Recent Advances in Materials and Manufacturing Technology: Select Proceedings of ICAMMT 2022 9819929202, 9789819929207

Citation preview

Lecture Notes in Mechanical Engineering

Ramesh Kumar Nayak Mohan Kumar Pradhan Animesh Mandal J. Paulo Davim   Editors

Recent Advances in Materials and Manufacturing Technology Select Proceedings of ICAMMT 2022

Lecture Notes in Mechanical Engineering Series Editors Fakher Chaari, National School of Engineers, University of Sfax, Sfax, Tunisia Francesco Gherardini , Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy Vitalii Ivanov, Department of Manufacturing Engineering, Machines and Tools, Sumy State University, Sumy, Ukraine Editorial Board Francisco Cavas-Martínez , Departamento de Estructuras, Construcción y Expresión Gráfica Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain Francesca di Mare, Institute of Energy Technology, Ruhr-Universität Bochum, Bochum, Nordrhein-Westfalen, Germany Mohamed Haddar, National School of Engineers of Sfax (ENIS), Sfax, Tunisia Young W. Kwon, Department of Manufacturing Engineering and Aerospace Engineering, Graduate School of Engineering and Applied Science, Monterey, CA, USA Justyna Trojanowska, Poznan University of Technology, Poznan, Poland Jinyang Xu, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

Lecture Notes in Mechanical Engineering (LNME) publishes the latest developments in Mechanical Engineering—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNME. Volumes published in LNME embrace all aspects, subfields and new challenges of mechanical engineering. To submit a proposal or request further information, please contact the Springer Editor of your location: Europe, USA, Africa: Leontina Di Cecco at [email protected] China: Ella Zhang at [email protected] India: Priya Vyas at [email protected] Rest of Asia, Australia, New Zealand: Swati Meherishi at [email protected] Topics in the series include: . . . . . . . . . . . . . . . . .

Engineering Design Machinery and Machine Elements Mechanical Structures and Stress Analysis Automotive Engineering Engine Technology Aerospace Technology and Astronautics Nanotechnology and Microengineering Control, Robotics, Mechatronics MEMS Theoretical and Applied Mechanics Dynamical Systems, Control Fluid Mechanics Engineering Thermodynamics, Heat and Mass Transfer Manufacturing Precision Engineering, Instrumentation, Measurement Materials Engineering Tribology and Surface Technology

Indexed by SCOPUS and EI Compendex. All books published in the series are submitted for consideration in Web of Science. To submit a proposal for a monograph, please check our Springer Tracts in Mechanical Engineering at https://link.springer.com/bookseries/11693

Ramesh Kumar Nayak · Mohan Kumar Pradhan · Animesh Mandal · J. Paulo Davim Editors

Recent Advances in Materials and Manufacturing Technology Select Proceedings of ICAMMT 2022

Editors Ramesh Kumar Nayak Department of Materials and Metallurgical Engineering Maulana Azad National Institute of Technology Bhopal, India Animesh Mandal School of Minerals, Metallurgical and Materials Engineering Indian Institute of Technology Bhubaneswar Bhubaneswar, India

Mohan Kumar Pradhan Department of Mechanical Engineering National Institute of Technology Raipur, India J. Paulo Davim Department of Mechanical Engineering University of Aveiro Aveiro, Portugal

ISSN 2195-4356 ISSN 2195-4364 (electronic) Lecture Notes in Mechanical Engineering ISBN 978-981-99-2920-7 ISBN 978-981-99-2921-4 (eBook) https://doi.org/10.1007/978-981-99-2921-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

This book presents select proceedings of the 2nd International Conference on Advances in Materials and Manufacturing Technology (ICAMMT-2022). The book comprises the advances in the existing and new materials, manufacturing processes, evaluation of materials properties for the application in automotive, aerospace, marine, locomotive, automotive, and energy sectors. The topics covered include advanced metal forming, bending, welding and casting techniques, recycling and re-manufacturing of materials and components, materials processing, characterization and applications, multi-physics coupling simulation, and optimization, alternate materials/material substitution, thermally enhanced processes, and materials, composites and polymer manufacturing, powder metallurgy and ceramic forming, numerical modeling and simulation, advanced machining processes, functionally graded materials, non-destructive examination, optimization techniques, engineering materials, heat treatment, material testing, MEMS integration, energy materials, bio-materials, metamaterials, metallography, nanomaterial, SMART materials, and superalloys. In addition, it discusses industrial applications and covers theoretical and analytical methods, numerical simulations, and experimental techniques in the area of advanced materials and their applications. It also covers the application of artificial intelligence in advanced materials and manufacturing technology. The book is intended for academics, including graduate students and researchers, as well as professionals interested in interdisciplinary topics in the areas of materials, manufacturing, and application of artificial intelligence and machine learning in manufacturing and new materials development sectors. The book will be a valuable reference for beginners, researchers, engineers, and industry professionals working in the various fields of Materials and Mechanical engineering. Bhopal, India Raipur, India Bhubaneswar, India Aveiro, Portugal

Ramesh Kumar Nayak Mohan Kumar Pradhan Animesh Mandal J. Paulo Davim

v

Acknowledgements

The editors would like to thank and acknowledge all the participants who have contributed different chapters of the book, the entire organizing committee of ICAMMT-2022, learned reviewers, keynote speakers, and supporting staffs. The editors also deeply express gratitude to the generous support provided by MANIT, Bhopal, to organize the conference in the department of MME. The editors also thank the publisher (Springer) and every staff and student volunteer of the department and institute who has directly or indirectly assisted in accomplishing the conference successful. Finally, the editors would also like to express their gratitude to the Director of MANIT, Dr. N. S. Raghuwanshi, for providing all kinds of support and blessings to make the international conference successful.

vii

Contents

The Chip Formation Mechanism for the Machining of the EN8 Unalloyed Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sunil Kumar and Kalyan Chakraborty Parameter Optimization for Titanium Superalloy for Electrical Discharge Machining Using Advanced Optimization Techniques . . . . . . Neeraj Agarwal, Gurjeet Singh, Irshad Ahmed, Abhishek Agarwal, Rakesh Yadav, and Anil Singh Yadav A Deep Review: Techniques, Findings and Limitations of Traffic Flow Prediction Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . Abhay Narayan Tripathi and Bharti Sharma Effect of Normalizing Followed by Deep Cryogenic Soaking on Mechanical Properties of P91 Martensitic Steel . . . . . . . . . . . . . . . . . . . Elysee Nzita Tembwa, M. E. Makhatha, Pawan Kumar, Srijan Sengupta, and Ankit Dev Singh Effect of Tempering Variables on Mechanical Properties of P91 Martensitic Steel and Determination of Hollomon–Jaffe Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elysee Nzita Tembwa, M. E. Makhatha, Pawan Kumar, Srijan Sengupta, and Ankit Dev Singh Influence of Multi-pass Friction Stir Processing on Microhardness and Wear Properties of AA2014/ SiC–CNT Hybrid Surface Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vedant Soni, Vivek Pragada, Nilesh D. Ghetiya, and Shalok Bharti Experimental and Numerical Analysis of Strength Characterisation of Concrete with Recycled Concrete Aggregate: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aruna Ekka, Kavita Gidwani Suneja, Priyanka Dhurvey, Harsangeet Kaur, and Chandra Prakash Gour

1

11

21

35

45

55

65

ix

x

Contents

Effect of Nano-TiO2 Content (wt.%) on Hardness of Epoxy Polymer Matrix Nanocomposites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sai Babu Chanda, Amish Mishra, Akshaya Kumar Rout, Ramesh Kumar Nayak, and Santosh Kumar Nayak Development and Characterization of Nano-SiO2 -Enhanced Polymer Nanocomposites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amish Mishra, Sai Babu Chanda, Ramesh Kumar Nayak, Akshaya Kumar Rout, and S. Suresh Optimization Techniques Used in Machining Processes: A Review . . . . Diksha Jaurker, M. K. Pradhan, Siddharth Jaurker, and Raj Malviya

77

85

93

Coupled Temperature Displacement Finite Element Analysis of Friction Welding of Similar and Dissimilar Metals . . . . . . . . . . . . . . . . M. K. Pradhan and Deepansh Gill

103

Microstructural Effect of Stir-Cast and Squeeze Stir-Cast AZ91 Mg Under Variable Dry-Sliding Conditions . . . . . . . . . . . . . . . . . . . . . . . . . Kamal Kant Singh and Dharamvir Mangal

123

Behaviour Analysis of Layered Beam Using ANSYS . . . . . . . . . . . . . . . . . Badree Sahu, Priyanka Dhurvey, Parth Verma, Juned Raheem, and Aditya Bhargava

135

Thermal Performance Analysis of Eccentric Helical Coil Tube in Tube Heat Exchanger Using CFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sanyog Kumar and A. R. Jaurker

143

Analysis of Performance of Roughened Triangular Duct of Solar Air Heater . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Durgesh Kumar Dubey and A. R. Jaurker

157

Structural Analysis of Aircraft Wing Considering Titanium Alloy . . . . . Ruby Mishra, Himanshu Ranjan Kumar, Smaranika Nayak, Santosh K. Nayak, Swayam B. Mishra, and Basanta Ku. Nanda Effect of T6 Heat Treatment on Compressive Strength of Al6082 Reinforced with Multi-walled Carbon Nanotubes . . . . . . . . . . . . . . . . . . . . Madhusudan Baghel, C. M. Krishna, Anil Chourasiya, and Anurag Namdev Application of Digital Image Processing on Machined Surfaces: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saurabh Jain, M. K. Pradhan, and Amit Kumar Arm Fracture Detection Using Deep Convolution Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gorav Kumar Malik, Ashish Nainwal, Amrish, Vishwanath Bijalwan, and Vijay Bhaskar Semwal

171

179

191

203

Contents

xi

Manufacturing Techniques and Effect of Stacking Sequence on Mechanical and Tribological Properties of FRP Hybrid Composites: An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smaranika Nayak, Isham Panigrahi, Ruby Mishra, Diptikanta Das, and Santosh Kumar Nayak

213

Study of Gap Flow Simulation for Machining Gap in Electric Discharge Machining process—A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . Sudhanshu Kumar, Dharmendra Kumar, and Dilip Sen

223

Fabrication and Structural Analysis of Hybrid Metal Matrix Composites (MMC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. V. S. Phani, Basanta Kumar Nanda, Swayam Bikash Mishra, Santosh Kumar Nayak, and Ruby Mishra A Brief Review of Technical Parameters and Its Applications Used in Cold Spray Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ayaz Mehmood, Mohammad Zunaid, and Ashok Kumar Madan Finite Element Analysis of Uniaxial Compression Test . . . . . . . . . . . . . . . Hamza Naseem, Valluri Sai Prasanna, S. S. V. D. Pavan Kumar, and Kalluru Giri Evaluation of Microstructure, Mechanical Properties and Biocompatibility of Biodegradable Zinc-Based Alloys for Implants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammad Mohsin Khan, Abhijit Dey, Zainab Rubanee, Kausar Mushtaq, Mohammad Irfan Hajam, Sheikh Shahid Ul Islam, Musab Bashir Shah, and Akash Dwivedi

231

243 257

267

Thermal and Mechanical Analysis of Bimodular Beam . . . . . . . . . . . . . . . Saumya Shah and S. K. Panda

279

Design Analysis of a UAV for Medical Transport Purposes . . . . . . . . . . . . Ruby Mishra, Samyak Nikhil Siddhanta, Somesh Kumar Sharda, Santosh Kumar Nayak, Swayam Bikash Mishra, and Basant Ku Nanda

285

Parametric Optimization of the EDM Using a Genetic Algorithm on Machining of Al7075/SIC/WS2 Composites . . . . . . . . . . . . . . . . . . . . . . Amit Kumar, Mohan Kumar Pradhan, and Saurabh Jain Status of Bio-printing Inks and Their Compatibility with Current Printing Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shrushti Maheshwari, Rajesh Purohit, Deepen Banoriya, Anurag Namdev, and Deepa Ahirwar Firefighting and Extinguishing Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kondepu Jai Sai Nath, Kotapati Thanuja, Ch. Sai Ganesh, Kottnana Janakiram, and P. Joshua Reginald

295

307

321

xii

Contents

Noise Reduction of Jet/Aircraft Engine Fitted with N10 Chevron Nozzle and Comparative Analysis with N8 Nozzle at Varied Tip Angle (β) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Irfan Nazir Wani, Ankur Kulshreshtha, and Saragadam Chaitanya Study on Effect of Process Parameters on AJM of Partially Biodegradable Hybrid Composite Using TOPSIS Approach . . . . . . . . . . Itishree Rout, Trupti Ranjan Mahapatra, Arun Kumar Rout, Debadutta Mishra, and Akshaya Kumar Rout Effect of Artificial Roughness on Heat Transfer and Friction Factor in a Solar Air Heater: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anil Singh Yadav, Tabish Alam, Rajiv Saxena, Raj Kumar Yadav, Rajan Kumar, Abhishek Sharma, Yogesh Agrawal, K. Viswanath Allamraju, Monika Vyas, Manmohan Singh, and Subhendu Chakroborty An Overview of Extrinsic Strategies of Self-healing Materials . . . . . . . . . Deepa Ahirwar, Rajesh Purohit, and Savita Dixit A Study on Chemical Composition, Morphology, and Hardness of Spheroidal Graphite (SG) and Austempered Ductile Cast Iron (ADI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ramesh Kumar Nayak, Jatin Sadarang, Dheerendra Singh Patel, Sourabh Jain, and Rahul Kumar Effect of Parametric Variance on Wear and Mechanical Behavior of the Polymer Hybrid Composite: A Brief Review . . . . . . . . . . P. S. Yadav, Rajesh Purohit, Anurag Namdev, Madhusudan Baghel, and Yashwant Kumar Yadav

331

341

355

365

377

387

Experimental Analysis of Performance of Solar Water Heater for Optimizing Tilt Angle Under Effect of Wind Speed . . . . . . . . . . . . . . . Subhash Prasad and Bhupendra Gupta

399

AI Pathfinding Algorithm in 3D Game Development Strategy Optimization System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhuowen Fang

407

Optimal Design of Garden Landscape Space Environment Based on Interactive Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ming Chen and Renshu Wen

419

Research on Prediction of Global Climate Change Based on Grey Model and Climate Governance Partnership . . . . . . . . . . . . . . . . Chang Liu

429

Contents

Simulated Evacuation Analysis of a Secondary Building Based on Pathfinder, PyroSim, and Revit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shiyi Zhou, Danhong Chen, Sibo Liu, Yongxuan Shao, Hanmiao Zhang, and Pengfei Song Fire Evacuation System Based on UWB Indoor Positioning Technology and Small Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shiyi Zhou, Danhong Chen, Chengzhuo Ming, Rui Zhang, Shuwen Bai, and Tong Li Analysis and Research on the Related Technology of WebSocket Network Communication in the Public Area Comprehensive Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuxuan Wang

xiii

441

451

463

Safety Detection System of Perovskite Battery Materials Based on Intelligent Identification Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dehui Sun

475

Application of Industrial Robot in Injection Molding of Thin-Walled Porous Plastic Parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yongcheng Huang, Pengcheng Wang, Bin Yang, and Yanxia Zhang

485

Optimize of Power Supply Driving Circuit for Agricultural Blade Welding Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yufei Feng, Guoqing Jiang, Zuqi Zhang, Jianwei Shu, Jinfeng Dong, and Hua Liu

497

The Analysis of the Assessment of Head Protection Test of C-NCAP Version 21 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Tian, Limin Wang, and Lei Liu

509

Hospital Interior Public Space Environment Design System Based on 3D Virtual Spatial Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yi Fu and Yihao Xie

519

Information Construction Management System for Deep Foundation Pit Engineering Based on BIM Technology . . . . . . . . . . . . . . Xuefeng Gai

529

Construction of Landscape Simulation Design System for Characteristic Towns Based on Improved Fish Swarm Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ning Xian and Huize Guan From 5 to 6G—A Summary Review and Prospect of Contemporary Mobile Communication Technology . . . . . . . . . . . . . . . Yifan Jiang, Xinyu Huang, Yuxin He, Dicai Kang, and Jinyue Yu

539

551

xiv

Contents

Management System of Electric Firefighting Equipment Based on GeoMap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yi Cheng Mobile Peak Shaving Equipment Based on Rural Network Lines . . . . . . Jian Jiang, Fengliang Xu, Fayi Wang, Binghui Hu, and Qingwei Tang Collaborative Planning of Power Lines and Storage Configuration Considering Comprehensive Wind-Solar Power Consumption Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tianliang Yao, Zhiwei Li, Jinpeng Liu, Hu Wang, Liwei Zhang, and Chenglian Ma Optimization Model of Steel Cutting Based on Filling Algorithm . . . . . . Feng Jiang, Dongcheng Wang, Wenbai Chen, and Tingting Chen

563 575

587

601

UAV Cooperative Inspection Route Planning Based on Ant Colony Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . You Li, Wenqi Mao, Shuifeng Wu, and Guodong Li

613

GIS-Based Urban–Rural Planning Connection and Power Grid Planning Auxiliary Planning System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiayin Xu, Kun Li, Jincheng Bai, Tao Wang, and Zhiwei Li

623

Walking Stability of Biped Robot Based on Machine Learning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jianrui Zhang, Yitong Sun, Qian Jing, Yani Lu, Ning Mi, Xiao Lian, Sheng Dong, and Jianxiao Bian

635

Transmission and Transformation Engineering Management System Based on Distributed Load Balancing . . . . . . . . . . . . . . . . . . . . . . . Zhonghong Kang

645

Computer Image Intelligent Recognition System Based on Visual Communication Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shuangxiao Gou and Ling Mei

657

Numerical Analysis of Shape Memory Alloy Pipe Joint in Aerospace Hydraulic System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qingyu Liu and Shihong Xin

669

Performance Analysis and Optimization of Low Temperature Dual Pressure Waste Heat Power Generation (WHPG) System . . . . . . . Yuanjia Liu

683

Vehicle Automatic Driving Path Based on AdaBoost Algorithm . . . . . . . Peng Xie, Weiwei Tian, and Fei Qin Construction of TBM Operation Parameter Optimization Decision Model Based on Reinforcement Learning Algorithm . . . . . . . . Nan Jiang and Ling Zeng

695

709

Contents

Analysis on the Influencing Factors of Carbon Emission in World Based on LMDI and Ridge Regression . . . . . . . . . . . . . . . . . . . . . Shanshan Wu and Xiang Wang Relay Protection Stability of Intelligent Substation . . . . . . . . . . . . . . . . . . Xiuzhi Li and Guihua Qiu Weak Signal Data Collector Based on Time-Domain Aero-Electromagnetic Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lili Pang, Pan Zhang, Hanchuan Dong, Kang Li, Yanxin Shi, Lide Fang, and Zhonghua Zhang

xv

719 737

747

Design of Anti-islanding Intelligent Monitoring System for Photovoltaic Microgrid Based on IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . Huaizhong Chen, Riliang Xu, and Ziqing Xie

757

Mobile IoT Region Segmentation Algorithm Based on Multi-layer Sensing with Edge Computing . . . . . . . . . . . . . . . . . . . . . . . Dongmei He

769

Key Point Detection of Power Tower Based on Improved Yolov5 . . . . . . Changxin Zhao, Yandong Cui, Zushan Ding, and Chuang Cao

779

Analysis of Regional Logistics Efficiency Based on SE-DEA Model and FCM Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meijuan Liu

789

Logistics Picking Path Optimization Based on Improved Ant Colony Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qi Li

799

Vehicle Automatic Braking System Based on Multi-target Acquisition Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chengli Pang

811

Intelligent Fault Diagnosis of Electronic Engineering Equipment System Based on Machine Learning Algorithm . . . . . . . . . . . . . . . . . . . . . . Yan Yang

823

Content-Based Visual Information Retrieval Technique Using Adaptive Deep Learning Algorithms: A Review . . . . . . . . . . . . . . . . . . . . . Gaurav Singh and Hemant Kumar Soni

835

The Development Trend of Intelligent Logistics Based on Machine Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xin Zhang and Pengmin Jia

849

Optimization Algorithm of Building Drainage Pipe System Based on Isotope Tracking Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoting Liu, Ou Luo, Xialing Huang, and Hao Zhu

861

xvi

Contents

Optimization of Injection Moulding Process Parameters Using Hybridization Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Md. Mofid Alam, Padmakar Pachorkar, Gurjeet Singh, Neeraj Agarwal, Rakesh Yadav, Jagdish Prasad, and Ashish Srivastava Structure and Electric Properties of Ba (Ti, Zr)O3 Thin Films Using Sol–gel Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ling Huang, Bo Zheng, and Qian Wang Smart Agriculture Monitoring System to Prevent Water Wastage . . . . . Alapati Akash, Muthyala Lalith Krishna, Bommaganti Chandana, Kottnana Janakiram, and P. Joshua Reginald

873

887 897

Monolithic Integration of Cu(In,Ga)Se2 Thin Film Solar Modules by all Nanosecond Laser Scribing . . . . . . . . . . . . . . . . . . . . . . . . . Amol Badgujar, Bhushan Nandwalkar, and Sanjay Dhage

907

Parametric Optimization in Nd:YAG Laser Micro-drilling of Carbon Black/Epoxy Composite Utilizing GRA and Response Surface Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lipsamayee Mishra, Trupti Ranjan Mahapatra, Soumya Ranjan Parimanik, Sushmita Dash, and Debadutta Mishra

917

Static Structural Analysis of Below Knee Prosthesis Using Fea . . . . . . . . Prabhakar Nandivada, Jagana Nikhil, I. Arun Kumar, and Ravi Kumar Mandava

929

Toward Machinability Improvement of AISI 4340 Using CVD Multilayer TiN-Coated Carbide Insert Through MQL: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ashok Kumar Sahoo, Ramanuj Kumar, Amlana Panda, Purna Chandra Mishra, and Tanmaya Mohanty

939

Experimental Explorations on Mechanical Performance of Waste Marble Dust Powder and Banana Fibre Strengthened Hybrid Bio-composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J Joshua Gnana Sekaran, G. Gokilakrishnan, G. M. Pradeep, R. Saravanan, and R. Girimurugan

951

Effect of ZrO2 Nanofluid Concentrations in Hard Turning of AISI D2 Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saswat Khatai, Ashok Kumar Sahoo, Ramanuj Kumar, and Amlana Panda Study on Thermal and Electrical Conduction Properties of Nano Zinc Particle-Reinforced Polyester-Graded Composites . . . . . . . . . . . . . . Archana Nigrawal, Arun kumar Sharma, and Fozia Z. Haque

961

973

Contents

An Experimental Investigation on Overlapped Multipass Laser Transformation Hardening of Ti–6Al–4V Titanium Alloy Using Nd:YAG Laser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Duradundi Sawant Badkar Clean and Green Electricity Generation by Zirconia-Based Hydroelectric Cell Device Without Greenhouse Gas Emission . . . . . . . . . Rojaleena Das, Abha Shukla, Jyoti Shah, Sanjeev Sharma, Pritam Babu Sharma, and Ravinder Kumar Kotnala

xvii

981

995

A Short Review on Tribological Behaviour of Epoxy Composites Containing Different Fibres and Nanoparticles . . . . . . . . . . . . . . . . . . . . . . 1007 Anurag Namdev, Rajesh Purohit, Amit Telang, Madhusudan Baghel, and Raghvendra Singh

About the Editors

Dr. Ramesh Kumar Nayak is working as an Assistant Professor in the Department of Materials and Metallurgical Engineering, Maulana Azad National Institute of Technology, Bhopal, India. He graduated (M.Tech.) from the Indian Institute of Technology, Kanpur, India, in 2005. He has worked in several reputed organizations such as General Motors India Technical Centre, Bangalore; Hindustan Aeronautics Limited, Bangalore; and DENSO International India Pvt. Limited, Gurgaon in various roles. His research interest is FRP composites/nanocomposites, metal casting, solidifications, and process simulations. He teaches polymer engineering, solidification and casting, composite materials, and corrosion engineering to undergraduate and graduate scholars at MANIT, Bhopal. He has guided several Ph.D. and M.Tech. scholars in the area of composite materials and metal casting. He has published his work in peer-review international journals, reference books, and book chapters in the areas of manufacturing, metallurgy and materials science. He has been listed in World’s Top 2% Scientists by Stanford University-Elsevier study, 2022. Dr. Mohan Kumar Pradhan completed his M.Tech. and Ph.D. (mechanical engineering) from NIT Rourkela. He is working as Associate Professor, in the Department of Mechanical Engineering, National Institute of Technology, Raipur. Pripor to this Assistant Professor in Maulana Azad National Institute of Technology, Bhopal (An Institute of National Importance). He has over 25 years of teaching and research experience in manufacturing. His areas of teaching and research interest are Additive Manufacturing, non-traditional machining, metrology, micromachining, MEMS, hybrid machining, composites, and process modeling and optimization. He has more than 75 international journal publications, more than 65 technically edited papers, which were published in conference proceedings, 2 edited books, more than 5 conference proceedings volumes and more than 20 book chapters. He is on the editorial board and review panel of more than 5 international journals of repute. He has total 193 publications/h-index 16+, SCOPUS/h-index 12+, Google Scholar/h-index 18+). He is charted engineer, a life fellow of IIPE, and a life member of ISTE, IACSIT, IAENG, and MIE (I).He has been listed in World’s Top 2% Scientists by Stanford University-Elsevier study, consecutively in 2021 and 2022. xix

xx

About the Editors

Dr. Animesh Mandal is working as Associate Professor at School of Minerals, Metallurgical and Materials Engineering, IIT Bhubaneswar, since 2010. He obtained his M.Tech. and Ph.D. degree from IIT Kharagpur and worked as postdoctoral fellow in Worcester Polytechnic Institute, Massachusetts, USA. His areas of research interest include aluminium alloys, magnesium alloys, recycling of aluminium alloys, metal matrix composites, semisolid processing of metals and tribology of metals. He has guided several Ph.D. and M.Tech. scholars at IIT Bhubaneswar. He has completed different R&D projects sponsored by DST and other funding agencies. He has published his research outcomes in different reputed peer-reviewed journals on the topics of manufacturing, metallurgy and materials science. Prof. J. Paulo Davim is a Full Professor at the University of Aveiro, Portugal. He is also distinguished as honorary professor in several universities/colleges/institutes in China, India and Spain. He received his Ph.D. degree in Mechanical Engineering in 1997; M.Sc. degree in Mechanical Engineering (materials and manufacturing processes) in 1991, Mechanical Engineering degree (5 years) in 1986, from the University of Porto (FEUP), the Aggregate title (Full Habilitation) from the University of Coimbra in 2005 and the D.Sc. (Higher Doctorate) from London Metropolitan University in 2013. He is Senior Chartered Engineer by the Portuguese Institution of Engineers with an MBA and Specialist titles in Engineering and Industrial Management as well as in Metrology. He is also Eur Ing by FEANI-Brussels and Fellow (FIET) of IET-London. He has more than 35 years of teaching and research experience in manufacturing, materials, mechanical and industrial engineering, with special emphasis in machining and tribology. He has guided large numbers of postdoc, Ph.D. and master’s students as well as has coordinated and participated in several financed research projects. He has received several scientific awards and honors. He has worked as evaluator of projects for ERC-European Research Council and other international research agencies as well as examiner of Ph.D. thesis for many universities in different countries. He is the editor-in-chief of several international journals, guest editor of journals, books editor, book series editor and scientific advisory for many international journals and conferences. Presently, he is an editorial board member of 30 international journals and acts as reviewer for more than 100 prestigious Web of Science journals. In addition, he has also published as editor (and co-editor) more than 200 books and as author (and co-author) more than 15 books, 100 book chapters and 500 articles in journals and conferences (more than 300 articles in journals indexed in Web of Science core collection/h-index 61+/ 12500+ citations, SCOPUS/h-index 66+/15500+ citations, Google Scholar/h-index 84+/25000+ citations). He has been listed in World’s Top 2% Scientists by Stanford University-Elsevier study (Ranked 2021 single year at 1773 out of 190063 scientists, ranked at 16 in the field of industrial engineering and automation and ranked at 38 in the field of materials).

The Chip Formation Mechanism for the Machining of the EN8 Unalloyed Steel Sunil Kumar and Kalyan Chakraborty

Abstract The EN8 carbon steel (unalloyed) was machined at different machining conditions in a lathe. The Indoloy IK-20 tungsten carbide tool was used for the dry turning of the material. The true stress–true strain curve for the material was obtained and the stress–strain relationship was obtained as σ = 850.48εn . The von Mises stresses (VMSs) were determined for different experimental parameters. The collected chips for all the machining conditions were examined under the scanning electron microscope (SEM). The mechanism of chip formation was identified by observing the SEM images for the chips. The mechanism was further established with reference to the extent of von Mises stress generation. The mechanism of chip formation depends upon the experimental parameters. The machining chips were formed by successive lamellar shear sliding with and without the presence of dislocation. The von Mises stress increases during the lamellar shear sliding chip formation at the higher strain rate. The chip formation was influenced by the crack formation at higher speed and feed to reduce the level of von Mises stress. Keywords Dry turning · Experimental parameters · Strain rate

1 Introduction The EN8 steel is the (unalloyed) medium carbon steel. These steels are used for the production of the studs, bolts, keys, etc. The EN8-grade steel was machined in the CNC vertical milling machine by the shoulder milling. The machining was performed by the cryogenically treated, cryogenically treated and tempered (CCT) and untreated multi-layered carbide tool inserts. Base inserts were of the WS40PM and F40M grades. The wear resisting ability of the treated inserts was improved by the cryogenic treatments. The CCT tools showed improved microstructural and mechanical properties [1]. The effect of hardness on the machinability was improper according S. Kumar · K. Chakraborty (B) National Institute of Technology, Silchar, Assam 788010, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_1

1

2

S. Kumar and K. Chakraborty

to the previous literature review. Annealing, normalizing, quenching and tempering were done on the EN8 steel. The effect of heat treatment on the microstructure and hardness was compared. Improved hardness and microstructure were obtained with normalizing and tempering. Better machinability can be expected with the normalized and tempered EN8 steels [2]. The turning was done on the EN8 steel using the TiN-coated carbide tool insert (CNMG 120408). The experiments were done according to the response surface methodology. The cutting forces, surface roughness and tool wear were measured. The percentage contributions of input parameters on the responses were determined by the analysis of variance. The depth of cut (DOC) influenced the cutting force strongly. The roughness was influenced strongly by the DOC. The tool wear was highly influenced by the feed. The predictive model was established between the input parameters (the speed, feed, depth of cut, cutting force and surface roughness) and the output parameter (tool wear). Different algorithms were selected for the purpose. The BFGS quasi-Newton backpropagation algorithm was found effective in respect of the least mean squared error and computational time [3]. The effect of the thermal assisted machining on the EN8 steel was identified. The speed, feed and DOC were selected as the input parameters. The surface roughness and material removal rate were considered as output parameters. The value of surface roughness was less during the (thermal assisted) machining and the material removal rate was more [4]. The speed, feed and depth of cut were considered as the input parameters during milling of EN8 steel. The parameters were optimized to increase the MRR and to reduce the cutting force and the surface roughness. The experimental results were validated by the RSM using the GRA and the Taguchi method [5]. The Tin-coated carbide tool was used for the machining of the EN8 steel. The fresh tool and worn tool were selected for the experiments. The speed, feed and the doc were considered as the input parameters. The RSM model was established for the output responses as the force, the wear and the surface roughness. The ANOVA was performed. The optimum speeds were obtained for both the fresh and the worn tools. The particle swarm optimization was applied successfully [6]. The wear reduces with hardness, but the machinability becomes poorer [7]. Increased speed and feed increase the tool wear. The temperature increases with the speed and decreases with the feed. The coated tools provided better results during the machining of the heattreated EN8 steel (up to 20HRC) [8]. The EN8 steel was machined by using the cryogenic treated and the untreated tool. The cryogenic tool performed better due to the less chatter during the machining [9]. EN8 cylindrical bar was machined in the CNC to optimize the surface roughness considering speed, cutting fluid and DOC as process parameters [10]. The tempering after normalizing for EN8 steel is suggested for better machinability [11]. The literatures on the chip formation mechanism for the machining of EN8 steel are scarce. In this paper, the effect of machining parameters on the mechanism of chip formation for the EN8 steel is presented. The aim of the present work is to establish the chip formation mechanism during the machining of the EN8 steel.

The Chip Formation Mechanism for the Machining of the EN8 …

3

Table 1 Experimental parameters Expt. No. 1

v, m/min 38.894

d, mm

f , mm/rev

Expt. no

v, m/min

d, mm

f , mm/rev

1

0.1

6

86.431

1.5

0.1

2

86.431

1

0.1

7

86.431

1

0.1

3

216.079

1

0.1

8

86.431

1

0.26

4

86.431

0.5

0.1

9

86.431

1

0.41

5

86.431

0.8

0.1

2 Experimental Procedure The EN8 steel was obtained in the round form. The length and diameter of the work piece were 600 mm and 76 mm, respectively. The dry turning was done on the lathe (speed, v, range: 38.89–216.079 m/min., feed, f , range: 0.1–0.41 mm/rev., depth of cut, d, range: 0.5–1 mm) using the Indoloy IK-20 tungsten carbide tool. The principal cutting-edge angle of the tool was 48°. The machining was performed at different machining parameters (Table 1). The machining chips were collected for each experimental condition. Subsequently, the SEM study for the chip surfaces was done. The chip reduction coefficients (CRC, ratio of formed and uncut chip thickness) were determined for different experimental conditions. The Brinell hardness for the specimen was measured. The universal tensile test was done for this material.

3 Results The Brinell hardness for the specimen was found to be 91.318 BHN. The true stress and the true strain graphs were found, and the data were used to determine (graphically) the value of the strength coefficient (K) and the strain hardening exponent (N). “K” and “N” were found as K = 850.48 MPa and N = 0.1608. The power law equation for the specimen was established as (Eq. 1) σ = 850.48ε0.1608 ,

(1)

where σ is the true stress and ε is the true strain. The von Mises stress [12] was determined by using Eq. (2) S = 1.74 K (ln ζ ) N ,

(2)

where S = VMS, K = strength coefficient, ζ = CRC and N = strain hardening index. The experiment number, CRC and estimated VMS (MPa) are (1, 2.5, 1459.45), (2, 2.66, 1474.66), (3, 2.79, 1486.19), (4, 3.65, 1542.41), (5, 2.68, 1476.72), (6, 2.61, 1469.75), (7, 2.65, 1473.46), (8, 3.36, 1526.55), (9, 2.46, 1454.49).

4

S. Kumar and K. Chakraborty

4 Discussion 4.1 The VMS and Chip Type The VMS increases with the increase of the speed (expt. no. 1, 2 and 3). This is attributed to the strain rate hardening effect for the higher speed. The type of chip changes from the short tubular chip to the short continuous chip (1 → 2 → 3) (see Fig. 1a, b). The VMS reduces with the increase of the DOC (expt. no. 4, 5 and 6). This is attributed to the thermal softening for the increased depth of cut. The short continuous chips were produced (4 → 5 → 6) (see Fig. 1c, d). The VMS increases and then decreases with the increase of the feed (expt. no. 7, 8 and 9). The chip type changes from the short continuous to the discontinuous type (7 → 8) (see Fig. 1e, f). This is due to the initial thermal softening effect followed by the stain hardening effect with the increase of the feed (up to feed = 0.26 mm/rev.). However, at very high feed (0.41 mm/rev.), the VMS has reduced due to the excessive strain hardening and numerous crack generation (8 → 9). The small broken chips has formed due to numerous cracks (see Fig. 1f).

4.2 The Study Using SEM At v = 38.89 m/min., DOC = 1 mm, f = 0.1 mm/rev. (expt. no. 1), the material side flow occurs (see Fig. 2a). The chip forms by the successive shear lamellar sliding mechanism. The intergranular fracture (or crack) has occurred during the lamellar sliding of the grains. Absence of the dislocations for the lamellae (see Fig. 2b) indicated that the deformation took place for the lower strain rate. This happens as the chip was formed for the lower cutting speed (38.89 m/min.). This was further indicated by the lesser amount of the von Mises stress (1459.45 MPa). But when the machining was done for v = 216.08 m/min., DOC = 1 mm, f = 0.1 mm/rev. (expt. no. 3), it was seen that the chip was formed with the severe shear sliding mechanism (see Fig. 3a, b). Intergranular fracture (or crack) has occurred during shear sliding. The presence of the intragranular dislocations indicated that the deformation had taken place for the higher strain rate (see Fig. 3c). This operates to increase the hardness due to the strain rate hardening. This finding is observed in the previous section (Sect. 4.1) also. This was further revealed by the increased value of the von Mises stress (1486.19 MPa). The chip formation was based on the successive lamellar sliding mechanism (see Fig. 3d). At v = 86.43 m/min., DOC = 0.5 mm, f = 0.1 mm/min (expt. no. 4), the machining chip was formed by the successive shear sliding mechanism. Intergranular fracture (or crack) has occurred during shear sliding. The shear lamellae were seen (see Fig. 4a). The machining chip was formed by the successive shear sliding mechanism with the intergranular fracture (or crack). The presence of dislocations was due to the higher strain rate deformation (see Fig. 4b–d). The von Mises stress at this condition was found to be

The Chip Formation Mechanism for the Machining of the EN8 …

von Mises stress, MPa

1490 1480 1470 1460 1450

5

3 2

1 doc=1mm, f=0.1 mm/rev. 0

50

100

150

200

1

3

2

250

Speed,m/min (a) von Mises stress, MPa

1600

v=86.431 m/min., f=0.1 mm/rev. 4

1500 1400

0

von Mises stress, MPa

1600

5

6

1 DOC, mm (c)

2

7

0

8

0.2 0.4 Feed mm/rev. (e)

4

6

5

(d)

v=86.431 m/min., doc=1 mm

1500 1400

(b)

7

8

9

9

0.6 (f)

Fig. 1 a VMS versus cutting speed (expt. no. 1, 2, 3), b chip type (expt. no. 1, 2, 3), cVMS versus DOC (expt. no. 4, 5, 6), d chip type (expt. no. 4, 5, 6), e VMS versus feed (expt. no.7, 8, 9), f chip type (expt. no. 7, 8, 9)

relatively higher (1542.41 MPa), and this also indicated that the chip was formed for the higher strain rate deformation. When the machining was done at the increased doc (expt. no. 6), the chip was formed by the shear deformation of the successive lamellae (see Fig. 5). The absence of dislocations for the interior of the lamellae was due to the deformation for the higher temperature. This finding is also in agreement with the previous result (Sect. 4.1). This caused the lower values of the von Mises stresses (1476.72 and 1469.75 MPa) with the increased DOC. At v = 86.43 m/min., DOC = 1 mm, f = 0.1 mm/rev. (expt. no.7), the chip formation was occurred by the successive shear sliding mechanism. The von Mises stress (1473.46 MPa) indicated that the chip was formed by the successive shear sliding of the deformed grain (see Fig. 6). When the feed was increased to 0.26 mm/rev. (expt. no. 8), it was observed that the chip was formed by the successive lamellar shear sliding mechanism through shear band formation (see Fig. 7a, b). The presence of the numerous dislocations (see Fig. 7c) was due to the chip formation mechanism for the higher strain rate. This was further indicated by the higher value of the von Mises stress (1526.55 MPa). This happened because of the excessive strain rate hardening. This observation is also in agreement with the finding in the previous section (Sect. 4.1). When the

6

S. Kumar and K. Chakraborty

Fig. 2 SEM images of chip side surfaces, msf: material side flow, igf: intergranular fracture

a

b msf igf

machining was done for the higher feed ( f = 0.41 mm/rev.) (expt. no. 9), the chip was formed by the successive sliding of the massive deformed grain (see Fig. 8a). The chip formed for the excessive strain hardened state. The presence of the cracks in the chip was due to the strain hardening (see Fig. 8b) during the chip formation. The transgranular cracks in the chip are due to the strain hardening (see Fig. 8c). This was further indicated by the lower value of the von Mises stress (1454.49 MPa). The stress concentration arises due to the presence of the cracks. The crack propagation during the chip formation occurs due to this. Thus, less force is required to remove material. Therefore, the VMS reduces at this machining condition. The same observation has been noted in the previous section (Sect. 4.1).

a

b

c

sb

d

igd

Fig. 3 SEM images of chip side surfaces, sb: shear bands, igd: intragranular dislocations

a

b

c

d

igf igf

igf

Fig. 4 SEM images of side surfaces, igf: intergranular fractures, dis: dislocations

dis

The Chip Formation Mechanism for the Machining of the EN8 …

7

Fig. 5 SEM image of chip side surface

Fig. 6 SEM image of chip side surface along with the undersurface

b

a

c

sb d

Fig. 7 SEM images of chip side surfaces, sb: shear bands, d: dislocations

8

S. Kumar and K. Chakraborty

a

b

c

Fig. 8 SEM images of chip side surfaces, igc: intergranular cracks, tgc: transgranular cracks

5 Conclusions The VMS increases with the increase of the speed at the constant DOC and the feed due to the strain rate hardening. The VMS reduces with the increase of the DOC at the constant speed and the feed due to the thermal softening. The VMS increases for the moderate increase of the feed due to the strain hardening. This occurs for the constant speed and the DOC. The VMS decreases for the high feed due to the excessive strain hardening with the dislocations and the crack generation. This occurs for the constant speed and the DOC. Overall, chip formation can take place by (a) gross deformation of the grain, (b) shear sliding of the deformed grain, (c) lamellae formation and shear sliding along with intergranular cracks, (d) lamellae formation, shear sliding with dislocation, (e) lamellae formation, shear sliding with inter granular cracks, dislocation in the grain and transgranular crack generation, (f). lamellae formation, shear sliding with dislocation and sub grain formation due to dislocation climb. A correlation can be attainable between VMS and the mechanism of chip formation. The change in chip type is due to the change in material properties at different experimental conditions. Acknowledgements The SEM images were obtained from IIT Kanpur. Accordingly, authors are grateful to IIT Kanpur.

References 1. Mahendran R, Rajkumar PL, Nirmal R, Karthikeyan S, Rajeshkumar L (2021) Effect of deep cryogenic treatment on tool life of multilayer coated carbide inserts by shoulder milling of EN8 steel. J Braz Soc Mech Sci Eng 43:378. https://doi.org/10.1007/s40430-021-03100-7 2. Palash B, Arnab K, Dhiraj M, Prasanta KB (2018) Effect of heat treatment on microstructure behavior and hardness of EN 8 steel. In: International conference on mechanical, materials and renewable energy. IOP Conf Ser: Mater Sci Eng 377:012065. IOP Publishing. https://doi.org/ 10.1088/1757-899X/377/1/012065 3. Thangarasu SK, Shankar S, Mohanraj T, Devendran K (2020) Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network. Proc IMechE Part C: J Mech Eng Sci 234(1):329–342 (2020). IMechE SAGE. https://doi.org/ 10.1177/0954406219873932

The Chip Formation Mechanism for the Machining of the EN8 …

9

4. Singh H, Sharma S, Gaba M (2016) Analysis of surface roughness and material removal rate in dry and thermal assisted machining of EN8. Int J Eng Sci Res Technol 5(3):737–743.Thomson Reuters. https://doi.org/10.5281/zenodo.48350 5. Nangare AS, Jadhav VS (2020) Optimization of process parameters for machining of en8 steel on cnc vertical milling machine. In: Optimization Methods in Engineering. Lecture notes on multidisciplinary industrial engineering book series (LNMUINEN). Springer, pp 503–511 6. Thangarasu SK, Shankar S, Navin Prasath R (2019) Experimental study and optimisation in turning process of EN8 steel using RSM with hybrid algorithm approach. Int J Bio-Inspired Comput 13(4):242–256 7. Biswas P, Kundu A, Mandal D (2017) An experimental investigation on hardness and micro structure of heat treated EN 9 steel. IOP conf Ser Mater Sci Eng 225 8. Roopa D, Mudakappanavar VS, Suresh R, Chavan TK (2022) Influence of process parameters on tool wear and temperature of coated HSS tools on drilling of hardened EN8 alloy steel. https://doi.org/10.1016/j.matpr.2021.09.169 9. Murali D, Narayanamoorthy K, Aagashram N, Baig R, Gnanasekaran K, Ramesh K (2020) Performance comparison of cryogenic treated tool vs untreated tool using an accelerometer sensor. IJEAT 10(2):12–14 10. Gurugubelli S, Chekuri RBR, Penmetsa RV (2022) Experimental investigation and optimization of turning process of EN8 steel using Taguchi L9 orthogonal array. Mater Today Proc 2214– 7853 11. Biswas P, Kundu A, Mondal D, Bardhan PK (2018) Effect of heat treatment on microstructure behavior and hardness of EN8 steel. IOP Conf Ser Mater Sci Eng 377 12. Astakhov VP (2006) Tribology of metal cutting. In: Briscoe BJ (ed) Tribology and interface engineering series, no.52. Elsevier

Parameter Optimization for Titanium Superalloy for Electrical Discharge Machining Using Advanced Optimization Techniques Neeraj Agarwal , Gurjeet Singh , Irshad Ahmed , Abhishek Agarwal , Rakesh Yadav , and Anil Singh Yadav

Abstract A Titanium superalloy BT20L is machined using electrical discharge machining (EDM). In this research paper, parameter optimization for material removal rate (MRR), surface roughness (SR), tool wear rate (TWR) and radial overcut (ROC) is done using the Jaya algorithm (JA). The objective is to maximize the MRR and to minimize SR, ROC and TWR. Response Surface Methodology is used to create a regression model which is used for single and multi-objective optimization. Jaya algorithm is run for 100 iterations for each objective function. Each objective function is tested three times using the Jaya algorithm. It has been found that the Jaya algorithm produces similar optimum solutions in each run. Multi-objective optimization (MOO) is used to optimize multiple responses at the same time. To solve the multi-objective optimization problem, a combined objective function equation is developed that combines four objectives into a single equation. This single equation is used for optimization, which is similar to single-objective optimization. JA is used to optimize the parameters of this MOO. Keywords Jaya algorithm · Advanced optimization · EDM · Electric discharge machining · Optimization · Titanium alloy

N. Agarwal (B) · G. Singh · I. Ahmed · R. Yadav · A. S. Yadav Department of Mechanical Engineering, IES College of Technology, Bhopal, Mathya Pradesh, India e-mail: [email protected] A. Agarwal Department of Mechanical Engineering, University Institute of Technology—R.G.P.V., Bhopal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_2

11

12

N. Agarwal et al.

1 Introduction EDM is a nonconventional machining method. There is a workpiece and the tool is immersed in dielectric media. A potential difference is applied between two electrodes. Due to potential difference, sparking takes place between the two electrodes. There is a high frequency of sparking in the machining zone [1]. Due to sparking, a very high temperature is developed in the machining area. Due to this, a small amount of material is melted and evaporated. To enhance the material removal (MRR), there is a mechanism of flushing that exists at the place of sparking [2]. Titanium alloy has excellent engineering properties; hence, it is widely used in the aerospace industry [3]. EDM is suitable for Titanium alloy machining [4]. EDM has a few controllable input parameters like discharge voltage (V ), peak current (Ip), pulse on time (Ton), pulse off time (Toff), duty factor (t) electrode polarity, electrode gap and dielectric flushing [5]. There are four important input parameters Ip, Ton, V and ‘t’ which are considered as input. MRR is selected as output [6]. A regression model is developed between the control parameters and responses. This model is used for the optimization of the process. There are many advanced optimization algorithms used for the optimization. The Genetic algorithm [7], Bat algorithm [8], Particle swarm optimization [9], Ant colony optimization, Teaching–learning-based optimization [10] and many more are used as optimization algorithms. Jaya algorithm (JA) is getting more popular because of its ease of implementation and higher convergence rate [11, 12]. Rao and Saroj proposed a new variant of JA, which determines population size automatically [13]. Agarwal et al. optimized AISI D2 tool steel for the EDM process using the Jaya algorithm. They optimized MRR, radial overcut and tool wear rate [14]. The process of optimizing more than two quality measures is known as multi-objective optimization [15, 16]. Generally, MOO generates a single combination of both objective functions (quality measures) [17]. Agarwal et al. combined two objectives MRR and TWR into a single function known as relative wear ratio (RWR), in which they optimized as a single-objective optimization [18]. In general, metaheuristic methods are excellent for solving optimization problems. The results may be optimal or very close to optimal. Occasionally, an optimization problem gets stuck in the local optima. Jaya algorithm is a better optimization method for an engineering optimization problem. EDM is most suitable for Titanium alloy machining. It was found from the literature review that peak current, ‘Ton’ and ‘Toff’ are the most important parameter to control the EDM process. This research paper considered tool lift time as an additional control factor. Generally, the proper tuning of the parameter is required to apply the optimization algorithm; hence, Jaya algorithm is used in this research paper because no tuning of parameters is required. This enhances the reliability of the algorithm. Furthermore, it is simple to implement.

Parameter Optimization for Titanium Superalloy for Electrical …

13

2 Experimental Details A plate (50 × 50 × 10 mm) of Titanium superalloy was selected as the workpiece and 10 mm copper bar was selected as an electrode. Both are submerged in a dielectric fluid with straight polarity. There are four important input parameters Ip, Ton, Toff and tool lift time ‘TLT’ which are selected as input parameters to control the process, and other control parameters are kept constant throughout experiments. Twenty-seven experiments were carried out using Response Surface Methodology in accordance with the experiment design [19]. The machining duration is fixed for 20 min for each operation. MRR, TWR, SR and ROC are selected as quality measure. All EDM machining has been done at the Central Institute of Plastic Engineering and Technology, Bhopal, on Electronica S50 CNC. SR is measured with a profilometer. The weight of the workpiece and tool is measured at the beginning and end of the experiment. A selection of input parameters is shown in Table 1. Ton is the spark duration (µs). Ip is the maximum current flow for the sparking. Toff is pulse off time, and TLT is tool lift time. The observed MRR, SR, ROC and TWR responses are recorded in Table 2. The following formula is used to calculate the MRR; MRR =

Mf − Mi . Tp

(1)

A similar formula is used to calculate the TWR, where Mf is the final weight of the workpiece, Mi is the initial weight of the workpiece and Tp is the machining period. The regression model for MRR, SR, TWR and ROC is developed using Minitab [20]. After removing non-significant terms, the final MRR (in mg/min) is shown in Eq. (2), SR model in Eq. (3), ROC model in Eq. (4) and TWR model in Eq. (5). The R-squared value for the regression model is 0.96, 0.96, 0.97 and 0.96 for the MRR, SR, ROC and TWR, respectively. MRR = 13.84 − 2.68 Ip + 0.859 Ton − 0.0815 Toff + 0.86 TLT + 0.1835 Ip2 − 0.01067 Ton2 − 0.01652 Ip ∗ Ton − 0.1879 Ip ∗ TLT + 0.001138 Ton ∗ Toff, Table 1 Range of input parameters Parameter

Peak current Ip (A)

Level 1

9

Pulse on time

Pulse off time

Ton (µs)

Toff (µs)

10

20

Level 2

12

20

50

Level 3

15

50

100

Tool lift time TLT (µs) 1.5 3 4.5

(2)

14

N. Agarwal et al.

Table 2 Experimental observations S. No

Ip (A)

Ton (µs)

Toff (µs)

TLT (µs)

MRR (mg/min)

SR (µm)

ROC (mm)

TWR (mg/min)

1

9

10

20

1.5

8.25

6.059

0.119

0.57

2

9

10

50

3

4.17

4.75

0.121

0.61

3

9

10

100

4.5

1.06

4.19

0.127

0.55

4

9

20

20

3

11.67

5.859

0.162

0.45

5

9

20

50

4.5

8.56

6.35

0.189

0.3

6

9

20

100

1.5

5.57

6.979

0.15

0.52

7

9

50

20

4.5

8.27

7.86

0.196

0.46

8

9

50

50

1.5

8.279

0.213

0.41

9

9

50

100

3

8.07

6.14

0.168

0.32

10

12

10

20

3

8.75

7.229

0.217

0.73

11

12

10

50

4.5

3.9

6.59

0.203

0.33

12

12

10

100

1.5

4.01

8.7

0.224

0.84

13

12

20

20

4.5

8.22

7.469

0.217

0.65

14

12

20

50

1.5

13.17

8.76

0.262

0.81

15

12

20

100

5.97

7.34

0.229

0.31

16

12

50

20

1.5

12.5

8.86

0.259

0.59

17

12

50

50

3

10.45

7.73

0.241

0.33

18

12

50

100

4.5

5.35

7.7

0.227

0.23

19

15

10

20

4.5

9.2

8.68

0.274

1.23

20

15

10

50

1.5

13.7

10.16

0.287

1.7

21

15

10

100

0.263

1.72

22

15

20

20

1.5

17.03

10.43

0.301

2.08

23

15

20

50

3

16.47

9.48

0.314

1.59

24

15

20

100

25

15

50

20

3

26

15

50

50

4.5

27

15

50

100

1.5

3

3

4.5

12

6.82

7.249

9.67

7.01

0.259

1.43

11.91

10.10

0.289

1.52

8.15

8.82

0.276

1.51

12.24

0.299

1.81

13.8

SR = − 0.07 + 0.8134 Ip + 0.03708 Ton + 0.02416 Toff − 0.776 TLT + 0.3300 TLT2 − 0.1001 Ip ∗ TLT − 0.01039 Toff ∗ TLT,

(3)

ROC = − 0.2477 + 0.03394 Ip + 0.00766 Ton + 0.000908 Toff + 0.0222 TLT − 0.000065 Ton2 − 0.000008 Toff2 − 0.000233 Ip ∗ Ton − 0.002259 Ip ∗ TLT,

(4)

Parameter Optimization for Titanium Superalloy for Electrical …

15

TWR = 5.944−1.093 Ip−0.00317 Ton−0.000654 Toff + 0.176 TLT + 0.05642 Ip2 −0.02278 Ip ∗ TLT,

(5)

3 Jaya Algorithm The Jaya algorithm has few parameters as ‘k’ population size (five in this case), ‘j’ numbers of design variables (four in this case) and ‘i’ as number of iterations (hundred in this case). Initial solutions are random numbers within the range of upper and lower bounds and the corresponding fitness (objective) value. The solution is modified from Eq. (6) and a new fitness value is calculated. JA is a greedy algorithm; hence, better solution is kept in the system (between old and new). The flow diagram of the JA is given in Fig. 1. | | | |   ' X jki = X jki + r1 ji X j,best,i − | X jki | − r2 ji X j,worst,i − | X jki | ,

(6)

where ‘j’ as jth variable value, ‘i’ as the current iteration number, ‘k’ as the candidate number and X j,best,i = best candidate solution among all candidates, while X j,worst,i is worst candidate value.

4 Optimization The objective is to maximize MRR and to minimize TWR, SR and ROC. Step 1: Population size five is considered. Design variable is chosen as random numbers in between upper and lower bounds. Step 2: Identify the best candidate and worst candidate among the existing solution. Step 3: Calculate the new value of variables from following equation and the corresponding MRR is calculated from Eq. (6) Step 4: Now compare the old candidate and new candidate for better objective function value. Select better solution for all candidates. Step 5: Repeat a similar procedure from step-2 to step-4 for the second iteration. Step 6: Repeat the method for 100 times (step-2–step-5). Multi-objective optimization: In this case, a single-objective function is created that incorporates all objective functions into one. This single function optimizes using Jaya algorithm. Figure 2 illustrates the impact of parameters on MRR, while Fig. 3 demonstrates the influence of a parameter on SR. Figure 4 displays the influence of parameters on ROC and Fig. 5 depicts the effect of parameters on TWR.

16

Fig. 1 Jaya algorithm flow diagram

N. Agarwal et al.

Parameter Optimization for Titanium Superalloy for Electrical …

17

Fig. 2 Influence of parameters on MRR

Fig. 3 Influence of parameters on SR

Mimimize Z =

ROC TWR MRR SR + + − SRmin ROCmin TWRmin MRRmax

(7)

5 Results and Discussion Table 3 shows the single-objective optimization. It is obvious from the figure that MRR increases with increment in ‘Ip’ because more power is supplied to the machining area. Similarly, surface roughness increases due to a higher power supply. More energy supply causes the poor surface finish. ‘Ton’ represents spark duration;

18

N. Agarwal et al.

Fig. 4 Influence of parameters on ROC

Fig. 5 Influence of parameters on TWR

hence, higher ‘Ton’ leads to higher energy supply. As a result of the ‘Ton’ enhancement, MRR and SR increase. ‘Toff’ is the spark-off time duration between the two sparks. ‘TLT’ is the tool lift time after the spark. Hence, higher ‘Toff’ and ‘TLT’ decrease the energy supplied in the machining zone. This is the reason to lower MRR and SR with an increase in ‘Toff’ and ‘TLT’. ROC and TWR have also nearly similar trends as MRR. Table 4 shows the multi-objective optimization.

Parameter Optimization for Titanium Superalloy for Electrical …

19

Table 3 Optimum value of MRR SR, ROC and TWR S. No

Response

Ip

Ton

1

MRR

15

29.798

Toff

TLT

Optimum value

20

1.5

19.8539

2

SR

9

10

100

4.1061

4.4499

3

ROC

9

10

100

1.5

0.1163

4

TWR

10.5758

50

100

4.5

0.1754

Table 4 Multi-objective optimization and corresponding MRR, SR, ROC and TWR Ip

Ton

Toff

TLT

MRR

SR

ROC

TWR

9

22.9293

20

2.2273

12.3495

6.0224

0.1702

0.5241

6 Conclusion Titanium superalloys are difficult to machine with the conventional machining process. EDM is employed to machine the Titanium superalloy BT20L, and the subsequent step involves optimizing parameters using the Jaya algorithm. There are four input parameters considered in this study which are ‘Ip’, ‘Ton’, ‘Toff’ and ‘TLT’. The Jaya algorithm was successfully applied to optimize MRR, SR, ROC and TWR as single-objective optimization. Because the Jaya algorithm is used for single-objective optimization, a combined equation is developed that gives equal weightage to each objective. This combined equation is optimized using the JA. The JA is implemented using the commercial software MATLAB; it is found that JA produces an optimal result. It is a reliable optimization algorithm and easier to implement. It is suitable for engineering optimization problems and multi-objective optimization.

References 1. Abbas NM, Solomon DG, Bahari MF (2007) A review on current research trends in electrical discharge machining (EDM). Int J Mach Tools Manuf 47:1214–1228 2. Ho KH, Newman ST (2003) State of the art electrical discharge machining (EDM). Int J Mach Tools Manuf 43:1287–1300 3. Boyer RR (1996) An overview on the use of titanium in the aerospace industry. Mater Sci Eng A 213:103–114 4. Ribeiro MV, Moreira MRV, Ferreira JR (2003) Optimization of titanium alloy (6Al–4V) machining. J Mater Process Technol 143–144:458–463 5. Kumar S, Singh R, Batish A, Singh TP (2012) Electric discharge machining of titanium and its alloys: a review. Int J Mach Mach Mater 11:84–111 6. Kao JY, Tsao CC, Wang SS, Hsu CY (2010) Optimization of the EDM parameters on machining Ti–6Al–4V with multiple quality characteristics. Int J Adv Manuf Technol 47:395–402 7. Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197

20

N. Agarwal et al.

8. Yang X-S, Gandomi H (2012) Amir, Bat algorithm: a novel approach for global engineering optimization. Eng Comput 5:464–483 9. Eberhart RC, Shi Y (2001) Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 congress on evolutionary computation, vol 1, pp 81–86 10. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43:303–315 11. Rao R, Venkata J (2016) A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7:19–34 12. Rao RV, More KC, Taler J, Ocło´n P (2016) Dimensional optimization of a micro-channel heat sink using Jaya algorithm. Appl Therm Eng 103:572–582 13. Rao RV, Saroj A (2017) A self-adaptive multi-population based Jaya algorithm for engineering optimization. Swarm Evol Comput 37:1–26 14. Agarwal N, Shrivastava N, Pradhan MK (2020) Optimisation of EDM process parameters using Jaya Algorithm. Mater Today: Proc 24:825–834 15. Marler RT, Arora JS (2004) Survey of multi-objective optimization methods for engineering. Struct Multidisc Optim 26:369–395 16. Kalyanmoy D (2014) Search methodologies. Springer, Boston, pp 403–449 17. Rao RV, More KC (2017) Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm. Energy Convers Manag 140:24–35 18. Agarwal N, Shrivastava N, Pradhan MK (2020) Optimization of relative wear ratio during EDM of titanium alloy using advanced techniques. SN Appl Sci 2:99 19. Habib SS (2009) Study of the parameters in electrical discharge machining through response surface methodology approach, Appl Math Model 33:4397–4407 20. Sohani MS, Gaitonde VN, Siddeswarappa B, Deshpande AS (2009) Investigations into the effect of tool shapes with size factor consideration in sink electrical discharge machining (EDM) process. Int J Adv Manuf Technol 45:1131–1145

A Deep Review: Techniques, Findings and Limitations of Traffic Flow Prediction Using Machine Learning Abhay Narayan Tripathi and Bharti Sharma

Abstract The evolution of machine learning techniques has generated creative solutions for smart cities, by which a life of human life can be easier. Amid the growing transportation data, the accurate traffic flow prediction has become a great requirement and hence regarded as an important for so many cites of reasonably a good size, which is a matter of worry which creates obstacle to continuous urban development. Nowadays, transportation data are exploding in the nature of big data. Presently, available traffic flow prediction models are less effective for many real-world applications. A short time ago, Intelligent Traffic System using deep learning has surfaced as a constructive and fruitful tool to lessen urban congestion and accurate traffic flow forecast. This study’s objective is to provide a thorough, well-organized assessment of the literature, which will include 29 publications from 2014 that were pulled from Web of Science, Scopus, and ScienceDirect. The extracted information includes the gaps, limitations, and future scopes for accurate and effective traffic movement prediction. Our research reflects that Convolutional Neural Network (CNN), Stacked Autoencoder (SAE), Long Short-Term Memory or hybrid are ML techniques that have been used frequently for the better and improved performance. In this paper, the proposed techniques are compared with shallow and traditional models. The Authors believe that this study provides an efficient manner for traffic estimation in smart cities. Keywords Connected vehicles · Predicting traffic jams · Traffic flow · Deep learning · And Hybrid machine learning

A. N. Tripathi (B) · B. Sharma DIT University, Dehradun, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_3

21

22

A. N. Tripathi and B. Sharma

1 Introduction An estimation of the flow count at present scenario is highly required for everyone be it individual travelers, houses from business, and government sectors [1]. For gaining the major forecasting potential, helps everyone to make better travel decisions, minimize density, reduce pollution, and for improving traffic regularization efficiency. The main feature of flow count forecasting is to give a flow statistics for traffic planning and management. Traffic flow prediction has richly attained the high order of care with the swift growth of Intelligent Transportation Systems. This is very important factor for the incorporation of ITS subsystems, especially for them who use at advance level to manage novel flow management, buses, trains, and other transportation systems, and vehicle engaged in trade for advance function at present time. The huge demand even in limited network, traffic congestion has become a global and vital problem. There are certain identified factors which cause traffic snarl like complex traffic network, not balanced traffic flow, and not certain event as road accident. Traffic flow depends on the various parameters like vehicle density, speed, and traveling time. Smooth and effective traffic flow will help in fuel consumption, reduction in traveling time and road accidents, traffic congestion, air pollution, and energy consumption. A day of a week, weather, and season are some contextual factors on which traffic flow prediction depends. From historical data, a relationship can be derived from the traffic count values and a combination of external factors. Traffic flow prediction mainly spatiotemporal time series traffic data collected from various places like Facebook, Twitter, mobile GPS, crowd sourcing, etc. Due to the explosion of data and traditional traffic sensors, the concept and implementation of big data transportation have emerged. Data-driven model is playing an essential part in traffic flow dealing and control [2, 3]. For traffic flow prediction, there are many models but are less efficient and effective. This motivates for introspection for new model based on deep learning architecture pertaining to traffic movement forecasting. Many years have gone for researchers for developing the model for traffic flow prediction. When forecasting was thought to be very important, values considered at a particular time interval were dealt with Autoregressive Integrated Moving Average model [3, 4], whereas Support Vector Regression model in the work of [5–8] is used for the short-term forecasting model. The two main machine learning algorithms backpropagation model [5] and therefore NN model were also implemented [9]. However, the traditional neural network is still less effective. Contextual factors and traffic events like weather, accident, etc. affect traffic flow estimation. Therefore, the deep network architecture is required. After the emergence of fast learning, deep learning became more powerful for the better prediction [10, 11]. For the exact and accurate flow count and prediction management two methods have been used one is Long Short-Term Memory and other is Staked Autoencoder. To gain or acquire knowledge from the data of traffic count, a relationship between spatiotemporal data

A Deep Review: Techniques, Findings and Limitations of Traffic Flow …

23

is established, and Stacked Autoencoder has been used [12]. For learning features, a greedy layer-wise technique is performed. The center of attention of this paper in on the successful application for projecting traffic flow counts using short-term and long-term memory (LSTM), a DL that is an Artificial Neural Network. For arraigning into categories, performing operations, and making future flow count predictions, the time series data are used by LSTM networks, since timing is uncommon for important events. A neural network comprising many layers of sparse autoencoders is called as Stacked Autoencoder, where input layer is the attached to the hidden layer’s output. The next layer uses the learned data of the preceding layer and carries on till the training is completed.

2 Literature Review Established keywords have been introduced for searching the articles. As per the language option, only English language is selected. Time span of search is from year 2004 to 2022.The database search (like Web of Science and Scopus) yielded 74 studies (with duplicates removed). Initially, 29 articles were selected based on information in the title and abstract and met the inclusion criteria. Criteria: Only studies addressing the prediction of traffic flow applying machine learning techniques were eligible for consideration. Yu and Zhang [11] proposed Switching Autoregressive Integrated Moving Average (ARIMA) model using Sigmoid function, and it was observed that Switching ARIMA model is better, applicable, and effective than conventional model ARIMA model for pattern recognition. MRSE and MARE are better. Kumar et al. [12] considered the diverse conditions using Artificial Neural Network (ANN) for predicting short-term traffic flow. It has been observed that the result is optimum of the model and correlation coefficient, mean absolute error, and root mean square error (MAE), standard deviation and c2-test have been used. Kumar et al. [13] considered a rural route’s short-term traffic flow forecast utilizing an ANN. From 5 to 15 min, the time interval for predicting traffic flow was increased using Artificial Neural Network for constant and optimum result. Lv et al. [14] discussed traffic forecasting model by using a Stacked Autoencoder (SAE) where result showed superior performance and average accuracy is over 93% with other models. Kumar and Vanajakshi [4] discussed data-driven approach in the paper by using algorithm called Seasonal Autoregressive Integrated Moving Average (SARIMA), and the experimental result revealed that SARIMA overcomes the problem of ARIMA in terms of data availability for limited input projection of the short-term traffic flow. Duan et al. [15] aimed the performance evaluation for traffic movement prediction considering deep learning approach at different times by using multiple SAE models. The result concluded that MAE and RMSE are larger at daytime while MRE is at nighttime.

24

A. N. Tripathi and B. Sharma

Koesdwiady et al. [16] discussed traffic flow estimation considering weather condition and a deep learning approach using deep belief network (DBN), where the performance of traffic flow prediction was improved for connected cars. DBN exhibits best prediction accuracy. Fu et al. [17] aimed traffic flow prediction neural network as NN with GRU and long short-term memory LSTM NN. The result depicted that proposed model outperformed traffic flow prediction than ARIMA in average, and GRU NNs have reduced MAE at about 10% than ARIMA model 5% than LSTMNN model. Soua et al. [18] explains how big data, deep learning, and the Dempster–Shafer theory can be used to anticipate traffic flow. Deep belief network (DBN) is used and result revealed that DBN gives around 82% accuracy in short-term traffic movement forecasting using big data originated from diverse sources. Soua et al. [18] stated about traffic count by using TensorFlow based neural network DNN (deep neural network) and also discussed deep neural networks. To distinguish traffic flow congestion from non-congestion, TPI was used and results show that new algorithm and model could measure congestion with 99% accuracy. Li et al. [19] employed a combined approach taking into account spatial and temporal data to forecast short-term highway traffic flow. A great improvement was found by using ARIMA and SVR which reflected accuracy and stability. Jia et al. [20] focused on exogenous factor rainfall impact using a DBN and LSTM are deep learning techniques for predicting traffic flow. Experimental result showed the better performance than BPNN and ARIMA, metropolitan traffic count considering effect of rainfall. Arif et al. [21] states predicting traffic flow using deep learning and the KNN nonparametric regression model. It was found that traffic movement was very effective, this model considered and compared three parameters MRE, MRSE, MAPE and APE. Tampubolon and Hsiung [22] supervised deep learning-based FC-DNN, a fully connected deep neural network, which is taken into consideration for traffic flow prediction. When using sample data, it was discovered that MAPE for our traffic count predicting is within 5%, if not between 15 and 20%. Li et al. [23] discussed short-term traffic flow projection based on LSTM. The experiment’s findings showed that there was a considerable effect on prediction accuracy. Zhang et al. [24] discussed to extract the spatiotemporal properties, and a Convolutional Neural Network (CNN) model with two convolution layers and three linked layers was used. Traffic flow prediction is very much effective and accurate as per the experimental result. Luo et al. [25] considered KNN and LSTM for traffic flow forecasting. Hybrid methodologies indicated that when compared to ARIMA, the method can, on average, produce a 12.59% improvement. Qu et al. [26] aimed on a deep neural network using for forecasting daily longterm traffic flow, used a DNN (Deep Neural Network). The result reflected that the proposed model outperformed the conventional prediction model in terms of prediction accuracy.

A Deep Review: Techniques, Findings and Limitations of Traffic Flow …

25

Yi et al. [27] LSTM-based prediction of short-term traffic flow. The outcome of the experiment revealed that the performance of prediction was significantly impacted. Sumalatha et al. [28] discussed Multi-Layer Perceptron Neural Networks using artificial neural system for predicting short-term road traffic flow. The results exhibited by the model were quite satisfactory. Li and Xu [29] discussed SVR for machine learning traffic prediction in intelligent transportation. Algorithm is faster and better for accurate for short-term traffic flow prediction. This study finds that the RMSE is decreased by 29.71% and 47.22%, respectively, while SVR’s MAPE declines by 19.94% and 42.86%, respectively, and classification error rate is rate is the lowest 3.22%. Chen et al. [30] discussed better prediction of traffic flow using wavelet neural networks. Because IPSO-prediction WNN’s accuracy is much according to the experimental results, IPSO-WNN can predict short-term traffic flow with a practicality and efficacy that is greater than that of PSO-WNN and WNN. Qiao et al. [31] discussed long short-term memory and one-dimensional Convolutional Neural Networks The neural network 1DCNN-LSTM uses one-dimensional convolution to predict short-term flow. Result shows that the forecasting accuracy is high. Zheng et al. [32] discussed a composite model having CNN and LSTM networks for predicting traffic flow to extract geographical and short-term temporal parameters, Attention-based Conv-LSTM module was developed. Essien et al. [33] discussed prediction ilising traffic data, analysed urban traffic flow from twitter using Deep b-directional Long Short-Term Memory (LSTM) and Stacked Autoencoder (SAE) architecture. The result showed that the MAE was reduced from 8 to 5.5 veh/h by adding Twitter data along with traffic, rainfall, weather, and temperature datasets, delivering a more precise forecast of traffic flow. Zheng and Huang [34] compared In order to predict traffic flow for time series analysis, LSTM network is employed by them. BPNN and ARMA model. The experimental results indicated that the LSTM network gives better performance as compared to the BPNN model versus the ARIMA model in terms of prediction precision. The mean RMSEs are 61.1699, 26.1699, and 14.4438 for the ARIMA, BPNN, and LSTM, respectively. Lu et al. [35] presented the experimental findings, which demonstrated that the recommended dynamic weighted mixed model performs better. Rajendran and Ayyasamy [36] discussed model for short-term traffic forecasting in cities. It is more useful for forecasting India’s abnormal traffic situation. Hou et al. [37] short-term traffic flow prediction with weather conditions utilizing Stacked Autoencoder (SAE) and Radial Basis Function (RBF) employing deep learning algorithms and Data Fusion. Results indicate that the integrated framework is more effective and useful for prediction (Table 1).

26

A. N. Tripathi and B. Sharma

Table 1 Literature review S. No.

Ref. No.

Technique

Main findings

Limitation

1

[11]

Switching autoregressive integrated moving average (ARIMA) model using Sigmoid function

It was observed that Switching ARIMA model is better, applicable, and effective than conventional model ARIMA model for pattern recognition. MRSE and MARE are better

The model parameters of time series has been done manually

2

[12]

Artificial neural network (ANN)

Model performance is better

The hybrid model can be successfully applied in the Indian traffic scenario

3

[13]

Artificial neural network (ANN)

Artificial neural network performance improved when the prediction interval was extended from 5 to 15 min

For non-urban Indian highways, ANN may be utilized to estimate short-term traffic flow in mixed traffic circumstances

4

[14]

A stacked autoencoder (SAE) model

The result shows that superior performance and average accuracy is over 93% with other models

Investigation of other deep learning algorithms is interesting on different public open datasets and also prediction layer has been just a logistic regression

5

[4]

• SARIMA • Data-driven approach

SARIMA overcomes the problem SRIMA only applies to of ARIMA in terms of data input data with a small availability with little input sample size predicting future traffic flow

6

[15]

Multiple SAE models

MAE and RMSE are larger at daytime. while MRE is at nighttime

SAE is better, but only limited parameters are used

7

[16]

• Deep belief network (DBN) for traffic and weather prediction • Decision-level data fusion scheme

DBN exhibits best forecasting accuracy

The consideration has not been made for non-traditional and social networks information

8

[17]

• LSTM • GRUNN

Outperformed traffic flow prediction than ARIMA GRU NNs have, on average, reduced MAE by 10% compared to ARIMA models and 5% compared to LSTMNN models

RNNs are tested with limited hidden states

(continued)

A Deep Review: Techniques, Findings and Limitations of Traffic Flow …

27

Table 1 (continued) S. No.

Ref. No.

Technique

Main findings

Limitation

9

[18]

• DBN • DSET

82% accuracy has been measured Only Twitter social by DBN considering media has been heterogeneous big data for considered short-term traffic flow prediction

10

[18]

Deep neural network (DNN) based on TensorFlow

A distinction has been done between congested and non-congested traffic conditions by using TPI, and results indicate that model could estimate congestion with 99% accuracy

11

[19]

• ARIMA • SVR

The prediction performance can Model is not validated be improved by using a hybrid for incidents, work zone, model that includes accuracy and and weather conditions stability

12

[20]

• DBN • LSTM

Experimental result shows the better performance than BPNN and ARIMA, evaluating the effects of rainfall on urban traffic flow

It would be interesting to introduce more deep learning method, considering more additional factors

13

[21]

K-nearest neighbor (KNN)

The findings of a model that took into account the three factors MRE, MRSE, MAPE, and APE demonstrate accurate traffic flow prediction

For predicting traffic flow, the model has not taken into account multivariate time series; however, other regression models might be investigated

14

[22]

FC-DNN

Our traffic flow prediction’s Mean Absolute Percentage Error (MAPE) is 5% or less when utilizing sample data and 15–20% when using non-sample data. Over-fitting is decreased when a deep network is trained more quickly using BN and dropout

Early stopping technique can be used for the training. The training error does not improve that much considering spatiotemporal

15

[23]

LSTM

The outcome reveals that the size of hidden layer neuron has a great impact on the performance of prediction

Gated recurrent unit (GRU) can be considered in future for more accurate prediction

A potential method in the field of transportation engineering is the use of TensorFlow™ to large data. MultiGPUs of high-degree performance could be built in future TensorFlow™ research, some of the limitations described above may be overcome

(continued)

28

A. N. Tripathi and B. Sharma

Table 1 (continued) S. No.

Ref. No.

Technique

Main findings

Limitation

16

[24]

To extract the spatiotemporal properties, a CNN was used

Traffic count prediction is very much effective and accurate as per the experimental result

Contextual factors such as speed, road accidents, weather have not been considered

17

[25]

• KNN • LSTM

A mixed traffic flow forecasting shows that this method can improve on average by 12.59% over ARIMA

Weather, incidents, and other factors have not been considered

18

[26]

DNN

The performance of proposed model outperformed in terms of prediction accuracy, the traditional prediction model

The consideration has not been made for accidents and construction activities

19

[27]

• LSTM-RNN model

It is worthwhile to keep working on the model because it shows promising outcomes

In order to forecast the near term in roadway networks, the length of the sequence, the effects of parameters like dropout, and the number of neurons can be taken into consideration

20

[28]

Artificial neural network (ANN)

The model shows the satisfactory The same principle can results be applied to other congested intersections or bottleneck traffic areas in Hyderabad, and similar analyses can be done. Time series models use the same dataset to anticipate the near-term traffic flow

21

[29]

• SVR • SVM

• Comparatively the performance HOG features are not of algorithm is effective and expanded better than previous to forecast short-term traffic flow. The MAPE of SVR results in reductions of 19.94% and 42.86%, while the RMSE results in reductions of 29.71% and 47.22%, respectively. Classification error rate is rate is the lowest 3.22% (continued)

A Deep Review: Techniques, Findings and Limitations of Traffic Flow …

29

Table 1 (continued) S. No.

Ref. No.

Technique

Main findings

Limitation

22

[30]

Wavelet neural Because IPSO forecasting network (WNN) WNN’s accuracy is significantly prediction algorithm higher in this case. Instead of using WNN and PSO-WNN to predict short-term traffic flow, IPSO-WNN is a viable and effective alternative

23

[31]

1DCNN-LSTM

Result shows that the forecasting True consideration has accuracy is high not been made for weather conditions and other exogenous factors

24

[32]

Firstly, model was built on CNN and LSTM network, attention-based Conv-LSTM

Prediction accuracy is improved

25

[33]

• Deep bi-directional LSTM • SAE

Giving a more precise forecast of The study is restricted to traffic flow, the MAE decreased single arterial road from 8 to 5.5 vehicles per hour. Along with traffic, rainfall, meteorological, and temperature records, tweet data was also included

26

[34]

This study analyzes the impacts of the ARMA model, BPNN, and LSTM network on traffic flow forecasting

For the ARIMA, BPNN, and LSTM, the mean RMSEs were, in that order, 61.1699, 26.1699, and 14.4438. This shows that the LSTM network fared superior than both the BPNN and the ARIMA models in terms of prediction accuracy

27

[35]

• LSTM • ARIMA • Used to capture linear and nonlinear data

The suggested dynamic weighted All influencing factors mixed model has better effect are not covered

28

[36]

• Structure pattern • Regression

Model is more beneficial in forecasting the abnormal traffic state in India

Training model sample is small in this paper

In this paper, road network has been considered relatively simple and small

The characteristics of machine learning based on big data to forecast the traffic count under various exogenous factors are not utilized

The other crucial outside variables include weather, detours caused by construction or road damage, and traffic incidents can be extended in future work (continued)

30

A. N. Tripathi and B. Sharma

Table 1 (continued) S. No.

Ref. No.

Technique

Main findings

Limitation

29

[37]

• Stacked autoencoder (SAE) • Radial basis function (RBF)

Results show that the combined framework is better and valuable for prediction

Some more influential factors can be considered

3 Comparative Analysis of Deep Learning Architectures There was a GRU of a Recurrent Neural Network (RNN) made a base for the investigation of several traffic flow sections considering traffic. Therefore we have designed and develop a Instead of using the neural network’s input is a time series of typical traffic flow, use a spatial temporal dependence matrix of parameters to predict traffic flow [38, 39]. ARIMA, SVM, and LSTM were chosen as the control tests [40]. A more accurate way of predicting traffic flow is based on spatiotemporal dependence than the methods currently used in the mainstream [41, 42]. For traffic volume forecasting, this section includes merits, demerits, and best solution of different deep learning architectures like SAE, LSTM, and CCN. For timely and accurate traffic flow prediction, Mean Absolute Error [43] is used. Some other key methods like MRE and RMSE [18], MAPE [44], and precision and accuracy [43] are also taken into consideration. It has been observed that in the case of traffic events and contextual factors, SAE is much better including weather and sentiments with spatial and temporal traffic data [44, 45]. It is displayed in Table 2 [44, 46–48]. Table 2 Merits and demerits of DL architectures DL architecture

Merits

Demerits

Best solution

LSTM

Most suitable for time series spatiotemporal traffic data

When unexpected traffic events occurs, performance comes down

A combination of Bayesian and LSTM network can be used

SAE

It handles time series data efficiently and can be trained greedy layer-wise technique

Sparse Autoencoders are used for more accuracy, hence more processing time

Selection of appropriate algorithm will give good results with less autoencoders

CNN

CNN is more useful for spatial data. For fetching image data, surveillance camera can be used

Features’ extraction related to time from images is difficult

To model spatiotemporal data, both CNN and LSTM can be used

A Deep Review: Techniques, Findings and Limitations of Traffic Flow …

31

4 Conclusion, Limitations, and Future Work The performance of the proposed techniques is compared with existing machine learning and deep learning baseline models to determine their effectiveness of traffic flow prediction. It will help other researchers and academician as a reference for future work. A deep literature review has been made to select 29 papers for this comprehensive study. The contextual and external factors have not been addressed, and also limited dataset has been used. These factors refrain the performance of traffic flow prediction. Various techniques and algorithms have been proposed by the researchers to measure the most effective approach for traffic estimation. The traffic flow algorithms specifically CNN, LSTM, and SAE have shown high-quality outcomes in improving the overall performance. This study provides future research scope based on different literary sources and studies indexed in greater databases.

References 1. Zhang N, Wang F-Y, Zhu F, Zhao D, Tang S (2008) DynaCAS: computational experiments and decision support for ITS. IEEE Intell Syst 23(6):19–23. https://doi.org/10.1109/mis.2008.101 2. Zhang J, Wang F-Y, Wang K, Lin W-H, Xu X, Chen C (2011) Data-driven intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst 12(4):1624–1639. https://doi.org/ 10.1109/tits.2011.2158001 3. Philip Chen CL, Zhang C-Y (2014) Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf Sci 275:314–347. https://doi.org/10.1016/j.ins.2014. 01.015 4. Kumar SV, Vanajakshi L (2015) Short-term traffic flow prediction using seasonal ARIMA model with limited input data. Eur Transp Res Rev 7(3). https://doi.org/10.1007/s12544-0150170-8 5. Zeng D, Xu J, Gu J, Liu L, Xu G (2008) Short term traffic flow prediction based on online learning SVR. In: 2008 Workshop on power electronics and intelligent transportation system. https://doi.org/10.1109/peits.2008.134 6. Ahn JY, Ko E, Kim E (2015) Predicting spatiotemporal traffic flow based on support vector regression and Bayesian classifier. In: 2015 IEEE fifth international conference on big data and cloud computing. https://doi.org/10.1109/bdcloud.2015.64 7. Hu J, Gao P, Yao Y, Xie X (2014) Traffic flow forecasting with particle swarm optimization and support vector regression. In: 17th International IEEE conference on intelligent transportation systems (ITSC). https://doi.org/10.1109/itsc.2014.6958049 8. Haiyan W, Youzhen W (2015) Vessel traffic flow forecasting with the combined model based on support vector machine. In: 2015 International conference on transportation information and safety (ICTIS). https://doi.org/10.1109/ictis.2015.7232151 9. Bao-ping C, Zeng-qiang M (2009) Short-term traffic flow prediction based on ANFIS. In: 2009 International conference on communication software and networks. https://doi.org/10. 1109/iccsn.2009.140 10. Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527 11. Yu G, Zhang C (2004) Switching ARIMA model based forecasting for traffic flow. In: 2004 IEEE International conference on acoustics, speech, and signal processing. https://doi.org/10. 1109/icassp.2004.1326286

32

A. N. Tripathi and B. Sharma

12. Kumar K, Parida M, Katiyar VK (2013) Short term traffic flow prediction in heterogeneous condition using artificial neural network. Transport 30(4):397–405. https://doi.org/10.3846/ 16484142.2013.818057 13. Kumar K, Parida M, Katiyar VK (2013) Short term traffic flow prediction for a non urban highway using artificial neural network. Procedia Soc Behav Sci 104:755–764. https://doi.org/ 10.1016/j.sbspro.2013.11.170 14. Lv Y, Duan Y, Kang W, Li Z, Wang F-Y (2014) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst1–9. https://doi.org/10.1109/tits.2014.234 5663 15. Duan Y, Lv Y, Wang F-Y (2016) Performance evaluation of the deep learning approach for traffic flow prediction at different times. In: 2016 IEEE international conference on service operations and logistics, and informatics (SOLI). https://doi.org/10.1109/soli.2016.7551691 16. Koesdwiady A, Soua R, Karray F (2016) Improving traffic flow prediction with weather information in connected cars: a deep learning approach. IEEE Trans Veh Technol 65(12):9508– 9517. https://doi.org/10.1109/tvt.2016.2585575 17. Fu R, Zhang Z, Li L (2016) Using LSTM and GRU neural network methods for traffic flow prediction. In: 2016 31st Youth academic annual conference of Chinese association of automation (YAC). https://doi.org/10.1109/yac.2016.7804912 18. Soua R, Koesdwiady A, Karray F (2016) Big-data-generated traffic flow prediction using deep learning and dempster-shafer theory. In: 2016 International joint conference on neural networks (IJCNN). https://doi.org/10.1109/ijcnn.2016.7727607 19. Li L, He S, Zhang J, Ran B (2016) Short-term highway traffic flow prediction based on a hybrid strategy considering temporal–spatial information. J Adv Transp 50(8):2029–2040. Portico. https://doi.org/10.1002/atr.1443 20. Jia Y, Wu J, Xu M (2017) Traffic flow prediction with rainfall impact using a deep learning method. J Adv Transp 2017:1–10. https://doi.org/10.1155/2017/6575947 21. Arif M, Wang G, Chen S (2018) Deep learning with non-parametric regression model for traffic flow prediction. In: 2018 IEEE 16th international conference on dependable, autonomic and secure computing, 16th international conference on pervasive intelligence and computing, 4th international conference on big data intelligence and computing and cyber science and technology congress (DASC/PiCom/DataCom/CyberSciTech). https://doi.org/10.1109/dasc/ picom/datacom/cyberscitec.2018.00120 22. Tampubolon H, Hsiung P-A (2018) Supervised deep learning based for traffic flow prediction. In: 2018 International conference on smart green technology in electrical and information systems (ICSGTEIS). https://doi.org/10.1109/icsgteis.2018.8709102 23. Li J, Gao L, Song W, Wei L, Shi Y (2018) Short term traffic flow prediction based on LSTM. In: 2018 Ninth international conference on intelligent control and information processing (ICICIP). https://doi.org/10.1109/icicip.2018.8606716 24. Zhang W, Yu Y, Qi Y, Shu F, Wang Y (2019) Short-term traffic flow prediction based on spatiotemporal analysis and CNN deep learning. Transportmetrica A: Transp Sci 15(2):1688–1711. https://doi.org/10.1080/23249935.2019.1637966 25. Luo X, Li D, Yang Y, Zhang S (2019) Spatiotemporal traffic flow prediction with KNN and LSTM. J Adv Transp 2019:1–10. https://doi.org/10.1155/2019/4145353 26. Qu L, Li W, Li W, Ma D, Wang Y (2019) Daily long-term traffic flow forecasting based on a deep neural network. Expert Syst Appl 121:304–312. https://doi.org/10.1016/j.eswa.2018. 12.031 27. Yi H, Bui K-HN, Jung H (2019) Implementing a deep learning framework for short term traffic flow prediction. In: Proceedings of the 9th international conference on web intelligence, mining and semantics—WIMS2019. https://doi.org/10.1145/3326467.3326492 28. Sumalatha V, Dingari M, Jayalakshmi C (2020) Short term road traffic flow forecasting using multi layer perceptron neural networks. In: International conference on mathematical sciences and applications (ICMSA-2019). https://doi.org/10.1063/5.0014561 29. Li C, Xu P (2020) Application on traffic flow prediction of machine learning in intelligent transportation. Neural Comput Appl 33(2):613–624. https://doi.org/10.1007/s00521-020-050 02-6

A Deep Review: Techniques, Findings and Limitations of Traffic Flow …

33

30. Chen Q, Song Y, Zhao J (2020) Short-term traffic flow prediction based on improved wavelet neural network. Neural Comput Appl 33(14):8181–8190. https://doi.org/10.1007/s00521-02004932-5 31. Qiao Y, Wang Y, Ma C, Yang J (2020) Short-term traffic flow prediction based on 1DCNNLSTM neural network structure. Mod Phys Lett B 35(02):2150042. https://doi.org/10.1142/ s0217984921500421 32. Zheng H, Lin F, Feng X, Chen Y (2021) A hybrid deep learning model with attention-based Conv-LSTM networks for short-term traffic flow prediction. IEEE Trans Intell Transp Syst 22(11):6910–6920. https://doi.org/10.1109/tits.2020.2997352 33. Essien A, Petrounias I, Sampaio P, Sampaio S (2020) A deep-learning model for urban traffic flow prediction with traffic events mined from twitter. World Wide Web 24(4):1345–1368. https://doi.org/10.1007/s11280-020-00800-3 34. Zheng J, Huang M (2020) Traffic flow forecast through time series analysis based on deep learning. IEEE Access 8:82562–82570. https://doi.org/10.1109/access.2020.2990738 35. Lu S, Zhang Q, Chen G, Seng D (2021) A combined method for short-term traffic flow prediction based on recurrent neural network. Alex Eng J 60(1):87–94. https://doi.org/10.1016/j.aej.2020. 06.008 36. Rajendran S, Ayyasamy B (2020) Short-term traffic prediction model for urban transportation using structure pattern and regression: an Indian context. SN Appl Sci 2(7). https://doi.org/10. 1007/s42452-020-2946-2 37. Hou Y, Deng Z, Cui H (2021) Short-term traffic flow prediction with weather conditions: based on deep learning algorithms and data fusion. Complexity 2021:1–14. https://doi.org/10.1155/ 2021/6662959 38. Sharma PK, Gautam A, Warudkar V, Ahmed S, Bhagoria JL (2020) Analysis of wind characteristics parameters with the application of lidar and mast. https://doi.org/10.1002/we.2580/ v2/response1 39. Kumar Sharma P, Gautam A, Baredar P, Warudkar V, Bhagoria JL, Ahmed S (2021) Analysis of terrain of site Mamatkheda Ratlam through wind modeling tool ArcGIS and WAsP. Mater Today: Proc 46:5661–5665. https://doi.org/10.1016/j.matpr.2020.09.638 40. Gautam A, Sharma PK, Baredar P, Warudkar V, Bhagoria JL, Ahmed S (2021) Modeling of atmospheric boundary flows using experimental investigation over complex terrain in a non-neutral condition. Mater Today: Proc 46:5681–5686. https://doi.org/10.1016/j.matpr.2020. 09.735 41. Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22(4):679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001 42. Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?— arguments against avoiding RMSE in the literature. Geosci Model Dev 7(3):1247–1250. https:/ /doi.org/10.5194/gmd-7-1247-2014 43. Tofallis C (2015) A better measure of relative prediction accuracy for model selection and model estimation. J Oper Res Soc 66(8):1352–1362. https://doi.org/10.1057/jors.2014.103 44. Ali U, Mahmood T (2018) Using deep learning to predict short term traffic flow: a systematic literature review. In: Intelligent transport systems—from research and development to the market uptake, pp 90–101. https://doi.org/10.1007/978-3-319-93710-6_11 45. Gautam A, Warudkar V, Bhagoria JL (2021) Recurrent neural network analysis for accurate extrapolation of the wind velocity. Adv Clean Energy Technol 453–462. https://doi.org/10. 1007/978-981-16-0235-1_36 46. Chen C, Hu J, Meng Q, Zhang Y (2011) Short-time traffic flow prediction with ARIMAGARCH model. In: 2011 IEEE intelligent vehicles symposium (IV). https://doi.org/10.1109/ ivs.2011.5940418 47. Lippi M, Bertini M, Frasconi P (2013) Short-term traffic flow forecasting: an experimental comparison of time-series analysis and supervised learning. IEEE Trans Intell Transp Syst 14(2):871–882. https://doi.org/10.1109/tits.2013.2247040 48. Gautam A, Warudkar V, Bhagoria JL (2022) A comparative analysis of wind characteristics for distinct terrains of India. Sustain Energy Technol Assess 52:102089. https://doi.org/10.1016/j. seta.2022.102089

Effect of Normalizing Followed by Deep Cryogenic Soaking on Mechanical Properties of P91 Martensitic Steel Elysee Nzita Tembwa, M. E. Makhatha, Pawan Kumar, Srijan Sengupta, and Ankit Dev Singh

Abstract Normalizing is a widely used heat treatment process for steel to induce desired mechanical and microstructural properties. In the present investigation, P91 martensitic steel was subjected to normalizing, followed by deep cryogenic soaking (DCS) using a muffle furnace and liquid nitrogen chamber. The specimens were reheated in the temperature range of 980–1150 °C and held isothermally for 10– 135 min to homogenize the temperature. It is further subjected to air cooling at room temperature followed by deep cryogenic cooling at a temperature of − 196 °C for a time of 30 min in liquid nitrogen. The volumetric % of retained austenite (RA) envisages a logarithmic inclination with increasing austenitizing time for both “normalized” and “normalized followed by DCS” samples. It is ascribed to the steady reduction in thermal stability of the reversed austenite which was obtained at the elevated temperature during the austenitizing. However, the trend did not show either a constant (after reaching critical condition) or decreasing nature; therefore, it is suggested that the thermal equilibrium was not achieved. The variation in hardness also envisages a logarithmic inclination. The reduction in carbide stability at elevated austenitizing temperature (AT) and extensive recovery at the larger austenitizing time leads to reducing the hardness during the process.

E. N. Tembwa · M. E. Makhatha · P. Kumar (B) Faculty of Engineering and the Built Environment, Department of Engineering Metallurgy, University of Johannesburg, John Orr Building, DFC, 25 Louisa St, Doornfontein, Johannesburg 2028, South Africa e-mail: [email protected] M. E. Makhatha e-mail: [email protected] S. Sengupta · A. D. Singh Metallurgical and Materials Engineering Department, Indian Institute of Technology Jodhpur, Jodhpur 342001, India e-mail: [email protected] A. D. Singh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_4

35

36

E. N. Tembwa et al.

Keywords Austenitizing · Normalizing · Deep cryogenic cooling · Retained austenite · Hardness

1 Introduction The P91 martensitic steel is one of the furthermost extensively castoff materials in engineering applications [1, 2]. It is used to completely transform into martensite during normalizing. The high strength, better creep resistance, higher thermal conductivity and lower thermal expansion enable it to use in the high-pressure pipes, elbows and engineering components in thermal power plant [3–8]. The low carbon content also makes it useful in welding applications [5]. It has also resistance to thermal-induced stresses. This important class of steel is generally subjected to heat treatment to further improve its mechanical and thermal properties. One such process is normalizing. The cryogenic soaking can also be an additional process to normalizing which can transform the RA to martensite and improve the mechanical properties of P91 martensitic steels [9, 10]. There are some researchers studied the heat treatment process of martensitic steel. Li et al. stated the presence of RA in a tinny film amid planks of martensite [1–5, 11]. They also found that this austenitic retention was stable even at the prolonged austenitizing/soaking time. However, they reported against the direct carbide precipitation during the deep cryogenic cooling. Zhou et al. studied the austenitic refinement and martensitic lath and pinning outcome of steady carbide to improve the martensitic steel at higher temperatures [12]. It is also possible that the untransformed austenite can be obtained after normalizing if the martensitic steel is subjected to cooling between martensitic start temperature and martensitic finish temperature. It can be possible that the dissolution of carbon and other alloying elements can depress the martensitic transformation and stabilize the RA. The outcome of such a normalizing process has the process parameters like austenitizing temperature and time (ATT). It means that by controlling these two parameters, one can induce the desired properties in such materials. Therefore, it feels that a further quantitative study is required to investigate the effect of process parameters during normalizing followed by deep cryogenic cooling of martensitic steel. In the present investigation, P91 martensitic steel was subjected to the AT range of 980–1150 °C and times in 10–135 min’ range. The volumetric percentage of RA and the hardness was optimized for the normalizing process and normalizing followed by a deep cryogenic soaking (DCS) process.

Effect of Normalizing Followed by Deep Cryogenic Soaking …

37

2 Materials and Methods The material under examination was P91 martensitic steel. The chemical composition is shown in Table 1. The normalizing followed by DCS was done on the distinct sample in the form of cubical specimens having a length of 10 mm, width of 10 mm and height of 10 mm a using muffle furnace and liquid nitrogen chamber. The specimens were reheated in the temperature range of 980–1150 °C and held isothermally for 10–135 min to standardize the temperature through the specimen. The samples were then subjected to air cooling at room temperature followed by deep cryogenic cooling at a temperature of − 196 °C for a time of 30 min in liquid nitrogen. The heat treatment parameters are shown in Table 2. The distinct phases were studied using an X-ray diffraction tester having a copper X-radiation. The amount of RA was measured according to the ASTM Standard E975 by measuring the relative intensity of α (110) and γ (110) peaks. Table 1 Chemical composition of P91 martensitic steel Element

C

Mn

P

Mo

Ni

Co

V

Si

Weight (%)

0.10

0.38

0.01

0.83

0.15

0.02

0.19

0.29

Table 2 Heat treatment process parameters

Heat treatment process

Temperatures (°C)

Time (minutes)

Austenitization

980

10, 25, 45,75 and 135

1000 1040 1060 1100 1130 1150

Cooling in still air at room temperature (normalizing) Cryogenic soaking

− 196

30

38

E. N. Tembwa et al.

Fig. 1 X-ray diffraction (XRD) spectra of “normalized” (at an AT of 1040 °C, austenitizing time of 25 min) and “normalized at the same condition followed by DCS” at − 196 °C for 30 min

3 Results and Discussion 3.1 X-Ray Diffraction Spectra for Normalized and Normalized Followed by Cryogenic Soaking Samples The X-ray diffraction (XRD) spectra of “normalized” (at an AT of 1040 °C, austenitizing time of 25 min) and “normalized at the same condition followed by DCS” at − 196 °C for 30 min is shown in Fig. 1. It has observed martensite, austenite and carbide (Cr23 C6 ) peaks. It is suggested that the carbide was formed at the austenitic grain boundaries which had the high energy density sites, and the same observation was also reported by other researchers [4, 13–15]. The spectral analysis of the sample when austenitized in the temperature range of 980–1150 °C, austenitizing time of 25 min followed by cryogenic soaking in liquid nitrogen at − 196 °C for 30 min showed a diffraction angle of 44 and 47° as shown in Fig. 2. The increase in intensity envisages a larger diffraction angle which confirms the presence of increased martensite and is also suggested that the carbon saturation induced the stresses in the martensitic lattice.

3.2 Variation of Volumetric % of RA and Hardness with ATT The variation in volumetric % of RA with ATT for “normalized” and “normalized followed by DCS” samples is shown in Figs. 3 and 4, respectively. The volumetric % of RA envisages a logarithmic inclination (conservative in nature) with increasing time, which can be ascribed to the gradual decrease in the thermal stability of the

Effect of Normalizing Followed by Deep Cryogenic Soaking …

39

Fig. 2 XRD spectra of “normalizing followed by DCS” sample at the AT range of 980–1150 °C, austenitizing time of 25 min, and DCS in liquid nitrogen at − 196 °C for 30 min

reversed austenite which was formed at the elevated temperature. A similar observation was also made by Zhang et al. [16]. The thermal stability of reversed austenite is influenced by experimental conditions, initial morphology and materials chemistry. The amount of RA (volumetric %) was increased with the time in a logarithmic-like trend. However, it did not show either a constant (after reaching critical condition) or a decreasing nature as reported by other researchers [16–18]. Therefore, it can be suggested that the thermal equilibrium was not achieved for the AT (980–1150 °C) and time range (10–135 min) adopted in the experimentation. The volumetric % of austenite was obtained maximum at 1150 °C for the given austenitizing time range. However, the austenite retention in samples subjected to normalizing followed by DCS was decreased for the same ATT range as compared to “normalized samples without DCS”. The dissimilarity in hardness with ATT for “normalized” and “normalized followed by DCS” samples is shown in Figs. 5 and 6, respectively. The hardness also envisages a logarithmic inclination with increasing time. The reduction in the carbide stability at elevated AT envisages a reduction in the hardness for both the “normalized” and “normalized followed by DCS” samples (Figs. 5 and 6). However, as the austenitizing time was increased, the hardness showed a decreasing trend because the larger time provided the extensive recovery which further leads to the disintegration/rearrangement of dislocations which reduces the hardness [19, 20].

E. N. Tembwa et al.

Austenite (vol %)

40

20 18 16 14 12 10 8 6 4 2 0

0

50

100

150

Austenizing time (minute)

980 deg C 1000 deg C 1040 deg C 1060 deg C 1080 deg C 1100 deg C 1130 deg C 1150 deg C Log. ( 980 deg C) Log. (1000 deg C) Log. ( 1040 deg C) Log. (1060 deg C) Log. (1080 deg C) Log. (1100 deg C) Log. (1130 deg C) Log. (1150 deg C)

Fig. 3 Variation of volumetric % of RA with ATT 16

980 deg C 1000 deg C

14

1040 deg C 1060 deg C

Austenite (vol %)

12

1080 deg C

10

1100 deg C

8

1150 deg C

1130 deg C Log. ( 980 deg C)

6

Log. (1000 deg C) Log. ( 1040 deg C)

4

Log. (1060 deg C) Log. (1080 deg C)

2 0

Log. (1100 deg C)

0

50

100

150

Log. (1130 deg C) Log. (1150 deg C)

Austenizing time (minute) Fig. 4 Variation of volume % of RA with ATT after the normalizing followed by the deep cryogenic cooling

3.3 Optimization of ATT The parameters which affected the normalizing process were ATT. A range of ATT was used; hence, the response of the sample (volumetric % of austenite and hardness) was optimized in terms of variables (ATT). The response surface methodology (RSM) was used for analysis/optimization. The suitable/optimized combination of ATT (variables) provided the desired volumetric % of austenite and the hardness. The

Effect of Normalizing Followed by Deep Cryogenic Soaking … 380

980 deg C 1000 deg C

370

Hardness (HV)

41

1040 deg C

360

1060 deg C

350

1100 deg C

1080 deg C 1130 deg C

340

1150 deg C Log. ( 980 deg C)

330

Log. (1000 deg C)

320

Log. ( 1040 deg C) Log. (1060 deg C)

310 300

Log. (1080 deg C)

0

50

100

150

Austenitizing time (minute)

Log. (1100 deg C) Log. (1130 deg C) Log. (1150 deg C)

Fig. 5 Variation in hardness with ATT 420 410

Hardness (HV)

400 390 380 370 360 350 340 330 320

0

50

100

150

980 deg C 1000 deg C 1040 deg C 1060 deg C 1080 deg C 1100 deg C 1130 deg C 1150 deg C Log. ( 980 deg C) Log. (1000 deg C) Log. ( 1040 deg C) Log. (1060 deg C) Log. (1080 deg C) Log. (1100 deg C) Log. (1130 deg C) Log. (1150 deg C)

Austenizing time (minute) Fig. 6 Variation in hardness with ATT after the normalizing followed DCS

RSM image for volumetric % of RA (response) as the function of ATT for “normalized” and “normalized followed by DCS” samples is shown in Fig. 7. The optimum (minimum) response (volumetric % of RA) in the range of 2% was obtained at an AT in the range of 980–1080 °C with a time range of 10–135 min. The RSM image for hardness (response) as the function of ATT for “normalized” and “normalized

42

E. N. Tembwa et al.

followed by DCS” samples is shown in Fig. 8. The optimum (maximum) hardness (response) in the range of 400–500 HV was obtained at two different combinations of ATT. These were either at the AT in the range of 980–1040 °C with a time range of 10–25 min or at a time in the range of 10–30 min with an AT range of 1060–1100 °C.

Fig. 7 RSM image showing the variation in volumetric % of RA with ATT for, a “normalized” sample, b “normalized followed by deep cryogenic cooling” sample

Fig. 8 RSM image showing variation in hardness with ATT for, a “normalized” sample, b “normalized followed by deep cryogenic cooling” sample

Effect of Normalizing Followed by Deep Cryogenic Soaking …

43

4 Conclusion The principal premise of current research yields significant results as: 1. The XRD spectra of the martensitic steel showed martensite, austenite and carbide peaks. It is suggested that the carbide was formed at the austenitic grain boundaries which had the high energy density sites. The increase in intensity envisages a larger diffraction angle which confirms the presence of increased martensite and is also suggested that the carbon saturation induced the stresses in the martensitic lattice. 2. The volumetric % of RA envisages a logarithmic inclination with increasing ATT for both “normalized” and “normalized followed by DCS” samples, which ascribed to the gradual decrease in the thermal stability of the reversed austenite which was formed at the elevated temperature. However, it did not show either a constant (after reaching critical condition) or a decreasing nature; therefore, the thermal equilibrium was not achieved for the AT (980–1150 °C) and time range (10–135 min). The variation in hardness also envisages a logarithmic inclination. The reduction in carbide stability at elevated AT envisages a reduction in the hardness for both the “normalized” and “normalized followed by DCS” samples. However, as the austenitizing time was increased, the hardness showed a decreasing nature because the larger time provided the extensive recovery which leads to annihilation/rearrangement of dislocations and reduces the hardness. 3. The RSM analysis showed an optimum response (volumetric % of RA) in the range of 2% obtained at an AT in the range of 980–1080 °C with a time range of 10–135 min. However, the optimum hardness in the range of 400–500 HV was obtained at two different combinations of ATT. These were either at an AT in the range of 980–1040 °C with a time range of 10–25 min or at a time in the range of 10–30 min with an AT range of 1060–1100 °C.

References 1. Golden BJ et al. (2014) Microstructural modeling of P91 martensitic steel under uniaxial loading conditions. J Pressure Vessel Technol 136(2) 2. Shibli A, Starr F (2007) Some aspects of plant and research experience in the use of new high strength martensitic steel P91. Int J Press Vessels Pip 84(1–2):114–122 3. Santella ML et al. (2001) Martensite formation in 9 Cr-1 Mo steel weld metal and its effect on creep behavior. In: Proceedings of EPRI conference on ‘9Cr materials fabrication and joining technologies’, Myrtle Beach, CA 4. Pandey C, Giri A, Mahapatra MM (2016) Effect of normalizing temperature on microstructural stability and mechanical properties of creep strength enhanced ferritic P91 steel. Mater Sci Eng A 657:173–184 5. Dak G, Pandey C (2020) A critical review on dissimilar welds joint between martensitic and austenitic steel for power plant application. J Manuf Process 58:377–406 6. Zhang K, Aktaa J (2016) Characterization and modeling of the ratcheting behavior of the ferritic–martensitic steel P91. J Nucl Mater 472:227–239

44

E. N. Tembwa et al.

7. Pandey C et al. (2018) Some studies on P91 steel and their weldments. J Alloys Compd 743:332–364 8. Pandey C, Mahapatra MM (2016) Effect of heat treatment on microstructure and hot impact toughness of various zones of P91 welded pipes. J Mater Eng Perform 25(6):2195–2210 9. Uygur I et al. (2015) The effects of cryogenic treatment on the corrosion of AISI D3 steel. Mater Res 18:569–574 10. Zheng S-Q et al. (2015) Effect of deep cryogenic treatment on formation of reversed austenite in super martensitic stainless steel. J Iron Steel Res Int 22(5):451–456 11. Li S et al. (2010) Influence of deep cryogenic treatment on microstructure and evaluation by internal friction of a tool steel. Cryogenics 50(11–12):754–758 12. Zhou X et al. (2015) Phase transformation behavior and microstructural control of high-Cr martensitic/ferritic heat-resistant steels for power and nuclear plants: a review. J Mater Sci Technol 31(3):235–242 13. Li H, Mitchell D (2013) Microstructural characterization of P91 steel in the virgin, service exposed and post-service re-normalized conditions. Steel Res Int 84(12):1302–1308 14. Vidyarthy RS, Dwivedi DK (2018) Microstructural and mechanical properties assessment of the P91 A-TIG weld joints. J Manuf Process 31:523–535 15. Tembwa EN (2018) Softening response of as-normalized and cryogenic-soaked P91 martensitic steels. University of Johannesburg (South Africa) 16. Zhang S et al. (2015) Investigation of the evolution of retained austenite in Fe–13% Cr–4% Ni martensitic stainless steel during intercritical tempering. Mater Des 84:385–394 17. Nakada N et al. (2011) Temperature dependence of austenite nucleation behaviour from lath martensite. ISIJ Int 51(2):299–304 18. Pei W et al. (2008) Investigation on phase transformation of low carbon martensitic stainless steel Zg06Cr13Ni4Mo in tempering process with low heating rate 19. Kumar P et al. (2020) EBSD investigation to study the restoration mechanism and substructural characteristics of 23Cr–6Ni–3Mo duplex stainless steel during post-deformation annealing. Trans Indian Inst Metals 73(6):1421–1431 20. Kumar P et al. (2021) Restoration mechanism and sub-structural characteristics of duplex stainless steel with an initial equiaxed austenite morphology during post-deformation annealing. Key Eng Mater 882. Trans Tech Publications Ltd.

Effect of Tempering Variables on Mechanical Properties of P91 Martensitic Steel and Determination of Hollomon–Jaffe Parameter Elysee Nzita Tembwa, M. E. Makhatha, Pawan Kumar, Srijan Sengupta, and Ankit Dev Singh

Abstract The P91 martensitic steel is one of the most extensively used materials in engineering applications which is used to completely transform into martensite during normalizing. In the present investigation, P91 martensitic steel samples were subjected to “tempering” using a muffle furnace. The X-ray diffraction (XRD) spectra showed an increased peak intensity and displacement of peak angles with increasing tempering temperature (tempering-T) and holding time which indicated that the martensitic lattice was in a stressed state because of carbon saturation. An increasing peak for precipitates with an increasing tempering temperature and time (tempering-TT) was also obtained. A decrease in hardness and an increase in the impact toughness with increasing tempering-T were observed during tempering. For a given tempering temperature, the hardness decreased with holding time and it reciprocated to nearly constant values indicating that the equilibrium condition was also reciprocating. However, the impact toughness of the material was increased with tempering-T and holding time but did not reciprocate to constant/nearly constant which indicated that the equilibrium envisages impact toughness was not achieved. The volumetric fractions of precipitates of carbides/nitrides were also higher at the

E. N. Tembwa · M. E. Makhatha · P. Kumar (B) Faculty of Engineering and the Built Environment, Department of Engineering Metallurgy, University of Johannesburg, John Orr Building, DFC, 25 Louisa St, Doornfontein, Johannesburg 2028, South Africa e-mail: [email protected] M. E. Makhatha e-mail: [email protected] S. Sengupta · A. D. Singh Metallurgical and Materials Engineering Department, Indian Institute of Technology Jodhpur, Jodhpur 342001, India e-mail: [email protected] A. D. Singh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_5

45

46

E. N. Tembwa et al.

elevated tempering-T, and for a given temperature, it increased with time. The hardness was decreased and impact toughness was increased with increasing Hollomon– Jaffe tempering parameter. The equivalent hardness–toughness values were obtained after tempering at different conditions considering closer values of the Hollomon– Jaffe tempering parameter and a specific hardness was obtained at a combination of lower tempering-T and holding time (of 60 min) provided the similar tempering parameter considering the equivalent (to hardness) impact toughness (of 172 J) when tempering was done at the higher temperature for 10 min. Keywords Austenitizing · Impact toughness · Hardness · Tempering · Hollomon–Jaffe parameter

1 Introduction The P91 martensitic steel is one of the most widely used materials which completely transforms into martensite during normalizing [1, 2]. The excellent mechanical, thermal and microstructural properties like high thermal conductivity, low thermal expansion, high strength and enhanced creep resistance of such martensitic steel envisage its use in thermal power plants, pipe industries, welding industries and other engineering industries [3–8]. The P91 grades of martensitic steel were subjected to austenitizing followed by quenching to improve its hardness. However, this process is sometimes provided excessive hardness but diminishes the impact strength of the materials. Hence, the excessive hardness of the material is relieved by a process called tempering. In tempering, the as-quenched (after austenitizing) steel is further subjected to heating below melting temperature (and also lower than prior austenitizing temperature), holding the material at the same temperature for a predefined time (called holding time) and then followed by air cooling. However, the tempering process also depends on process parameters like temperature and time. The optimized process parameters lead to provide the desired microstructural and mechanical properties. In the “tempering heat treatment” process, the Hollomon–Jaffe parameter (also called as Larson–Miller parameter) defines the effect of tempering at a given temperature and holding time [9, 10]. The Hollomon–Jaffe parameter plays an important role to define the outcome of various tempering processes [11]. Parker investigated the tempering at low levels and reported that the hardness decreased as tempering increased. They also stated that reduction in hardness lasts as a smooth curve until a very high degree of tempering and established a relationship with microalloying elements to the hardness during the process [12]. However, Paul et al. used the activation energy for the tempering process to study such phenomena [13]. In the light of the effect of microstructure, Revilla et al. reported the effect of the rate of heating on the distribution of carbide (precipitate mostly) by considering the high-angle and low-angle misorientations [14]. However, despite various such findings, it still feels that a quantitative analysis of the Hollomon–Jaffe parameter for such martensitic steel for a range of tempering times is required. In the present

Effect of Tempering Variables on Mechanical Properties of P91 …

47

investigation, P91 martensitic steel was subjected to the tempering and its impact strength, hardness and volumetric % formation of precipitates were investigated. The Hollomon–Jaffe parameter of such material was calculated using three different tempering temperatures and tempering time in the range of 5–480 min.

2 Materials and Methods The alloy under investigation was P91 martensitic steel having distinct chemical composition and mechanical properties [1, 2]. The samples were subjected to “tempering” using a muffle furnace. The distinct samples with a dimension of 55 mm in length, 10 mm in width and 10 mm in thickness were reheated to an austenitizing temperature of 1030 °C, holding it at the same temperature for 20 min to homogenize the temperature through the sample followed by quenching to induced desired hardness. The quenched samples were then subjected to tempering at a temperature of 750, 760 and 780 °C. The tempering time (tempering-t) was in the range of 5 min– 480 min followed by air cooling. The hardness and impact toughness were measured for each sample to calculate the Hollomon–Jaffe tempering parameter. The X-ray spectroscopy was done using Rigaku Ultima IV X-Ray Diffraction tester having a copper X-radiation to identify the different phases present in the microstructure. The amount of carbide/nitride precipitates was calculated according to the ASTM Standard E975 by measuring the relative intensity of α (110) and γ (110) peaks. However, the carbide volumetric fraction was calculated using the process adopted in [15].

3 Results and Discussion 3.1 X-Ray Diffraction Spectra in Tempering The X-ray diffraction spectra for samples austenitized at a temperature of 1030 °C for 20 min followed by quenching and then subjected to tempering at a temperature of 760 °C for a time of 5, 10, 15, 30, 45 and 480 min are shown in Fig. 1. An increased peak intensity and displacement of peak angles with increasing temperingTT were observed. This rise in peak intensity indicated that the martensitic lattice was in a stressed state because of carbon saturation [16]. The spectra also indicated an increasing peak for precipitates like CrC, NbC, VC and Cr2 N with an increasing tempering-TT. Therefore, it was confirmed that the tempering at higher temperatures and longer time envisages precipitate formation in the lattice. A similar observation was also made by other researchers [5].

48

E. N. Tembwa et al.

Fig. 1 X-ray diffraction spectra for samples austenitized at a temperature of 1030 °C for 20 min followed by quenching and then subjected to tempering at a temperature of 760 °C for a time of 5, 10, 15, 30, 45 and 480 min

3.2 Variation in Hardness and Impact Toughness in Tempering A decrease in hardness and an increase in the impact toughness with increasing tempering-T were observed as shown in Figs. 2 and 3, respectively. For a given tempering temperature, the hardness decreased and it reciprocated to nearly constant as the tempering-t was increased from 5 to 480 min which suggested that the equilibrium condition was also reciprocating. However, the impact toughness of the material was increased (for a given tempering temperature) with increasing tempering-t as shown in Fig. 3. The curvature nature of the impact toughness versus tempering-t was increasing, and it did not reciprocate to constant/nearly constant which indicated that the thermal equilibrium was not achieved. It is therefore suggested that the solid-state reaction occurred during tempering which envisages the “as-quenched martensite” (when austenitized samples were subjected to quenching) transformed into “tempered martensite”. This “tempered martensite” comprises dispersed spheroids of cementite (carbides) which subsequently reduced the hardness and increased the impact toughness, and the same phenomena were reported by other researchers [17, 18]. The internal stresses retained during quenching were relived after tempering at a higher temperature and time by the process of recovery, which also leads to a further decrease in the hardness and increases the toughness of the material. It is also

Effect of Tempering Variables on Mechanical Properties of P91 …

49

suggested that the retained dislocations which were stable at room temperature also rearranged themselves during tempering and envisage a lower hardness and higher impact toughness at a higher tempering-T [12, 19, 20]. The volumetric fractions of precipitates of carbides/nitrides were higher at the elevated tempering-TT for a given temperature and it increased with time as shown in Fig. 4. This observation once again confirms the reduction of hardness and enhancement in the impact toughness which was already reported in Figs. 2 and 3. Similar observations were also made by Tadeusz et al. [21]. 400 350

Hardness (HV)

300 250 200 150 100 750 deg C

50 0

0

100

200

300

760 deg C 400

780 deg C 500

600

Tempering time (minute) Fig. 2 Variation in hardness with tempering-t at a temperature of 750, 760 and 780 °C 400

Impact toughness (J)

350 300 250 200 150 100 50 0

750 deg C 0

100

200

300

760 deg C 400

780 deg C 500

600

Tempering time (minute) Fig. 3 Variation in impact toughness with tempering-t at a temperature of 750, 760 and 780 °C

E. N. Tembwa et al.

Volumetric fractions of precipitates of Carbide/Nitride precipitate

50 18 16 14 12 10 8 6 4 750 deg C

2 0

0

100

200

760 deg C 300

400

780 deg C 500

600

Tempering time (minute) Fig. 4 Variation in volumetric fractions of precipitates with tempering-t at a temperature of 750, 760 and 780 °C

3.3 Hollomon–Jaffe Parameter The outcome of the heat treatment is a function of temperature and time also called as process parameter of tempering heat treatment. The optimized combination of temperature–time leads to the desired microstructure and properties. It can be understood that the same/equivalent mechanical properties can be induced in the microstructure refinement either with a lower temperature and a relatively longer holding time or a higher temperature with a relatively shorter holding time. Tempering is one of the most important heat treatment processes for steel to improve its toughness by reducing/releveling the internal stresses in the microstructure. In the heat treatment process, the Hollomon–Jaffe parameter (also called as Larson–Miller parameter) defines the effect of tempering at a given temperature and holding time. It is calculated using the process adopted in [10] as: HP = T {C + log t},

(1)

where t represents the holding (at a constant temperature) time in hour, T represents the tempering-T in K and C is a constant. The Hollomon–Jaffe tempering parameter (H P ) was determined using Eq. (1). It was observed that the hardness was decreased with increasing H P ; however, the impact toughness was increased with an increasing tempering parameter as shown in Figs. 5 and 6, respectively. It was observed that the equivalent hardness–toughness values were obtained after tempering at different conditions considering closer values of H P . It means that the optimum combination of temperature and time leads to a similar value of H P . A specific hardness (262 HV) obtained at a combination of tempering-T (of 750 °C) and holding time (of 60 min) provided a similar tempering parameter H P considering the equivalent impact hardness (of 172 J) when tempering was done at the temperature of 780 °C for 10 min.

Effect of Tempering Variables on Mechanical Properties of P91 … 750 deg C 760 deg C 780 deg C Linear ( 750 deg C) Linear ( 760 deg C) Linear ( 780 deg C) Linear ( 780 deg C)

400 350

Hardness (HV)

51

300 250 200 150 100 50 0

30

31

32

33

34

Tempering parameter, HP Fig. 5 Hardness versus Hollomon–Jaffe tempering parameter 400

Impact toughness (J)

350 300 250 200

750 deg C 760 deg C 780 deg C Linear ( 750 deg C) Linear ( 760 deg C) Linear ( 780 deg C)

150 100 50 0 45

46

47

48

49

Tempering parameter , HP Fig. 6 Impact toughness versus Hollomon–Jaffe tempering parameter

4 Conclusion The current work yields significant results as: 1. The X-ray diffraction spectra for samples austenitized at a temperature of 1030 °C for 20 min followed by quenching and then subjected to tempering at a temperature of 760 °C for a time of 5, 10, 15, 30, 45 and 480 min showed an increased peak intensity and displacement of peak angles with increasing tempering-TT. This rise in peak intensity indicated that the martensitic lattice was in the stressed state because of carbon saturation. The spectra also indicated an increasing peak for precipitates like CrC, NbC, VC, Cr2 N with an increasing tempering-TT. 2. A decrease in hardness and an increase in the impact toughness with increasing tempering-T were observed during tempering. For a given tempering temperature, the hardness decreased with holding time and it reciprocated to nearly

52

E. N. Tembwa et al.

constant values indicating that the equilibrium condition was also reciprocating. However, the impact toughness of the material was increased with tempering-T and holding time but did not reciprocate to constant/nearly constant which indicated that the thermal equilibrium was not achieved for toughness. The volumetric fractions of precipitates of carbides/nitrides were also higher at the elevated tempering-T, and for a given temperature, it increased with time. 3. The hardness was decreased and impact toughness was increased with increasing Hollomon–Jaffe tempering parameter H P . The equivalent hardness–toughness values were obtained after tempering at different conditions considering closer values of H P and a specific hardness (262 HV) was obtained at a combination of tempering-T (of 750 °C) and holding time (of 60 min) provided the similar tempering parameter H P considering the equivalent impact hardness (of 172 J) when tempering was done at the temperature of 780 °C for 10 min.

References 1. Shibli A, Starr F (2007) Some aspects of plant and research experience in the use of new high strength martensitic steel P91. Int J Press Vessels Pip 84(1–2):114–122 2. Golden BJ et al. (2014) Microstructural modelling of P91 martensitic steel under uniaxial loading conditions. J Press Vessel Technol 136(2) 3. Pandey C, Mahapatra MM (2016) Effect of heat treatment on microstructure and hot impact toughness of various zones of P91 welded pipes. J Mater Eng Perform 25(6):2195–2210 4. Santella ML et al. (2001) Martensite formation in 9 Cr-1 Mo steel weld metal and its effect on creep behavior. In: Proceedings of EPRI Conference on ‘9Cr materials fabrication and joining technologies’, Myrtle Beach, CA, USA 5. Pandey C, Giri A, Mahapatra MM (2016) Effect of normalizing temperature on microstructural stability and mechanical properties of creep strength enhanced ferritic P91 steel. Mater Sci Eng A 657:173–184 6. Zhang K, Aktaa J (2016) Characterization and modeling of the ratcheting behavior of the ferritic–martensitic steel P91. J Nucl Mater 472:227–239 7. Dak G, Pandey C (2020) A critical review on dissimilar welds joint between martensitic and austenitic steel for power plant application. J Manuf Process 58:377–406 8. Pandey C et al. (2018) Some studies on P91 steel and their weldments. J Alloys Compd 743:332–364 9. Hollomon JH (1945) Time-temperature relations in tempering steel. Trans AIME 162:223–249 10. Janjuševi´c Z et al. (2009) The investigation of applicability of the Hollomon–Jaffe equation on tempering the HSLA steel. Chem Ind Chem Eng Quart/CICEQ 15(3):131–136 11. Saeglitz M, Krauss G (1997) Deformation, fracture, and mechanical properties of lowtemperature-tempered martensite in SAE 43xx steels. Metall Mater Trans A 28(2):377–387 12. Parker J (2013) In-service behavior of creep strength enhanced ferritic steels Grade 91 and Grade 92–Part 1 parent metal. Int J Press Vessels Pip 101:30–36 13. Paul VT et al. (2014) Microstructural characterization of weld joints of 9Cr reduced activation ferritic martensitic steel fabricated by different joining methods. Mater Charact 96:213–224 14. Revilla C, López B, Rodriguez-Ibabe JM (2014) Carbide size refinement by controlling the heating rate during induction tempering in a low alloy steel. Mater Des (1980–2015) 62:296– 304 15. Kellai A et al. (2018) Qualitative and quantitative assessment of γ and δ phases in duplex stainless steel weldments by the X-ray diffraction technique. In: The 6th international conference on welding, non destructive testing and materials industry (IC-WNDT-MI’18)

Effect of Tempering Variables on Mechanical Properties of P91 …

53

16. Scheuer CJ et al. (2014) Effects of heat treatment conditions on microstructure and mechanical properties of AISI 420 steel 17. Canale LCF et al. (2014) Hardenability of steel. Compr Mater Process 12:39–97 18. Mabruri E et al. (2018) Effect of high temperature tempering on the mechanical properties and microstructure of the modified 410 martensitic stainless steel. AIP Conf Proc 1964(1). AIP Publishing LLC 19. Kumar P et al. (2020) EBSD Investigation to study the restoration mechanism and substructural characteristics of 23Cr–6Ni–3Mo duplex stainless steel during post-deformation annealing. Trans Indian Inst Metals 73(6):1421–1431 20. Kumar P et al. (2021) Restoration mechanism and sub-structural characteristics of duplex stainless steel with an initial equiaxed austenite morphology during post-deformation annealing. Key Eng Mater 882. Trans Tech Publications Ltd. 21. Nykiel T, Hryniewicz T (2014) Transformations of carbides during tempering of D3 tool steel. J Mater Eng Perform 23(6):2050–2054

Influence of Multi-pass Friction Stir Processing on Microhardness and Wear Properties of AA2014/SiC–CNT Hybrid Surface Composites Vedant Soni, Vivek Pragada, Nilesh D. Ghetiya, and Shalok Bharti

Abstract Friction Stir Processing (FSP) is a well-established solid-state severe plastic deformation surface alteration procedure. Fabrication, processing, and synthesis of materials with just surface alterations are all parts of FSP. As a result, the metal’s base characteristics are preserved, and the result is a surface composite. FSP has lately been popular for producing surface composites that improve the tribological, mechanical, and microstructural properties of certain metals. Alloys of aluminum are used widely due to their light weightiness and corrosion resistance. AA2014 is used in the aircraft and automotive industries for its good machinability properties and good strength-to-weight ratio. In the current experiment, FSP was employed to produce a surface composite of AA2014 by using hybrid nano-sized SiC–CNT reinforcement. The number of passes has an important impact in determining the qualities of the surface composite in FSP. This paper examines the effect of the number of passes while keeping rotational and traversal speeds constant during FSP. After the experiment, analysis was carried out for microhardness and tribological properties. One-, two-, and three-pass FSPs were used to process the AA2014. The properties of the base metal, one-pass, two-pass, and three-pass Friction Stir Processed (FSPed) specimens were studied and compared by performing the experiment three times. From the average results of conducted experiments, it can be deduced that by increasing the number of passes, the microstructure improves in addition to improved coefficient of friction (COF), microhardness, and wear resistance for prepared specimens. Keywords Friction stir processing · Multi-pass · Hybrid reinforcement · Surface composite · AA2014

V. Soni · V. Pragada · N. D. Ghetiya (B) · S. Bharti Department of Mechanical Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat 382481, India e-mail: [email protected] S. Bharti Department of Mechanical Engineering, CT University, Ludhiana, Punjab 142024, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_6

55

56

V. Soni et al.

1 Introduction Friction Stir Processing (FSP) is a novel process for preparing surface composites. Conventional techniques can be used to create surface composites. FSP is a manufacturing, processing, and synthesizing strategy for materials that only display surface property improvement and thus retain their base properties’ basic characteristics. Mishra et al. [1] developed FSP in 1999, which is based on the principle of Friction Stir Welding. The technique, FSP is used to enhance the base metal’s grain structure. The other name of FSP is ‘solid-state process’ since the base metal remains solid throughout the process. Superplasticity is created by FSP that occurs in metals, like Al and Mg. To enhance tribological and mechanical properties of material, FSP is used. Properties such as hardness, wear resistance, and grain size improve after undergoing FSP. With progress in the FSP technique, it is used to fabricate surface composites. Surface composite is employed where surface characteristics play a significant role. To produce the surface composite, different researchers changed different parameters to improve the resultant properties. Satyanarayana [2] used AA6061 to investigate FSP for five passes and deduced that the tribological and mechanical properties were enhanced. Similarly, Suganeswarn [3] used AA7075/SiC for FSP and concluded improvements in wear behavior, hardness, and microstructural for surface composite which were improved post-FSP. Prabhu [4] employed AA6082 for FSP with reinforcement particle SiCp and discovered that employing three-pass FSP increased the tensile, microhardness, and wear resistance of the surface composite. Other research has shown that FSP is the best approach for creating surface composites with enhanced characteristics over the underlying material [5–7]. Surface composites have gained popularity in recent years because they are suitable for applications requiring high strength, wear resistance, hardness, low weight, and durability at elevated temperatures leaving base characteristics uncompromised [8]. Aluminum alloy is used in a variety of applications, including vehicle chassis, aviation constructions, and the military industry. When compared to unreinforced AA2014, hybrid surface composites utilizing SiC–CNT as reinforcement have higher mechanical characteristics, and the agitating effect of the method aids in superplasticity and grain refining [9, 10]. For the homogeneous dispersion of the reinforced particles in the matrix, tool traverse speed, depth, and rotating speed are important considerations. Three experiments of Aluminum alloy 2014–SiC–CNT hybrid composites produced using FSP are carried out in this study with the main focus on the number of passes and its influence on mechanical characteristics without changing other parameters.

Influence of Multi-pass Friction Stir Processing on Microhardness …

57

2 Methodology and Materials The base material used for this research is AA2014 (Aluminum alloy 2014). A 300 mm long, 75 mm wide, and 5 mm thick metal plate is employed. The chemical constitution of the base metal utilized is depicted in Table 1. SiC–CNT hybrid particles in the ratio 50:50 of grain size are used as reinforcement particles. The average size of reinforcement Sic–CNT was 10 nm. The hole approach was employed in the study to incorporate reinforcement particles into the base metal. A vertical milling CNC was used to create holes. On the surface of AA2014 plate, 94 holes, 3 mm deep and a 2 mm wide, are bored in a zigzag pattern to pack reinforcement for producing surface composite. The design was employed to avoid the reinforcement particles’ clumping. Figure 1 depicts the FSP hole layout. Table 1 Chemical composition of the base metal AA2014 wt%

Cu

Mg

Mn

Cr

Si

Al

4.4

0.5

0.6

0.1 (max)

0.8

Balance

Fig. 1 a Zigzag pattern of hole AA2014 plate, b representational illustration of hole size

58

V. Soni et al.

Fig. 2 Setup for experiment fixture arrangement for FSP

3 Methodology FSP is carried out using a vertical head milling machine. To avoid vibrations, a workpiece fixture was employed during the procedure. Two separate tools were utilized for FSP. The most common tool used to cover the reinforcement into holes was a tool without a pin. And then, there is the FSP tool. The second tool is composed of hardened H13 steel with a 5 mm diameter cylindrical pin shape, 18 mm shoulder diameter, and 3.5 mm height. During the fabrication of the surface composite AA2014/SiCCNT, the tool’s traverse speed was set to 63 mm/min, the rotating speed was set to 710 RPM, and the tool’s tilt angle remained constant at 3° throughout the procedure [11]. One-pass, two-pass, and three-pass FSPs were carried out to prepare the surface composite. Figure 2 shows the experimental setup and the clamps used to hold the workpiece. Composite FSPed specimens were segmented out of the surface. The specimens were then ground with various grades of emery paper ranging from P100 to P2000. Before conducting any microscopic investigations, the samples were properly polished using 3-micron and 1-micron diamond paste and subsequently etched with Keller reagent to reveal the grain structure. FSPed samples were subjected to microhardness and wear property tests. Three sets of four samples were examined, three of which had various numbers of passes and one of base metal as a control in one set. Table 2 displays the example code for various pass FSPs, while Fig. 3 depicts the processed samples with various pass counts.

4 Results and Discussion The microstructure of the FSPed zone was studied at 100X magnification using an optical microscope. The optical pictures of distinct zones in the treated samples are shown in Fig. 4. The grain fineness has improved with an increase in pass, according

Influence of Multi-pass Friction Stir Processing on Microhardness … Table 2 FSP sample nomenclature

59

Nomenclature

Sample type

No. of pass

Presence of SiC–CNT

I

Base metal AA2014

–

–

J

1p AA2014

I

Present

K

2p AA2014

II

Present

L

3p AA2014

III

Present

Fig. 3 Fabricated AA2014/SiC–CNT

to photographs. Figure 4 depicts the change between the nugget zone (NZ) and the thermomechanically affected zone (TMAZ) (i). In comparison to TMAZ, NZ has a larger concentration of reinforcing particles. Figure 4 (ii) shows reinforcement particles in NZ. The distribution of the reinforcement increases by increasing the passes. Figure 4 (iii) shows the transition between TMAZ and heat-affected zone (HZ). The appearance of SiC–CNT throughout the treated zone was discovered, and the reinforcement particles were distributed properly. Furthermore, it is assumed that SiC–CNT is evenly distributed in NZ. Because the regularity of reinforcing particles in NZ and TMAZ differs, a prominent interface line distinguishes the two zones. The TMAZ detects the reinforcement particles, indicating that the FSP settings were chosen correctly [12, 13]. The Vickers Microhardness Testing equipment is utilized to test the microhardness of the FSP specimens according to ASTM E92-17, with a load of 200 gf and a load duration of 10 s. Figure 5 shows a comparison of microhardness of different passes and base metal. The microhardness of the basic metal AA2014 was 95 HV. The maximum hardness of 147.8 HV in CS and 164.8 HV in TS is attained in three passes, indicating that FSP is aided in the hardening of the base metal. The ductility of the treated section rises after FSP, resulting in an improvement in scratch resistance. An increase in scratch resistance induces an increase in the treated zone’s hardness. As the grain size decreases, there is an increase in hardness, in accordance with the Hall-Petch equation, which states that hardness and grain size

60

V. Soni et al.

Fig. 4 Microscopy of cross-sections of a Sample J, b Sample K, and c Sample L

Fig. 5 AA2014/SiC–CNT microhardness profile at cross-section and top section

are inversely related. Figure 5 shows the average hardness of various cross-sections for varied FSP pass counts. It is observed that hardness rises in the NZ for CS and in the AS for TS [14, 15]. Three sets of the FSPed samples were subjected to a dry wear test on a Pin-on-Disk Tribometer following ASTM G99-17. Samples measuring 5 mm in length and 6 mm

Influence of Multi-pass Friction Stir Processing on Microhardness … Table 3 Mass lost during the wear test

61

Samples

Weigh before test (g)

Weight after test (g)

Wear rate (m3 /m)

Sample I

0.344

0.340

5.8 × 10–5

Sample J

0.350

0.347

4.3 × 10–5

Sample K 0.342

0.339

4.4 × 10–5

Sample L 0.362

0.360

2.8 × 10–5

in diameter were made. A 10 N force was applied across a 1000 m sliding distance, with a track diameter of 80 mm that remained constant during the test. Table 3 shows how the weight decrease was utilized to determine wear. The surface qualities of the FSPed zone improve as the hardness increases following FSP. The main objective of the experiment is to observe the change in microhardness and wear resistance after undergoing FSP; thus, COF and wear resistance are compared to demonstrate the change in surface properties. The FSPed zone’s wear resistance improves as the surface characteristics improve. Furthermore, the reinforcing material operates as distinct particles in the surface composite, preventing any exterior deterioration due to friction. COF decreases as wear resistance ameliorates, enhancing the friction qualities of the treated material [16]. COF varies with sliding distance, as seen in Fig. 6. The average wear of Sample L is the

Fig. 6 Graph of COF versus distance of sliding for FSPed samples

62

V. Soni et al.

Fig. 7 Graph of the rate of wear versus time for FSPed samples

least, showing that the number of passes being increased results in increased wear resistance, as illustrated in Fig. 7.

5 Conclusion In this research, Friction Stir Processing (FSP) was performed on three sets of AA2014 using nano-sized reinforcing material SiC/CNT. The treated material was put through a series of tests, including microhardness and dry wear testing. The results obtained for various tests are average result from three experiments. An optical microscope was also used to examine the microstructure of the treated material. The subsequent inferences were drawn from the results of the experiment: • The microstructure of the treated alloy was refined when the number of FSP passes was increased. • A rise of 18.47% in one pass, 11.04% in two passes, and 13.43% in three passes for CS was seen to improve microhardness qualities. • When comparing three passes to two passes and one pass, the wear resistance rose by 20% and 19%, respectively.

Influence of Multi-pass Friction Stir Processing on Microhardness …

63

• As the number of passes grew, the COF decreased, resulting in an improvement in the treated material’s friction qualities. According to the findings, the FPS improves the wear and hardness properties of the base metal. This information may then be utilized in future study to improve the situation.

References 1. Mishra RR, Mahoney M, McFadden SS, Mara NN, Mukherjee AA (1999) High strain rate superplasticity in a friction stir processed 7075 Al alloy. Scr Mater 42:163–168. https://doi. org/10.1016/S1359-6462(99)00329-2 2. Satyanarayana MVNV, Adepu K, Chauhan K (2020) Effect of overlapping friction stir processing on microstructure, mechanical properties and corrosion behavior of AA6061 alloy. Met Mater Int. https://doi.org/10.1007/s12540-020-00757-y 3. Suganeswaran K, Parameshwaran R, Mohanraj T, Radhika N (2021) Influence of secondary phase particles Al2 O3 /SiC on the microstructure and tribological characteristics of AA7075based surface hybrid composites tailored using friction stir processing. Proc Inst Mech Eng Part C J Mech Eng Sci 235:161–178. https://doi.org/10.1177/0954406220932939 4. Prabhu S, Perumal E (2020) Arulvel: development of multi-pass processed AA6082/SiCp surface composite using friction stir processing and its mechanical and tribology characterization. Surf Coat Technol 394:125900. https://doi.org/10.1016/j.surfcoat.2020.125900 5. Hashemi R, Hussain G (2015) Wear performance of Al/TiN dispersion strengthened surface composite produced through friction stir process: a comparison of tool geometries and number of passes. Wear 324–325:45–54. https://doi.org/10.1016/j.wear.2014.11.024 6. Bharti S, Ghetiya ND, Patel KM (2020) Micro-hardness and wear behavior of AA2014/Al2 O3 surface composite produced by friction stir processing. SN Appl Sci 2:1760. https://doi.org/ 10.1007/s42452-020-03585-2 7. Durmu¸s HK, Meriç C (2007) Age-hardening behavior of powder metallurgy AA2014 alloy. Mater Des 28:982–986. https://doi.org/10.1016/j.matdes.2005.11.022 8. Sachit, TS, Sapthagiri Prasad N, Aameer Khan M (2018) Effect of particle size on mechanical and tribological behavior of LM4/SiCp based MMC. Mater Today Proc 5:5901–5907. https:// doi.org/10.1016/j.matpr.2017.12.189 9. Lu D, Jiang Y, Zhou R (2013) Wear performance of nano-Al2 O3 particles and CNTs reinforced magnesium matrix composites by friction stir processing. Wear 305:286–290. https://doi.org/ 10.1016/j.wear.2012.11.079 10. Maurya R, Kumar B, Ariharan S, Ramkumar J, Balani K (2016) Effect of carbonaceous reinforcements on the mechanical and tribological properties of friction stir processed Al6061 alloy. Mater Des 98:155–166. https://doi.org/10.1016/j.matdes.2016.03.021 11. Bharti S, Ghetiya ND, Patel KM (2021) A review on manufacturing the surface composites by friction stir processing. Mater Manuf Process 36:135–170. https://doi.org/10.1080/10426914. 2020.1813897 12. Sutton MA, Yang B, Reynolds AP, Taylor R (2002) Microstructural studies of friction stir welds in 2024-T3 aluminum 323:160–166 13. Moustafa E (2017) Effect of multi-pass friction stir processing on mechanical properties for AA2024/Al2 O3 nanocomposites. Materials (Basel). 10:1053. https://doi.org/10.3390/ma1009 1053 14. Shojaeefard MH, Akbari M, Khalkhali A, Asadi P (2018) Effect of tool pin profile on distribution of reinforcement particles during friction stir processing of B4C/aluminum composites. Proc Inst Mech Eng Part L J Mater Des Appl 232:637–651. https://doi.org/10.1177/146442 0716642471

64

V. Soni et al.

15. Mahmoud TS, Mohamed SS (2012) Improvement of microstructural, mechanical and tribological characteristics of A413 cast Al alloys using friction stir processing. Mater Sci Eng A 558:502–509. https://doi.org/10.1016/j.msea.2012.08.036 16. Hosseini SA, Ranjbar K, Dehmolaei R, Amirani AR (2015) Fabrication of Al5083 surface composites reinforced by CNTs and cerium oxide nano particles via friction stir processing. J Alloys Compd 622:725–733. https://doi.org/10.1016/j.jallcom.2014.10.158

Experimental and Numerical Analysis of Strength Characterisation of Concrete with Recycled Concrete Aggregate: A Case Study Aruna Ekka, Kavita Gidwani Suneja, Priyanka Dhurvey, Harsangeet Kaur, and Chandra Prakash Gour

Abstract Rapid growth in the construction sector in India is leading to the scarcity of natural materials required for construction. In the process, construction and demolition waste is also generated. Disposal and management of C&DW are posing as major issue in the construction sector. In construction activities, use of recycled concrete aggregate can help in addressing these issues. For suitable and sustainable applications of recycled concrete aggregate, its properties and performance in concrete are studied in this research work. Particle size distribution and water absorption of recycled concrete aggregate extracted from construction and demolition waste are determined. High water absorption of 4.62% of RCA is obtained. Compressive strengths of concrete with various mix proportions using recycled concrete aggregate are studied. Recycled aggregate concrete made with full substitution of natural coarse aggregate with coarse-recycled concrete aggregate achieved characteristic compressive strength of 30 MPa. A numerical analysis is performed on experimental values of strength. Single-factor ANOVA method is used for test results after 28 days of curing. ANOVA results show that 100% replacement of natural aggregate with cRCA using IS design is fully acceptable as a sustainable alternative to conventional concrete. Keywords Recycled concrete aggregate · Recycled aggregate concrete · Compressive strength · Construction and demolition waste · ANOVA

A. Ekka · K. G. Suneja Energy Centre, Maulana Azad National Institute of Technology, Bhopal, India P. Dhurvey (B) · H. Kaur · C. P. Gour Department of Civil Engineering, Maulana Azad National Institute of Technology, Bhopal, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_7

65

66

A. Ekka et al.

1 Introduction Rapid urbanisation and modernisation globally are seeing a surge in the construction sector. This is not only putting huge pressure on the availability and sustainability of natural construction materials but also adding to the problem of waste management from demolition and construction. According to the World Bank projections, there will be 2.59 billion tonnes of garbage generated globally in 2030 and 3.40 billion tonnes of waste in 2050 [1]. Circular Economy (CE) is envisaged as a solution. This entails a change in perspective of how the economics of production works. By redesigning products in an eco-friendly manner and using raw ingredients that can be “made to be made once more”, waste can be reduced, profit can be created, and improvements can be made to the environmental outcomes [2]. The waste hierarchy for C&DW under EU directives follows—preparing for reuse, recycling, downcycling, and disposal [3].

1.1 Recycled Concrete Aggregate Construction and demolition waste is produced during new construction, dismantling ageing infrastructure, and rehabilitating already existing structures. Recycled concrete aggregate is a major item that can be retrieved from the processing of C&DW. In the dry process, C&DW can be crushed and sorted to obtain RCA. It can further be washed to remove the cement. The wet process can be used to extract manufactured sand. Recycled concrete aggregate can have structural as well as nonstructural applications. Because hydrated cement is present, RCA has different characteristics from natural aggregates, which influences the strength and other characteristics of the concrete in which it is employed. RCA contains attached mortar and adhered cement due to which its performance differs from natural aggregates. Adhered mortar of up to 24.2% can be found in RCA [4]. RCA generally has higher porosity and water absorption. These must be considered while developing mixes to account for workability, appropriate binding of material elements, and environmental effects on the hardened cement paste. Properties of RCA such as Impact Value, Abrasion Value, and Crushing Value determine the strength of the concrete and thus its end application in various construction activities as given in Table 1.

1.2 Improvement of Properties of RCA Crushing is the most common method of recovery of recycled concrete aggregate. Recycling techniques use smart crushers, rotor crushers, impact crushers, and jaw crushers [19, 20]. RCA contains adhered cement. Crushing fairly removes the

Experimental and Numerical Analysis of Strength Characterisation …

67

Table 1 Comparison of properties of recycled concrete aggregate References

Water absorption (%)

Density (kg/m3 )

Los Angeles index (%)

Crushing value (%)

Chinzorigt et al. [4]

3.84

2430

–

21.2

Kalinowska-Wichrowska et al. [5]

1.8–2.5

2580

–

11.8–13.6

Vinay Kumar et al. [6]

4.5

1425

–

–

Rao et al. [7]

2.68

–

–

–

Abed et al. [8]

5.6–7.91

2612.29–2569.88

36.11–39.34

–

Lei et al. [9]

4.0

2531

–

17.6

Wagih et al. [10]

2.5–6.9

1200–1410

31–47

–

Shahidan et al. [11]

2.03

1250

–

–

Sharkawi et al. [12]

1.9–5.08

1100–1260

–

28.5–54 –

Gebremariam et al. [13]

4.16–9.65

–

26

Kabir et al. [14]

1.78–4.32

2200–2461

24.34–31

Surya et al. [15]

2.76

1340

29.24

28.87

Paul [16]

3.2

2630–2770

–

10.8–12.5

Panda and Bal [17]

1.25

1377

26.65

37.14

Marie and Mujalli [18]

10.5

–

40

–

adhered mortar from coarse aggregate (CA), whereas adhered mortar in fine aggregates (FA) is not easily removed and needs to undergo further processing to remove cementitious material. To remove these, innovative technologies of Heating Air Classification System (HAS) and Advanced Dry Recovery (ADR) are used where recycled aggregates of various sizes are obtained, i.e. coarse, fine, and ultra-fines are produced [13]. With CO2 treatment, the mechanical characteristics of RCA may be enhanced. The characteristics of RCA, including water absorption, porosity, and Crushing Value, are improved by the formation of silica gel (SiO2 ) and calcium carbonate (CaCO3 ) by the reaction of CO2 gas with the calcium silicate hydrate and calcium hydroxide contained in the attached mortar [21]. To extract loosely attached mortar particles, apply a low-concentration acid treatment [22]. Lime treatment and mechanical treatment of RCA are done to enhance the characteristics of RCA and its application in high-grade concrete [23].

1.3 Applications in Construction Activities Processing of C&DW produces many items which can be reused or recycled. Material flow analysis shows that the application of C&DW can minimise the use of raw

68

A. Ekka et al.

materials by 17–35% under different construction scenarios in a study for Vienna [24]. Great Britain has achieved 29% usage of RCA in construction mainly due to policy formulation and its successful implementation [25]. Crushed concrete contains CA, FA, and ultrafine aggregates. FA can be used in cement bricks and very fine aggregates in asphalt fillers [12]. It can also be used in bituminous paving layers [26, 27]. Recycled concrete aggregate can also have applications in concrete road pavement construction [28, 29]. Experimental research on the application of RCA in prestressed and precast concrete has also been done [30]. Applications of various classes of RCA have been made in structural elements of buildings (walls, columns, and floors) [31]. Recycled concrete aggregates can also be used for CO2 sequestration [32–34].

1.4 Design Mix of RAC The most basic approach to studying and characterising RAC is a simple replacement of recycled aggregates as per design mix proportions of guidelines of various standards such as IS, ACI, BS, EN. To increase the characteristics of RAC, however, researchers have put forward and used various novel techniques. The attached/ adhered/residual mortar volume is taken into account using the equivalent mortar volume (EMV) approach to assess the extent of NCA replacement [35, 36]. EMV method considerably reduces the total cement demand of the mix and has good structural application [37]. Optimal RCA content of up to 63.53% has been achieved by this method when implemented with various mixing methods [38–40]. In Particle Packing Method (PPM), the fractions of coarse and fine aggregates are so determined such that the packing density of the mix is increased and the void content is minimised resulting in reduction of cement and water consumption [41, 42]. Fly ash is used as a filler material to fill the spaces between fine aggregates in the Densified Mixture Design Algorithm (DMDA) method. Fly ash and fine aggregates are combined to fill the spaces between coarse aggregates to increase the density of the aggregate mixture and reduce the void content [43, 44]. In aggregate skeleton theory, FA/cement proportion is considered the determining factor of the strength of concrete. Coarse aggregate is treated as reinforcement for cement mortar and sand as reinforcement cement paste [45]. Besides experimental methods, innovative statistical approaches are being made to design mixed proportions. One such technique is response surface methodology (RSM). Usually, a Design of Experiment (DoE) software is used for the forecast of a concrete mix’s strength taking into consideration various input variables that affect the characteristics of concrete. It is later validated with an experimental model [46, 47].

Experimental and Numerical Analysis of Strength Characterisation …

69

2 Research Significance and Objectives Niti Aayog and European Union’s paper “Resource Efficiency and Circular Economy” of January 2019 states that an estimated 100 Mt of C&DW was produced in India in 2018 of which only 10–30% was recovered and utilised in land levelling and backfilling [48]. Full recovery, reuse, and recycling are not being done and the full potential of C&DW is not being utilised. IS 383(2016) [49] stipulates the extent of use of RCA up to 25% in plain concrete, up to 20% in reinforced concrete and up to 100% in lean concrete (< 15 MPa). Demonstrated strength of recycled aggregate concrete and its various uses and applications in academic research can lead to better recovery and utilisation of RCA and its wider adoption by the construction sector. The goal of this research is to determine the degree to which CA and FA may be substituted in concrete with a strength of M30 according to IS design.

3 Experimental Programme 3.1 Materials For experimental laboratory study and testing of Recycled Aggregate Concrete, C&DW was obtained from dismantled concrete pavement road of M30 grade from which RCA was processed. For the test sample of RAC, a coarse fraction of RCA was employed to substitute natural aggregate 100%. Natural river sand and natural crushed stone aggregates were used for the control sample. A fine fraction of RCA was used for 25% replacement of natural sand in combination with 100% substitution of natural aggregate with the coarse fraction for the test sample of RAC. We utilised OPC grade 43 for making concrete as per the direction of IS:8112-2013 [50], and having a specific gravity of 3.15. For mix proportions, potable water adhering to IS:456-2000 [51] was used. The particle size distribution of RCA, fRCA, and NFA is shown in Figs. 1 and 2. Fig. 1 Particle size distribution of RCA

70

A. Ekka et al.

Fig. 2 Particle size distribution of fRCA & NFA

3.2 Recycled Concrete Aggregate Water absorption of RCA was determined as per IS:2386(Part III)-1963 [52] and found to be 4.62% which is considerably greater than the natural aggregate. This was attributable to the adhering mortar’s high porosity. The particle size distribution of all-in aggregate of RCA determined as per IS:2386(Part I)-1963 [53] conforms to the coarse fraction as per specifications of IS:383-2016 [49]. Fine fraction of RCA was found to be coarser than natural fine aggregate conformed to Zone I fine aggregate grading as per IS:383-2016 [49].

3.3 Design Mix IS:10262-2019 [54] was referenced to design the trial mixes. The mix design of C1, C2, and C3 is achieved in M30-grade concrete. The control sample, designated C1, had only natural materials. C2 was designed with 100% substitution of NCA, NCA with cRCA, and C3 was designed with 100% substitution of NCA with cRCA and 25% of NFA, NFA with fRCA. Table 2 lists the design mix proportions. Table 2 Design mixes for experiment Design mix

Reference

C1

IS:10262-2019

Target length (N/ mm2 )

(kg/m3 )

Water

M30

171

Cement

cRCA

NCA

NFA

fRCA

w/c

450

–

1177

651

–

0.38

C2

IS:10262-2019

M30

166

437

1193

–

660

–

0.38

C3

IS:10262-2019

M30

166

437

1156

–

523

148

0.38

cRCA = coarse-recycled concrete aggregate, NCA = natural coarse aggregate, NFA = natural fine aggregate, fRCA = fine recycled concrete aggregate, w/c = water content

Experimental and Numerical Analysis of Strength Characterisation …

71

Fig. 3 Mixing process of concrete (CA = coarse aggregate, FA = fine aggregate, C = cement, s = seconds)

3.4 Casting and Curing of Trial Samples In the conventional mixing process of concrete, cement, CA, and FA are dry mixed, and then, water is added and mixed further to obtain fresh concrete, as shown in Fig. 3. For this experiment, water and cement were added in two steps. First, the concrete mixer was used to dry mix CA, FA, and 50% of the cement for 30 s. After adding half of the water, the mixture was swirled for 30 s, after which reminder of cement was added and stirred for another 30 s. The mixture was agitated for 60 s after adding the remaining water. Fresh concrete was placed in greased steel cube moulds of size 150 mm in three layers. Compaction was done on a table vibrator. Poured concrete was allowed to be set in moulds for 24 h. After removing the moulds, the concrete cubes took 7 days and 28 days to cure in water before testing. The cubes were taken from the curing process just before testing (in accordance with IS:516-1959) [55], their weight and dimensions were measured, and they were then placed in the compressive machine with load applied until cube failure and the results were tabulated. The characteristic compressive strength of the sample design mix is considered to be the same as the average compressive strength of the three specimens is tested at 28 days.

4 Result and Discussion The gain of strength of RAC samples was faster than conventional concrete at 7 days, but the final gain of strength of RAC samples at 28 days was slightly lower than conventional concrete. Concrete continues to gain strength beyond 28 days; hence, it leaves the scope of further investigation of characterisation of RAC as shown in Fig. 4 and given in Table 3. ANOVA test results show that 100% replacement of natural aggregate with cRCA using IS design fully is acceptable as an alternative to conventional concrete (p-value of C1 vs. C2 being > 0.05) as shown in Table 4. However, fRCA is not ideal for replacement of sand (p-value of C1 vs. C3 < 0.05) as the addition of fRCA in the

72

A. Ekka et al.

C1

C2

C3

Compressive Strength ( N/mm 2)

45 38.22

40

38.07

36.74

35 30

25.48

26.82

26.67

25 20 15 10

7 days

28 days Time (Days)

Fig. 4 Comparison of compressive strength of various design mixes

Table 3 Compressive strength, flexural strength, and modulus of elasticity of various design mixes Design mix

7 days’ compressive strength (N/ mm2 )

Characteristic compressive strength f ck (28 days) (N/mm2 )

Flexural strength √ f cr = 0.7 f ck (N/ mm2 ) IS:456-2000

Modulus of√Elasticity Ec = 5000 f ck (N/ mm2 ) IS:456-2000

C1

25.481

38.222

4.33

30,912

C2

26.815

38.074

4.32

30,852

C3

26.667

36.741

4.24

30,307

Table 4 Single-factor ANOVA test results of 28 days’ compressive strength

Design mix

p-value

C1 versus C2

0.724

C1 versus C3

0.109

mix proportion has feaFurther investigations of the super fines present in fRCA can present solutions for any further processing of fRCA to achieve proper grading for structural applications (Table 4).

Experimental and Numerical Analysis of Strength Characterisation …

73

5 Conclusion and Future Scope 1. Recycled aggregate concrete with 100% replacement of natural aggregate produced comparable and good compressive strength of 38.07 MPa with IS Design Mix and can be recommended for various construction applications. 2. Poor performance of RAC with 25% replacement of natural sand shows that fine recycled aggregate (36.74 MPa compressive strength being less than the target strength) cannot be used in concrete, so about 30–40% of concrete waste would still have to go for landfill disposal or for non-structural application in construction activities or for any other innovative applications. 3. Construction and demolition waste will be part of the construction industry due to End-of-Life of buildings and infrastructure, natural disasters such as earthquakes and landslides, and man-made disasters such as wars. Hence, C&DW management will be critical for sustainable and eco-friendly construction of which recycled concrete aggregates will be a major component. 4. Further investigations are required into the properties recycled fine aggregate, especially its water absorptions, and the reasons for it is being so high and the appropriate laboratory methods to accurately quantify it.

Conflict of Interest None declared.

References 1. Kaza S, Yao LC, Bhada-Tata P, Van Woerden F (2018) What a Waste 2.0: a global snapshot of solid waste management to 2050. Urban Development. World Bank. © World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/30317. License: CC BY 3.0 IGO 2. Zanni S, Simion IM, Gavrilescu M, Bonoli A (2018) Life cycle assessment applied to circular designed construction materials. Procedia CIRP 69:154–159. https://doi.org/10.1016/j.procir. 2017.11.040 3. Zhang C, Hu M, Di Maio F, Sprecher B, Yang X, Tukker A (2022) An overview of the waste hierarchy framework for analyzing the circularity in construction and demolition waste management in Europe. Sci Total Environ 803:149892. https://doi.org/10.1016/j.scitotenv. 2021.149892 4. Chinzorigt G, Lim MK, Yu M, Lee H, Enkbold O, Choi D (2020) Strength, shrinkage and creep and durability aspects of concrete including CO2 treated recycled fine aggregate. Cem Concr Res 136(January):106062. https://doi.org/10.1016/j.cemconres.2020.106062 5. Kalinowska-Wichrowska K, Pawluczuk E, Bołtryk M (2020) Waste-free technology for recycling concrete rubble. Constr Build Mater 234:117407. https://doi.org/10.1016/j.conbuildmat. 2019.117407 6. Vinay Kumar BM, Ananthan H, Balaji KVA (2018) Experimental studies on utilization of recycled coarse and fine aggregates in high performance concrete mixes. Alex Eng J 57(3):1749–1759. https://doi.org/10.1016/j.aej.2017.05.003

74

A. Ekka et al.

7. Rao KJ, Keerthi K, Vasam S (2018) Acid resistance of quaternary blended recycled aggregate concrete. Case Stud Constr Mater 8(January):423–433. https://doi.org/10.1016/j.cscm.2018. 03.005 8. Abed M, Nemes R, Tayeh BA (2020) Properties of self-compacting high-strength concrete containing multiple use of recycled aggregate. J King Saud Univ—Eng Sci 32(2):108–114. https://doi.org/10.1016/j.jksues.2018.12.002 9. Lei B, Li W, Tang Z, Li Z, Tam VWY (2020) Effects of environmental actions, recycled aggregate quality and modification treatments on durability performance of recycled concrete. J Market Res 9(6):13375–13389. https://doi.org/10.1016/j.jmrt.2020.09.073 10. Wagih AM, El-Karmoty HZ, Ebid M, Okba SH (2013) Recycled construction and demolition concrete waste as aggregate for structural concrete. HBRC J 9(3):193–200. https://doi.org/10. 1016/j.hbrcj.2013.08.007 11. Shahidan S, Azmi MAM, Kupusamy K, Zuki SSM, Ali N (2017) Utilizing construction and demolition (C&D) waste as recycled aggregates (RA) in concrete. Procedia Eng 174:1028– 1035. https://doi.org/10.1016/j.proeng.2017.01.255 12. Sharkawi AM, El Mofty SEDM, Showaib EA, Abbass SM (2018) Feasible construction applications for different sizes of recycled construction demolition wastes. Alex Eng J 57(4):3351–3366 13. Gebremariam AT, Di Maio F, Vahidi A, Rem P (2020) Innovative technologies for recycling end-of-life concrete waste in the built environment. Resour Conserv Recycl 163(April):104911. https://doi.org/10.1016/j.resconrec.2020.104911 14. Kabir S, Al-Shayeb A, Khan IM (2016) Recycled construction debris as concrete aggregate for sustainable construction materials. Procedia Eng 145:1518–1525. https://doi.org/10.1016/ j.proeng.2016.04.191 15. Surya M, Kanta Rao VVL, Lakshmy P (2013) Recycled aggregate concrete for transportation infrastructure. Procedia—Soc Behav Sci 104(m):1158–1167. https://doi.org/10.1016/j.sbspro. 2013.11.212 16. Paul SC (2017) Data on optimum recycle aggregate content in production of new structural concrete. Data Brief 15:987–992. https://doi.org/10.1016/j.dib.2017.11.012 17. Panda KC, Bal PK (2013) Properties of self compacting concrete using recycled coarse aggregate. Procedia Eng 51(NUiCONE 2012), 159–164. https://doi.org/10.1016/j.proeng.2013. 01.023 18. Marie I, Mujalli R (2019) Effect of design properties of parent concrete on the morphological properties of recycled concrete aggregates. Eng Sci Technol, Int J 22(1):334–345. https://doi. org/10.1016/j.jestch.2018.08.014 19. Nedeljkovi´c M, Visser J, Šavija B, Valcke S, Schlangen E (2021) Use of fine recycled concrete aggregates in concrete: a critical review. J Build Eng 20. Tam VWY, Soomro M, Evangelista ACJ (2021) Quality improvement of recycled concrete aggregate by removal of residual mortar: a comprehensive review of approaches adopted. Constr Build Mater 288:123066. https://doi.org/10.1016/j.conbuildmat.2021.123066 21. Hosseini Zadeh A, Mamirov M, Kim S, Hu J (2021) CO2 -treatment of recycled concrete aggregates to improve mechanical and environmental properties for unbound applications. Constr Build Mater 275:122180. https://doi.org/10.1016/j.conbuildmat.2020.122180 22. Ismail S, Ramli M (2013) Engineering properties of treated recycled concrete aggregate (RCA) for structural applications. Constr Build Mater 44:464–476. https://doi.org/10.1016/j.conbui ldmat.2013.03.014 23. Prasad D, Pandey A, Kumar B (2021) Sustainable production of recycled concrete aggregates by lime treatment and mechanical abrasion for M40 grade concrete. Constr Build Mater 268:121119. https://doi.org/10.1016/j.conbuildmat.2020.121119 24. Lederer J, Gassner A, Fellner J, Mollay U, Schremmer C (2021) Raw materials consumption and demolition waste generation of the urban building sector 2016–2050: a scenario-based material flow analysis of Vienna. J Clean Prod 288:125566. https://doi.org/10.1016/j.jclepro. 2020.125566

Experimental and Numerical Analysis of Strength Characterisation …

75

25. Tangtinthai N, Heidrich O, Manning DAC (2019) Role of policy in managing mined resources for construction in Europe and emerging economies. J Environ Manage 236(February):613– 621. https://doi.org/10.1016/j.jenvman.2018.11.141 26. Gopalam J, Giri JP, Panda M (2020) Influence of binder type on performance of dense bituminous mixture prepared with coarse recycled concrete aggregate. Case Stud Constr Mater 13:e00413. https://doi.org/10.1016/j.cscm.2020.e00413 27. Gopalam J, Prakash Giri J, Panda M (2020) Effect of filler on bituminous base layer containing recycled concrete aggregates. Int J Transp Sci Technol 9(3):239–248. https://doi.org/10.1016/ j.ijtst.2020.04.002 28. Maduabuchukwu Nwakaire C, Poh Yap S, Chuen Onn C, Wah Yuen C, Adebayo Ibrahim H (2020) Utilisation of recycled concrete aggregates for sustainable highway pavement applications; a review. Constr Build Mater 235:117444. https://doi.org/10.1016/j.conbuildmat.2019. 117444 29. Kox S, Vanroelen G, Van Herck J, de Krem H, Vandoren B (2019) Experimental evaluation of the high-grade properties of recycled concrete aggregates and their application in concrete road pavement construction. Case Stud Constr Mater 11:e00282. https://doi.org/10.1016/j. cscm.2019.e00282 30. Brandes MR, Kurama YC (2016) Use of recycled concrete aggregates in precast/prestressed concrete. Procedia Eng 145:1338–1345. https://doi.org/10.1016/j.proeng.2016.04.172 31. Yoda K, Shintani A (2014) Building application of recycled aggregate concrete for upperground structural elements. Constr Build Mater 67(PART C):379–385. https://doi.org/10.1016/ j.conbuildmat.2013.12.096 32. Dos Reis GS, Cazacliu B, Artoni R, Torrenti J-M, Hoffmann CS, Lima EC (2021) Coupling of attrition and accelerated carbonation for CO2 sequestration in recycled concrete aggregates. Clean Eng Technol 3:100106. https://doi.org/10.1016/j.clet.2021.100106 33. Suescum-Morales D, Kalinowska-Wichrowska K, Fernández JM, Jiménez JR (2021) Accelerated carbonation of fresh cement-based products containing recycled masonry aggregates for CO2 sequestration. J CO2 Utilization 46(November 2020). https://doi.org/10.1016/j.jcou. 2021.101461 34. Tam VWY, Butera A, Le KN, Li W (2020) Utilising CO2 technologies for recycled aggregate concrete: a critical review. Constr Build Mater 250:118903. https://doi.org/10.1016/j.conbui ldmat.2020.118903 35. Kim J, Sadowski Ł (2019) The equivalent mortar volume method in the manufacturing of recycled aggregate concrete. Czasopismo Techniczne 11:123–140. https://doi.org/10.4467/235 3737xct.19.119.11335 36. Anike EE, Saidani M, Olubanwo AO, Tyrer M, Ganjian E (2020) Effect of mix design methods on the mechanical properties of steel fibre-reinforced concrete prepared with recycled aggregates from precast waste. Structures 27(July):664–672. https://doi.org/10.1016/j.istruc.2020. 05.038 37. Hayles M, Sanchez LFM, Noël M (2018) Eco-efficient low cement recycled concrete aggregate mixtures for structural applications. Constr Build Mater 169:724–732. https://doi.org/10.1016/ j.conbuildmat.2018.02.127 38. Rajhans P, Gupta PK, Ranjan RK, Panda SK, Nayak S (2019) EMV mix design method for preparing sustainable self compacting recycled aggregate concrete subjected to chloride environment. Constr Build Mater 199:705–716. https://doi.org/10.1016/j.conbuildmat.2018. 12.079 39. Rajhans P, Chand G, Kisku N, Panda SK, Nayak S (2019) Proposed mix design method for producing sustainable self compacting heat cured recycled aggregate concrete and its microstructural investigation. Constr Build Mater 218:568–581. https://doi.org/10.1016/j.con buildmat.2019.05.149 40. Kisku N, Rajhans P, Panda SK, Pandey V, Nayak S (2020) Microstructural investigation of recycled aggregate concrete produced by adopting equal mortar volume method along with two stage mixing approach. Structures 24(February):742–753. https://doi.org/10.1016/j.istruc. 2020.01.044

76

A. Ekka et al.

41. Pradhan S, Kumar S, Barai SV (2017) Recycled aggregate concrete: particle packing method (PPM) of mix design approach. Constr Build Mater 152:269–284. https://doi.org/10.1016/j. conbuildmat.2017.06.171 42. Liu S, Minne P, Luli´c M, Li J, Gruyaert E (2021) Implementation and validation of Dewar’s particle packing model for recycled concrete aggregates. Constr Build Mater 294. https://doi. org/10.1016/j.conbuildmat.2021.123429 43. Vo DH, Yehualaw MD, Hwang CL, Liao MC, Tran Thi KD, Chao YF (2021) Mechanical and durability properties of recycled aggregate concrete produced from recycled and natural aggregate blended based on the densified mixture design algorithm method. J Build Eng 35(July 2020):102067. https://doi.org/10.1016/j.jobe.2020.102067 44. Babalola OE, Awoyera PO, Tran MT, Le DH, Olalusi OB, Viloria A, Ovallos-Gazabon D (2020) Mechanical and durability properties of recycled aggregate concrete with ternary binder system and optimized mix proportion. J Market Res 9(3):6521–6532. https://doi.org/10.1016/j.jmrt. 2020.04.038 45. Ge P, Huang W, Zhang J, Quan W, Guo Y (2021) Mix proportion design method of recycled brick aggregate concrete based on aggregate skeleton theory. Constr Build Mater 304(March):124584. https://doi.org/10.1016/j.conbuildmat.2021.124584 46. Zhang Q, Feng X, Chen X, Lu K (2020) Mix design for recycled aggregate pervious concrete based on response surface methodology. Constr Build Mater 259. https://doi.org/10.1016/j.con buildmat.2020.119776 47. Habibi A, Ramezanianpour AM, Mahdikhani M (2021) RSM-based optimized mix design of recycled aggregate concrete containing supplementary cementitious materials based on waste generation and global warming potential. Resour, Conserv Recycl 167(October 2020):105420. https://doi.org/10.1016/j.resconrec.2021.105420 48. Resource Efficiency and Circular Economy-current status and way forward, Niti Aayog and European Union. https://www.eurei.com/pdf/publication/NA_EU_Status%20Paper%20&% 20Way%20Forward_Jan%202019.pdf 49. Bureau of Indian Standards, IS 383:2016—Coarse and fine aggregate for concrete—specification (3rd Revision) 50. Bureau of Indian Standards, IS 8112:2013—Ordinary Portland Cement, 43 grade—specification (2nd Revision) 51. Bureau of Indian Standards, IS 456:2000—Plain and Reinforced Concrete—code of practice (4th Revision) 52. Bureau of Indian Standards, IS:2386 ( Part III )—1963—Methods of Test for Aggregates for Concrete Part III Specific Gravity, Density, Voids, Absorption and Bulking (Eight Reprint March 1997) 53. Bureau of Indian Standards, IS:2386 (Part I)—1963—Methods of Test for Aggregates for Concrete Part I Particle Size and Shape (Eleventh Reprint August 1997) 54. Bureau of Indian Standards, IS 10262:2019—Concrete mixing proportioning—guidelines (2nd Revision) 55. Bureau of Indian Standards, IS:516-1959 (Reaffirmed 1999)—Methods of tests for strength of concrete

Effect of Nano-TiO2 Content (wt.%) on Hardness of Epoxy Polymer Matrix Nanocomposites Sai Babu Chanda, Amish Mishra, Akshaya Kumar Rout, Ramesh Kumar Nayak , and Santosh Kumar Nayak

Abstract The polymer matrix composites exhibit better mechanical properties. However, still there exist some mechanical failures under specific structural applications like aerospace, automobile, and shipbuilding. To overcome the above-said mechanical failures, the nanofiller was intercalated with epoxy polymer matrix at different wt%. It is expected that the high specific surface area and wrinkled surface morphology of nanofillers would lead to a better reinforced interface. The current study focuses on the effect of different wt% of nano-TiO2 filler in the epoxy polymer matrix on the hardness of the nanocomposites. The nanoparticles were mixed with the epoxy polymer matrix through mechanical stirring, followed by magnetic stirring and ultrasonication. The presence of nanofillers in the epoxy matrix was confirmed through XRD, SEM–EDS, and FTIR analysis. The hardness test was conducted to obtain the increment in the hardness of the nanocomposites. It is observed that there is a substantial increment of hardness in the nanocomposites with the increase in nano-TiO2 content. Keywords Nano-TiO2 · Composites · Nanocomposites · Sonication · Mechanical · Hardness

1 Introduction The nanocomposites have versatile usage in the field of different industries like automobile, aerospace, shipbuilding, surface coatings, adhesives, painting materials as well as electronic devices, and so on. It is because of their unique features when

S. B. Chanda · A. Mishra · R. K. Nayak (B) Department of Materials and Metallurgical Engineering, MANIT, Bhopal 462003, India e-mail: [email protected] A. K. Rout · S. K. Nayak School of Mechanical Engineering, KIIT Deemed to be University, Bhubaneswar-24, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_8

77

78

S. B. Chanda et al.

compared with the metals and their alloys. The polymer matrix composites are noncorrosive, biodegradable, antifungal in nature, very light in weight, and strength-toweight ratio is higher compared to metal matrix composites [1, 2]. The improvement of fiber-reinforced polymer composites may be achieved through hybridization of fibers [3, 4]. The manufacturing of these nanocomposites is easy and cost effective. The epoxy resins are very brittle in nature when they are faced with the high impact and fracture strength. This brittleness leads to many problems in the products which need more impact and fracture resistance. Researchers were attempted to solve these problems by different approaches like intermediary phase and chemical reactions [2]. Strength of the bond can be increased by incorporation of the nanofillers which acts as a bridge between the fibers and epoxy [5]. As another key factor affecting the mechanical properties of composites is the dispersion status of nanofillers in epoxy matrix. Inorganic nanofillers like Al2 O3 and TiO2 were incorporated into the polymer matrix to improve its strength and modulus. However, agglomeration of nanoparticles in the polymer matrix is creating hindrance. By addition of the nano-TiO2 fillers into the polymer matrix, it changes the physical and mechanical properties [6]. With the inorganic fillers, the composites were becoming stiffer. Salehian et al. [7] found that addition of 21 nm titanium dioxide nanoparticles at different wt% in a vinyl ester resin improves its mechanical properties. Chen et al. [8] found that the dispersion of nano-TiO2 is important to achieve better mechanical properties. Dispersibility and structural analysis of the various carbon nanofillers were characterized and compared to get their enhancement polymers. It is observed that dispersion of the nanoparticles in pure epoxy polymer matrix is a challenge. Fan et al. [9] observed that the one of the probable solutions to close the pores/voids and improvement of interface/ interphase strength is by adding nanofillers in GFRP composites. Among the different nanofillers, inorganic nanofillers are the most promising because of their availability, low fabrication cost, and readily optimization of mechanical and thermal properties at the design stage [10]. The parameter used while making the polymer matrix is also playing a crucial role in well-dispersed nanoparticles in polymer matrix. The present study focuses on the development of titanium dioxide nanoparticles embedded epoxy polymer matrix composites, evaluate the physical properties to ensure the presence of the nanoparticles in polymer matrix like (SEM/XRD and FTIR) and determine the hardness of the nanocomposites by using micro-Vickers hardness test.

2 Materials and Methods The nanocomposites are fabricated with different weight percentage of nano-TiO2 (1–3 wt%) in the epoxy matrix. The major constituent of the whole composite is epoxy resin (LY556) which is used to adhere the other particle in the nanocomposites. The nanoparticles (TiO2 ) are purchased from the market (SRL company). TheTiO2 particles are in the range of 15–20 nm. The X-ray diffraction (XRD) experiment is conducted for these nanoparticles in order to confirm whether these were really TiO2 or not. The acetone is used for making the epoxy less viscous and increases

Effect of Nano-TiO2 Content (wt.%) on Hardness of Epoxy Polymer …

79

Fig. 1 Flow sheet of fabrication method of nanocomposites

the homogeneity of the mixture by proper mixing of the solution. As nanoparticles are having high aspect ratio that they form agglomerations and lead to attain inhomogeneity. The acetone decreases the surface tension of the nanoparticles by clearing the surface film over it and controls the formation of agglomerations. The microweighing balance is used to measure the weight of nanoparticles. Figure 1 shows the schematic diagram of fabrication method adopted in the study. Electron dispersion spectroscopy (EDS) was done to confirm chemical composition of the composite. The Vickers microhardness was measured for the nanocomposites.

3 Results and Discussions 3.1 XRD Analysis X-ray diffraction investigates the nanoparticles’ presence in the polymer nanocomposites. Desired crystal structures were calculated from the diffraction peaks of the constructive interference. It follows Bragg’s law, but destructive interference does not follow it. The wave length (ň) is exposed to the sample at an angle of θ toward tangential surface and detected at an angle of 2θ. Figure 2 shows the XRD analysis of different nanocomposites. It is observed that crystallographic structure of particles (TiO2 ) is present in it. The crystalline structure of titanium at different phases like rutile, anatase was found with different miller indices. The miller indices like (1 1 0), (0 0 1), (1 1 1), (1 3 0), (0 0 2) were obtained from the analysis done by the X-pert high score with standard pattern data.

80

S. B. Chanda et al.

Fig. 2 XRD graph analysis of 1wt%, 2wt%, 3wt% polymer matrix and pure TiO2 nanoparticles

3.2 SEM–EDS Analysis Energy-dispersive spectroscopy is also known to be energy-dispersive X-ray spectroscopy. This analysis is done for the samples for knowing their chemical composition and approximate characterization. Figures 3 and 4 show the SEM–EDS analysis of plan and nanocomposites, respectively. It is observed that in plain composites, the presence of TiO2 peak is not available, and in nanocomposites, the TiO2 peak is available.

Fig. 3 SEM–EDS analysis of plain composites

Effect of Nano-TiO2 Content (wt.%) on Hardness of Epoxy Polymer …

81

Fig. 4 SEM–EDS analysis of nanocomposites

3.3 FTIR Analysis FTIR spectroscopy is very important analytical tool for the nanocomposites because it provides information about structure of the sample. The interaction of infrared light with sample is known as infrared (IR) spectroscopy. FTIR spectroscopy is best at finding functional group and band. It performs operation worth respect to the bandwidth. It is extensively used in the both qualitative and quantitative observations of the nanocomposites. In qualitative carbon structures, metal oxides were analyzed. In the quantitative nanofiller, influence on the properties of the nanocomposites was done. It depends on the electromagnetic radiation (EM) of movement of the chemical bonds of the system. At a particular energy level fraction of the incident radiation is absorbed at specified energy level. To know about the functional growth, absorption bandwidth of each element in the all the nanocomposites, FTIR analysis has been done. Figure 5 shows the FTIR analysis of different nanocomposites. The specific absorption peak is displayed at 920 cm−1 . The declination of the wave takes place due to the addition of hardener, because there is a reaction between the hardener and epoxy the OH group is formed. Specific absorption peaks are occurring at the 3300 cm−1 and 1081–1108 cm−1 . There absorption bandwidth drops at 1637 cm−1 . The hardener is belongs to the polyamide group. Adding epoxy to TiO2 and hardener, absorption wave in between 914 and 920 cm−1 slowly gets fade out due to increase in the content of the TiO2 . The OH group forms strong over 990 cm−1 and 1081–1108 cm−1 , respectively. The strength of absorption width at 915_920 cm−1 tends to fade out along with increase in the methyl isobutyl ketone. The contrary to the 3300 cm−1 it enables the TiO2 fully dispersed in the polymer matrix due to increase of the isobutyl ketone.

82

S. B. Chanda et al.

Fig. 5 FTIR analysis of 1wt%, 2wt%, and 3wt% of TiO2 nanocomposites

3.4 Hardness The hardness test is done for the polymer nanocomposites by using micro-Vickers hardness tester. In this machine, there is an indenter which gives the impression of the indentation and the mean diameter is calculated. The indentation shape is rhombus, because it consists of two perpendicular diagonals. In this study, 25 gms load was applied, by calculating average value out of five readings taken as final outcome of each sample. Figure 6 shows the effect of nano-TiO2 on microhardness of the nanocomposites. It is observed that with the increase in the nano-TiO2 content, the micro-Vickers hardness of the nanocomposites increases. This may be due to good interface bond between nano-TiO2 and epoxy matrix.

4 Conclusions This study emphasizes on the preparation methodology, characterization, and mechanical properties’ evaluation of the nano-titanium dioxide/epoxy nanocomposites. The nanoparticles were first dispersed in the epoxy resin and cured at room

Effect of Nano-TiO2 Content (wt.%) on Hardness of Epoxy Polymer …

83

Fig. 6 Vickers hardness values of nanocomposites

temperature. Five different samples were prepared pure titanium dioxide, 1 wt% of nano-titanium dioxide-epoxy, 2 wt% nano-titanium dioxide-epoxy, 3 wt% nanotitanium dioxide-epoxy. Then, hardness test has been done for all samples including cured ones and the results varied. It shows that hardness values of the high wt% of the nanofillers were high and with low wt% of the nanofillers were low in comparison. It is due to the dispersed TiO2 nanoparticles in the sample gives resistance to not to penetrate the indenter on the surface of the sample. The hardness values for pure epoxy, 1 wt% TiO2 , 2 wt% TiO2 , and 3wt% TiO2 were 0.14, 0.16, 0.17, and 0.19GPa, respectively. It observed that the hardness value is increased by 37% form pure epoxy to the 3 wt% TiO2 of samples.

References 1. Nayak S, Nayak RK, Panigrahi I (2021) Effect of nano-fillers on low-velocity impact properties of synthetic and natural fibre reinforced polymer composites- a review. Advances in Materials and Processing Technologies 0:1–24. https://doi.org/10.1080/2374068X.2021.1945293 2. Jesthi DK, Mohanty SS, Nayak A et al (2018) Improvement of mechanical properties of carbon/ glass fiber reinforced polymer composites through inter-ply arrangement. IOP Conf Ser: Mater Sci Eng 377 3. Khandai S, Nayak RK, Kumar A et al (2019) Assessment of mechanical and tribological properties of Flax/Kenaf/glass/carbon fiber reinforced polymer composites. Mater Today: Proc 18:3835–3841. https://doi.org/10.1016/j.matpr.2019.07.322 4. Saroj S, Sarangi RK, Das D, Nayak RK (2019) Enhancemenet of mechanical and vibrational properties of plywood for packaging and container applications. Mater Today: Proc 18:3952– 3957 5. Nayak RK, Mahato KK, Routara BC, Ray BC (2016) Evaluation of mechanical properties of Al2 O3 and TiO2 nano filled enhanced glass fiber reinforced polymer composites. J Appl Polym Sci 133

84

S. B. Chanda et al.

6. Cazan C, Enesca A, Andronic L (2021) Synergic effect of TiO2 filler on the mechanical properties of polymer nanocomposites. Polymers 13:2017. https://doi.org/10.3390/polym1312 2017 7. Salehian H, Jenabali Jahromi SA (2015) Effect of titanium dioxide nanoparticles on mechanical properties of vinyl ester-based nanocomposites. J Compos Mater 49:2365–2373. https://doi. org/10.1177/0021998314546140 8. Chen W-J, Zhu Y-F, Wang S-X et al (2012) Effects of carbon nanofillers on enhancement of polymer composites. J Appl Phys 112:074302. https://doi.org/10.1063/1.4755806 9. Fan XJ, Lee SWR, Han Q (2009) Experimental investigations and model study of moisture behaviors in polymeric materials. Microelectron Reliab 49:861–871. https://doi.org/10.1016/ j.microrel.2009.03.006 10. Hu Y, Du G, Chen N (2016) A novel approach for Al2O3/epoxy composites with high strength and thermal conductivity. Compos Sci Technol 124:36–43. https://doi.org/10.1016/j.compsc itech.2016.01.010

Development and Characterization of Nano-SiO2 -Enhanced Polymer Nanocomposites Amish Mishra, Sai Babu Chanda, Ramesh Kumar Nayak , Akshaya Kumar Rout , and S. Suresh

Abstract The addition of nano particles in polymer matrix composites gives better strength by decreasing the crack propagation in polymer matrix nanocomposites. In this study, the mixing of nano-SiO2 fillers was done by a mechanical, magnetic stirrer and followed by probe sonication for different time periods. Epoxy/silica nanocomposites having 1 wt%, 2 wt%, 3 wt%, 4 wt% of nano-SiO2 were prepared experimentally. The samples were characterized using XRD, SEM–EDS, and FTIR, to know the presence of nano-SiO2 in the nanocomposites. The effect of cure and uncure of the composites on the microhardness was studied. It is observed that the incorporation of nanosilica in epoxy matrix increases the microhardness. Furthermore, the effect of curing has improved the hardness of nanocomposites. Keywords Nano-SiO2 · Nanocomposites · Epoxy · Microhardness

1 Introduction Fiber-reinforced polymer (FRP) matrix composite is used in variety of applications including automobile, aerospace, marine, and general engineering due to its increased mechanical strength and chemical inertness. The most important property of polymer matrix composite is that they do not corrode and they can be extensively used in areas where moisture and atmosphere play a major role. These FRP composites are reinforced with various organic and inorganic type nanofillers to improve further its strength and toughness. The nanoscale size particles such silica, titania A. Mishra · S. B. Chanda · R. K. Nayak (B) Department of Materials and Metallurgical Engineering, MANIT, Bhopal 462003, India e-mail: [email protected] A. K. Rout School of Mechanical Engineering, KIIT Deemed to Be University, Bhubaneswar-24, India S. Suresh Department of Chemical Engineering, MANIT, Bhopal 462003, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_9

85

86

A. Mishra et al.

can improve mechanical properties. Processing of polymer nanocomposites plays important role for dispersing nanofibers into the polymer matrix in order to achieve the desired properties of the final nanocomposite. Nanocomposites have fillers of size 1–100 nm dispersed in the polymer matrix. Many organic and inorganic type nanofillers are used such as CNT or carbon nanotubes in which multi-wall carbon nanotubes (MWCNTs) and single-wall carbon nanotubes (SWCNTs) are used. Inorganic nanofillers such as TiO2 , SiO2, Al2 O3, SiC, and ceramics are used at nanoscale level as filler materials. Based on the nanofiller type and processing conditions, various techniques are used. In in situ polymerization process, the nanofillers are first dispersed in the monomer and reaction occurs for polymerization. In solution processing, the nanofiller is first dispersed in solvent and mixed thoroughly using magnetic stirrer, then ultrasonication is done for proper dispersion of nanoparticles. Tang et al. [1] found that the interlaminar fracture toughness increases and ILSS decreases with increase in nano-SiO2 content. Nayak et al. [2–5] found that with the addition of nano-TiO2 and Al2 O3 in epoxy polymer matrix improve the flexural strength and retain its properties in hydrothermal treatment compared to plain composites. Karnati et al. [6] found that nanosilica has good potential to increase the mechanical properties of nanosilica which enhances fiber-reinforced polymer composites. The addition of 2 wt% nanosilica particles into the epoxy polymer increases fracture toughness and wear resistance of the nanocomposites as compared to plain epoxy composites in a seawater environment [7]. Different researchers have found that addition of nanosilica in fiber-reinforced polymer composites improves the mechanical properties for different engineering applications [8–11]. However, to achieve the full potential of the nanoparticles in the polymer matrix, the dispersion of nanoparticles in epoxy polymer matrix is a challenge. The parameters used while making the polymer matrix is also playing a crucial role in well-dispersed nanoparticles in polymer matrix. Therefore, in this investigation to achieve better dispersion of nanosilica particles of size 10–15 nm in the epoxy matrix, ultrasonic probe is used. The presence of nanoparticles is ensured through XRD, SEM–EDS, and FTIR analysis. Furthermore, the effect of nanofillers on microhardness is evaluated and compared with plain composites.

2 Materials and Methods The epoxy resin constitutes of diglycidyl of bisphenol A (DGEBA) of LY-556 and hardener (HY-951), which is a primary amine of aliphatic group. Nanofiller which was used in this study was nanosilica having size between 10 and 15 nm and purchased from SRL India Pvt. Ltd. The silica was first dispersed in acetone and then mixed with epoxy resin to avoid agglomeration. Beakers of 100 ml, 200 ml, 500 ml, were arranged. Acetone is now measured in a glass beaker which is about 10% of the epoxy. Acetone was mixed with the epoxy to reduce the viscosity of the epoxy resin and helps for dispersion of nanoparticles in the epoxy resin. The SiO2 nanoparticles were kept in the oven of temperature at 1000 C in order get rid of the moisture content if any present

Development and Characterization of Nano-SiO2 -Enhanced Polymer …

87

Fig. 1 Fabrication method of nanocomposites

and also to reduce the surface functionalization which causes the agglomeration. The nanoparticles were combined with acetone and magnetic stirring was conducted at specific parameters. Figure 1 shows the fabrication technique used to manufacture nanocomposites. In magnetic stirring, the colloidal nanoparticles get separated and dispersed in a homogeneous manner. Then, the mixture was taken to mix with epoxy. The magnetic stirring was performed for 1 h at 200 rpm and at room temperature. The ultrasonication probe was used at room temperature with ice kept around the beaker containing epoxy/silica mixture. The ultrasonication was done at 220 v for 1 h time for proper dispersion. Degassing is conducted to the whole mixture which leads to release of the all the gas which was entrapped into the mixture. This experiment was conducted at the temperature of 27 °C room temperature for the time period of 1 h. The mixture kept in the oven for 12 h at the temperature of 80 °C in order to remove the acetone present in it. Then hardener was mixed in the ratio 10:1 [12–15]. The sample was poured into the mold cavities of desired shapes. After the preparation of samples, the samples were cured in hot air oven at 140 °C for 6 h followed by furnace cooling. There were four nanocomposites having different weight percents of nano-SiO2 (1–4 wt%) and one-plain epoxy composites were prepared. The density of the samples was measured and found no significant difference between them.

3 Results and Discussions 3.1 XRD Analysis The X-ray diffraction experiment of pure silica and nano-SiO2 -enhanced polymer composites is done to confirm the presence of silica nanofiller in the nanocomposites. Figure 2 shows the XRD plots for pure silica, epoxy, and nanocomposites having 1, 2,

88

A. Mishra et al.

Fig. 2 XRD plots of nanocomposites having different wt% of nano-SiO2

3, and 4 wt% nano-SiO2 . The pure silica being amorphous in nature gives amorphous peak. The peak here is at 23° 2-theta angles for pure amorphous nanosilica. The XRD graphs for 1 wt%, 2 wt%, 3 wt%, and 4 wt% nanosilica/epoxy are shown in Fig. 1. The 2-theta angle selected for our study was from 5° to 90°. This is because nanosilica gives peaks at low angles. No crystalline peaks were observed, indicating amorphous structure of the samples as expected. They all showed an amorphous peak at about 2-theta 22.51. The intensity of the nanocomposites is weaker than that of the pure sample. It implies that the intensity is varying with the incorporation of silica nanoparticles. The degree of crystallinity of epoxy matrix decreases and the diffraction peaks are reduced in the nanocomposites.

3.2 SEM–EDS Analysis Energy-dispersive spectroscopy (EDS) is also known to be energy-dispersive Xray spectroscopy. This analysis is done for the samples for collecting about their chemical composition and characterization. The SEM–EDS analysis was performed for pure epoxy and nano-SiO2- enhanced nanocomposites. SEM images were taken

Development and Characterization of Nano-SiO2 -Enhanced Polymer …

89

Fig. 3 SEM–EDS of a without nano-SiO2 and b with nano-SiO2 filler

for pure epoxy, 1 wt%, 2 wt%, 3 wt%, and 4 wt% SiO2 content nanocomposites. Figure 3 shows the SEM–EDS of (a) without nano-SiO2 and (b) with nano-SiO2 filler composites. From the EDS of pure epoxy sample, it can be observed that silica is not present in the sample and the amount of carbon is very high due to presence of organic polymer epoxy which consists of carbon, oxygen, and chorine. Some traces of chorine was also found which is due to chemical formula of the epoxy and hardener. The acquisition parameters were taken to be accelerating voltage of 10 kV, probe current is about 1.0000 nA, and Real time—68.68. Live time—50.00. It gives graphical data of the KeV and counts which indicate chemical composition that sample attains. The nano-SiO2 contains 52% of the mass, oxygen of 42.46% of the mass, and silica 5.34% by mass%. This declares that the samples were prepared which consists of the enough amount nanofillers which enhances the properties of the material both in mechanical and chemical.

3.3 FTIR Analysis FTIR was performed on pure epoxy, 1%, 2%, 3%, 4 wt% epoxy/silica samples so as to know the bonding between the polymer resin and nanoparticles and also to know that weather dispersant acetone is present in the samples after preparation or not. The FTIR transmittance spectra were plotted for pure epoxy and silica nanoparticles’ epoxy matrix nanocomposites. The two curves are similar but differ in the absorption intensity. Figure 4 shows the FTIR analysis of nano-SiO2 -enhanced polymer composites. There are two absorption bands located at 3440 and1630 cm− 1 in all the transmittance curves. The absorption band at around 3440 cm− 1 is attributed to the O–H stretching vibration of surface hydroxyl groups involved in hydrogen bonds. The absorption band at 1630 cm− 1 corresponds to the O–H bending vibration of absorbent water molecules. The absorption bands at 1110, 960, 810, and 470 cm− 1 are due to the Si–O–Si stretching vibration. The corresponding absorption intensity of the Si–O–Si stretching vibration is stronger for pure-silica nanoparticles. The bonding process between the nanoparticles can be explained in the following

90

A. Mishra et al.

Fig. 4 FTIR analysis of nano-SiO2 -enhanced polymer composites

three steps. First, hydrolyzation took place to convert RSi(OMe)3 into silanol and methanol, next, condensation occurred between the hydroxyls to form the silanol and particle, and finally, the hydroxyls reacted with resin molecules of epoxy which is present in the nanocomposite. Thus, the amount of Si–O–Si bonds increases, which would probably contribute to the absorption of Si–O–Si stretching vibration.

3.4 Vickers Microhardness The hardness test was performed by Vickers microhardness tester. The hardness test is done for the polymer nanocomposites as well as for plain epoxy. In this machine, there is a diamond type of indenter which gives the impression of the indentation and by using that mean diameter is calculated and the load that has been given is variable. The indentation shape is of rhombus due to the presence of two perpendicular diagonals. Figure 5 shows the microhardness values for plain and nanocomposites. There are five readings of each sample which was taken to have the mean average values of

Development and Characterization of Nano-SiO2 -Enhanced Polymer …

91

Fig. 5 Effect of nano-SiO2 on microhardness of the nanocomposites

all the readings. Readings for pure epoxy, 1 wt% SiO2 , 2%SiO2 , 3% SiO2, and 4% SiO2 were taken. As the size of the indentation increases, then the hardness value decreases. It is observed that with the increasing in the wt% of nanosilica, increases the hardness of the nanocomposite gradually. Figure 5 also so shows the comparison of two samples consisting of cured nanocomposites and uncured nanocomposites. This can be inferenced from the graph that curing at 140 °C increases the hardness values by a marginal amount. This can be due to removal of reduction of porosity present in the uncured samples and better cross-linking of polymer.

4 Conclusions This study emphasizes on the preparation methodology, characterization, and mechanical properties’ evaluation of the nanosilica/epoxy nanocomposites. The silica nanoparticles were of amorphous type and have a particle size varying around 10–15 nm size. Process like magnetic stirring, ultrasonication through probe, and degassing was done for better dispersion and less porous nanocomposites. Five different samples were prepared pure silica, 1, 2, 3, and 4% by weight of nanosilica. Studies on the crystallographic structures and the morphological images have been done for all the samples. The qualitative and quantitative analysis method has been used, i.e., Fourier transformer infrared spectroscopy (FTIR). SEM–EDS was performed to confirm the presence of nano-SiO2 in polymer matrix composites. The improvement of mechanical properties was confirmed though microhardness test. The hardness values for pure epoxy, 1% SiO2 , 2% SiO2 , and 3% SiO2 were 0.16, 0.16, 0.17, and 0.23 GPa, respectively. It observed that there has been an increase in the hardness by 39% of the nanocomposites compared to plan epoxy. The improvement of microhardness of nano-SiO2 enhances composites to create opportunity to

92

A. Mishra et al.

be used in glass/carbon/Kevlar fiber-reinforced polymer composites to increase the mechanical strength and toughness of the hybrid composites.

References 1. Tang Y, Ye L, Zhang D, Deng S (2011) Characterization of transverse tensile, interlaminar shear and interlaminate fracture in CF/EP laminates with 10wt% and 20wt% silica nanoparticles in matrix resins. Compos A Appl Sci Manuf 42:1943–1950. https://doi.org/10.1016/j.compos itesa.2011.08.019 2. Nayak RK (2019) Influence of seawater aging on mechanical properties of nano-Al2 O3 embedded glass fiber reinforced polymer nanocomposites. Constr Build Mater 221:12–19 3. Nayak RK, Mahato KK, Ray BC (2016) Water absorption behavior, mechanical and thermal properties of nano TiO2 enhanced glass fiber reinforced polymer composites. Compos A Appl Sci Manuf 90:736–747 4. Nayak RK, Ray BC (2017) Water absorption, residual mechanical and thermal properties of hydrothermally conditioned nano-Al2 O3 enhanced glass fiber reinforced polymer composites. Polym Bull 74:4175–4194 5. Nayak RK, Ray BC (2018) Influence of seawater absorption on retention of mechanical properties of nano-TiO2 embedded glass fiber reinforced epoxy polymer matrix composites. Arch Civ Mech Eng 18:1597–1607 6. Karnati SR, Agbo P, Zhang L (2020) Applications of silica nanoparticles in glass/carbon fiberreinforced epoxy nanocomposite. Compos Commun 17:32–41. https://doi.org/10.1016/j.coco. 2019.11.003 7. Han W, Chen S, Campbell J et al (2016) Fracture toughness and wear properties of nanosilica/ epoxy composites under marine environment. Mater Chem Phys 177:147–155. https://doi.org/ 10.1016/j.matchemphys.2016.04.008 8. Khodadadi A, Liaghat G, Vahid S et al (2019) Ballistic performance of Kevlar fabric impregnated with nanosilica/PEG shear thickening fluid. Compos B Eng 162:643–652. https://doi. org/10.1016/j.compositesb.2018.12.121 9. Jumahat A, Soutis C, Jones FR, Hodzic A (2010) Improved compressive properties of a unidirectional Cfrp laminate using nanosilica particles. Adv Compos Lett 19:096369351001900604. https://doi.org/10.1177/096369351001900604 10. Manap N, Jumahat A, Rahman NA, Rahman NAA (2020) NaOH treated Kenaf/glass hybrid composite: The effects of nanosilica on longitudinal and transverse tensile properties. J Phys: Conf Ser 1432:012046. https://doi.org/10.1088/1742-6596/1432/1/012046 11. Hashim UR, Jumahat A, Mahmud J (2019) Improved tensile properties of basalt fibre reinforced polymer composites using silica nanoparticles. Materialwissenschaft und Werkstofftechnik 50:1149–1155. https://doi.org/10.1002/mawe.201800071 12. Jesthi DK, Nayak R (2020) Influence of glass/carbon fiber stacking sequence on mechanical and three-body abrasive wear resistance of hybrid composites. Mater Res Express. https://doi. org/10.1088/2053-1591/ab6919 13. Jesthi DK, Nayak RK (2019) Improvement of mechanical properties of hybrid composite through interply rearrangement of glass and carbon woven fabrics for marine applications. Compos B Eng. https://doi.org/10.1016/j.compositesb.2019.03.042 14. Jesthi DK, Nayak RK (2019) Evaluation of mechanical properties and morphology of seawater aged carbon and glass fiber reinforced polymer hybrid composites. Compos Part B: Eng 106980. https://doi.org/10.1016/j.compositesb.2019.106980 15. Jesthi D, Nayak R (2020) Influence of glass/carbon fiber stacking sequence on mechanical and three-body abrasive wear resistance of hybrid composites. Mater Res Express 7. https://doi. org/10.1088/2053-1591/ab6919

Optimization Techniques Used in Machining Processes: A Review Diksha Jaurker , M. K. Pradhan , Siddharth Jaurker , and Raj Malviya

Abstract In the rapidly advancing and spontaneous manufacturing industry, optimizing methods in machining processes is critical for a production plant to adapt efficiently to intense competitiveness and rising global demand for premium products. Optimization techniques in machining processes are regarded as a critical instrument for the continuous enhancement of quality of output. These methods involve the modeling of parameters and their relationships and the calculation of optimal processing conditions. But, identifying ideal machining conditions using cost-effective mathematical models is a complicated research endeavor, and modeling and optimization approaches have experienced significant growth and extension over time. The strategic vision of numerous modeling and optimization methodologies in machining processes has been assessed in this paper. Keywords Optimization · Multi-objective optimization · Genetic algorithm · Advanced processing techniques · Artificial neural network · Response surface methodology · Taguchi method

1 Introduction Optimal parameters of machining are of major relevance in production contexts, as machining operation economy is critical to market competitiveness. The machining D. Jaurker (B) Shri G.S. Institute of Technology & Science, Indore, India e-mail: [email protected] M. K. Pradhan Department of Mechanical Engineering, National Institute of Technology, Raipur, India e-mail: [email protected]; [email protected] S. Jaurker Govt. Polytechnic College, Damoh, India R. Malviya National Institute of Technology Tiruchirappalli, Tiruchirappalli, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_10

93

94

D. Jaurker et al.

cost on numerical control (NC) machines is very much dependent on parameters of machining, and ideal values must be calculated. Because of the expensive machining of NC machines, it is necessary to run equipment as most possibly effective for achievement of requisite pay back [1]. Machining research focuses on the properties of input work materials, tools, and machine parametrization that influence operational efficiencies and responses. Parametric optimization of the process, which establishes the areas of essential process controlling variables resulting desired outputs or responses with tolerable variances and ensures a reduced cost of manufacture, can result in a considerable enhancement in process efficiency [2]. For a long time, many numerical and analytical approaches for determining a function’s extreme limits have been employed in scientific computations. Although these strategies work satisfactory in certain practical scenarios but often fail in complicated designing circumstances. The number of design parameters in real-world design issues might be quite enormous, and its impact upon the value to be optimized could intricate and non-linear. The function of objective may contain several local extrema, but the designer is only concerned with the global extremum. Classical techniques cannot solve such issues or could solely calculate localised extremes. In these difficult circumstances, modern optimization algorithms provide answers as they identify a result close to the global optimum in a fair amount of computational and time cost. The optimization methodologies are divided into two categories, as mentioned be following: 1. Conventional optimization approaches: They are predictable algorithms that follow specific rules to go from one answer to the subsequent one. Such algorithms have been around since a while and has been effectively deployed in a wide range of engineering design challenges. Some examples are design of experiment (DOE) which includes Taguchi method, factorial design and response surface methodology (RSM), and mathematical iterative search which includes dynamic programming, non-linear programming and linear programming-based algorithms, etc. 2. Non-conventional optimization approaches: They are stochastic in nature and use probabilistic transition principles. These approaches are very new and are acquiring prominence owing to specific qualities that the deterministic algorithm lacks. Differential evolution (DE), particle swarm optimization (PSO), biogeographybased optimization (BBO), harmony elements algorithm (HEA), artificial immune algorithm (AIA), artificial bee colony (ABC), continuous ant colony algorithm (CACO), non-dominated sorting genetic algorithm II (NSGA-II), landscape adaptive PSO (LAPSO), adaptable neuro-fuzzy inference structure (ANFIS), and more algorithms are examples. 3. Hybrid optimization approaches: They are combination of conventional and nonconventional optimization approaches employed together to ensure the accuracy and reliability of out results. Initially, the machine operator is used to choose the cutting conditions for machining using knowledge gained from experience, yet even a good operator would struggle to get the best results every time. The machined component’s quality is determined

Optimization Techniques Used in Machining Processes: A Review

95

by the parametric values. Later, Taylor’s [3] tool life equation helped in optimizing the machining for better quality and cost minimization. Conventional mathematical computing methodologies have been utilized to tackle optimizing tasks in design; however, they have the below mentioned drawbacks: • Traditional approaches perform poorly across a wide range of problem areas. • They were ineffective for tackling multi-modal situations because they produce localized optimum solution. • They were ineffective while tackling multi-objective optimization issues. • They were ineffective in dealing with issues with a high number of restrictions. Given the limitations of standard optimization approaches, efforts are being made to use evolutionary optimization techniques to solve mechanical design optimization issues. GA is the most widely used evolutionary optimization approach. GA, on the other hand, delivers a near-optimal solution to a complicated issue with a large set of constraints and variables. That is mostly because it is difficult to determine the appropriate regulatory parameters, such as size of population, mutation rate, and crossover rate. As a result, attempts should be made to continue applying more modern optimization methods to alter existing algorithms and build hybridized algorithms that are very effective, robust, and capable of producing right results.

2 Conventional Optimization Approaches Ribeiro et al. [4] investigated the impacts of altering four process parameters of milling, namely axial depth , feed rate, radial depth, and cutting speed. The impact of these factors on surface roughness is investigated separately, as well as the interaction of certain of them, for milling machining of hardened steel using the Taguchi optimization technique. A L16 orthogonal array was constructed for this purpose, with two alternative levels established for each parameter, equivalent to sixteen experimental experiments. The effect of each parameter on surface roughness was then determined using analysis of variance (ANOVA) on experimental data. The Taguchi approach proved to be fairly robust, allowing us to quantify each machining factor’s impact and interplay in our inquiry. Abhan et al. [5] optimized turning operation for minimization of roughness of surface considering temperature of lubricant, rate of feed, and cutting depth, using Taguchi method and found that it is appropriate for analyzing the surface roughness. It is discovered that the this method’s parameters design ensures a straightforward, methodical, and efficient way for optimizing machining settings. Lin et al. [6] applied L18 orthogonal array based on Taguchi method to optimized magnetic force-assisted EDM for maximization of material removal rate and minimization of roughness of surface with taking into account the polarity of machining, duration of current, and maximum current and voltage. He found that the operating stability of magnetic force-aided EDM was higher.

96

D. Jaurker et al.

Sastry et al. [7] applied RSM on the process parameters like rate of feed, speed of cutting, and cutting depth to minimize roughness of surface and maximization of MRR for resin and aluminum alloy. Makadia et al. [8] employed RSM to investigate the influence of process parameters on surface quality. Response surface contours were created in order to determine the best condition for a given surface quality. The established prediction equation demonstrates that the feed rate, followed by tool nose radius, has the greatest effect on surface quality.

3 Non-conventional Optimization Approaches In the literature, numerous implementations of optimization techniques in metal cutting process parameter optimization challenges have been published. The GA methodology is used to optimize and search problems by utilizing the natural evolution process. It was used by Kant et al. [9] providing an ANN approach combined with a GA for predicting and optimizing machining settings to achieve the lowest possible surface roughness. The anticipated values are similar to the vales obtained from experiments. When the results obtained from ANN were compared with the fuzzy logic models and regression, it is evident that the constructed ANN model performs better than the other models. A GA-based milling parameter optimization system was modeled by Rai et al. [10] for parametric optimization of a multitool milling operation. According to the findings, the built system has improved functional capabilities and provides more accurate predictions than the previous models. Yadav et al. [11] applied approach of ANN along with non-dominated sorting GA for parametric optimization of slotted electro-discharge abrasive grinding operation, studied the influence of applied current, duration of the pulse, speed of the grinding wheel, and grit number on rate of material removal and roughness of the surface. Sangwan et al. [12] combine ANN and GA to provide a technique for finding the optimal machining settings which yields in the lowest roughness of surface. In this study, a real machining experiment was utilized to assess the capabilities of the ANN-GA approach for roughness of surface prediction and optimization. A feedforward ANN is formed by gathering data collected during the turning operation of Ti-6Al-4V. The 3D surface and contour plots generated throughout the research may be utilized to determine the ideal machining parameters for achieving certain surface roughness levels. Jiang et al. [13] used a hybrid GA in conjunction of a self-adaptive penalty function and crossover to optimize the computation and simulation of operation while accounting for cutting fluid utilization and operation cost. This permits each generation to remain a member of the non-feasible solution in effort to accomplish the globally optimum over both parts of the feasible and non-feasible solutions. The model’s use is demonstrated by a case study of a cylinder turning process. Wang et al. [14] offer a optimization strategy of multiple objectives for machining parameters while accounting for energy consumption, it remains limited in terms of cutting process subcategory and optimization algorithm. On the one hand, this research only explains energy models of turning operations, whereas additional

Optimization Techniques Used in Machining Processes: A Review

97

machining processes will need to be explored in future. However, the generated solutions after a limited number of calculations by NSGA-II cannot be guaranteed to be optimum, even though the precision is sufficient in this article. Krishnaraj et al. [15] provide an experimental assessment of a full factorial approach performed on slim CFRP composite structures with a K20 carbide drill to determine optimal cutting parameters by varying drilling settings. The contribution rates of hole quality metrics were assessed using analysis of variance (ANOVA). To find the ideal cutting parameters for drilling, the multiple objective optimization employs GA. The tool wear of the carbide drill was determined at optimum cutting speed and feed. Kant et al. [9] provide an ANN approach combined with a GA for predicting and optimizing machining settings to achieve the lowest possible surface roughness. The anticipated values are similar to the vales obtained from experiments. When the results obtained from ANN were compared with the fuzzy logic models and regression, it is evident that the constructed ANN model performs better than the other models. Gao et al. [16] for alloy based on nickel’s cutting, he designed a model of the thermal and stress field employing dynamic numerical simulation and FEM, along with integrating high-speed machining experiments and orthogonality research methodologies. Cutting efficiency is influenced by force of cutting and wearing of tool. The GA-optimized processing parameters of speed of cutting, cutting depth in axial depth of cut direction, and feed for each tooth were used to construct the force of cutting and wearing of tool deduction system. Yang [17] suggested an optimization approach for selecting the optimal EDM process parameters. The SA approach was used to adjust process parameters to obtain enhancement in MRR while minimizing roughness of surface. Rao et al. [18] stated that electrochemical machining (ECM) variables such flow velocity of electrolyte, voltage applied, and rate of tool feed, all have a significant influence in increasing process performance metrics. To discover the optimal set of variables for an ECM technique, PSO was used. The three machining performance parameters that are measured are MRR, life of tool, and geometrical accuracy. The suggested algorithm’s results are compared to previously reported results acquired using various optimization approaches. Pradhan [19] This study suggests a mixed, unified technique combining GR analysis, and principal component analysis for optimizing EDM settings. The GR reduces optimization of numerous responses into optimization of a single response problem. ANOVA is used to identify parameters that have a remarkable influence, and responses are optimized by adjusting these operation variables. It was discovered that the primary important operation variable is duty cycle. Razfar et al. [20] developed a prediction model for level of surface roughness utilizing experimental data acquired during face milling operation of stainless steel (X20Cr13) using a PSO-based ANN to manage the optimization condition. The estimated surface roughness values agree well with experimental values measured with the expected optimal machine settings. Samanta et al. [21] employed ABC algorithm to find the optimum set of process parameters electrochemical discharge machining, electrochemical machining, and

98

D. Jaurker et al.

Table 1 List of published work employing hybrid optimization methods for machining processes S. No.

Author

Machining process

Optimization method

Remark

1

Bhaskar et al. [1]

Profit maximizing

GA, PSO, TS, and CACO

PSO always produces superior outcomes when using unconvtactics, A L16 orthogonal array was constructed for this purpose. The effect of each parameter on roughness of surface was then evaluated using ANOVA

2

Pradhan [23]

EDM

RSM and GA

The main important element for surface quality was discovered to be pulse period, preceded by duty factor, current applied, and supplied voltage. The GR grade provided the best circumstances for the machining settings. The weighting factor of the input variables is determined using ANOVA

3

Pradhan

EDM

RSM and GA [24]

The experiments were designed using RSM, followed by GA analysis to optimize, the weightage of the obtained values was defined by utilizing the entropy measurement method. ANOVA was utilized

4

Huang [26]

Wire-EDM

GR and Taguchi An L18 mixed-orthogonal array table and gray relational study and statistical approach, it was discovered that the table feedrate influences material removal and the width of gap and roughness of surface were controlled by pulse duration

5

Maiyara et al. [25]

End milling processes

GR and Taguchi On a L9 orthogonal array, 9 experiment trials were performed using the Taguchi approach. A GR grade derived from the GRA is used to address the end milling process given various performance factors. And the ANOVA is utilized to discover the most important attribute

6

Jayaraman et al. [27]

turning

GR and Taguchi The important contribution of parameters is assessed by ANOVA, once the optimal amounts of process parameters were chosen focusing on the GR grade values. To authenticate the lab results, a verification test was performed

7

Pradhan et al. [30]

EDM

GA, RSM, and TOPSIS

TOPSIS was utilized to do multi-criteria choice making and GRA converts to the GR coefficient. ANOVA is used to identify factors that have a substantial impact, and optimization of outcomes is achieved by adjusting these operational parameters

8

Cue et al. [31]

ANFIS-ACO, GA, and SA

Compared against ANFIS-ACO, GA, and SA, the experiment indicated that PSO outperforms all other algorithms

9

Rao et al. [32]

Wire-EDM

RSM and ABC

ABC algorithm’s effectiveness is evaluated on grounds of rate of convergence and solution’s accuracy. The ABC method’s rate of convergence was discovered to be quite great

10

Satishkumar et al. [33]

CNC multitool drilling

GA, SA, and ACO

ACO can find a near-optimum result in an incredibly wide solution space compared to GA and AS, and it is totally independent of problem and generalized

11

Kumar et al. [28]

Turning

BNN, RNN, and WPCA

Found that all three methods are suitable to predict the responses

12

Gangil [29]

EDM

VIKOR and RSM

Multi-criteria decision-making-based VIKOR approach, in conjunction with RSM, has been used to optimize numerous quality attributes. RSM’s established statistical equation is used to perform the conformation check

Optimization Techniques Used in Machining Processes: A Review

99

electrochemical micro-machining. This approach performed both single-objective optimization and multi-objective optimization. The findings acquired while implementing the ABC technique for optimization of these three processes are compared to the results obtained by the previous studies, demonstrating the ABC algorithm’s applicability and relevancy in improving the effectiveness measures of the investigated processes. Wu et al. [22] implemented modified continuous ant colony optimization (MCACO) to the multi-pass turning model, and the performance of MCACO demonstrates a significant enhancement in the unit as compared to various conventional and unconventional methodologies.

4 Hybrid Optimization Techniques For the optimization of a machining process, many researchers have employed more than one optimization methods for a single problem to compare the different methods, like Bhaskar et al. [1] used GA, PSO, CACO, and TS, Pradhan [23, 24] employed RSM and GA, some used GR and Taguchi [25–27]. Kumar et al. [28] used back propagation neural network (BNN), recurrent neural network (RNN), and weighted principal component analysis (WPCA). Gangil et al. [29] used multi-criteria decisionmaking-based VIKOR approach, inconjunction with RSM. Table 1 shows the work of different researchers employing hybrid optimization techniques.

5 Conclusion An approach to model and optimize the cutting machining conditions has revealed an intriguing possibility for improving process and product quality. The standard blueprint for process parameter optimization in machining operations aims to offer a solitary, centralized, and structured method for identifying optimal machining conditions in diverse process optimization problems. It encompasses one or many existing designing and optimization methodologies, attempting to make the framework a cohesive and efficient. Furthermore, it seeks to give adaptability in the use of appropriate procedures depending on their intrinsic capability and on-hand complexity of the problem, emphasizing the relevance of situation criticality and analysis.

References 1. Baskar N, Asokan P, Prabhaharan G, Saravanan R (2005) Optimization of machining parameters for milling operations using non-conventional methods. Int J Adv Manuf Technol 25(11):1078– 1088

100

D. Jaurker et al.

2. Montgomery DC (2007) Introduction to statistical quality control. John Wiley & Sons 3. Taylor FW (1906) On the art of cutting metals, vol 23. American Society of Mechanical Engineers 4. Ribeiro JE, César MB, Lopes H (2017) Optimization of machining parameters to improve the surface quality. Procedia Struct Integrity 5:355–362 5. Abhang L, Hameedullah M (2012) Optimization of machining parameters in steel turning operation by Taguchi method. Procedia Eng 38:40–48 6. Lin Y-C, Chen Y-F, Wang D-A, Lee H-S (2009) Optimization of machining parameters in magnetic force assisted EDM based on Taguchi method. J Mater Process Technol 209(7):3374– 3383 7. Sastry MNP, Devi KD, Reddy KM (2012) Analysis and optimization of machining process parameters using design of experiments. Ind Eng Lett 2(9):23–32 8. Makadia AJ, Nanavati J (2013) Optimisation of machining parameters for turning operations based on response surface methodology. Measurement 46(4):1521–1529 9. Kant G, Sangwan KS (2015) Predictive modelling and optimization of machining parameters to minimize surface roughness using artificial neural network coupled with genetic algorithm. Procedia CIRP 31:453–458 10. Rai JK, Brand D, Slama M, Xirouchakis P (2011) Optimal selection of cutting parameters in multi-tool milling operations using a genetic algorithm. Int J Prod Res 49(10):3045–3068 11. Yadav RN, Yadava V (2013) Multiobjective optimization of slotted electrical discharge abrasive grinding of metal matrix composite using artificial neural network and nondominated sorting genetic algorithm. Proc Inst Mech Eng, Part B: J Eng Manuf 227(10):1442–1452 12. Sangwan KS, Saxena S, Kant G (2015) Optimization of machining parameters to minimize surface roughness using integrated ANN-GA approach. Procedia CIRP 29:305–310 13. Jiang Z, Zhou F, Zhang H, Wang Y, Sutherland JW (2015) Optimization of machining parameters considering minimum cutting fluid consumption. J Clean Prod 108:183–191 14. Wang Q, Liu F, Wang X (2014) Multi-objective optimization of machining parameters considering energy consumption. Int J Adv Manuf Technol 71(5):1133–1142 15. Krishnaraj V, Prabukarthi A, Ramanathan A, Elanghovan N, Kumar MS, Zitoune R, Davim J (2012) Optimization of machining parameters at high speed drilling of carbon fiber reinforced plastic (CFRP) laminates. Compos Part B: Eng 43(4):1791–1799 16. Gao DQ, Li ZY, Mao ZY (2011) Study of high-speed machining parameters on nickel-based alloy gh2132. Adv Mater Res 189:3142–3147 17. Yang S-H, Srinivas J, Mohan S, Lee D-M, Balaji S (2009) Optimization of electric discharge machining using simulated annealing. J Mater Process Technol 209(9):4471–4475 18. Rao R, Pawar P, Shankar R (2008) Multi-objective optimization of electrochemical machining process parameters using a particle swarm optimization algorithm. Proc Institution Mech Eng, Part B: J Eng Manuf 222(8):949–958 19. Pradhan M (2013) Estimating the effect of process parameters on MRR, TWR and radial overcut of EDMed AISI D2 tool steel by RSM and GRA coupled with PCA. Int J Adv Manuf Technol 68(1):591–605 20. Razfar M, Asadnia M, Haghshenas M, Farahnakian M (2010) Optimum surface roughness prediction in face milling x20cr13 using particle swarm optimization algorithm. Proc Inst Mech Eng, Part B: J Eng Manuf 224(11):1645–1653 21. Samanta S, Chakraborty S (2011) Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Eng Appl Artif Intell 24(6):946–957 22. Wu J, Yao Y (2008) A modified ant colony system for the selection of machining parameters. In: 2008 Seventh international conference on grid and cooperative computing. IEEE, pp 89–93 23. Pradhan MK (2013) Estimating the effect of process parameters on surface integrity of EDMed AISI D2 tool steel by response surface methodology coupled with grey relational analysis. Int J Adv Manuf Technol 67(9):2051–2062 24. Pradhan M (2013) Optimization of MRR, TWR and surface roughness of EDMed D2 steel using an integrated approach of RSM, GRA and entropy measutement method. In: 2013 International conference on energy efficient technologies for sustainability. IEEE, pp 865–869

Optimization Techniques Used in Machining Processes: A Review

101

25. Maiyar LM, Ramanujam R, Venkatesan K, Jerald J (2013) Optimization of machining parameters for end milling of Inconel 718 super alloy using Taguchi based grey relational analysis. Procedia Eng 64:1276–1282 26. Huang J, Liao Y (2003) Optimization of machining parameters of wire-EDM based on grey relational and statistical analyses. Int J Prod Res 41(8):1707–1720 27. Jayaraman P et al (2014) Multi-response optimization of machining parameters of turning aa6063 t6 aluminium alloy using grey relational analysis in taguchi method. Procedia Eng 97:197–204 28. Kumar R, Modi A, Panda A, Sahoo A, Deep A, Behra P, Tiwari R (2019) Hard turning on JIS S45c structural steel: an experimental, modelling and optimisation approach. Int J Autom Mech Eng 16(4):7315–7340 29. Gangil M, Pradhan M (2018) Optimization the machining parameters by using VIKOR method during EDM process of titanium alloy. Mater Today: Proc 5(2):7486–7495 30. Pradhan MK et al (2018) Optimisation of EDM process for MRR, TWR and radial overcut of d2 steel: a hybrid RSM-GRA and entropy weight-based TOPSIS approach. Int J Ind Syst Eng 29:273 31. Cus F, Balic J, Zuperl U (2009) Hybrid ANFIS-ants system based optimisation of turning parameters. J Achievements Mater Manuf Eng 36(1):79–86 32. Rao R, Pawar P (2009) Modelling and optimization of process parameters of wire electrical discharge machining. Proc Inst Mech Eng, Part B: J Eng Manuf 223(11):1431–1440 33. Satishkumar S, Asokan P (2008) Selection of optimal conditions for CNC multitool drilling system using non-traditional techniques. Int J Mach Mach Mater 3(1–2):190–207

Coupled Temperature Displacement Finite Element Analysis of Friction Welding of Similar and Dissimilar Metals M. K. Pradhan

and Deepansh Gill

Abstract Friction welding is a solid-state joining process that is widely used in leading industries such as automotive and aerospace, where complex parts play an important role. It is a low-cost process in which temperatures increase but do not reach melting points, unlike fusion welding, and with small temperature fields, heataffected zones (HAZs) are much narrower. Contact zone temperatures, frictional heat generation, and flow characteristics all play important roles in determining the ideal weld zone. That is why research into friction behaviour, joining behaviour, and temperature modelling is considered necessary. This research establishes a comparison between finite element analysis (FEA) and experimental study in order to encapsulate the correct welding parameters as well as determine the temperature fields, and heat distribution flow stresses at the required zones. Keywords Finite element method (fem) · Copper alloys (ca) · Nickel alloys (na) · Stainless steel (ss) · Aluminium alloys (aa) · Ansys · Abaqus

1 Introduction Welding is an extensively used fabrication process which finds its applications in various industries to manufacture various shapes and sizes while involving the use of different materials. With such a diversity, welding is generally used in areas where a high strength to weight ratio is needed as, the bond is metallurgically formed by the fusion of two or more metals. The metallurgically formed bond is obtained after the energy conversion from different sources into coalescence [1]. The energy being localized at the interface of two metals forms different zones, namely the fusion zone, the heat-affected zone, and the base metal. The arc at the interface results in a rapid rise in temperature, mainly above the melting point, so that fusion may M. K. Pradhan (B) Department of Mechanical Engineering, National Institute of Technology, Raipur, India e-mail: [email protected]; [email protected] D. Gill Maulana Azad National Institute of Technology, Bhopal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_11

103

104

M. K. Pradhan and D. Gill

Fig. 1 Direct/continuous drive friction welding [3]

happen. In some cases, surplus material is also added in the fusion zone to increase the strength. Localized heat discharge in the weld zone becomes an important parameter of weld success as there can be certain outcomes if the discharge of heat is not dealt with properly, and the quality of the weld will be compromised [2]. There are three steps in the friction welding process: heating, burn up, and upsetting/forging [3, 4] which are represented altogether in Fig. 1. Friction welding is branched in three categories mainly depending upon shape of the workpiece and mechanism of heat generation, namely [3, 4]. 1. Rotary friction welding 2. Linear friction welding 3. Orbital friction welding The need for the developmental studies in this field is linked with the commercial usage of the process, which will have optimized design parameters that will reduce the experimental costs for different operating conditions. The process is modelled for different input parametric combinations. If we go ahead with the experimental methodology, then time and cost become a constraint, which is why computational methods were introduced. Several studies have been done in the field of friction welding, mostly done using experimental methods to understand the behaviour of friction welding. The works are focussed on the after effects of the welding, which mainly include the microstructural studies, scanning electron microscopy-X-ray diffraction, tensile, and bend testing of the welded structure. This study is mainly focussed on the computational aspect of the study [1]. Finite element modelling is an important technique which can approximate the temperature distribution, flow stresses, and upsetting characteristics of the weld.

Coupled Temperature Displacement Finite Element Analysis . . .

105

In the previous research, Zhang et al. [5] investigated 3D rigid viscoplastic material modelling using von Mises constitutive law by FEM to describe the FW process on nickel-based super alloy GH4169 and C25 steel for temperature profile distribution, frictional stress, and upsetting velocity computation and discovered that stress by friction is greater inland of the friction junction than at the edge, and the deformation phase of friction stress remains nearly the same duality. Li et al. [6] investigated a 2D model of CDFW using 20 mild steel in a redeveloped ABAQUS environment, the effect of friction/forge pressure, and rotating speed on temperature and upset using experimental flow stress curves dependent on temperature and strain rates at different temperatures. The author used DFLUX subroutine based on Python scripting to define heat generation at the interface which is having variation with RPM, axial loads, and flow stresses. The author’s key findings are that as friction/forge increases, upset increases, and temperature reaches a quasi-static state; similarly, as heating time increases, temperature reaches stability, and upset length increases. Li et al. [7] performed numerical simulation of friction welding processes in an ABAQUS environment for low-carbon steel and high-performance superalloy FGH96 using arbitrary Lagrangian-Eulerian (ALE) joined with Johnson-Cook plasticity and found, upon increasing welding time, the high-temperature zone widens from the weld interface due to the heat conduction within the specimen, and the largest flash is obtained after the forging process. Bennett et al. [8] performed inertia friction welding considering phase transformation and validated the FEM model against experimental data for process parameter optimization and further validated the model for residual stresses against experimental process for compositions of austenitic and martensitic steels and found that microstructural effects and volume change associated with the martensitic transformation are included in the post-weld cooling analysis and have an important role in the accuracy of residual stress determination. Schmicker et al. [9] performed direct drive friction welding with a modified Carreau fluid constitutive model and evaluated the fully temperature coupled FEM model for process parameter optimization on steels and found that Carreau fluid model in association with Johnson-cook power law provides robust simulation of the friction welding process, featuring a steady transition from solid to liquid material behaviour. Seli et al. [10] modelled friction welding using Johnson-Cook viscoplastic material model for evaluating mechanical and interfacial properties of alumina-mild steel model and experimentally validating it by various testings and found bending strengths depending on friction time and interlayer thickness in increasing order; by fully coupled thermal-mechanical FE model, the peak temperature, the fields of temperature, deformation, stresses, and strains are successfully analyzed where maximum values are mostly predicted to be around the periphery of the rubbing surface. Asif et al. [11] performed friction welding modelled with Johnson-cook constitutive model to evaluate thermal history, predicted upsetting profiles, validated the same using experimentally for UNS S31803 duplex stainless steel, and discovered maximum temperature gained while frictional heating is less than melting point, and thermal effects are greater over mechanical effects which was concluded based on hardness testing. Ajith et al. [12] carried out optimization for friction welding (FW) parameters of duplex stainless steel (DSS) UNS S32205 carrying out the central composite

106

M. K. Pradhan and D. Gill

array of 30 experiments on UNS S32205 stainless steels and found that friction force was the significant parameter in changing tensile strength and hardness, followed by upset force and speed rotation. While burn-off length had a negative effect on tensile strength, while burn-off length increased, the tensile strength decreased. Jedrasiak et al. [13] modelled heat generation in linear friction welding using a small strain finite element method for Ti-alloys. Constitutive data was experimentally determined with temperature and strain rate dependency from best fit curves; the author discovered that material deformation maps indicated that deformation was concentrated within narrow ranges of temperature and flow stress; and the predicted power history was in reasonable agreement. Ansaripour et al. [14] draw a comparison between GA and HS on MATLAB using both single and multi-objective optimization in GA and HS to address the problem of optimizing the process parameters for reducing the residual stresses and distortion in the samples and thus establish a better mechanism for the same problem. Lashgari et al. [15] modelled rotary friction welding in an ABAQUS environment and employed the ALE technique for the simulation to investigate the microstructure, tensile properties, hardness, wear resistance, and corrosion properties of the weld joint and found that the microstructure of the WZ and HAZ changed from the elongated columnar to a fine equiaxed morphology owing to the co-effect of temperature rise and plastic deformation resulting in dynamic crystallization. Finite element analysis revealed that the temperature can go up to 1100 ◦ C during friction welding, and the presence of dimples on the fracture surface indicates that the mode of fracture is ductile. However, oxide films had a negative influence on the tensile strength, and further, optimization is required to eliminate these defects. Objective of the study is to perform finite element analysis of similarly welded UNS S31803 stainless steel to analyze the weld for temperature and flow stress distribution for different inputs and to predict FEM-based failure and validate it against experimental values.

2 Finite Element Modelling of Friction Welding Friction is modelled in terms of temperature, at lower temperature classical friction law, i.e. Amonton’s law (Coulomb’s law) is followed which is given as: τ f = μ.F

(1)

where τ f is frictional shear stress, μ is friction coefficient, and F is applied axial pressure. At higher temperature, flow stresses come into picture, and material nature changes to viscoplastic so frictional shear stress becomes equal to shear yield stress which is given as: σy (2) τ f = τy = √ 3

Coupled Temperature Displacement Finite Element Analysis . . .

107

Fig. 2 Friction stress behaviour as function of normal stress [3]

√ where τ y is frictional shear stress, σ y is tensile yield stress, and 1/ 3 is the constant friction coefficient which is acting [11]. In some journals, σ y is referred as σs which is equivalent flow stress, and consequently, τ y becomes shear flow stress [6, 7] as shown in Fig. 2. Thermal modelling is done by solving the governing differential equation for conduction mode of heat transfer which is ( ( Cp ∂T ∂2 T ∂2 T ∂2 T + + + Q = ρ. (3) . 2 2 2 ∂x ∂y ∂z k ∂t where T is temperature field, and it is partially differentiated with respect to x, y, z directions and t time. ρ is density, C p is specific heat capacity, and k is thermal conductivity. Q is heat generation due to frictional rubbing between workpieces [7]. So, heat generation has been a fractional distribution between sticking and sliding friction conditions. Thus, it is shown to be generated in two parts. ( ( σy Q = δ.η.γ .μ.F + (1 − δ).η.γ . √ 3

(4)

where γ is the slipping rate; η is heat efficiency; δ is fraction related to sliding friction; μ is coefficient of friction; σs is equivalent flow stress; F is axial pressure [7, 11] and [6].

108

M. K. Pradhan and D. Gill

2.1 General Assumptions The foremost assumption is that duplex stainless steel material is to be treated as homogeneous and isotropic. Properties do not show much variation with space but some properties that are utilized are temperature-dependent. Heat generation and axial loading are assumed to be uniformly distributed over the entire interface. And heat transfer is assumed to happen along the axial direction. In reality, radial flow of heat happens but to avoid the intricacy of modelling. Holding components, i.e. chucks, are not taken into consideration in this simulation.

2.2 Geometry and Meshing A three dimensional workpiece is modelled in ANSYS and ABAQUS using CAD modelling module in the respective softwares. Similar workpieces in terms of dimensions were taken, i.e. 16 mm in diameter and 100 mm in length, and both cylindrical workpieces were assembled along the axis to form the interface or junction. The nature of problem is thermo mechanical which involves the stress and temperature fields, so the element is decided in such a way that it can handle both thermal and mechanical loads. In ABAQUS, “C3D8RT” is an 8 noded thermally coupled brick element which is the suitable element for ABAQUS/Explicit. Later, mesh refinement was carried out until the result variation becomes irrespective of mesh refinements and can be considered acceptable. This model was chosen for simulations because it was thought to be the best. An adaptive mesh technique has been employed in the model due to the fact that HAZs are narrow in friction welding, and plastically deformed zone is also taken into consideration, so near the junction, fine mesh has been employed, and in remaining body, relatively coarser mesh is employed to optimize the cost to computational time ratio. There are total of 87,248 elements and 91,208 nodes. Contact modelling has been given a special attention, a penalty-based frictional contact pair agreement based on tangential behaviour is employed.

2.3 Boundary Conditions Nodes of the non-rotating workpiece are completely constrained along the frictional contact, while the rotating component is allowed two certain degree of freedoms, one along the axis and other rotary along the same axis. Load is applied on end of the rotating workpiece perpendicular to the surface of frictional contact. Load is given in two increments as friction and forge, respectively. Thermal boundary conditions come into action as soon as heat generation happens in plasticized zone, the outer surface of workpiece interacts with the ambient (air), and heat loss mechanism happens through the means of convection and radiation. Convective heat transfer

Coupled Temperature Displacement Finite Element Analysis . . .

109

Fig. 3 Boundary condition representation

coefficient of air is taken as 25 W/m2 − K, and material emissivity is taken as 0.4. Initial of temperature at all nodes is taken as 25 ◦ C. Boundary conditions are as shown in the Fig. 3 [11].

2.4 Material Modelling The material of study is UNS S31803 Stainless Steel that comes under Grade 2205 that has a superior pitting resistance, high strength, stress corrosion, crevice corrosion, and fracture resistance. The material modelling used in most of the studies including [6, 7, 11] used the mechanism of damage which is Johnson-Cook model. This is a von Mises stress-based functional model in this, equivalent tensile/compressive stress is temperature-dependent, and strain rate-dependent criterion undertakes temperaturedependent material properties and their relation with development of strain under a constant of variable strain rate. The equation is as follows: [ ( (] [ ( ( ] ∈ ˙ T − Troom m . 1− σ = (A + B.∈ n ). 1 + C. ln 1 + ∈ ˙0 Tmelt − Troom

(5)

In the above equation, first bracketed term represents elasto-plasticity behaviour of the material in initial loading phase governed by the material constants (X is initial yield stress and Y is hardening constant) and strain (∈ ) with hardening exponent (n), second bracketed term represents the viscosity behaviour of the material governed by the strain rate (∈ ˙ ) and reference strain rate (∈ ˙0 ), while the third bracketed term represents the material softening behaviour under the effect of temperature (T) in reference to room temperature (Troom ) and melting temperature of the material (Tmelt ) and m as thermal softening constant. These material constants need to be determined experimentally. The stress-strain curves of UNS S31803 stainless steel, which vary with temperature, are presented in Fig. 4 [16] (Table 1).

110

M. K. Pradhan and D. Gill

Fig. 4 Flow stress and young’s modulus variation with temperature [11] Table 1 Johnson-Cook material parameters X (MPa) Y (MPa) n m 731

840.5

0.1904

0.965

Melting Transition C temp. (◦ C) temp. (◦ C)

∈ ˙0

1410

1

25

0.0124

Table 2 is the design of experiment table which mentions about the combination of input parameters to design the process parameters with the respective output values of temperature, stress, and upset.

3 Results and Discussions of FEM 3.1 Stress Distribution The maximum equivalent stress field calculations were performed in ABAQUS, and the distribution varies with time as the process goes on, peak equivalent stresses appear during the forge phase of welding. The value of stresses rises from the range of 500–800 MPa in the friction phase to 1100–1300 MPa in the forge phase at the end of welding. The stresses appear near the joint section, as the material softens at the junction the stresses begins to fall down, and the stress concentration happens near the joint just after the weld region as shown in Fig. 5.

Coupled Temperature Displacement Finite Element Analysis . . . Table 2 Design of input variables FrP (MPa) FoP (MPa) FrT (s)

40 40 50 50 50 30 30 30 50 40 50 30 50 50 30 30 30 30 40 50 40 40 40 50 40 40 30 40 40 40

70 70 60 60 60 80 60 80 80 70 60 60 80 80 80 60 80 60 70 80 70 80 70 70 70 70 70 70 70 60

4 4 5 5 3 5 5 5 5 4 3 3 3 3 3 3 3 5 4 5 4 4 5 4 3 4 4 4 4 4

111

FoT (s)

Temperature Stress (von (◦ C) Mises) (MPa)

Upset length (mm)

3 3 4 2 4 2 2 4 4 3 2 2 4 2 4 4 2 4 3 2 3 3 3 3 3 4 3 2 3 3

840.2 840.2 964 955 939 900 819 950 981 840.2 905.6 753 898.5 895 949.3 758.4 944 784 840.2 971.4 840.2 844.1 903.7 966 840 850 844.3 849 840.2 849.6

6.98 6.98 7.5 8.19 8.33 8 6.7 9.1 9.96 6.98 8.32 7 8.27 7.54 9.7 6.65 9.1 7.3 6.98 9.75 6.98 7.2 7.08 9.55 6.85 7.3 6.89 6.85 6.98 6.75

1348 1348 1340 1325 1370 1343 1290 1380 1477 1348 1199 1096 1470 1429 1370 1236 1320 1300 1348 1440 1348 1370 1345 1350 1330 1350 1330 1325 1348 1320

3.2 Temperature Distribution The maximum temperature is obtained at the weld interface, and then, it reduces along the length forming a temperature field near the weld zone. The peak surface temperatures obtained are in the range of 750–990 ◦ C, while the temperatures inside the weld reach nearly 1300 ◦ C where the heat generation is exactly happening.

112

M. K. Pradhan and D. Gill

Fig. 5 Stress distribution at 50 MPa friction pressure and 80 MPa forge pressure

Asif et al. [11] have performed the surface peak load experiments using infrared thermometers, and the data from FEM is compared taken from [11]. The flash begins to appear at the end of frictional phase, and it grows from the beginning of forging phase. As the flash begins to form, it starts to cool down under the influence of conduction, convection, and radiation mode of heat transfer. At the same time, heat flows in the axial direction, and it begins to cool as we move away from the weld zone. This proves the fact that the heat-affected zones are much narrower in friction welding. The temperature distribution is represented in the Figs. 6 and 7, the latter shows the heat generation inside the material which is happening due to the surface to surface interaction and breaking of the asperities.

3.3 Axial Shortening/Upsetting Axial shortening or upsetting is known as the loss in length of the workpiece after the weld. As in the metal forming process, the volume of metal remains constant before and after forming, which can be represented here also because the volume of metal lost by the workpieces is expelled outwards as flash, and thus, volume constancy is maintained. Axial shortening is depicted in Fig. 8, and it is clearly visible that flash area is having a different colour coding than main workpiece, and thus, the movement of edge from its original position to a final one is the exact measure of upset. The upsetting range obtained from the design of experiment is from 6.75 to 10 mm depending on the input parameter value. Flash expulsion is also varying according to the value of load and combination of heating time with forge time.

Coupled Temperature Displacement Finite Element Analysis . . .

113

Fig. 6 Temperature distribution at 50 MPa friction pressure and 60 MPa forge pressure

Fig. 7 Cut section of temperature distribution at 50 MPa friction pressure and 60 MPa forge pressure

3.4 Comparative Study of FEM with Experimental Observations The experimental validation for temperature and upset length has been done in the Tables 3 and 4 using the base study [11].

114

M. K. Pradhan and D. Gill

Fig. 8 Axial shortening at 50 MPa friction pressure and 80 MPa forge pressure Table 3 Temperature comparision with published experimental results Results Study’s FEM Iteration 12 27 8 1 22 30 13 5

753 844.3 950 840.2 844.2 849.6 898.5 939

812.6 934.01 953.45 897.23 970.21 970.47 962.62 950.54

Table 4 Upset length comparision with published experimental results Results Study’s FEM Iteration 12 27 8 1 22 30 13 5

7 6.89 9.1 6.87 7.2 6.8 8.27 8.33

6.96 8.36 9.61 7.75 8.84 9.67 7.81 8.78

Study’s expt. results 770.5 880.7 927.6 847.4 937.1 929.4 920.9 905.3

Study’s expt. results 7.5 9.1 10.4 8.3 9.5 10.4 8.4 9.1

Coupled Temperature Displacement Finite Element Analysis . . .

115

4 Results and Discussions of RSM 4.1 Effect of Parameters on Stress The stress response surfaces are extracted from the design expert by deriving the empirical relation as mentioned in (6) after applying the necessary algorithm of backward elimination. σ = 1214.99 + 40.8333 × A + 62.3889 × B + 61.2083 × C + 29.2222 × D + (−18.9375) × BC + (−18.9375) × C D

(6)

where A is friction pressure; B is forge pressure; C is friction time; D is forge time and σ being the equivalent stress. Equation (6) is the coded stress equation obtained from the design expert which is employed to model the response surfaces for the stress variation dependent on different process parameters. It is clear that the dominant factors for determination of stresses are A, B, C, D, BC, and CD. With increase

Fig. 9 Effect of forge timing on stresses in interaction with forge and friction pressure

116

M. K. Pradhan and D. Gill

Fig. 10 Effect of forge timing on stresses in interaction with friction time and pressure

in the basic four factors: A, B, C, and D, the stress value rises, and the trend can be observed in the response surfaces for respective stress curves. Friction pressure and forge pressure play a considerable role in determining the stresses in weld. And as the interaction between friction pressure and forge pressure is plotted in Fig. 9 which shows that with increase in both forge and friction time, the stresses grow and maximum value as 1470 MPa at 50 and 80 MPa friction and forge pressure, respectively; while Fig. 10 shows that with increase in both friction time and pressure, the stresses grow and maximum value as 1477 MPa at friction time 3 s but pressure of 50 MPa.

4.2 Effect of Parameters on Temperature The temperature response surfaces are extracted from the design expert by deriving the empirical relation and represented in (7) after applying the necessary algorithm of backward elimination.

Coupled Temperature Displacement Finite Element Analysis . . .

117

Fig. 11 Effect of friction timing on temperature in interaction with forge and friction pressure

T = 780.833 + 123.797 × A + 33.65 × B + 19.1833 × C + (−40.4125) × AB + 11.5625 × AC + 50.6167 × A2

(7)

Equation (7) is the coded temperature equation obtained from the design expert which is employed to model the response surfaces for the temperature variation dependent on different process parameters. The dominating factors are A, B, C, AB, and AC. Friction pressure, forge pressure, and friction time play a considerable role in ascending order for determining the temperatures in weld, and the respective trends can be observed in the response surfaces for temperature. And as the interaction between friction pressure and forge pressure is plotted in Fig. 11 which clearly shows that with increase in both forge and friction time, the temperature grows to 981 ◦ C; while in Fig. 12, the interaction between friction pressure and time has been plotted which shows the with increase in both friction time and pressure, the temperature grows to 981 ◦ C at friction time of 5 s and friction pressure 50 MPa.

118

M. K. Pradhan and D. Gill

Fig. 12 Effect of friction timing on temperature in interaction with friction time and pressure

4.3 Effect of Parameters on Upset The upset response surfaces are extracted from the design expert by deriving the empirical relation as mentioned in (8) after applying the necessary algorithm of backward elimination. Upset length = 5.6725 + 1.02097 × A + 0.66 × B + 0.101111 × C + (−0.316875) × AB + 0.268125 × AC + 1.22139 × A2

(8)

Equation (8) is the coded upset length equation obtained from the design expert which is employed to model the response surfaces for the upset length variation dependent on different process parameters. It can be noted that the dominant factors are A, B, C, AB, and AC. Friction pressure, forge pressure, and friction time show the trend of increasing upset length in ascending order with increase in respective values, and they play a considerable role in determining the upsetting in weld. And as the interaction between friction pressure and forge pressure is plotted in Fig. 13 which clearly shows that with increase in both forge and friction time, the upset

Coupled Temperature Displacement Finite Element Analysis . . .

119

Fig. 13 Effect of friction timing on upset in interaction with forge and friction pressure

length increases, and the maximum value is 9.55 mm at 50 MPa friction pressure and 70 MPa forge pressure, while Fig. 14 clearly shows that with increase in both friction time and pressure, the upset length increases, and the maximum value is 9.96 mm at 50 MPa friction pressure and 5 s friction time.

5 Conclusions Continuous drive friction welding of duplex stainless steel was simulated using 3D non-linear finite element model developed in ABAQUS. The thermal history and axial shortening were studied numerically, and the effect to each parameter on the output was also studied. The following conclusions were drawn from the study. The simulation results of temperature distributions and peak temperatures and axial shortening showed a fair agreement with actual loading and timings (95 and 80%, respectively. With increased friction time, the temperature and flash formation are affected, while less effect has been seen on stresses. With the increase in forge time, the temperature and flash formation are affected less, while a considerable effect has been seen on stresses.

120

M. K. Pradhan and D. Gill

Fig. 14 Effect of friction timing on upset in interaction with friction time and pressure

References 1. Gill D, Pradhan MK (2022) A review on advances in friction welding of dissimilar metals. In: Popat KC, Kanagaraj S, Sreekanth PSR, Kumar VMR (eds) Advances in mechanical engineering and material science. Springer Nature Singapore, Singapore, pp 181–197 2. Singh R (2020) Applied welding engineering: processes, codes, and standards. ButterworthHeinemann 3. Maalekian M (2007) Friction welding-critical assessment of literature. Sci Technol Welding Joining 12(8):738–759 4. Uzkut M, Ünlü BS, Yilmaz SS, Akda˘g M (2010) Friction welding and its applications in today’s world. In: Proceedings of the 2nd international symposium on sustainable development, International Burch University Sarajevo, pp 8–9 5. Zhang QZ, Zhang LW, Liu WW, Zhang XG, Zhu WH, Qu S (2006) 3D rigid viscoplastic FE modelling of continuous drive friction welding process. Sci Technol Weld Joining 11(6):737– 743 6. Li W, Wang F (2011) Modeling of continuous drive friction welding of mild steel. Mater Sci Eng: A 528(18):5921–5926 7. Li W, Shi S, Wang F, Zhang Z, Ma T, Li J (2012) Numerical simulation of friction welding processes based on abaqus environment. J Eng Sci Technol Rev 5(3)

Coupled Temperature Displacement Finite Element Analysis . . .

121

8. Bennett C, Attallah M, Preuss M, Shipway P, Hyde T, Bray S (2013) Finite element modeling of the inertia friction welding of dissimilar high-strength steels. Metallurgical and Mater Trans A 44(11):5054–5064 9. Schmicker D, Naumenko K, Strackeljan J (2013) A robust simulation of direct drive friction welding with a modified carreau fluid constitutive model. Comput Methods in Appl Mech Eng 265:186–194 10. Seli H, Awang M, Ismail AIM, Rachman E, Ahmad ZA (2013) Evaluation of properties and fem model of the friction welded mild steel-al6061-alumina. Mater Res 16(2):453–467 11. Shrikrishana KA, Sathiya P et al (2015) Finite element modelling and characterization of friction welding on uns s31803 duplex stainless steel joints. Eng Sci Technol an Int J 18(4):704– 712 12. Ajith P, Husain TA, Sathiya P, Aravindan S (2015) Multi-objective optimization of continuous drive friction welding process parameters using response surface methodology with intelligent optimization algorithm. J Iron and Steel Res Int 22(10):954–960 13. Jedrasiak P, Shercliff H (2019) Modelling of heat generation in linear friction welding using a small strain finite element method. Mater Design 177:107833 14. Ansaripour N, Heidari A, Eftekhari SA (2020) Multi-objective optimization of residual stresses and distortion in submerged arc welding process using genetic algorithm and harmony search. Proc Institut Mech Eng Part C: J Mech Eng Sci 234(4):862–871 15. Lashgari H, Li S, Kong C, Asnavandi M, Zangeneh S (2021) Rotary friction welding of additively manufactured 17–4ph stainless steel. J Manuf Process 64:1517–1528 16. Chen J, Young B (2006) Stress-strain curves for stainless steel at elevated temperatures. Eng Struct 28(2):229–239

Microstructural Effect of Stir-Cast and Squeeze Stir-Cast AZ91 Mg Under Variable Dry-Sliding Conditions Kamal Kant Singh and Dharamvir Mangal

Abstract The microstructural developments of stir-cast (SC) and squeezed stir-cast (SSC) AZ91 Mg-alloy have been examined in this study under variable dry-sliding wear conditions. Tribological tests have been investigated on tribo-testing (pin-ondisk) arrangement with an applied load of 50N at a fixed sliding speed of 3 m/s under an atmospheric environment. The result of delamination of sub-surfaces of SC-AZ91 Mg-alloy reveals a high wear rate with a low friction coefficient. Also, the originations of cracks around the interfaces of SC-AZ91 Mg alloy have been observed in-between the primary alpha-Mg phase and discrete beta-Mg17 Al12 phase. In contrast, the result of delamination of sub-surfaces of SSC-AZ91 Mg-alloy experiences a low rate of wear and a high friction coefficient. This is due to the induced friction which trends minimizes the loss of wear rate and formed a barrier of (material removal) wear debris near the sub-surface area of SSC Mg-specimen. Thus, the evaluated results demonstrate an inverse relation in-between the wear rate and coefficient of friction of SC and SSC-AZ91 Mg-alloys. Keywords AZ91 Mg mono-composite · Stir-cast · Squeezed stir-cast · Wear rate · Microstructure · Coefficient of friction

1 Introduction Mg-based composites play an important role in industrial applications like electronic, transportation, and implant equipment [1]. Despite this, AZ91 Mg-based alloys exhibit remarkable fabrication properties and mechanical properties at environmental temperatures but are deficient in their tribological properties [2]. The microstructure of AZ91 Mg alloy generally shows the grain bounds near the intermetallic beta phase (Mg17 Al12 ) due to its brittle nature [3]. However, their wear K. K. Singh (B) · D. Mangal Mechanical Engineering Department, Gautam Buddha University, Greater Noida, Uttar Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_12

123

124

K. K. Singh and D. Mangal

behavior can be enhanced by improving their ductility by using the metal matrix composite approach [4]. In this approach, when the proper dissipation of the betaMg17 Al12 phase has been treated under elevated temperature and dipped into heat treatment solution of the AZ91 Mg alloy, then ductility has been improved significantly. Singh et al. [5] demonstrated the proper amalgamation of hard (B4 C) and soft (Cr) reinforcements in Mg-based composites, and their wear results show that the inclusion of reinforcements enhances the wear resistance of Mg composites significantly. Also, Aydin et al. [6] experimented on Mg-based composites and evaluated the loss of wear rate of AZ91 Mg-based composites specimens by varying the dry-sliding parameters such as the sliding speed from 1 to 2.5 m/s under the nominal load of 20– 30 N. Their studies reveal that wear rate came into two-type wear regimes categories: severe and mild type wear. Although mild type wear includes two wear domains, i.e., delamination and oxidation wear, whereas the severe type wear regime constitutes melt and plastic deformation-induced type wear. Their study also illustrates the transition of wear regime of each composite. Recently, Oteyaka et al. [7] demonstrated the influence of wear behavior of aging heat-treated and deep cryogenic (DC) treatment of AZ91 Mg-alloy by evaluating its microstructural properties which depict the eutectic phase of intermetallic betaMg17 Al12 precipitates of AZ91 Mg composites. Their research suggests that the micrographs of worn surfaces of DC-treated AZ91 Mg-alloys show twin frames of beta-phases. Their results also show improved hardness under DC treatment but wear rate slightly lower under the dry medium of AZ91 Mg-alloy specimens. Furthermore, Koppad et al. [8] evaluated the wear and frictional coefficient behavior of AZ31 Mg composites through powder metallurgy and sintering techniques. Their study signifies that the friction coefficient shows the decrement with the increment of load while other wear parameters show minimal effects of AZ31 Mg-alloy composites. However, diverse investigations are examined regarding the dry-sliding behavior through different techniques of AZ91 Mg-based composites operated in variable load and sliding speed parameters under high temperature as well as heat treatment conditions [9, 10]. But, the key objective of current research is to develop a correlation between the wear behavior and microstructural evolution of stir cast and squeezed stir cast AZ91 Mg-based mono-composites through sub-surface analysis and under variable dry-sliding conditions.

2 Experimentations 2.1 Fabrication The compound distribution of AZ91 alloy is shown in Table 1. The AZ91 Mg-alloy is fabricated through stir casting and squeezed stir casting process then machined into cylindrical form pins having a length of 50 mm and diameter of 10 mm so that the tribological test has been performed. After testing both (stir-cast and squeezed

Microstructural Effect of Stir-Cast and Squeeze Stir-Cast AZ91 Mg …

125

Table 1 Investigated chemical distribution of AZ91 Mg alloy Mg-alloy

Chemical distribution (wt. %)

AZ91D

Al 9.5

Zn 1.2

Mn 0.11

Si 0.12

Fe 0.02

Mg Rest

stir cast) specimens of AZ91 Mg-alloy, half sections of each cylindrical pin have been subjected to microstructural characterization. These stir cast and squeeze-cast AZ91 Mg are named SC-AZ91 and SSC-AZ91, respectively.

2.2 Dry-Sliding Wear Tests A tribo-machine (Ducom. KOM Lab, ME department, DTU, Delhi) has been considered for using a dry-sliding test on both SC-AZ91 and SCC-AZ91 cylindrical samples by using pin-on-disk configurations assembled with EN8 mild steel counter-disk as shown in Fig. 1. Before testing, the AZ91 specimens have been cleaned with ethanol solution and then polished on a polishing machine. The dry-sliding wear tests have been operated at a sliding speed of 3 m/s with an applied load of 50N with a distance of 1000 m under an atmospheric environment. The wear-out cylindrical pins have been weighed concurrently by using an electronic-type balance. And, microscopic worn analysis has been performed on the leftover wear debris which was collected after each test. However, the rate of wear loss of performed AZ91 Mg specimens is calculated concurrently with a difference in the weight loss per unit after each sliding test. The normal and frictional force is evaluated by load cell which estimates the friction coefficient value during testing. Each tribo-test has been repeated 4-times to confirm the accurate value in wear loss and frictional data so that the average values of the coefficient of friction and wear loss rate have been evaluated. Fig. 1 Pin-on-disk tribo-machine considered for dry-sliding test

126

K. K. Singh and D. Mangal

2.3 Characterization of Fabricated AZ91 Mg Alloys Microstructural characterizations of SC-AZ91 and SCC-AZ91 composite specimens have been analyzed by an electron microscope (Zeiss Ultra 60, Nanotechnology Lab, Jamia Millia Islamia, Delhi). Also, the structural morphologies of sub-surface regimes, worn surfaces, and wear debris are investigated with the same microscopic instrument. Diffraction patterns were identified by using a diffractometer from Nanoscience and Nanotechnology Lab, JMI, Delhi, and evaluated the various phases of both (SC and SCC) AZ91 specimens using a diffractometer. Rockwell-hardness testing machines (Instron, ME department, JMI, Delhi) have been used to calculate the hardness at an applied load of 100 kgf with a stagnant time of 15 s. Averages of five hardness values have been considered for evaluation.

3 Results and Discussions 3.1 Effect of SC- and SCC-AZ91 Mg Alloys on Microstructural Structures Figure 2a and b represents the distinctness in the microscopic characterization of SC and SCC-AZ91 Mg-based mono-composites. The stir-cast AZ91 Mg sample depicts two phases of alpha-Mg (having alpha plus beta eutectic phase) and beta phase of Mg17 Al12 which are dispersed at the grain boundary interfaces. Whereas, squeezed stir-cast AZ91 specimen consists of supersaturated alpha-Mg phase as well as beta phase of Mg17 Al12 nearer to the grain boundaries. Around 25.4 ± 5% and 5.25 ± 2% are the area fraction of beta-Mg17 Al12 phase of stir cast and squeezed stir cast AZ91 Mg- alloys have been observed, respectively. It is well-known that the squeezed pressure leads to the proper dissipation of precipitate particles [11]. Thus, during squeeze stir cast specimen reveals the proper dissipation of the beta phase into the alpha-Mg phase with minimal defects. This is the prime reason for the reduction of area fraction in the SCC-AZ91 mono-composite in comparison with the SC-AZ91 Mg mono-composite.

3.2 The Behavior of Frictional Force Gajalakshmi et al. [12] studies depicted a model representing the frictional force which mainly includes two factors adhesion and plowing. However, the plowing factor arises when the plastic deformation occurs due to the rubbing surfaces whereas the adhesion factor emerges due to the formation of adhesion force in-between the rubbing surfaces. Figure 3a–b depicts the microscopic images of wear surfaces of SCAZ91 and SSC-AZ91 Mg specimens, respectively. The microscopic images showing

Microstructural Effect of Stir-Cast and Squeeze Stir-Cast AZ91 Mg …

127

Fig. 2 Microscopic images of investigated samples a stir cast b squeezed stir cast AZ91 Mg alloys

the existence of grooves on the worn surfaces represent the plowing. The plowing factor restricts the frictional component as compared to the adhesive frictional component. Figure 3a–d also illustrates the widening of the width of the grooves of squeezed stir cast specimens as compared to stir cast specimens and the formation of wavy-type deformed layers on the worn-out surfaces. These wavy-type deformed layers confirm the plastic deformation of squeezed stir-cast AZ91 Mg-specimen as shown in Fig. 3b. However, Table 2 demonstrates the evaluated frictional data of both (SC and SSC) type AZ91 specimens which represent the high coefficient of friction value and a significant reduction in the brittle intensity of SSC-AZ91 Mg in comparison with SC-AZ91 Mg-based mono-composite. This rise in brittle intensity occurs due to the existence of the hard beta phase of Mg17 Al12 . Thus, increase in the ductility causes the enhancement of the frictional plowing component due to plastic deformation of the worn-out surfaces. In contrast, stir cast AZ91 Mg-specimen leads to an increase in load-bearing capacity due to the presence of hard beta-precipitates and results in a low coefficient of friction value.

3.3 Wear Behavior and Microstructural Correlation According to delamination wear theory, when the shearing force is exerted due to the sliding contact, the interfacing strength (i.e., shear strength) in-between the matrix and beta phase is exceeded [13]. Thus, de-cohesion generated in the sub-surface region causes long and fragile wear debris on the surfaces. Figure 3c evidences crated plate-type pattern of worn-out wear debris confirming the delamination of the wear mechanism. However, summarized Table 2 signifies that the loss of wear rate SCC-AZ91 Mg alloy has been reduced up to 63.2% in comparison with stir cast AZ91 Mg alloy specimen. Mazaheri et al. [14] research represented that hardened AZ91 Mg-alloy exhibits high wear loss rate as compared to stir cast AZ91 Mg monocomposite due to the presence of a brittle beta phase of Mg17 Al12 . This justifies that the high amount of beta-precipitates in hardened AZ91 Mg alloy specimen depresses

128

K. K. Singh and D. Mangal

Fig. 3 Worn-out surfaces of the investigated samples a and c stir cast and b and d squeezed stir cast AZ91 Mg alloys under a load of 50N and sliding speed of 3 m/s

Table 2 Summarized data values from the current investigation Properties

Squeezed stir cast AZ91 alloy

Stir cast AZ91 alloy

Change in Percentage (%)

Rockwell hardness (HRB)

72.2 ± 0.4

58.6 ± 1.2

+ 18.8

Friction coefficient

0.24 ± 0.06

0.11 ± 0.04

+ 118

Wear rate (X10 −10 m3 /m)

1.05 ± 1.6

2.86 ± 0.6

− 63.2

that area and activates the contact pressure which causes the rise in the wear loss rate. Thus, an enhancement in the level of ductility of the hardened AZ91 Mg specimen develops the brittle beta phase of Mg17 Al12 . Thus, during dry-sliding conditions, the squeeze stir-cast AZ91 Mg specimen experiences a low wear loss rate. Moreover, Patel et al [15] illustrated that the induced friction is the major factor that affects the loss of wear rate of AZ6- alloy as observed in the microstructural sub-surface. Similarly, Fig. 4a and b positively signifies the enormous existence of

Microstructural Effect of Stir-Cast and Squeeze Stir-Cast AZ91 Mg …

129

beta phase in an interfacial zone of stir cast AZ91 Mg-specimen, whereas in squeezed stir cast AZ91 Mg-specimen signifies a meager amount of beta phase of Mg17 Al12 . In contrast, the existence of brittle beta-Mg17 Al12 phase in the sub-surface area of squeeze stir-cast AZ91 specimen is subjected to the cracking and cohesiveness at the interfaces of both phases as shown in Fig. 4c. This causes an increment in the wear rate of a squeezed stir-cast specimen. The developments of sub-layers of alphaMg and beta-Mg17 Al12 phases are shown in Fig. 4d. These sub-layers confirm the existence of the eutectic-type phase during sliding contact. The wear conditions of squeezed stir cast AZ91 Mg mono-composite reveal the enhancement of the ductility alpha-Mg phase occurs due to significant plastic deformation. However, the plastic deformation has been observed due to the friction, which arises near the surface region of squeezed stir AZ91 Mg specimen representing a minimal wear rate. However, the microscopic graphs of collected wear debris of the SSC-AZ91 Mg specimen are demonstrated in Fig. 5a–d. It is observed that the worn-out debris of

Fig. 4 Sub-surfaces of the investigated samples a and c stir cast and b and d squeezed stir cast AZ91 Mg alloys under a load of 50N and sliding speed of 3 m/s

130

K. K. Singh and D. Mangal

SC-AZ91 Mg represents the brittleness, whereas SSC-AZ91 Mg shows a wavy-type pattern with ductile nature. The study of worn-out debris of SC-AZ91 Mg exhibits cleavages and SSC-AZ91 specimen exhibits river-like pattern splits into sub-layers which fused each other (Fig. 5c and d). Figure 6 illustrates the diffraction patterns of left-out wear debris of SC-AZ91 and SSC-AZ91 specimens. It is demonstrated that dominant textures have been observed to change from pyramidal into basal plane structures in case of wear debris of SCAZ91 and SSC-AZ91 mono-composite samples, respectively. Specifically, the SSCAZ91 mono-composite signifies high peak intensity (basal plane) at 2 θ at 35.5 in comparison with prismatic and pyramidal SC-AZ91 (non-basal slip planes) 2 θ peak intensity values of 33.5 and 37.5, respectively. The variation peak intensities represent the crystallographic structure because of the initiation of plastic deformation at various planes of SSC-AZ91 and SC-AZ91 specimens. The high intensity of squeezed stir cast AZ91 specimen signifies basal plane plastic deformation as well as a low wear rate.

Fig. 5 Collected worn-out debris of AZ91 Mg alloy samples a and c stir cast and b and d squeezed stir cast under a load of 50N and sliding speed of 3 m/s

Microstructural Effect of Stir-Cast and Squeeze Stir-Cast AZ91 Mg …

131

Fig. 6 Diffraction patterns of stir cast and squeeze cast AZ91 Mg wear debris under a load of 50N and sliding speed of 3 m/s

The alignment variation in the basal plane shows contradictory data as reported by Ye le et al. [16]. Their research determines the aligned basal planes on the worn surfaces which affect the wear resistance properties of AZ91 Mg-based monocomposite. Their study also indicates wear rate increases steadily due to shear stress and friction. This illustrates that wear rate occurs when slipping deformation triggers under a critical shear stress of the basal (Mg) plane. However, the SSC-AZ91 specimen undergoes improved wear resistances when strong basal intensity occurs. Figure 4b represents the microstructural surface of the SSC-AZ91 Mg specimen having alpha-Mg and beta- Mg17 Al12 eutectic phases. This significantly represents the smooth sliding wear for the alpha-Mg phase due to the alignment of the Mg (i.e., basal) plane. Subsequently, the beta-Mg17 Al12 phase provides a significant hardness value as well as forms a barrier to the material removal surface region. This explains that the secondary beta phase surpasses the material removal rate and optimizes the wear rate of squeezed stir cast AZ91 Mg mono-composite. In contrast, for stir-cast AZ91 Mg-specimen, there is no such type of eutectic phase that has been observed near the sub-surface region. Moreover, the presence of irregular beta-precipitates near the interface of the grain boundaries induces inter-granular fracture. These fractures have been observed close to the sub-surfaces which build up the wear rate of stir cast AZ91 Mg mono-composite. After concluding the discussions, the result illustrates that AZ91 Mg-alloys reveal an inversely proportionate relationship between the loss of wear rate and the friction coefficient [17, 18]. This inverse relationship had also been demonstrated in the study

132

K. K. Singh and D. Mangal

of Khonsari et al. [19]. Their study suggested the dry-sliding wear behavior of Alalloy substrates coated with MoS2 coatings. Their research signifies that the applied squeezed is advantageous for the AZ91 Mg-alloys for enhancing the wear resistance properties.

4 Conclusions The following conclusions have been withdrawn for the stir cast and squeezed stir cast AZ91 Mg mono-composites: 1. Under the dry-sliding selected operating conditions, the behavior of stir cast and squeezed stir cast AZ91 Mg mono-composites depicts the delamination type wear mechanisms and illustrates an inverse relation in-between the loss of wear rate and friction coefficient. 2. The friction coefficient of squeezed stir cast AZ91 Mg mono-composite is incremented up to 118% as compared to stir cast AZ91 Mg mono-composite. This increment positively affects the proper dissipation of beta phase of Mg17 Al12 and increases the ductility level also. 3. However, the loss of wear rate of squeezed stir cast AZ91 Mg mono-composite has decrement up to 63.2%in comparison with stir cast AZ91 Mg monocomposite. The construction of the eutectic phase of alpha and beta leads to minimizing the loss of wear rate behavior and leads to the development of barrier film of wear debris (material removal) of AZ91 Mg alloys near the sub-surface region. 4. The enhancement in the wear rate of stir-cast AZ91 Mg mono-composite is due to the rise in crack formation intensity and de-cohesion which is observed due to the brittle phase of beta-Mg17 Al12 close to the sub-surface area.

References 1. Guan H, Xiao H, Ouyang S, Tang A, Chen X, Tan J, Pan F (2022) A review of the design, processes, and properties of Mg-based composites. Nanotechnol Rev 11(1):712–730 2. Rathod S, Sharma DK, Dani M, Rank P, Savaliya N (2021) Effect of friction stir processing on AZ91 Mg-alloy: a review. Jurnal Kejuruteraan 33(4):793–800 3. Zhao Z, Zhao R, Bai P, Du W, Guan R, Tie D, Guo Z (2022) AZ91 alloy nano-composites reinforced with Mg-coated graphene: phases distribution, interfacial microstructure, and property analysis. J Alloys Compounds 902:163484 4. Singh S, Chauhan NR (2022) Optimization of adhesive wear behavior of B4 C/AZ91D-Mg composites. Adv Mater Process Technol 1–15 5. Singh S, Chauhan NR (2021) Experimental investigation of mechanical and thermal study of Mg/B4 C/Cr hybrid composites. Indian J Pure Appl Phys (IJPAP) 59(5):379–385 6. Aydin F, Durgut R (2021) Estimation of wear performance of AZ91 alloy under dry sliding conditions using machine learning methods. Trans Non-ferrous Metals Soc of China 31(1):125– 137

Microstructural Effect of Stir-Cast and Squeeze Stir-Cast AZ91 Mg …

133

7. Öteyaka MÖ, Karahisar B, Öteyaka HC (2020) The impact of solution treatment time (T6) and deep cryogenic treatment on the microstructure and wear performance of magnesium alloy AZ91. J Mater Eng Perform 29(9):5995–6001 8. Kaviti RVP, Jeyasimman D, Parande G, Gupta M, Narayanasamy R, Koppad PG (2019) Improving the friction and wear characteristics of AZ31 alloy with the addition of Al2 O3 nanoparticles. Mater Res Expr 6(12):126505 9. Gassama B, Ozden G, Oteyaka MO (2022) The effect of deep cryogenic treatment on the wear properties of AZ91 magnesium alloy in dry and in 0.9 wt% NaCl medium. S¯adhan¯a 47(1):1–13 10. Matta AK, Koka NSS, Devarakonda SK (2020) Recent studies on particulate reinforced AZ91 magnesium composites fabricated by stir casting-a review. J Mech Energy Eng 4 11. Ramanathan A, Krishnan PK, Muraliraja R (2019) A review on the production of metal matrix composites through stir casting-furnace design, properties, challenges, and research opportunities. J Manuf Process 42:213–245 12. Gajalakshmi K, Senthilkumar N, Prabu B (2019) Multi-response optimization of dry sliding wear parameters of AA6026 using hybrid gray relational analysis coupled with response surface method. Measurem Control 52(5–6):540–553 13. Kumar A, Kumar S, Mukhopadhyay NK, Yadav A, Kumar V, Winczek J (2021) Effect of variation of SiC reinforcement on wear behavior of AZ91 alloy composites. Materials 14(4):990 14. Jalilvand MM, Mazaheri Y (2020) Effect of mono and hybrid ceramic reinforcement particles on the tribological behavior of the AZ31 matrix surface composites developed by friction stir processing. Ceram Int 46(12):20345–20356 15. Harwani D, Badheka V, Patel V, Li W, Andersson J (2021) Developing super-plasticity in magnesium alloys with the help of friction stir processing and its variants–a review. J Market Res 12:2055–2075 16. Fan Y, Ye L, Tian Q, Zhuang Y, Zhang Y, Li X (2021) Effects of aligned graphene sheets on mechanical properties of ZK61 alloy. Mater Sci Eng A 801:140417 17. Zhai W, Bai L, Zhou R, Fan X, Kang G, Liu Y, Zhou K (2021) Recent progress on wear-resistant materials: designs, properties, and applications. Adv Sci 8(11):2003739 18. Kumar S, Kumar A, Poddar A, Asthana P (2022) Investigation on wear behavior of aluminium matrix micro and nano-composites. Mater Today: Proc 56:2839–2845 19. Ghatrehsamani S, Akbarzadeh S, Khonsari MM (2022) Experimentally verified prediction of friction coefficient and wear rate during running-in dry contact. Tribol Int 170:107508

Behaviour Analysis of Layered Beam Using ANSYS Badree Sahu, Priyanka Dhurvey , Parth Verma , Juned Raheem, and Aditya Bhargava

Abstract Reinforced concrete structures play an increasingly important role in civil and industrial construction, along with the technology of new concrete materials. With adequately designed layered or in-filled sections, crack resistance can be improved, along with the shear and flexure behaviour of the beam member. In the present work, the bending stresses, shear stress and deflection of the simply supported homogeneous beams with conventional solid concrete and brick-filled layered composite beams are analysed using ANSYS 2021 R2 software and compared. The results show that the shear and flexural behaviour is sufficiently improved for a layered beam filled with bricks. Fewer stresses were observed near the neutral axis due to the replacement of lightweight material like bricks in the mid portion. With a brick fill beam, the structure weight reduces and achieves economy. Keywords Concrete brick-filled layered beam · Concrete beam · In-filled replacement zone · ANSYS

1 Introduction In civil construction engineering, the weight and cost reduction of the structure have been a great concern for the engineers, which can achieve with the help of modern technological enhancement by adopting many chances in the construction work. In the last few decades, extensive research has been done to make the components lighter, more economical and energy-efficient. When more extensive open floors and high-rise buildings are to be constructed, the designer or the contractor wants to make the structure lightweight. Many options are available to make these types of construction activities simpler. For example, pre-stressed concrete construction is one of the methods available to make the structural component of the smaller B. Sahu · P. Dhurvey (B) · P. Verma · J. Raheem · A. Bhargava Department of Civil Engineering, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_13

135

136

B. Sahu et al.

dimensions, which results in the lighter weight of the whole structure. Other available technological methods are also there like bubble deck technology and composite construction. These methods used for the construction of the lighter weight structures are very advanced in technicality and require very skilled personnel for design as well as in site development phases. Some new approaches are also being developed to make the structure strong and of lighter weight also. Sandwich or layered beam is one method used by combining the different materials for the construction or making of the members and using them as a structural component. In sandwich-type construction, the designer is more concerned about the stiffness of the materials used in the construction. Sandwich members are produced by making the outer layers of the strong material and some lightweight material that should also have good compatibility with the top and bottom layers can replace the inner core [1]. Carbon fibre-reinforced polymer skins or glass fibre-reinforced polymer layers combined with some lightweight core material can produce members of very light and robust enough to sustain the load properly [2]. In civil construction, the lightweight members are produced with sandwich members. Still, it can also be achieved by using concrete made of lighter materials, e.g. concrete made of lightweight aggregates, autoclaved aerated block combined with concrete and foamed concrete. With the help of these new technologies, the weight of the structural components is reduced to a greater extent. This construction makes the structure more economical, uses some waste material cheaper and has a positive environmental impact [3]. Flexural members are an essential component of any civil structure, as these members transfer the loads laterally by themselves to the column, through which the load is transferred to the ground soil. As we know, in the case of RCC beams, the strength of concrete present near the NA is not utilized efficiently, and this can be understood by the flexural member’s bending stress diagrams [4]. Concrete used in this zone can be replaced by the other available economic options, which are strong as well as chemically inactive with the concrete. Materials such as aerated autoclaved concrete (AAC) blocks and bricks can be used to make the in-filled beam. By these feasible approaches, the economy and the strength needed in the structure can be achieved. These infill-type beams have combined benefits of both materials. Before using any material to combine with concrete, their bonding and other properties should also be taken into consideration [5–11]. The present work concerns the stress and deflection behaviour analysis of conventional concrete and concrete with brick-filled layered composite beams using ANSYS 2021 R2 software. First, the analysis of conventional concrete beam is done with the ANSYS software. Validation of ANSYS results was done with analytical results. Then with the help of stress block diagram, brick replacement zone was obtained and concrete brick-filled layered composite beams were designed and analysed with conventional concrete beam to check its behaviour under uniformly distributed load (UDL) [12–15].

Behaviour Analysis of Layered Beam Using ANSYS

137

2 Methodology To better understand the stress and deformation behaviour of the in-filled beam. First, the same solid concrete beams were analysed and validated with the analytical results. The generalized equations used for the simply supported conventional concrete beam carrying UDL are as follows: Moment of inertia, I =

bd 3 12

(1)

Maximum bending moment at the centre of the beam M = Maximum bending stress, σmax =

wL 2 8

M y I

Maximum deflection at the centre, δmax =

5 wL 4 384 E I

(2) (3)

(4)

Then, for in-filled layered beam replacement zone calculation is done. Replacement zone is the zone in the structural component from where the main material of the member can be replaced with the compatible lightweight material that is being used as the infill material. For the present work, brick is used as in-filled material. From the bending stress analysis of the beam, it is clear that material near the neutral axis (NA) is not stressed much. So, the material with lesser Young’s modulus and lesser weight can be used to make the structure lightweight and sustainable. Infill material can be placed within the cross section of the beam near the computed depth of NA. The estimation of any in-filled material that may replace concrete near NA called the replacement zone in the reinforced concrete beam was obtained by the stress diagram used in the limit state design [4], This infill replacement zone is shown in Fig. 1 by shaded lines. Equating total tension with total compression in the beam gives,   X u = 0.87 f y∗ Ast /(0.36 f ck ∗ b) as per IS : 456 − 2000

(5)

  ' d = 2∗ cover + bar diameter ,

(6)

where depth of NA from top fibre-X u , yield strength of steel-f y , area of tension steel-Ast , concrete characteristic compressive strength-f ck . To maintain proper bond below the in-filled materials between steel and concrete, concrete layer thickness, d needed. Here in this calculation, it has been assumed that the provision of a minimum thickness of concrete, not less than the cover, to satisfy the durability behaviour is provided on either side of the reinforcement satisfies the bond requirement.

138

B. Sahu et al.

Fig. 1 Replacement zone of brick infill beam [4]

Xb =

4 X u (1 − f bk / f ck )1/2 7

(7)

dmin = 3 X u /7 + X b

(8)

dmax = D−d '

(9)

Infill replacement zone = dmax −dmin

(10)

Figure 2 illustrates the cross sections of solid and brick in-filled beams with 450 mm width and 800 mm depth. Length of the beam is taken as 4000 mm with a load of 25 KN/m. ANSYS 2021 R2 software is used to analyse the beam. Figure 3 shows the properties of the materials employed for the analysis. Design modeller is used for the construction of the geometry of the member. During the modelling of the geometry of the layered beam, better relative motion between the different layers of the section is provided by a proper connection introduced between the different layers of the beam, as shown in Fig. 4. After the construction of the geometry, uniform meshing is defined for meshing 3D solid elements.

3 Result and Verification Equations obtained the analytically calculation of conventional concrete beam with homogenous cross section from (1) to (4). Three-dimensional model is developed using ANSYS mechanical, solid beam results are compared with analytical results as shown in Tables 1, and 2 shows the effect of brick in-filled beam. The results of brick in-filled concrete are higher (almost twice) than solid concrete beams because of the

Behaviour Analysis of Layered Beam Using ANSYS

Conventional concrete beam

139

Brick in-filled beam *Dimensions are in mm

Fig. 2 Cross sections of solid and infill beam

Fig. 3 Material properties used in the ANSYS model design

Fig. 4 Contact region of the beam in ANSYS design modeller for different layers

low Young’s modulus of brick material. But this can be overcome with reinforced fibre and will match the desired results. The variation of the normal and shear stresses, along with the deflection of solid concrete and concrete brick in-filled beams, is Fig. 5. As observed in Fig. 5a–d that the shear and flexure behaviours are sufficiently enhanced with brick fill layered beams. Fewer stresses were observed near the neutral axis due to the replacement of lightweight material like bricks in the mid-portion.

140 Table 1 Comparison of analytical and ANSYS results for solid concrete beam

Table 2 ANSYS results for concrete brick in-fill beam

B. Sahu et al.

Results

Solid beam (Analytical)

Solid beam (ANSYS)

% Variation

Bending stress

1.04 MPa

1.05 MPa

1.02

Deflection

0.19 mm

0.21 mm

10.52

Shear stress

0.20 MPa

0.18 MPa

9.45

Results

Brick in-filled beam (ANSYS)

Bending stress

2.00 MPa

Deflection

0.40 mm

Shear stress

1.10 MPa

As can be seen, they are comparable, and about 35–45% of concrete can be replaced by brick material with reinforced fibre.

4 Conclusions This work aims to analyse the in-filled beams/layered beams for which numerical computation was adopted. The reliability of the numerical results was checked and validated. The results show that the flexure behaviours are sufficiently enhanced with brick fill-layered beam. Fewer stresses were observed near the neutral axis due to the replacement of lightweight materials like bricks in the mid-portion. This study includes the investigation of the stresses and deflection behaviour of the solid and in-filled beam member. Based on the results obtained from the ANSYS and analytical calculations, the following points can be concluded:1. There are very few differences in the results obtained from the numerically calculated and obtained from the software analysed results. 2. From Table 1, it can be observed that the member’s bending stress, shear stress and deflection are in a considerable range. 3. Table 2 shows that with lightweight in-filled material like brick results in higher bending stress and shear stress but are in the permissible range, but in the case of deflection, the variation is significant. 4. The results obtained for the layered beams come around twice the values obtained for the solid beams; this is because of the lower value of Young’s modulus of elasticity for the in-filled material, i.e. brick, and this can be taken care of with reinforced fibre like semi-precast reinforced section. A concrete brick fill beam reduces the structure’s weight and achieves economy. So about 35–45% of concrete can be replaced by brick material along with reinforced fibre.

Behaviour Analysis of Layered Beam Using ANSYS

a) Normal stress of concrete beam

141

b) Normal stress of brick in-filled beam

c) Shear stress of concrete beam

d) Shear stress of brick in-filled beam

e) Deflection of concrete beam

f) Deflection of brick in-filled beam

Fig. 5 Contour plot of solid concrete and concrete brick in-filled beam

References 1. Abbadi A, Koutsawa Y, Carmasol A, Belouettar S, Azari Z (2009) Experimental and numerical characterization of honeycomb sandwich composite panels. Simul Model Pract Theor 17:1533– 1547 2. Vimonsatit V, Wahyuni A S, Macri P, Nikraz H (2010a) Experimental investigation of shear strength and behaviour of lightweight sandwich reinforced concrete slab. In: Proceedings ACMSM21, 7–10 December, Melbourne 3. Vimonsatit V, Wahyuni AS, Mazlan MA, Nikraz H (2010b) Use of lightweight concrete as infill of reinforced concrete sections. In: Proceedings, ACMSM 21, December 7–10 4. Patel R, Dubey SK, Pathak KK (2013) Analysis of RC brick filled composite beams using MIF. Proc Eng 51:30–34 5. Eramma H, Madhukaran, Karthik BS (2014) Behavior of concrete grade variation in tension and compression zones of Rcc beams. Int J Adv Technol Eng Sci 2 ISSN: 2348–7550

142

B. Sahu et al.

6. Holschemacher K, Iskhakov I, Ribakov Y, Mueller T (2012) Laboratory tests of two-layer beams consisting of normal and fibered high strength concrete: ductility and technological aspects. Mech Adv Mater Struct 19(7) 7. Fujikake K, Li B, Soeun S (2009) Impact response of reinforced concrete beam and its analytical evaluation. J Struct Eng 135(8):938–950 8. Saatci S, Vecchio FJ (2009) Nonlinear finite element modeling of reinforced concrete structures under impact loads. ACI Structural J 106(5):717–725 9. Sousa JBM, da Silva AR (2010) Analytical and numerical analysis of multilayered beams with interlayer slip. Sci Direct Eng Struct 32:1671–1680 10. Sun M, Zhu J, Li N, Fu CC (2017) Experimental research and finite element analysis on mechanical property of SFRC T-beam. Hindawi Adv Civil Eng 2017:2721356 11. Omran HY, Zangeneh P, EL-Hacha R (2009) Finite element modelling of steel-concrete composite beams strengthened with prestressed CFRP plate. International Institute for FRP, December-2009, pp 187–192 12. Ferdous W, Manalo A, Khennane A, Kayali O (2015) Geopolymer concrete filled pultruded composite beams-concrete mix design and application. Sci Direct Cement and Concrete Composites 58:1–13 13. Do TMD, Lam TQK (2021) Design parameters of double layers steel fiber concrete beams. In: Proceedings of the XIII international scientific conference on architecture and construction 2020. Springer, Singapore, pp 299–321 14. Do TMD, Lam TQK, Ngo VT, Nguyen TTN (2022) Two-layered steel fiber concrete beam with concrete grade change in layers. In: Resilient infrastructure . Springer, Singapore, pp 427–443 15. IS: 456–2000, Plain and Reinforced Concrete Code of Practice

Thermal Performance Analysis of Eccentric Helical Coil Tube in Tube Heat Exchanger Using CFD Sanyog Kumar and A. R. Jaurker

Abstract Effects of dean no. (De) of annulus side (1300–2200) and inclination angle (β) (0–180°) of inner tube on annulus side Nusselt no. (Nua) and friction factor (fa) in eccentric (e = 0, 0.5, 2, 3.5 mm) tube in tube helical coil heat exchanger is investigated. Ethylene glycol (EG) and air are used as cold fluid and hot fluid on inner and annulus side, respectively, using k−ε standard model. Result shows that on increasing De, Nu increases but f decreases. Highest value of Nu is achieved for eccentric helical coil tube in tube (β = 0°, e = 2) in De = 2200, which is 1.05 and 1.01 times higher than concentric helical coil tube in tube (HCTT) and straight concentric tube in tube. Also, the effect of De is analyzed on thermo hydrodynamic index (η). On increasing De, η decreases slightly, but it is higher for HCTT. On increasing β (e = 2), Nua decreases but fa increases by 4.9, 4.2, 3.38, 1.75%, and 3.89, 5, 6.43, 7.38%, respectively. η value decreases slightly. Similar results also calculated at e = 0.5, 3.5 mm. Keywords Eccentric · Heat exchanger · Helical coil · Tube in tube

Nomenclature Cp ρ μ k Nu f De η

Specific heat (J/Kg-K) Density (Kg/m3 ) Viscosity (N-m/sec) Thermal conductivity(w/m–K) Nusselt number Friction factor Dean number Thermo hydrodynamic index

S. Kumar (B) · A. R. Jaurker Department of Mechanical Engineering, Jabalpur Engineering College, Jabalpur, Madhya Pradesh 482011, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_14

143

144

β e Nus fs P T p

S. Kumar and A. R. Jaurker

Inner tube inclination angle Eccentricity Nusselt no. of straight pipe Friction factor of straight pipe Pitch Temperature (K) Pressure (Pa)

Subscript ii io oi oo T ci T co T hi T ho

Inner diameter of inner pipe Outer diametet of inner pipe Inner dia. of outer pipe Outer dia. of outer pipe Cold fluid inlet temperature Cold fluid outlet temperature Hot fluid inlet temperature Hot fluid outlet temperature

Abbreviations HCTT SCTT a i

Helical coil tube in tube Straight coil tube in tube Annulus Inner

1 Introduction Nowadays focus is on extracting more heat at the lower possible cost and surface area. So, designer use variety of techniques for compact design of heat exchanger. Some active and passive methods are used. In active methods, external power source such as pump, surface vibration, and mechanical aids are used. In passive methods, no external power is required. To make inner surfaces corrugated, straight pipe twisted, and to use some external twisted semi-circular plates, nanoparticles, etc. Yadav et al. [1, 2] numerically investigated that tube having twisted tape insert with different tape length ratios are more effective than smooth tube. Kumar et al. [3] found that more heat transfer is achieved in tube-in-tube helical coil than shell and single helical coil due to centrifugal force formation. Dean [4, 5] found that due to the formation of

Thermal Performance Analysis of Eccentric Helical Coil Tube in Tube …

145

strong vortex flow in helical coil, it is more efficient than straight tube heat exchanger. Dean [4] found that in steady condition, incompressible flow velocity decrease with respect to the coil ratio. Aly [6] numerically calculated the thermal performance and pressure drop in double tube using Al2 O3 /water nanofluid. He found that heat both increase with curvature ratio and nanoparticle concentration. Mukesh Kumar et al. [7] numerically studied about thermal performance and pressure drop in helical coil tube using MWCNT/ water nanofluid. Dravid et al. [8] experimentally found the effect of centrifugal force on laminar flow heat transfer in helical coil tube. Navid et al. [9] numerically found effect of eccentricity on Nusselt no. and friction factor and revealed that both increase with eccentricity. Elattar et al. [10] numerically found effect of no. of inner coil and external coil inclination angle on heat transfer, pumping power and effectiveness. Also studied that k−ε RNG model validates good with experimental data as compare to k−ε standard model. Mishra and Gupta [11] found effect of coil pitch and dia. On pressure drop of Newtonian fluid flowing through helical coil. Dawood et al. [12] found the effect of nanoparticle size and volume concentration of ethylene glycol based nanofluids on Nusselt no. Erfan et al. [13] found the effect of different cross section of helical coil tube on Nusselt no. and also proposed different application of helical coil. Helical coil heat exchanger uses in gas turbine which finds application in aircraft, airplane etc. so compressed air needs to be cooled otherwise it will melt the turbine blade. Shaobei et al. [14] carried out study about helical coil tube in tube with double cooling source to obtain cooled air for advance aeroengine. Sun et al. [15] study the heat transfer characteristics of turbulent hydrocarbon fuel flow in tube in tube helical coil numerically. Greg et al. [16] experimentally studied application of heat exchanger to cool the compressor bleed air used in gas turbine engine and found that fuel-to-air system is more efficient than air to air system. From the above study it is clear that very little work has done in the field of making tube in tube helical coil, eccentric with fuel to air heat transfer system for thermal performance enhancement. So, in present work, after making eccentric tube in tube helical coil inner tube is inclined at different angles eccentrically, and their effect is studied on Nusselt no., friction factor and thermal performance factor.

2 Modeling of Eccentric HCTT Eccentric HCTT counter flow HE is modeled in ANSYS 18 CFD fluent using new design modular in system of intel core i5 and 8 GB RAM. Copper is used as heat exchanger (HE) material with ethylene glycol (EG) as cold fluid and air as hot fluid. Cold fluid is flowing inside inner tube, and air is flowing in the annulus region (Fig. 1; Tables 1 and 2).

146

S. Kumar and A. R. Jaurker

a

D= 100mm

P= 20mm

L

b doi= 12

doo= 12.7 mm

dio= 6.35mm

e= 2

dii= 5.85

Fig. 1 a Model of eccentric HCTT Heat exchanger. b Eccentric HCTT (β = 0°, e = 2 mm) Table 1 Properties of copper

Table 2 Properties of EG

Properties

Numerical value

k

401

P

8960

CP

0.385

Properties

Numerical value

k

0.252

ρ

1114.4

CP

0.057

μ

2415

Thermal Performance Analysis of Eccentric Helical Coil Tube in Tube …

147

2.1 Grid Independent Test Mesh is generated with octahedral and wedge elements. After face and line meshing inflation layer, edge size (0.02 mm) and first layer thickness (0.002 mm) with growth rate of (1.02, 1.04, 1.09, 1.11, 1.14) are added. To decrease the error of present results, grid-independent test shown in Fig. 2 is performed. According to which after 1,118,575 no. of elements (growth rate = 1.04), outer cold fluid temp is constant, so it is considered as no. of elements (Fig. 3).

Fig. 2 Grid independent test

Fig. 3 Meshing

148

S. Kumar and A. R. Jaurker

2.2 Governing Equations Continuity, energy, and momentum Eqs. (1–5) are used for flowing condition analysis. Conservation of mass given by continuity equation is ∂ρU1 ∂ρ ∂ρU2 ∂ρU3 + + + =0 ∂t ∂ x1 ∂ x2 ∂ x3

(1)

∂V ∂U + =0 ∂x ∂ x1

(2)

∂ρ =0 ∂t

(3)

Momentum equation 

∂T ∂U +v ρ u ∂x ∂y

 = −ρg

∂ρ ∂2 y +μ 2 ∂x ∂x

(4)

Energy equation   ∂T ∂T ∂2T +v =k 2 ρC p u ∂x ∂y ∂y

(5)

2.3 Boundary Condition and Setup Copper material properties are shown in Table 1. Cold fluid is made to flow with constant inlet velocity of 2 m/sec, while annulus side dean no. (De) is ranged between (1300 and 2200). Tci = 300 K, Thi = 800 K. e = 0, 0.5, 2, 3.5 mm, and β = 0, 45, 90, 135, 180°. Steady state k−ε standard model, coupled scheme, secondorder discretion equation, and standard initialization with accuracy of 10–6 is used for numerical solution of eccentric HCTT. Outer surface as adiabatic wall. System coupling is used for inner and outer wall. Velocity inlet and pressure outlet condition for both fluid are used.

2.4 Thermal and Hydraulic Performance Parameter The hydraulic dia. is defined by four times of the ratio of effective flow area to wetted perimeter. Fluid flowing between two circular cross section, the cross-section area

Thermal Performance Analysis of Eccentric Helical Coil Tube in Tube …

149

Aa is evidently π4 (d2io − d2oi ) but the wetted perimeters for heat transfer and pressure drops are different. Heat transfer wetted perimeter de =

4π(d2oi − d2io ) 4 × flow area = = (d2oi − d2io )/dio wetted peremetre 4π dio

(6)

In the analysis of friction not only results affected by the resistance of the outer pipe inner surface but also affected by inner pipe outer surface. Total wetted perimeter is π (doi − dio ) and for the pressure drop in annulus dh =

4 × flow area = 4π (d2oi − d2io )/4π(doi + dio ) = dio − doi (7) frictional wetted perimetre De = Re(dh /D)0.5

(8)

Re = (ρvdh )/μ

(9)

Nu = hde /k

(10)

  f = (dp∗ d∗h 2)/ ρ ∗ v2 ∗ L

(11)

   f Nu / 0.33 N us fs

(12)

 η=

for efficient enhancement technique, ï value at least larger than unity. More ï value, better the overall thermo-hydraulic performance. h = q/(π dh L)

(13)

q = mC p(To − Ti )

(14)

q = heat transfer (w), m = mass flow rate (Kg/sec).

3 Model Validation In Figs. 4 and 5 proposed model gives a good relation with A. Dravid equation [8] for Nui and Mishra gupta eq. [11] for f by changing the De in the tube side in the range of (50–1000) and Re (5500–8500).

150

S. Kumar and A. R. Jaurker

Fig. 4 Nusselt no. validation

50

Nui

40 30 20

Present work

10

A. Dravid

0

0

500

1000

1500

Dei

0.05

Fig. 5 Friction factor validation

0.04 0.03 f

Mishra & Gupta 0.02 Present work

0.01 0 5500 6500 7500 8500 Re

4 Results and Discussion 4.1 Effect of Dean Number Figures 6 and 7 show the effect of variation in annulus side De in the range of (1300–2200) on Nua and fa of eccentric HCTT (β = 0˚, e = 2 mm), and the result is compared with concentric HCTT and SCTT in the same condition. Graph shows on increasing the De, Nu increases but f decreases. At the same De, Nu for eccentric HCTT is more than the concentric HCTT and SCTT. Highest Nu is achieved for eccentric HCTT at the dean no. of 2200, which is 1.01 and 1.05 times higher than concentric HCTT and SCTT. On increasing the De, f for eccentric and concentric HCTT is almost same, but for SCTT, is lower. The reason for it may be the converged flow produced in eccentric helical coil which develops the turbulence in fluid flow due to which collision between the atom increased hence both the Nu and f increases. Figure 8 shows the variation in η due to change in De. According to graph, ï is highest for eccentric HCTT.

Thermal Performance Analysis of Eccentric Helical Coil Tube in Tube …

Fig. 6 Nu versus De

Fig. 7 f versus De

151

152

S. Kumar and A. R. Jaurker

Fig. 8 η versus De

4.2 Effect of Inclination Angle (β) of Inner Tube Figure 9 shows the variation in Nu and f due to change in β of inner tube. According to graph, on increasing β from (0–180°) Nu decreases but increases for β (180–360°). Also, Nu values are symmetrical for (0–180°) and (180–360°).

Fig. 9 Nu and f versus β

Thermal Performance Analysis of Eccentric Helical Coil Tube in Tube …

153

Fig. 10 ï versus β

On increasing β (0–180°), f increases but decreases for β (180–360°). Also, f values are symmetrical for (0–180°) and (180–360˚). Figure 10 shows the variation in η, for the variation in β. According to graph, ï value decreases for increasing in β (0–180°) and increases for β (180–360°). Also, the η value is symmetrical for (0–180°) and (180–360°). All above values are for eccentric HCTT (e = 2 mm, De = 1900). Above graph shows the same trend for e = 0.5, 3.5 mm (Figs. 11 and 12).

154

S. Kumar and A. R. Jaurker

(a)

(b)

Fig. 11 a Cold fluid at inlet, b cold fluid at outlet

(a)

(b)

Fig. 12 a Hot fluid at inlet. b Hot fluid at outlet

5 Conclusion • With increase in De, Nu increases. Nu for Eccentric HCTT is greater than concentric HCTT and SCTT. • On increasing De, f decreases. f value for eccentric HCTT and concentric HCTT is almost same but higher than SCTT. • ï value is highest for eccentric HCTT and greater than unity. • With increase in β (0–180°) Nu decreases and f increases but for (180–360°) Nu increases and f decreases. • ï value decreases for β (0–180°) and increases for (180–360°) • Highest Nu is achieved for β (0°) and lowest for 180° • Highest f is achieved for β (180°) and lowest for 0°

Thermal Performance Analysis of Eccentric Helical Coil Tube in Tube …

155

References 1. Yadav AS, Shrivastava V, Sharma A, Sharma SK, Dwivedi MK, Shukla OP (2021) CFD Simulation on thermo-hydraulic characteristics of a circular tube with twisted tape insert. Mater Today Proc 47(Part 11):2790–2795 2. Yadav AS, Shrivastava V, Dwivedi MK, Shukla OP (2021) 3-Dimensional simulation and correlation development for circular tube equipped with twisted tape. Mater Today Proc 47(Part 11):2662–2668 3. Kumar V, Saini S, Sharma M, Nigam KDP (2006) Pressure drops and heat transfer study in tube helical coil heat exchanger. Chem Eng 61:4403–4416 4. Dean WR (1927) Note on the motion of fluid in a curved pipe. Phil Mag Ser 7(4):208–223 5. Dean WR (1928) The streamline motion of fluid in a curved pipe. Phil Mag Ser 7(5):673–695 6. Wael IAA (2014) Numerical study on heat transfer and pressure drops using nanofluid in coiled tube in tube heat exchanger. Energy Convers Managem 79:304–316 7. Mukesh Kumar PC, Chandrasekar M (2019) CFD analysis on heat and flow characteristics in double helical coil heat exchanger handling MWCNT/ water nanofluid. Heliyon 5:e02030 8. Dravid AN, Smith KA, Merrill EW, Brian PLT (1971) Effect of secondary fluid motion on laminar flow heat transfer in helically coiled tubes. Department of Chemical Engineering Massachusetts Institute of Technology, Cambridge, Massachusetts, pp 021 39 9. Zade NM, Akar S, Rashidi S, Esfahani JA (2017) Thermo-hydraulic analysis for a novel eccentric helical screw tape inserts in a three-dimensional tube. Appl Therm Eng 124:413–421 10. Elattar HF, Fouda A, Nada SA, Refaey HA, Al-Zahrani A (2018) Thermal and hydraulic numerical study for a novel multi tubes in tube helically coiled heat exchangers: effects of operating/geometric parameters. Int J Therm Sci 128:70–83 11. Mishra P, Gupta SN (1979) Momentum transfer in curved pipes: Newtonian fluids. Ind Eng Chem Des Dev 18 12. Dawood HK, Mohammed HA, Sidik NAC, Munisamy KM, Alawi OA (2017) Prediction of the effect of nano particle size on Nusselt no. using ethylene glycol based nanofluid. Int Commun Heat and Mass Transf 82:29–39 13. Khodabandeh E, Safaei MR, Akbari S, Akbari OA, Alrashed AAAA (2018) Application of nanofluid to improve the thermal performance of horizontal spiral coil utilized in solar ponds: geometric study. Renew Energy 122:1e16 14. Liu S, Huang W, Bao Z, Zeng T, Qiao M, Meng J (2021) Analysis, prediction and multiobjective optimization of helically coiled tube-in-tube heat exchanger with double cooling source using RSM. Int J Therm Sci 159:106568 15. Sun F, Li Y, Sunden B, Xie G (2019) The behaviour of turbulent heat transfer deterioration in supercritical hydrocarbon fuel flow considering thermal resistance distribution. Int J Therm Sci 141:19–32 16. Bruening GB, Chang WS (1999) Cooled cooling air system for turbine thermal management. The American society of mechanical engineers. Three Park Avenue New York, pp 10016–5990

Analysis of Performance of Roughened Triangular Duct of Solar Air Heater Durgesh Kumar Dubey and A. R. Jaurker

Abstract Numerical analysis of thermal performance of solar heater with the roughness equipped on one of the surface. Here triangular-shaped channel is used and fluid used in air. In this research work, various geometry of roughness elements (such as triangular, rectangular, semicircular, square, and trapezoidal) are taken in order to study their effect on heat transfer in turbulent regime (4000 < Re < 20,000) in triangular duct solar heater. ANSYS 18 is used for simulation purpose under steady-state condition. Excellent enhancement in performance of 2.76 is found at Re = 20,000 in trapezoidal rib (h/w = 2). The best values of TPP found on varying roughness geometry are 2.56 for triangle (h/w = 2), 2.54 for rectangle (h/w = 2), 2.4 for rectangle (h/w = 0.67), 2.06 for square, and 1.96 for semicircular rib at Re = 20,000. Keywords Solar air heater · Roughness geometries · Nusselt number · Friction factor

Nomenclature Dh K havg P Cp T L u w

Hydraulic diameter (m) Thermal conductivity (W/m*K) Coefficient of heat transfer (W/m2* K) Pressure (Pa) Specific heat (W/Kg*K) Temperature (K) Length (m) Inlet velocity Width of rib (m)

D. K. Dubey (B) · A. R. Jaurker Department of Mechanical Engineering, Jabalpur Engineering College, Jabalpur, Madhya Pradesh 482011, India e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_15

157

158

p h

D. K. Dubey and A. R. Jaurker

Pitch Height of rib

Dimensionless h/Dh f Nu Pr Re p/h h/w h’/h

Relative roughness height Friction factor Nusselt number Prandtl number Reynolds number Relative roughness pitch Rib aspect ratio Parallel sides ratio (Trapezoidal rib)

Greek symbols μ ρ ε k G Gt

Viscosity (Ns/m2 ) Density (kg/m3 ) Dissipation rate Turbulent kinetic energy (m2 /s3 ) Molecular thermal diffusivity (=μ/Pr) Turbulent thermal diffusivity (=μt / Pr)

Abbreviations S.A.H R S SC T TR RR

Solar air heater Rectangular rib (h/w = 2) Square rib Semicircular rib Trapezoidal rib Triangular rib Rectangular rib (h/w = 0.67)

Analysis of Performance of Roughened Triangular Duct of Solar Air Heater

159

1 Introduction Since the demand of energy is increasing with time, so there is the need to increase in the generation of energy. It is required to decrease the dependency on fossil fuel because they are limited in quantity and also affect the environment. The problem of fuel can be solved by using solar radiation as it is free, available in unlimited quantity, and also pollution free. Solar heater is a type of heat exchanger which uses solar radiation as input and transfers this to the fluid. The heat energy transferred to the fluid can be used as end system. When the fluid used is air, then the solar heater is called as solar air heater. There are two methods to increase the performance of S.A.H. First by changing the shape of the duct, Cebeci and Bradshaw et al. [1] Physical and computational aspects of convective heat transfer, Page no.130, here various ducts of shapes such as square, triangular, hexagon, semicircular, rectangle, circular, and ellipse are considered and among all the geometry of duct triangular duct has the minimum friction factor. Second by applying roughness on the surfaces, Singh et al. [2] have performed numerical analysis to compare the thermal performance of semicircular and triangular duct. It is observed that triangular duct has less efficiency but it also has less friction factor across the duct than the semicircular duct. Leung et al. [3] have performed experimental analysis to find out the effect of triangular duct apex angle in turbulent flow conditions. Five different values of apex angles and three rib roughness values on the surfaces are considered. It is observed that the duct with apex angle of 60° has high thermal performance. Mahanand et al. [4] have performed numerical analysis to find out the effect of quarter circular rib with different arrangements in the performance of solar air heater. The maximum thermal performance parameter of 1.88 observed at Reynolds number 15,000 and relative rib roughness pitch of 7.14. Rajneesh et al. [5] have performed numerical analysis to find rectangular geometry’s roughness effect on performance in triangular channel solar heater. Excellent enhancement in the performance of 1.89 is observed p/h = 10, ratio of h/Dh of 0.04, h/w = 4 at Re = 15,000. Anil et al. [6] have performed numerical analysis to find out the effect of semicircular in solar air heater. It is observed that the best performance increment factor of 1.74 obtained at Pitch = 15 mm and P/h = 10.71 at Reynolds number of 15,000. Luo et al. [7] have done experimental analysis to find out the effect of square rib in solar air collector solar air heater of equilateral triangular duct. Optimum value of relative height equal to 0.18 and relative spacing between the ribs equal to 7.22 corresponds to the best performance and with increase in rib height, flow friction increase linearly and obtained a maximum value when the relative spacing between ribs is 7.22. From the literature, it is evident that the performance of S.A.H. is strongly influenced because of roughness element in absorber plate. Triangular duct has minimum friction factor in comparison with S.A.H. with rectangular duct. Hence, for triangular duct requires less pump power and also limited study is available for the triangular channel of S.A.H. So that’s why in the present work triangle cross section of the duct of S.A.H. is taken. Since the working fluid is air and air having less thermal

160

D. K. Dubey and A. R. Jaurker

conductivity which can result in poor performance of the device. Hence, to improve the device performance roughness elements are employed. The roughness element induces turbulence in the flow of air which as result breaks the viscous laminar sublayer of airflow and increases the heat transfer. Various analyses have been done by providing different roughness element in the surface. It is observed that in every analysis is improved. This research work is to find out the geometry of rib which can improve the performance better and what parameters are responsible for it. The roughness elements presented are semicircular, square, rectangular, triangular, and trapezoidal. This research work is done on ANSYS 18.

2 Problem Description From Fig. 1, it is clear that the whole fluid flow area is breakdown into three sections, i.e., inlet section, exit section, and test section. The lengths L 1, L 2, and L 3 are 900 mm, 700 mm, and 400 mm, respectively. Depending on the length of side of triangular channel, hydraulic diameter (Dh ) can be obtained as 88.89 mm. A constant heat flux of 1000 W/m 2 is assuming to incident on the test section’s surface which contain ribs. Roughness elements of different geometries are created on absorber plate, and the same condition is applied in each roughness element to find out the overall performance in the same conditions.

Fig. 1 Proposed solar air heater

Analysis of Performance of Roughened Triangular Duct of Solar Air Heater

161

2.1 Numerical Procedure In this research work, air is flowing through S.A.H. with distinct geometries of roughness elements equipped on the surface. Only fluid flow area is examined for the simulation purpose. The current calculation makes the following assumptions: • The flow is incompressible. • The output of this numerical analysis is obtained in steady state. • A thermo-hydraulic property of surface attached to roughness is constant, and temperature of fluid flowing is equal to surrounding temperature, i.e., 300 K. Geometry of solar air heater is drawn in Catia V5 version as shown in Fig. 2 with roughness details according to Table 1 and then imported to ANSYS 18 fluent for further simulation purpose. The finite volume methods in the ANSYS software are used for solving of governing continuity, energy, and momentum equation. The meshing of the proposed geometry is done using ANSYS ICEM and ensured the mesh quality to be more than 0.60. Near the roughness element, the finer meshing has been done to study about the flow near the element. The thermal properties of the fluid is analyzed with the inlet condition according to Table 2.

2.2 Governing Equations Modeling of fluid flow using with the help of momentum, continuity, and energy equations. This can be expressed as:Continuity equations:∂ρu j =0 ∂x j

(1)

162

D. K. Dubey and A. R. Jaurker

Triangular rib

Square rib

Rectangular rib (h/w=0.67)

Rectangular rib (h/w=2)

Trapezoidal rib (h/w=2) (h’/h=0.75)

Semicircular rib

Fig. 2 Schematic of proposed roughness shape

Analysis of Performance of Roughened Triangular Duct of Solar Air Heater Table 1 Range/values of parameter in present research work

Table 2 Inlet air properties

Parameter

Value/Range

h

4 mm

P

44 mm

Dh

88.89 mm

Re

4000–20,000

(h/Dh )

0.045

(h/w)

0.67 and 2.0

(P/h)

11

(h’/h) (Trapezoidal rib)

0.75

Inlet properties

Value

T

300 K

P

1.01325 Pa

Pr

0.7296

ρ

1.225 kg/m3

K

0.0242 W/m/K

Cp

1006.3 J/kg/K

μ

1.7894 * 10–3 Ns/m2

163

Momentum equation:      ∂ ρu i u j ∂u j ∂u j ∂ ∂u i ∂P ∂ ∂u i + + = μ + μt + ∂x j ∂ xi ∂x j ∂x j ∂ xi ∂x j ∂x j ∂ xi

(2)

Energy equation: ∂  ∂ ∂T ρu j T − =0 (┌ + ┌t ) ∂ xi ∂ xi ∂x j

(3)

k−ε turbulence model is used in the current research. Here k shows turbulent kinetic energy, and ε shows the dissipation of kinetic energy. The equations used to represent K−ε turbulence model are:  ∂(ρku i ) ∂k ∂ αk μe f f − G k + ρε = 0 − (4) ∂ xi ∂x j ∂x j   ∂(ρεu i ) ∂ε ε ∂ ε2 αε μe f f − C1ε G k + C2ε ρ = Rε , − (5) ∂ xi ∂x j ∂x j k k where

164

D. K. Dubey and A. R. Jaurker

Table 3 Deviation of Nu and f with the number of element Number of elements

Nusselt number, Nu

% variation in Nu

Friction factor, f

% variation in f

368,125

47.285

573,224

54.545

15.35

0.016705

6.49

775,894

60.215

10.39

0.01607

3.8

963,433

62.69

4.11

0.015775

1.8

114,017

63.187

0.792

0.01567

0.66

0.017865

ui = velocity component in corresponding direction 2 μeff = eddy viscosity = ρCμ kε C μ = constant = 0.09 Gk = generation of turbulence kinetic energy αk , αε , C1ε , and C2ε are constant, and the value is 1.39, 1.39, 1.42, and 1.68, respectively.

2.3 Grid Independence Test This test is performed in solar air heater with square roughness at Re = 12,000 at the same boundary condition. Meshing is done in ANSYS ICEM with mesh quality more than 0.60. The meshing method starts with coarse element having maximum size of 10 mm, and then again the method is repeated with finer elements, predicting deviation of Nu and f with the number of elements (Table 3).

2.4 Boundary Conditions and Solutions The inlet velocity given depends upon Reynolds number from 4000 to 20,000, and at outlet pressure, outlet condition is given. The surface of test section having rib roughness is assumed to have uniform heat flux of 1000 W/m2 and remaining surface, no slip, and insulated conditions are considered. The properties of air at inlet are given in Table 2. Since RNG K−ε turbulence model gives more accurate result than any other model in ANSYS 18 for flow through the channel [4]. Hence, above turbulent model is used for further calculation. Double precision solvers and simulations are selected for further simulation. An upwind discretization of second order is selected for each transport equation. A convergence criterion of 10–6 is selected for good accuracy. To start the iteration, select the standard initialization, and start computation from the inlet, the number of iterations performed by the solver varies due to the different geometries.

Analysis of Performance of Roughened Triangular Duct of Solar Air Heater

165

3 Data Reduction For roughened S.A.H. Nusselt number is calculated by:Nur = h ∗avg Dh /K

(6)

For artificially roughened S.A.H, friction factor can be calculated by:fr =

(ΔP/l) · D 2∗ ρ ∗ v ∧ 2

(7)

Nusselt number for smooth S.A.H is given by Dittus–Boelter correlation is given by, Nus = 0.023∗ Re0.8 ∗ Pr0.4

(8)

Friction factor in smooth S.A.H is given by Blasius equation:f s = 0.0791∗ Re−0.25

(9)

Normalized Nusselt number (Nunorm) offers fact about the increase in Nu due to roughness added to surface with respect to smooth surface. Nunorm =

Nur Nus

(10)

Friction penalty ( f p ) gives information about the increment in friction factor due to roughness added to surface with respect to smooth surface. fp =

fr fs

(11)

With the addition of ribs in surfaces, Nu and f both increase. Hence, both factors must be considered to compare the performance of different roughness shapes. Thermal hydraulic performance parameter (TPP) gives information about the increment in thermal performance due to roughness added to surface with respect to smooth surface. TEF =

Nu norm 1

fp3

(12)

166

D. K. Dubey and A. R. Jaurker

120

100

Nusselt Number D.D.Luo

0.07

Friction Factor D.D. luo

0.06

Nusselt Number Present Friction Factor Present

0.05 0.04

60

f

Nu

80

0.08

0.03

40

0.02 20

0.01 0

0 4000

8000

12000

16000

20000

Re

Fig. 3 Validation of proposed model with Luo et al. [1]

4 Model Validation ANSYS 18 software used for the predicting the values of Nu and f of roughened triangular channel with the fluid flowing in turbulent regime (4000 < Re < 20,000). The result predicted by the software is compared to experimental results of Luo [1]. Figure 3 represents the comparison of the numerically predicted result and experimental result given by Luo [1]. Predicted values of Nusselt number at low Re show maximum deviation from experimental result, i.e., 3.89%. But for higher Re, this deviation from experimental values is less than 1%. Predicted values of friction factor show less than 6% of deviation from the experimental values. From the above deviation values, it can be considered that the methodology used is predictable and this methodology can be used for analysis.

5 Result and Discussion This numerical analysis is done to find the effect of the roughened surface of S.A.H with varying Reynold number from 4000 to 20,000 on Nu, f , and TPP.

5.1 Effect of Roughness Element in Nusselt Number The impact of roughened surface on Nu is shown in Fig. 4. From the graph, it makes evident that with increment in Re, Nunorm also increases. Hence, the best value of Nunorm is observed at the highest Re, i.e., 20,000. Besides this, the T shows the

Analysis of Performance of Roughened Triangular Duct of Solar Air Heater

167 Rect. Rib h/w=2

4.5

Rect. Rib h/w=0.67

4 trapezoidal (h/w=2) (h'/h = 0.75)

Nu norm

3.5

triangular rib (h/w=2) Square rib

3

Semicircular rib

2.5

2

1.5

1 0

4000

8000

12000

16000

20000

Re

Fig. 4 Plot between Nu norm and Re for different rib shapes

maximum increment in Nu when compared to other roughness shapes with the value of 3.92 while the Nunorm for other rib geometry is 3.11 for TR, 3.52 for R, 3.3 for RR, 2.8 for S, and 2.52 for SC. High value of Nunorm is found in T. Nu increases because of the inducement of turbulence in the main flow. In case SC, there is no sharp edge. So there is smooth flow over the rib which induces less turbulence in the main flow. Hence, the semicircular rib will show low value of Nunorm . According to Kumar et al. [8] in case TR (h’/h = 0) and T (h’/h = 0.75), with increase in h’/h value from 0 to 0.75, Nusselt number increase and from 0.75 to 1, Nu decreases. So the highest value of Nusselt number is obtained at h’/h = 0.75. It has been also concluded that with increase in e/w value Nusselt number also increases.

5.2 Effect of Roughness Element in Friction Factor With the provision of roughness element in absorber plate, it increases Nusselt number but f also increases with it. This increment f p in increases pump power requirement. Hence, less f is desirable. Figure 5 represents the variation of f p of different roughness geometry with varying Re from 4000–20,000. f p value decreases with increase in Re [4]. So, higher friction penalty is observed at Re = 4000. Because of the formation of eddies in case of geometry having sharp edges. So the geometry having sharp edges has high friction factor compared to smooth geometry. The highest friction penalty was found in case of T with the value of 4.25 at Re = 4000.

168

D. K. Dubey and A. R. Jaurker Rect. Rib (h/w=2)

4.5

Rect. Rib (h/w=0.67)

4

Trapezoidal (h/w=2) (h'/h = 0.75)

3.5

triangular (h/w=2)

3

fp

Square rib

2.5 Semicircular rib

2

1.5

1 0

4000

8000

12000

16000

20000

Re

Fig. 5 Plot between f p and Re for different rib shapes

For different shaped geometries f p for TR is 3.02, R is 3.55, RR is 3.32, S is 3.1, SC is 2.48. The variation in friction penalty appears due to the geometries of surface roughness. Since sharp edges form eddies which in result increase the pressure drop and increase in pumping power. Every sharp edge geometry produces eddies while smooth geometry does not produce any eddies. So, that’s why SC has less friction penalty compared to other geometry. According to Rajneesh et al. [8], same explanation is followed in case of friction factor as followed in Nusselt number.

5.3 Effect of Roughness Element in Thermo-Hydraulic Performance Parameter (TPP) For the design of S.A.H, it is desirable to increase the heat transfer; but with the application of surface roughness, there is increment in f . As the f increases, the device requires more pump power. This increment in pump power is not desirable. Therefore, both Nunorm and f p should be taken into account when designing a solar air heater. Thermo-hydraulic performance parameters provide information on improving the performance of S.A.H with rough surfaces compared to smooth surfaces. Therefore, the value of TPP must be greater than 1. Figure 6 represents the variations of the TPP at various geometric roughness elements. TPP increases with increasing Re, and the same trend continues with the roughness of each rib. However, at Re = 20,000, the TPP value will be higher. Best value obtained 2.7532 for T. The TPP for the various roughness elements is 2.5751 for R and 2.4276 for RR, 2.5885 for TR, 2.081 for S, and 1.9861 for SC.

Analysis of Performance of Roughened Triangular Duct of Solar Air Heater

169 Rect. Rib (h/w=2)

3

Rect. Rib (h/w=0.67)

2.5

Trapezoidal (h/w=2) (h'/h=0.75)

TPP

triangular (h/w=2)

2

Square rib

Semicircular rib

1.5

1 0

4000

8000

12000

16000

20000

Re

Fig. 6 Plot between TPP and Re

6 Conclusion The work is done on ANSYS 18 to study the impact of different roughness geometry on the performance of S.A.H and about the parameter which affect the performance. The roughness element is put in such a manner that the fluid incident perpendicular to the roughness surfaces. Nunorm and fp values have been studied with different Reynolds numbers with different surface roughness shapes. Different roughness geometries are rectangular (h/w = 0.67), rectangular (h/w = 2), semicircular, trapezoidal (h/w = 2), square, and triangular. The findings concluded from the present investigation are as follows:• Since the value of TPP in greater than 1 in each case, so it can be said that application of roughness element in the surface of test section of solar air heater enhances its performance. • Due to the direct strike to the slope line of trapezoidal rib, there is better mixing of secondary flow to the mainstream. That’s why it has maximum Nunorm that is 3.92 when compared to other geometry of roughness element. • Since in semicircular rib, there are no sharp edges. So smooth flow takes place over the surface. Thus, no eddies formed. So, proper mixing of secondary flow with main flow does not occur and pressure drop is increased in less amount. That’s why low normalized Nusselt number and friction penalty semicircular rib is observed.

170

D. K. Dubey and A. R. Jaurker

• Due to the direct strike to the slope line of trapezoidal rib, there is more amount of eddies formed as compared to any other case. With the maximum number of eddies, pressure drop increases to maximum value. Hence, the maximum enhancement in friction factor observed in trapezoidal rib. • Highest value of TPP is observed in trapezoidal rib, i.e., 2.7532

References 1. Cebeci T, Bradshaw P (1988) In: Physical and computational aspects of convective heat transfer, Springer, Germany 2. Singh S (2018) Thermal performance analysis of semicircular and triangular cross-sectioned duct solar air heaters under external recycle. J Energy Storage 20:316–336 3. Leung CW, Wong TT, Kang HJ (1998) Forced convection of turbulent flow in triangular ducts with different angles and surface roughness. Heat Mass Transf 34:63–68 4. Mahanand Y, Senapati JR (2021) Thermo hydraulic performance analysis of solar air heater (SAH) with quarter-circular ribs on the absorber plate: a comparative study. Int J Therm Sci 161:106747 5. Kumar R, Kumar A, Goel V (2017) A parametric analysis of rectangular rib roughened triangular duct solar air heater using computational fluid dynamics. Sol Energy 157:1095–1107 6. Yadav AS, Shrivastava V, Chouksey VK, Sharma A, Sharma SK, Dwivedi MK (2021) Enhanced solar thermal air heater: a numerical investigation. Mater Today Proc 2214–7853 7. Luo DD, Leung CW, Chan TL (2004) Forced convection and flow friction characteristics of air cooled horizontal equilateral triangular ducts with ribbed internal surfaces. Int J Heat Mass Transf 47:5439–5450 8. Kumar R, Goel V, Kumar A (2017) Investigation of heat transfer augmentation and friction factor in triangular duct solar air heater due to forward facing chamfered rectangular ribs: a CFD analysis. Renew Energy

Structural Analysis of Aircraft Wing Considering Titanium Alloy Ruby Mishra, Himanshu Ranjan Kumar, Smaranika Nayak, Santosh K. Nayak, Swayam B. Mishra, and Basanta Ku. Nanda

Abstract Structural analysis is a significant part of the strategy and advancement of the aircraft structure. For smooth flight, the design of aircraft wings, which is one of the important parts, is very essential. Apart from this, the selection of material is also an important factor for the design. In the past research, all the structural design and modal proposed were with aluminium. In our research, we would like to explore with titanium alloy. Different views of the air foil wing were developed by using solid works, and modal analysis was done by using Ansys. The modes of vibration, with its corresponding natural frequency and mode shapes, also found out. Keywords Air foil wings · Modal analysis · Natural frequencies · Structural analysis

1 Introduction The past decade has been significant research devoted to design and modal analysis of the aircraft wing using aluminium alloy. The air frame of aircraft resists the aerodynamic forces and stresses induced because of the weight of the fuel, crew, and payload [1]. Aircraft wing comprises NACA0012, with maximum thickness of 12%, maximum camber 0% with maximum camber position 0%. A 3D model of an aircraft wing has been developed, and the modal analysis has been done using ANSYS [2]. This modal analysis deals with the mechanical structures under dynamic forces excitation. Considering it, the overall performance of the system in positive operating conditions can be improved. This aeroplane background information regarding air foil wing is shown in Fig. 1 [2]. The structure of the air foil wing along with its advantages, application, features, and design procedure has been studied. The aeroplane wing comprises a special shape known as air foil. The air foil is developed so R. Mishra (B) · H. Ranjan Kumar · S. Nayak · S. K. Nayak · S. B. Mishra · B. Ku. Nanda School of Mechanical Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be a University, Bhubaneswar, Odisha, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_16

171

172

R. Mishra et al.

Fig. 1 Parts and stages of wings [2]

that the air which is travelling at the top of the wing travels faster and farther than the air which is travelling below the wing. For any aircraft to fly, one must lift the total weight of the aeroplane, including fuel, passengers, and fuselage body. To hold these total loads, the wings of the aeroplane help to generate most of the lift in the air, and the aeroplane must be impoverished through the air. A wing’s aerodynamic effectiveness is articulated as its lift-to-drag ratio. A high lift-to-drag ratio needs a lesser thrust to drive the wings through the air at an adequate lift. In this Fig. 1, we can observe that there are various stages of the aircraft wings. Remotely worked drones are furnished through a slower jet. The diameter of the principal rotor of drone is 3 m, and weight of the lift capacity is 22.6 kg. In each 45 min, it takes closely 1 gallon of petrol. One such study unlocked its groundwork for the progress of aircraft system along with a tougher par value and bigger size of VMD droplet [3]. Remote-controlled UAV sprayed pesticides and fertilizer with this device. The sprayer is manually actuated by an RF-controlled nozzle, and the UAV is controlled by manual flight plans. Indian agriculture required production and protection materials to achieve high productivity [4]. The mechanical perception of flying robot is also useful here for improved investigation [5, 6]. A highly integrated and ultra-low-power MSP430 single-chip microcomputer with a separate functional module was used in the system [5]. Some reviews are also done by using the concept of robotic arm mechanisms and its simulations. The 5-R robotic arm system used artificial intelligence and deep learning to repeatedly notice the pompous component [6]. The development of a robotic manipulator was done after it was identified [7], and needle deviation was measured for observation [8]. Different methods were used in the mechanism to analyse vibration [9]. Analysis and path simulation were conducted using the novel methodology, and an experiment was demonstrated while taking into account the theoretical model [10, 11]. The improved needle manoeuvre was located using various sensors [12]. A counterweight was used to automate an examination

Structural Analysis of Aircraft Wing Considering Titanium Alloy

173

in a newly built 4-R mechanism for support [13]. For the goal of numerous multidisciplinary works, optimal design was completed in four-bar linkages [14]. Multilevel safety measures are recommended for preventing COVID-19 contamination in a healthcare facility using various techniques such AI, mobile apps, and system design modification [15]. It is investigated how a robotic manipulator is designed and how it functions utilising artificial intelligence [16–18]. In [19, 20], modelling and vibrational research were conducted. [21–23] examined the design, model development, and vibrational analysis of an aircraft wing. In order to enable stable flight with vertical take-off, a revolutionary hybrid fixed-wing flying robot design that combines both fixed-wing and rotary-wing principles is applied and also used for medical purposes [24–26]. We conducted the structural modal analysis of the air foil wing based on these works.

2 Methodology 2.1 Material Selection and Theoretical Modal Analysis of Air Foil Wing We have considered titanium alloy for wing design. Titanium alloys are companionable with carbon fibres and are used to avoid voltaic corrosion difficulties. The better usage of design in response to mechanical and thermal properties is related to high manoeuvrability and supersonic cruise speed. All simulations are done by Solid Works 2021 and Ansys. It determines how a product will function with various specifications. Here, the software package used for theoretical model analysis is SOLIDWORKS 2021.The material properties study is given in Table 1. The different views of the air foil wing are shown by using Solid works 2021 in Fig. 2.

2.2 Modal Analysis of Air Foil Wing Here, air foil wings are considered as cantilever beam as its root chord is static into the fuselage with the support of arms, and other end which is known as tip chord is free, and analysis is done considering unique boundary condition using soilid works 2021. The model of air foil wing is complicated to manufacture due to lack of resources, and the cost is also very high. So, to corroborate, a modal analysis is done here to find out natural frequency. The physical structure exhibited in this paper is an air foil cross section of the aircraft wing. The dimension of the chord length at the free end is 0.5m and at the fixed end is 1.1m and the length of the wing is 4m. The model is tapered and made of titanium alloy structure. The physical model of the aircraft wing is shown in Fig. 3.

174

R. Mishra et al.

Table 1 Properties of titanium alloy

Fig. 2 Different views of the air foil wing

The complete modal analysis with titanium alloy by using Ansys is shown in Fig. 4a–g, considering different natural frequencies in HZ. In each natural frequency, the beam behaves differently.

2.3 Structural Analysis of Air Foil Wing The structural analysis is done considering titanium alloy for the different natural frequencies, which is shown in Fig. 5a–f. The total deformation is more in the tip of the wing for each case.

Structural Analysis of Aircraft Wing Considering Titanium Alloy

Fig. 3 Physical model of aircraft wing

Fig. 4 a–g Cantilever beam numerical frequency with its respective mode shapes

175

176

R. Mishra et al.

Fig. 5 a–f Total deformation along with respective modes considering different natural frequencies

3 Discussion From the above results, we can accomplish that the total deformation in titanium alloy is acceptable. As titanium alloy is abundantly tougher to twist and cut than other alloy, it would significantly increase its manufacturing costs. But titanium keeps its strength at high temperatures. We can use titanium alloy in order to give the more strength to the structure and because of lightweight reduces the weight of the wing.

4 Conclusion This paper mostly shows the modal analysis of an aircraft wing using titanium alloy. We performed both the theoretical and structural analysis by considering the aircraft wing as a cantilever beam for different mode shapes. The simulation is done under

Structural Analysis of Aircraft Wing Considering Titanium Alloy

177

application of external loads by Ansys, and it is observed that the deformations considering different natural frequencies with different loads, and boundary conditions are very less. The boundary condition is applied under the cantilever beam condition according to the assumption that the wing is attached to the fuselage body (which holds the passenger cargo) in a fixed condition. We observe the varying behaviour in different modes. After analysis, it is found that the natural frequency determined by this approach is quite promising, and the procedure opted for the analysis is correct. We are using titanium alloy because it is more compatible with carbon fibres and is used to avoid galvanic corrosion problems.

References 1. Khadse NA, Zaweri SR (2015) Model analysis of aircraft wing using ANSYS workbench software package. 4(07) ISSN: 2278-0181 2. Kathivaran T, Mohammed Huda A, Parthiban K (2018) Structural and model analysis of subsonic aircraft wing using ANSYS workbench. 5(10) ISSN: 2395-0056 3. Huang Y, Hoffmann WC, Lan Y, Wu W, Fritz BK (2015) Development of a spray system for an unmanned aerial vehicle platform. Appl Eng Agricul 25(6):803–809 4. Kale S, Khandagale S, Gaikwad S, Narve S, Gangal P (2015) Agriculture dronefor spraying fertilizer and pesticides. Int J Adv Res in Comput Sci Softw Eng 5(12):804–807 5. Maity R, Mishra R, Pattnaik PK (2022) A review of flying robot application in health care. In: Smart healthcare analytics: state of the art, Springer Singapore, pp 103–111 6. Maity R, Mishra R, Pattnaik PK (2021) Flying robot path planning techniques and its trend. Mater Today Proc 1–8 7. Shah S, Mishra R, Pramanik S, Kundu A, Pandit S, Mallick A (2020) Design and deviation analysis of a semi-automated needle manipulation system using image processing technique. In: First international conference on sustainable technologies for computational intelligence. Advances in intelligent systems and computing, vol 1045. Springer, Singapore. https://doi.org/ 10.1007/978-981-15-0029-9_22 8. Shah SK, Mishra R, Ray LS (2020) Solution and validation of inverse kinematics using deep artificial neural network. Mater Today Proc 26:1250–1254. https://doi.org/10.1016/j.matpr. 2020.02.250 9. Shah SK, Ruby M, Prasanna D, Mohapatro G (2019) Fabrication and deviation analysis of a 5-axis robotic arm using image processing technique. Mater Today Proc 18:4622–4629. https:/ /doi.org/10.1016/j.matpr.2019.07.446 10. Gourishankar M, Ruby M, Shubham S, Taniya G (2019) Real-time vibration analysis of a robotic arm designed for CT image guided guided diagnostic procedures. In: Advances in interdisciplinary engineering. Lecture Notes in mechanical engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-6577-5_17 11. Shah SK, Mishra R, Mohapatro G (2018) Experimental and theoretical design analysis and modeling of a CT image guided robotic arm. In: 2018 international conference on engineering, applied sciences, and technology (ICEAST), Phuket, pp 1–4. https://doi.org/10.1109/ICEAST. 2018.8434464 12. Shubham Kamlesh S, Mishra R (2018) Advanced path simulation of a 5R robotic arm for CT guided medical procedures. Mater Today Proc 5(2):6149–6156. https://doi.org/10.1016/j. matpr.2017.12.221 13. Shah SK, Mishra R, Choudhury S (2018) Preliminary design of an 7 DOF robotic manipulator positioning biopsy needle. Mater Today Proc 5(9):19140–19146. https://doi.org/10.1016/ j.matpr.2018.06.268

178

R. Mishra et al.

14. Mishra R, Mohapatro G, Behera R (2018) Structural and dynamic analysis of optimized four bar mechanism considering counterweight in coupler link. Mater Today Proc 5(2):5467–5474. https://doi.org/10.1016/j.matpr.2017.12.135 15. Mishra R, Kanti Naskar T, Acharya S (2018) Optimum design of elastic and flexible linkages for motion and path generation. Mater Today Proc 5(2):4629–4636. https://doi.org/10.1016/j. matpr.2017.12.034 16. Manoranjan M, Ruby M (2021) Modern and Integrated approach for safety issues in healthcare during covid-19. Ilkogretim Online 20(5):1029–1034. 6 17. Shah S, Mishra R (2021) Modelling and optimization of robotic manipulator mechanism for computed tomography guided medical procedure. Scient Iranica. https://doi.org/10.24200/sci. 2021.57259.5149 18. Shah S, Mishra R, Szczurowska A, Guzi´nski M (2021) Non-invasive multi-channel deep learning convolutional neural networks for localization and classification of common hepatic lesions. Pol J Radiol 86(1):440–448. https://doi.org/10.5114/pjr.2021.108257 19. Shubham Kamlesh S, Ruby M, Prasad MBS, Om P (2020) Prediction of abnormal hepatic region using ROI thresholding-based segmentation and deep learning-based classification. Int J Comput Appl Technol 64:4:382–392 20. Srividhya S, Nehru K, Salamon Rago A, Selvan P (2020) Vibrational analysis of an aircraft wing model using ANSYS workbench. 8 ISSN: 2321-9939 21. Venkatesan SP (2018) Modelling and analysis of aircraft wing with and without winglet. 42(4) 22. Ninian D, Daaka SM (2017) Design, development and testing of shape shifting wing model. 4(01) 23. Srinivasa Rao K, Ashok Chakravarty M, Sreedhara Babu G, Rajesh M (2018) Modelling and simulation of aerfoil element. 5(02) 24. Maity R, Mishra R, Patnaik PK, Sain M (2022) Design and analysis of hybrid fixed-wing type flying robot. Wireless Commun Mobile Comput. https://doi.org/10.1155/2022/3978898 25. Dey S, Mishra R, Mohapatra M, Sabut S (2022) Micro active catheters and embolization techniques: a brief review based on design and working efficacy. Biomed Eng Appl Basis and Commun 34(4). https://doi.org/10.4015/S1016237222300012 26. Nalla BT, Devarajan Y, Subbiah G, Sharma DK, Krishnamurthy V, Mishra R (2022) Investigations of combustion, performance, and emission characteristics in a diesel engine fueled with prunus domestica methyl ester and n-butanol blends. Environ Progress and Sustain Energy 41(4). https://doi.org/10.1002/ep.13811

Effect of T6 Heat Treatment on Compressive Strength of Al6082 Reinforced with Multi-walled Carbon Nanotubes Madhusudan Baghel, C. M. Krishna, Anil Chourasiya, and Anurag Namdev

Abstract An adapted stir casting technique was employed to synthesize Al6082 composites embedded with 0, 0.3, 0.6, 0.9, and 1.2 wt.% of MWCNTs. As-cast composites were then heat treated with oil quenching to enhance their resistance against compressive loading. The microstructure of heat-treated nanocomposites was examined using an optical microscope, while energy dispersive X-Ray spectrometer (EDX) was employed to evaluate the presence of elements. Microstructural examination showed the grain refinement with MWCNTs up to 0.9 wt.% and agglomeration with 1.2 wt.% of MWCNTs. Maximum improvement in compressive strength was displayed by Al6082 composites incorporated with 0.9 wt.% of MWCNTs. These nanocomposites showed 80.9% enhancement over as-cast alloy in non-heat-treated condition, while 81.67% enhancement was shown over peak-aged Al alloy in the heat-treated condition. Keywords MWCNTs · Stir casting · Microstructure · Artificial aging · Compressive strength

1 Introduction Nowadays, materials with light weight, high specific stiffness, and strength are highly preferred in many sectors such as automobile and aerospace [1]. This requirement plays a vital part in the emergence of metal matrix composites [2]. Lower density, good electrical conductivity, and high strength at lower weight of aluminum (Al) alloys make them ideal matrix materials [3]. Carbon nanotubes (CNTs) are a M. Baghel (B) · C. M. Krishna · A. Chourasiya Department of Mechanical Engineering, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India e-mail: [email protected] A. Namdev Department of Mechanical Engineering, Institute of Engineering and Technology, Lucknow, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_17

179

180

M. Baghel et al.

promising reinforcement for Al composites owing to their good thermal conductivity, higher strength, higher aspect ratio, and excellent electrical conductivity [4]. CNTs-reinforced Al matrix composites can be produced using solid-state processing and liquid-state processing [5]. Stir casting is the most preferred technique due to the bulk production of complicated designs at low cost. The stir casting process consists of melting of Al alloy in furnace followed by incorporation of preheated CNTs and pouring of melt composite in preheated dies [6]. Fabrication of CNTs/Al composite using stir casting faces some issues like poor distribution of CNTs, anisotropic properties, low wettability, and interfacial reaction [7]. Nonhomogeneous dispersion of CNTs results in clusters formation which deteriorates properties of composites [8], while poor interfacial bonding leads to pull-out of CNTs during load transfer. Therefore, some modifications are required during fabrication to obtain the best properties of CNTs-reinforced Al composites, such as the addition of wetting agent, coating of CNTs with alloying element, and use of ultrasonic stirrer [9]. Compressive strength is one of the essential mechanical properties of materials in resisting large deformation with high stiffness required in the components of automobiles and aerospace sector [10, 11]. Choi et al. [12] used the conventional powder metallurgy technique to fabricate uniaxially aligned CNTs in the Al matrix. Strengthening efficiency of MWCNTs matched well with the discontinuous fibers in the composites having grain sizes of 200 and 72 mm. Macro and nanoscale compressive strength were evaluated by Bakshi et al. [13] in case of CNTs/Al-Si composites. Nanoscale testing showed 17.5% and 27% enhancement in compressive yield strength in case of 5 and 10 wt.% addition of CNTs, respectively. While, macroscale testing showed an enhancement of 27% and 77% for the same content if CNTs. Saheb et al. [14] prepared CNTs/Al6061 composite using ball milling and measured the compressive strength. Agglomeration of CNTs, porosity, and poor wetting between CNTs and Al6061 resulted in decreased ductility and compressive strength. Akbarpour et al. [15] fabricated Al-CNT composites by using flake powder metallurgy. Microstructural characterization revealed that CNTs were homogeneously distributed in the matrix. An enhancement of 24.87% in compressive strength was obtained with addition of 4 vol.% of CNTs due to grain refinement of Al matrix and strengthening effect of CNTs. Most researchers have worked on tensile characteristics of CNTs/Al composites. However, nanocomposites used in the devices also undergo the compression force. For example, fuselage of aircraft undergoes compressive loading during carrier landing, and connecting rod in vehicle also experiences compressive load during combustion. Based on the available literature, it can be summarized that a very less amount of work has been performed in inspecting the compressive behavior of CNTsreinforced Al composites. In this study, Al6082/MWCNTs composites were fabricated using an ultrasonic stirrer-assisted stir casting method, and heat treatment (T6) was also performed on the nanocomposites for compressive strength enhancement.

Effect of T6 Heat Treatment on Compressive Strength of Al6082 …

181

Table 1 Chemical constituents of Al6082 alloy Elements

Mn

Fe

Si

Mg

Zn

Ti

Cu

Cr

Al

Wt. %

0.56

0.26

1.05

0.76

0.018

0.023

0.026

0.032

Balance

Fig. 1 FESEM micrograph of MWCNTs representing tangled CNTs

2 Experimental Work 2.1 Selection of Materials Al6082 was chosen as a matrix material having chemical composition displayed in Table 1. Multi-walled carbon nanotubes (MWCNTs), procured from SRL Pvt. Ltd., were picked as reinforcing particles having diameter of 30–50 nm and length of 10–30 μm. Figure 1 represents the FESEM image of MWCNTs showing tangled CNTs.

2.2 Synthesis of MWCNTs Reinforced Al6082 Composites An ultrasonic stirrer-aided stir casting setup equipped with (Fig. 2a) was employed to fabricate 0, 0.3, 0.6, 0.9, and 1.2 wt.% MWCNTs/Al nanocomposites. Ingots of Al6082 were placed in a graphite crucible which was put inside electrical resistance furnace and temperature was enhanced to 800 °C to melt the ingots completely. A coating of graphite paste was applied on the blades of the stirrer inclined at 45° to prevent direct contact between the stirrer and molten matrix. Dies were also coated

182

M. Baghel et al.

with graphite paste to remove the casting smoothly after solidification. A muffle furnace (Fig. 2b) was used to preheat the MWCNTs powder and dies at 300 °C for 40 min to vaporize any unsought ingredient from the surface of MWCNTs and to bring down the temperature difference between molten composite and dies at the time of pouring. Once the Al ingots are melted, coverall flux is added to remove the dross, and dry nitrogen gas is passed through to circumvent the melt oxidation. For wettability enhancement between MWCNTs and Al melt, 2 wt.% magnesium tablets were also added. Then, Al melt was stirred at 450 rpm by using a mechanical stirrer resulting in the formation of a vortex. Preheated MWCNTs powder was added on the outskirts of the vortex, and stirring was continued for uniform dispersion. To prevent the clustering of MWCNTs, the composite melt was subjected to ultrasonication for 5 min at 20 kHz resulting in the improvement in the distribution of MWCNTs. Again, melt composites were stirred for 5 min and poured in preheated dies. After the solidification, casting was taken out and specimens were prepared for compression test.

Fig. 2 a Setup for stir casting process with ultrasonic stirrer, b muffle furnace, and c specimen for compression test

Effect of T6 Heat Treatment on Compressive Strength of Al6082 … Table 2 T6 heat treatment parameters

Parameters

Description

Solutionizing

495 ± 5 ºC

Quenching medium

Oil

Aging temperature

185 °C

Aging time

3, 6, 9, 12, 15, 18, 21, and 24 h

183

2.3 Heat Treatment of Nanocomposites Al6082 nanocomposites reinforced with MWCNTs were subjected to T6 heat treatment cycle to enhance their compressive strength. Optimization of aging time was performed to determine the peak age condition where highest compressive strength is achieved. During heat treatment, as-cast alloys were subjected to solutionizing for 6 h to dissolute the solute, followed by quenching for supersaturated solution attainment, and then furnace heating for different time durations. Specimens were then air-cooled, and hardness was calculated to detect the peak aged condition. Hardness is improved due to hardening and strengthening of Al matrix resulting in the enhancement of compressive strength. Based on the peak-aged condition, all specimens were heat-treated, and compressive strength was evaluated for each composition. Table 2 shows T6 heat treatment parameters.

2.4 Characterization An optical microscope with an image analyzer (Model 7001-1 MS, Tension, India) was used to examine the microstructure of heat-treated MWCNTs/Al6082 composites in the peak-aged condition. For this purpose, all the specimens were polished, and then Keller’s reagent was used for etching. A hardness testing machine (FH-11) with a diamond indenter was used to measure the hardness of nanocomposites. Specimens were prepared according to ASTM E18 standard, and testing was performed under loading of 100 g for 10 s. ASTM E9 standard (diameter of 13 mm and height of 26 mm) was used to prepare compression test specimens. A universal testing machine was used for compression tests at a rate of 0.0001 s−1 . The average of three values was calculated for each composition. Variation of hardness and compressive strength with addition of MWCNTS was drawn using origin software.

184

M. Baghel et al.

3 Results and Discussion 3.1 Morphological Analysis Morphology of nanocomposites after T6 heat treatment is shown in Fig. 3, along with EDX analysis displaying the alloying elements. Figure 3a shows α-Al phase having lamellar eutectic structure. It can be concluded that inter-dendritic phase covers the solid solution of α-Al phase. Dendritic region contains Si particles, and the α-Al phase is eutectic. Figure 3b–e shows the microstructure of MWCNTs/Al6082 nanocomposites after T6 heat treatment. Al6082 nanocomposites containing MWCNTs of 0.3–0.9 wt.% show grain refinement with the inclusion of MWCNTs. But clustering of MWCNTs is observed for Al6082 nanocomposites embedded with 1.2 wt.% of MWCNTs that deteriorates the strength of nanocomposites. EDX analysis of composites containing 0.6 wt.% of MWCNTs is depicted in Fig. 3f, representing the peaks of constituents present in the composites. Carbon peak represents the presence of MWCNTs, while oxygen peak shows oxide film which is developed during stir casting.

3.2 T6 Heat Treatment Cycle Maximum compressive strength is obtained at peak aged condition that is decided on the basis of optimization. As-cast Al alloy and nanocomposites were first used to determine peak aging time by subjecting them to T6 heat treatment cycle with values as described in Table 2. Micro-hardness is evaluated for all the T6 heat-treated specimens after their respective periods, as shown in Fig. 4. Hardness of Al6082 alloy increases gently to a maximum value of 88.82 HV after 15 h and then decreases with time. While in the case of nanocomposites, a maximum value of hardness is obtained after 12 h and then again decreases. Hence, it can be concluded that peak aging time is 12 and 15 h for nanocomposites and as-cast alloy, respectively. Improvement in hardness of composites is due to advancement in precipitation hardening behavior of Al and retarding in the over-aging activity with the incorporation of MWCNTs which decreases peak aging time.

3.3 Compressive Strength All the as-cast alloy and nanocomposites are heat-treated according to their peakaged conditions. After heat treatment, all specimens undergo the uniaxial compression test. Variation in compressive yield strength (YSc ) of nanocomposites under various conditions is represented in Fig. 5. For as-cast composites, yield strength is observed to be improved with incorporation of MWCNTs. Minimum YSc of 70 MPa

Effect of T6 Heat Treatment on Compressive Strength of Al6082 …

185

Fig. 3 Optical microscopic images of T6 heat treated a Al6082 alloy, nanocomposites containing MWCNTs of b 0.3 wt.%, c 0.6 wt.%, d 0.9 wt.%, e 1.2 wt.%, and f EDX analysis of 0.6 wt.% MWCNTs/Al6082

is for as-cast Al6082 alloy, which is further enhanced with addition of 0.3 wt.% MWCNTs (21.42% improvement). Maximum value of yield strength is obtained in case of 0.9 wt.% MWCNTs/Al6082 composites (57.14% higher than Al6082 alloy). Improvement in compressive strength with an increase in the content of MWCNTs is owing to effective transfer of load from Al to MWCNTs and grain refinement as observed in microstructural examination. Another reason is homogeneous dispersion of MWCNTs which causes strengthening of grain boundaries and results in the hindering of dislocation movement as reported in previous literature [16]. YSc for 1.2 wt.% of MWCNTs decreases to 107 MPa due to agglomeration of MWCNTs as can be seen in Fig. 3e. It may be noted that heat treatment improves the YSc of all the composites. An increment of 35.71% is observed in the case of peak-aged Al6082

186

M. Baghel et al.

Fig. 4 Hardness variation of nanocomposites with aging time

alloy, and highest value of 157 MPa is obtained for Al6082 composites embedded with 0.9 wt.% of MWCNTs. Enhancement in compressive strength is mainly due to the dislocation strengthening after artificial aging and difficulties in shearing the Mg2 Si precipitates. A similar trend is observed in the case of ultimate compressive strength (UCS), as shown in Fig. 6. For non-heat-treated composites, UCS values increase with the inclusion of MWCNTs, and Al6082 composites containing 0.9 wt.% of MWCNTs show highest value of 521 MPa (80.9% higher than Al alloy). UCS of nanocomposites is observed to be improved with heat treatment, and it is better than the results obtained by Morsi et al. [17]. UCS of peak-aged Al6082 alloy is 322 MPa, and incorporation of MWCNTs up to 0.9 wt.% enhances UCS to maximum value of 585 MPa. Addition of 1.2 wt.% MWCNTs causes agglomeration which decreases the UCS. Figure 7 shows the specimens after the compression test representing fracture along with the height.

4 Conclusions Ultrasonic probe agitator-aided stir casting technique was utilized for the synthesis of different amounts of MWCNTs reinforced Al6082 nanocomposites. Effect of artificial aging on compressive strength of MWCNTs/Al6082 composites was observed. The following conclusions are extracted: 1. Addition of Mg and employment of ultrasonic probe stirrer improved the wettability and dispersion of MWCNTs in Al matrix. 2. Distribution of MWCNTs was uniform up to 0.9 wt.% addition, but clustering was seen in the case of 1.2 wt.%.

Effect of T6 Heat Treatment on Compressive Strength of Al6082 …

Fig. 5 Effect of heat treatment on compressive yield strength

Fig. 6 Effect of heat treatment on ultimate compressive strength

187

188

M. Baghel et al.

Fig. 7 Compression test specimens after testing

3. Peak aging time was 15 h for Al6082 alloy, while for nanocomposites, it was 12 h owing to precipitation hardening acceleration. 4. For as-cast composites, maximum increment of 57.14% and 80.9% in YSc and UCS, respectively, were shown by Al6082 composites containing 0.9 wt.% of MWCNTs over as-cast alloy. 5. In case of artificially aged composites, a maximum increment of 65.26% and 81.67% in YSc and UCS, respectively, was displayed by Al6082 composites embedded with 0.9 wt.% of MWCNTs over peak-aged Al6082 alloy.

References 1. Fereiduni E, Ghasemi A, Elbestawi M (2020) Selective laser melting of aluminum and titanium matrix composites: recent progress and potential applications in the aerospace industry. Aerospace 7 2. Miracle DB (2005) Metal matrix composites - From science to technological significance. Compos Sci Technol 65:2526–2540 3. Rohatgi P (1991) Cast aluminum-matrix composites for automotive applications. Jom 43:10–15 4. Behabtu N, Young CC, Tsentalovich DE (2013) Strong, light, multifunctional fibers of carbon nanotubes with ultrahigh conductivity. Science 80(339):182–186 5. Baghel M, Krishna CM (2021) Development of CNTs reinforced metal matrix composites and their mechanical and tribological behaviour: a review. IJARET 12:658–668 6. Baghel M, Krishna CM, Suresh S (2022) Development of Al-SiC composite material from rice husk and its parametric assessment. Mater Res Express 9 7. Esawi AMK, Farag MM (2007) Carbon nanotube reinforced composites: potential and current challenges. Mater Des 28:2394–2401 8. Liao J, Tan MJ (2011) Mixing of carbon nanotubes (CNTs) and aluminum powder for powder metallurgy use. Powder Technol 208:42–48 9. Baghel M, Krishna CM (2022) Synthesis and characterization of MWCNTs/Al6082 nanocomposites through ultrasonic assisted stir casting technique. Part Sci Technol 0:1–13 10. Silvestre N, Faria B, Canongia Lopes JN (2014) Compressive behavior of CNT-reinforced aluminum composites using molecular dynamics. Compos Sci Technol 90:16–24 11. Noguchi T, Magario A, Fukazawa S (2004) Carbon nanotube/aluminium composites with uniform dispersion. Mater Trans 45:602–604

Effect of T6 Heat Treatment on Compressive Strength of Al6082 …

189

12. Choi HJ, Kwon GB, Lee GY, Bae DH (2008) Reinforcement with carbon nanotubes in aluminum matrix composites. Scr Mater 59:360–363 13. Bakshi SR, Keshri AK, Agarwal A (2011) A comparison of mechanical and wear properties of plasma sprayed carbon nanotube reinforced aluminum composites at nano and macro scale. Mater Sci Eng A 528:3375–3384 14. Saheb N (2013) Compressive behavior of spark plasma sintered CNT reinforced AL2124 and AL6061 nanocomposites. Adv Mater Res 652–654:33–37 15. Akbarpour MR, Pouresmaeil A (2018) The influence of CNTs on the microstructure and strength of Al-CNT composites produced by flake powder metallurgy and hot pressing method. Diam Relat Mater 88:6–11 16. Mokdad F, Chen DL, Liu ZY (2016) Deformation and strengthening mechanisms of a carbon nanotube reinforced aluminum composite. Carbon N Y 104:64–77 17. Morsi K, Esawi AMK, Lanka S (2010) Spark plasma extrusion (SPE) of ball-milled aluminum and carbon nanotube reinforced aluminum composite powders. Compos Part A Appl Sci Manuf 41:322–326

Application of Digital Image Processing on Machined Surfaces: A Review Saurabh Jain , M. K. Pradhan , and Amit Kumar

Abstract Image processing has been shown to be a useful tool for analysis in a variety of domains and applications. Many machining parameters, such as machining time and cutting speed, side cutting edge angle, true rake angle, depth of cut, nose radius, feed rate, and so on, influence to machined surfaces. Analysis of machined surfaces aids in forecasting whether a component will succeed or fail when placed into operation. This study proposes to focus on a review of image processing applications. Keywords Image processing · Machined surfaces · Vision system

1 Introduction Computer algorithms are used to process images in digital image processing technique. It has several benefits over the analogue image processing technique. Digital image processing offers a a variety of algorithms that may be used to the supplied data. We can prevent some processing issues in digital image processing, such as noise generation and signal distortion. For a machining surface, it affects with different parameters like power, cutting force, surface finish, temperature, sound energy, current, vibrations, etc., which are affected by the machining conditions and the cutting tool’s structure [1]. By the DIP technique, the characterisation of the machined surfaces can be classified [2]. Figure 1 shows the stages of digital image processing for analysing the image in which the machined surface comes after the machining operation, which is clicked with the help of a CCD camera with an effective lighting environment. After that, texture analysis and feature extraction are done with a computer programme that could be based on different algorithms and different methods. S. Jain (B) · A. Kumar Maulana Azad National Institute of Technology, Bhopal, India e-mail: [email protected] M. K. Pradhan Department of Mechanical Engineering, National Institute of Technology, Raipur, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_18

191

192

S. Jain et al.

Fig. 1 Flow diagram of stages in digital image processing

The pattern on the surface is recognised that we can collect in a numerical data for further processing.

2 Image Processing Application • • • • • •

System for computerised visual inspection Interpreting scenes using remote sensing Techniques for biomedical imaging Defence monitoring Retrieval of evidenced images Object tracing for movements.

Application of Digital Image Processing on Machined Surfaces: A Review

193

3 Methodology • Preparation of specimens by machining processes • Vision system installation, CCD camera, advance image processing computers, scattering light • Software development, algorithm for calculating characteristics of machined surfaces • Experimentation • Result and analysis.

4 Review Literature The research work is carried out in various DIP techniques which applied of machining surfaces for identification of applications of DIP in this field, as experienced by various researchers (Table 1). Liu et al. [3] establish a novel roughness measuring technique on the basis of a colour distribution statistical matrix and regression correlation approach. They had a great measurement range, showed a fair amount of precision, and were, to some extent, unaffected by the texture’s orientation or the light source’s intensity. When the texture created on the image is good and has a bright light source, the newly found CDSM technique accepts it. Jeyapoovan and Murugan [4] done classification of roughness value with image processing by finding the Ra value using distance-based classification with Euclidean and Hamming distance as input. Using MATLAB software, a signal vector was created from the intensity of an image’s pixels. It was discovered that when the pixel intensity of the image is good, Euclidean and Hamming distances may assess surface roughness. Ali et al. [5] have done surface roughness characterisation based on vision system, of EDMed surfaces, utilising the MATLAB application and the regression correlation approach to estimate the Rda and Rdq using 2D images, images of EDMed surfaces were taken using red LED light source and CMOS camera. From the picture pixel intensity matrices, a unique signal vector was produced for each of the photos. The signal vector was used to determine the mean, skewness, and kurtosis. Ali et al. [6] have carried out evaluation of roughness on EDMed surfaces with the help of image processing by 2D wavelet transformed method, and the MATLAB toolbox was used for processing. The mean and SD of the 4th and 5th level detailed coefficients used as input to calculate Ra, Rda, and Rdq. The link between the parameter of image SD and the parameters Rda, Rdq, and Ra from stylus was obviously linear. Shivanna et al. [2] estimate parameters of 3D surface roughness for EDM component with vision system under which EDMed surface is clicked with the help of CCD

194

S. Jain et al.

Table 1 Literature review Name of Researcher

Lightning system

Method used

Type of measurement/machining

Remarks

Al-kindi and Shirinzadeh [15]

Natural lightning

Extraction of optical surface roughness characteristics from 1st order statistics and face milling, comparison of two lighting models, Ra prediction and intensity-topography compatibility, and light diffused model.

Ra pridiction, face milling

Surfaces that were rather rough were examined utilising the suggested ITC approach.

Akbari et al. [7]

Scattered pattern illumination

First order statistical Ra prediction, texture analysis using Milling histograms (four descriptors) & MLPNN

Devillez et al. [27]

White light WLI by varying and interferometer (WLI) automatic focussing

Dhanasekar and Ramamoorthy [28]

White illumination system

High resolution Grinding (Ra picture reconstruction prediction), milling using POCS, histogram-based texture analysis, frequency domain

The stylus Ra value and vision roughness value have a very strong association

Gadelmawla [8, 9]

Microscope

Study the effect of pps and GLCM

Milling, face turning, reverse engineering to correlate cutting conditions with Ra

No improvement to the pps value, the SD increases by increasing the surface roughness

Elango and Karunamoorthy [12]

Diffused light coming ANOVA and from various grazing Taguchi’s orthogonal angles array

Ra prediction, face turning

ANOVA and the S/N ratio method provide comparable results

Dawson and Kurfess [29]

WLI

Measurement of tool volume reduction from the union of a surface profile & a CAD model

Depth measurement of crater

Grooved inserts that are challenging to measure

Kiran et al. [19]

Phase shifting via grating projection, light scattering, and light sectioning

Three illumination Milling, griding, methods that are shaping, Ra frame averaging, 2nd prediction order co-occurrence statistics, and frame averaging were compared for smooth, medium rough, and rough images

Depth measurement of crater

ANN to accurately establish the relationship between actual Ra and 3D texture features effectively estimate Ra Grooved inserts that are challenging to measure

Comparison mostly between three different lighting systems

(continued)

Application of Digital Image Processing on Machined Surfaces: A Review

195

Table 1 (continued) Name of Researcher

Lightning system

Method used

Type of measurement/machining

Remarks

Khalifa et al. [14]

No light used

Magnification, statistical texture analysis (1st & 2nd order), Edge enhancement, GLCM

Chatter vibration detection in turning, calculation of Ra value

Separating a chatter-rich process from a chatter-free process using surface pictures

Tarng and Lee [20]

2 light sources placed Polynomial network at a sharp angle to the with self-organised workpiece’s axis adaptive learning (v, f, d Ga as input and Ra as output)

Nakao [22]

Optical fibre light

Lee et al. [21]

A scattered blue light Two frequency in inclination of 45’ domain parameters, grayscale standard deviation, and an abductive network (with three texture descriptors as input and Ra as the output)

Ra prediction, turning The MAE between the Ra value measured by the vision system and that measured by the stylus instrument is less than 11.32%, according to many verification tests on S55C steel. Effective application of developed computer vision is possible

Palani and Natarajan [11]

No light used

BPNN, spatial domain, and frequency-based texture analysis

Ra prediction, end milling

A self-organised ANN to simulate a Ra inspection vision measurement system has been created

Tsai et al. [25]

Fluorescent illumination system

ANN and Fourier analysis

Ra prediction, milling, shaping

The surface picture is described by the FT method in terms of frequency components

Zhang et al. [26]

No light used

Shape features, Laws filter, SVM with RBFNN kerne, Gabor filter, DCT, GLCM

Defect categorization and identification in polished and ground surfaces

82% success rate utilising the Gabor filter and GLCM in conjunction with SVM

Ra prediction, turning Even under diverse turning procedures, the Ra value of a turned component may be predicted with some degree of accuracy if the image of the turned surface and the turning circumstances

Component labelling, Measurement of drill thresholding burr

There is 2% & 3% error in measurement of burr height and thickness

196

S. Jain et al.

camera & a DIP algorithm is develop using MATLAB. By that effective correlation is obtained between roughness by the vision system & optical roughness parameters. Akbari et al. [7] used four texture descriptors to a MLPNN as inputs: the AM, SD, average Ra value, and RMS Ra value, in order to evaluate the roughness of milled surfaces. Although the complete surface area has been assessed to provide a more precise estimate, they have not given any quantitative error estimates with reference to the surface roughness based on stylus. Gadelmawla et al. [8] created software, named reverse engineering for identification & estimation of the machining conditions, f, d, and v from the images of machined surfaces with the texture descriptors by GLCM. But, none of the GLCM settings have been tuned. Additionally, they have only tested this approach with images of machined surfaces. Gadelmawla et al. [9] performed an autonomous surface assessment by computing the GLCM of ten different types of machined surface pictures with changing orientation, parameters, and distance. Previously, they extracted 4 features from the GLCMs and found that, with the exception of the GLCM’s SD, the majority of the features had differing sensitivity to rough (turned) and smooth lapping surfaces. Jian and jin [10] developed a quick online approach for analysing surface texture to categorise images of machined surface with straight feed marks. After initially binarizing the images, they convert all of the pixels in to 1 which are along in a vertical line, when the fraction of single valued pixels in that vertical line is greater than half of all the pixels on that line. The width between two succeeding white lines was measured, and the average of all texture widths in the image was calculated. According to them, this texture’s breadth was a roughness descriptor. But their approach is rather sloppy and less precise. Palani and Natarajan [11] predict the surface roughness value with online method utilising end milling operation with input parameters such as d, f, v, main peak frequency, principal component magnitude square, and Ra. The difference between surface roughness that is based on stylus and the projected surface roughness was 2.47%. However, progressive wear monitoring can also be done using their method. Elgo and Karunamoorthy [12] utilised Taguchi’s L9 ANOVA and orthogonal array to create an experiment that examined the variation in diffused light coming from various grazing angles on a face-turning specimen spun at various striation angles. They discovered that an ideal grazing angle of 75◦ and a striation inclination of 90◦ were necessary to get correct Ga using the Ga value as a texture descriptor & discovered that an ideal grazing angle of 75◦ and a striation inclination of 90◦ were necessary to get correct Ga. However, this technique may be used effectively for purposes such as monitoring increasing wear. Kashiwagi et al. [13] used the image histogram and cross-correlation approach to capture images of the cutting dust surfaces and measure the width of the imprinted line. They noticed that the width was reducing as the cutting time or tool sharpness increased. Khalifa et al. [14] employed statistical and textural analysis, edge enhancement, and magnification in order to find chatter in turned surface images. Utilising a composite Laplacian filter, the images were improved. Then, using filtered images, Ga, the histogram’s mean, SD, and variance were assessed. The GLCM analysis of improved

Application of Digital Image Processing on Machined Surfaces: A Review

197

images was then carried out. In order to distinguish between images with chatter and those without it entropy(S), inertia(I) and energy characteristics were retrieved from the GLCM. In that, he calculated chatter vibration value by vision system and Ra value. Kindi et al. [15]developed the intensity-topography compatibility (ITC) approach to characterise image data by taking three aspects into account: lightning, reflectance, and surface characteristics. They extracted values for all surface roughness parameters from the grey level histogram, including average roughness, maximum peak height, RMS roughness, maximum peak to valley, skewness, and kurtosis. However, utilising their technique, no research on the link between wear and Ra value has been conducted. Kim et al. [16] designed a magnetic jig to hold the camera and lighting system in order to measure flank wear for a 4-fluted end mill using a machine tool. They looked at the signal-to-noise ratio of measurements taken using a novel jig equipped with a microscope and CCD camera. For additional enhancement, they installed a fibre optic directed lighting system in the lens. However, the measurement system rather than the image processing method is more susceptible in this work. Kindi et al. [17] looked at how a digital imaging system may be used to evaluate surface quality. Ra value was calculated using the distance between grey level peaks and the number of grey level peaks per unit length of a scanned line in the grey level image. This one dimensional-based method does not completely utilise the two dimensional information of the surface image and is sensitive to the choice of lay direction, light, and noise. Cuthbert and Huynh [18] increased the complexity of the research by doing a statistical texture analysis on the optical Fourier transform pattern produced on the ground surface images. The standard deviation, mean, kurtosis, skewness, and RMS height of the image’s grey level histogram were then calculated. This approach has two shortcomings. Only surfaces with an average surface roughness of 0.4 mm could be investigated due to the tendency of rougher surfaces to create a diffused pattern in the camera. It was difficult to utilise in online inspection since the imaging lenses required to be precisely and intricately aligned. Kiran et al. [19] had established certain characteristics for evaluating the roughness of images of ground, shaped, and milled surfaces using the grey level intensity histogram, but they had not correlated those characteristics with tool wear or profilerbased surface roughness. Lee and Tarng [20] used both frequency and spatial domain features of machined surface images as inputs to an abductive network to predict the value of surface roughness without taking into account the machining circumstances. They used the frequency coordinates, the greatest eigenvalue of the covariance matrix of the standardised power spectrum, and the standard deviation of the grey level as inputs to the abductive network. They considered the spatial and frequency domain characteristics of the image texture in their investigation. Lee et al. [21] used ANFIS to enhance their model for predicting surface roughness using picture texture variables such as standard deviation of grey level, arithmetic mean grey level, and spatial frequency. Compared to the polynomial network approach, they were able to obtain a surface roughness difference between antici-

198

S. Jain et al.

pated and measured of no more than 8%. Their findings also show that inaccuracy decreases with increasing surface roughness values. These techniques, however, have only been used in turning operations with a single workpiece material and cutting tool combination. Additionally, monitoring of progressive wear has not been done. Nakao [22] took pictures of the drilling burrs and processed them to keep track of the drilling operation. Here, picture data was measured using traditional image processing methods such noise reduction, labelling, and binary image processing. Here, using coordinate data, the processed image was used to measure the burr height and thickness. Yoon and Chung [23] measured the burr width and hole quality in micro-drilled holes using an edge detection method. Additionally, they used the “shape from focus” (SFF) approach to assess burr height. To provide consistent illumination, a halogen lamp was utilised as the front light and an LED as the backlight in this instance. Persson [24] developed a non-contact approach to detect RA value of surface by combining the angular speckle correlation methodology. A speckle pattern produced by a coherent He-Ne laser on a machined surface and photographed at various illumination angles. The computed correlation between the collected speckle patterns at various lighting angles revealed that rougher surfaces exhibited lower correlation values. For rougher surfaces, a lower correlation value has been noted. Although this method may be used to assess surface roughness whilst it is being manufactured, the precision of this approach is constrained by the setup’s correct angular orientation. However, this restriction may be removed by measuring the tilt of the setup using a laser interferometric approach. Tsai et al. [25] attempted to create a homogeneously lighted image of a machined surface using a standard fluorescent illumnination system that was located at an incidence angle of around 10◦ regards to the specimen’s surface normal. In order to capture images in the direction of the light, the camera was additionally positioned at 10◦ angle with regard to the normal of the specimen’s surface. But this setup could only be appropriate for specimens with flat surfaces, not curved ones. Zhang et al. [26] extracted the best features from Laws filter bank, discrete cosine transform (DCT), GLCM, and Gabor filter bank to create an accurate defect detection and classification system. For the aim of classification, they utilised RBFNN and support vector machines (SVMs). With the use of the Gabor filter, GLCM, and SVM in combination, they achieved an 82 per cent success rate. Devillez et al. [27] used the white light interferometry (WLI) method to quantify the depth of crater wear and identify the ideal cutting parameters (v, f) in orthogonal dry turning of 42CrMo4 steel with an uncoated carbide insert to get the best surface finish. A vertical scanning process has been used in the white light interferometry technique to obtain the optimal focus locations for each and every point present in the object to be measured. Due to the fact that this technology is offline, measuring the crater depth of grooved inserts or inserts with chip breakers is particularly challenging. Dhanasekar and Ramamoorthy [28] pre-processed the machined surface images of the ground, milled, and shaped components before applying a geometric search approach for edge recognition. The surface roughness value measured by the stylus-

Application of Digital Image Processing on Machined Surfaces: A Review

199

based surface profiler and the vision-based method has shown to be highly correlated. They did not investigate the relationship between increasing tool wear and visionbased surface roughness. In this literature, different types of machined and non-machined surfaces undergo digital image processing on that various machining parameters, scattering lighting effects, and surface texture are considered. Many applications of digital image processing are found in this literature for machining surfaces, under which they calculate surface roughness, surface texture, micro-cracks, depth measurement, burr measurement, and defect detection on polished surfaces by different methods such as ANN, GLCM, BPNN, SD of grey level, cubic convolution interpolation, and Taguchi’s orthogonal array. In that study, researchers used scattering light as ambient light [7, 15], white light [27–29], microscope [8], diffused light [12, 19, 20], fluorescent light [25], fibre optic light [22], and machining operations as grinding, milling, shaping [7, 19, 25, 28, 30], face milling, face turning [8, 12, 15], turning [14, 20, 21], drilling [22], and EDM process [2, 5, 6] is used for the machined surfaces.

5 Conclusion The use of image processing technologies for surfaces that have been machined is explored in this study. The automation capabilities of unmanned machining centres can be improved with the use of image processing techniques. The automatic identification of numerous surface defects (such as material fracture, micro-cracks, roughness, burrs, etc.) that are highly challenging to identify by other modalities may be done quickly and easily using digital image processing techniques. A robust system (including lights, a camera, and a quicker algorithm) should be developed for real-time machined surface monitoring techniques in the future. The following are some established findings from the review: 1. This method is suitable for characterisation of any machined surfaces online without making contact. 2. There is no need to navigate on uneven surfaces. 3. The measurement happens at a rapid rate. 4. There is not any further surface deterioration.

References 1. Dutta S, Pal S, Mukhopadhyay S, Sen R (2013) Application of digital image processing in tool condition monitoring: a review. CIRP J Manuf Sci Technol 6(3):212–232 2. Shivanna D, Kiran M, Kavitha S (2014) Evaluation of 3d surface roughness parameters of EDM components using vision system. Procedia Mater Sci 5:2132–2141 3. Liu J, Lu E, Yi H, Wang M, Ao P (2017) A new surface roughness measurement method based on a color distribution statistical matrix. Measurement 103:165–178

200

S. Jain et al.

4. Jeyapoovan T, Murugan M (2013) Surface roughness classification using image processing. Measurement 46(7):2065–2072 5. Ali M, Jailani S, Mariappan M, Anandan M, Pavithran V (2019) Vision based surface roughness characterization of flat surfaces machined with edm, tech. rep 6. Ali JM, Jailani H, Murugan M (2022) In-situ surface roughness evaluation of surfaces machined with EDM by image processing. J Nondestr Eval 41(2):1–11 7. Akbari AA, Fard AM, Chegini AG (2006) An effective image based surface roughness estimation approach using neural network. In: 2006 world automation congress. IEEE, pp 1–6 8. Gadelmawla E (2004) A vision system for surface roughness characterization using the gray level co-occurrence matrix. NDT & e International 37(7):577–588 9. Gadelmawla E (2011) Estimation of surface roughness for turning operations using image texture features. Proc Inst Mech Eng, Part B: J Eng Manuf 225(8):1281–1292 10. Jian Z, Jin Z (2010) Surface roughness measure based on average texture cycle. In: 2010 second international conference on intelligent human-machine systems and cybernetics, vol 2. IEEE, pp 298–302 11. Palani S, Natarajan U (2011) Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2d Fourier transform. Int J Adv Manuf Technol 54(9):1033–1042 12. Elango V, Karunamoorthy L (2008) Effect of lighting conditions in the study of surface roughness by machine vision-an experimental design approach. Int J Adv Manuf Technol 37(1):92– 103 13. Kashiwagi H, Nagayama Y, Shibuta K, Watanabe H, Oka H (2003) “Estimation of tool wear by use of image processing of cutting dust. In: SICE 2003 annual conference (IEEE Cat (03TH8734)), vol 2, IEEE, pp 1723–1726 14. Khalifa OO, Densibali A, Faris W (2006) Image processing for chatter identification in machining processes. Int J Adv Manuf Technol 31(5):443–449 15. Al-Kindi GA, Shirinzadeh B (2007) An evaluation of surface roughness parameters measurement using vision-based data. Int J Mach Tools Manuf 47(3–4):697–708 16. Kim J-H, Moon D-K, Lee D-W, Kim J-S, Kang M-C, Kim KH (2002) Tool wear measuring technique on the machine using CCD and exclusive jig. J Mater Process Technol 130:668–674 17. Al-Kindi G, Baul R, Gill K (1992) An application of machine vision in the automated inspection of engineering surfaces. Int J Prod Res 30(2):241–253 18. Cuthbert L, Huynh V (1992) Statistical analysis of optical Fourier transform patterns for surface texture assessment. Meas Sci Technol 3(8):740 19. Kiran M, Ramamoorthy B, Radhakrishnan V (1998) Evaluation of surface roughness by vision system. Int J Mach Tools Manuf 38(5–6):685–690 20. Lee B, Tarng Y (2001) Surface roughness inspection by computer vision in turning operations. Int J Mach Tools Manuf 41(9):1251–1263 21. Lee B, Juan H, Yu S (2002) A study of computer vision for measuring surface roughness in the turning process. Int J Adv Manuf Technol 19(4):295–301 22. Nakao Y (2001) Measurement of drilling burr by image processing technique. Kanagawa University, Japan 23. Yoon H-S, Chung S-C (2004) Machine vision inspection system of micro-drilling processes on the machine tool. Trans Korean Soc Mech Eng A 28(6):867–875 24. Persson U (2006) Surface roughness measurement on machined surfaces using angular speckle correlation. J Mater Process Technol 180(1–3):233–238 25. Tsai D-M, Chen J-J, Chen J-F (1998) A vision system for surface roughness assessment using neural networks. Int J Adv Manuf Technol 14(6):412–422 26. Zhang X, Krewet C, Kuhlenkötter B (2006) Automatic classification of defects on the product surface in grinding and polishing. Int J Mach Tools Manuf 46(1):59–69 27. Devillez A, Lesko S, Mozer W (2004) Cutting tool crater wear measurement with white light interferometry. Wear 256(1–2):56–65 28. Dhanasekar B, Ramamoorthy B (2010) Restoration of blurred images for surface roughness evaluation using machine vision. Tribol Int 43(1–2):268–276

Application of Digital Image Processing on Machined Surfaces: A Review

201

29. Dawson TG, Kurfess TR (2005) Quantification of tool wear using white light interferometry and three-dimensional computational metrology. Int J Mach Tools Manuf 45(4–5):591–596 30. Kumar J, Soota T, Rajput S, Saxena KK (2021) Machining and optimization of zircaloy-2 using different tool electrodes. Mater Manuf Process 36(13):1513–1523

Arm Fracture Detection Using Deep Convolution Neural Network Gorav Kumar Malik, Ashish Nainwal, Amrish, Vishwanath Bijalwan, and Vijay Bhaskar Semwal

Abstract Emergency medical professionals who lack subspecific musculoskeletal knowledge view emergency X-rays often out of need and wrong fractures represent approximately four of every five diagnostic mistakes in certain EDs documented.The experts use the X-ray picture to analyse the broken arm. The detection of manual cracks is repeated, and the risk of error is high. A possible solution to this problem is computer-based detection systems, which can provide clinicians with a second, confident opinion quickly. This paper presents an approach for detecting arm bone fractures based on data normalisation and CNN classification in recent advances in deep learning and the artificial intelligence subfield. The proposed CNN also examines positive or negative expectations of arm cracks, and proposed scheme is capable of achieving an average accuracy of 76.15%. The classification is carried out by the convolution neural network (CNN). Keywords X-ray · Convolution neural network · Bone fracture

G. K. Malik (B) · A. Nainwal · Amrish Department of ECE, FET Gurukul Kangri (Deemed to Be)University, Haridwar, Uttarakhand, India e-mail: [email protected] A. Nainwal e-mail: [email protected] Amrish e-mail: [email protected] V. Bijalwan Department of ECE, Institute of Technology Gopeshwar, Chamoli, Uttarakhand, India e-mail: [email protected] V. B. Semwal Department of CSE, MANIT, Bhopal, Madhya Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_19

203

204

G. K. Malik et al.

1 Introduction Every year, over 1.7 billion individuals are impacted by musculoskeletal disorders, which lead to substantial and long-term pain and impairment [1]. CT scanning is allowing doctors to make better diagnoses and provide better treatments for musculoskeletal problems.The number of outstanding radiologists, however, is far less than the number of patients who are above average. Even though the duty load of radiologist is excessive, they cannot handle it. This puts them at a significant disadvantage because of all the medical imaging data. This, in turn, has necessitated the rapid implementation of assistance technology for radiologists in this area.The major goal of medical image processing is to extract the information that is clinically relevant from the picture. Advances have been made in the area of medical imaging, not only in acquiring medical pictures but also in the methodology and skill used in interpreting them. Fracture is the most common bone ailment amongst humans. Injuries are just fractures that develop because of accidents. Bone fractures may be distinguished according to their type, with typical, transverse, comminuted, oblique, spiral, segmented, avulsed, impacted, torus, and greenstick fractures all falling into different groups. Rapid and accurate diagnosis is an exceptionally active area of study where computer technologies are created to help quickly and precisely identify a patient’s condition. In the early 1970s, CAD applications in the medical sector began when a decision tree analysis was used [2, 3]. CAD algorithms have demonstrated potential to assist radiologists in clinical settings, thanks to the introduction of deep learning techniques [4]. For the time being, the deep learning technique has been used for the identification of lung nodules and wrist fractures. Currently, for bone fracture detection, there is no evidence of benefit. Ryder [5] observed a bone fracture by monitoring to the echoes from the sound waves which travelled through bone. According to the work of Raghavendra et al. [6], a new CNN-based classification model has been created to assist in the diagnostic process of thoracolumbar fractures. In order to investigate automated categorization of osteoporotic vertebral fractures, a CNN trained on posteroanterior and lateral radiographs was used by Ebsim et al. [7] to identify wrist fractures. The article, by England et al. [8], utilised deep convolutional neural networks (CNN) to find traumatic paediatric elbow joint effusion. In this study, researchers Urakawa et al. [9] developed a computer programme called VGG-16 to evaluate an anterior-view radiograph of the proximal femurs. VGG-16 was then used to identify intertrochanteric hip fractures. The algorithm achieved an accuracy of 95.5%. Tomita et al. [10] combined a deep residual network (ResNet) with a long short-term memory (LSTM) network.To train a basic binary classification model, Rajpurkar et al. [11] used MURA, a large dataset of musculoskeletal radiographs that included 40,895 radiographs, and used a 169-layer DenseNet for the model. Inception-v3 is used by Badgeley et al. [12] to predict hip fracture, which include confounders (patient and healthcare). To identify femoral neck fractures in X-ray, researchers used AlexNet and GoogLeNet, which had an accuracy of 94.4%. Kim et al.[13] used lateral wrist radiographs to create a novel case prediction model

Arm Fracture Detection Using Deep Convolution Neural Network

205

that incorporates the concepts of Inception-v3. For the most part, the research that we found used machine learning models to classification tasks rather than detection tasks, such as identifying whether a radio graph is fractured or non-fractured. In this work, the X-ray picture is denoise, and the texture, shape, and frequency features are extracted. These features are used on a convolution neural network to categorise the picture as arm fracture or no fracture images.

2 Method and Material 2.1 Proposed Methodology The proposed method based on advanced CNN of eight layers and the overview of the method is illustrated in Fig. 1. First, the X-ray images are pre-processed for equal

Fig. 1 Methodology of proposed work.

206

G. K. Malik et al.

Table 1 Details of each layer of CNN Model Layer Layer name No. of output 1 2 3 4 5 6 7 8

Convolution Max-pooling Convolution Max-pooling Convolution Max-pooling Flatten Fully-connected

16 × 20 8 × 10 8 × 10 4×5 4×5 4X5 40 2

Activation function

Filter size Stride

ReLU ReLU ReLU ReLU ReLU ReLU – Softmax

5 2 3 2 3 2 – –

1 2 1 2 1 2 – –

sizes, and images have features such as variation of intensity, low contrast, and high noise rates that cause interpretation and classification challenges. The quality of these images should be improved in the initial stage of pre-processing through a combined method for contrast improvement and noise reduction. Some threshold techniques are subsequently used to remove bone picture noises. Pre-processes play a key role when extracting the features correctly and accurately. By proper pre-processing of X-ray pictures, the classification step can also be simplified. 21 * 21 masks are used for noise removal and contrast improvement. Second, we used a novel CNN classifier for extraction of features and classification. CNN network is designed to extract features from pre-processed image, and there are total three different types features extracted: textures, shapes, and frequency features. We designed three layer deep learning algorithm to detect and locate fractures in arm X-ray images and then trained it on labelled data set. To assess whether the trained model can help clinicians improving fracture detection in emergency medicine or not, classifier used as binary classifiers where either “fracture present” or “fracture absent” is classified. A standard 3-layered neural network during experimentation used features as an input for training and testing for designed CNN architecture. The classifier is trained with various data sets to train the classification processes. The precision of the individual classifiers is assessed. Table 1 demonstrates the classification accuracy performance.

2.2 Database Our studies make use of the MURA dataset [11], which is provided by the Stanford Machine Learning Group. The multi-view radiography anatomy (MURA) project, which is a multi-volume data collection including 40,895 multi-view radio graphic pictures of the upper extremity, includes most of the radio graphic images of the shoulder, elbow, forearm, wrist, hand, and fingers. Every picture is saved as an 8-bit png. To draw inferences, we conducted an experiment that consisted of selecting all

Arm Fracture Detection Using Deep Convolution Neural Network

207

3392 pictures that included an image of the humerus, elbow, and forearm as training data, and 612 pictures as test data. Training models of detection should be immune to negative pictures. The 612-image test dataset was divided by MURA previously. The training dataset and the test dataset are not equivalent.

2.3 Image Pre-processing We face two difficulties with the X-ray pictures in MURA’s original dataset. One is the noise, and the other is the dark backdrop of pictures. For these two problems, we thus need to carry out picture preparation. We analyse the pictures using a morphological approach to minimise the impact of noise. For processing a greyscale picture, a morphological procedure with a kernel of 21 × 21 is used. The opening procedure may remove the isolated noise in a picture and, in the meantime, identify the primary region. This refers to the region including all bones and fractures in the major area of the picture. We utilise the cumulative distribution of the normal distribution to conduct the grey stretch of the original image to enhance the brightness of the picture. Here, the major area pixel variance is considered to be the variation of normal dispensing. Since the region with fractures is frequently the brightest in the main area, we choose as an average normal distribution the highest pixel value of the main area, making the transformation sensitive to the area with a fracture. The contrast between the whole picture may be enhanced through the two procedures, and the fracture region becomes sharper and brighter in the converted photographs.The output of pre-proceesed image is shown in Fig. 2.

Fig. 2 Pre-processing of image a original image of MURA data set, b noise removed image, c filtered image by 21 × 21 mask, d extracted feature of image.

208

G. K. Malik et al.

2.4 Feature Extraction The feature descriptors are the main part of the computerised clinical picture investigation, and the motivation behind extracting the features is to measure and give pivotal data to the classifiers. This distinguishes a fractured arm from a non-fractured arm. The two-dimensional image is converted to a feature vector in this step and used as input for a data mining classifier. Here three different kind of features are used for classification. The Texture Features: Texture characteristics of the bone are extracted, such as orientation of bone and shape. Texture characteristics of the edges are used different morphological values that are provided as inputs to trainees in the neural network and help to detect fractures in the picture. Such features such as contrast, energy, entropy, standard deviation, mean, and variance are extracted by grey level co-occurrence matrix (GLCM), correlation, Gabor orientation (GO) [14], Markov random field (MRF) [15], and intensity gradient direction (IGD). The Shape Features: For extracting shape-based features, the region of interest in an arm fractured image and statistics-based features were used. Statistics such as perimeter, region centroid coordinates, axis length, minor axis lengths, and orientation are some of the features in shape-based extraction obtained through rapid-hough transformation [16]. Frequency Features: DWT was widely used as a rapid algorithm for obtaining wavelet transformations of medical X-ray images [17]. The DWT analyses these images by breaking them down into gross approximations and details representing lowand high-frequency images. The approximation can be further calculated to give the approximation and details at the next degradation level, until the required level is reached. And the fourth-level D-4 approximation coefficient represents the low image frequency. The mean and variance values are calculated from each factor; following the DWT on bone fractured images, a two-dimensional matrix is arranged for collected features with every column representing an extracted features and the row showing different features representing X-ray image. The final column is tell about its a arm fractured images or not by column with a Boolean value of 0 and 1.

2.5 Convolution Neural Network We build a CNN model with just eight basic layers, including three convolutional layers, three pooling layers, a fully-connected layer, and one functional layer (the flatten layer). Figure 3 shows the structure of CNN model. The convolutional function is computed as follows in the convolution layer:

Arm Fracture Detection Using Deep Convolution Neural Network

209

Fig. 3 CNN structure. y(m 1 , m 2 ......, m n ) =

∞ ∑

∞ ∑

∞ ∑

x( p1 , p2 ....., pn ) f (m 1 − p1 , m 2 − k2 , ......m n − kn )

p1 =−∞ p2 =−∞ pn =−∞

(1) Where n, x, f, and y are the model dimensions, the input data, the receptive field of the area, and the output features. n may be 1, 2, and 3. For example, n = 1 denotes a onedimensional CNN model, implying that the convolutional operation in this model operates in just one direction. To identify each pixel, the convolutional direction is used as the depth direction. n = 2 denotes a two-dimensional CNN model in which the convolutional operation is performed in two dimensions (height and width). As a result, the 2D CNN model concentrates on the correlations between the pixels in the input layer. In a three-dimensional CNN, n = 3, since the convolutional process traverses all three dimensions (height, width, and depth, where depth is set to one in this research). Thus, a 3D CNN model allows for a more in-depth examination of both the connections between the input layers of each pixel and the correlations between the pixels in the input layer. In the ReLU activation, the unidirectionality and one-end saturation properties are achieved. It adds sparsity to the CNN model to manage the disappearance of the gradient efficiently, significantly increasing the convergence speed of the CNN

210

G. K. Malik et al.

model. Therefore, we use the ReLU activation function in the model to improve the feasibility and efficiency of the training process in the CNN model. The pooling layer is a key component of our CNN architecture. This study uses the maximum combination to identify the function maps and reserves the strongest characteristics with the maximum number of characteristics in a particular timeframe in each function map. The pooling window dimension influences the amount and computational cost of the training model. The flatten layer is frequently used to convert from the convolution layer to the fully-connected layer by converting the input from multidimensional space to the single dimension space. The fully-connected layer primarily functions as an integrator and a classifier.The integrater ensures that the completely-connected layer combines picture features through several convolution layers and grouping layers in the feature maps to achieve a high level of characteristics. The classifier is used to map the functional map created by the convolution layers to a vector of the fixed feature and then calculates the resulting class and the error between the output and the actual value. The complete detail of each layer is given in Table 1. Due to good performance, ReLU activation function is used in each convolution and max-pooling layer. In last layer which is fully-connected layer, softmax activation function is used. Stride is an important component of convolutional neural networks which is used to control the amount of movement applied to the picture or video.

3 Result and Discussion This study showed that of convolutional neural network model can be trained with Xray images to detect arm fractures similar with highest diagnostic accuracy. Further, this study has shown that in emergency, clinicians have the assistance of the trained model to significantly improve their ability to detect arm fractures, thus reducing diagnostic mistakes and improving the efficiency of clinicians. Our experiment is pretrained on MURA dataset by the convolutional neural network. The architecture is finally trained. The image input is resized as similar pixel labelling is required. There are four images in a mini-batch. In the CNN training process, we train the proposed network with 3292 pictures and test the model with 932 pictures. The results of the identification of arm fractures with the training picture were 76.15%. The result of the detection of the bone fracture is displayed automatically in the classification. The filtered image in Fig. 2 obtained when filtered is provided with CNN training features. Three neural networks are tested to give 76.15 % accuracy in the CNN classification, which is shown in Table 2.This also shows the 79.95% sensitivity and 71.73% specificity for fractured image detection. It is worth noting that some fractures still cannot be detected precisely by our model, and two factors play crucial role for that first quality of data sets used for training purpose second size of datasets. Neural network (CNN) data-depended method itself makes the model very sensitive to data quality.

Arm Fracture Detection Using Deep Convolution Neural Network

211

Table 2 Confusion matrix

Original

Fractured image Normal image

Predicted Fractured image

Normal image

Accuracy

Sensitivity

Specificity

1459

366

76.15

79.95

71.73

443

1124

76.15

71.73

79.95

Fig. 4 Training and validation accuracy and loss plot

Figure 4 shows training and validation accuracy and loss plot. The graph depicts the relationship between accuracy and loss against the number of epochs. The accuracy of the forecast is estimated to be 76.15%, which is also a very good result for CNN trained as MURA dataset, and the results of the solution can therefore be comparable to the maximum indexes of best radiologist performance [18].

4 Conclusion In this work, a novel approach based on the convolution neural networks used, the resulting X-ray images are pre-processed by a 21 × 21 kernel masking for the new approach based on the convolution of neural networks. CNN is the combination with three different layers, and features are extracted from the database X-ray images and feed to the CNN which consists of sets of convolution and pool layers. The experimental results showed when testing and classification of the approach done that the produced bone fracture screening system and detect all anomalies in the fractured bone region, and especially in small areas, the experimental results of our model are based on a smaller data set with images of low quality, and the results

212

G. K. Malik et al.

of fracture detection for a larger data set of higher quality images can be further enhanced. Therefore, in actual clinical environments, we believe that the neural convolution network has strong potential application.

References 1. 2014 report—bmus: the burden of musculoskeletal diseases in the united states, (n.d.). Available: http://www.boneandjointburden.org/2014-report 2. Potter B, Potter MC Method and apparatus for automated medical diagnosis using decision tree analysis, 22 Mar 1988, uS Patent 4,733,354 3. Yang AY, Cheng L, Shimaponda-Nawa M, Zhu H-Y (2019) Long-bone fracture detection using artificial neural networks based on line features of x-ray images. In: 2019 IEEE symposium series on computational intelligence (SSCI). IEEE, pp 2595–2602 4. Xie H, Yang D, Sun N, Chen Z, Zhang Y (2019) Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recogn 85:109–119 5. Ryder D, King S, Oliff C, Davies E (1993) A possible method of monitoring bone fracture and bone characteristics using a noninvasive acoustic technique. In: International conference on acoustic sensing and imaging. IET, pp 159–163 6. Raghavendra U, Bhat NS, Gudigar A, Acharya UR (2018) Automated system for the detection of thoracolumbar fractures using a CNN architecture. Future Gener Comput Syst 85:184–189 7. Ebsim R, Naqvi J, Cootes TF (2018) Automatic detection of wrist fractures from posteroanterior and lateral radiographs: a deep learning-based approach. In: International workshop on computational methods and clinical applications in musculoskeletal imaging. Springer, pp 114–125 8. England JR, Gross JS, White EA, Patel DB, England JT, Cheng PM (2018) Detection of traumatic pediatric elbow joint effusion using a deep convolutional neural network. Am J Roentgenol 211(6):1361–1368 9. Urakawa T, Tanaka Y, Goto S, Matsuzawa H, Watanabe K, Endo N (2019) Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skeletal Radiol 48(2):239–244 10. Tomita N, Cheung YY, Hassanpour S (2018) Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Comput Biol Med 98:8–15 11. Rajpurkar P, Irvin J, Bagul A, Ding D, Duan T, Mehta H, Yang B, Zhu K, Laird D, Ball RL et al (2017) Mura: large dataset for abnormality detection in musculoskeletal radiographs. arXiv preprint arXiv:1712.06957 12. Badgeley MA, Zech JR, Oakden-Rayner L, Glicksberg BS, Liu M, Gale W, McConnell MV, Percha B, Snyder TM, Dudley JT (2019) Deep learning predicts hip fracture using confounding patient and healthcare variables. NPJ Digital Med 2(1):1–10 13. Kim D, MacKinnon T (2018) Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol 73(5):439–445 14. Roslan R, Jamil N (2012) Texture feature extraction using 2-d gabor filters. In: 2012 International symposium on computer applications and industrial electronics (ISCAIE). IEEE, pp 173–178 15. Kato Z, Pong T-C (2006) A markov random field image segmentation model for color textured images. Image Vision Comput 24(10):1103–1114 16. Hari C, Jojish JV, Gopi S, Felix V, Amudha J (2009) Mid-point hough transform: a fast line detection method. In: 2009 annual IEEE India conference. IEEE, pp 1–4 17. Geetha C, Pugazhenthi D (2018) Classification of Alzheimer’s disease subjects from MRI using fuzzy neural network with feature extraction using discrete wavelet transform 18. Bone X-ray deep learning competition. Available: https://stanfordmlgroup.github.io/ competitions/mura/

Manufacturing Techniques and Effect of Stacking Sequence on Mechanical and Tribological Properties of FRP Hybrid Composites: An Overview Smaranika Nayak, Isham Panigrahi, Ruby Mishra, Diptikanta Das, and Santosh Kumar Nayak

Abstract Composite materials have been found to be the top most choice among different manufacturing industries against the conventional metal components. Their excellent properties in terms of strength, rigidity, light weight, corrosion resistance, impact resistance, and durability have proved to be the most promising material in the global market. Currently, composites are gaining more attention from different researchers and academicians due to improved sustainable performances in varied areas of application (automobile, aerospace, marine, transport, construction, biomedical, and sports). The presence of the constituent fibres along with different manufacturing techniques largely decides the performance of the composite materials. An overview of different manufacturing techniques is undertaken to select the best-suited technique to develop fibre-reinforced polymer (FRP) composite for wide range of applications. The effect of symmetrical/usymmetrical stacking sequence on mechanical and tribological properties of fibre-reinforced polymer composites has been investigated successfully. Keywords Hybrid composites · Stacking sequence · Flexural · Impact · Fabrication methods

1 Introduction FRP composites are considered as the best alternative lightweight material against the traditional heavier metals and alloys. The complete revival of materials with FRP composites has become predominant in recent years. Their widespread use and of course its application have reached a particular level creating enormous competition in the global market. Its application includes automotive, aerospace structures, ship S. Nayak (B) · I. Panigrahi · R. Mishra · D. Das · S. K. Nayak School of Mechanical Engineering, KIIT Deemed to Be University, Bhubaneswar, Odisha 751024, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_20

213

214

S. Nayak et al.

buildings, civil structures, biomedical industries, and many more. Its unique characteristics are due to high strength, high modulus, good corrosion resistance, high impact resistance, high fracture resistance, low maintenance cost, low coefficient of thermal expansion, and lightweight [1–4]. Therefore, the main focus is now on developing lightweight and durable composite materials. Glass fibre-reinforced polymer (GFRP) composites are widely used in many applications due to their advantageous properties like low cost, high impact resistance, easy availability, and high strain to failure. Carbon fibre-reinforced polymer composites, on the other hand also possesses superior properties like high strength, stiffness, low density, etc. However, it has poor strain–to-failure behaviour, low impact resistance, and is three times more expensive than GFRP composites. These drawbacks of individual fibres can be overcome by combining with other fibres in the same matrix system for enhancing the overall properties of the resultant composite and also eliminating the weakness of individual fibres. Thus, there is a great need to combine the properties of different fibres in a single matrix system. The tremendous growth in hybrid composite areas has been found in several research works. Figure 1 shows the increasing growth rate of CFRPs and GFRPs in different applications (aerospace, automobile, wind tunnel, marine, and others) from 2014 to 2024 [5]. Significant advancements in manufacturing processes and technology have created the niche for high-performance CFRPs and GFRPs. It has been estimated that there will be an improvement of 58.6% of rate of increase of glass fibres by 2024. However, the increase in growth percentage of carbon fibres will remain below the glass fibres due to high cost [6]. Fibre hybridization can be considered as the most appropriate solution to ascertain individual fibre-reinforced polymer composites [7, 8]. Hybridization has given a new horizon to the functionality and applicability of composite materials which was not available earlier. In a composite, although fibres and matrix are the two phases present, interphase/interface is considered as the core which plays an important role in the transfer of load from matrix to fibre [9]. An interface is generally present at the juncture of fibre and matrix and is mostly a three-dimensional region. It possesses specific physico-chemical properties [10]. The importance of interface in the performance of composites is widely known which has created interest among researchers and academia. However, in transverse loading conditions, the interphase/interface is considered to be the weakest region of fibre-reinforced polymer composites where premature failure begins. Once, there is an initiation of crack, it propagates through the matrix and subsequently, the fibre-matrix interface causes fracture of the composite structure. Thus, interfacial strength is considered to be very serious in composite structures and is directly related to its overall strength [11]. Hence, enhancement of matrix strength is essential to improve the overall strength of the composite structure as the matrix is weaker in comparison to fibres. In the present investigation, emphasis is given to different manufacturing techniques for developing hybrid composites and effect of symmetrical/usymmetrical stacking sequence on mechanical and tribological properties.

Manufacturing Techniques and Effect of Stacking Sequence …

215

Fig. 1 Growth trend of CFRPs and GFRPs in terms of billion USD, 2015–2024

2 Hybrid Composites Hybrid composites have gained potential interests among several researchers and academicians due to its tailored properties. It is basically made up of two or more reinforcing fibres in a single matrix system. In hybrid composites, one of the fibre belong to the category of high cost, high strength, and high modulus like carbon/ graphite and the second fibre belongs to the low strength, low modulus and low cost category like glass/evlar. There are other characteristic features of hybrid composite which includes: effective use of different fibre materials, reduction in cost, weight savings, improved fracture toughness, impact resistance, corrosion resistance, and higher fatigue life [12]. Broadly, hybrid composites are divided into interplay (interlaminated) hybrids, intraply hybrids, and intermingled hybrids [13, 14]. The hybridization of glass/carbon fibre in polymer matrix composites resulted in improvement of mechanical properties which is termed as positive hybridization effect [15]. It leads to the enhancement of strength and failure strain. The mechanical behaviour of carbon/glass fibre-reinforced polymer composites which were fabricated by filament winding method with varying volume fraction was investigated by Sonparote and Lakkad [16]. Several research works are also available on in-plane mechanical properties of hybrid composites [17–19]. A review paper was published by Kretsis [20] by focusing on the mechanical properties of unidirectional and continuous glass/carbon hybrid composites. Swolfs [21] also published a research paper which highlighted on different mechanical properties like tensile, flexural, impact,

216

S. Nayak et al.

fatigue and also on the hybrid effect. The behaviour of hybrid composites can thus be termed as a balancing act between pros and cons of individual fibres [22].

3 Fabrication of Hybrid Composite 3.1 Hand Lay-Up Technique The hand lay-up method is one of the oldest and easiest methods of preparing composite laminates. This method is useful for large as well as smaller components. No special tools are required in the fabrication, but the quality depends upon the hand of the person who is performing the lay-up process. The following steps are used to prepare the composite [23, 24]: • Initially, a Teflon sheet is spread on the mould, and then sheet-releasing agents like silicon sprays are sprayed for the quick and easy release of the composite laminate. • Then a coating of thermoset/thermoplastic matrix (combination of matrix + hardener) is spread on the Teflon sheet followed by laying up of reinforcing fibres (natural/synthetic). • A roller is then applied to remove moisture and additional matrix from the surface of fibre. This process is repeated until desired no. of layers of composites is achieved. • Then again silicon spray is applied on the Teflon sheet and it is spread on the last layer of matrix. Initial curing is done by placing a weight of 10 kg on the surface of the mould at room temperature for 24 h to get rid of excess matrix and air bubbles. • Finally, the desired laminate is taken out and as per different ASTM standards, the specimens were cut and final curing is done in a hot air oven at 140 °C for 6 h.

3.2 Compression Moulding This method is used for the production of different composite components by the application of high pressure. It is a closed moulding technique where two matched moulds are used for fabrication. The base plate is motionless while upper plate is movable. The raw materials (fibres, matrices, SMC (sheet moulding compounds), BMC (bulk moulding compounds), and prepegs) are placed in the mould for the production. Depending upon the size of the components, required amount of pressure and heat is applied to the mould. Generally, at room temperature curing is done and the product is taken out after curing for further processing. Both the types of thermoplasts [25] as well as thermoset [26, 27] type of fibre reinforced composites are produced.

Manufacturing Techniques and Effect of Stacking Sequence …

217

3.3 Pultrusion This method is used to develop different hybrid composite components [28] having uniform cross section such as beams, pipes, channels, tubes, golf club shafts, rod stock, and fishing rods. It is a continuous process where the roving of fibres is impregnated with resin by passing through the resin bath. Then the resin-rich fibres are pulled through preform stations to acquire the required shape and then pulled through the heated die. This method is suitable for thermoset-based fibre-reinforced composites. The matrix used is mostly polyester and vinyl ester resin. This method is useful in developing components for electrical and construction applications.

3.4 Bag Moulding Processes In this method, flexible diaphragm or bag is used for fabrication, and the process is similar to the hand lay-up technique. But the only difference is that the bags are required to maintain the desired pressure on the composite. Moreover, this is a costly method of fabricating the composite because it requires a flexible bag, mould, vacuum pump or air pump, and good releasing agent. This method is used for small and medium components, the complex shapes are also easily obtained without the requirement of external weights. The surface finish of the product is very fine. The bag moulding process consists of two methods: (a) Pressure bag (b) Vacuum bag

3.5 Resin Transfer Moulding This method also involves a closed moulding technique [29], where two moulds are used that is a lower male mould and an upper female mould. It is considered to be a low-temperature and low-pressure process where resin is poured over the reinforcing fibre [5]. In the beginning, all the fibres are placed at the top of the bottom mould cavity. The upper mould is placed and tightly clamped with the bottom mould. The top mould contains epoxy inlet hole and vacuum hole. On the top of the epoxy hole, the hoper is placed which contain the epoxy in it. The vacuum is provided in the vacuum hole with the aid of vacuum pump. Due to the pressure difference in the cavity, the epoxy moves into the mould cavity. When the mould cavity is fully filled with epoxy then the vacuum hole is sealed. In this process, good surface finish is acquired on the surface.

218

S. Nayak et al.

3.6 Filament Winding This method is used to make different cylindrical structures like pipes, tubes, shafts, bottle, and cylinder. The fibres used in this process are in the form of roving. The filament winding contains mandrel, carriage, creel, roving fibres, and control unit. The creel is mounted on the carriage, and it contains the epoxy. The carriage has linear motion parallel to the mandrel axis, while the mandrel has rotational motion, and carriage speed is controlled by the control unit. The fibres from the roving move into the carriage for an epoxy bath then wind on the mandrel. The carriage position and speed depends on the winding pattern. It is possible to obtain different hoop or circumferential pattern, a helical pattern, and longitudinal pattern using this method [30]. The outer surface finish obtained in this method is not fine.

4 Effect of Stacking Sequence There are numerous works available in the literature which gives a clear perspective of the future scenario of hybrid composites. The mechanical properties like tensile, impact, and flexural strength are enhanced by reinforcing different fibres with their respective advantageous properties. The weakness of one reinforcing fibre is balanced by the strength of another reinforcing fibre in the resultant hybrid composite. Finally, the effect of symmetrical and unsymmetrical stacking sequence of fibres on the different mechanical and tribological properties is mentioned as follows.

4.1 Effect of Symmetrical Stacking Sequence The major challenges that are being faced by automobile companies in different aspects are cost effectiveness, weight reduction, recyclability, and inadequate experience with the use of advanced composite materials. It has been observed that if the structural components are made up of GFRP composites, around 20–35% reduction in weight occurs in comparison to CFRP composites where 40–60% reduction occurs [31]. Research indicates that the high cost of carbon fibres restricts the use of CFRP to only luxury cars and in concept design although it possesses high stiffness, strength and is more lighter as against GFRP composites [32, 33]. Extensive research works are available in the literature related to mechanical properties of glass/carbon fibre reinforced composite [34–37]. Dong and Davies [35] studied the flexural behaviour of an intraply arrangement of glass/carbon fibre-reinforced hybrid composite. They studied the flexural behaviour both analytically by classical lamination theory and numerically by finite element method. It was observed that the flexural strength followed rule of mixtures (RoM) which indicates absence of hybrid effect. Turscan and Meszaros [38] investigated the mechanical properties of CF

Manufacturing Techniques and Effect of Stacking Sequence …

219

and CF/GF composites with two different epoxy/vinyl ester and epoxy/unsaturated polyester hybrid matrices. Hybrid matrices resulted in improvement of interlaminar properties of CF/GF composites against plain epoxy matrix due to good energyabsorbing behaviour. Zhang et al. [39] investigated the effect of stacking sequence on strength of glass/carbon fibre reinforced hybrid composites. The results indicate the increase in flexural strength with fifty percent reinforcement of carbon layers and presence of carbon layers on the exterior side. With alternate arrangement of glass and carbon layers, an increase in the compressive strength was also reported. The increase in impact strength with symmetrical arrangement of glass/carbon fibre reinforced hybrid composites are reported by several researchers and academicians [40–42]. Jesthi et al. [43] investigated the effect of symmetrical stacking sequence of glass/carbon fibre layers on the tribological behaviour. Response surface methodology was used to optimize the values of specific wear rate obtained by three body abrasive wear test. A good agreement was obtained between the experimental and model-predicted results. Several researchers and academicians have also reported the three-body abrasive wear of glass/fibre-reinforced hybrid composites where wear rate is affected by applied load and sliding distance [44–46].

4.2 Effect of Unsymmetrical Stacking Sequence Numerous works are available related to symmetrical stacking sequence of hybrid glass/carbon fibre-reinforced composites. A few research investigations on unsymmetrical stacking sequence of glass/carbon hybrid composites are as follows. Dong and Davies [47] experimentally investigated the effect of unsymmetrical stacking sequence of glass/carbon fibres on mechanical properties. They reported the increase in flexural modulus with increase in span-to-depth ratio. Also, the flexural strength increases with increase in glass fibre volume fraction than carbon fibre volume fraction. The low-velocity impact behaviour of glass/carbon fibre-reinforced intralayer hybrid composites was experimentally investigated by C Zhang et al. [48]. The impact tests were conducted using drop weight impact test. The different damages were seen using fluorescent dye penetration and visual inspection. The intralayer hybrid composite with carbon/glass fibre layers in the ratio of 1:1 possessed better impact resistance. The hybrid composite with carbon and glass fibre layers in the ratio of 1:4 exhibited less damage than the hybrid with 1:2 ratio. Papa et al. [49] investigated the effect of stacking sequence of glass and carbon fibre-reinforced vinyl ester resin composite on the flexural and low-velocity impact behaviour and also the effect of hybridization. The flexural modulus was not affected by the stacking sequence of reinforcing fibres. Harsha et al. [50] experimentally investigated the wear behaviour of unidirectional and bidirectional glass and carbon fibres along with pure epoxy at varying impingement angles. The bidirectional glass fibre-reinforced epoxy composites possessed better impact resistance than unidirectional glass and carbon fibre-reinforced epoxy composites followed by pure epoxy. Apart from this, there

220

S. Nayak et al.

are several other work related to varying stacking sequence of glass and carbon fibre-reinforced hybrid composite [49, 51, 52].

5 Conclusion The hybridization of fibre-reinforced polymer composites plays a vital role in increasing the characteristic features of the composites. This has enabled to increase the widespread applicability of composite materials in numerous applications. The stacking sequence of reinforcing fibres greatly influence the properties of composite materials obtained through hybridization. Due to the improvement in different properties the reliability and durability feature of composite materials are greatly enhanced. The possibility of combining low-cost material with high cost and high-strength material with low strength material is achieved through hybridization resulting in higher strength and cost-effective material. An overview of different manufacturing techniques as well as effect of symmetrical/unsymmetrical stacking sequence on mechanical and tribological properties have been undertaken to investigate its suitability for wide range of applications. Their improved sustainable performances in varied areas of application (automobile, aerospace, marine, transport, construction, biomedical, and sports) have made fibre-reinforced polymer composite a favourable alternative over conventional metals/alloys.

References 1. Kisner A, Rainert KT, Ferrari F, Nau CT, Barcellos IO, Pezzin SH, Andreaus J (2013) Chemical functionalization of polyamide 6.6 fabrics. React Funct Polym 73:1349–1356 2. Isa MT, Ahmed AS, Aderemi BO, Taib RM, Mohammed-Dabo IA (2013) Effect of fiber type and combinations on the mechanical, physical and thermal stability properties of polyester hybrid composites. Compos B Eng 52:217–223 3. Sarasini F, Tirillò J, Ferrante L, Valente M, Valente T, Lampani L, Gaudenzi P, Cioffi S, Iannace S, Sorrentino L (2014) Drop-weight impact behaviour of woven hybrid basalt–carbon/epoxy composites. Compos B Eng 59:204–220 4. Nayak RK, Mahato KK, Ray BC (2016) Water absorption behavior, mechanical and thermal properties of nano TiO2 enhanced glass fiber reinforced polymer composites. Compos A Appl Sci Manuf 90:736–747 5. Rajak DK, Wagh PH, Linul E (2021) Manufacturing technologies of carbon/glass fiberreinforced polymer composites and their properties: a review. Polymers 13:3721 6. Wan Y, Takahashi J (2021) Development of carbon fiber-reinforced thermoplastics for massproduced automotive applications in Japan. J Compos Sci 5:86 7. Nayak S, Nayak RK, Panigrahi I, Sahoo AK (2019) Tribo-mechanical responses of glass fiber reinforced polymer hybrid nanocomposites. Mater Today Proc 18:4042–4047 8. Nayak S, Sadarang J, Panigrahi I, Nayak RK, Maurya M (2020) Optimization of composite leaf spring for reduced weight and improved noise, vibration, and harshness in an electric vehicle. Noise Vib Worldwide 51:127–138 9. Drzal LT (1986) The interphase in epoxy composites. In: Dušek K (ed) Epoxy Resins and Composites II. Springer, Berlin, Heidelberg, pp 1–32

Manufacturing Techniques and Effect of Stacking Sequence …

221

10. Papanicolaou GC, Paipetis SA, Theocaris PS (1978) The concept of boundary interphase in composite mechanics. Colloid Polym Sci 256:625–630 11. Dirand X, Hilaire B, Soulier JP, Nardin M (1996) Interfacial shear strength in glass-fiber/ vinylester-resin composites. Compos Sci Technol 56:533–539 12. Naik NK, Ramasimha R, Arya H, Prabhu SV, ShamaRao N (2001) Impact response and damage tolerance characteristics of glass–carbon/epoxy hybrid composite plates. Compos B Eng 32:565–574 13. Bunsell AR, Harris B (1974) Hybrid carbon and glass fibre composites. Composites 5:157–164 14. Bradley PD, Harris SJ (1977) Strategic reinforcement of hybrid carbon fibre-reinforced polymer composites. J Mater Sci 12:2401–2410 15. Summerscales J, Short D (1978) Carbon fibre and glass fibre hybrid reinforced plastics. Composites 9:157–166 16. Sonparote PW, Lakkad SC (1982) Mechanical properties of carbon/glass fibre reinforced hybrids. Fibre Sci Technol 16:309–312 17. Koronis G, Silva A, Fontul M (2013) Green composites: a review of adequate materials for automotive applications. Compos B Eng 44:120–127 18. Hapuarachchi TD, Ren G, Fan M, Hogg PJ, Peijs T (2007) Fire retardancy of natural fibre reinforced sheet moulding compound. Appl Compos Mater 14:251–264 19. Selver E, Potluri P, Soutis C, Hogg P (2015) Healing potential of hybrid materials for structural composites. Compos Struct 122:57–66 20. Kretsis G (1987) A review of the tensile, compressive, flexural and shear properties of hybrid fibre-reinforced plastics. Composites 18:13–23 21. Swolfs Y, Gorbatikh L, Verpoest I (2014) Fibre hybridisation in polymer composites: a review. Compos A Appl Sci Manuf 67:181–200 22. Rajkumar G, Srinivasan J, Suvitha L (2015) Natural protein fiber hybrid composites: effects of fiber content and fiber orientation on mechanical, thermal conductivity and water absorption properties. J Ind Text 44:709–724 23. da Silva LJ, Panzera TH, Velloso VR, Christoforo AL, Scarpa F (2012) Hybrid polymeric composites reinforced with sisal fibres and silica microparticles. Compos B Eng 43:3436–3444 24. Mishra S, Mohanty AK, Drzal LT, Misra M, Parija S, Nayak SK, Tripathy SS (2003) Studies on mechanical performance of biofibre/glass reinforced polyester hybrid composites. Compos Sci Technol 63:1377–1385 25. Narendar R, Dasan KP, Nair M (2014) Development of coir pith/nylon fabric/epoxy hybrid composites: mechanical and ageing studies. Mater Des 1980–2015(54):644–651 26. Huda MS, Drzal LT, Mohanty AK, Misra M (2006) Chopped glass and recycled newspaper as reinforcement fibers in injection molded poly (lactic acid) (PLA) composites: a comparative study. Compos Sci Technol 66:1813–1824 27. Pérez-Fonseca AA, Robledo-Ortíz JR, Ramirez-Arreola DE, Ortega-Gudiño P, Rodrigue D, González-Núñez R (2014) Effect of hybridization on the physical and mechanical properties of high density polyethylene–(pine/agave) composites. Mater Des 64:35–43 28. Athijayamani A, Thiruchitrambalam M, Natarajan U, Pazhanivel B (2009) Effect of moisture absorption on the mechanical properties of randomly oriented natural fibers/polyester hybrid composite. Mater Sci Eng, A 517:344–353 29. Erden S, Ho K (2017) 3—Fiber reinforced composites. In: Seydibeyo˘glu MÖ, Mohanty AK, Misra M (eds) Fiber technology for fiber-reinforced composites. Woodhead Publishing, pp 51–79 30. Quanjin M, Rejab MRM, Idris MS, Bachtiar B, Siregar JP, Harith MN (2017) Design and optimize of 3-axis filament winding machine. IOP Conf Ser Mater Sci Eng 012039. IOP Publishing 31. Das S (2001) The cost of automotive polymer composites: a review and assessment of DOE’s lightweight materials composites research. Oak Ridge National Laboratory. ORNL/TM-2000/ 283 32. Al-Qureshi HA (2001) Automobile leaf springs from composite materials. J Mater Process Technol 118:58–61

222

S. Nayak et al.

33. Hosseinzadeh R, Shokrieh MM, Lessard LB (2005) Parametric study of automotive composite bumper beams subjected to low-velocity impacts. Compos Struct 68:419–427 34. Dong C, Davies IJ (2014) Flexural and tensile moduli of unidirectional hybrid epoxy composites reinforced by S-2 glass and T700S carbon fibres. Mater Des 1980–2015(54):893–899 35. Dong C, Davies IJ (2013) Flexural properties of E glass and TR50S carbon fiber reinforced epoxy hybrid composites. J Mater Eng Perform 22:41–49 36. Dong C, Davies IJ (2013) Flexural properties of glass and carbon fiber reinforced epoxy hybrid composites. Proc Inst Mech Eng, Part L: J Mater: Des Appl 227:308–317 37. Pandya KS, Veerraju C, Naik NK (2011) Hybrid composites made of carbon and glass woven fabrics under quasi-static loading. Mater Des 32:4094–4099 38. Turcsan T, Meszaros L (2017) Mechanical performance of hybrid thermoset composites: effects of matrix and reinforcement hybridization. Compos Sci Technol 141:32–39 39. Zhang J, Chaisombat K, He S, Wang CH (2012) Hybrid composite laminates reinforced with glass/carbon woven fabrics for lightweight load bearing structures. Mater Des 1980– 2015(36):75–80 40. Sánchez-Gálvez V, Paradela LS, Gálvez F (2014) Analytical simulation of high-speed impact onto hybrid glass/carbon epoxy composites targets. Procedia Eng 88:101–108 41. Sutcliffe MPF, Aceves CM, Stronge WJ, Choudhry RS, Scott AE (2012) Moderate speed impact damage to 2D-braided glass–carbon composites. Compos Struct 94:1781–1792 42. Sayer M, Bekta¸s NB, Demir E, Çallio˘glu H (2012) The effect of temperatures on hybrid composite laminates under impact loading. Compos B Eng 43:2152–2160 43. Jesthi DK, Nayak RK (2020) Influence of glass/carbon fiber stacking sequence on mechanical and three-body abrasive wear resistance of hybrid composites. Mater Res Express 7:015106 44. Suresha B, Chandramohan G, Samapthkumaran P, Seetharamu S (2007) Three-body abrasive wear behaviour of carbon and glass fiber reinforced epoxy composites. Mater Sci Eng, A 443:285–291 45. Nayak S, Sadarang J, Panigrahi I, Kumar Nayak R (2018) Development of carbon/glass fibre reinforcement polymer hybrid composite through modeling and simulation. Mater Today Proc 5:17838–17844 46. Nayak S, Nayak RK, Panigrahi I (2021) Improvement of low-velocity impact and abrasive wear resistance of carbon/glass fiber-reinforced polymer hybrid composites. Trans Indian Inst Met 74:2245–2254 47. Dong C, Davies IJ (2012) Optimal design for the flexural behaviour of glass and carbon fibre reinforced polymer hybrid composites. Mater Des 37:450–457 48. Zhang C, Rao Y, Li W (2020) Low-velocity impact behavior of intralayer hybrid composites based on carbon and glass non-crimp fabric. Compos Struct 234:111713 49. Papa I, Boccarusso L, Langella A, Lopresto V (2020) Carbon/glass hybrid composite laminates in vinylester resin: bending and low velocity impact tests. Compos Struct 232:111571 50. Harsha AP, Jha SK (2008) Erosive wear studies of epoxy-based composites at normal incidence. Wear 265:1129–1135 51. Muñoz R, Martínez-Hergueta F, Gálvez F, González C, LLorca J (2015) Ballistic performance of hybrid 3D woven composites: experiments and simulations. Compos Struct 127:141–151 52. Özben T (2016) Impact behavior of hybrid composite plates dependent on curing and different stacking sequences. Mater Testing 58:442–447

Study of Gap Flow Simulation for Machining Gap in Electric Discharge Machining process—A Review Sudhanshu Kumar, Dharmendra Kumar, and Dilip Sen

Abstract The EDM is generally used in the drilling hole having high aspect ratio. However, in the deep drilling, the machining speed may decrease or even stop due to aching phenomenon. This is because of improper debris removal from the sparkgap zone. Improper flushing decreases material removal rate, surface finish and also increases machining time and chances of secondary arching and short circuiting. In order to increase the flushing during machining process and to improve the machining parameter, better understanding of the dielectric distributions and flow directions is required. Recent trends and understanding in the field of flow simulation in EDM process have been reported in present paper. Keywords Flushing · Simulation · Debris · Electric discharge machining process

1 Introduction Electric discharge machining (EDM) is the most applicable machining process for making complex-shaped dies and deep hole with high aspect ratio. It works on the principle of material removal by series of electric spark between the electrodes, submerged in dielectric fluid [1] also known as spark erosion process [2]. As a potential difference is applied between electrodes [3], an electric field is established due to which an electrostatic force subjected on free electrons. This results the tool to emit the electrons if the bonding energy of free electron is less, such emission is named as cold emission. These electrons are then getting accelerated, gain velocity and energy, and collide with dielectric molecules [4, 5]. This leads to formation of plasma in the gap. Many of the electrons and ions, due to low electrical resistance flows from tool and job and vice versa, are called avalanche motion of electrons [4]. Such activity of ions and electrons is visually seen as a spark. On impact of S. Kumar (B) · D. Kumar · D. Sen Department of Mechanical Engineering, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_21

223

224

S. Kumar et al.

electrons and ions with the surface of the workpiece and tool respectively, the kinetic energy is transformed into thermal energy and leads to extreme rise in temperature about 10,000 °C [5, 6]. This melts the material and then dielectric fluid flush out the eroded material from the cavity, in the form of tiny particles, called flushing [7, 8]. During drilling of blind hole having high aspect ratio, the flow of dielectric is not reached properly at bottom side. This increases the chances of secondary arching and also formation of recast layer on machined surface and results in reduction in MRR and quality of machined surface [9, 10]. Zhang et al. [11] presented a paper in which they analyzed the debris removal in the gap flow for small hole in EDM for TI alloy. Author simulated the gap flow field at different depth, for changed flushing velocity, and at different tool diameter. Normally, with the increase in depth of hole, the velocity of fluid at the bottom gap results in inefficient removal of debris but, on increasing the flushing velocity of the fluid, fluid at the bottom side takes more debris removal from the hole. Also, a large size diameter is useful in the debris removal. But when the aspect ratio increases beyond 3, the effect of tool diameter and flushing velocity on debris removal from the hole reduces obviously, which results in increase in accumulation of debris at the bottom gap. Chang et al. [12] used external fluid to establish gap flow field model by using CFD software to analyze the distribution of flow field and pressure in the machining gap. The simulated result clarifies that debris removal is increased with increasing the inlet velocity and size of electrode, after certain level of depth it get decreased. Muthuramalingam et al. [13] proposed a flushing control mechanism in which unwanted arching is reduced by controlling gap voltage and pressure in the gap, which result in enhancement in flushing process. Effective flushing enhanced the surface qualities of machined surface. Mullya and Karthikeyan [14, 15] observed the flow of dielectric in the gap in EDM process. They analyze the effect of inlet velocity of fluid, rotation of tool, and gap size on debris movement and recast layer formation validated with CFD analysis. Chuvaree and Kanlayasiri [16] compared the performance of EDM process for making deep hole by using multi-hole inferior flushing technique and that of with side surface flushing. They concluded that the multi-hole interior flushing provides good surface finish and high MRR. When aided with tool rotation, flushing improves and results in 35.28% increment in MRR. Li et al. [17] compared the flushing of mono-hole in traditional solid electrode and flushing of multi-hole in a bunched electrode. Author found that bunched electrode with multi-hole has more surface area, and flow velocity is about 10 times higher than that of traditional solid electrode. It has effective flushing condition results in increase in MRR which is about three times larger than the traditional solid electrode. Barman et al. [18] adopt the destructive method to study the surface texture and its characteristics of blind hole with high aspect ratio. As for internal flushing, Munz et al. [19] investigated that the flushing conditions in EDM process depend on pressure of the dielectric fluid. Flow rates become very high in such a narrow gap between the tool and workpiece when pressure becomes very high. Author used different flow rates to remove debris and found that by increasing the dielectric flow rate, gas bubble, and debris generated during machining flush away easily. This improved the MRR, and TWR. Uhlmann et al. [20] suggested ultrasonic vibration-assisted EDM to improve flushing. Jose

Study of Gap Flow Simulation for Machining Gap in Electric Discharge …

225

et al. [21] performed an experiment to analyze the influence of discharge gap of 0.5, 1, 1.5 mm at different voltage on EDM performance. They found that the smaller gap between tool and machining surface will assist to concentrate the spark at a particular point, which minimizes the spread of discharge energy from the machining zone. Author applied jet flushing at regular interval to remove the debris from the gap, which result in increasing MRR and surface quality of the machined surface. Singh et al. [22] compared the performance of EDM process with multi-hole rotary tool assisted with gas, EDM process with stationary tool, and EDM with rotary tool. Generally, rotary tool provides centrifugal effect in dielectric fluid, which result in improvement in flushing as compared to stationary tool, and if it is aided by multi-hole, furthermore increment in flushing results in increment in MRR around 75% and reduction in rate of tool wear is about 30%. Xiaoming Yue et al. [23] observed an impact of jets of metal vapor from tool on material removal from the surface of workpiece in EDM process. Guodong et al. [24, 25] found that the behavior of bubble at different depth plays very important role on debris removal. Mathai et al. [26] presented the effect of different flushing strategies on process responses like TWR, MRR, and surface roughness during planetary EDM. Comparison of responses obtained under both strategies has shown that side injection flushing with high-pressure dielectric fluid supply is more effective than side suction flushing strategy for planetary EDM of Ti6Al4V. Kumar et al. [27] performed a boring operation in EDM using different tool movement strategies like helical, radial, and die-sinking at different tool diameter and found that tool movement promotes self-flushing that improves machining performance. All three-tool movement strategy increases material removal rate, surface finish and decreases tool wear rate. Kumar et al. [28] probed the influence of tool movement on machining performance of EDM. Various tool movement strategies have been reviewed and concluded that tool movement improves self-flushing and results in improvement in machining performance like MRR, surface finish, and TWR. Also found that radial tool movement provides better acceptances, minimal overcut, less wear in tool, better finish in surface and dimensional precision. Gupta et al. [29] use rotational tool and powder abrasive to improve machining performance. They found that tool rotation exerted centrifugal force on dielectric fluid to move radially outwards that force the eroded particle away from the machining area and addition of abrasive particles make better discharge distribution that improves self-flushing, which results in increase in MRR. It can be noted that tool rotation with addition of abrasive powder increases MRR by 25.53% and decreases surface roughness by 21.22%. Dave et al. [30] performed an experiment in which they provide orbital motion by actuating orbital radius to decouple the hole from the tool by which it is being drilled. Orbital motion in the tool increases side gap between tool and hole, which results in improvement in flushing.

226

(a) v=2m/s

S. Kumar et al.

(b) v=4m/s

(c) v=6m/s

(d) v=8m/s

Fig. 1 Effect of inlet velocity on the debris distribution [12]

2 Effect of Various Parameter on Gap Flow Field It has been observed that it is very difficult to flush out the debris from deep hole, and it may require either high velocity of fluid to push the debris away or may have optimum side and bottom gap. So, there are some parameters which influenced the gap flow field like velocity and pressure of the dielectric, size of hole, etc.

2.1 Effect of Inlet Velocity on Gap Flow Field On the impact of the dielectric fluid, the debris moves toward the right-side gap, and by increasing the flow rate, the debris at the bottom gap becomes less. This can be evident from Fig. 1. As per shown in Fig. 1 with the increase in velocity, the accumulated debris moves upside from the right corner.

2.2 Effect of Machining Depth on Gap Flow Field It has been observed that machining depth has also effect on MRR due to difficulty in flushing out the eroded particle from the machining zone. It is very hard to remove the debris from the bottom with increasing the depth as per shown in Fig. 2. When depth is (δ = 0.5 mm), the debris movement velocity is high about 6 m/s, and it is 4 m/s when ( δ= 1.5 mm). Therefore, when the processing depth becomes very large, the electrode needs to be picked up to remove the debris.

Study of Gap Flow Simulation for Machining Gap in Electric Discharge …

227

Fig. 2 Effect of machining depth on gap flow field [12]

2.3 Combine Effect of Depth and Velocity on Gap Flow Field Machining depth as well as fluid velocity may have combined effect on debris removal. It is analyzed that from Fig. 3a, b, higher fluid velocity (5.70 m/s) in the gap is obtained for 0.5 mm deep hole having fluid velocity of 5 m/s at inlet as compared to the fluid velocity (2.70 m/s) in the gap is obtained for 0.5 mm deep hole having fluid velocity of 2 m/s at inlet. So, less debris left when using higher fluid velocity. But, when depth is increasing, it is very difficult to remove the debris even at higher velocity of fluid. Figures 4b and 5 show that with high velocity, more quantity of fluid is injected into the deep gap. Even at this high inlet velocity, the maximum fluid velocity obtained in the gap is 2.31 m/s and 5.98 m/s for 2 mm and 3.5 mm deep hole, respectively, for 5 m/s inlet velocity. So, as the depth of hole increases, there are more debris accumulated at the bottom gap. Here, the tool diameter plays important role because larger diameter is helpful for increasing the flow of fluid and debris removal. But on increasing the depth of the hole to a certain limit, whose depth becomes more than the three times of its diameter, the impact of tool diameter, and flushing velocity on debris removal lessens obviously, which results in a more debris accumulated at the bottom gap. Liu et al. [31] established the gap flow field of 3-D cylindrical model in which author analyzed the influence of bubble generation and the debris removal for single pulse discharge by fluent software. It is observed that bubbles generated by breaking the dielectric fluid at high temperature during pulse discharging help to remove the debris. It is because of pressure difference between the surrounding fluid medium, bubble forces debris to move away. It can be seen from Fig. 6a that during flushing, a velocity field is formed from left side to right side and also the debris is distributed from left to right. So, the removal effect due to flushing is greater as compared to that of bubble.

228

S. Kumar et al.

Fig. 3 Gap flow field with flushing for 0.5 mm deep hole a at 2 m/s and b at 5 m/s [11]

Fig. 4 Gap flow field with flushing for 2 mm deep hole a at 2 m/s and b at 5 m/s [11]

Fig. 5 Representations of gap flow field with flushing at 5 m/s and 3.5 mm deep hole [11]

Fig. 6 a Velocity of fluid in the gap and, b distribution of debris in the gap [31]

Study of Gap Flow Simulation for Machining Gap in Electric Discharge …

229

3 Conclusion A study has been summarized to understand the distribution of debris and fluid flow in the machining gap. Secondary discharge and arcing during the EDM process are one of the major issues which lowers the MRR as well as makes poor quality of surface. These can be visualized using gap flow simulation of EDM process. It has been observed that debris removal from the working gap is very critical that leads to the arcing problem. Proper flushing out the debris from the cavity is very important, and it can be analyzed using flow simulation. In the literature, good attempt has been made to understand the flow characteristics of dielectric fluid and debris distribution between the bottom and side gap by performing simulated environment as well as performing various experiments to overcome these limitations of EDM. Acknowledgements The authors gratefully acknowledge the Department of Science and Technology (DST), Gov. of India, SERB under SRG scheme (SRG/2020/000675) for their financial support of this research.

References 1. Tsai HC, Yan BH, Huang FY (2003) EDM Performance of Cr/Cu-based composite electrodes. Int J Mach Tools Manuf 43(3):245–252 2. Schulze HP (2015) Energetic consideration of the spark erosion using different process energy sources and pulse-unit-gap conditions. Key Eng Mater:701–706 3. Abu Qudeiri JE, Saleh A, Ziout A, Mourad A-HI, Abidi MH, Elkaseer A (2019) Advanced electric discharge machining of stainless steels: assessment of the state of the art, gaps and future prospect. Materials 12. https://doi.org/10.3390/ma12060907 4. Jain VK (2009) Advanced machining processes. Allied Publishers Pvt. Ltd., New Delhi 5. McGeough JA (1988) Electro discharge machining. Advanced methods of machining. Chapman & Hall, London, p 130 6. Boothroyd G, Winston AK (1989) Non-conventional machining processes. Fundamentals of machining and machine tools. Marcel Dekker Inc., New York, p 491 7. Ho KH, Newman ST (2003) State of the art electrical discharge machining (EDM). Int J Mach Tool Manuf 43:1287–1300 8. Makenzi MM, Ikua BW (2012) A review of flushing techniques used in electrical discharge machining, ISSN 2079-6226. In: Proceedings of the 2012 mechanical engineering conference on sustainable research and innovation, vol 4, 3–May 2012 9. Pandey PC, Shan HS (1980) Modern machining processes. Tata Mcgraw Hill Publishing Co. Ltd, New Delhi 10. Kuppan P, Narayanan S, Rajadurai A, Adithan M (2015) Effect of EDM parameters on hole quality characteristics in deep hole drilling of Inconel 718 superalloy. Int J Manuf Res, vol 10(1) 11. Zhang S, Zhang W, Liu Y, Ma F, Su C, Sha Z (2017) Study on the gap flow simulation in EDM small hole machining with Ti Alloy. Adv Mater Sci Eng 2017:23 (Article ID 8408793). https:/ /doi.org/10.1155/2017/8408793 12. Chang W, Xi Y, Li H (2020) Simulation of gap flow field in EDM process used oil-in-water working fluid. Key engineering materials, vol 841, pp 232–237. ISSN: 1662-9795. https://doi. org/10.4028/www.scientific.net/KEM.841.232

230

S. Kumar et al.

13. Muthuramalingam T, Vasanth S, Babu LG, Saravanakumar D, Karthikeyan P (2019) Flushing pressure automation for efficient machining in EDM process. In: 2019 7th international conference on control, mechatronics and automation (ICCMA), pp 232–236. https://doi.org/10.1109/ ICCMA46720.2019.8988592 14. Mullya SA, Karthikeyan G (2018) CFD simulation of dielectric fluid flow in micro electro discharge milling process. Mater Today: Proc 5(11):24792–24798. ISSN 2214-7853. https:// doi.org/10.1016/j.matpr.2018.10.277 15. Mullya S, Karthikeyan G (2018) Dielectric flow observation at inter-electrode gap in microelectro-discharge-milling process. Proc Inst Mech Eng, Part B: J Eng Manuf 232(6):1079–1089. https://doi.org/10.1177/0954405416662082 16. Chuvaree S, Kanlayasiri K (2018) Improving the performance of EDM deep hole using multi hole interior flushing electrode. IOP Conf Ser: Mater Sci Eng 361:012013. https://doi.org/10. 1088/1757-899X/361/1/012013 17. Li L, Gu L, Xi X et al (2012) Influence of flushing on performance of EDM with bunched electrode. IntJ Adv Manuf Technol 58:187–194. https://doi.org/10.1007/s00170-011-3357-8 18. Barman S, Hanumaiah N, Puri AB (2015) Investigation on shape, size, surface quality and elemental characterization of high-aspect-ratio blind micro holes in die sinking micro EDM. Int J Adv Manuf Technol 76:115–126. https://doi.org/10.1007/s00170-013-5243-z 19. Munz M, Risto M, Haas R (2013) Specifics of flushing in electrical discharge drilling. Procedia CIRP 6:83–88 20. Uhlmann E, Domingos DC (2013) Investigations on vibration-assisted EDM-machining of seal slots in high-temperature resistant materials for turbine components. Procedia CIRP 6:71–76 21. Jose J, Paul L (2022) Experimental analysis of the influence of discharge gap on EDM performance. Mater Today: Proc 55:394–398. ISSN 2214-7853. https://doi.org/10.1016/j.matpr.2021. 10.515 22. Singh NK, Singh Y, Sharma A, Prasad R (2021) Experimental investigation of flushing approaches on EDM machinability during machining of titanium alloy. Mater Today: Proc 38:139–145. ISSN 2214-7853. https://doi.org/10.1016/j.matpr.2020.06.165 23. Li Q, Yang X (2020) Influence of tool electrode material on material removal and surface integrity in electrical discharge machining. Procedia CIRP 95L383–388. https://doi.org/10. 1016/j.procir.2020.01.195 24. Li G, Natsu W, Yang J, Yu Z (2022) Bubble flushing effect in micro EDM drilling and its relation with debris. J Mater Process Technol 305:117590. ISSN 0924-0136. https://doi.org/ 10.1016/j.jmatprotec.2022.117590 25. Li G, Natsu W, Yang J, Yu Z (2018) Study on debris behavior and its influence on EDM characteristics in deep micro-hole machining. Procedia CIRP 68:578–581. ISSN 2212-8271. https://doi.org/10.1016/j.procir.2017.12.117 26. Mathai V, Dave H, Desai K (2015) Effect of flushing strategies on responses during Planetary EDM of Ti-6Al-4V 27. Kumar S, Dave HK, Desai KP (2016) Experimental investigation on performance of different tool movement strategies in EDM process for boring operation. Int J Adv Manuf Technol 87:1609–1620. https://doi.org/10.1007/s00170-016-8443-5 28. Kumar S, Sen D (2022) Effect of tool movement in electro-discharge machining process—a review. Recent Adv Manuf Process Syst:391–401 29. Gupta A, Kumar A, Beri N, Ghubade A (2020) Effects of tool rotation and powder abrasive on EDM performance of H13 tool steel. Int J Manuf Technol Manage 34(5) 30. Dave HK, Desai KP, Raval HK (2011) Effect of the orbital tool movement on the material removal rate during electro discharge machining. In: Proceeding of international conference on advance trends & engineering materials and their application. Montreal, Canada, pp 365–370 31. Liu Y, Liu C, Zhang W, Zhang S, Ma F, Yang D, Sha Z (2019) Simulation study on gap flow field in EDM micro-hole machining based on gas-liquid-solid three-phase flow. IOP Conf Ser Mater Sci Eng 692(1):6. https://doi.org/10.1088/1757-899X/692/1/012033

Fabrication and Structural Analysis of Hybrid Metal Matrix Composites (MMC) K. V. S. Phani, Basanta Kumar Nanda, Swayam Bikash Mishra, Santosh Kumar Nayak, and Ruby Mishra

Abstract Aluminium metal matrix composites are widely used in the aerospace and automotive industries due to their light weight, increased strength, corrosion resistance, and high toughness. The hybrid metal matrix composite is made up of three (3) constituents: Al 7075, SiC, and graphite. Al 7075 is used for high strength and good corrosive resistance, SiC strengthens the reinforced particles, and graphite improves the welding properties. In this study, the stir casting process is used to fabricate hybrid metal matrix composites (MMC), i.e., Al7075/SiC/graphite, with different weight fractions of SiC and a constant 1 wt.% of graphite. This hybrid metal matrix composite has been subjected to a variety of tests, including tensile, hardness, and toughness tests. The impact strength of the composite increases to 42%–56% when 0.5, 1.0, 1.5, and 2 wt% SiC is added to the base alloy, while the graphite constant remains constant at 1 wt%. Similarly, tensile strength is increased by 43%, and Rockwell hardness strength is increased by 11%. In the hybrid MMC, microstructure analysis is also performed at various SiC compositions. Keywords Hybrid alloy · Stir casting · Tensile test · Hardness test · Impact Test · Microstructure

1 Introduction Aluminium alloys are used in a wide range of engineering applications, such as the automotive, marine, and defence industries. Metal matrix composites (MMCs) have significantly higher specific strength, specific modulus, damping capacity, and wear resistance than unreinforced alloys. When compared to aluminium, MMC has many distinguishable advantages such as increased elasticity, corrosion resistance, and thermal expansion. Particle and fibre reinforcements significantly enhance the properties of MMCs. Stir casting is a quick and low-cost method of producing aluminium K. V. S. Phani · B. K. Nanda (B) · S. B. Mishra · S. K. Nayak · R. Mishra KIIT University, Bhubaneswar, Odisha 751024, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_22

231

232

K. V. S. Phani et al.

metal matrix composites. In a furnace, aluminium alloy is melted, and then reinforcements are added to the molten metal, which is cranked at high speeds. The molten hybrid metal alloy is then casted into a fixed die to form the component’s desired shape and size that the first paragraph of a section or subsection is not indented. Many researchers have previously worked on this hybrid composite with metal matrix to examine its various mechanical characteristics. Suresh et al. [1] focused on wear properties of aluminium composites to observe that there was increase in tensile strength at reduced rate of wear. Davidson et al. [2] also compared aluminium metal matrix composites to find the mechanical properties of both coated and uncoated Al 6061 alloy. The tensile characteristics of the alloy at optimum values of the speed, temperature, and time were studied by Khosravi et al. [3] to show SiC particles within the matrix with porosity content. Saheb et al. [4] increased the weight percentage of SiC and graphite to find relatively increase in hardness of ceramic materials. Kumar et al. [5] discovered that density was decreased with increase in porosity as the mass fraction of bamboo leaf ash (BLA) particles was increased. The friction and wear behaviour of Al–Mg–Cu alloys and Al–Mg– Cu-based composites containing SiC particles were investigated by Hassan et al. [6] at a pressure of 3.18 MPa and sliding speed of 0.393 m/s using a pin-on-disk wear testing machine to investigate the effects of adding copper as alloying element and silicon carbide as reinforcement particles. Leng et al. [7] developed the squeeze casting with variable volume fractions of graphite to fabricate SiC-Gr-Al composites to study the linearity between tensile strength and elastic modulus. Krishna et al. [8] compared the mechanical properties of Al6061-SiC and Al6061-SiC/graphite hybrid composites to observe that tensile strength was highly influenced by weight fractions. Three different paths were explored to developed Al–Zr nanocomposite alloys by Srinivasarao et al. [9] using both mechanical alloying and spark plasma sintering processes to find a good amount of plasticity in the sintering method due to excellent bonding between the powder particles and the coarse Al grains. Sivananthan et al. [10] prepared aluminium alloy metal matrix composites to study their mechanical properties such as hardness, tensile, and compressive strength. P. Gnaneswaran et al. [11] studied about 10% extra copper-coated steel fibre to show 50% greater wear resistance than LM6. Bhaskar et al. [12] developed Mg-based composites for bioimplants, commonly reinforced with Mg fibres and stir casting technology. Karthik et al. [13] used aluminium alloy AA 5083 –H321, and zirconium oxide (ZrO2) as additional of reinforced particles in study of hybrid MMC. The mechanical properties of Al7075/SiC and graphite composites were investigated in this study using the stir casting technique with different volume percentages of Al7075-SiC while keeping the graphite percentage constant. These tests were conducted with various levels of composite content in order to determine the optimum level.

Fabrication and Structural Analysis of Hybrid Metal Matrix Composites …

233

Fig. 1 Schematic diagram of stir casting set-up

2 Preparation of Metal Matrix Composite 2.1 Stir Casting Process Stir casting process is the most commonly used commercial technique for making aluminium-based composites as it is least expensive of all the proven manufacturing methods. It comprises furnace, reinforcement feeder, and mechanical stirrer as in the schematic diagram (Fig. 1) and stir casting set-up (Fig. 2). Bottom pouring furnace is generally used for stir casting to avoid sedimentation in the crucible. The stirrer creates the necessary vortex with the help of flat-bladed impeller attached to a stirring rod. The regulator attached with a variable speed motor controls the rotational speed of the stirrer. The reinforcement powder is fed into the melt by the feeder inside the furnace. One permanent mould (Fig. 3) is used for pouring the mixed slurry. Here, matrix materials are kept at the bottom furnace for melting, and the reinforcements are preheated in another furnace at a temperature of 800 °C for separating humidity and other impurities. Then the stirrer starts forming vortex for certain time and the feeder pours reinforcement particles with constant feed rate at the centre of the vortex. In this way, the feeding of reinforcement particles is completed. The molten metal composite is put into a preheated mould and left for natural cooling and solidification. Then post-casting processes like machining, testing, and inspection are performed. In this study, five no. of samples are prepared to investigate the different mechanical properties with different process parameters (Fig. 4).

2.2 Sample Preparation and Procedure As shown in Fig. 1, the crucible dimensions for preparing Al 7075 samples are 40 mm length and 20 mm width (4). To improve wettability, the calculated amount of SiC

234

K. V. S. Phani et al.

Fig. 2 Stir casting set-up

Fig. 3 Permanent mould

Fig. 4 Crucible

and graphite particles (Figs. 5 and 6) are placed in the preheater and preheated to 700 °C. Table 1 displays the properties of reinforcement materials. The charge of Al-7075 alloy is melted and kept at 800 °C (usually above its liquid temperature). The preheated SiC and graphite particles (as shown in Figs. 5 and 6) are then slowly added to the molten Al 7075 and mixed at a speed of 300 rpm. The molten metal mixture is then poured into a die and allowed to cool in air to solidify. Finally, the required metal matrix composite in solid state is ejected from the die for further study.

Fabrication and Structural Analysis of Hybrid Metal Matrix Composites …

235

Fig. 5 SiC powder

Fig. 6 Graphite powder

Table 1 Properties of reinforcement materials Materials

Density gm/cc

Elastic modulus (MPa)

Young’s modulus (GPa)

Tensile strength (MPa)

Hardness

Graphite

1.81

1000

42

50

4000

SiC

3.12

412

91

245

2850

2.3 Casting Parameters The different casting parameters for the five samples are as given in Table 2.

3 Testing of Hybrid Metal Matrix Composite Different mechanical properties have been tested for the fabricated metal matrix composite as described below:

236

K. V. S. Phani et al.

Table 2 Casting Parameters Sample

Stirring time (before Stirring time (after ceramic feeding), min ceramic feeding), min

Hydraulic press load, Kg

Stirring temp., °C

1

5

0

22

800

2

5

7

22

800

3

5

7

22

800

4

5

7

22

800

5

5

7

22

800

Fig. 7 Rockwell hardness test

3.1 Hardness Test The ASTM E-10-08 Rockwell hardness test (Fig. 7) is performed on Al 7075 alloy and Al7075-SiC-graphite hybrid composite specimens. The hardness impressions are taken in three different locations, with the average result taken into account. The average hardness values of the samples are then compared, as shown in Fig. 13.

3.2 Tensile Test Tensile tests on specimens are performed successfully on the Universal Testing Machine (UTM) in accordance with the ASTM E8 standard, as shown in Fig. 8. Table 3 contains the test results, and Fig. 14 depicts a comparison of tensile strengths (Figs. 7 and 8).

Fabrication and Structural Analysis of Hybrid Metal Matrix Composites …

237

Fig. 8 Tensile test

Table 3 Mechanical properties of hybrid MMC material Samples

Hybrid MMC material

Rock well hardness

Charpy toughness

Izod toughness

Tensile strength

1

Al 7075 + 1 wt.% graphite

100.7

29

31

200

2

Al 7075 + 1 wt.% graphite + 0.5 wt.% SiC

111.45

33

33

250

3

Al 7075 + 1 wt.% graphite + 1 wt.% SiC

103.30

36

51

275

4

Al 7075 + 1 wt.% graphite + 1.5 wt.% SiC

88.89

39

40

295

5

A7075 + 1 wt.% graphite + 2 wt.% SiC

108

40

42

246

3.3 Charpy Impact Test The Charpy test is performed on Al 7075 and hybrid composites in accordance with ASTM E23 standards. Figures 9 and 10 show the Charpy test values of Al 7075 alloy and Al 7075-SiC-graphite hybrid composites with SiC. Graphite reinforcement outperformed the base alloy in terms of impact strength. Due to the homogeneous distribution and moderate feed rate of ceramic particles, the energy absorbing capacity of the material increases with an increase in reinforcement up to 1 wt.% graphite.

238

K. V. S. Phani et al.

Fig. 9 Charpy test

Fig. 10 Charpy test specimen

3.4 Izod Impact Test The Izod impact test is carried out in accordance with the ASTM E23 standard, as shown in Figs. 11 and 12. Table 3 shows the impact strength of Al 7075 alloy and Al 7075-SiC-graphite hybrid composite. Due to increased ductility, increasing the SiCgraphite ceramic reinforcement particles up to 1 wt.% SiC increase impact strength (Figs. 13, 14, 15 and 16).

3.5 Micro Structure Analysis Optical microstructures of Al 7075 + 1 wt.% graphite, Al 7075 + 1 wt.% graphite + 0.5 wt.% SiC, Al 7075 + 1 wt.% graphite + 1 wt.% SiC, Al 7075 + 1 wt.%

Fabrication and Structural Analysis of Hybrid Metal Matrix Composites … Fig. 11 Impact test

Fig. 12 Impact test specimen

Fig. 13 Bar chart of hardness test

Fig. 14 Bar chart of tensile test

239

240

K. V. S. Phani et al.

Fig. 15 Bar chart of Charpy test

Fig. 16 Bar chart of Izod impact test

Fig. 17 Microstructure analysis

graphite + 1.5 wt.% SiC, and Al7075 + 1 wt.% graphite + 2 wt.% SiC are shown in Fig. 17a–e, respectively. The micrographs Fig. 17a–e represent uniform distribution and homogeneity of reinforcements in the appropriate matrix within of Al7075-SiC-graphite composite. Also the micrographs clearly indicate about the increased filler contents within the composites. The assessment of experimental results for all five types of MMC samples is given in Fig. 18.

Fabrication and Structural Analysis of Hybrid Metal Matrix Composites …

241

Fig. 18 Comparison of experimental results

4 Results and Discussion Because of the uniform distribution of the reinforcement particles and good bonding between matrix and reinforcement, the highest tensile strength is observed at 1 wt.% graphite and 1.5 wt.% SiC-reinforced composite, which is 43% higher than welted base Al 7075 alloy specimen. Tensile strength is higher in SiC composites with 1 and 1.5 weight percent than in base alloys. The average hardness values of Al 7075 alloy and Al 7075-SiC-graphite hybrid composite specimens are evaluated at different distances according to ASTM E10-08. This is the average of three different locations’ impressions. The hardness value is highest at 0.5 wt.% SiC because of the more uniformly distributed of the reinforcing particles and the good bonding action. The Charpy test according to ASTM-E23 was adopted to evaluate Al7075 and Al7075-SiC-graphite hybrid composite materials with SiC. The outcomes of these tests are shown in Fig. 10. The graphite reinforcement outperformed the base alloy in terms of impact resistance. Due to its homogeneous distribution, the energy absorbing capacity of the material increases with a rise in reinforcement up to 1 wt.% graphite. Toughness is increased by 56% when 1wt% graphite is introduced to the base alloy. The Izod impact test is carried out in accordance with the ASTM E23 standard. Increased SiC and graphite reinforcement particles increase the impact strength of Al 7075 alloy and Al 7075-SiC-graphite hybrid composite. The Izod impact strength decreases as the wt.% of graphite increases due to the clustering effect. The fracture structure is examined to determine the toughness and failure mode of each specimen.

5 Conclusion In this study, the microstructures, ageing behaviour, and mechanical properties of composites are investigated to reach at the following conclusions: • Traditional powder metallurgy techniques could be used to introduce different volume percentages of particles into Al 7075 aluminium matrix.

242

K. V. S. Phani et al.

• The particle dispersion in the aluminium matrix is not uniform, and most reinforcements diffused at grain boundaries, clustering occurred, and aggregation increased as particulate content increased. • The ageing kinetics and precipitate phases of Al7075 are unaffected by silicon carbide particles. All materials in this study have the highest yield strength and ultimate strength. With 1% vol., SiC/ Al7075 composites have highest yield strength and ultimate strength. • Traditional testing machines are used here to measure and analyse the mechanical properties that leading some deviations from the accuracy, which is the major limitation of the work.

References 1. Suresh KR, Niranjan HB, Jebaraj PM, Chowdiah MP (2003) Tensile and wear properties of aluminium composites. Wear 255(1–6):638–642 2. Davidson AM, Regener D (2000) A comparison of aluminium-based metal-matrix composites reinforced with coated and uncoated particulate silicon carbide. Compos Sci Technol 60(6):865–869 3. Khosravi H, Bakhshi H, Salahinejad E (2014) Effects of compocasting process parameters on microstructural characteristics and tensile properties of A356–SiCp composites. Trans Nonferrous Met Soc China 24(8):2482–2488 4. Saheb DA (2011) Aluminium silicon carbide and aluminum graphite particulate composites. ARPN J Eng Appl Sci 6(10):41–46 5. Kumar BP, Birru AK (2017) Microstructure and mechanical properties of aluminium metal matrix composites with addition of bamboo leaf ash by stir casting method. Trans Nonferrous Met Soc China 27(12):2555–2572 6. Hassan AM, Alrashdan A, Hayajneh MT, Mayyas AT (2009) Wear behavior of Al–Mg–Cu– based composites containing SiC particles. Tribol Int 42(8):1230–1238 7. Leng J, Wu G, Zhou Q, Dou Z, Huang X (2008) Mechanical properties of SiC/Gr/Al composites fabricated by squeeze casting technology. ScriptaMaterialia 59(6):619–622 8. Krishna MV, Xavior AM (2014) An investigation on the mechanical properties of hybrid metal matrix composites. Procedia Eng 1(97):918–924 9. Srinivasarao B, Suryanarayana C, Oh-Ishi K, Hono K (2009) Microstructure and mechanical properties of Al–Zrnanocomposite materials. Mater Sci Eng, A 518(1–2):100–107 10. Sivananthan S, Reddy VR, Samuel CS (2020) Preparation and evaluation of mechanical properties of 6061Al-Al2O3 metal matrix composites by stir casting process. Mater Today: Proc 1(21):713–716 11. Gnaneswaran P, Hariharan V, Chelladurai SJ, Rajeshkumar G, Gnanasekaran S, Sivananthan S, Debtera B (2022) Investigation on mechanical and wear behaviors of LM6 aluminium alloy-based hybrid metal matrix composites using stir casting process. Adv Mater Sci Eng 10:2022 12. Kandpal BC, Johri N, Kumar L, Tyagi A, Joshi V, Gupta U (2022) Stir casting technology for magnesium-based metal matrix composites for bio-implants-a review. Mater Today: Proc 13. Karthik R, Gopalakrishnan K, Venkatesh R, Krishnan AM, Marimuthu S (2022) Influence of stir casting parameters in mechanical strength analysis of Aluminium Metal Matrix Composites (AMMCs). Mater Today: Proc

A Brief Review of Technical Parameters and Its Applications Used in Cold Spray Process Ayaz Mehmood , Mohammad Zunaid , and Ashok Kumar Madan

Abstract The cold spray process is a high-impact coating deposition technology that deposits powder particles on the substrate surface that range in size from microns to nano. Since the spray particles are not melted during the cold spray process, their physical and chemical characteristics are preserved in their original state. The cold spray process characteristics make it special and ideal for several engineering applications. This is a novel technology, and marginal data is available as latest. Around three decades ago, this technique was developed, but it has yet to establish itself as a feasible industrial technology. As a result, commercialization of this coating method will necessitate efforts as well as support from public and private sources. Cold spray is likely to become a feasible coating technique all over the world in the next decade. This paper briefly covers the cold spray coating methods, including their working principle, types, parameters, applications and challenges. The outcome of this study is that using a cold spray procedure improves coating quality. Keywords Coatings · Cold spray · Particle deformation · Application and comparison · Thermal coating

A. Mehmood (B) Department of Mechanical Engineering, Mewat Engineering College (Wakf), Nuh, Haryana 122107, India e-mail: [email protected] A. Mehmood · M. Zunaid · A. K. Madan Department of Mechanical Engineering, Delhi Technological University, Bawana Road, Delhi 110042, India e-mail: [email protected] A. K. Madan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_23

243

244

A. Mehmood et al.

1 Introduction Cold spray technology refers to a solid-state coating and uses high kinetic energy carrier gases typically nitrogen or helium [1], sometimes air [2] to deposit nearly on any substrate surface of the deposited (powdered) coating material at the temperature below its melting, to form deposition of the coating material [3]. The cold spray technique is appropriate for situations where spraying processes based on high temperatures such as high velocity oxy fuel (HVOF), plasma spray, wire arc and flame arc are inefficient and concerns such as oxidation, porosity in the coating and weak adhesion that must be addressed [4]. Cold spray technology is efficient for the critical repair of materials; hence, it is one of the alternate solutions that has recently been developed for the repair of the complex metallic parts used in various important fields such as aeronautical, medical, marine, defense, petrochemical, nuclear, agriculture, oil and gas turbine industries[5–7]. Cold spray can be used to deposit a wide range of materials including metals, composites, ceramics and polymers for corrosion and structural repairs of intricate parts. Copper, aluminum and titanium are examples of smaller, softer and oxygen-sensitive materials that give best deposition efficiency using cold spray [8, 9]. The cold spray techniques reduce surface corrosion, coating porosity, phase transitions, residual thermal stresses and the formation of a heat-affected zone (HAZ); all of these are significant challenges in the various processes other than cold spray (thermal) processes. Cold spray coating is used to produce thick, dense coatings on the substrate, with thicknesses lying between 100 µm to 1500 µm [8]. Cold spray is a significant innovation in the world of additive manufacturing, and it differs from thermal spraying in some ways as shown in Fig. 1. The design of coatings and optimization of the cold spray process are dependent on factors such as the velocity of powder particles when using different gases [11]. Dense coatings can be achieved by using high gas pressure and low temperature, resulting in low porosity and high deposition efficiency on substrate, which has gained popularity in recent years [12]. Hence, it was previously reported that the use of He propellant gas was better than that of N2 for producing a dense coating [13]. The impact of temperature and deposition phenomena was investigated using aluminum particles when strikes by a hard steel substrate [14]. Same particle impact velocity is achieved by varying particle impact temperature by using LPCS and HPCS, to give size-dependent insight into deposition/adhesion, and three distinct particle sizes are Fig. 1 Thermal spray and cold spray process comparison [10]

A Brief Review of Technical Parameters and Its Applications Used …

245

examined, with differing intrinsic impact temperatures as a consequence [12]. The effects of particle impact temperature on deposition parameters (deposition efficiency and critical velocity), adhesion and coating qualities (apparent ductility) are explored for the combination of soft particle/hard substrate material [15]. Deposition efficiency in cold spray based on the impact of input parameters results in coating quality, adhesion strength and bonding mechanism. These parameters include gas used, gas flow temperature, pressure, nozzle geometry, standoff distance, angle of spray and transverse speed of torch [16]. These input parameters have a significant impact on the final coating quality and the bonding process at the interface [17]. This process is influenced by several factors that affect coating development and deposition effectiveness. These factors include substrate and powder materials, particle shape, amount of oxidation and spraying temperature [18]. High deposition efficiency, in-situ micro-grit blasting, adaptable substrate-coating material combinations and cheap process costs are typical advantages of the cold spray process in addition to its low process energy requirements [19]. The literature states that the particle exit velocity to critical velocity ratio determines the deposition efficiency [20]. The powder preparation method, as well as mechanically blended Ni with B4 C in various proportions, is significant [21]. However, the ceramic content was never greater than 20% by volume. The deposition efficiency of B4 C particles in cold spray coatings was significantly increased by powder cladding. Al2 O3 with Ni increases alumina content in the coating and prevents ceramic particle fracture. As a result, the ceramic contribution increased from 7.3% to 30.5% in coatings sprayed with mechanically blended commercial Ni–Al2 O3 and Ni-coated Al2 O3 [22]. The cold spray process has a high striking velocity range (300–1200) m/s, with temperatures of about 800 °C; however, 2000 °C temperature is used in the thermal spraying process [23].

2 Working Principle The kinetic energy of the sprayed powdered particle must be greater than its critical velocity to make the proper coating [24]. A powder particle undergoes plastic deformation and sticks to the surface when its critical velocity exceeds its threshold value [22]. To acquire the proper coating on the deposited surface, several parameters must be checked and evaluated. Some of the key parameters that affect coating are spray particle size, carrier gas temperature and pressure, impact velocity on substrate and design of the nozzle. As the temperature of carrier gas increases, there is a simultaneous increase in the velocity and powder temperature [25]. Another important factor is to obtain the desired supersonic speed with Mach number (1.2–5), and for this purpose, the most effective nozzle has been used known as the De Laval nozzle [26]. During the flow of carrier gas with solid particles, velocity increases somewhat with a decrease in temperature and pressure in the converging portion near the beginning of the throat, and velocity reaches its maximum value in the diverging section with a simultaneous sharp fall in pressure and temperature, as shown in Fig. 2.

246

A. Mehmood et al.

Fig. 2 Flow characteristics of De Laval nozzle [27]

Fig. 3 Variation of gas temperature and particle velocity for a cold spray with other spray processes [16]

When the cold spray process is compared with the other thermal spray processes, it is found that the temperature is significantly lower than 1000 °C and the impact velocity can exceed up to 1200 m/s in the cold spray process. The essential distinctions between the cold spray process to other spray processes are depicted in Fig. 3.

3 Types of Cold Spray Coating System 3.1 High-Pressure Cold Spray System (HPCS) In the HPCS process, particles of powder are fed into a powder feeder and mixed with the effective carrier gas just before the spraying nozzle in the prechamber section after that mixture is moved axially toward the mainstream. To avoid the reverse flow of the carrier gas toward the powder feeder, keep the upstream pressure of the spraying nozzle converging portion high before accelerating gas input into the section, as shown in Fig. 4. This is achieved when the solid powdered particles are subjected to rapid acceleration at a velocity from 600 to 1200 m/s that has sufficient velocity (kinetic energy) to create effective plastic deformation and impart mechanical or metallurgical bonding over the substrate surface [27].

A Brief Review of Technical Parameters and Its Applications Used …

247

Fig. 4 High-pressure cold spray working cycle [28]

Table 1 Major difference between the two methods is studied here [30, 31, 33] HPCS

LPCS

Pressure 25–30 bars

Pressure 5–10 bars

Axial injection of powder with carrier gas

Radial injection of powder at the nozzle throat

Carrier gases (He, N2 , or air) are preheated up to 1000 °C

Carrier gases (He or N2 ) are preheated up to 500 °C

Efficiency 90%

Efficiency 50%

Powder feed rate (kg/h) 4.5–13.5

Powder feed rate(kg/h) 0.3–3

Spraying materials are normally Al, Cu, Cu-Sn, Ni, NiCr, NiAl, Ta or Ti

Spraying materials is limited to low-strength Sn, Zn, Al, Cu and Ni

Electric power (KW) 17–47

Electric power (KW) 3.3

Particle size (µm) 5–50

Particle size (µm) 5–30

Spraying distance (mm) 10–50

Spraying distance (mm) 5–15

3.2 Low-Pressure Cold Spray System (LPCS) In the LPCS process, the carrier gas is forced through a De Laval nozzle that has been heated with gas to improve its aerodynamic properties. As shown in Fig. 5, the powder of the solid particles is fed radially downstream at the critical portion (throat) of the nozzle at below pressure of 101.329 kPa (1 atm), with the heated gas velocity varied from 300 to 600 m/s at the diverging portion of the nozzle and feeding powder particles are successfully pulled in from the powder feeder [29] (Table 1).

4 Cold Spray Parameters Careful selection of parameters is important for proper coating strength and obtaining desired mechanical and chemical properties [34]. The parameters are summarized below.

248

A. Mehmood et al.

Fig. 5 Low-pressure cold spray working cycle [28]

4.1 Gas Temperature and Pressure These two determined the successful coating deposition of sprayed particles, and if the temperature of the powder particles gets increased, it causes a negative impact on their critical velocity. The change in temperature and pressure causes an impact on particle properties and velocity which results in oxidation and porosity reducing the adherence of coatings and the efficiency of the deposition [32].

4.2 Standoff/Spraying Distance The axial gap from the spraying gun to the coating on the substrate surface is known as standoff distance. Temperature and speed of powder particles are highly affected if the standoff distance is 50 mm at maximum, while 120 mm–300 mm is for other thermal spraying processes [35].

4.3 Transverse Speed of Spray Nozzle It determines the maximum amount of spray particles of coating powder that can be sprayed over the surface of the substrate using the spray nozzle. Low deposition efficiency can be formed on the substrate coating due to the higher transverse gun speed of the nozzle and vice versa [36].

A Brief Review of Technical Parameters and Its Applications Used …

249

Fig. 6 Different types of spraying angles [37]

4.4 Spraying Angle The spraying angle must be affected by variables, such as microstructure, coating thickness and particle deposition efficiency [37]. Overall, the spraying angle can be classified into three different groups as shown in Fig. 6.

4.5 Substrate Temperature If the temperature of the substrate is exceptionally high during spraying, then thermal softening can be taken place, thus resulting in a reduction in powder particle speed during coating [38].

4.6 Spraying Nozzle The selection of an appropriate nozzle material and design is the important factor, and the material used is either tungsten carbide or polymers. The nozzle can be interpreted into three levels convergent, conical convergent/divergent and cylindrical convergent/ divergent. The width of the critical section (throat), inlet carrier gas (compression), outlet carrier gas (expansion) and passage dimension of the whole nozzle is different in all these three types of nozzle. A diameter of 400 µm particles can be sprayed by using these nozzles [39].

4.7 Substrate Preparation Before beginning the spraying procedure, some basic requirements must be met, such as the coating surface being free from dust, scratches, oxide and chemical residues. The two different stages of substrate preparation are [40] SiC used for abrading

250

A. Mehmood et al.

having 5 µm roughness and grit blasting with Al2 O3 powder, and in grit blasting, the shape of powder particles, size of particles, impingement angle and the value of the roughness on the coated substrate surface should be defined clearly in all aspects. So, there is an increase in adhesion bonding strength as well as deposition efficiency, due to higher particle deformation of powder on rough coating surfaces [41].

5 Application of Cold Spray Process Cold spray is used to repair fabricated parts, maintenance work and increase the overall performance of machinery components in various industries. The durability of the complex parts enhances, and replacements of these parts are reduced [33]. Five different categories of cold spray coatings, with their practical applications, are discussed here:

5.1 Metallic Cold Spray Coatings This coating is based on materials such Al, Cu, Zn, Ni, etc., and its alloys are used in various applications [27]. Alloys of Al coating in the aeronautical industry with Al can prevent corrosion due to their low oxidation nature [31]. Cu coating is used in electronic fields and industries such as heat exchangers maintenance and conductive systems, because Cu coatings offered excellent resistance due to corrosion [42]. Zn coatings and their alloy provide excellent protection from corrosion in various industries. The alloy of Ni coatings in the field of power generation is due to resistance to corrosion at high temperatures [43]. Al–5Fe–V–Si metal coatings in the automobile industry (IC engines) have excellent protection from high corrosion resistance [22].

5.2 Metal Matrix Composite Cold Spray Coatings Metal matrix composites offer numerous benefits in repair, maintenance and service life of the most machine and power plants [44]. TiAl3 –Al used in aerospace and power generating industries for repair and maintenance services and has corrosion resistance and work at high temperature [45]. Cobalt- and nickel-based alloys are used for repair and maintenance in nuclear power plant systems [46]. The coatings produced by the Al + SiC and Al + Al2 O3 are free from corrosion resistance having a lot of demand in the electrical industry [40]. The carbon nanotubes-Cu coating produces coating surfaces having good corrosive resistance that is frequently employed as heat sinks in the electronics industry [47]. The WC–Co and WC–10Co–4Cr coatings are commonly used in power generation sectors and turbine-related sectors as wearresistant and corrosion-resistant [48].

A Brief Review of Technical Parameters and Its Applications Used …

251

5.3 Ceramic Cold Spray Coatings The coating generated by SiC is generally employed in the sector of power generation, mining and petrochemical industries because of their excellency at high temperatures in corrosion resistance [49]. The Al2 O3 is utilized for the coating that improves wear resistance [33]. The coatings generated by TiO2 are commonly used in the production of photocatalysts [49].

5.4 Nanocrystalline Cold Spray Coatings MCrAlY (where M = Co, Ni, or Co/Ni) coatings applied in first- and second-stage turbine blades and nozzle guide vanes used as corrosion-resistant overlays or bonding coatings for use with thermal barrier coatings. This coating application in aeroengine and industrial turbines [50].

5.5 Polymer Cold Spray Coatings The coating generated by polyethylene and polyamides has the quality of protection from corrosion; hence, it is generally used in chemical-related industries [51].

6 Challenges in Cold Spray Process Brittle, ceramics and other hard materials should require additional ductile binders, whereas it can work on composites materials [52]. Fine bonded coating on the ceramic substrate which are having low bonding strength slight ductility of the substrate is required [53]. High-quality coatings like MCrAlY, Inconel and others are usually created by using expensive helium gas in order to obtain high velocity for surface deposition [54]. Nozzle clogging occurs when particle velocity and temperature are increased, and this challenge affects the nozzle efficiency and can be sorted out by using a blended powder [55].

7 Conclusion Plenty of industries across the world adopted cold spray coating as a low-temperature coating process, reasonably inexpensive and environment friendly, that allows for excellent coatings characteristics and local deposition in precisely specified areas. At

252

A. Mehmood et al.

very low temperatures, many engineering materials such as cermet, ceramic mixture and complicated metal with its alloys can be deposited on any substrate. The most effective, adaptable and fruitful method for dealing with the diversity of challenging and sophisticated systems is the cold spray coating process, although there are wide scopes in this field to optimize this process and perform other useful work in the field of repair and maintenance. A lot of research is further needed to optimize some of the working parameters such as nature of gas, control of temperatures, material selection and design parameters such as optimized nozzle design, nozzle material and analysis of critical velocity for various particle/substrate combinations. Further analysis in this field is required to improve the deposition efficiency with brittle, ceramic materials and improve coating quality.

References 1. Ozdemir OC, Widener CA, Helfritch D, Delfanian F (2016) Estimating the effect of helium and nitrogen mixing on deposition efficiency in cold spray. J Therm Spray Technol 25(4):660–671. https://doi.org/10.1007/s11666-016-0394-8 2. Koivuluoto P, Heli, Vuoristo (2009) Effect of ceramic particles on properties of cold-sprayed Ni20Cr+Al 2 O 3 coatings. J Therm Spray Technol 4(18):555–562 3. Assadi H, Kreye H, Gärtner F, Klassen T (2016) Cold spraying—a materials perspective. Acta Mater 116:382–407. https://doi.org/10.1016/j.actamat.2016.06.034 4. Maledi NB, Oladijo OP, Botef I, Ntsoane TP, Madiseng A, Moloisane L (2017) Influence of cold spray parameters on the microstructures and residual stress of Zn coatings sprayed on mild steel. Surf Coat Technol 318:106–113. https://doi.org/10.1016/j.surfcoat.2017.03.062 5. Yin S et al (2010) Cold spray additive manufacturing and repair: fundamentals and applications. Addit Manuf. https://doi.org/10.1016/j.addma.2018.04.017 6. Ghelichi R, Guagliano M (2009) Coating by the cold spray process : a state of the art 8:30–44. https://doi.org/10.3221/IGF-ESIS.08.03 7. Fox S, Li L (2012) Expanding the scope of prosumption: a framework for analysing potential contributions from advances in materials technologies. Technol Forecast Soc Change 79(4):721–733. https://doi.org/10.1016/j.techfore.2011.10.006 8. Moy CKS, Cairney J, Ranzi G, Jahedi M, Ringer SP (2010) Investigating the microstructure and composition of cold gas-dynamic spray (CGDS) Ti powder deposited on Al 6063 substrate. Surf Coat Technol 204(23):3739–3749. https://doi.org/10.1016/j.surfcoat.2010.04.016 9. Samareh B, Stier O, Lüthen V, Dolatabadi A (2009) Assessment of CFD modeling via flow visualization in cold spray process. J Therm Spray Technol 18(5–6):934–943. https://doi.org/ 10.1007/s11666-009-9363-9 10. Pathak S, Saha GC (2017) Development of sustainable cold spray coatings and 3D additive manufacturing components for repair/manufacturing applications: a critical review. Coatings 7(8). https://doi.org/10.3390/coatings7080122 11. Silvello A, Cavaliere PD, Albaladejo V, Martos A, Dosta S, Cano IG (2020) Powder properties and processing conditions affecting cold spray deposition 12. Nastic A, Jodoin B, Poirier D, Legoux J (2020) Surface & coatings technology particle temperature effect in cold spray : a study of soft particle deposition on hard substrate. Surf Coat Technol 406:126735. https://doi.org/10.1016/j.surfcoat.2020.126735 13. Dikici B, Yilmazer H, Ozdemir I, Isik M (2016) The effect of post-heat treatment on microstructure of 316L cold-sprayed coatings and their corrosion performance. J Therm Spray Technol 25(4):704–714. https://doi.org/10.1007/s11666-016-0402-z

A Brief Review of Technical Parameters and Its Applications Used …

253

14. Nastic A, Jodoin B, Poirier D, Legoux JG (2021) Particle temperature effect in cold spray: a study of soft particle deposition on hard substrate. Surf Coat Technol 406:126735. https://doi. org/10.1016/j.surfcoat.2020.126735 15. Nastic A, Jodoin B (2018) Evaluation of heat transfer transport coefficient for cold spray through computational fluid dynamics and particle in-flight temperature measurement using a high-speed IR camera. J Therm Spray Technol 27(8):1491–1517. https://doi.org/10.1007/s11 666-018-0787-y 16. Singh S, Raman RKS, Berndt CC, Singh H (2021) Influence of cold spray parameters on bonding mechanisms: a review. Metals (Basel) 11(12). https://doi.org/10.3390/met11122016 17. Dowding I, Hassani M, Sun Y, Veysset D, Nelson KA, Schuh CA (2020) Particle size effects in metallic microparticle impact-bonding. Acta Mater 194(May):40–48. https://doi.org/10.1016/ j.actamat.2020.04.044 18. Maev RG, Leshchynsky V (2006) Air gas dynamic spraying of powder mixtures: theory and application. J Therm Spray Technol 15(2):198–205. https://doi.org/10.1361/105996306X10 8048 19. Kutz M (2018) Handbook of environmental degradation of materials, 3rd ed. William and U. Andrew Publishing, Norwich, NY, pp 469–488 20. Raoelison RN, Aubignat E, Planche M, Costil S, Langlade C, Liao H (2016) Surface & coatings technology low pressure cold spraying under 6 bar pressure deposition : exploration of high deposition efficiency solutions using a mathematical modelling. Surf Coat Technol 302:47–55. https://doi.org/10.1016/j.surfcoat.2016.05.068 21. Feng C et al (2012) B 4 C/Ni composite coatings prepared by cold spray of blended or CVDcoated powders 21(June):561–570. https://doi.org/10.1007/s11666-012-9774-x 22. Moridi A, Hassani-Gangaraj SM, Guagliano M, Dao M (2014) Cold spray coating: review of material systems and future perspectives. Surf Eng 30(6):369–395. https://doi.org/10.1179/ 1743294414Y.0000000270 23. Dimitris K, Jeandin M, Irissou E, Legoux JG, Knapp W (2012) Laser-assisted cold spray (LACS). Nd YAG laser. https://doi.org/10.5772/36104 24. Champagne V, Helfritch D, Champagne V, Helfritch D (2015) Critical assessment 11 : structural repairs by cold spray critical assessment 11 : structural repairs by cold spray 0836. https://doi. org/10.1179/1743284714Y.0000000723 25. Sudharshan P, Srinivasa Rao D, Joshl S (2007) Effect of process parameters and heat treatments on properties of cold sprayed copper coatings. J Therm Spray Technol 16:425–434 26. Sun W et al (2022) Current implementation status of cold spray technology: a short review. J Therm Spray Technol 31(4):848–865. https://doi.org/10.1007/s11666-022-01382-4 27. Karthikeyan J (2006) Evolution of cold spray technology. Adv Mater Process 164(5) 28. Singh H, Sidhu TS, Kalsi SBS, Karthikeyan J (2013) Development of cold spray from innovation to emerging future coating technology. J Brazilian Soc Mech Sci Eng 35(3):231–245. https://doi.org/10.1007/s40430-013-0030-1 29. Huang G, Gu D, Li X, Xing L, Wang H (2014) Numerical simulation on syphonage effect of laval nozzle for low pressure cold spray system. J Mater Process Technol 214(11):2497–2504. https://doi.org/10.1016/j.jmatprotec.2014.05.014 30. Li WY et al (2014) Modelling of impact behaviour of cold spray particles: review. Surf Eng 30(5):299–308. https://doi.org/10.1179/1743294414Y.0000000268 31. Rokni MR, Nutt SR, Widener CA, Champagne VK, Hrabe RH (2017) Review of relationship between particle deformation, coating microstructure, and properties in high-pressure cold spray. J Therm Spray Technol 26(6):1308–1355. https://doi.org/10.1007/s11666-017-0575-0 32. Li CJ, Li WY, Liao H (2006) Examination of the critical velocity for deposition of particles in cold spraying. J Therm Spray Technol 15(2):212–222. https://doi.org/10.1361/105996306X10 8093 33. Da Silva FS, Cinca N, Dosta S, Cano IG, Guilemany JM, Benedetti AV (2017) Cold gas spray coatings: basic principles, corrosion protection and applications. Eclet Quim 42(1):9–32. https:/ /doi.org/10.26850/1678-4618eqj.v42.1.2017.p09-32

254

A. Mehmood et al.

34. Grigorievb S, Okunkovab A et al (2015) Cold spraying: from process fundamentals towards advanced applications. Surf Coat Technol 5:77–84 35. Li C, Li W, Wang Y, Yang G, Fukanuma H (2005) A theoretical model for prediction of deposition efficiency in cold spraying 489:79–85. https://doi.org/10.1016/j.tsf.2005.05.002 36. Fernando Santos da Silva AVB, Cinca N, Dosta S, Garcia Cano I, Guilemany JM (2017) Cold gas spray coatings: basic principles, corrosion protection and applications. Eclética Química J 42:9–32 37. Li CJ, Li WY, Wang YY, Fukanuma H (2003) Effect of spray angle on deposition characteristics in cold spraying. Therm Spray 2003 Adv Sci Appl Technol, pp 91–96 38. Watanabe MFY, Yoshida C, Atsumi K, Yamada M (2014) Influence of substrate temperature on adhesion strength of cold-sprayed coatings. J Therm Spray Technol 24(1–2):86–91 39. Tang W, Liu J, Chen Q, Zhang X, Chen Z (2013) The Effects of two gas flow streams with initial temperature and pressure differences in cold spraying nozzle Wenyong. Surf Coat Technol. https://doi.org/10.1016/j.surfcoat.2013.12.019 40. Da Silva FS, Bedoya J, Dosta S, Cinca N, Cano IG, Guilemany JM, Benedetti AV (2017) Corrosion characteristics of cold gas spray coatings of reinforced aluminum deposited onto carbon steel. Corros Sci 114:57–7 41. Klassen T, Gärtner F, Schmidt T, Kliemann JO, Onizawa K Donner KR, Gutzmann H, Binder K, Kreye H (2010) Basic principles and application potentials of cold gas spraying. Materwiss. Werksttech 41:575–584 42. Partovi-Nia R et al (2015) Corrosion of cold spray deposited copper coating on steel substrates. Corrosion 71(10):1237–1247. https://doi.org/10.5006/1757 43. Fernandez R, Jodoin B (2017) Effect of particle morphology on cold spray deposition of chromium carbide-nickel chromium cermet powders. J Therm Spray Technol 26(6):1356–1380. https://doi.org/10.1007/s11666-017-0580-3 44. Science E (2023) On the characteristics of cold spray technology and its application in aerospace industries on the characteristics of cold spray technology and its application in aerospace industries. https://doi.org/10.1088/1755-1315/719/3/032023 45. Champagne V et al (2018) Dissimilar metal joining and structural repair of ZE41A-T5 cast magnesium by the cold spray (CS) process, pp 1–43 46. Sample CM, Champagne VK, Nardi AT, Lados DA (2020) Factors governing static properties and fatigue , fatigue crack growth , and fracture mechanisms in cold spray alloys and coatings/ repairs : a review 47. Bakshi SR, Singh V, Balani K, McCartney DG, Seal S, Agarwal A (2008) Carbon nanotube reinforced aluminum composite coating via cold spraying. Surf Coat Technol 202:5162–5169 48. Koivuluoto H, Lagerbom J, Vuoristo P (2007) Microstructural studies of cold sprayed copper, nickel, and nickel-30% copper coatings. J Therm Spray Technol 16(4):488–497. https://doi. org/10.1007/s11666-007-9060-5 49. Wang Y, Normand B, Mary N, Yu M, Liao H (2017) Effects of ceramic particle size on microstructure and the corrosion behavior of cold sprayed SiCp/Al 5056 composite coatings. Surf Coat Technol 315:314–325 50. Bonadei A, Marrocco T (2013) Cold sprayed MCrAlY+X coating for gas turbine blades and vanes A. Surf Coat Technol. https://doi.org/10.1016/j.surfcoat.2013.08.019 51. Bush TB, Khalkhali Z, Champagne V, Schmidt DP, Rothstein JP (2017) Optimization of cold spray deposition of high-density polyethylene powders. J Therm Spray Technol 26:1548–1564 52. Chu X, Che H, Teng C (2020) A multiple particle arrangement model to understand cold spray characteristics of bimodal size 316L/Fe powder mixtures. Surf Coat Technol 381:125–137

A Brief Review of Technical Parameters and Its Applications Used …

255

53. Yu SQ, Yang XW et al (2018) Research progress of cold spray coating protection on magnesium alloy surface 5:43–56 54. Terms RT (2017) NRC Publications Archive Archives des publications du CNRC Cold sprayability of mixed commercial purity Ti Plus Ti6Al4V metal powders 55. Foelsche A (2020) Nozzle clogging prevention and analysis in cold spray

Finite Element Analysis of Uniaxial Compression Test Hamza Naseem, Valluri Sai Prasanna, S. S. V. D. Pavan Kumar, and Kalluru Giri

Abstract In this paper, the typical uniaxial compression test of a cylindrical specimen has been modeled using the finite element method. Ideal cases for compression testing have been developed using only the specimen model under load and displacement-controlled test conditions. The results of these cases show that the displacement boundary condition is closer to the strength of material data. Therefore, the finite element model of the actual compression test setup consisting of the cylindrical specimen and platens has been analyzed for displacement-controlled test conditions. The frictional contact conditions between specimen and platens have been modeled using different values of friction coefficient representing smooth and rough contacts. The contact tractions at the specimen-platen interfaces and the corresponding states of stress and displacement in the specimen have been obtained under these actual test conditions. All these results help in understanding and addressing the ‘barreling,’ commonly found in the uniaxial compression testing of cylindrical specimens. The presented results also suggest that the radial motion must be allowed at the specimen-platen interfaces for developing uniform uniaxial compression in the entire specimen—a valuable input for designing a new compression platen. Keywords Lateral expansion · Interfacial friction · Contact shear stress

1 Introduction Uniaxial compression tests are conducted to obtain the compressive properties of materials. The compression test is preferred when the material undergoes large plastic strain during the loading since the plastic strain range is much larger as compared to the tension test due to the absence of necking (plastic instability). In polymeric materials, yield and post-yield behaviors may significantly differ in tension and H. Naseem (B) · V. Sai Prasanna · S. S. V. D. Pavan Kumar · K. Giri Department of Aeronautical Engineering, Bharath Institute of Higher Education and Research, Chennai 600073, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_24

257

258

H. Naseem et al.

compression loading. Moreover, compression test data have significant importance in material forming processes. The compression test is also used for measuring contact friction since the barreling of the cylindrical directly depends on the friction at the platen-specimen interfaces [1]. Therefore, the compression test plays an important role in material characterization [2–4]. In a typical compression test, a cylindrical specimen of circular or square or rectangle cross-section is placed between two parallel platens (also called anvils), and compressive load is gradually applied. The standard dimensions of the compression specimen can be obtained from ASTM E9 for metallic materials and ASTM D695 for plastics. Further, both ends of the specimen are machined flat and parallel to avoid any eccentricity which could cause bending of the specimen during the longitudinal compression and affect plastic deformation [4, 5]. Following the above standards, different compression properties (e.g., yield strength, modulus) are estimated from the test data. Moreover, various data processing methods are available to obtain the true stress-true strain curve from the load–displacement data of the compression test [6]. Further, Gruber [5] suggests methods to eliminate the following two issues from the load–displacement data of the test: (a) machine compliance and (b) tilted loading axis due to imperfect machining of the specimen ends. Nevertheless, there are three major problems found in compression testing as compared to tensile testing: (a) non-homogeneous deformation, (b) inaccurate measurement of the specimen deformation, and (c) buckling of the specimen. Nonhomogeneous deformation of the specimen has been explored in this paper. The compressive longitudinal loading produces longitudinal compression and lateral expansion (due to Poisson’s effect) of the specimen. However, the frictional contacts between specimen and platens restrict the lateral expansion of specimen at the specimen-platen interfaces. It causes significant frictional stresses at the interfaces and leads to non-homogeneous lateral deformation of the specimen, resulting in either ‘barreling’ or ‘bollarding’ in the specimen (Fig. 1). In barreling, the lateral expansion of the specimen is maximum at the mid-section and minimum at the end section. Barreling is often observed while compressing metals and polymeric materials. In bollarding, the lateral expansion of the specimen is minimum at mid-section and maximum at the end section. Excessive lubrication at the specimen-platen interface may cause bollarding in the specimen [3, 4, 7]. In either case, the state of stress in the specimen does not remain uniaxial. The contact shear traction at the interfaces creates a non-uniform triaxial state of stress in some part or most part of the specimen. Nevertheless, it is reminded that the purpose of the compression test is to create a pure uniaxial compression state of stress and strain throughout the specimen. Specimen height to diameter ratio and friction coefficient play vital roles in the non-homogeneous deformation of the specimen. In the literature, the commonly adopted methods to mitigate the non-homogeneous deformation are as follows: (a) conical platens and a specimen with conical cavities at either end [8], (b) cylindrical head blocks made of specimen material [9], and (c) lubricants including PTFE (Teflon) sheets, graphite powder [3, 4]. However, they do not completely eliminate frictional stresses at the interfaces and some amount of non-homogeneous deformation still exists.

Finite Element Analysis of Uniaxial Compression Test

259

Fig. 1 (a) Undeformed specimen of height H and diameter D, (b) barreling in the deformed specimen, and (c) bollarding in the deformed specimen. For the deformed specimens: height H d , diameter at the ends De , diameter at the center Dc

In this paper, the typical compression testing of a cylindrical specimen with platens has been modeled and analyzed using the finite element method (ANSYS-APDL). Polyvinyl chloride (PVC) is the specimen material. The interface between specimen and platen has been appropriately modeled using the contact wizard available in ANSYS-APDL. These analyses are required to understand the stress and deformation fields at the specimen-platen interfaces and within the specimen. For the validation of the above analyses, ideal cases of compression testing—based on the theoretical model involving only cylindrical specimens without platen, have been first developed and analyzed under force and displacement-controlled settings. The results of these ideal cases are verified with the strength of material results so that these ideal cases can serve as reference cases, and the experimental validation can be minimized.

2 Methodology 2.1 Models and Materials Finite element 3D models of specimen and platen were created in ANSYS-APDL (Fig. 2). Specimen dimensions were obtained from ASTM D695. Both the specimen and platens were meshed using the solid 8-node brick element. Ideal and actual compression test setup boundary conditions corresponding to different test cases are shown in Figs. 3 and 4, respectively. Specimen material was polyvinyl chloride (E = 3.275 GPa; v = 0.40), and platen material was mild steel (E = 210 GPa; v = 0.33). PVC (plastic) and rubbers are used as bushes and washers, subjected to compression loading [10]. Here, PVC has been chosen as the test material due to its linear elastic behavior in pre-yield region. Static structural analyses (linear elastic) were performed.

260

H. Naseem et al.

Fig. 2 Meshed 3D models of (a) specimen alone and (b) top and bottom platens with specimen

Fig. 3 Different ideal cases of compression test and the corresponding boundary conditions

Fig. 4 Test cases of actual compression test setup and the corresponding boundary conditions including the interface contact conditions: (i) bonded always, ( j) frictional, and (k) frictionless

Finite Element Analysis of Uniaxial Compression Test

261

2.2 Boundary Conditions and Analyses Figure 3 represents ideal loading cases 1a, 1b, 2a, and 2b wherein the platens are absent and force or displacement may be applied directly at the specimen ends. In cases 1a and 1b, the displacement and force were applied respectively at both the ends of the specimen to compress it. In cases 2a and 2b, the displacement and force were applied respectively at the top end of the specimen only, and another end of the specimen was constrained to move in the longitudinal direction. Figure 4 shows actual compression test setup conditions in case 3 and case 4, wherein the specimen is compressed in between the two platens. The specimen-platen interface conditions were captured through one of the following contact conditions: i (bonded always), j (frictional), and k (frictionless). The friction coefficient (µ) for frictional and frictionless contacts are 0.5 and 0.05, respectively. The top platen is connected to the actuated crosshead, while another platen is connected to the fixed crosshead of the testing machine. The applied displacement or force boundary conditions are such that the stresses in the specimen are always below the yield strength.

3 Computational Results and Discussions Finite element analyses of the only specimen with boundary conditions as shown in Fig. 3 are discussed first to find their deviations from strength of material. Results are sorted for the nodes on the cylindrical surface along the length of the specimen and for the nodes on the end surface along the radial direction. Figure 5 shows the variations of longitudinal normal compressive stress and shear stress along the length of the cylindrical specimen for cases 1a, 1b, 2a, and 2b. Whereas, the constant longitudinal normal compressive stresses obtained using strength of material formula are as follows: 64.5 MPa, 72.5 MPa, 64.5 MPa, and 72.5 MPa, respectively. The deviation in the compressive stress at the specimen ends, as reflected in Fig. 5a for cases 1b and 2b, is due to the application of force boundary conditions. Similarly, the shear stresses must be zero as per strength of material; however, the computational results show small deviations at the ends where force boundary conditions are applied (Fig. 5b). Figure 6 shows the variation of longitudinal compression and lateral expansion of specimen along its length for cases 1a, 1b, 2a, and 2b. The longitudinal compression of the specimen matches with values obtained from the strength of material. However, there are small deviations for lateral deformation in cases 1b and 2b at the ends where the external forces are applied. Figure 7 shows the variation of shear stresses on the end surface of the specimen along its radius for cases 1a, 1b, 2a, and 2b. Results for the bottom end surface are also included for cases 2a and 2b. Significant shear stresses develop at the top end surface where the force boundary condition is applied; however, the bottom end surface does not undergo significant shear stress

262

H. Naseem et al.

Fig. 5 Variations of (a) longitudinal normal compressive stress and (b) shear stress along the specimen length in the different loading cases: 1a, 1b, 2a, and 2b. Specimen length is 50.8 mm

and has similar shear stresses to the remaining cases. Figure 8a shows the variation of radial displacement of the nodes at the end surface of the specimen along its radius for cases 1a, 1b, 2a, and 2b. The radial displacement of nodes closer to force boundary conditions shows initial radial offset in cases 1b and 2b. This explains the respective significant shear stresses shown in Fig. 7. In the remaining cases, the nodes show expected monotonically increasing radial displacement starting from zero at the center. The results shown in Figs. 5, 6, 7, and 8a indicate that the FE models of ideal cases match closely with the strength of material approach. Therefore, cases 1a, 1b, 2a, and 2b can be considered as references or baselines for future comparison. Case 2a will serve as the reference for cases 3i, 3j, and 3k, whereas case 2b will be the reference for cases 4i, 4j, and 4k. The details of cases 3s and 4s are shown in Fig. 4 along with the specimen-platen interface conditions. These results also suggest that the displacement-controlled testing is closer to strength of material approach.

Fig. 6 Variations of (a) longitudinal compression and (b) lateral expansion due to Poisson’s ratio along the specimen length in the different loading cases: 1a, 1b, 2a, and 2b

Finite Element Analysis of Uniaxial Compression Test

263

Fig. 7 Variations of (a) radial and (b) tangential shear stresses at the specimen end surfaces

Fig. 8 Radial displacement of nodes at the end surfaces for (a) the ideal cases 1a, 1b, 2a (top and bottom), 2b (top and bottom) and (b) the actual compression test cases 3i, 3j, and 3k under displacement boundary conditions

Therefore, the results of the compression test with displacement boundary conditions (case 3s) are only presented in the following paragraphs. Figure 8b compares the radial displacement of the nodes at the end surface of the specimen for cases 3i, 3j, and 3k with case 2a. The radial displacement of case 3k matches closely with case 2a due to the frictionless specimen-platen interface contact condition. However, the radial displacement of the nodes in cases 3i and 3j is constrained, and the nodes are not able to move radially outwards. Moreover, the nodes do not tend to move tangentially in all cases. These displacements lead to the development of significant radial shear stresses at the interface (Fig. 9a) as compared to tangential shear stress (Fig. 9b) for cases 3i and 3j. Nevertheless, case 3k shows insignificant shear stresses at the end surfaces and is comparable to ideal case 2a. The variations in longitudinal normal compressive stress and shear stress along the length of the cylindrical specimen for cases 3i, 3j, and 3k have been compared with the respected baseline—case 2a in Fig. 10. Due to friction at the specimen-platen

264

H. Naseem et al.

Fig. 9 Comparison of (a) radial shear stress and (b) tangential shear stress at the end surfaces in actual compression test condition with the ideal test condition

interfaces in cases 3i and 3j, the state of stress near the interfaces is not uniaxial. However, the central portion (~ 60% of the length) of the specimen is still under uniaxial compression. Therefore, if the strain measurement is made from this portion then only, the stress–strain curve will provide uniaxial compression properties. Upon further loading (post-yield), more and more parts of the specimen will undergo nonuniaxial compression. For case 3k, the shear stress is approximately equal to baseline case 2a since it is the frictionless condition at the interfaces in case 3k. Similarly, the variations in longitudinal compression and lateral expansion of specimen along its length for cases 3i, 3j and 3k have been compared with the respective baseline case 2a in Fig. 11. Due to friction at the specimen-platen interfaces in cases 3i and 3j, barreling has been observed near the interfaces (~ within 5% of the specimen length). Negligible barreling is seen in case 3k due to frictionless boundary

Fig. 10 Comparison of (a) longitudinal normal compressive stress and (b) shear stress along the specimen length in cases: 3i, 3j, and 3k with the respective baseline case 2a

Finite Element Analysis of Uniaxial Compression Test

265

Fig. 11 Comparison of (a) longitudinal compression and (b) lateral expansion due to Poisson’s ratio along the specimen length in cases: 3i, 3j, and 3k with the respective baseline case 2a

conditions at the interfaces. Nevertheless, the lateral expansion in the central portion of the specimen is free from the end effects for all the cases.

4 Conclusions Detailed finite element analyses of the compression testing of a cylindrical specimen have been presented. Ideal test cases were ideated and analyzed with both the displacement and force boundary conditions to develop reference cases for comparison. The results of these ideal cases match closely with the strength of material. Therefore, these cases have well served for validation of the actual compression test cases. Moreover, the results also show that the cases with displacement boundary conditions are closer to the strength of material approach. Subsequently, the actual compression test cases have been analyzed only using displacement boundary conditions. In these actual test cases, the platen-specimen interface contact conditions have also been modeled. The results of actual test cases show that the specimen material points at the specimen-platen interface tend to move radially outwards, though, their displacement in the tangential direction is not significant. Nevertheless, their movement is constrained due to friction and that leads to significant radial shear stress at the interface. The longitudinal normal compressive stress and shear stress indicates that the central portion of the specimen still undergoes uniaxial compression, and the platen-specimen interface effects are restricted to the ends of the specimen. The results also confirm that the frictionless platen-specimen interface condition will be closer to ideal compression. All the presented results provide a comprehensive understanding of the typical compression test. Moreover, these results also suggest that if a new compression platen is designed which allows the radial outward motion at the platen-specimen interface then the issues, like non-homogenous deformation

266

H. Naseem et al.

and non-uniform axial compression of the specimen during the typical compression test, may be resolved.

References 1. Yao Z, Mei D, Shen H, Chen Z (2013) A friction evaluation method based on barrel compression test. Tribol Lett 51(3):525–535 2. Jerabek M, Major Z, Lang RW (2010) Uniaxial compression testing of polymeric materials. Polym Testing 29(3):302–309 3. Hu HC (2005) Continuum mechanics and plasticity. Chapman and Hall/CRC, USA 4. Bhaduri A (2018) Mechanical properties and working of metals and alloys. Springer, Singapore 5. Gruber JA (2018) Accurate data reduction for the uniaxial compression test. Exp Tech 42(2):209–221 6. Samantaray D, Mandal S, Bhaduri AK (2011) A critical comparison of various data processing methods in simple uni-axial compression testing. Mater Des 32(5):2797–2802 7. Banerjee JK, Cárdenas G (1985) Numerical analysis on the barreling of solid cylinders under axisymmetric compression. J Eng Mater Technol 107:145–147 8. Hsu TC, Young AJ (1967) Plastic deformation in the compression test of pure copper. J Strain Anal 2(2):159–170 9. Wu HC, Chang KJ, Schwarz J (1976) Fracture in the compression of columnar gained ice. Eng Fract Mech 8(2):365–370 10. Grosu E (2022) Applications of polyvinylchloride (PVC)/thermoplastic nano-, micro-and macro blends. In: Polyvinylchloride-based blends. Springer, Cham, pp 75–89

Evaluation of Microstructure, Mechanical Properties and Biocompatibility of Biodegradable Zinc-Based Alloys for Implants Mohammad Mohsin Khan , Abhijit Dey, Zainab Rubanee, Kausar Mushtaq, Mohammad Irfan Hajam, Sheikh Shahid Ul Islam, Musab Bashir Shah, and Akash Dwivedi

Abstract The use of biodegradable metallic materials for implants is growing in popularity within the biomaterials industry due to their superior mechanical properties and degradation rates compared to polymeric materials. Zinc and its alloys have been studied in recent years as possible candidates for biodegradable stent applications. This study aims to formulate and evaluate a new series of Zn–Ti/Cu alloys with the goal of finding an alloy that meets the standard mechanical properties required for biodegradable implants. The alloys were produced using vacuum induction melting, and various experiments were conducted to determine the effect of adding titanium and copper on the mechanical properties of zinc. The study looked at factors such as microstructure, mechanical properties, cytotoxicity, and antibacterial properties. The results showed that copper provided improved cytocompatibility and effectively eliminated bacterial reactions, and the mechanical results indicated that zinc-copper alloys had greater strength than other alloys. Thus, it can be concluded that Zn–Ti/ Cu alloys are a viable option for biodegradable implant applications. Keywords Biodegradable metal · Osteoporosis · Implants · Zinc-based alloys

1 Introduction The issue of osteoporosis is posing a grave threat to society and has been ranked as the second most harmful disease by the World Health Organization (WHO) [1]. There is an extensive rise in the number of cases of osteoporosis among both older M. M. Khan (B) · A. Dey · Z. Rubanee · M. I. Hajam · S. S. U. Islam · M. B. Shah · A. Dwivedi National Institute of Technology Srinagar, Srinagar, India e-mail: [email protected] K. Mushtaq SSM College of Engineering, Parihaspora, Jammu and Kashmir, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_25

267

268

M. M. Khan et al.

and younger generations in recent times which has led to an increase in implant demand. There is a widespread prevalence of non-biodegradable implants nowadays, which not only offer a risk of infection but also need the patient to endure the same surgical trauma during the removal of the implant. Hence, it is quite prudent to switch to biodegradable implants to address the challenges associated with nonbiodegradable implants. There are three major types of biodegradable implants, viz., magnesium-based, iron-based, and zinc based. Magnesium-based implants (standard electrode potential = − 2.73/SCE) are currently regarded as superior to iron-based implants since iron-based implants (standard electrode potential = − 0.44/SCE) degrade quite slowly, posing a threat to biosafety and leading to the inflammation of the surrounding tissues. In contrast to iron-based implants, magnesium-based implants degrade rapidly and release excessive H2 gas. This leads to the absorption of the implant even before the bone healing time and an increase in toxicity within the human body. Zinc-based implants (standard electrode potential = − 0.76/SCE) are in the early phase of development, but considering their degradation rate matching with that of the bone healing time, they seem to be an ideal option for the implant. Moreover, their ease of fabrication, excellent mechanical properties, and satisfactory biocompatibility weigh in the favour of zinc being used as implants. Zinc, being one of the key micronutrients required by the human body, poses no hazard to human metabolism after degradation. The average zinc intake for a human body ranges between 4 and 14 mg/day and the normal plasma level ranges between 70 and 120 mg/dL [2]. Biodegradable implants are expected to serve the following basic purposes [3]: 1. The implant both as a material and its by-products after degradation must not produce any negative host response. 2. To be compatible with the body tissue and blood. 3. To avoid any effect of stress shielding by having an elastic modulus matching that of the natural bone. Zinc has a hexagonal close-packed (H.C.P.) structure, having lattice parameters a = 0.2665 nm and c = 0.4947 nm. The elastic modulus (E) of zinc (97 ± 14 GPa), lies in between the elastic modulus of Fe (E = 210 GPa) and Mg (E = 39–46 GPa) [4] Since, a low modulus of elasticity supports a proper transfer of mechanical loading between bone tissue and implant, hence the stress shielding factor of zinc will be relatively higher than magnesium but lower than iron. Pure zinc is considered to be soft and brittle. To improve these properties of pure Zn, it is alloyed with certain other elements. Presently, we have around thirty types of Zn-based binary, ternary, and quaternary alloy systems that are being studied under various biomedical applications. These alloys include a combination of elements like calcium (Ca), Iron (Fe), Aluminium (Al), Magnesium (Mg), Strontium (Sr), Manganese (Mn), Zirconium (Zr), Germanium (Ge), Silver (Ag), Titanium (Ti), Lithium (Li), and Bismuth (Bi) [5]. The microstructure of binary zinc alloy consists of a matrix of αZn and secondary phases known as intermetallic phases. These are hard and brittle. Continuous and fine distribution of these phases improves mechanical properties [6]. Copper (Cu) is a vital element in human biology. Cu ions are essential in promoting

Evaluation of Microstructure, Mechanical Properties …

269

antibacterial activities. Cu has a solid solubility of 2.75 wt% at 425 °C, depicting medium solubility in zinc alloys, with decreasing solubility as temperature decreases. Thus, solid solution strengthening is expected to be achieved with the addition of copper to zinc [7]. In the present work, binary Zn-1 wt.% Cu and Zn-x wt.% Ti (x = 0.5, 1) alloys are fabricated by vacuum-assisted casting. The effects of Cu and Ti concentration on microstructure, mechanical properties, cytotoxicity, and antibacterial property are discussed, and the results are compared systematically.

2 Materials and Method 2.1 Material Preparation Zn–Ti alloys were prepared by melting zinc chips (99.9% purity) with pure titanium rod (99.7% purity), and Zn–Cu alloy were prepared using zinc chips and Cu (99.9% purity) in an electric furnace. The metals were put in a zirconia crucible. The temperature in the crucible was maintained at 580 °C for 20 min to melt the materials followed by maintaining at 10 min for the homogenization of the melt. The alloy compositions were maintained as follows: 0.5 wt% of Ti, 1 wt% of Ti, and 1 wt% of Cu, respectively, and the balance being that of zinc. S. No.

Alloying element

Base material

1

0.5 wt.% Ti

Zinc

2

1 wt.% Ti

Zinc

3

1 wt.% Cu

Zinc

2.2 Microstructure The product obtained was in the shape of rods of diameter 10 mm and height 32 mm; these were sectioned and ground to make the end faces parallel to obtain proper results for experiments that were to follow. 1. The samples were mounted in epoxy and later polished up to a grit size of 2000. 2. Further, the samples were finely polished using alumina slurry and 1 μm diamond paste. 3. The samples were then dipped in the etchant [using Palmerton’s reagent consisting of 200 g chromium trioxide (CrO3 ), and 15 g sodium sulfate (Na2 SO4 ), 1000 ml H2 O] for a few seconds.

270

M. M. Khan et al.

The microstructure of the samples was observed under a scanning electron microscope.

2.3 Mechanical Testing Hardness Testing. The hardness of the test materials was measured using Vickers Hardness Tester. Hardness measurements were conducted as per the ASTM standard E 92–82 [10], which defines the standard test method for hardness measurement. The specimen was polished, and opposite faces were made parallel. The parameters for hardness measurement were set at 15 kg load with a dwell time of 5 s, square pyramidal diamond indentor with included angles 136°. Five readings were taken from each composition. Tensile Testing. Small size round tensile specimens having a gauge diameter (ϕ) of 8 mm and a length of 30 mm were machined using wire electric discharge machining (WEDM). The tensile tests were conducted at room temperature using a computerized universal testing machine at a strain rate of 1.68 × 10–4 s−1 as per ASTM Standard, ‘E8’ [8]. An extensometer was used to measure the elongation. Young’s modulus values were obtained by performing a linear regression on the steepest region of the engineering stress vs. elongation curves. Thereafter, using 0.2% proof stress, the ultimate tensile strength of the sample was evaluated. Compression Testing. Cylindrical specimens having a diameter (ϕ) of 8 mm and a length of 12 mm were machined using WEDM. The compression tests were conducted at room temperature, using a computerized universal testing machine. The strain rate for the specimens was set at 2.22 × 10–3 s−1 as per ASTM Standard, ‘E9’ [9]. An extensometer was connected to the inside edges of the compression blocks to determine the amount of compression. The tests were performed without any type of lubricant. Young’s modulus was determined by analyzing the steepest part of the compressive stress vs. strain curves through linear regression. For each material, one sample was was tested for failure, and four additional samples were subjected to stress levels high enough to calculate the 0.1% and 0.2% offset yield stresses based on the test data. Fractography. Fracture studies were performed on the samples impacted by the mechanical tests. The samples were cleaned ultrasonically in acetone, dried and mounted on specimen stubs. The specimens were observed under a scanning electron microscope. A sputtered gold coating was used to lower the charging effects and enhance the secondary electron yield on specimens with very rough surfaces.

Evaluation of Microstructure, Mechanical Properties …

271

2.4 Biocompatibility Studies Cell Culture. Mesenchymal immature cells were extracted from the bone marrow of Delia Cres goat, resuscitated, and cultured in an α-MEM (supplemented with 10% foetal bovine serum, 1% penicillin (100 U/mL), and streptomycin sulfate (100 mg/ mL) culture medium in a cell culture incubator (37 °C, 5% CO2 and saturated humidity). The cells were studied under a microscope. Once 90% of the mesenchymal cells were confluent, the cultures were digested using 0.25% trypsin and centrifuged at a force of 300 N for 300 s. The cell pellet after the collection was resuspended. Cell concentrations were measured using a Cell Counting Kit-8 (CCK-8). This was developed as a standardized procedure to evaluate the cellular proliferation of the mesenchymal cells. The concentrations of the resuspended mesenchymal cells after adjustment were seeded in equal amounts in a 24-well plate (100 μm well). Incubator was used for cell culture maintained at 37 °C, with 5% CO2 and saturated humidity for 24–48 h [11]. Bacteria Culture. Four strains were obtained for bacteria culture tests. Staphylococcus aureus (ATCC 25,923), Staphylococcus epidermidis (ATCC 35,984), methicillin-safe S. aureus (MRSA), and methicillin-safe S. epidermidis (MRSE287). MRSE287 were revived, single clonal provinces were gathered, refined in tryptone soy stock (TSB) culture medium, shaken at 37 °C with a consistent temperature shaker at a speed of 2 rps, and refining was performed at high-impact conditions for approximately 14 h. The further bacterial suspension (1 mL) was centrifuged at 125 rps for 300 s. The supernatant was disposed off and the pelleted microscopic organisms were gathered. A culture medium was added to the microscopic organisms and vortexed. The convergence of the bacterial suspension was acclimated to 1 × 106 cfu/mL as per the McFarland turbidimetry technique.

3 Results and Discussions 3.1 Microstructure Figure 1 shows the SEM micrographs of the test materials. It can be seen that the titanium-added zinc-based alloys contain η-Zn phase (the matrix) and an intermetallic compound TiZn16 phase, whereas copper-added alloy contained η-Zn phase (the matrix) and ε-CuZn5 phase. The η-Zn matrix phase seems to be white and irregular, while the TiZn16 phase appeared to be a granular eutectic intermetallic compound. The majority of the TiZn16 eutectic precipitates are scattered along grain boundaries, with a small portion of the TiZn16 phase distributed in the grain matrix of the η-Zn phase. The η-Zn phase also contains numerous cellular crystal grains with indistinct grain boundaries. According to the Zn–Ti phase diagram, the small, rod-like, and granular Ti–rich phase is most likely the TiZn16 phase. The Zn–Ti phase diagram

272

M. M. Khan et al.

Fig. 1 Microstructure of the zinc-based alloy containing a 1 wt. % Cu, b 0.5 wt. % Ti and c 1 wt. % Ti

shows that the solubility of Ti in the Zn matrix is very low (only 0.007–0.015 wt.% at 300 °C). However, when the Ti concentration exceeds 0.21 wt.% and the temperature reaches around 418.6 °C, a eutectic reaction occurs and the TiZn16 phase forms with an excess of Ti and Zn, which is dispersed along the grain boundary and within the grains. Due to the rapid solidification of the alloy, the Ti underwent a nonequilibrium eutectic reaction, resulting in varying Ti concentrations in the different precipitation phases, with concentrations concentrated in specific locations such as eutectic branches and leading to severe segregation. The only elements in the finer granular phase are zinc and copper. Interpreted from the Zn–Cu phase diagram, this intermetallic phase is CuZn5 .

3.2 Hardness Hardness tests performed on Vicker’s hardness showed higher values for Zn-1 wt.% Cu (86 Hv) due to the presence of intermetallic phases of ε Cu–Zn5 which provided greater hardness compared to those of Zn–Ti alloys. Also, the hardness in Zn–Ti alloys increased with increasing Ti percentage due to the greater distribution of Zn– Ti16 intermetallics. Also, these alloys retained their hardness at elevated temperatures as the dispersed intermetallics are not reactive with the matrix phase. Table 1 gives the value of hardness for Zn-0.5 wt.% Ti, Zn-1 wt.% Ti, and Zn-1 wt.% Cu. Table 1 Sample hardness

S. No.

Sample

Avg. hardness (Hv)

1

0.5 wt.% Ti

61

2

1 wt.% Ti

73

3

1 wt.% Cu

86

Evaluation of Microstructure, Mechanical Properties …

273

Fig. 2 Load–displacement diagram for tensile test

3.3 Tensile Test The tensile strength of Zn–Ti under the tensile test increased with increasing Ti content from 0.5 to 1%. However, the tensile strength for Zn-1 wt.% Cu was found to be greater as compared to the 0.5% Ti and 1% Ti. Therefore, it is suggested that the addition of 1 percent Cu to the Zn alloys enhanced their average tensile strength, resulting in higher strength (Fig. 2).

3.4 Compression Test The compressive strength of Zn under the compression test initially decreased and then increased with the Ti content of 0.5% and 1%, respectively (Fig. 3). However, the compressive strength for Zn with 1 wt.% Cu was found to be greater as compared to the 0.5 wt.% Ti and 1 wt.% Ti. As a consequence, it is anticipated that the Zn alloy’s average compressive strength might be improved by adding 1% Cu, resulting in higher strength. Fig. 3 Load–displacement diagram for compression test

274

M. M. Khan et al.

Fig. 4 SEM micrograph of the fracture surface under the tensile test of a 0.5 wt.% Ti b 1 wt.% Ti and c 1 wt.% Cu dispersed zinc-based alloy

3.5 Fractography The SEM micrograph of Zn–Ti alloy samples taken after the tensile test confirmed the presence of fracture surfaces. From the micrograph of Zn-0.5 wt.% Ti, Fig. 4a, the mode of failure can be easily predicted as that of brittle. Cleavage planes are supposed to have been generated due to the coarse-grained nature of the alloy. Also, the low elongation percentage of 9% is in perfect consonance with the obtained result of brittle fracture. From the SEM micrograph Zn-1 wt% Ti, Fig. 4b, the presence of a cleavage surface was observed all over the fracture surface. Although this alloy had the highest elongation percentage of 13%, the mode of failure was found to be brittle, as confirmed by the SEM images. Cleavage planes were also present on the surface of Zn-1 wt% Cu alloy, Fig. 4c. It can also be observed that the intermetallic phases of Zn–Ti were present, which increased with an increase in the concentration of Ti in the alloys.

3.6 Biocompatibility Studies Cytotoxicity The results obtained from the undiluted, 25% dilution, and 50% dilution extracts on the mesenchymal cells showed an increasing trend in cytotoxicity, with an increase in concentration. Undiluted extracts inhibited cell proliferation. Further, it was observed that with an increase in the weight percentage of Ti from 0.5 to 1% cell growth showed a negative impact. The cell activity for the Zn-1 wt.% Cu extract showed enhanced results. The Zn-1 wt.% Cu concentrate had the biggest impact on cell multiplication in comparison with the other specimens. These outcomes show that the Zn–Ti composite is somewhat cytotoxic to mesenchymal cells; however, Zn-1 wt.% Cu compound can essentially further develop cytocompatibility. Figures 5 and 6 show the optical images of Zn-1 wt.% Cu, Zn-0.5 wt.% Ti, Zn-1 wt.% Ti, after 24 and 48 h of immersion in cell media, respectively.

Evaluation of Microstructure, Mechanical Properties …

275

Fig. 5 Cell activity in cell culture after 24 h of test samples containing a 1 wt.% Cu, b 0.5 wt.% Ti and c 1 wt.% Ti

Fig. 6 Cell activity in cell culture after 48 h of test samples containing a 1 wt.% Cu, b 0.5 wt.% Ti, and c 1 wt.% Ti

Bacteria Culture The plating gradient dilution method was used to test the bacteriostatic efficiency of pure Zn-0.5 wt.% Ti, Zn-1 wt.% Ti, and Zn-1 wt.% Cu alloys. The Zn-0.5 wt.% Ti group demonstrated substantially greater antibacterial activity after 24 h of bacterial co-culture. However, compared to Zn-1 wt.% Ti and Zn-0.5 wt.% Ti, Zn–Cu alloys had a significantly greater bacteriostatic efficiency on all four bacterial strains. There were considerable clonal bacterial colonies in the Zn-1 wt.% Ti group but none in the Zn-1 wt.% Cu alloy group. The adherence of two drug-resistant bacterial strains (MRSE287 and ATCC43300) on the specimen surface was studied. A significant quantity of bacterial growth was seen on the surface of Zn-1 wt.% Ti, resulting in clustered and multi-layered accumulations with a composite structure. On the Zn0.5 wt.% Ti surface, just a few bacterial colonies grew. In contrast, bacteria only attached to the surface of Zn-1 wt.% Cu alloys in small concentrations. More interestingly, the bacterial cells on the surfaces of the Zn–Cu alloys exhibited morphologies of shrinkage, deformation, and even disintegration and rupture. Figure 7 shows the optical images of Zn-1 wt.% Cu, Zn-0.5 wt.% Ti, Zn-1 wt.% Ti, after 24 h of immersion in bacteria culture respectively.

276

M. M. Khan et al.

Fig. 7 Bacterial activity in bacteria culture for 24 h of test samples containing a 1 wt.% Ti, b 0.5 wt.% Ti and c 1 wt.% Cu

4 Conclusions In the current work, the casting technique was used to create the Zn-X alloys (X = 0.5 wt.% Ti, 1 wt.% Ti, and 1 wt.% Cu) for biodegradable implant material applications. The Zn-X (Ti, Cu) alloys’ microstructure, mechanical characteristics, tensile test, compression test, hardness, fractography, cytotoxicity, and anti-bacterial capabilities were all thoroughly examined. The primary findings are as follows: 1. The elasticity of the Zn–Cu alloy was viewed as more prominent when contrasted with the 0.5% Ti and 1% Ti. In this manner, it is recommended that the normal rigidity of the Zn be enhanced by the addition of 1% Cu. Similarly, the compressive strength for Zn-1 wt.% Cu presented better results when contrasted with the 0.5% Ti and 1% Ti. 2. Alloying with 0.5 wt.% Ti, 1 wt.% Ti, and 1 wt.% Cu in Zn-X increases its hardness because of precipitation strengthening and grain refining. The highest hardness of 86 HV was found in Zn-1 wt.% Cu trailed by 73 HV in Zn-1 wt.% Ti and 61 HV in Zn-0.5 wt.% Ti. 3. In terms of cytocompatibility, it was understood that Zn–Cu alloys performed better than Zn–Ti alloys as copper is the essential trace element in the human body for tissue healing and growth. In the case of Zn–Ti alloys, it was found that the extracts containing a diluted solution of 25% showed no toxicity. Also, Zn-0.5 wt.% Ti showed better cytocompatibility results compared to Zn-1-wt.% Ti as elevated levels of Ti ions could inhibit osteocalcin production and lead to bone resorption. 4. Zn–Cu presented strong antibacterial efficacy for both positive as well as negative strains of staphylococci.

Evaluation of Microstructure, Mechanical Properties …

277

References 1. Lin J et al (2020) A biodegradable Zn-1Cu-0.1Ti alloy with antibacterial properties for orthopedic applications. Acta Biomater 106:410–427. https://doi.org/10.1016/j.actbio.2020. 02.017 2. Li HF, Shi ZZ, Wang LN (2020) Opportunities and challenges of biodegradable Zn-based alloys. J Mater Sci Technol 46:136–138. https://doi.org/10.1016/j.jmst.2019.12.014 3. Li Y, Yang C, Zhao H, Qu S, Li X, Li Y (2014) New developments of ti-based alloys for biomedical applications. Materials (Basel) 7(3):1709–1800. https://doi.org/10.3390/ma7 031709 4. Shi ZZ et al (2020) Design biodegradable Zn alloys: Second phases and their significant influences on alloy properties. Bioact Mater 5(2):210–218. https://doi.org/10.1016/j.bioactmat. 2020.02.010 5. Yang N, Balasubramani N, Venezuela J, Almathami S, Wen C, Dargusch M (2021) The influence of Ca and Cu additions on the microstructure, mechanical and degradation properties of Zn–Ca–Cu alloys for absorbable wound closure device applications. Bioact Mater 6(5):1436–1451. https://doi.org/10.1016/j.bioactmat.2020.10.015 6. Kabir H, Munir K, Wen C, Li Y (2021) Recent research and progress of biodegradable zinc alloys and composites for biomedical applications: biomechanical and biocorrosion perspectives. Bioact Mater 6(3):836–879. https://doi.org/10.1016/j.bioactmat.2020.09.013 7. Jiang J, Qian Y, Huang H, Niu J, Yuan G (2022) Biodegradable Zn–Cu–Mn alloy with suitable mechanical performance and in vitro degradation behavior as a promising candidate for vascular stents. Mater Sci Eng C 112652. https://doi.org/10.1016/j.msec.2022.112652 8. ASTM E8 (2010) ASTM E8/E8M standard test methods for tension testing of metallic materials 1. Annu B ASTM Stand 4C:1–27. https://doi.org/10.1520/E0008 9. Products M, Modulus T, Modulus C, Barrier TF, Rates S (2014) Standard test methods of i:1–9. https://doi.org/10.1520/E0009-19.2 10. ASTM Standard E92-82 (1997) ASTM E92-82 standard test method for vickers hardness of metallic materials. Annu B ASTM Stand 4 82(Reapproved):1–27 11. Jia B et al (2021) Biodegradable Zn–Sr alloy for bone regeneration in rat femoral condyle defect model: in vitro and in vivo studies. Bioact Mater 6(6):1588–1604. https://doi.org/10. 1016/j.bioactmat.2020.11.007

Thermal and Mechanical Analysis of Bimodular Beam Saumya Shah and S. K. Panda

Abstract The paper demonstrates the analysis of cantilever beam having dissimilar modularity in tensile and compressive tests liable with thermal and mechanical loading. The prime objective is to evaluate the governing differential equations to determine the displacements of the bimodular beam under combined loading of thermal and mechanical loading. The relation between change in temperature for conduction and convection for particular value of length of cantilever beam has been assessed. Keywords Thermo-elastic · Governing differential equations · Bimodular beam

1 Introduction It is noted that many materials such as concrete, graphite, ceramics, and some composites reveal dissimilar tensile and compressive Young’s modulus of elasticity. Such substances are denoted as bimodulus substances [1–3]. Bimodulus materials are first introduced by Timoshenko [4]. Some authors [5, 6] calculate for elastic solutions of cantilever bimodulus beam for mechanical load. Other authors have evaluate the thermal-elastic deformation of bimodulus materials but mostly are based on thin and thick plates [7, 8]. The early studies of homogeneous anisotropic thin plates of temperature bending with the help of Classical Laminated Plate Theory (CLPT) depend upon Kirchhoff–Love hypothesis [9, 10]. In other paper, Pathak et al. done the analysis of buckling behavior of composite rectangular plate under in-plane elasticthermal load with the help of ANSYS APDL software package [11]. The results show that natural frequency of a variable thickness rectangular plate is inversely S. Shah (B) · S. K. Panda Department of Mechanical Engineering, Indian Institute of Technology (B.H.U.), Varanasi, Uttar Pradesh 221005, India e-mail: [email protected] S. K. Panda e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_26

279

280

S. Shah and S. K. Panda

proportional to the temperature. Some authors studied the consequences of variation of temperature on the strength of composites [12]. An investigation has been done on the fracture of composite structures liable to thermo-elastic behavior with the help of a microscopic mechanical model. The present paper explores the governing differential equation to examine the deflections of cantilever bimodulus beam liable to thermal and mechanical loading. Also, the relation between change in temperature for conduction and convection for particular value of length of the cantilever beam has been demonstrated through graph.

2 Problem Description and Analytical Model A conventional model of cantilever beam with non-identical modular of elasticity in tension and compression is considered. A system of Cartesian coordinate is expressed into analytic model. The beam length is ‘l’ and thickness is ‘h. ’ The elastic modulus of tension and compression are E t and E c , respectively, and Poisson’s ratio and Thermal Expansion Coefficient hold constant. The mathematical model of the described issue depends on the elastic theory having following assumptions: • Substance is presumed to be continuous, homogeneous, and isotropic. • Different nonlinear relations are considered for stress and strain in tensile and compressive domain, shown in Fig. 1. • Euler–Bernoulli hypothesis says that cross-sections of the transverse plane remain plane and perpendicular to the neutral axis after deformation of the beam and their shape and area will not change. • Beam is at ambient temperature at the fixed end and beam loses heat at the free end. • In the lack of body forces, the elemental equation for static problem under thermal and mechanical loading is as follows: εx x =

1 [σx x − ν(σ yy+ σzz )] + αΔT E

(1)

σ

Fig. 1 Stress–strain curve of a bimodulus material

Et Ec

ϵ

Thermal and Mechanical Analysis of Bimodular Beam

{ E=

281

E t (σ > 0) E c (σ < 0)

h , θ ∂ΔT ∂ΔT ∂ΔT =− , = = 0 (as beam loses heat at the free end) (2) ∂x k ∂y ∂z where E—Young’s modulus of elasticity ν—Poisson’s ratio α—Thermal Expansion Coefficient ΔT —temperature change (conduction) h , —heat transfer coefficient k—thermal conductivity θ —change in temperature (convection).

3 Application A cantilever bimodulus composite beam liable to concentrated shear force at free end and thermal loading at fixed end is presented in Fig. 2. The Airy’s function is φ = a2 x 2 + b2 x y + c2 y 2 + a3 x 3 + b3 x 2 y + c3 x y 2 + d3 y 3 + a4 x 4 + b4 x 3 y + c4 x 2 y 2 + d4 x y 3 + e4 y 4 Applying boundary conditions {y {h (1) −0h σx x |x=0 dy.1 + y20 σx x |x=0 dy.1 = 0 { y2 [ {h[ ] ] (2) −0h σx x |x=0 dy.1 y + y20 σx x |x=0 dy.1 y = 0 2 {y {y (3) −0h τx y dy.1 + −0h τx y dy.1 = −P 2

2

P h

x

y0

y

Fig. 2 Cantilever bimodulus beam

l

282

S. Shah and S. K. Panda

Using strain displacement relations, we get 6P 2 x y + αΔT x + U (y) h3 E

(3)

6P νx y 2 + αΔT y + V (x) h3 E

(4)

u=− v=

where U (y) and V (x) are constant. Using stress–strain relations and strain displacement equations, we get the values of constants. Also a condition is imposed at fixed end, x = l, y = 0, u = v = 0,

∂v =0 ∂x

Finally, 2P 3 4P 6Pl 2 6P 2 3 x νy (1 + ν)y y − + + h3 E h3 E h3 E h3 E αh , θ 2 3P (1 + ν) + y + αΔT x − αΔT l − hE 2k 6P 2P 4P 6P v = 3 νx y 2 + 3 x 3 + 3 l 3 − 3 l 2 x + αΔT y h E h E h E h E

u=−

4 Result and Discussion The study has been focused on predicting the behavior of bimodulus beam liable to thermal and mechanical loading. The mathematical expressions derived in the previous section are being used to examining the consequences of change in temperature. The beam bending effects dilations of reverse signs to occur on the upper and lower halves. One half of the beam is contract and heated, and the other half is expanded and cooled. Therefore, a transverse temperature gradient and concentration gradient are obtained in presence of finite thermal expansion. The rate of heat transfer is same for conduction and convection. So, there may be a relation between change in temperature for conduction and convection for particular value of length

Thermal and Mechanical Analysis of Bimodular Beam

283

Change in temperature in convection (K)

250

200

150

100

50

0 0

200

400

600

800

1000

Change in temperature through conduction (K)

Fig. 3 Graph between change in temperature though conduction and convection

of the cantilever beam. Figure 3 shows that there is a linear relation between the conduction and convection temperatures.

5 Conclusion This paper reveals the Young’s modulus of elasticity of a cantilever beam with nonidentical modularity in tensile and compressive tests subjected to combined loading consisting of mechanical load at free end and thermal load at fixed end of the beam. The governing differential equation has been derived and explained for combined loading conditions. A linear relation has been derived between change in temperature through conduction and convection.

References 1. Jun-yi S, Hai-qiao Z, Shi-hong Q, Da-lin Y, Xiao-ting H (2010) A review on the research of mechanical problems with different moduli in tension and compression. J Mech Sci Technol 24(9):1845–1854 2. Jones RM (1976) Apparent flexural modulus and strength of multimodulus materials. J Compos Mater 10(4):342–354 3. Jones RM (1977) Stress-strain relations for materials with different moduli in tension and compression. AIAA J 15(1):16–23 4. Timoshenko SP (1933) Strength of materials. D. Van Nostrand Company, New York 5. Yang Q, Zheng BL, Zhang K, Li J (2014) Elastic solutions of a functionally graded cantilever beam with different modulus in tension and compression under bending loads. Appl Math Model 38:1403–1416

284

S. Shah and S. K. Panda

6. Sitar M, Kosel F, Brojan M (2014) Large deflections of nonlinearly elastic functionally graded composite beams. Arch Civ Mech Eng 14:700–709 7. Vel SS, Batra RC (2001) Generalized plane strain thermoelastic deformation of laminated anisotropic thick plates. Int J Solids Struct 38:1395–1414 8. Praveen GN, Reddy JN (1988) Nonlinear transient thermoelastic analysis of functionally graded ceramic-metal plates. Int J Solids Struct 35(33):4457–4476 9. Pell WH (1946) Thermal deflection of anisotropic thin plates. Brown University. Q Appl Math 4:27–44 10. Stavsky Y (1963) Thermoelasticity of heterogeneous aeolotropic plates. J Eng Mech Div Proc ASCE 89:89–105 11. Pathak DK, Purohit R, Soni A, Gupta HS (2021) Buckling analysis of composite laminated plate in different boundary conditions under thermo mechanical loading. Mater Today Proc Part 1 44(5):2211–2214 12. Ye J, Wang Y, Li Z, Saafi M, Jia F, Huang B, Ye J (2020) Failure analysis of fiber-reinforced composites subjected to coupled thermo-mechanical loading. Compos Struct 235:111756

Design Analysis of a UAV for Medical Transport Purposes Ruby Mishra, Samyak Nikhil Siddhanta, Somesh Kumar Sharda, Santosh Kumar Nayak, Swayam Bikash Mishra, and Basant Ku Nanda

Abstract The significance and use of UAVs/drones in the healthcare sector has increased drastically since the onset of the COVID-19 Global Pandemic. Hence, we studied the field of UAVs and alongside fabricate our own model, which could help in transport of medicines/medical equipment. Our primary aim is to do a case study about the use of drones in this field and then design a drone according to the findings of the case studies. We’ve used SOLIDWORKS and Ansys for design and analysis. Other applications, control technology, and the principles behind the flight of drone have also been covered. Keywords Unmanned arial vehicles · Healthcare sector · Medicines · Control technology · Turbulence

1 Introduction The term “drones” is more properly used to refer to unmanned aerial vehicles (UAVs) or unmanned aircraft systems (UAS). A drone, in its simplest form, is a flying robot that can be remotely controlled or fly on its own using software-controlled flight plans in their embedded systems and on-board sensors and GPS. A powered aerial vehicle is referred to be a UAV if it does not have a human pilot on board, can fly autonomously or under remote control, can be expendable or recoverable, and can carry a lethal or nonlethal payload. Drones with single or multiple rotors, fixed wings, and VTOL capabilities were the three main types utilized (vertical take-off or landing). Here is an explanation of some of the related review work that was done. A novel, distinct early real-time coronavirus detection and tracking system, was presented, according to Mohammed et al. [1] (UAV). The suggested apparatus R. Mishra (B) · S. N. Siddhanta · S. K. Sharda · S. K. Nayak · S. B. Mishra · B. K. Nanda School of Mechanical Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be a University, Bhubaneswar, Odisha, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_27

285

286

R. Mishra et al.

can measure ground temperatures from above [1]. The COVID-19 pandemic situations offer a management architecture for simulation-based and real-time pandemic situations. It gathers and saves a sizable amount of data in a predetermined amount of time [2]. Drones are utilized to monitor disaster sites and areas with biological hazards. Telecom drones are utilized for both diagnosis and treatment. Drones can serve as dependable delivery systems for laboratory and microbiological samples [3]. Artificial intelligence (AI) has the potential to fight fatal illnesses. Technology responds to the situation by learning, adapting, and changing its behaviour. It searches for the optimal position in the conflict with the coronavirus [4]. Drones that can be controlled remotely have a sluggish jet. The main rotor of the drone has a lift capacity of around 22.7 k and is three metres in diameter. Previously, it used roughly 1 gallon of gasoline every 45 min. One such study laid the groundwork for the creation of aeroplanes with a crop-based system, a higher par value, and larger VMD droplets [5]. Pesticides and fertilizer are sprayed using the remote-controlled unmanned aerial vehicle (UAV) equipment [6]. The UAV is controlled by manual flight plans, and the sprayer is manually operated by an RF controlled nozzle, using a spraying system that an unmanned aerial vehicle control (UAV). A highly integrated drone is used to deliver medicine in an emergency [7]. The 5-R robotic arm system used artificial intelligence and deep learning to repeatedly notice the pompous component [8]. The development of a robotic manipulator was carried out after it was identified [9], and needle deviation was measured for observation [10]. Different techniques were used in the mechanism to analyses vibration [11]. Design analysis and path simulation were accomplished using cutting-edge methodology, and experiments were conducted in [12, 13] while taking into account the theoretical model. Different sensors were used to plan the progressive needle device’ placement [14]. A mechanical investigation was carried out employing a counterweight in a newly built four-bar mechanism to sustain these [15]. For elastic four-bar connections, the best design was completed [16]. A healthcare facility is granted multilevel protection ways for preventing COVID-19 contamination employing various techniques like AI, smartphone apps, and workflow design modification [17]. Artificial intelligence and robotic manipulator design concepts are investigated in the context of biopsy procedures [18–20]. For a more thorough investigation, the flying robot’s mechanism and concept are also used here [21, 22]. From these papers, we gleaned broad concepts for developing a sophisticated drone with medical applications.

1.1 Underlying Principles of Drones The basic principle on which drones/UAVs work is the principle of stable flight. Four forces like lift, gravity force or weight, thrust, and drag force are acting on the drones in which, lift and drag are considered aerodynamics forces because of the movement through the air. These help in the following flight control parameters [23] which are flight path planning, trajectory generation, and trajectory regulation. All these forces balance out each other as shown in Fig. 1.

Design Analysis of a UAV for Medical Transport Purposes

287

Fig. 1 Principle of balanced forces [23]

2 Design Analysis We created a simple quadcopter assembly, which when produced in masses can be easily designed and reproduced, make special arrangements/allow space for accessories to be fit in on the main assembly as per the type of medical drug/equipment, etc., being needed to transport. Accessories are made which can easily fit onto the main body and can be altered depending on the medicine/medical equipment being transported and be able to transport medicines/medical equipment under varying natural and load conditions. The design is kept user friendly, lightweight, and energy efficient. We use Solid works software to create the model and then proceed with CFD analysis of the model in Ansys using Ansys Fluent system.

2.1 Materials Used Drone body and arms: carbon fibre-reinforced composites (CRFCs). Battery: lithium-ion batteries. Motors: conventional magneto-electric motors. Propeller (fan): polystyrene (Thermoplastic)—ABS. Holders: aluminium.

2.2 Different Components of the Drone Drones contain many technological components which include the following things: Electronic Speed Controllers (ESC), an electronic circuit that controls a motor’s speed and direction, flight controller, GPS module, battery, antenna, receiver, cameras, sensors (ultrasonic sensors and collision avoidance sensors), accelerometer (which measures speed), and altimeter (which measures altitude). Different accessories used in drones are shown in Fig. 2.

288

R. Mishra et al.

Fig. 2 Different accessories used in drones

Fig. 3 CAD model of drone

In Fig. 2, different accessories used in drones are shown, and in Fig. 3, the CAD model of drone is shown. Solid works software is used to design the model from scratch and also to assign materials to specific parts of the drone, as mentioned above. Figure 4 shows the control volume meshing analysis. Ansys software is used to study the behaviour of the motion characteristics of the drone when flying through air.

Design Analysis of a UAV for Medical Transport Purposes

289

Fig. 4 Control volume meshing for analysis

2.3 CFD Analysis of Turbulence CFD analysis is also done for observing air flow in the body of drone. Figures 5 and 6 show the meshing of Pre-CFD Processing and the turbulence of drones while moving. FOR CFD analysis, we will use the Fluid Flow (Fluent) Module of Ansys.

Fig. 5 Mesh analysis pre-CFD processing

290

R. Mishra et al.

Fig. 6 Turbulence intensity

3 Result Analysis Two perspectives—velocity and energy—are used to explain how winds affect the UAV. Finally, if the UAV flew into a dangerous wind field, it might not be able to follow the specified flight route very well or it might possibly get out of control. According to “The viewpoint of the energy transfer process” part, the kinetic energy of UAV can currently be increased to improve stability [24]. So the turbulence intensity should be low. After doing CFD analysis of the moving drone, it is observed in Figs. 6 and 7 that the turbulence intensity is very low and the turbulence kinetic is also in acceptable condition.

Design Analysis of a UAV for Medical Transport Purposes

291

Fig. 7 Turbulence kinetic error

4 Conclusion The use of drones for medical purposes has many benefits, including quick assistance, a reduction in travel time to the patient, a reduction in complications in the injured due to a short time to wait for rescue, support and improvement of basic operations of medical emergency teams, and the ability to reach locations inaccessible for basic medical transport. The progress of research in the medical applications of drone technology was slower than the other fields. So here, we are attempting to design a versatile flying UAV for the medical transport purposes. Using Solid works, a UAV model is designed, and static analysis and CFD analysis were made. From the CFD analysis, it is found that the turbulence in the propeller blades is stable while moving. It is found that usage of drones for medical transport was a huge win for both the society and nature. This is because it not only reduces delivery time, human interference, overall costs, etc., but it also reduced carbon footprints and emissions as compared to conventional methods of medical transport.

References 1. Mohammed MN, Hazairin NA, Al-Zubaidi S, Sairah AK, Mustapha S, Yusuf E (2020) Toward a novel design for coronavirus detection and diagnosis system using IoT based drone technology. Int J Psychosoc Rehabil 24(7). ISSN: 1475-7192 2. Kumar A, Sharma K, Singh H, Naugriya SG, Gill SS, Buyya R (2020) A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic 3. Rosser JC, Vignesh V, Terwilliger BA, Parker BC (2018) Surgical and medical applications of drones: a comprehensive review 22(3): e2018.00018

292

R. Mishra et al.

4. Vaishnavi P, Agnishwar J, Padmanathan K, Umashankar S, Preethika T, Annapoorani S, Subash M, Aruloli K (2020) Artificial intelligence and drones to combat COVID–19. https://doi.org/ 10.20944/preprints202006.0027.v1 5. Huang Y, Hoffmann WC, Lan Y, Wu W, Fritz BK (2015) Development of a spray system for an unmanned aerial vehicle platform. Appl Eng Agric 25(6):803–809 6. Kale S, Khandagale S, Gaikwad S, Narve S, Gangal P (2015) Agriculture drone for spraying fertilizer and pesticides. Int J Adv Res Comput Sci Softw Eng 5(12):804–807 7. Konert A, Smereka J, Szarpak L (2019) The use of drones in emergency medicine: practical and legal aspects. Hindawi no. 3589792 8. Shah S, Mishra R, Pramanik S, Kundu A, Pandit S, Mallick A (2020) Design and deviation analysis of a semi-automated needle manipulation system using image processing technique. In: First international conference on sustainable technologies for computational intelligence. Advances in intelligent systems and computing, vol 1045. Springer, Singapore. https://doi.org/ 10.1007/978-981-15-0029-9_22 9. Shah SK, Mishra R, Ray LS (2020) Solution and validation of inverse kinematics using deep artificial neural network. Mater Today Proc 26:1250–1254. https://doi.org/10.1016/j.matpr. 2020.02.250 10. Shah SK, Mishra R, Prasanna D, Mohapatro G (2019) Fabrication and deviation analysis of a 5-axis robotic arm using image processing technique. Mater Today Proc 18:4622–4629. https:/ /doi.org/10.1016/j.matpr.2019.07.446 11. Gourishankar M, Mishra R, Shubham S, Taniya G (2019) Real-time vibration analysis of a robotic arm designed for CT image guided diagnostic procedures. In: Advances in interdisciplinary engineering. Lecture notes in mechanical engineering. Springer, Singapore. https://doi. org/10.1007/978-981-13-6577-5_17 12. Shah SK, Mishra R, Mohapatro G (2018) Experimental and theoretical design analysis and modeling of a CT image guided robotic arm. In: 2018 International conference on engineering, applied sciences, and technology (ICEAST). Phuket, pp 1–4. https://doi.org/10.1109/ICEAST. 2018.8434464 13. Shah SK, Mishra R (2018) Advanced path simulation of a 5R robotic arm for CT guided medical procedures. Mater Today Proc 5(2):6149–6156. https://doi.org/10.1016/j.matpr.2017. 12.221 14. Shah SK, Mishra R, Choudhury S (2018) Preliminary design of an 7 DOF robotic manipulator positioning biopsy needle. Mater Today Proc 5(9):19140–19146. https://doi.org/10.1016/ j.matpr.2018.06.268 15. Mishra R, Mohapatro G, Behera R (2018) Structural and dynamic analysis of optimized four bar mechanism considering counterweight in coupler link. Mater Today Proc 5(2):5467–5474. https://doi.org/10.1016/j.matpr.2017.12.135 16. Mishra R, Kanti Naskar T, Acharya S (2018) Optimum design of elastic and flexible linkages for motion and path generation. Mater Today Proc 5(2):4629–4636. https://doi.org/10.1016/j. matpr.2017.12.034 17. Mohapatra M, Mishra R (2021) Modern and integrated approach for safety issues in healthcare during covid-19. Ilkogretim Online 20(5):1029–1034 18. Shah S, Mishra R (2021) Modelling and optimization of robotic manipulator mechanism for computed tomography guided medical procedure. Scientia Iranica. https://doi.org/10.24200/ sci.2021.57259.5149 19. Shah S, Mishra R, Szczurowska A, Guzi´nski M (2021) Non-invasive multi-channel deep learning convolutional neural networks for localization and classification of common hepatic lesions. Pol J Radiol 86(1):440–448. https://doi.org/10.5114/pjr.2021.108257 20. Shah SK, Mishra R, Mishra BSP, Pandey O (2020) Prediction of abnormal hepatic region using ROI thresholding-based segmentation and deep learning-based classification. Int J Comput Appl Technol 64(4):382–392 21. Maity R, Mishra R, Pattnaik PK (2022) A review of flying robot application in health care. In: Smart healthcare analytics: state of the art. Springer Singapore, pp 103–111

Design Analysis of a UAV for Medical Transport Purposes

293

22. Maity R, Mishra R, Pattnaik PK (2021) Flying robot path planning techniques and its trend. Mater Today Proc 1–8 23. Xue X, Lan Y, Sun Z, Chang C, Hoffmann WC (2016) Develop an unmanned aerial vehicle based automatic aerial spraying system. Comput Electron Agric 1(28):58–66 24. Wang BH, Wang DB, Ali ZA, Ting Ting B, Wang H (2019) An overview of various kinds of wind effects on unmanned aerial vehicle. Measure Control 52(7–8):731–739. https://doi.org/ 10.1177/0020294019847688

Parametric Optimization of the EDM Using a Genetic Algorithm on Machining of Al7075/SIC/WS2 Composites Amit Kumar , Mohan Kumar Pradhan , and Saurabh Jain

Abstract Hybrid metal matrix composites are well known for a variety of cuttingedge applications in the domain of automotive, aerospace, agricultural machinery, electronics equipment, and turbine blades, among others. These materials outperform metals and alloys in terms of good strength, lesser density, low coefficient of thermal expansion, and high wear resistance. In this study, different amounts of WS2 (0.75%, 1%, and 1.5%) were used to make an Al7075 alloy matrix reinforced with 10% SiC at a constant proportion. Additionally, a CNC electric discharge machine was used to assess the composite’s machinability. The input parameters like wt.% of WS2 , current, voltage, and pulse on time have been considered to form a Taguchi-based L9 array which is designed to perform a set of experiments. Responses like metal removal rate, radial overcut, surface roughness, and tool wear rate have all been studied to determine the impact of machining parameters. The regression mathematical model has been generated by using Minitab 18 software for all four responses. Simultaneously, multi-objective genetic algorithm has been employed by using MATLAB to get optimal parameters. Keywords Electrical discharge machining · Multi-objective optimization · Taguchi method · Hybrid metal matrix composite · Genetic algorithm

1 Introduction Aluminum metal matrix composites (AMMCs) include multiple reinforced elements with aluminum as a base matrix. The main components of hybrid composites are a single base matrix and two or more reinforcements [1]. Metal matrix composites A. Kumar (B) · S. Jain Maulana Azad National Institute of Technology, Bhopal, India e-mail: [email protected] M. K. Pradhan Department of Mechanical Engineering, National Institute of Technology, Raipur, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_28

295

296

A. Kumar et al.

(MMCs) are known for their good wear, low thermal coefficient of expansion, high specific strength and stiffness, corrosion, and high-temperature resistance among other well-known good performance in service and consequently high demand [2]. Due to the aforementioned advantages, research into composite materials has gained attention in recent years. Due to the process’s low cost and availability of a variety of materials and processing conditions, AMMCs made using conventional stir casting are an attractive result. The stir casting method makes sure that the reinforced particles are distributed uniformly, which increases the matrix’s strength [3]. The manufacturing industry found it extremely difficult to machine these hybrid metal matrix composites because of challenges like the work material’s extreme abrasiveness when using traditional machining techniques like drilling, turning, sawing, etc. To achieve better machining characteristics, non-traditional machining techniques like Ultrasonic Machining (USM), Electric Discharge Machining (EDM), Electrochemical Machining (ECM), etc. are typically preferred [4]. The ability to work with any material that is electrically conductive makes EDM, one of these unconventional processes, the perfect choice for cutting these more modern materials [5]. Using a conductive electrode, the EDM process creates intricate profiles through electrically conductive workpieces. The sparks get generated between the workpiece and electrode due to the voltage gap at a certain distance. The successive sparks erode the surface of the workpiece and eroded parts get flushed away by the fluid [6]. The main issue in the EDM is to choose the control variables such as input current (I p ), voltage (V ), and ON time (T on ) in such a way that Material removal rate (MRR) increases, and concurrently, surface roughness (Ra), tool wear rate (TWR), and radial overcut (ROC) should diminish [7]. When machining the Al-SiC nanocomposites, Gopalakannan et al. [8] used the response surface methodology to optimize the EDM machining parameters. Since the process parameters in the EDM process have different effects on the performance parameters, it is challenging to identify a single optimal set of process parameters from multiple parameters [9]. This paper reports the multi-objective optimization of EDM parameters on AL7075/SIC/WS2 composites. It is a challenging mathematical problem to find an optimum in a large, multidimensional space, especially when the function is complex and there are many criteria and constraints [10]. A genetic algorithm is the best, global-search, and population-based optimization method that is often used in manufacturing. A multi-objective optimization technique called non-dominating sorting genetic algorithm-II was used in the past by researchers who investigated the use of GA in electronic design management to maximize the outcome of the process. This offers an optimization model for EDM parameters based on genetic algorithms to simulate a decision [11].

Parametric Optimization of the EDM Using a Genetic Algorithm …

297

Table 1 Composition of the AA7075 Element

Cu

Mg

Zn

Si

Ti

Cr

Fe

Pb

Mn

%

1.5

2.4

6

0.5

0.5

0.5

0.25

0.2

0.2

2 Methodology 2.1 Fabrication of Specimen SiC and WS2 were utilized as reinforcement materials, with the aluminum alloy AA7075 serving as the base material. Table 1 displays the aluminum alloy composition that was employed in this study. Using the stir casting technique, the hybrid composites (Al7075 + SiC + WS2 ) were fabricated. In a graphite crucible, the required amount of Al7075 alloy was taken, and an electric furnace was used to heat it. The temperature in the furnace was increased from 750 °C to 900 °C. At a speed of 300–400 RPM, to form a vortex, the molten metal was agitated by a graphite impeller. Separate SiC and WS2 reinforcement particles were used, and an Al7075 matrix was blended with them. SiC (10 wt.%) and WS2 (0.75 wt.%, 1 wt.%, and 1.5 wt.%) have both been used in the composite at constant proportions. At 450 °C, WS2 and SiC powders were preheated to remove the moisture. The melt begins to form a vortex after the stirring has finished rotating. For an additional 5 min, the melt with reinforcement was stirred. Getting a uniformity of particles in the composite material is important even after the feeding of reinforcement particles has stopped. A metal mold made of cast iron was poured by the molten substance carefully, where it was left to cool and solidify for 3–4 h.

2.2 Taguchi’s Design of Experiment Traditional experimental design techniques are too complicated and challenging to use. For smooth conduction of the experiment, there is a need to design a special experimental procedure to see the effects of control parameters and to evaluate the performance appearances in the best machining condition. Taguchi method is used in this study which is a good tool for parametric design. To investigate the all input parameters, this method creates a unique design of orthogonal arrays. There were four input parameters such as wt.% (0.75, 1, 1.5), I p (8, 10, 12 A), T on (75, 100, 150 μs), and V (50, 60, 70 V) used as control variables in this research. Nine experiments were carried out using L9 orthogonal arrays in accordance with the Taguchi design concept.

298

A. Kumar et al.

Table 2 Experimental values of EDM responses Exp Wt. of I p (amp.) T on (μs) V (V) MRR WS2 (%) (mm3 / min)

TWR (mm3 / min)

ROC (μm) Ra (μm)

1

0.75

8

75

50

38.14446 0.145768 5.6458

6.9

2

0.75

10

100

60

17.76367 0.090511 5.7056

10.928

3

0.75

12

150

70

40.06992 0.119039 5.6782

11.91

4

1.0

8

100

70

18.3321

0.292309 5.6931

8.041

5

1.0

10

150

50

19.13204 0.378279 5.6258

10.381

6

1.0

12

75

60

39.21962 0.362711 5.6365

8.844

7

1.5

8

150

60

18.03273 0.230593 5.6248

10.323

8

1.5

10

75

70

18.2872

9

1.5

12

100

50

39.71567 0.101573 5.5806

0.046769 5.6782

7.12 9.723

2.3 Experimentation on EDM The experiments studies were performed on CNC electrical discharge machines. The AMMC workpiece with a size of 100 × 50 × 10 mm and a 10 mm diameter electrolytic pure copper rod were used as an electrode. The experiment was processed on the given material, and the impact of the above parameters was seen on MRR, TWR, ROC, and Ra. All the responses based on the EDM parameters are calculated and shown in Table 2.

2.4 Regression Analysis The process of developing statistics for predicting the relationships between variables is known as regression analysis. Understanding how the values of the dependent variable change when the value of any one of the independent variables is changed while the values of other independent variables are kept constant is useful. The estimation goal in each situation is the regression function, which is a function of the independent variables [12]. To create the regression model, Minitab 18 software was used by considering the relevant data. In order to create the regression model, a two-sided confidence interval with a 95% confidence interval has been taken. The suggested mathematical models for MRR by considering the process variable are obtained as follows in Eqs. 1–4. Y (1) = 769.1 − 155.9 ∗ X 1 − 77.58 ∗ X 2 − 1.153 ∗ X 3 − 8.002 ∗ X 4 + 62.17 ∗ X 1 ∗ X 1 + 4.131 ∗ X 2 ∗ X 2 + 0.005076X 3 ∗ X 3 + 0.06608X 4 ∗ X 4,

(1)

Parametric Optimization of the EDM Using a Genetic Algorithm …

299

Y (2) = −2.121 + 4.031 ∗ X 1 − 0.1912 ∗ X 2 − 0.006937 ∗ X 3 + 0.05399 ∗ X 4 − 1.787 ∗ X 1 ∗ X 1 + 0.009203 ∗ X 2 ∗ X 2 + 0.000034 ∗ X 3 ∗ X 3 − 0.000473 ∗ X 4 ∗ X 4,

(2)

Y (3) = 4.747 − 0.2181 ∗ X 1 + 0.1278 ∗ X 2 + 0.001621 ∗ X 3 + 0.009708 ∗ X 4 + 0.06809 ∗ X 1 ∗ X 1 − 0.006675 ∗ X 2 ∗ X 2 − 0.000008 ∗ X 3 ∗ X 3 − 0.000053 ∗ X 4 ∗ X 4,

(3)

Y (4) = −39.99 − 16.16 ∗ X 1 + 1.399 ∗ X 2 + 0.2038 ∗ X 3 + 1.231 ∗ X 4 + 6.439 ∗ X 1 ∗ X 1 − 0.04654 ∗ X 2 ∗ X 2 − 0.000705 ∗ X 3 ∗ X 3 − 0.01019 ∗ X 4 ∗ X 4,

(4)

where variables X1, X2, X3, and X4 denote the wt.% of WS2, I p , T on , and V, respectively. Y (1), Y (2), Y (3), and Y (4) stand for all four response variables.

3 Result and Discussion 3.1 The Impact of Process Variables on the Characteristics of Machining In order to analyze the S/N ratios, the Analysis of Variance technique called ANOVA is used. It is a statistical method that includes graphical analysis of the distinct effects and visually highlighting the significant influences of various substantial variables. Metal Removing Rate The size of the erosive surface at the specific machining regime as well as the contamination of the gap between the electrode and the workpiece are both factors that affect the stability of the machining process needed to achieve higher MRR in EDM [13]. The MRR is one of the important EDM responses which was calculated by Eq. 5.   MRR mm3 /min. =

Loss in weight (gm) × 60   The density of material gm/mm3 × Time (s)

(5)

Figure 1 says that when the wt.% WS2 increases, MRR decreased, and when I p increases, MRR first decreases from 8 to 10 amps before rapidly increasing. During the initial rise in T on and voltage, the MRR initially decreased from 75 to 100 μs and 50 to 60 V, respectively, before steadily increasing.

300

A. Kumar et al.

Main Effects Plot for MRR(mm3/min) Data Means

Wt.(%)

40

Ip (amp.)

V(volts)

Ton (μ sec)

Mean

35

30

25

20

0.75

1.00

1.50

8

10

12

75

100

150

50

60

70

Fig. 1 Impact of control parameters on MRR

Tool Wear Rate The TWR increases across all I p and T on stages as a result of the tool’s motion. The loss in volume of a tool per unit time is known as TWR. It is written as Eq. 6. TWR =

ΔW Loss in volume = , t ρ∗t

(6)

where ΔW is the weight loss in the tool during machining, t denotes the amount of time spent during cutting, and ρ = 8960 kg/m3 is the electrode’s density. The relationship between EDM parameters and TWR is shown in Fig. 2. It has been seen that as V and the wt.% of reinforcements (WS2 ) in hybrid composite samples increase, the TWR initially rises to 0.75–1 wt.% and 50–60 V, respectively, before sudden decreases. Initially, the TWR decreased by 10 A and 100 μs, respectively, when the I p and T on increase concurrently, before gradually increasing afterward. Radial Overcut A toolmaker’s microscope was used to measure the diameter of the EDMed hole for calculating the ROC. The discharge gap and electrode wear-induced ROC have a significant impact on the dimensional accuracy of the EDM machined surface. The relationship between EDM parameters and ROC has been illustrated in Fig. 3. As can be observed, the very first TWR rises from 8 to 10 A and 75 to 100 V, respectively, as I p and Ton rise, before eventually lowering. The ROC gradually increases when the voltage is increased simultaneously.

Parametric Optimization of the EDM Using a Genetic Algorithm …

301

Main Effects Plot for TWR(mm3/min) Data Means

Wt.(%)

0.35

Ip(amp.)

Ton (μ sec)

V(volts)

Mean

0.30

0.25

0.20

0.15

0.10 0.75

1.00

1.50

8

10

12

75

100

150

50

60

70

Fig. 2 Impact of control parameters on TWR

Main Effects Plot for ROC (μm) Data Means

Wt.(%)

5.69

Ton (μ sec)

Ip (amp.)

V(volts)

5.68 5.67

Mean

5.66 5.65 5.64 5.63 5.62 5.61 5.60 0.75

1.00

1.50

8

10

12

75

100

150

50

60

70

Fig. 3 Impact of control parameters on ROC

Surface Roughness One of the most significant aspects of the EDM process is surface roughness, which has an impact on the component’s fatigue resistance. The Surftest SJ-210 portable stylus-type profilometer was used to measure the Ra value. Figure 4 shows how EDM parameters affect the Ra, and it is clear that the Ra value decreases when the wt.% of reinforcements increases. When the voltage initially increases from 50 to 60 V, then Ra rises steadily along with the I p and T on and then is decreased to 70 V.

302

A. Kumar et al.

Main Effects Plot for Ra (μm) Data Means 11.5

V(volts)

Ton (μ sec)

Ip(amp.)

Wt.(%)

11.0 10.5

Mean

10.0 9.5 9.0 8.5 8.0 7.5

0.75

1.00

1.50

8

10

12

75

100

150

50

60

70

Fig. 4 Impact of control parameters on Ra

3.2 Genetic Algorithm (GA)-Based Optimization GA provides an optimization model for EDM parameters to produce a decision [14]. In this research, there is more than one objective. The MRR is only one response that needs to maximize and other than that TWR, Ra, and ROC are required to minimize to get optimum parameters. These parameters were conflicting in nature; it is very difficult to select an optimal combination of machining parameters to obtain higher MRR while maintaining TWR, Ra, and ROC. In order to achieve these four optimum EDM responses, a genetic algorithm-based optimization technique is very useful. This approach is applicable to both linear and nonlinear optimization problems [15]. Beginning with a population of arbitrary strings that represent design or decision variables, GA operations in MATLAB are performed. After that, the fitness value can be evaluated for each string. To create a new population of points, the population is then subjected to three main operations: reproduction, cross-over, and mutation. The new population is assessed and tested for termination once more. The population is iteratively operated and analyzed by the three operators if the termination condition is not satisfied. The process is carried out repeatedly until the termination criterion is reached [16]. The problem in the present study has been mathematically stated as: Maximize MRR = f 1 (Wt.%, I p , T on , V ), 1/TWR = f 2 (Wt.%, I p , T on , V ), 1/ROC = f 3 (Wt.%, I p , T on , V ), 1/Ra = f 3 (Wt.%, I p , T on , V ). Subject to 0.75 ≤ Wt.% ≤ 1.5, 8 ≤ I p ≤ 12, 75 ≤ T on ≤ 150, 50 ≤ V ≤ 70.

Parametric Optimization of the EDM Using a Genetic Algorithm …

303

To find the optimum parameters by GA, it needs some input required value which has to be set in the MATLAB optim tool as follows in Table 3. In the optim tool of multi-objective optimization by genetic algorithm, a total of 25 sets of the optimal solution have been found after 147 iterations (from Table 4), where f 1, f 2, f 3, and f 4 show four function values and x1, x2, x3, and x4 shows the four variables. Out of these, the best optimal parametric combination was found on the index (6) which is wt.% of WS2 ≈ 0.75, I p ≈ 12 A, T on ≈ 150 μs, and V ≈ 60 V. This combination provides the highest possible MRR = 41.62734 mm3 /min, and other lowest possible values were TWR = 0.154251 mm3 /min, ROC = 5.661129 μm, Ra = 8.67217 μm. Table 3 Parameters’ set in the GA optimization tool Used values

GA constraints Type of population

Double vector

Population size

50

Function (creation)

Constraint dependent

Maximum no. of generation

100* no. of variables

No. of problem variables

3

Fraction (cross-over)

0.8

Mutation rate

0.01

Function (hybrid)

fgoalattain

Stall generation

100

Function tolerance

1e−4

Population fraction (Pareto front)

0.35

Table 4 Decision variables and Pareto front-function values Index f 1

f2

1

21.46881

0.027357 5.733115 10.98911

0.756348

9.833234 112.7682 69.97106

2

−0.59482

0.326389 5.667392 10.14567

1.250243

9.398951 113.7624 60.77698

3

20.75799

0.030973 5.733629 10.82452

0.756348

9.528546 112.5494 69.98668

4

38.85255

0.17294

1.487514 11.99831

5

21.97219

0.379584 5.607032

6

41.62734

0.154251 5.661129 13.20917

7

13.55029

0.411217 5.646786

8

31.16494

0.342564 5.598691 10.35992

f3

5.57846

f4

11.48442

x1

5.518954 1.232062

x2

x4

138.7925 52.09299

8.018326 75.11931 50.06313

0.764753 11.89872

6.923623 1.15882

x3

129.4002 59.40935

8.116738 77.61297 58.3208

1.221499 11.92943

112.2005 51.80587

9

1.855643 0.361726 5.662709

9.382819 1.138864

9.062094 102.2246 57.70044

10

9.248987 0.377026 5.638976

7.649928 1.199298

8.413052 87.59687 54.65333

11

26.57627

0.051921 5.722118 11.55124

0.771175 10.6667

119.5439 69.89955 (continued)

304

A. Kumar et al.

Table 4 (continued) Index f 1 12

f2

f3

0.085454 0.310451 5.664944

f4 9.61517

x1

x2

x3

x4

1.286868

9.111796 105.9194 60.58174

6.473363 1.071032

8.046739 75.79743 53.87856

13

19.11397

0.409167 5.633787

14

35.43139

0.239395 5.632761 12.02085

0.844305 11.49996

135.3643 52.02207

15

24.10917

0.250239 5.601771 10.95692

1.386681 11.47042

128.7305 52.23593

16

15.69656

0.418103 5.638656

17

20.44944

0.259536 5.596399 10.38163

1.409024 10.5401

18

20.75799

0.030973 5.733629 10.82452

0.756348

9.528546 112.5494 69.98668

1.254063

9.193204 91.55146 60.67249

1.5

9.548672 109.384

6.567911 1.165591

8.042986 75.38104 56.83134 150

50

19

2.023138 0.328218 5.666585

20

5.274848 0.069704 5.676025 10.09659

21

4.894216 0.266996 5.66795

8.121864 1.348892

9.194639 84.04247 62.84104

22

8.95241

6.988052 1.254244

8.927806 75

23

3.913539 0.099213 5.664589

9.88218

1.498616

9.332337 102.3493 61.97381

24

3.393607 0.333387 5.665506

8.295445 1.254091

9.167106 86.26191 60.70568

25

5.959119 0.219636 5.63353

0.361653 5.645153

8.72477

10.05503

1.391968 10.07812

65.92906 56.43664

112.8029 53.36106

4 Conclusion This study examines how the parameters of EDM affect the machining responses of a given composite surface AL7075/SIC/WS2 . The experiments were conducted using various parametric settings based on Taguchi orthogonal array. Using Minitab 18 software, ANOVA analysis and Taguchi methodologies were used to model the potential outcomes and characteristics of the EDM machining settings. To show the relationship between the responses and the control factor, a mathematical regression model was developed. A genetic algorithm was employed for process optimization to get multi-objective outputs. The following parameters: wt.% of WS2 = 0.75, I p = 12 A, T on = 150 μs, and V = 60 V have been recommended to get the maximum productivity. For MRR, it was seen that I p is the most important parameter, followed by T on . The TWR has mainly been affected by I p followed by WS2 wt.%. The ROC and Ra are largely dominated by wt.% of WS2 followed by voltage.

References 1. Alaneme KK, Olubambi PA (2013) Corrosion and wear behavior ice husk Ash/alumina reinforced al–mg–si alloy matrix hybrid composites. J Market Res 2(2):188–194 2. Rohatgi P, Schultz B (2007) Lightweight metal matrix composites stretching the boundaries of metals. Mater Matters 2:16–19 3. Thomas AT, Parameshwaran R, Muthukrishnan A, Kumaran MA (2014) Development of feeding and stirring mechanisms for stir casting of aluminium matrix composites. Proc Mater Sci 5:1182–1191

Parametric Optimization of the EDM Using a Genetic Algorithm …

305

4. Ho KH, Newman ST (2003) State state-of-the-art discharge machining (EDM). Int J Tools Manuf 43(13):1287–1300 5. Mohd Abbas N, Solomon DG, Fuad Bahari M (2007) A review on current research trends in electrical discharge machining (EDM). Int J Mach Tools Manuf 47(7–8):1214–1228 6. Silambarasan S, Prabhakaran G (2018) Int J Mech Prod Eng Res Dev (IJMPERD) 8(8):426– 430. ISSN (P): 2249-6890; ISSN (E): 2249-8001 7. Pradhan MK, Biswas CK (2010) Neuro-fuzzy and Neural Network based prediction of various responses in electrical discharge machining of AISI D2 steel. Int J Adv Manuf Technol 50(5– 8):610 8. Gopalakannan S, Senthilvelan T, Ranganathan S (2012) Proc Eng 38:685–690 9. Gouda D, Panda A, Nanda BK, Kumar R, Sahoo AK, Routara BC (2021) Recently evaluated electrical discharge machining (EDM) process performances: a research perspective. Mater Today Proc 44:2087–2092 10. Wojciech P (2009) Genetic algorithms, a nature-inspired tool: survey of applications in materials science and related fields. Mater Manuf Process 24:174–197 11. Rao JP, Kumar LS (2019) Int J Prod Technol Manage (IJPTM) 10(1):49–59, Article ID: IJPTM_ 10_01_005 12. Pradhan MK (2012) Determination of optimal parameters with multi-response characteristics of EDM by response surface methodology, grey relational analysis, and principal component analysis. Int J Manuf Technol Manage 26(1–4):56–80 13. Josko V, Junkar M (2004) Online selection of rough machining parameters. J Mater Process Technol 149:256–262 14. Kumar R, Pradhan MK, Kumar R (2014) Modeling and optimization of milling parameters on AL-6061 alloy using multi-objective genetic algorithm. In: 5th international and 26th all India manufacturing technology, design, and research conference (AIMTDR 2014) December 12th–14th, 2014 15. Nakhjavani OB, Ghoreishi M (2006) Multi-criteria optimization of laser percussion drilling process using artificial neural network model combined with genetic algorithm. Mater Manuf Process 21:11 16. Paszkowicz W (2009) Genetic algorithms, a nature-inspired tool: survey of applications in materials science and related fields. Mater Manuf Process 24:179–197

Status of Bio-printing Inks and Their Compatibility with Current Printing Techniques Shrushti Maheshwari, Rajesh Purohit, Deepen Banoriya, Anurag Namdev, and Deepa Ahirwar

Abstract Over the past decades, there has been a drastic growth in additive manufacturing which is also referred to as 3D printing. The ability of 3D printing to lay material in a 3-D matrix with great precision, even with the most intricate designs, is quickly making them mainstream in production, industrial, academics, medicine, dentistry, biomedical and other sectors. Although there has been vast development, great potential lies ahead in increasing its versatility in promising fields like bioprinting, hybrid 3-D printing and many more. Owning the potential to fabricate patient-specific designs and medical devices at a practical cost within a suitable time, 3D bioprinting is establishing itself as a dominant technology for the new era medical revolution. However, bioprinting being a budding field lacks in diversity of bio-printable materials or 3D inks and the compatibility of these inks with the current printing techniques. This study objects to highlight the current developments in bio-printing ink and factors affecting it’s printability. A brief overview of ink requirements for current printing techniques is also provided. Keywords 3D printing · Bioprinting · Direct ink writing (DIW) · Bio-inks · Fused deposition modelling · Stereolithography (SLA) · Laser-guided direct writing (LGDW)

1 Introduction 3D printing is a process of laying down sequential layers of materials at a predetermined layer thickness and speed. These materials can be ceramics, biomaterials, metallic elements, concrete, plastics, or other adhesives. Although printing speed, S. Maheshwari (B) Department of Mechanical Engineering, Indian Institute of Technology, Dhanbad, India e-mail: [email protected] R. Purohit · D. Banoriya · A. Namdev · D. Ahirwar Department of Mechanical Engineering, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_29

307

308

S. Maheshwari et al.

Step-1

Step-2

Step-3

Step-4

Fig. 1 Step 1: Imaging of targeted tissue or part for the comprehensive study of tomographic and architectural complexities. Step 2: Developing a computer-assisted 3D model. Step 3: Digitally sliced image or 2D cross-sectional thin horizontal slices (with changeable size and orientation) consisting of text-based command lists are prepared. These also include information about ink parameters and print directions. Step 4: Importing the slices in bio-printer [1]

processing speed and resolution have drastically developed over current years, the lack of diversities in 3D printable materials continues. The compatibility and flowability of printing inks with the most recent printing techniques are critical to rapidly evolving fields like 3-D printing biomaterials, tissues, and high-viability cells. The advancements in printing inks aimed at embryonic biomedical applications and their compatibility with presently accessible printing techniques are the subject of this study. Bio-printing is the course of producing cell patterns by layer-by-layer deposition, thus constructing living human constructs with comparable genetic, biochemical and mechanical properties for properly recuperating tissues, supports and body parts. Constituents used for bio-printing are termed biomaterials. These materials should possess (1) Easy printability, (2) Biocompatibility, i.e. acceptable by the human body, (3) Allow tissues hindrance free restoration, (4) Appropriate mechanical properties, (5) Proper rate of degradation and (6) Upon degrading should not release any hazardous by-products. Figure 1 shows the steps required in bio-printing.

2 3D Bioprinting Techniques 2.1 Fused Deposition Modelling (FDM) In this process, ink is forced through the nozzle in the semi-molten state at suitable viscosity, or it is melted to form a predetermined deposit on the build platform. The obligatory ink is made in the form of a solid filament (preferably wire) which is then heated to a semi-molten state before extrusion. The filament is moved between the rollers to propagate the semi-molten extrude further. A temperature-controlled extruder excretes out the filament material. Extruder force can be pneumatically or

Status of Bio-printing Inks and Their Compatibility with Current …

309

mechanically controlled with the help of a lead screw. The pushed-out extrude is placed on a platform sequentially. After finishing, one layer platform gets depressed and then the next layer gets deposited, as shown in Fig. 2. The main parameters deciding the concluding attributes of the material are (1) Layer thickness or height, (2) Support material, (3) Infill density, (4) Build orientation, (5) Raster angle, (6) Printing speed and (7) Bed temperature. Ink Requirements for the FDM process: Mostly, these systems are compatible with a broad range of ink. (1) Viscosity should be greater than 6 × 107 mPa/s [3]. (2) Should be molten at the temperature range of (200–250 °C) [4]. (3) Steep rate of solid to melt is required as the viscous melt is necessary for pre-extrusion and after extrusion, solidification is needed. (4) The ratio of melt viscosity to elastic modulus ought to be below 5 × 105 s−1 [5]. Bio-inks suitable for the FDM process: Poly(caprolactone): PCL has the belowmentioned attributes, making PCL suitable for the melting-based extrusion process. (1) Bio-resorbable (2) Low cost (3) PCL has shear-thinning properties; hence, its viscosity can be manipulated according to requirement by exploiting shear stress which in turn makes it prime material to be used in fused deposition modelling (4) High thermal stability. Table 1 shows PCL-based bio-inks. Poly(lactic acid): Poly-lactic acid (PLA) is a dominant polymer used in FDM printing owing to attributes like biocompatibility, renewability, biodegradability and lesser cost. PLA has a melting point of 170–180 °C, which makes it appropriate for creating filaments. It is squeezed out at a temperature of 200–300, 170–180 °C [8]. One of the problems that need to be addressed with PLA is the release of acidic by-products upon degradation. A significant decrease in the physiological Fig. 2 Fused deposition modelling (FDM) [2]

310

S. Maheshwari et al.

Table 1 PCL-based bio inks Materials Fabrication process

Key findings

References

PCL + Chitosan

FDM M.P. of PCL scaffolds = 130 °C Nozzle speed: 1–3 mm.s−1 Pressure-1.5–3 bar Chitosan hydrogel was filled inside the pores of PCL scaffolds

It has reinforced compressive strength and a [6] suitable microenvironment for osteogenesis and cell growth. It is a promising three-dimensional scaffold for in vivo bone

PCL + β-TCP

FDM

PCL + β-TCP have shown better physical, biological and mechanical properties than PCL alone

[7]

P.H. is observed due to the release of lactic acid. To overcome this, PLA is amalgamated with ceramics (calcium phosphates) to produce a composite which reduces acidic releases. Composite thus formed also tends to increase compressive strength henceforth, making it suitable for musculoskeletal manufacturing techniques. Polyether ether ketone (PEEK): PEEK is a semi-crystalline-shaped thermoplastic polymer that can be used in FDM printers. It has a melting temperature of 330–340 °C and a service temperature of 260 °C [9]. As the poly manufacturer’s high melting point was previously unsuitable for use with FDM technology, recent advances have allowed it to be more widely adopted, consequently leading to PEEK usage in FDM. Peng Geng et al. experimented with parameters affecting the 3D printing of PEEK by FDM and found that melt pressure, extrusion speed and extrusion force are the prominent factors which play a significant role in surface morphology as well as in deciding the diameter of filament [10]. Polybutylene terephthalate (PBT): PBT is a semi-aromatic thermoplastic polymer which is either amorphous or semi-crystalline. Its thermophysical and mechanical properties greatly depend on the degree of crystallinity: the more the crystallinity, the harder and more robust the plastic. Although PBT is biocompatible and shows less creep at elevated temperatures, its applications have been limited in FDM owing to its high melting temperature of 220–225 °C. However, Tellis et al. experimented with filaments of PBT in the FDM process to fabricate bone scaffolds of canine trabecular bones. It was found that fabricated bone scaffolds were capable of matching trabecular bone porosity and thereby establishing its potential in biomimetic scaffolds and its compatibility with fused deposition modelling [11].

Status of Bio-printing Inks and Their Compatibility with Current …

311

2.2 Stereolithography (SLA) SLA is a method of light-assisted three-dimensional printing in which a laser is used to cure photopolymer resin into the desired shape. A UV laser is used in typical SLA machines to cure an explicit layer of photosensitive resin from the tank. To precisely trace a 2D programmed silhouette of the object to be made, the ultraviolet laser is precisely controlled. The resin is cured or hardened by the laser, resulting in a thin, sliced, solid layer. A previously formed slice, the build plate, or the tank’s bottom are held in place by this slice. Then, the build plate moves away based on the layer thickness. The tank can be moved in this way by peeling, sliding, or shaking. This procedure is recurring until the complete entity is formed. Due to the use of a laser to solidify the material, SLA can achieve higher resolution than FDM. The X.Y. or horizontal resolution of SLA varies depending on the size of the laser spot and can be anywhere from 30 to 140 microns. The upright resolution, or resolution in the Z direction, ranges from 25 to 200 microns. Speed and quality are two contrasting parameters. As a result, a delicate equilibrium ought to be maintained. Ink requirement for the SLA process: (1) Biocompatible materials should be stable under exposure to U.V. light. (2) Low in viscosity and (3) Optically transparent [12]. Bio-inks suitable for the SLA process: Poly(propylene fumarate) (PPF): PPF is primarily utilised in SLA owing to its photo-crosslinking capability. Additionally, it is biodegradable and has significant mechanical properties. It is primarily processed in SLA by creating a solution with diethyl fumarate (DEF) as a solvent and PPF as the base polymer. SLA necessitates the use of a photoinitiator in combination with the above-stated solution. Bisacryl phosphine oxide is used in this example. It’s crucial to strike a good balance between PPF and DEF. With a PPF-DEF ratio greater than 0.5, it has been observed that mechanical strength declines suggestively. Alternatively, adding DEF makes the solution less viscous, which makes it easier to print. Karissa Nettleton and colleagues investigated the relationship between the molecular mass of PPF scaffold samples printed using continuous digital light processing (cDLP) stereolithography and the rates at which bone is degraded and rebuilt in vivo. It was discovered that PPF with less mass had a higher healing rate; however, the in vivo effect of molecular mass was obscured at 12 weeks [13]. Most importantly, there was no inflammation and host cell acceptability. Poly(D, L-lactide) (PDLLA): PDLLA is an oligomer which has spread its tentacles in the medical field because of the gainful prospect of increasing its rheological, biomechanical, and thermal attributes by exploiting the ratio of D and L isomers in its formulations. In addition to PDLLA, its composite with nanosized Hydroxyapatite (H.A.) was processed for the replacement of damaged tissues and organs. A. Ronca et al. prepared composite scaffolds of H.A. nanopowder embedded in PDLLA oligomers through SLA using ethyl-2,4,6-trimethyl-benzoyl phenyl phosphinate as photoinitiator and N-methyl-2-pyrrolidone (NMP) as a diluent. With cumulative ceramic content, the resin becomes more viscous; therefore, NMP is a non-reactive diluent, which helps maintain the viscosity required for SLA. In addition, a small

312

S. Maheshwari et al.

amount of tocopherol inhibitor and Orange Orasol G dye were used to cease premature polymerization. It was studied that the modulus of elasticity amplified with a surge in the concentration of H.A. powders [14]. Gelma and PEGDA-based ink: Zhu et al. [15] investigated a novel cell-filled cartilage tissue construction using a stereo lithography-based 3D bio-printer. A biocompatible photoinitiator, numerous concentrations of polyethene glycol diacrylate (PEGDA), transforming growth factor-beta 1 (TGF-1) entrenched nanospheres made up through a core–shell electrospraying method, and a 10% gelatin methacrylate (GelMA) base make up the printable resin. Printability was found to be significantly enhanced when PEGDA was added to GelMA hydrogel, While the swelling ratio decreased, compressive modulus also increased in proportion to PEGDA. The highest rates of cell viability and proliferation are pragmatic when cells are grown on hydrogels containing 5%/10% (PEGDA/GelMA). The chondrogenic differentiation of the captured MSCs can be enhanced by the TGF-1 embedded in nanospheres, which can withstand a continual release for up to 21 days. As a result, this ink has much potential for regenerating cartilage.

2.3 Direct Ink Writing (DIW) DIW, as depicted in Fig. 3, is a 3D printing technique based on extrusion that is similar to FDM in that it uses a nozzle to plunge materials layer by layer upon a fabrication platform. With this technology, materials can be deposited in a highly viscous liquid state, preserving their shape after deposition. Because it can print an extensive series of materials, including hydrogels, plastic, food, ceramics, and even living cells, DIW is more versatile than FDM. Printing speed, the size of the nozzle, the material’s viscosity and density, and the thickness that is sustained between the layers are the essential constraints that govern the resulting characteristics of the product. Like FDM, DIW has trouble making long structures with sections that aren’t supported. However, this issue can be quickly resolved by employing filler materials that can be destroyed or dissolved in the post-printing process. Through sintering, heating, UV curing, and drying, the post-fabrication process also hardens the product and improves its mechanical properties. Laser-guided direct writing (LGDW): LGDW is a method that deposits cells with micrometre precision. For cell deposition on a variety of surfaces and matrices, this method makes use of a weakly focused laser beam. A laser beam is focused near a cell suspension in laser-guided bioprinting, where it is used to encapsulate and assist cells onto a recipient substrate. The in-depth operation of laser-assisted bioprinting is depicted in Fig. 4. Significant apprehension in employing laser-guided direct writing or laser-aided bioprinting is the high cell mortality rate due to thermal damage occurring from nanosecond laser irritation. However, Hopp et al. [16] eradicated the abovementioned issue by employing a femtosecond-based laser and observed a decrease in cell death.

Status of Bio-printing Inks and Their Compatibility with Current …

313

Fig. 3 Direct ink writing [15]

Fig. 4 Laser-assisted bio-printing [17]

Ink Requirements for DIW: (1) Fast rate of gelation. (2) Should provide sufficient integrity after printing out. Bio-inks suitable for DIW and LGDW: Hydrogel Inks: Hydrogels are complicated three-D networks of hydrophilic polymers that can hold much water and are based on both synthetic and natural polymers. The ability of the hydrogel to provide an environment that facilitates the encapsulation of viable cells and protects the cell without preventing cell–cell interaction is the primary benefit of its excellent biocompatibility and biodegradability. In order to use hydrogel inks, properties like (1) Under working pressure should flow smoothly; viscosity is required to control this parameter. (2) After printing, it should provide sufficient integrity; yield stress is necessary for controlling this parameter. (3) The shear thinning property can be used to control the rapid rate of gelation [18]. Formulating a polymer solution that immediately forms a network after printing is the traditional process for designing hydrogel ink. Using external stimuli like temperature, light, or ion concentration, the newly formed web could be materially or chemically cross-linked [19]. Hydrogels for 3D bioprinting are

314

S. Maheshwari et al.

mostly made of natural polymers like gelatin, cellulose, collagen, fibrinogen, alginate, and agar, as well as synthetic polymers like polyacrylamide [20], polyurethane [21], and polyethene glycol (PEG) [22]. As the concentration of polymers and the density of cross-linking decrease, the proliferation rate toward a target tissue decreases. However, due to their increased viscosity, sophisticated polymer concentrations have been found to be advantageous for extrusion-based DIW [23]. High polymer concentration also increases mechanical properties. Therefore, a suitable balance must be found between these two opposing requirements for a particular application. With more viscous inks, high pressure, and nozzles with small diameters, shear stress rises, which can cause cell death during extrusion. 35 per cent or more of cells die when shear stresses exceed 60 MPa [24]. Since then, the resolution of bioprinting hydrogels with DIW has been limited by shear stress. Gelatin-methacryloyl (GelMA): It is a gelatin derivatized with methacrylamide and methacrylate groups semi-synthetic hydrogel. Iliyana Pepelanova and the others GelMa was also publicized to backing cartilage tissue formation with chondrocytes and MSCs [26] and experimented with that GelMA hydrogels can be synthesised with a detailed degree of functionalization (DoF) and accustomed to the proposed application as a 3D cell culture platform. Bertassoni and others [27] found that significantly higher pressure was required to disperse cell-laden GelMa from a glass capillary; however, there were no significant changes to cell vitality or proliferation. Ink requirements in printing neural tissues: The neural cell’s vitality, growth, and signalling are affected by the composite ink’s elastic modulus or stiffness. Brain tissues were found to be compatible with a stiffness of about 0.5 MPa, which is much lower than that of bone or cartilage tissues [28]. As a result, soft hydrogels with low interfacial tension are required so that cells can freely move across the tissue implant line. A combination of two or more printing inks, one with the necessary biological property and others to manage stiffness can accomplish this [16]. Bio-printing and nucleic acid delivery can be combined in a variety of ways, according to recent research. A bio-ink that is activated by a gene is one of them. A gene-activated bio-ink, as depicted in Fig. 5 [29], could be produced by encapsulating the desired nucleic acid and its delivery mechanism into a bio-printable material in a single step.

2.4 Inkjet Printing Inkjet printing is programmable, without-contact printing that enables the disposition of minimal volumes (1–100 pico-litres) [30] of individual droplets, with cell vitality and low cost as well. Droplets are expelled from a nozzle by either of the two mechanisms: (1) Drop by the drop (DOD) (2) Continuous inkjet (CIJ). Out of these two, DOD is considered suitable for tissue engineering. Furthermore, DOD can be divided into three categories depending on the process of depositing inks: (1) Thermal (using heat to compression before the nozzle), (2) electromagnetic (3) mechanical,

Status of Bio-printing Inks and Their Compatibility with Current …

315

Fig. 5 Gene-activated bio-inks [29]

as shown in Fig. 6. In DOD inkjet printing, individual drops of diameter varying in the choice of 25–50 μm are produced as per requirements. Continuous streams of 100 m-diameter individual droplets are ejected during CIJ printing. It has been discovered that electromagnetic and thermal inkjet printing is not commonly used in tissue engineering due to their tendency to affect cell walls and lack of vitality after sonication at 15–25 Hz. Consequently, thermal inkjet is utilised to increase cell vitality [31]. Multiple inkjet print heads consisting of a considerable number of individual nozzles have also been industrialised to speed up the entire process. A speciality of inkjet printing lies in its accuracy in placing droplets even of the tiniest diameter, also termed as a spatial resolution ranging between 10 and12 μm. Ink Requirements for Inkjet printing: In this process, properties like viscosity and surface tension determine the ink requirements as surface tension determines the shape of the drop after it emerges from the nozzle and on the substrate. (1) The ink’s viscosity should be below ten centipoises. (2) The ideal surface tension is between 28 and 350 mN m−1 [32]. Thermal Inkjet Printing: Bubbles are formed as a result of superheating the ink and expanding them further until the nozzle releases ink. The heating temperature can rise as high as 300 °C, but it only lasts for a few microseconds and is very localised, so it doesn’t raise the ink temperature more than 10 °C. As a result, biologically printed DNA, cells, tissues, and other organs remain viable. In this instance, approximately 85% of cells have been found to be viable [33]. Application of Inkjet Printing: Table 2 presents the application of bio-inks used in inkjet printing.

316

S. Maheshwari et al.

Fig. 6 Different inkjet printing [32]

Table 2 Application of inkjet printing in bio-printing Bio-printing part

The ink used and process details

References

Vessel tissue

Collagen, gelatin, cells exhibited spreading increases with the stiffness of bio-ink

[34]

Skin

Collagen; cell human dermal fibroblasts, keratinocyte

[35]

3 Summary of the 3D Bio-printing Process Table 3 presents the summary of the 3D bio-printing process, its advantages, inkrequirement, and current constraints present in the printing process.

Status of Bio-printing Inks and Their Compatibility with Current …

317

Table 3 Bio-printing process summary S. No.

3D printing process

Advantages

Ink requirements for Current constraints bio-printing, considering different processes

1

Fused deposition modelling (FDM)

1. Easy to use 2. Supports a wide range of biomaterials ranging from polymers to bio-composite, cells and dermal fibroblasts 3. Prominent bio-inks:ex: PCL, PLA, PEEK, PBT

1. Viscosity > 10 mPa/s.2 2. Temperature range 200–250 °C 3. Ratio of elastic modulus to melt viscosity < 5 × 105 s−1

1. Temperature achievable is not sufficient for high melting point inks 2. Structures with long unsupported sections or sharp overhangs are challenging to create using this as extruded filament appears to have a dearth of material strength to support them just after extrusion

2

Stereolithography (SLA) Digital laser stereolithography (DLP)

1. Better horizontal resolution as well as vertical resolution 2. Recent development allows biodegradable (natural-synthetic) cross-linkable polymers ex: PPF, PDLLA, Hydrogel, Gelma + PEGDA

1. Optically transparent 2. Low in viscosity 3. Biocompatible materials; should be stable under exposure to U.V. light

Due to the harsh UV-based cross-linking, trapping of resins within the end product, therefore, needs much post-processing

3

Direct ink writing (DIW) Laser direct ink writing (LDIW)

1. Its versatility comes from the fact that it can print a wide range of materials, including food, hydrogels, ceramics, plastics, and even living cells

1. Fast rate of gelation 2. Should provide sufficient integrity after printing out 3. Higher rates of drying 4. Should support polymer solubility in the solvent used &preferably be shear thinning

Filler materials are needed with long unsupported structures and post-processing is required to remove filler materials properly. Dual nozzles are also needed in most cases, as supporting material must be printed parallel to genuine inks

4

Inkjet printing Thermal inkjet printing

1. Fast fabrication speed 2. High resolution 3. High cell viability in bio-printing

1. Viscosity < 10 centipoise 2. 28 < Surface tension < 350 Mn m−1

Material viscosity is primarily the significant constraint here. As the viscosity increases, the clogging problem inside the nozzle is prone to occur. Also, cell viability decreases significantly due to high shear force if the material is not shear-thinning

318

S. Maheshwari et al.

4 Conclusion Recent developments have significantly increased the resolution, speed, accuracy and versatility of 3D printing methods and machines available. This has led to the applicability of 3D printing in complex structure creation, soft tissue engineering, cell mimicry and all other biomedical requirements. However, there are still contrasting parameters in both fields which are resisting the gap between the biomedical and engineering applications to be filled. One of them is the lack of biocompatible printing materials, which fulfil mechanical properties, degradation kinetics and another compatibility requirement for printing through current printers available along with producing no harmful by-products upon degradation inside the body they are implanted. Advances in multi-material bio-inks, hydrogel-based bioinks, composite bio-inks and polymeric bioinks have led to tissue, healthy cells and even DNA printing. The full potential can be realised by overcoming the current constraints by further improving current printing techniques and simultaneously developing more biocompatible bioinks.

References 1. Kang HW, Lee SJ, Ko IK, Kengla C, Yoo JJ, Atala A (2016) A 3D bioprinting system to produce human-scale tissue constructs with structural integrity. Nat Biotechnol 34(3):312–319 2. Mobaraki M, Ghaffari M, Yazdanpanah A, Luo Y, Mills DK (2020) Bio inks and bioprinting: a focused review. Bioprinting 18:e00080 3. Murphy SV, Atala A (2014) 3D bioprinting of tissues and organs. Nat Biotechnol 32(8):773–785 4. Jose RR, Rodriguez MJ, Dixon TA, Omenetto F, Kaplan DL (2016) Evolution of bioinks and additive manufacturing technologies for 3D bioprinting. ACS Biomater Sci Eng 2(10):1662– 1678 5. del Burgo JMDA, D’Amato R, Méndez JAJ, Ramírez AS, Haro FB, Heras ES (2019) Real time analysis of the filament for FDM 3D printers. In: Proceedings of the seventh international conference on technological ecosystems for enhancing multiculturality, pp 354–360 6. Dong L, Wang SJ, Zhao XR, Zhu YF, Yu JK (2017) 3D-printed poly (ε-caprolactone) scaffold integrated with cell-laden chitosan hydrogels for bone tissue engineering. Sci Rep 7(1):1–9 7. Shim JH, Won JY, Park JH, Bae JH, Ahn G, Kim CH, Lim DH, Cho DW, Yun WS, Bae EB, Jeong CM, Huh JB (2017) Effects of 3D-printed polycaprolactone/β-tricalcium phosphate membranes on guided bone regeneration. Int J Mol Sci 18(5):899. https://doi.org/10.3390/ijm s18050899 8. Suganuma J, Alexander H (1993) Biological response of intramedullary bone to poly-L-lactic acid. J Appl Biomater 4(1):13–27 9. Schmidt M, Pohle D, Rechtenwald T (2007) Selective laser sintering of PEEK. CIRP Ann 56(1):205–208 10. Wu W, Geng P, Li G, Zhao D, Zhang H, Zhao J (2015) Influence of layer thickness and raster angle on the mechanical properties of 3D-printed PEEK and a comparative mechanical study between PEEK and ABS. Materials 8(9):5834–5846 11. Claase MB, Grijpma DW, Mendes SC, de Bruijn JD, Feijen J (2003) Porous PEOT/PBT scaffolds for bone tissue engineering: preparation, characterisation, and in vitro bone marrow cell culturing. J Biomed Mater Res Part A Official J Soc Biomater Jpn Soc Biomater Aust Soc Biomater Korean Soc Biomater 64(2):291–300

Status of Bio-printing Inks and Their Compatibility with Current …

319

12. Jakus AE, Rutz AL, Shah RN (2016) Advancing the field of 3D biomaterial printing. Biomed Mater 11(1):014102 13. Nettleton K, Luong D, Kleinfehn AP, Savariau L, Premanandan C, Becker ML (2019) Molecular mass-dependent resorption and bone regeneration of 3D printed PPF scaffolds in a critical-sized rat cranial defect model. Adv Healthcare Mater 8(17):1900646 14. Ronca A, Ambrosio L, Grijpma DW (2013) Preparation of designed poly (D, Llactide)/nanosized hydroxyapatite composite structures bystereolithography. Actabiomaterialia 4:5989–5996 15. Zhu W, Cui H, Boualam B, Masood F, Flynn E, Rao RD, Zhang Z-Y, Zhang LG (2018) 3D bioprintingmesenchymal stem cell-laden construct with core–shell nanospheres for cartilage tissue engineering. Nanotechnology 29(18):185101 16. Knowlton S, Anand S, Shah T, Tasoglu S (2018) Bioprinting for neural tissue engineering. Trends Neurosci 41(1):31–46 17. Hopp B, Smausz T, Szabó G, Kolozsvári L, Nogradi A, Kafetzopoulos D, Fotakis C (2012) Femtosecond laser printing of living cells using absorbing film-assisted laser-induced forward transfer. Opt Eng 51(1):014302 18. Vanaei S, Parizi MS, Salemizadehparizi F, Vanaei HR (2021) An overview on materials and techniques in 3D bioprinting toward biomedical application. Eng Regeneration 2:1–18 19. Stanton MM, Samitier J, Sanchez S (2015) Bioprinting of 3D hydrogels. Lab Chip 15(15):3111– 3115 20. Malda J, Visser J, Melchels FP, Jüngst T, Hennink WE, Dhert WJA, Groll J, Hutmacher DW (2013) 25th-anniversary article: engineering hydrogels for biofabrication. Adv Mater 25(36):5011–5028 21. Kengla C, Lee SJ, Yoo JJ, Atala A (2020) 3-D bioprinting technologies for tissue engineering applications. In: Rapid prototyping of biomaterials. Woodhead Publishing, pp 269–288 22. Wen CH, Hsu SC, Hsu SH, Chang SW (2019) Molecular structures and mechanisms of waterborne biodegradable polyurethane nanoparticles. Comput Struct Biotechnol J 17:110–117 23. Fakhruddin K, Razak SIA, Nayan NHM, Kadir MRA (2020) 3D bioprinting of a tissueengineered human heart. In: Cardiovascular engineering. Springer, Singapore, pp 243–259 24. Das S, Basu B (2019) An overview of hydrogel-based bioinks for 3D bioprinting of soft tissues. J Indian Inst Sci 1–24 25. Nair K, Gandhi M, Khalil S, Yan KC, Marcolongo M, Barbee K, Sun W (2009) Characterisation of cell viability during bioprinting processes. Biotechnol J Healthcare Nutr Technol 4(8):1168– 1177 26. Pepelanova I, Kruppa K, Scheper T, Lavrentieva A (2018) Gelatin-Methacryloyl (GelMA) hydrogels with defined degree of functionalisation as a versatile toolkit for 3D cell culture and extrusion bioprinting. Bioengineering 5(3):55. https://doi.org/10.3390/bioengineering5030055 27. Ng WL, Lee JM, Zhou M, Yeong WY (2020) Hydrogels for 3-D bioprinting-based tissue engineering. In: Rapid prototyping of biomaterials. Woodhead Publishing, pp 183–204 28. Bertassoni LE, Cecconi M, Manoharan V, Nikkhah M, Hjortnaes J, Cristino AL, Barabaschi G, Demarchi D, Dokmeci MR, Yang Y, Khademhosseini A (2014) Hydrogel bioprintedmicrochannel networks for vascularisation of tissue engineering constructs. Lab Chip 14(13):2202–2211 29. Thompson AB, Tipton CR, Juel A, Hazel AL, Dowling M (2014) Sequential deposition of overlapping droplets to form a liquid line. J Fluid Mech 761:261–281 30. Daly AC, Freeman FE, Gonzalez-Fernandez T, Critchley SE, Nulty J, Kelly DJ (2017) 3D bioprinting for cartilage and osteochondral tissue engineering. Adv Healthcare Mater 6(22):1700298 31. Derby B (2010) Inkjet printing of functional and structural materials: fluid property requirements, feature stability, and resolution. Annu Rev Mater Res 40:395–414 32. Guvendiren M, Molde J, Soares RM, Kohn J (2016) Designing biomaterials for 3D printing. ACS Biomater Sci Eng 2(10):1679–1693 33. Angelopoulos I, Allenby MC, Lim M, Zamorano M (2020) Engineering inkjet bioprinting processes toward translational therapies. Biotechnol Bioeng 117(1):272–284

320

S. Maheshwari et al.

34. Parsa S, Gupta M, Loizeau F, Cheung KC (2010) Effects of surfactant and gentle agitation on inkjet dispensing of living cells. Biofabrication 2(2):025003 35. Luo Y, Wei X, Huang P (2019) 3D bioprinting of hydrogel-based biomimetic microenvironments. J Biomed Mater Res B Appl Biomater 107(5):1695–1705 36. Tanrikulu IC, Westler WM, Ellison AJ, Markley JL, Raines RT (2020) Templated collagen “double helices” maintain their structure. J Am Chem Soc 142(3):1137–1141

Firefighting and Extinguishing Robot Kondepu Jai Sai Nath, Kotapati Thanuja, Ch. Sai Ganesh, Kottnana Janakiram , and P. Joshua Reginald

Abstract The primary goal of the article is to build a robot that can locate and extinguish the fire on its own. Minor human errors cause fires in industry, commercial spaces, and hospitals, among other places. Between 2010 and 2014, more than 1.2 lakh people perished in India as a consequence of fires, according to the National Crime Records Bureau (NCRB). Fire detecting and extinguishing robots are being developed to help in firefighting without the need for human involvement. There are several firefighting devices available for clearing out wildfires and carrying out flames in residential areas. The robot we have described here might be used to stop fires from spreading in their initial stages. Such robots might carry out activities like fire detection and total destruction without endangering the lives of the firefighters or creating hazardous situations. Robotics technology has improved to the point that it is now feasible to replace humans with robots in firefighting. Because of a lack of technological innovation, firefighting has frequently proven dangerous, resulting in severe and devastating losses. The robot is built using various components and for the movement of the robot two DC motors and a castor wheel at the front. To power the robot, a 12 V battery is utilized, and then, a 12 V DC pump for suction and a servo motor for axial spraying of water are employed. Several sensors are linked to the Arduino Uno board. A software called Arduino IDE is used to write the programming language that is utilized to provide inputs to the robot from sensors. Keywords Autonomous robot · Fire extinguish · Arduino Uno board · Firefighters

K. Jai Sai Nath · K. Thanuja · Ch. Sai Ganesh · K. Janakiram (B) · P. Joshua Reginald Vignan’s Foundation for Science, Technology and Research, Vadlamudi Village, Guntur, Andhra Pradesh, India e-mail: [email protected] P. Joshua Reginald e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_30

321

322

K. Jai Sai Nath et al.

1 Introduction Fire Detection and Extinguishment by Autonomous Systems: A robot is being developed to assist in the search and extinguishment of flames without the use of humans. A range of firefighting systems is available for battling home fires and forest fires. Our suggested device is a tiny robot that may be used to prevent flames from spreading in their initial stages. Such robots can perform fire detection and extinguishment activities without harming firefighters lives or placing them in risky circumstances. Additionally, the tiny size of the robot and its autonomous functioning enabling it to be utilized in the case of a fire in a compact, confined place with a chaotic environment that is impossible for people to approach. Likewise, this robot seems to have the ability to increase firefighting services’ capability, reliability, machinability, economy, and excellence. Basically, robots are used for a variety of tasks and for the comfort of human beings. As a result of technological advancement, human involvement has decreased. These days, fire accidents occur regularly, causing damage to human life and property, making it difficult for firefighters to rescue lives. In these situations, a firefighting robot is used to preserve property and human lives and the surrounding area from fire accidents by using a fire-detecting IoT-based firefighting robot. The authorities may begin picturing the fire area and communicating with people who are trapped once they are notified. Section 1 defines introduction about the autonomous firefighting robot, Sect. 2 explains about the literature on fire detection robots, Sect. 3 defines about motivation and theme of the project, and Sect. 4 defines working of the system with the specified algorithm.

2 Literature Survey on Fire Detection Robots Adilshah has created firefighting robot. This research project proposes and builds a fire-extinguishing robot that locates the fire and, after starting the water pump, puts it out with sprinklers. The three flame sensors on this robot allow for efficient fire detection. This proposed design for an autonomous fire-extinguishing robot powered by an Arduino detects the presence of a fire and puts it out without the assistance of a person. When it detects a fire and activates the water pump to put it out, it includes gear motors and a motor driver that control the robot’s movements. This little robot has a water ejector that it may use to spray water over the flames. The water ejector pipe may be moved in the required direction using a servo motor. The entire system is controlled by an Arduino Uno microcontroller [1]. Sazzad Sayyed et al. have created firefighter robot. The attached camera on this prototype captures a video feed in continuous format of its surroundings, which is used to indicate the existence of flame in its surroundings. A technique for image analysis was created. Following confirmation of the presence of fire in one direction, the robot adjusts itself and moves toward that place. After arriving, a pyro sensor is used to check for the existence of fire, and if the result is affirmative, the firefighting

Firefighting and Extinguishing Robot

323

activity starts and proceeds until the flame has died out. The robot’s functionality and accuracy are evaluated in a range of scenarios, as well as its predicted performance in terms of fire detection [2]. Diwanji et al. have created fighting robot. In this paper, the developers split robot creation into three components: hardware, electronics, and programming. The robot is propelled by direct current (DC) motors which have a steering wheel. A 12 V water pump is used for the purpose of water suction and spraying servo motor (SM) for axial water spraying. The microcontroller board used here is Arduino Uno board (AUB), and it is linked to a variety of sensors. For the movement of the robot, a suitable program has been developed using a software called Arduino integrated development environment suit that helps to sense the inputs and make the robot to be moved [3]. Imran et al. have created intelligent firefighting robot with deep learning. The bot will be turned on as soon as it receives information about the fire accident. Control points could be marked using the mission planner with the use of the GPS module once the position has been established using the Google API. Through the use of computer vision technology, the bot will arrive at the location on its own. To make autonomous driving possible, lane, obstacle, and traffic light detections are carried out. The Yolov5 design is crucial to detection techniques. As soon as it arrives at its target, the fire and heat sensors will start to detect a fire, and the nozzle, which has a 180° rotational range, will begin to spray water to put it out. At first, only one bot will be deployed to help, but if it is unable to meet all of the needs and demands, the GSM module will transmit a message of inefficiency so that other bots will arrive at the location and be able to fix the issue. The bot is rapidly cooled with a specific alloy of stainless steel to safeguard the electrical components. The suggested system will reduce the workload for firefighters as well as human errors made when fighting fires [4].

3 Motivation Detecting a fire and attempting to extinguish, it is a dangerous task for a fire extinguisher, and it may put his life at risk. The purpose of this mission is to provide a technical solution to the problem indicated above. Robots are programmed to locate fires before they spread out of control. It might be used in combination with firefighters to reduce the risk of injury to victims. The firefighting robot is presented in this article. This robot is a mechanical device capable of performing a complex set of actions on its own [5]. The AUB is the system’s brain and is used to control the whole system. The hardware, electronic, and programming components of robot development are split into three categories. The robot’s driving system includes two DC motors and a castor wheel for steering. A 12 V water pump is used for the purpose of water suction and spraying. Axial water spraying (ASM) (From 0 to 60°). The AUB may also connect to a variety of sensors. For the movement of the robot, a suitable program has been developed using a software called Arduino integrated

324

K. Jai Sai Nath et al.

development environment suit that helps to sense the inputs and make the robot to be moved [1]. Figure 1 describes about the components that are used in for the designing of the robot. The flame sensor, Arduino Uno board, 5 V power supply, motor driver, 12 V battery for external power source, two motors for left and right directions, 12 V dc motor pump, 5 V relay, and servo motor are all displayed in the above block diagram. The design has three flame sensors, one on each side of the chassis and one in the center, to detect fire or flame [4]. These three flame sensors are linked to an Arduino Uno board for input, and the board is powered by a 5 V battery. The required code for the sensors to function is written and dumped to the board via a type B USB cable. The motor driver utilized here is an IC called L298n that drives the motor wheels, and the power source for the moment of wheels is provided by an externally attached 12 V battery [5]. When the robot is in the moment, the servo motor is utilized to control the ultrasonic sensor, which helps to detect potential issues. The 12 V water pump is powered by a 12-V battery and ensures that water is sprayed when the flame sensor detects a fire. When the flame sensor senses a fire, the relay module instantly turns on the motor pump.

Fig. 1 Block diagram of firefighting robot

Firefighting and Extinguishing Robot

325

4 Design Flow The following are the processes for programming the robot to identify and put out the fire. 1. 2. 3. 4.

Start. Take note of the flame sensor’s inputs. Continue if no fire is seen (both the motors will be in ON condition). If the right sensor detects flame, the robot will travel to the right and halt before a particular distance triggering the direct current water pump and SM. (The motor moving right is deactivated, and the motor moving left is activated). 5. Whereas if the sensor on the left detects a flame, the robot should move to the left side and wait for a particular distance before triggering the direct current water pump and SM. (The motor on the left side is switched down, and the right motor is turned up). 6. When the focal sensor detects a flame, the robot will advance and halt at a particular distance before initiating the SM and direct current water pump (Wheels of both the motors will be powered on). 7. Hold back.

5 Implementation Instead of endangering a firefighter’s life, we want to create and develop a firefighting robot that will be used to control any fire-related crisis. Our project is mostly made up of AUB, flame sensors, motor driver, motors, servo motor, water reservoir, relay switch, water pump, and a chassis to house all of the equipment. The power supply is linked to both the AUB and the motor driver [6]. When a fire is spotted, the robot will advance and extinguish it. This section examines the hardware components that will be used in this project. Arduino Uno Board: The Arduino Uno board is generally a microcontroller board that is primarily used for integrating external devices for a variety of applications. The board has 14 general pins, out of which 6 pins are used for pulse width modulation, 6 analog pins, and some power supply pins with provided grounds, a type B USB jack for dumping code externally, and a power jack cable [7]. It also contains a transmitting and receiving signal indication to make it easier for users to see whether the code has been dumped and sent, as well as a reset button to clear the code that has already been dumped.

326

K. Jai Sai Nath et al.

6 Robotic Structure To create a robot, we first need a chassis that can withstand external calamities. The robot includes two motors at the back and a tiny castor wheel for forward mobility. All the required equipment is placed on the chassis with the connections made. On the body, a water tank with an attached water motor is placed, all of the required equipment is placed on the chassis with the connections made, and a battery is connected to power up the robot. Fire Sensor: These sensors, as pictured, have an infrared receiver called photodiode that senses fire. The sensor is mounted with the pins, named VCC, 5 V input, and ground. It contains a flame detector for flame detection, as well as a power indication that indicates the power supply and an indicator that indicates when a fire is detected [8]. Motor Driver (L293D): The L293D is a 16-pin motor driver IC that can control two direct current (DC) motors in any direction at the same time. At voltages ranging from 4.5 V to 36 V, the L293D can deliver bidirectional driving currents of up to 600 mA (at pin 8). (This is per channel.) It may be used to control tiny dc motors such as toy motors. At times, it might get extremely hot [9]. Ultrasonic Sensor: This is the sensor that is used to avoid obstacles or to determine the distance between them. The sensor has four pins: one for power, one for echo, one for ground, and one for trigger. When the reflected signal is received, the echo pin generates a pulse. The ultrasonic sound pulses are started by a trigger pin. With the assistance of this sensor, we can also make the robot to be at a certain distance away from the fire to avoid any burns.

7 Functioning of Robot When you switch on the robot, it keeps moving forward with both motors switched on. In this case, the flame sensors do not detect a fire [10]. As a result, the pump is used to pump the water and the servo motor which are both powered down. When the middle flame sensor senses a fire, the robo bogs down, travels a certain distance with both motors engaged, and ultimately comes to a complete standstill [11]. Following that, the pump is used to pump the water and the servo motor which are both powered on. As a result, until the flames are extinguished, water is thrown evenly upon them. The water pump and servo motor that are used for the movement of water spray are shut off after the flame is extinguished, and the robot continues its forward movement [8]. While the left flame sensor detects a fire, the robot bogs down, travels to the left

Firefighting and Extinguishing Robot

327

a fair distance with the motor on the right side is on and the motor on the left side is off condition, and it comes to a complete stop at that distance. The pump that is used to pump the water and servo motor that is used for the movement of water spray are then both powered on. As a result, until the flames are extinguished, water is sprayed evenly upon them. After the flame is extinguished, the pump that is used to pump the water and servo motor that is used for the movement of water spray are powered off, then the robot resumes its forward movement.

8 Results Figure 2 shows the connections in real-time project. The components are working with accuracy and the detection of fire by the three flame sensors that are implemented on the chassis. Figure 2a shows the connection of sensors with the action taken by the sensors will be controlled by Arduino when the power supply is on. Figure 2b describes about all the components that are required to design a robot which are attached to the chassis of the robot with the connections done and its ready to put off the fire. Figure 3a shows that initially, the robot moves, whenever the fire is detected, else it stops. In the figure after fire detection, the robot has stopped. Figure 3b shows that after detection of the fire, the robot is putting off the fire by spraying water. The pump automatically turns on after detection of fire and sprays the water by detecting particular flame sensor direction. In this figure, fire is detected at middle flame sensor.

Fig. 2 a Connection of components. b Firefighting robot prototype

328

K. Jai Sai Nath et al.

Fig. 3 a Initial condition of the robot. b Robot spraying the water

9 Conclusion and Future Scope The main objective was to develop a robot prototype. To achieve the output we needed, we modified the design and updated the code as required. The experiments on the robot were determined by the distance traveled. The capacity of the sensors to detect fire will be decreased as the range between peers increases. As a contrary, it performs effectively on level ground. With a little more development, this robot might be capable of traveling on difficult terrain. The absence of a camera is the main limitation for accidental monitoring of this research, also using of GPS tracking devices can increase their effectiveness overall. Consequently, a GPS tracking system helps firefighters by mapping the fire, directing rescue operations, detecting and directing firefighters to the hotspot, and dispatching more support to the appropriate location.

References 1. Adilshah PMR et al (2022) Autonomous firefighting robot using Arduino. Int J Res Eng Sci Manage 5(4):194–197 2. Sazzad Sayyed AQM, Hasan MT, Mahmood S, Hossain AR (2019) Autonomous fire fighter robot based on image processing. In: 2019 IEEE region 10 symposium (TENSYMP), pp 503– 507. https://doi.org/10.1109/TENSYMP46218.2019.8971157 3. Diwanji M, Hisvankar S, Khandelwal C (2019) Autonomous fire detecting and extinguishing robot. In: 2019 2nd International conference on intelligent communication and computational techniques (ICCT). IEEE 4. Imran IM et al (2022) Intelligent fire-fighting robot with deep learning. In: 2022 International conference on communication, computing and internet of things (IC3IoT). IEEE

Firefighting and Extinguishing Robot

329

5. Al Rakib MdA et al (2022) Fire detection and water discharge activity for firefighting robots using IoT. Eur J Eng Technol Res 7(2):128–133 6. Raj PA, Srivani M (2018) Internet of robotic things based autonomous firefighting mobile robot. In: 2018 IEEE international conference on computational intelligence and computing research (ICCIC). IEEE 7. Zaman HU et al (2018) Autonomous firefighting robot with optional Bluetooth control. In: 2018 4th International conference on computing communication and automation (ICCCA). IEEE 8. Mittal S et al (2018) CeaseFire: the firefighting robot. In: 2018 International conference on advances in computing, communication control and networking (ICACCCN). IEEE 9. Pramod BN et al (2019) Firefighting robot. In: 2019 International conference on information and communication technology convergence (ICTC). IEEE 10. Jalani J et al (2019) Automatic fire fighting robot with notification. IOP Conf Ser Mater Sci Eng 637(1) 11. Abhishek A (2021) A survey on Arduino based autonomous firefighting robot using IOT. https:/ /doi.org/10.13140/RG.2.2.16948.17280

Noise Reduction of Jet/Aircraft Engine Fitted with N10 Chevron Nozzle and Comparative Analysis with N8 Nozzle at Varied Tip Angle (β) Irfan Nazir Wani, Ankur Kulshreshtha, and Saragadam Chaitanya

Abstract Since the early twenty-first century, noise convention around airport have turn into more stringent due to the environmental impact that noise pollution has on the general public as the future development of the aircraft industry become constrained by its noise emission, engine manufacturer has work in a new solution to trim down the jet overall noise. Aircraft noise, which radiates from the exhaust of an airplane engine, is a major factor in aircraft noise. The present provision in the ability of passive aircraft sound diminution technology is represented by chevron nozzles. This type of nozzle marks triangular grooves in the nozzle trailing edges that improve mixing between the jet and the ambient air to yield a speedy decay of the jet aircraft plume, consequently reducing jet noise. In this research, the N10 chevron nozzle is designed with various angle change model analyses and compared with the N8 chevron nozzle which is similar to the chevron nozzle installed in the trailing edge of the nozzle used in Bombardier CRJ900. Different models have been studied based on the geometric parameter of the chevron to acquire efficient blueprint constraints of chevrons in the nozzle. The selective design in N8 and N10 V-shape chevron nozzle with tip angle 60° were modeled in CATIA, were analyzed in ANSYS fluent software. During the investigation, acoustics power level of individual’s model is considered at takeoff because the thrust needs more during takeoff of aircraft as compared to cruise for obtaining an efficient model. Keywords Noise · Engine · Chevron · Acoustic · Temperature · Velocity · CATIA · CFD

I. N. Wani (B) · A. Kulshreshtha · S. Chaitanya School of Mechanical and Aerospace Engineering, NIMS Institute of Engineering and Technology, NIMS University, Jaipur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_31

331

332

I. N. Wani et al.

1 Introduction Aircraft noise becomes a big issue now a day for both the environmental and technological fields. In the environment, it causes much more noise pollution where the airport is located near the city [1]. On the other hand, the dropping noise of aircraft within the area near the airport (aerodromes) is at a standstill. Aeronautical and aerospace engineers work hard toward further and make much better designs such as engines that reduce the sound in airdrome locality. During airplane takeoff and land, the engine contains a high weight creating an additional force that will tend to provide more sound compared to the airplane in the sky [2]. The majority part research has been practical going on aircraft engine nozzle to go faster the mixture of the shear layer without dropping their performance. Aircraft noise comprises turbulent mixing sound and during the case of defectively prolonged jets, distress noise. Unstable mixing noise is extremely hard to manage because a turbulent mixture is caused by eddy formed by mixing air streams of different temperatures at different speeds [3]. Generally, outside air and high-speed gasses from the engine results cause in eddies [4]. Of all the sound sources, the sound of a plane is considered to be the second, the 1st noise is considered rocket noise during its launch. The community is so worried about the superiority of their environment to sound is cited as the primary cause of concern. The nozzle is used to raise speed in the direction of additional continuous moreover to manage the same flow of air combination on or after the turbine. Inside a nozzle, static energy (because of flow behind the turbine) is changed to kinetic energy and this kinetic energy creates tension among the stream and the surroundings as a result of this sound that will be generated by the jet engine. This is why it is necessary to reduce the sound in the nozzle discharge so we can modify the size of the nozzle and we can modify the geometry of the nozzle (such as using fresh geometry). A chevron is a triangular dagger pattern around the circumference of an exhaust nozzle used in modern jet engines to help reduce jet noise. Chevron is used to reduce output noise. Chevron nozzle successfully used on Boeing747-8 jet engine with GEnx-2B67 engine and Rolls Royce 1000 jet engine [5].

2 Chevrons Configuration Chevron is used for the current suspension study. Chevrons geometry can be defined by the constraints as quantity of chevron in nozzle (N ), chevron length (L), tip angle (β), and penetration angle (α). The underneath diagram characterizes the simple graphic representation of chevron geometry with its angel and figure that describes the constraints (Fig. 1). The base of a single chevron (b) can be calculated as: b=

πD N

(1)

Noise Reduction of Jet/Aircraft Engine Fitted with N10 Chevron Nozzle …

333

Fig. 1 Schematic representation of the geometrical parameters of a chevron [1, 5]

The relation among the tip angle (β), base of chevron (b), and length of chevrons can be calculated using trigonometric functions.   β b tan = 2 2L

(2)

After the combination and rearranging of Eqs. (1) and (2), we get   β π L × tan ×N = D 2 2

(3)

The length of the nozzle depends on the outlet diameter of the nozzle and is defined as; L ' = 4.25 × D

(4)

Height (h) of the chevron is given as; h=

b 2

(5)

wherever b h D N β α L

Base of chevron Height of chevron Exit diameter of the nozzle Number of chevrons Tip angle Penetration angle Length of nozzle.

From these methods, we can say that the distance of the chevrons is depend in the number of chevrons and tip angle β to is play chevron confirmations so, we can write basically by way of N xβγ . Equation (3) which the ratio of length to diameter depends entirely on the number of chevrons in the end angle. This relationship indicates

334

I. N. Wani et al.

that these primary constraints are important because chevron geometry is dependent and you cannot change one constraint without changing the other. Therefore, these chevron configurations are chosen very carefully so that the results of the individual constraints can be derived. Designing used for the chevron nozzles we assume that the diameter of the nozzle exit is 100 mm, the tip angle (β) 60˚, penetration angle (α) as 5˚, the number of chevrons (N) is 10 so, that the configuration will be N8β60. The base of the chevron nozzle is, from Eq. (1) b=

πD N

So, that π × 100 8 = 31.41 mm 10

b= The length of chevron (L).

 31.41 60 = 2 2×L 100 2 × 0.57735 ∴ L = 28.8 mm 

Tan

For the length of chevron nozzle, L ' = 4.25 × D ∴ L ' = 4.25 × 100 ∴ L ' = 425 mm Considering the intake diameter (Di ) as 152.5 mm. Nozzle Pressure Ratio [NPR] Pte NPR = = Pti wherever Pti Pte Tti Tte γ

Pressure at nozzle inlet Pressure at nozzle exit Temperature at nozzle inlet Temperature at nozzle exit Specific ratio of heat.



 γ Tte Tti γ − 1

Noise Reduction of Jet/Aircraft Engine Fitted with N10 Chevron Nozzle …

335

2.1 Catia Model 2.1.1

Nozzles Design (Models)

Designs of various models of N8 and N10 chevron nozzles with a different angle β used for examination beside with baseline nozzle too are displayed. The main contours of the model are taken from the baseline of the nozzle. The base nozzle was also modeled in CATIA as a reference to the selected model (Figs. 2, 3 and 4; Tables 1 and 2). Designing nozzles with chevrons is not difficult if you know the geometrical parameters. It contains only basic calculations and simple formulas, and changing each limit may result in different values. In addition, chevron 3D models are easy to model because they do not have complex mechanisms and do not contain complex CATIA software tools [5] However, actual designs using chevrons in aircraft engines vary greatly and include more than the parameters considered in the study. Therefore, this scheme is used for fundamental analysis and not for pragmatic conditions.

Fig. 2 Baseline nozzle with chevrons (completely sizes are in mm)

Fig. 3 CATIA model of N8 and N10 chevron variants for nozzle N10β45 and N10β60

336

I. N. Wani et al.

Fig. 4 CATIA model a Baseline nozzle, b N8β60 nozzle, c N8β76.3 nozzle

Table 1 Data of different angles of N8 chevron [5] N

β (in degrees)

L (in mm)

D (in mm)

L/D

% Of Length with respect to Diameter

8

60

17.277

50.9

0.34013

35

8

46

12.8

50.9

0.26

26

8

31

10.3

50.9

0.3

21

8

101.5

8.15

50.9

0.1601

16

8

108

7.26

50.9

0.14271

14.3

Table 2 Data of different angles of N10 chevron S. No.

Nozzle type

1

Baseline chevron nozzle

2

Triangular chevron nozzle

3 4

Sum of chevron

Acoustic power

Mach number

Nozzle pressure

0

118

1.21

15.05

8

116

1.98

11.69

Triangular nozzle

10

115

2.03

9.91

Triangular nozzle

8

113

1.97

11.68

3 Model Computional Fluid Dynamics Study In the study, when analyzed, each model in this project has a uniform, unfenced solid mesh with a maximum face size of 0.050 m. This is because checking the quality of the faces before generating the 3D volume mesh will give you the best results if

Noise Reduction of Jet/Aircraft Engine Fitted with N10 Chevron Nozzle …

337

Fig. 5 CATIA Model of N10β60 nozzle meshing and N10β60 iterations

you provide the best mesh quality. Asymmetry was identified in the range of 0.4– 0.9, inferring the average mesh quality. The key parameter considered in this model analysis is the acoustic power level. Therefore, the analysis was mainly focused on the acoustic power level. However, static pressure fluctuations and velocity profiles were also investigated for different models at a stagnation temperature of 600 K. Theoretical calculations of nozzle flow parameters were performed based on a CF348C5 engine used in a Bombardier CRJ900 equipped with an N8 chevron nozzle. The model was considered pressure-based and the inlet and outlet pressures were considered to be 155,000 Pa and 154,555 Pa, respectively. The estimated speed at the entrance was 45.27 m/s (Fig. 5).

4 Results and Discussion The contours of the sound power levels and static pressure, temperature, turbulence, density, and velocity vectors for all models were clearly visualized. Comparisons with the calculated parameters show that they are nearly correct.

4.1 Acoustic Power Level Contours Figures 6 and 7 shows the sound pressures produced at the tip of the chevrons. The acoustic power level is high in exhaust of the chevron nozzle since of the mixture of hot air and cold air at different velocities from exhaust and bypass, respectively.

338

I. N. Wani et al.

Fig. 6 Acoustic model of baseline nozzle and N8β60

5 Concluding Remarks By getting acoustic analysis of chevron nozzles made it clear that noise suppression is achieved using chevrons. It also confirms that the chevron nozzle N10β60 has better performance than the N8β60 chevron and can work perfectly to reduce more noise by separating the eddy of the exhaust. Variations of the chevron nozzle N8 also showed that variations of various parameters and effective models were considered. The N8β108 offers the best noise cancelation and acoustic power as low as 100.45 dB. On the other hand, the N10β60 model has a low acoustic output of 99.1 dB. It should be noted that this lunar force is observed on a smaller surface area than the other models,

Noise Reduction of Jet/Aircraft Engine Fitted with N10 Chevron Nozzle …

339

Fig. 7 Acoustic model of N10β60 nozzle and N10β60 vector

resulting in lower pressure resistance. Consequently, considering all possible factors, we concluded that the effective model in the N8 configuration would be N8β101.6 and for N10 it would be N10β60 compared to N8β60 and recommended for use in aircraft engines as it provides standout performance. Therefore, it is appropriate to use N8 chevron nozzles on Bombardier CRJ900 CF34-8cs engines, which can be increased using N10β60.

340

I. N. Wani et al.

References 1. Modeling and analysis of different chevron nozzles for noise reduction in jet engines Swati Chauhan (IJIRSET) 9(6) 2. Rajshree V, Antony D, Palanisamy R, Prakash S, Ranjith Kumar R (2018) Design and analysis of a nozzle to enhance noise suppression. IJERT 3(03) 3. Nandita NH, Sruthi R, Suriyaprabha et al (2018) Flow visualization of the jet exiting a chevron nozzle. Aeron Aero Open Access J 2(2):62–65. https://doi.org/10.15406/aaoaj.2018.02.00031 4. Kaleeswaran P, Shanmugham Sundaram P, Experimental and Statistical analysis on the noise reduction using chevron nozzle in supersonic free jet 5. Wani IN et al (2022) Design and acoustic analysis of N8 chevron nozzle with varied tip angle (β). J Mech Civil Eng (IOSR-JMCE) 19(2):35–38

Study on Effect of Process Parameters on AJM of Partially Biodegradable Hybrid Composite Using TOPSIS Approach Itishree Rout, Trupti Ranjan Mahapatra, Arun Kumar Rout, Debadutta Mishra, and Akshaya Kumar Rout

Abstract In this study, epoxy-based partially biodegradable sustainable hybrid biocomposites are synthesized by using a mixture of Human Hair Fiber (HHF), Luffa Cylindrica (LC) and various weights as filler (0, 10, and 20 wt% of Incense stick ash (ISA)) via ultrasonicator assisted hand lay-up procedure. The machinability of the fabricated composites is investigated through the Abrasive Jet Machining (AJM) process. The experimentations are planned as per Taguchi’s L9 orthogonal array design of experiment (DOE) by considering the pressure, stand-off distance, composite type, and abrasive type as the controllable process parameters. The rate of material removal (RMR), overcut (OC), and surface roughness (Ra ) are considered to be the machinability indicators. The optimal process parameter settings that will result in the minimum OC, minimum Ra , and maximum RMR are determined for individual output responses by S/N ratio analysis. Further, a multi-criteria decisionmaking method, TOPSIS is implemented to perform multi-objective optimization. The assortments of optimum conditions obtained are justified by performing confirmatory tests. The optimum parameter obtained for the simultaneous optimization of output responses are 20 wt% ISA-filled composite, 3 bar pressure, 5 mm stand-off distance, and silicon carbide (300 µm) as the abrasive particle. Keywords Luffa cylindrica · Human-hair fiber · Incense stick ash · AJM · Taguchi’s method · TOPSIS

I. Rout · T. R. Mahapatra (B) · A. K. Rout · D. Mishra Department of Production Engineering, Veer Surendra University of Technology, Burla, Odisha 768018, India e-mail: [email protected] A. K. Rout School of Mechanical Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha 751024, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_32

341

342

I. Rout et al.

1 Introduction The use of NFs (NFs) as a reinforcing material in polymer matrix composites (PMCs) has been gaining popularity in recent years due to rising environmental pollution and depletion of petroleum resources. The most important factors in the development of NFs are light weight, biodegradability, inexpensive and eco-friendly [1]. NFs in PMCs provide numerous environmental benefits as they are entirely renewable besides the waste generated by them is organic. The NFs including the wood, sisal, jute, flax, coconut, palm, cotton, kenaf, bamboo, hemp, etc., have been befittingly used as reinforcement in NF-reinforced PMCs [2]. Thus, PMCs reinforced with NFs find their service in a variety of industries, including automobile, structural applications [3], packaging [4], medical storage equipment [5] and many more. It has been established that amidst other NFs the complete features of luffa cylindrica (LC) fibers are lignocellulosic (hemicelluloses + lignin + cellulose). The undesirable impurities of LC fibers are removed via appropriate treatment with chemicals (sodium hydroxide (NaOH) and benzoyl) so that augmented surface area with reduced water uptake is achieved [6]. The human hair fiber (HHF) is a biological waste material. It is low degradable and poses concern for the environmental experts. Although the hair fiber is discarded as a waste product, it contains several unique thermal, mechanical, and microstructural properties. Therefore, numerous researches have already been undertaken striving the recycling of HH as pesticides, agricultural fertilizer, wound healing medicines, making ropes etc. [7]. Moreover, to minimize the organic content, reduction in weight and cost of fabrication, various fillers are added during PMCs synthesis that improves the adherence capability leading to strength, stiffness, stability, and thermal resistance enhancement. In order to mitigate their applicability under diverse service conditions, various environmental unhealthy materials including the marble dust [8], blast furnace slag, fly ash, cenosphere [9], red mud [10], SiC particles, walnut shell powder [11], rice-husk ash, etc., have been employed as potential filler materials. The properties of various eco-friendly hybrid composites with different combinations of NFs and fillers have been explored by numerous researchers based on fabrication process, mechanical qualities, cost, environmental impact, and sustainability. As the resulted composites are more brittle in nature, the precise machining of them is generally undertaken via nonconventional machining such as the abrasive jet machining (AJM), and therefore, better understanding of the impact of process parameters is desirable to attain required efficiency and accuracy [12]. Although numerous studies related to synthesis and characterization of diverse NFPMCs have been undertaken, the machinability issues of NF-reinforced composites have been less outlined [13]. The machining speed, stand-off distance (SOD) and the pressure have been suggested as the most influencing parameters during the machining of NF composites using AJM [14]. Also, the suitable selection of the machining parameters is highly important for good surface quality and to avoid formation of cracks [15]. Jayakumar [16] used Taguchi’s L16 orthogonal array design to investigate the parameters of abrasive water jet process of Kenaf/E-Glass

Study on Effect of Process Parameters on AJM of Partially …

343

fiber-reinforced PMC laminate and found the improvements in surface quality when high value of abrasive flow rate and water jet pressure are used. From the assessment of previous research, it is understood that the synthesized novel NFPMCs need more exploration for their machinability because of their remarkable engineering applications [17]. Therefore, a new class of NFPMC incorporating LC, HHF as fiber material and the incense stick ash (ISA) as filler. A systematic parametric analysis is performed to explore the consequences of four distinct AJM machining parameters including the composite type, pressure, standoff distance and abrasive type on three crucial quality measures (i.e., the rate of material removal (RMR), surface roughness (Ra ) and overcut (OC)). The experimental planning is attained by Taguchi’s standardized DOE and the signal-to-noise ratio values are analyzed to obtain optimal input parameter combination for maximizing the RMR and minimizing the Ra and OC. Further, TOPSIS is implemented to attain the multiple performance optimization. Finally, confirmatory experiments are performed to make a comparison between the results obtained from both the methodologies to access their effectiveness.

2 Fabrication of Composite Samples 2.1 Materials The synthesis of the composites is carried out by utilizing the materials such as: (i) Polymer (LAPOX L12 epoxy resin with tensile modulus = 4.1 GPa, tensile strength = 110 MPa and density = 1.1 gm/cm3 ), (ii) Hardener of type K6, (iii) LC Fiber, (iv) short HHF and (v) ISA. The presently used epoxies as well as the hardener are supplied by Harippa Industries, Kolkata, India. The LCs collected locally and the HHFs are collected from local salons and spas. After drying for 7 days, the LC fibers are extracted carefully by removing the outer cover (dry skin) along with the seeds. Similarly, the HHFs are profoundly cleansed by dipping in detergent solution and subsequently dried under the sunlight. The dried fibers are subjected to alkali (5% NaOH) treatment for 5 h (Fiber − OH + NaOH → Fiber − O − Na + H2 O) for facilitating their mechanical bonding with the epoxy resin through enhanced surface roughness. After subsequent cleaning and thorough drying, the LC and HH fibers are chopped into approximately 5–8 mm and 3–4 mm length. The functional filler (ISA) is collected from local temples, sieved properly (particle size of 50–100 µm), cleaned with distilled water and thoroughly dried under sunlight for removing the external impurities. Both the chemically treated chopped fibers and ISA are now utilized for the composite fabrication.

344

I. Rout et al.

Table 1 Compositional weights of individual constituents of the composites Composite

LC fiber (gm)

HH fiber (gm)

Epoxy (gm)

Hardener (gm)

C1

40

40

ISA (gm) 0

400

40

C2

40

40

40

360

36

C3

40

40

80

320

32

2.2 Synthesis of Composites The versatile hand lay-up method has been taken on with the aid of a cast iron mold of dimension (265 mm × 265 mm × 5 mm) for the in-house synthesis of the present hybrid composites. To ensure the uniform mixing of the chemically (alkali) treated chopped HH and LC fibers as well as the ISA filler in the blend of epoxy resin and hardener (in 10:1 by weight ratio), an Ultrasonicator (Make: LABMAN, 2.5L) has been employed during the fabrication. After preparing the composites in the mold, they are allowed for curing at ambient condition for 72 h and subsequently removed from the mold. Teflon sheets and silicon spray are put in to facilitate easy removal of the composites by avoiding their adhesion with the mold. In the present analysis, the weight fractions of the ISA filler incorporated have been varied in the steps of as 0, 10 and 20 wt% of ISA and denoted as C1, C2 and C3, respectively. The detailed compositional weights of individual constituents are provided in Table 1. Finally, the presently fabricated composite samples are cut into appropriate sizes (25 mm × 25 mm × 4 mm) accordance with ASTM E1575 for performing the drilling using AJM.

3 Experimentation and Result Analysis 3.1 Abrasive Jet Machining (AJM) For the realistic machining of NFPMCs, it is necessary to find out the optimal machining parameters to attain better performance in terms of the tapper, overcut, surface finish, RMR, etc. The AJM set-up available at Production Engineering Department, VSSUT, India and integrated with high chromium-carbon steel nozzle has been utilized using three kinds of abrasive grits, i.e., Silicon carbide (SiC) with 300 µm (A1) and 600 µm particle size (A2) and fine glass powder blended Aluminum oxide (Al2 O3 ) of particle size 90 µm (A3). The other input parameters selected as the type of composites (C1, C2 and C3), pressure (3, 4 and 5 bars) and SOD (3, 4 and 5 mm) applied in different ranges as mentioned in Table 2. The experimentations are performed according to Taguchi’s L9 orthogonal array design using the RMR and Ra and OC as the key responses. The analysis is done using the Minitab-19 software. The ranks are allocated to the factor according to the delta values.

Study on Effect of Process Parameters on AJM of Partially … Table 2 Different input parameter levels

345

Levels

Level 1

Level 2

Level 3

Composite (C)

C1

C2

C3

Pressure (bar) (P)

3

4

5

SOD (mm) (S)

3

4

5

Abrasive types (A)

A1

A2

A3

After conducting the experiments, the first response RMR of the machined surface is calculated as: W = (W1 − W2 )/dt

(1)

where W1 and W2 are the initial and the final weight of the specimen (in gm); is the machine time in second. The digital weighing machine has least count of 0.0001 gm. Then, the cavity size is measured with the help of digital Vernier Caliper of least count 0.02 mm and the overcut is measured in terms of the cavity size produced in the work piece (D jt ) and the tool size (Dt ) as: OC =

D jt − Dt 2

(2)

Under the same machining conditions, Ra is measured by using Mitutoyo SJ210 Surface Roughness tester. The probe is ensured to be in contact with the specimen, and the mean values of observations taken at eight distinct locations are taken as the results.

3.2 Taguchi’s Analysis The experimental data acquired are then used to calculate the signal-to-noise (S/N) ratios. The S/N ratio is a technical term that compares the amount of background noise to the level of a desired signal (output). As the purpose of this study is to maximize the RMR and to minimize the OC and Ra , the S/N ratio for the maximum RMR is selected as a higher is the better characteristic and the S/N ratio for the lowest OC and Ra is described as a smaller is better characteristic. The S/N ratio (loss function logarithmic transformation) for larger-is-better criteria is calculated as:   sum(1/y 2 ) S = −10 log10 (3) N n The S/N ratio for smaller-is-better criteria is calculated as:

346

I. Rout et al.

 ∑  1 S = −10 log10 y2 N n

(4)

where n = number of observations and y = observed data. The experimental design and the acquired responses in AJM of the fabricated composites for RMR, OC and Ra are provided in Table 3. Table 4 provides each level of control parameters alongside the corresponding mean S/N ratio and rank according to the delta values for RMR, OC and Ra . In this investigation, the pressure has the highest delta value for RMR, followed by the type of abrasive, type of composite and SOD indicating that the pressure has the highest contribution towards RMR and it is awarded as rank 1. For Ra the SOD has the highest delta value, followed by type of abrasive, pressure, and type of composite signifying that the SOD has the highest contribution toward the Ra . However, for OC the pressure is rank one followed by type of abrasive, type of composite and SOD signifying that the pressure has the highest contribution towards the OC. The main effects plot for S/N ratios (used to examine the impact of each controllable parameters, significant contributors, and establish the ideal input parameter combination to attain optimal output) and normal probability plot of residuals for RMR, OC and Ra are graphically depicted in Figs. 1a–c and 2a–c, respectively. According to the results, the optimal combination process parameter setting for RMR, Ra and the OC are identified as (C3, 5 bar, 5 mm and A2), (C2, 5 bar, 3 mm and A3) and (C2, 5 bar, 4 mm and A1), respectively. A 3D surface plot represents the variation of the response with respect to the input parameters. The independent variables are taken in x and y axes and in z axis the dependent variable is taken. This 3D plot provides more information due to the added dimension in z-direction. In surface plot, it is easier to analyze the data from different perspectives. In this study, the parameter having rank 1 is taken in x-axis and the other parameters in y-axis. In z-axis, the various responses are considered. The plot for RMR (Fig. 1a–c) shows that the high value of RMR is generally obtained at mid-level of the process parameter. Similarly, for SOD (Fig. 1d–f)) the plot follows Table 3 Experimental design and results for RMR, OC and Ra of the composite fabricated S. No.

C (Composite type)

P (bar)

S (mm)

A (Abrasive Type)

RMR

Ra

OC

1

C1

3

3

A1

0.06

1.0965

1.783

2

C1

4

4

A2

0.158

1.0032

1.656

3

C1

5

5

A3

0.076

0.7511

0.183

4

C2

3

4

A3

0.039

0.7809

1.277

5

C2

4

5

A1

0.214

1.9166

0.103

6

C2

5

3

A2

0.27

0.2757

0.12

7

C3

3

5

A2

0.142

1.9166

1.725

8

C3

4

3

A3

0.137

0.3835

0.747

9

C3

5

4

A1

0.364

1.1862

0.013

− 23.13

− 15.54

− 14.16

− 20.91

− 17.61

− 14.31

6.59

3

1

2

3

Delta

Rank

1

8.98

P

C

Level

RMR (Larger is better)

4

0.11

− 17.55

− 17.62

− 17.66

S

Table 4 Response table for S/N Ratios

2

7.79

− 22.56

− 14.76

− 15.51

A

4

2.1659

0.3966

2.5625

0.5526

C

3

5.4986

4.0602

0.882

− 1.4344

P

Ra (Smaller is better)

1

9.1763

− 2.9385

0.2125

6.2378

S

2

6.9638

4.3192

1.8373

− 2.6447

A

3

7.031

4.713

6.442

11.744

C

1

29.703

− 4.413

2.019

25.294

P

OC (Smaller is better)

4

4.464

6.789

5.823

10.287

S

2

9.539

3.756

5.847

13.296

A

Study on Effect of Process Parameters on AJM of Partially … 347

348

I. Rout et al.

Fig. 1 Main effects plot for SN ratios of a RMR, b Ra , c OC

Fig. 2 Normal probability plot for a RMR, b Ra and c OC

the same pattern as that observed for the RMR. However, the OC (Fig. 1g–i) values decreases with increase in each input parameter values (Fig. 3).

3.3 TOPSIS (Technique of Order-Preference-Similarity to the Ideal-Solution) The TOPSIS methodology is useful for multi attributes decision-making problems. The steps involved in the TOPSIS method are according to [18, 19]. The rank is assigned based on the relative closeness (Ci ) values with the highest Ci value allocated as the Rank 1 for which the optimal combination of process parameters is established. Using the step-by-step methodology, the input and output data are entered and the normalized matrix and weight normalized decision matrix are attained. After computing the weighted normalized values, the ideal best and ideal worst values are calculated for each of the response values. As the objective requires to maximize the RMR and to minimize the other two response parameters i.e., OC and Ra , the ideal best values are the maximum RMR values and minimum OC and Ra values. Similarly, the ideal worst values are the minimum RMR values and maximum OC and Ra values. In the next step the Euclidean distance between ideal best and ideal worst is evaluated. Finally, the preference values and rank are established for the highest rank that is assigned as the best combination. The values calculated by weighted normalized and separation solution for the currently acquired results are provided in Table 5 alongside the respective ranks. The highest value is

Study on Effect of Process Parameters on AJM of Partially …

349

Fig. 3 Surface plot for the process responses

chosen as rank 1. According to the present results, the best run is run no. 5 and thereafter run no. 1 and run no. 7. Now, the ANOVA is utilized to evaluate the effect of process variables using the performance attributes. Tables 6 and 7 represent the response table for S/N ratios and the ANOVA results based on preference values, respectively. The abrasive type is the overall most influencing factor towards all the output responses considered. The results of factor reactions are acquired by implementing ‘higher-the-better’ criteria in Minitab-19. Figure 4 depicts the main effects plot for S/N ratios of preference value variation with composite types, pressure, SOD and abrasive types. The optimal parameter combination according to the preference value for simultaneous optimization of the output responses is obtained (C3, 3 bar, 5 mm, and A1).

350

I. Rout et al.

Table 5 Estimated preference value with respective rank Runs

Si+

Si−

Ci

Rank

1

0.1845

0.492

0.7273

2

2

0.413

0.1137

0.2158

7

3

0.4439

0.1674

0.2739

5

4

0.4467

0.0999

0.1827

9

5

0.0912

0.4559

0.8334

1

6

0.4939

0.1664

0.252

6

7

0.1819

0.4267

0.7011

3

8

0.4914

0.1317

0.2114

8

9

0.3342

0.2433

0.4213

4

Table 6 Response table for S/N ratios based on preference value Level

C

P

S

A

1

− 9.111

− 6.872

− 9.412

− 3.952

2

− 9.44

− 9.467

− 11.864

− 9.458

3

− 8.03

− 10.243

− 5.305

− 13.17

Delta

1.41

3.371

6.559

9.218

Rank

4

3

2

1

Table 7 ANOVA results based on preference value Source

DF

Adj SS

Adj MS

F-Value

Regression

4

0.427092

0.106773

3.95

P-Value 0.106

T

1

0.002274

0.002274

0.08

0.786

P

1

0.073461

0.073461

2.72

0.175

S

1

0.063592

0.063592

2.35

0.2

A

1

0.287766

0.287766

10.64

Error

4

0.108183

0.027046

Total

8

0.535276

0.031

3.4 Confirmatory Experiment for Taguchi Analysis and TOPSIS Approach The experimental results of individual responses for Taguchi analysis and the results of preference values for TOPSIS approach are verified by the confirmation test. The predicted and experimental output values of the optimal process parameters for Taguchi and TOPSIS approach are tabulated in Tables 8 and 9, respectively. By using Eq. (11), the predicted values of the responses can be evaluated.

Study on Effect of Process Parameters on AJM of Partially …

351

Fig. 4 Variation with C, P, S and A

ηopt = m +

0 ∑

(mi − m)

(11)

i=1

where m and mi are the mean of the response S/N ratio at the optimal level and S/N ratio at optimal parameter, respectively. From the confirmation test results it is observed that, for Taguchi analysis and TOPSIS, the responses show a satisfactory result within the improvement of responses. Using the Taguchi analysis, the improvement of RMR, Ra and OC are 9.44%, 29.06% and 44.44%, respectively, have been attained. Similarly, using TOPSIS for multi response optimization, an improvement in Ci after validation is 16.58%. The optimal parametric combination for simultaneous optimization through TOPSIS is attained as C3, 3 bar, 5 mm and A1. Therefore, confirmatory test results reveal that the improvement of Ci in the initial and experimental parameter setting using TOPSIS is satisfactory. Also, a better S/N ratio value (2.42178) as compared to the initial value (− 1.5829) is noticed.

352

I. Rout et al.

Table 8 Taguchi confirmatory test findings for RMR, Ra and OC Parameter

RMR (gm/s)

Ra (µm)

OC (mm)

Initial process parameter

Level

C3, 5, 4, A1

RMR (gm/s)

0.3644

S/N ratio (dB)

− 8.7684

S/N ratio Improvement (dB)

0.7835

% rise of RMR

9.44

Level

C2, 5, 3, A2

Ra (µm)

0.27574

S/N ratio (dB)

11.1900

S/N ratio improvement (dB)

2.2159

% decrease of Ra

29.06

Level

C3, 5, 4, A1

OC (mm)

0.013

S/N ratio (dB)

37.7211

S/N ratio Improvement (dB)

3.194

% decrease of OC

44.44

Table 9 TOPSIS confirmatory test findings

Optimal process parameters Prediction

Experiment

C3, 5, 5, A2

C3, 5, 5, A2 0.3988

− 7.95223

− 7.9849

C2, 5,3, A3

C2, 5,3, A3 0.21365

13.6719

13.4059

C2, 5, 4, A1

C2, 5, 4, A1 0.009

37.8934

40.9151

Initial process parameter

Optimal process parameters Prediction

Experiment

Level

C2, 4, 5, A1

C3, 3, 5, A1

C3, 3, 5, A1

RMR (gm/s)

0.214

0.4157

Ra (µm)

1.9166

0.1526

OC (mm)

0.013

Ci

0.8334

0.8914

S/N ratio

− 1.5829

2.42178

0.005 0.9991

Increase % of 16.58 Ci

4 Conclusion A new class of polymer matrix hybrid composites using a plant based (LC) and an animal based (HH) fiber-reinforced with different weight proportion of ISA has been fabricated using epoxy as the matrix material. The machinability of the currently

Study on Effect of Process Parameters on AJM of Partially …

353

synthesized composite in terms of RMR, OC and Ra using AJM is studied. It is observed that: 1. Highest RMR has been attained with 20 wt% ISA-filled composite at 5 bar pressure, whereas superior Ra and OC values are acquired with 10 wt% ISA-filled composites at 4 bar and 5 bar, respectively. According to the results, moderate stand-off-distance (4 mm) and SiC abrasive with 600 µm particle size are best suited for the machining of the composites using AJM. 2. The optimized combination of process parameter setting for RMR (C3, 5 bar, 5 mm and A2), Ra (C2, 5 bar, 3 mm and A3) and overcut (C1, 5 bar, 4 mm and A1) is derived via main effects plots for S/N ratios through Taguchi analysis and verified through confirmation test. 3. Upon implementing TOPSIS for multiobjective optimization, the optimized condition is found to be (C3, 3 bar, 5 mm and A1) with an enhanced performance score (16.58%) and improved predicted S/N ratio value.

References 1. Khalid MY, Rashid AA, Arif ZU, Ahmed W, Arshad H, Zaidi AA (2021) Natural fiber reinforced composites: Sustainable materials for emerging applications. Results Eng 11:100263 2. Boudjellal A, Trache D, Bekhouche S, Khimeche K, Razali MS, Guettiche D (2021) Preparation and characterization of Alfa fibers/graphene nanoplatelets hybrid for advanced applications. Mater Lett 289:129379 3. Nurazzi NM, Asyraf MRM, Fatimah Athiyah S, Shazleen SS, Rafiqah SA, Harussani MM, Kamarudin SH, Razman MR, Rahmah M, Zainudin ES, Ilyas RA (2021) A review on mechanical performance of hybrid natural fiber polymer composites for structural applications. Polymers 13(13):2170 4. Sun Z (2018) Progress in the research and applications of natural fiber-reinforced polymer matrix composites. Sci Eng Compos Mater 25(5):835–846 5. Anthony LS, Vasudevan M, Perumal V, Ovinis M, Raja PB, Edison TNJI (2021) Bioresourcederived polymer composites for energy storage applications: brief review. J Environ Chem Eng 9(5):105832 6. Premalatha N, Saravanakumar SS, Sanjay MR, Siengchin S, Khan A (2021) Structural and thermal properties of chemically modified luffa cylindrica fibers. J Nat Fibers 18(7):1038–1044 7. Yang L, Guo J, Zhang S, Gong Y (2017) Preparation and characterization of novel superartificial hair fiber based on biomass materials. Int J Biol Macromol 99:166–172 8. Rajawat AS, Singh S, Gangil B, Ranakoti L, Sharma S, Asyraf MRM, Razman MR (2022) Effect of marble dust on the mechanical, morphological, and wear performance of basalt fibre-reinforced epoxy composites for structural applications. Polymers 14(7):1325 9. Hegde S, Padmaraj NH, Siddesh V, Sunaya TS, Kini KA, Sanil VK (2021) Experimental investigation of mechanical sustainability and acoustic performance of fly ash cenosphere/ epoxy polymer composites. J King Saud Univ Eng Sci 10. Jena PK, Mohanty JR, Nayak S, Barik S (2022) A study on erosion wear behavior of benzoyl chloride modified Vetiver grass (Chrysopogon Zizanioides) and red mud as reinforcement in polymer based composites. J Nat Fibers 19(9):3253–3264 11. Pradhan P, Satapathy A (2022) Physico-mechanical characterization and thermal property evaluation of polyester composites filled with walnut shell powder. Polym Polym Compos 30:09673911221077808

354

I. Rout et al.

12. Nassar M, Arunachalam R, Alzebdeh KI (2017) Machinability of natural fiber reinforced composites: a review. Int J Adv ManufTechnol 88(9):2985–3004 13. Vinayagamoorthy R, Rajmohan T (2018) Machining and its challenges on bio-fiber reinforced plastics: a critical review. J Reinf Plast Compos 37(16):1037–1050 14. Jani SP, Kumar AS, Khan MA, Kumar MU (2016) Machinablity of hybrid natural fiber composite with and without filler as reinforcement. Mater Manuf Processes 31(10):1393–1399 15. Vigneshwaran S, Uthayakumar M, Arumugaprabu V (2018) Abrasive water jet machining of fiber-reinforced composite materials. J Reinf Plast Compos 37(4):230–237 16. Jayakumar K (2017) Abrasive water jet machining studies on Kenaf/E-glass fibre polymer composite. In: Proceedings of 10th international conference on precision, meso, micro and nano engineering (COPEN 10) 17. Rout I, Mahapatra TR, Mishra D (2022) Abrasive jet machining of incense stick ash filled luffa-human hair based sustainable bio-composite using Taguchi based GRA. J Nat Fibers 1–13 18. Bag R, Panda A, Sahoo AK, Kumar R (2022) Sustainable high-speed hard machining of AISI 4340 steel under dry environment. Arab J Sci Eng 1–24. https://doi.org/10.1007/s13369-02207094-9 19. Kumar R, Sahoo AK, Panda A (2022) Comparative performance analysis of coated carbide insert in turning of Ti-6Al-4V ELI grade alloy under dry, minimum quantity lubrication and spray impingement cooling environments. J Mater Eng Perform 31(1):709–732. https://doi. org/10.1007/s11665-021-06183-4

Effect of Artificial Roughness on Heat Transfer and Friction Factor in a Solar Air Heater: A Review Anil Singh Yadav , Tabish Alam , Rajiv Saxena , Raj Kumar Yadav , Rajan Kumar , Abhishek Sharma , Yogesh Agrawal , K. Viswanath Allamraju , Monika Vyas , Manmohan Singh , and Subhendu Chakroborty

Abstract In order to convert solar energy into thermal energy, solar air heater (SAH) is commonly used. These are used to convert the solar radiation into heat of working fluid. The effect of various geometric constraints on the friction characteristics and A. S. Yadav (B) · R. Kumar Mechanical Engineering Department, IES College of Technology, Bhopal, MP 462044, India e-mail: [email protected] T. Alam Architecture, Planning and Energy Efficiency, CSIR-Central Building Research Institute, Roorkee, Uttarakhand 247667, India R. Saxena Infinity Management and Engineering College, Sagar, MP 470001, India R. K. Yadav Mechanical Engineering Department, Adina Institute of Science and Technology, Sagar, MP 470002, India A. Sharma Department of Mechanical Engineering, BIT Sindri, Dhanbad, Jharkhand 828123, India Y. Agrawal Mechanical Engineering Department, Sagar Institute of Research and Technology, Bhopal, MP 462041, India K. Viswanath Allamraju Mechanical Engineering Department, Institute of Aeronautical Engineering, Hyderabad, Telangana 500043, India M. Vyas Civil Engineering Department, IES College of Technology, Bhopal, MP 462044, India M. Singh Computer Science and Engineering Department, IES College of Technology, Bhopal, MP 462044, India S. Chakroborty Department of Basic Sciences, IITM, IES University, Bhopal, MP 462044, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_33

355

356

A. S. Yadav et al.

heat transfer (HT) of a SAH has been studied. The paper aims to introduce the various parameters that are involved in the analysis of HT enhancement of SAH. There are various techniques that are involved in the analysis of HT enhancement of SAH. One of these is the use of a passive method, which involves the use of turbulators, ribs or fins on the surface. SAHs that are roughened can improve their HT properties compared to their plain counterparts. This is because the various surface patterns and shapes used in their design can help improve their performance. There have been many different sorts of studies conducted on the many facets of these HT augmentation approaches. Studies showed that combining the roughening process with a rough absorber plate can improve the performance of SAH duct. The goal of this study is to identify the most appropriate roughness component for solar air duct that can meet the different operating conditions. The findings of this study can be used to design and construct enhanced SAH duct system. Keywords Heat transfer · Fluid flow · Rib · Nusselt number · Solar air heater

1 Introduction Non-renewable energy sources are those that will run out or will not be able to be refilled in our lifetimes. Fossil fuels are mainly composed of carbon, which is the main component of these fuels. The period during which these fuels formed is referred to as the carboniferous period. Before the dinosaurs died out, Earth had a different landscape. There were vast seas and forests. During this time, algae, fungi, and plants grew in these areas, which absorbed sunlight and produced energy. When the organisms died, they drifted to the bottom of the lake or sea. The remains of these animals and plants then accumulated under the seabed, where they were crushed and subjected to high heat. Fossil fuels then formed due to the energy stored in these remains. Today, there are vast underground pockets that contain non-renewable energy sources. These areas are referred to as reservoirs. Due to the harmful effects of conventional energy sources, such as greenhouse gases, the focus has shifted toward the development of more sustainable sources such as wind, biomass, and solar. Due to the increasing energy consumption and the lack of resources, the global energy crisis is becoming more critical. Due to their environmental benefits and lower operating costs, renewable energy sources such as wind and solar are becoming more popular. In the past, these types of energy sources were very expensive to install and maintain. However, due to the advancements in technology, these costs have become more affordable. The cost of renewable energy has significantly decreased over the past couple of years, making it an increasingly feasible option for power producers. Despite the increasing popularity of renewable energy, its environmental benefits are still rising. Understanding the various costs and benefits associated with renewable energy sources is very important in order to make informed decisions regarding its future. These energy sources are expected to play a significant role in reducing greenhouse gas emissions. One of the most common factors that need to be considered

Effect of Artificial Roughness on Heat Transfer and Friction Factor …

357

when it comes to implementing renewable energy is the need to mix different types of energy. This can help achieve a goal that is sustainable. However, there are also various environmental issues that needs to be taken into account in order to fully realize its potential. Despite the various challenges that need to be considered when it comes to implementing renewable energy, it is still considered a viable option for the world. One of the most promising types of renewable energy is solar power, as it is environmentally friendly and can be easily affordable. Renewable energy is expected to play an important role in addressing climate change issues. Solar power is also becoming more popular due to its low cost and environmental benefits [1]. Various researchers have been working on improving the efficiency of thermal systems [2–5]. These efforts have resulted in the reduction of the size and energy consumption rates. Because of this, there has been a rise in the utilization of highly effective HT systems. To keep up with the increasing energy demand, engineers must focus on improving the efficiency of HT systems and reducing the energy waste. Aside from being an effective and sustainable energy source, solar energy is also regarded as a vital component of the development of sustainable energy. It is commonly used in various industries such as crop drying and water desalination. In order to transform the energy that the sun provides into thermal energy, air heaters are commonly used (Fig. 1). These are used to convert the solar radiation into heat of working fluid. They have various advantages over traditional air heaters, such as their low cost of installation and operation [6]. Unfortunately, SAHs are known to have poor thermal efficiency due to their low HT rate. Other factors such as the temperature rise and low thermal capacity can also limit their utilization [7]. To improve their thermal efficiency, air heaters can be utilized through various active and passive HT enhancement techniques [8–18]. These include improving HT rate that is more consistent with the system’s thermohydraulic state. It can be performed in various areas such as cooling systems, air conditioning, refrigeration, and central heating [19, 20].

Fig. 1 SAH [21]

358

A. S. Yadav et al.

2 HT Enhancement Techniques There are three main types of HT enhancement techniques: passive, active, and compound. The former involves using external power to perform the process, while the latter uses the system’s thermohydraulic state to improve its performance. In compound techniques, two or more active and passive techniques are used to achieve the better result. In the passive technique, the system’s geometry or surface is modified to increase the system’s thermohydraulic performance. This process is carried out through the use of rough surfaces, inserts, and ribs. The resulting increase in the system’s thermohydraulic state results in an increase in the overall HT rate. Compared to the other methods, the passive technique is more cost-efficient. On the other hand, active techniques require the use of external power to achieve their goals. This makes the process more complex. Due to the complexity of the process, most scientific fields limit the use of active techniques in their applications. In contrast, a compound technique combines the active and passive techniques to improve the performance of heat exchangers. This technique can be used to enhance the performance of a system by simultaneously using both the active and passive techniques. It has been regarded as a promising new method for improving the system’s thermal efficiency [22–24].

3 Basics of Artificial Roughness Element One of the most successful approaches to improve the forced convection HT coefficient in a solar collector duct is by utilizing artificial rib roughness elements. Most of the ribs to improve the HT coefficients that are used in the SAH are utilized to break the sub-layer of the laminar flow [25–27]. In various applications, such as nuclear reactors and gas turbine blade cooling systems, the presence of a sub-layer of viscous material can lead to a decrease in the HT coefficient [2, 28]. The rib element is only used in the hot interval. The other three spacer types are smooth and have a shield effect, which helps prevent the surface of the HT flow from getting distorted. In a SAH, the appearance of a thick plate can be a tempting factor that can lead to the improper HT. When the energy required to generate the disturbance from the fan comes from the exhaust, the force needed to make the wind flow through a duct must be enormous. In addition, turbulence should also occur near the surface of the sublet where only HT occurs. The current should not exceed the maximum permissible limit and the area should not be too viscous. To minimize the impact of the wind flow on the rib element, the height of the rib element should be kept very small. Although there are several parameters that can be used to describe the state of the harshness, the pitch and rib element height are the most critical factors. The various parameters that can be used to describe the state of the rib element, such as the pitch and rib element height, are usually determined as far away as dimensionless terms. Although the square rib element is commonly used, other types of rib elements, such as the circular, semi-circular, and square, have also been studied for heat-driven thinking.

Effect of Artificial Roughness on Heat Transfer and Friction Factor …

359

Although there have been many experimental studies on the effects of the rib element on the flow arrangement, little research has been conducted on the numerical aspects of this field due to the complexity of the flow arrangement and the mathematical constraints. The presence of the rib can also lead to the separation of the viscous sub-layer and increase the HT coefficient. The presence of the rib can also lead to the separation of the viscous sub-layer and increase the HT coefficient. The other factors that can affect the flow arrangement are the re-stabilization and the velocity separation.

4 HT Enhancement of SAH The low convective HT rate that a smooth SAH has usually results in a poor thermal performance for the SAH. It is essential to disrupt the laminar sublayer that forms under the absorbing surface in order to make the device work more effectively. This layer is formed to act as a thermal resistance to flowing air. The underside of an absorbing surface is often roughened due to the presence of small wires attached to the surface. The thermal performance is improved as a result of the flow of air across the roughened surface, which results in the separation and reattachment between the ribs. The addition of a secondary recirculation flow helps to improve the HT rate. It also helps in promoting the mixing between the heated surface and the core flow. However, this process can also increase the pressure drop, it ultimately leads in a larger requirement for pumping power. To prevent the resistance from becoming excessively high, the height of the inner rib should be maintained at a level that is low in proportion to the height of the duct. Various factors such as the shape of the wires and the ribs arrangement can also affect the performance of the SAH. In an effort to enhance the performance of the SAH, a number of different rib configurations have been investigated. Some of these include the curved ribs, V-ribs, discrete ribs, and staggered ribs [29–34]. Various investigators reported many ranges of values that affect the order and structures of the ribs, such as rib height, pitch and angle of arc. These rib elements are usually declared in rates of quantities, not of dimensions like that the “relative roughness pitch, relative roughness height, relative gap position, relative gap height, chamfer angle”, etc. Its roughness geometry studied by previous researchers has been reviewed and reported literature. The rough surfaces can be described by the key dimensions of their structure, such as the angle of arc, pitch, and rib height. In addition, various values that affect the structure and order of the ribs are also known to be declared [35]. These are usually declared in non-dimensional terms. Although the exact details of these rib elements are not known, previous researchers have been able to study their properties and report their findings by CFD analysis as well [36–38].

360

A. S. Yadav et al.

5 HT Enhancement of SAH The characterization of roughness can be described by using the various key dimensions geometrical parameters. Effect of rib element height (e) The presence of the roughness element under the absorbent plate leads to disruptions due to the establishment of flow separation zones on each side of the element, which can improve its performance and HT. The alignment and height of the ribs can also affect the flow. These elements can also prevent the flow from going through the entire surface. The secondary flow created by the rough surface can additionally contribute to the flow’s overall improvement. In their study, Prasad and Saini [39, 40] gave the following results: a. If e < δ There will be no roughness effect. b. If e > δ There will be more roughness effect on fluid pressure. c. If e ≥ δ There will be an improvement in the transfer of heat, where δ is the height of the laminar sub-layer. Effect of roughness element pitch (P) Webb et al. [41] observed that the height of the rib roughness can prevent the flow through exiting surface. If the rib element’s pitch isn’t correctly maintained, the flow will not be able to reattached. This issue can cause poor HT because the shear layer will not be reinserted to the pitch ratio of 8. It is at the point of reattachment that the greatest amount of heat is transferred. Effect of angle of arc (α) The alignment of the rough elements on the surfaces can also affect the performance of SAH duct. For instance, the angle of arc can affect the friction factor (fr) and the HT rate. The fr lowers as the arc’s angle falls. However, this is not the case with the inclined ribs, as the secondary flow generated by the rib increases the fr and Nusselt number (Nu). Taslim et al. [42] were able to study the flow produced by the rib due to the presence of vortexes at the leading end. After that, these forces travel along the rib till they reach the trailing end. The secondary flow generated by the rib is then generated by the cold fluid that moves toward the trailing end, where the rate of HT is quite low. Effect of duct height (H) For improving the efficiency of SAH duct the size of duct should be low. Due to the fact that the pumping frictional power consumption is related to (1/H3 ), the efficiency of the SAH duct will rapidly decline as the mass flow increases. Effect of aspect ratio (W/H) The aspect ratio of the SAH duct affects its performance. The higher the aspect ratio, the greater the amount of frictional power that is required due to the rise in turbulence.

Effect of Artificial Roughness on Heat Transfer and Friction Factor …

361

According to the Karwa et al. [43], high value of thermohydraulic performance (THP) can be achieved by providing low value of W/H. Effect of relative roughness height (e/D) Prasad and Mullick [7] investigated the impact of the effect of the circular wire-like elements’ geometric roughness parameter on the THP of the SAH duct. They found that the higher the e/D, the higher the Nu and the fr. As the e/D ratio of the rib’s height to the diameter of the air flow increases, the value of the Nu and the fr goes up. The researchers found that the rate of increase in the mean fr of the SAH duct’s performance can be influenced by the increasing e/D ratio. Effect of relative roughness pitch (P/e) Prasad and Saini [39] studied the effect of the P/e ratio on the flow pattern in the channel. It is calculated by dividing the distance between two adjacent ribs by the height of one rib. The low value of the P/e ( 0, positive jitter indicates that the packet arrives in advance; otherwise, negative jitter indicates that the packet arrives late or loses packets. VideoEngine: It is the framework for video media chains, from the camera of the camera to the network and from the network to the screen. VP8: It was developed and designed for low latency. It is the video codec from the WebM project, which is well suited for RTC.

Analysis and Research on the Related Technology of WebSocket …

473

Image enhancements: It will remove video noise, etc. from the images acquired through the camera.

4 Conclusion In view of a series of problems in the development of public place management system, this paper introduces some important technologies that can be used in the system and gives a detailed explanation from different application perspectives. The system adopts B/S model, SSH structure for system architecture, WebSocket and SIP technology for communication, VLC for video streaming, and WebRTC technology for video calling, so that the system can effectively achieve good operation and service purposes.

References 1. Karla T, Tarnawski J (2019) Soft real-time communication with WebSocket and WebRTC protocols performance analysis for web-based control loops. In: 2019 24th international conference on methods and models in automation and robotics (MMAR) 2. Miu A, Ferreira F, Yoshida N, Zhou F (2020) Generating interactive WebSocket applications in TypeScript. EPTCS 314:12–22 3. Murley P, Ma Z, Mason J (2021) WebSocket adoption and the landscape of the real-time web. In: Proceedings of the web conference, April 2021, pp 1192–1203 4. Rahatur Rahman Md., Akhter S (2015) Real time Bi-directional traffic management support system with GPS and WebSocket. In: 2015 IEEE international conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing 5. Bergkvist A, Burnett DC, Jennings C, Narayanan A (2012) WebRTC 1.0: real-time communication between browsers. In: Working draft, W3C, February 2012 6. Schulzrinne H, Wedlund E (2000) Application-layer mobility using SIP. ACM SIGMOBILE Mob Comput Commun Rev 4(3):47–57 7. Pimentel V, Nickerson BG (2012) Communicating and displaying RealTime data with WebSocket. IEEE Internet Comput 16(4):45–53 8. Maraševi´c J, Gavrovska A, Reality V (2020) WebRTC implementation for Web educational application development. Telecommunications forum (TELFOR) 9. Liu Q, Yang G, Zhao R, Xia Y (2020) Design and implementation of real-time monitoring system for wireless coverage data based on WebSocket. In: 2018 IEEE 3rd international conference on cloud computing and internet of things (CCIOT), 12 Mar 2020 10. Shu QL (2020) Design and implementation of college intelligent examination scheduling system based on C/S and B/S hybrid structure. Inform Technol Inform 04:16–19

Safety Detection System of Perovskite Battery Materials Based on Intelligent Identification Algorithm Dehui Sun

Abstract In recent years, neural network is a computational model that imitates animal neural structure and adopts the form of connection. The neural network consists of multiple neurons, grouped by layer, and the neurons in each layer are connected with other layers. In the forward reasoning operation, the output of the previous layer is taken as the next layer. The deep learning method uses a general learning process to learn feature representation from data and extract more effective image feature information, which surpasses the original manually designed image feature extraction method and has strong nonlinear modeling ability. With the continuous deepening of research, deep learning technology has achieved remarkable results in image classification, object detection, image segmentation, and other computer vision fields. Perovskite solar cells are solar cells that use perovskite type organic metal halide semiconductors as light absorbing materials and belong to the third generation of solar cells. The development of perovskite solar cells is in good condition, but there are still some key factors that may restrict the development of perovskite solar cells: 1. The stability of batteries. 2. The absorption layer contains soluble heavy metal Pb. 3. The theoretical research of perovskite solar cells needs to be enhanced. Keywords Neural network algorithm · Intelligent identification algorithm · Perovskite battery

1 Perovskite Solar Cells Since organic inorganic hybrid perovskite solar cells (OI PSCs) came out in 2009, their power conversion efficiency (PCE) has increased from 3.8 to 22.1% in just eight years, which is enough to highlight their huge application prospects. However, up to now, the application of PSCs has not been effectively promoted, one of the most D. Sun (B) Department of Basic Teaching, Shandong Jiaotong University, Weihai, Shandong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_44

475

476

D. Sun

important reasons is the poor battery stability. Recently, many papers reported that compared with OI PSCs, all inorganic perovskite solar cells (I-PSCs) have shown greater advantages in stability [4–6]. Therefore, the call for replacing OI PSCs with them is growing [1]. However, compared with OI PSCs, I-PSCs still have a fatal weakness that is hard to hide–the PCE is too low (no more than 10% at most). Although people have tried to use various means to improve the performance of IPSCs, these measures only improve the PCE of I-PSCs to a certain extent. Compared with the performance improvement rate of OI PSCs and their current maximum efficiency, the performance change of I-PSCs is still somewhat slow [2]. Therefore, it is urgent to find a new improvement method to further improve the PCE of IPSCs as soon as possible. The production process of perovskite battery/module is greatly different from that of crystalline silicon, which is more economical. Unlike the crystalline silicon route, which requires four steps of silicon material, silicon chip, battery chip, and module to prepare crystalline silicon modules, the preparation of perovskite modules requires only a single factory, and the production process takes much less time and energy consumption than crystalline silicon. In the mature state, after the GW level mass production, the investment in perovskite solar cell/module equipment and the cost per watt will be significantly lower than that of the crystalline silicon route. Therefore, from the perspective of material selection, Al and Ag are the best two types of materials (external media wrapping will make the resonance band of metal red shift). However, these two materials are very easy to be oxidized, so the performance stability of the battery is poor. Secondly, metal surface plasmon effect has two characteristics, namely far-field scattering and near-field enhancement [3]. However, in most cases, due to the specific structure of the battery, only one of them can be used. However, in this project, the combination of metal nanoparticle arrays and graphene as an electron transport layer can not only give play to their respective advantages, but also convert their disadvantages into favorable conditions for the electrode as a whole: first, both metal particle arrays and graphene have high electron mobility, and the energy level structure matches. Therefore, compared with traditional TiO2 , M/G-Electrode has more advantages in electron transport. Secondly, in addition to electron transfer, the metal array can play its surface plasmon effect to enhance the performance of I-PSCs, which is not available in traditional electrodes. Thirdly, due to the thin thickness of graphene [4], the metal array at this time can play both the far-field scattering and near-field enhancement effects, which makes it have more outstanding advantages than the general metal array electrode in manipulating light and improving the efficiency of photoelectric conversion dynamics [5]. Therefore, based on the above considerations, in this project, we will theoretically design M/G-Electrodes of various configurations on the basis of the most authentic IPSCs environment, and conduct numerical simulation on various performance indicators of I-PSCs using this electrode. In view of the widespread disconnection between theory and experiment, in this project, we plan to try to combine the finite difference time domain method (FDTD) with the first principle to simulate the electrical and optical parameters of various materials used in I-PSCs under real conditions, and introduce them into the industrial device design software Device (developed

Safety Detection System of Perovskite Battery Materials Based …

477

by Lumetallic). Thus, the most real I-PSCs performance indicators can be obtained through simulation (see the research scheme for details) [6]. Use the obtained battery performance characteristics to analyze its change rule, build a reasonable theoretical model, fundamentally dissect the reasons for the change of battery physical properties, and finally give the most suitable alternative scheme for the experiment, so as to improve the efficiency of the experiment, and truly achieve theoretical guidance. When exposed to sunlight, the calcium titanium ore layer first absorbs photons to generate electron hole pairs. Due to the difference of exciton binding energy of perovskite materials, these carriers either become free carriers or excitons. Moreover, because these perovskite materials often have lower carrier recombination probability and higher carrier mobility, the carrier diffusion distance and lifetime are longer. Then, these uncomposed electrons and holes are collected by the electron transport layer and hole transport layer respectively, that is, electrons are transferred from the calcium titanium deposit to the isoelectronic transport layer, and finally collected by FTO; The holes are transferred from the calcium titanium ore layer to the hole transport layer, and finally collected by the metal electrode. Of course, these processes are inevitably accompanied by some carrier losses, such as the reversible recombination of electrons in the electron transport layer and holes in the calcium titanium ore layer, the recombination of electrons in the electron transport layer and holes in the hole transport layer (the case of non-compact calcium titanium ore layer), and the recombination of electrons in the calcium titanium ore layer and holes in the hole transport layer. To improve the overall performance of the battery, the loss of these carriers should be minimized. Finally, the photoelectric current is generated through the circuit connecting the FTO and the metal electrode.

2 Neural Network Algorithm Neural network is composed of a large number of neurons, which are connected with each other and have high nonlinearity. The processing of neural network is determined by the number of neurons, the form of mutual connection, input and output. Neural network is an important technology of intelligent computing. It expands the processing of intelligent information and can handle complex nonlinear relations and logical operations [5]. It also provides an effective method to solve many problems (Fig. 1). Back propagation neural network [6], also known as BP neural network, is the most widely used network among all networks and the basis for other network applications. It is widely used in data compression, classification information, action technology, pattern analysis, and other fields. It has been proved mathematically that BP neural network can solve complex nonlinear mapping as long as there are enough neurons in the hidden layer [7–9]. Moreover, the self-learning ability of neural network can make a good decision plan by learning the correct data set. The construction of neural network in this paper is based on open neural network library, which is mainly used

478

D. Sun

Fig. 1 Initial structure diagram of neural network

Fig. 2 Perceptron and logical activation function diagram

in the field of deep learning and machine learning, and forms network models with different functions according to the needs of users. It is an advanced analysis software library [10] written in c++ , which has outstanding advantages in memory allocation and operation efficiency. In order to maximize efficiency, it is constantly optimized and parallelized (Fig. 2).

3 Model Design and Application 3.1 Optical Design Based on FDTD Numerical Simulation (a) According to literature research, a variety of M/G-Electrode space configurations are designed;

Safety Detection System of Perovskite Battery Materials Based …

479

(b) The finite difference time domain method is used to simulate the spatial distribution of electromagnetic fields caused by M/G-Electrodes of different spatial configurations in various areas of I-PSCs, in combination with the properties of the materials themselves (dielectric constant: can be obtained through the first principle or the corresponding database), so as to obtain the spatial optical properties of various areas of I-PSCs [7]; (c) Based on the electronic characteristics of M/G-Electrodes in 2.1.2, the reasons for the changes in the properties of light field inside the battery caused by M/ G-Electrodes with different spatial configurations are analyzed. mi

v0 (t) ei0 (t) − vi (t) dvi t + ∑ j (/=i) f i j + ∑w f iw = mi i dt τi

3.2 Calculation of Electronic Characteristics Based on the First Principle (a) First principles are used to simulate the physical properties of graphene such as energy level structure, electron transfer rate, surface defect formation energy under different parameters (doping, layers, etc.); (b) Based on the simulation results, the changes of graphene properties in (a) after the introduction of metal arrays are simulated [8]; (c) Analyze the reasons for changes in (a) and (b) results. vi0group

1 t= n−1

n ∑

v0j group t

j=1, j/=i

3.3 Device Software Based Device Performance Simulation and Scheme Design (a) The photovoltaic properties of I-PSCs under different spatial configurations of M/G-Electrodes, including PCE, short circuit current density (Jsc), open circuit voltage (Voc), filling factor (FF), IPCE, etc., are calculated using the parameters of materials (including optical properties of various regions of I-PSCs, electron mobility of various materials used by I-PSCs, material interface defect states, electrode work function, etc.) obtained in the previous calculation; (b) Analyze the rule that different M/G-Electrodes in (a) affect the battery performance, combine the simulation results in 2.1.1 and 2.1.2, explain the cause of this phenomenon, and determine the best experimental scheme in theory.

480

D. Sun

Q1 + Q2 ≤ 6 min 0.9[A1 (N − 1) + A2 B Q1 + Q2 T = ≤ 4 min 0.9[A1 (N − 1) + A2 B TP = TP1 + TP2 + TP3 + TP4 + TP5 ≤ 6 min TC = TP + TC1 + TC2 + TC3 ≤ 6 min \

T =1+

TR S E T = TD E T + TW A R N + (TP R E + TT R AV )

4 Analysis of Simulation Results The surface plasmon enhancement effect of metal nanoparticles can be taken advantage of by using M/G-Electrodes: first, part of the incident light will be localized to 5–10 nm around the metal particles (near-field effect), which greatly enhances the local electric field strength near the interface between the perovskite material and the electrode, thus improving the charge separation rate at the interface and effectively avoiding the recombination of electrons at the interface. See Nano Lett [11 2011, 1760–1765]. Secondly, the light scattered by particles will preferentially enter the battery interior and show angular distribution, thus increasing the effective optical path of scattered light in the light absorbing layer and improving the light absorption characteristics of the battery, see Nature Mater [9 2010, 205]. In addition, the scattered light entering the battery meets the coherent conditions, thus forming a local electric field coherent enhancement (plasmon focusing) in the battery. On the one hand, it improves the local field absorption of the battery, on the other hand, it accelerates the separation of electron hole pairs around the electric field, which can improve the battery’s IPCE and current density [9]. The longitudinal and transverse positions of the focus can be adjusted by adjusting the particle spacing and scattering light angle, so the controllability is good. In addition, after encountering the metal back electrode, the scattered light will return to the battery surface to interact with the metal particles and enter the battery again. If the conditions are appropriate, the scattered light will continue to move repeatedly in the battery until it disappears, so that the material can achieve 100% absorption of light (perfect absorption layer), see Nature[4922012,86]. As a zero band gap semiconductor, graphene, its electron mobility has reached 2.5 at room temperature × 105 cm2 /Vs, the resistivity is only 10–6 Ω·cm, so it is a natural electron transport layer material. Therefore, when the metal array and graphene are combined [10], they can also have greater advantages than traditional electrodes in conducting electrons (TiO2 electron mobility is only 20 cm2 /V·s). In addition, graphene can not only prevent the oxidation of metal particles, but also further improve the battery stability; At the same time, because of its thin thickness, it has no effect on the near-field effect of metal particles. Compared with traditional metal arrays, which can only use one of the far-field or near-field effects, M/G-Electrodes can play both roles at the same time. On the one hand, by controlling the number of graphene layers, doping, and other factors, we can control the

Safety Detection System of Perovskite Battery Materials Based …

481

electronic mobility rate, energy level structure, and other characteristics of graphene itself. At the same time, due to the change of effective refractive index, it will also affect the optical properties of metal arrays, so we can tap huge potential. The surface plasmon enhancement effect of metal nanoparticles can be taken advantage of by using M/G-Electrode: first, part of the incident light will be localized to 5–10 nm around the metal particles (near-field effect), which greatly enhances the local electric field strength near the interface between the perovskite material and the electrode, thus improving the charge separation rate at the interface and effectively avoiding the recombination of electrons at the interface. Secondly, the light scattered by particles will enter into the cell preferentially and show angular distribution, thus increasing the effective optical path of scattered light in the light absorbing layer and improving the light absorption characteristics of the cell. In addition, the scattered light entering the battery meets the coherent condition, thus forming a local electric field coherent enhancement (plasmon focusing). On the one hand, it improves the local field absorption of the battery, on the other hand, it accelerates the separation of electron hole pairs around the electric field, which can improve the battery’s IPCE and current density. The longitudinal and transverse positions of the focus can be adjusted by adjusting the particle spacing and scattering light angle, so the controllability is good. In addition, after encountering the metal back electrode, the scattered light will return to the battery surface to interact with the metal particles and enter the battery again. If the conditions are appropriate, the scattered light will continue to move repeatedly in the battery until it disappears, so that the material can achieve 100% absorption of light (perfect absorption layer). As a zero band gap semiconductor, graphene has an electron mobility of 2.5 at room temperature × 105 cm2 /V · s, the resistivity is only 10–6 Ω·cm, so it is a natural electron transport layer material. Therefore, when the metal array and graphene are combined, they can also have greater advantages than traditional electrodes in conducting electrons (TiO2 electron mobility is only 20 cm2 /Vs). Research on commercial polycrystalline silicon solar cells enhanced by metal nanoparticle arrays: With polycrystalline silicon solar cells as the research target, the far-field scattering effect of Ag nanoparticle arrays LSP is used to enhance their performance. By adjusting the size and surface coverage of Ag particles, the efficiency of commercial polysilicon cell chips has been successfully improved by 1.8%, and the current density has been improved by 2%. The assumption that the angular distribution of scattered light enhances absorption and then enhances efficiency is put forward. The experimental content is simulated and reproduced by FDTD method, and highly consistent results are obtained; The conjecture about the enhancement mechanism was successfully proved by the simulated electric field spatial distribution, cell absorption spectrum, current density, and other characteristics. Exploration of new quantum dot solar cell photoanode: the perovskite type material ZnTiO3 has high electron mobility (150–400 cm2 /vs) according to the first principle calculation. Compared with other photoanode materials, it is found that its electron mobility is 0.6–8 times that of SnO2 (50–250 cm2 /vs); It is 10–49 times of Zn2 SnO4 (10–15 cm2 /vs); 7–100 times of TiO2 (4–20 c cm2 /vs); It is at the same level as ZnO with high electron mobility (200–300 cm2 /vs). The energy band structure

482

D. Sun

Fig. 3 Assembly time frequency distribution

(band gap = 2.96 eV; conduction band bottom = −4.2 eV) is further calculated and studied, and it is predicted that ZnTiO3 has great potential as a new photoanode material for quantum dot solar cells. The quantum dot solar cell with ZnTiO2 /CdS/CdSe structure was prepared for the first time in the experiment, and the energy conversion efficiency of 1.95% and the current density of 5.96 mA/cm2 were obtained, compared with the mainstream photoanode material TiO2 under the same conditions (PCE = 1.83%; Jsc = 5.64 mA/cm2 ) and found that it has more excellent performance (Fig. 3). Theoretical exploration of LSP far-field scattering effect of metal nanoparticles to enhance PSC (preliminary research): Taking Ag and Al arrays of metal nanoparticles as battery electrodes, combining FDTD method and Device software, theoretically explored the influence of the far-field scattering effect of the two arrays on the performance of planar heterostructure PSC. The change of main parameters such as PCE, Jsc, Voc, and FF of the battery was analyzed by adjusting the conditions such as particle morphology and array period, and the efficiency was improved by 12.18 and 8.0% in theory. This work shows the feasibility of using metal arrays as electrodes in perovskite batteries, which is also an important support for the project. This is not available with traditional electrodes. Thirdly, due to the thin thickness of graphene, the metal array at this time can play both the far-field scattering and nearfield enhancement effects, which makes it have more outstanding advantages than the general metal array electrode in manipulating light and improving the efficiency of photoelectric conversion dynamics. Moreover, due to the existence of metal, graphene is also prevented from being used as an electronic transport layer alone, reducing the generation of leakage current. Of course, at this time, graphene will also be affected by the near-field effect, and the internal electron transfer rate will be further improved. Finally, the more important point is that the covering of graphene makes the metal array completely wrapped, eliminating the possibility of metal oxidation, thus further

Safety Detection System of Perovskite Battery Materials Based …

483

Fig. 4 Emergency evacuation heat map

improving the stability of the device. Although the above idea is reasonable, it is really difficult to realize it. Considering the complexity of the current perovskite battery preparation process and the expensive materials, it is obviously time-consuming, laborious and inefficient to directly adopt the experimental exploration method, so it is the most convenient way to theoretically explore the most qualified M/G-Electrode and use it to guide the experiment (Fig. 4).

5 Conclusion (1) The idea of introducing M/G-Electrode into I-PSCs as an electron transport layer was proposed for the first time. Compared with traditional electrodes, M/G-Electrode not only brings into play the advantages of metal arrays and graphene, but also complements their disadvantages. (2) Using the research idea of combining the finite difference time domain method with the first principle, and combining with the industrial device design software device, this paper proposes a scheme to study the battery performance in the real environment, which greatly reduces the time for experimental exploration, improves the experimental efficiency, and achieves the real theoretical guidance of the experiment. Acknowledgements Supported by the Shandong Jiaotong University Scientific Research Fund in 2020

484

D. Sun

Project Number: Z202010

References 1. Etgar L, Gao P, Xue Z, Peng Q, Chandiran AK, Liu B et al (2012) Mesoscopic CH3 NH3 PbI3 / TiO2 heterojunction solar cells. J Am Chem Soc 134:17396–17399 2. Kim HS, Lee CR, Im JH, Lee KB, Moehl T, Marchioro A et al. (2012) Lead iodide perovskite sensitized all-solid-state submicron thin film mesoscopic solar cell with efficiency exceeding 9%. Scient Reports 2:591 3. Kojima A, Teshima K, Shirai Y, Miyasaka T (2009) Organometal Halide Perovskites as visiblelight sensitizers for photovoltaic cells. J American Chem Soc 131:6050 4. Yin WJ, Shi T, Yan Y (2014) Unique properties of halide perovskites as possible origins of the superior solar cell performance. Adv Mater 26:4653–4658 5. Eperon GE, Paternò GM, Sutton RJ, Zampetti A, Haghighirad AA, Cacialli F et al (2015) Inorganic caesium lead iodide perovskite solar cells. J Mater Chem A 3:19688–19695 6. Kulbak M, Gupta S, Kedem N, Levine I, Bendikov T, Hodes G et al (2016) Cesium enhances long-term stability of lead bromide Perovskite-based solar cells. J Phys Chem Lett 7:167–172 7. Qiu X, Cao B, Yuan S, Chen X, Qiu Z, Jiang Y, et al. (2017) From unstable CsSnI3 to air-stable Cs2SnI6: a lead-free perovskite solar cell light absorber with bandgap of 1.48eV and high absorption coefficient. Solar Energy Mater Solar Cells 159:227–234 8. Moghe D, Wang L, Traverse CJ, Redoute A, Sponseller M, Brown PR et al (2016) All vapordeposited lead-free doped CsSnBr3 planar solar cells. Nano Energy 28:469–474 9. Wang N, Zhou Y, Ju M-G, Garces HF, Ding T, Pang S, et al. (2016) Heterojunction-depleted lead-free perovskite solar cells with coarse-grained B-γ-CsSnI3 thin films. Adv Energy Mater 1601130 10. Novoselov KS, Geim AK, Morozov SV, Jiang D, Katsnelson MI, Grigorieva IV et al (2005) Two-dimensional gas of massless Dirac fermions in graphene. Nature 438:197–200

Application of Industrial Robot in Injection Molding of Thin-Walled Porous Plastic Parts Yongcheng Huang, Pengcheng Wang, Bin Yang, and Yanxia Zhang

Abstract Plastic has the advantages of high specific strength, good processing characteristics, good corrosion resistance, and good performance design. It also has the performance requirements of some mechanical parts, which can save cost and facilitate molding and manufacturing. The demand for plastic applications is growing day by day, and it is widely used in various industries, such as household appliances, automobiles, decoration, aerospace. At present, most plastic parts are processed by traditional injection molding. The processed products are not only high precision, but also have strong consistency. This paper simulates the injection molding of thinwalled porous plastic parts based on Moldflow synergy software, and uses ABB industrial robot to carry out automatic handling of processed plastic parts. Keywords Injection molding · Thin-wall porous plastic parts · Industrial machinery · Automatic handling

1 Introduction At present, most plastic parts are processed by traditional injection molding. The processed products are not only high precision, but also have strong consistency. Because of its poor stiffness, thin-walled plastic parts are easier to warp and deform by traditional processing methods [1]. With the development of society, Industry 4.0 was put forward by Germany in 2013, and then the United States and Japan also put forward the corresponding manufacturing concept. Of course, China also proposed “Made in China 2025” in 2015 [2]. Industrial R&D, manufacturing and application are important indicators to measure the level of scientific and technological innovation and high-end manufacturing in a country. Industrial robots integrate manufacturing, automation, computer, and other science and technology. The talents Y. Huang (B) · P. Wang · B. Yang · Y. Zhang Guang Dong Technology College, Zhaoqing 526100, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_45

485

486

Y. Huang et al.

required for the design and R&D of industrial robots need not only systematic scientific knowledge, but also relevant industrial professional experience and knowledge. This paper is the application of industrial robot in injection molding machine, which greatly improves the work efficiency and work quality [3]. With the continuous development of modern science and technology, robots have gradually entered all aspects of human society, especially in industry. Industrial robots are divided into: handling robots, spot welding robots, laser processing robots, assembly robots, etc.; Four industrial robot families: ABB robots in Switzerland, Kuka robots in Germany, Yaskawa and Fanuc robots in Japan are leading the development of global industrial robots. More and more enterprises participate in the R&D and manufacturing of industrial robots, and more and more traditional enterprises use robots to solve labor problems, rather than manpower. With the rapid development of China’s economy and the continuous breakthroughs in science and technology, especially after the concept of “Made in China 2025” was put forward, the state and enterprises pay more and more attention to the training of innovative scientific and technological talents. China is also constantly cultivating innovative talents. Robots have also been introduced into domestic manufacturing and other fields, especially in industries such as palletizing, gluing, spot welding, arc welding, spraying, handling, and measuring.

2 Injection Molding of Thin-Walled Porous Plastic Parts Thin-wall plastic part means that the wall thickness of the molded plastic part is less than 1 mm (or 1.5 mm), or T /D (plastic part thickness T, plastic part diameter D, for disc-shaped plastic parts) is less than 0.05, which is defined as thin-wall injection molding. This study takes the air conditioning windshield shell as the analysis model, as shown in Fig. 1, with a size of 800 mm × 160 mm × 1.5 mm, round hole size is ∅ 10 mm, which is a typical thin-walled porous plastic part [4].

Fig. 1 Air conditioner wind shield

Application of Industrial Robot in Injection Molding of Thin-Walled …

487

Import the repaired model into Moldflow for 3D mesh generation. The side length of the mesh is generally 1.5–2 times of the minimum wall thickness of the product, that is, use the smaller global mesh with side length of 2.25 mm to divide the model into 3D mesh and repair the mesh defects. The number of tetrahedrons is 883,349, the connected node is 159,311, the connected area is 1, and the maximum aspect ratio is 19.58. The engineering material ABS (styrene butadiene acrylonitrile) is selected. It has the advantages of high height, stable size and easy forming and processing. In this experiment, the American Ineos brand is lustran ABS 1146. The recommended process parameters of the material library are die surface temperature of 80 °C and melt temperature of 260 °C. The plastic parts of air conditioner windshield are thinner, larger in area and more round holes. The first mock exam is five [5] gate feeding. In order to obtain the best gate position, the advanced gate locator of Moldflow software is used as the gate positioning algorithm to obtain five gate matching analysis diagrams. The best gate positions are n107539, n108060, n106000, n103028, and n104863. In order not to affect the appearance of plastic parts, point gate is used for pouring. Finally, the length of the main channel is 50 mm, the diameter of the nozzle is 5 mm, the beginning diameter of the main channel is 6 mm, and the end diameter is 8 mm. The water temperature at the cooling water inlet is 50 °C, and the whole gating system and cooling system are shown in Fig. 2.

Fig. 2 Gating system and cooling system

488

Y. Huang et al.

3 Automatic Loading and Unloading of Industrial Robot ABB’s IRB4600 industrial robot is adopted, with an arrival distance of 2.05 m, a payload of 60 kg and an arm load of 20 kg. IRB4600 is a very efficient generalpurpose robot. It is optimized to shorten the beat time. Its slim body can easily work in high-density work units. IRB4600 makes the layout of production units more compact. IRB 4600 adopts innovative and optimized design. The fuselage is compact and light, and the acceleration reaches the highest of its kind in ABB. Combined with its ultra-fast running speed, the cycle time obtained can be reduced by 25% compared with the industry standard. The large working range can realize the comprehensive optimization of arrival distance, cycle time, auxiliary equipment, and so on. The floor area is small, the swivel radius of shaft 1 is short, the elbow behind shaft 3 is thin, the upper and lower arms are small, and the wrist is compact. Mainly used for handling, sorting, painting and spot welding pallet stacking, finishing, polishing, and other fields [6–11] (Fig. 3).

Fig. 3 IRB4600 loading and unloading

Application of Industrial Robot in Injection Molding of Thin-Walled …

489

Fig. 4 Model drawing of suction cup tool

The main function of robot tools is to pick up and place products. In the design of tools, the system adopts suction cup adsorption tools, which are designed to match the size of robot flange and materials. In consideration of stability, we also need to add support to the suction cup body to strengthen stability. The plane dimension of suction cup panel is 500 × 330mm2 , the height from the flange connecting surface to the panel ground is 120 mm, and the height to the center point at the bottom of the suction cup is 148 mm. The suction cup tool model is shown in Fig. 4. The procedures for picking and placing industrial robots are as follows: MODULE Module1

490

Y. Huang et al.

PERS tooldata XYTool:=[TRUE,[[0,0,148],[1,0,0,0]],[10,[0,0,120],[1,0,0,0],0,0,0]]; PERS robtarget pActualPos:=[[1514.07,3.72009,1288],[1.80999E-06,1.16415E-10,-1,4.44715E-09]]; PERS robtarget pBase1_0:=[[-249.000208726,-1872.001012156,42.000223571],[0,0,1,0],[-2,0,-2,0]]; PERS robtarget pBase1_90:=[[-339,-1792,42],[0,0.707106776,0.707106787,0],[-2,0,-1,0]]; PERS robtarget pBase2_90:=[[-339,1782,42],[0,0.707106777,0.707106785,0],[1,0,-2,0]]; PERS robtarget pBase2_0:=[[-249,1872,42],[0,0,1,0],[1,0,1,0]]; PERS robtarget pHome:=[[1505,0,1288],[0,0,1,0],[0,0,0,0]]; PERS robtarget pPickH:=[[1514.07,3.72,975.62],[0,0,1,0],[0,0,0,0]]; PERS robtarget pPlace2:=[[-249,1362,342],[0,0,1,0],[1,0,1,0]]; PERS robtarget pPlaceH2:=[[-249,1362,975.618],[0,0,1,0],[1,0,1,0]]; PERS robtarget pPlace1:=[[-249,-1362,342],[0,0,1,0],[-2,0,-2,0]]; PERS robtarget pPlaceH1:=[[-249,-1362,975.618],[0,0,1,0],[-2,0,-2,0]]; PERS robtarget pPick:=[[1514,3.72,575.618],[0,0,1,0],[0,0,0,0]]; PERS bool bPalletFull1:=F; PERS bool bPalletFull2:=F; PERS num nMaduo:=1; PERS num nCount1:=0; PERS num nCount2:=0; PROC Main() chushihua; WHILE TRUE DO IF diMduoStart=1 AND nMaduo=1 AND bPalletFull1=FALSE THEN jisuanchengxu1; shiqu; maduo1; nMaduo:=2; ENDIF IF diMduoStart=1 AND nMaduo=2 AND bPalletFull2=FALSE THEN jisuanchangxu2;

Application of Industrial Robot in Injection Molding of Thin-Walled …

shiqu; maduo2; nMaduo:=1; ENDIF WaitTime 0.1; ENDWHILE ENDPROC PROC chushihua() pActualPos:=CRobT(\tool:=XYTool); pActualPos.trans.z:=pHome.trans.z; MoveL pActualPos,v2000,fine,XYTool\WObj:=wobj0; MoveJ pHome,v2000,fine,XYTool\WObj:=wobj0; bPalletFull1:=FALSE; nCount1:=1; bPalletFull2:=FALSE; nCount2:=1; nMaduo:=1; ENDPROC PROC jisuanchengxu1() TEST nCount1 CASE 1: pPlace1:=Offs(pBase1_0,0,0,0); CASE 2: pPlace1:=Offs(pBase1_0,500+10,0,0); /

CASE 3: pPlace1:=Offs(pBase1_90,0,330+10,0); CASE 4: pPlace1:=Offs(pBase1_90,330+10,330+10,0); CASE 5: pPlace1:=Offs(pBase1_90,660+20,330+10,0); CASE 6:

491

492

Y. Huang et al.

pPlace1:=Offs(pBase1_0,0,500+10,300); CASE 7: pPlace1:=Offs(pBase1_0,500+10,500+10,300); CASE 8: pPlace1:=Offs(pBase1_90,0,0,300); CASE 9: pPlace1:=Offs(pBase1_90,330+10,0,300); CASE 10: pPlace1:=Offs(pBase1_90,660+20,0,300); CASE 11: pPlace1:=Offs(pBase1_0,0,0,600); CASE 12: pPlace1:=Offs(pBase1_0,500+10,0,600); CASE 13: pPlace1:=Offs(pBase1_90,0,330+10,600); CASE 14: pPlace1:=Offs(pBase1_90,330+10,330+10,600); CASE 15: pPlace1:=Offs(pBase1_90,660+20,330+10,600); ENDTEST pPickH:=Offs(pPick,0,0,400); pPlaceH1:=Offs(pPlace1,0,0,400); IF pPickH.trans.z 20

Number of area segments

≥5

Channel environment

Gaussian channel/rice channel

Network operation time/ min

≤ 200

transmission rate of mobile IoT platform while reducing the BER, thus reducing the BER. Secondly, the network congestion control ability is better, and the nodes with superior performance are selected and set as cluster head nodes, which can effectively improve the stability of the mobile IoT system and effectively improve the network congestion phenomenon. Finally, the successful delivery rate of network data is higher, and the high-speed mobile characteristics of nodes are incorporated into the evaluation process of transmission architecture, and the transmission routing is optimized by the energy-routing dual factor, which improves the rate and success rate of data transmission while ensuring the performance of mobile object intelligent terminals, thus realizing the overall efficiency of the system.

5 Conclusion As an emerging multi-level cross-space modeling method, the research of mobile object perception technology will become an indispensable part of human social development and networked life in the future development. In this paper, we propose an efficient and secure partitioning algorithm for mobile IoT region based on multilayered sensing mechanism, which can effectively solve the above problems while ensuring the data transmission efficiency of mobile IoT terminals. The algorithm can adjust the system parameters in real time, so that the sensing device can track and locate the target accurately. Meanwhile, this paper uses a region segmentation sub-algorithm based on the chance collision information extraction mechanism and a routing stabilization sub-algorithm based on the energy-routing two-factor adjudication mechanism. By effectively fusing the sensing nodes in the mobile IoT system,

778

D. He

the information interaction between each target user and other intelligent terminals in the region is realized, which can effectively improve the system for the information transmission efficiency among the sensing nodes, so as to achieve the purpose of reducing the BER and improving the overall system performance. The experimental results show that the algorithm can effectively solve the problems of low data transmission efficiency and high BER of mobile object intelligent terminal, and it has good effect in practical application.

References 1. Li H, Shi G (2017) Design and implementation of vehicle intelligent terminal based on vehicle networking and intelligent technology. In: Proceedings of the 2017 5th international conference on mechatronics, materials, chemistry and computer engineering (ICMMCCE 201), no 1, pp 23–25 2. Calatayud A, Mangan J, Palacin R (2017) Connectivity to international markets: A multilayered network approach. J Transp Geogr 61–63 3. Tu W, Pop F, Jia W et al (2019) High-performance computing in edge computing networks. J Parallel Distrib Comput 230–231 4. Kousias K, Caso G, Alay Ö et al (2019) Empirical analysis of lorawan adaptive data rate for mobile internet of things applications. In: Wireless of the students, by the students, and for the students workshop, pp 12–15 5. Li J (2020) Research on L Company’s intelligent performance evaluation system. In: 2019 International conference on social science and education, pp 35–37 6. Kharrufa H, Al-Kashoash H et al (2018) A game theoretic optimization of RPL for mobile internet of things applications. IEEE Sens J 2520–2530 7. Das A, Ghoshal D (2016) Human skin region segmentation based on chrominance component using modified watershed algorithm. Procedia Comput Sci 856–858 8. Heidari M, Khuzani AZ, Danala G et al (2018) Improving performance of breast cancer risk prediction using a new CAD-based region segmentation scheme. Med Imaging 32–35 9. Hayashi Y, Shen C, Oda M et al (2020) Usefulness of fine-tuning for deep learning based multi-organ regions segmentation method from non-contrast CT volumes using small training dataset. Med Imaging 48–50 10. Oda M, Hayashi Y, Otake Y et al (2021) Lung infection and normal region segmentation from CT volumes of COVID-19 cases. Nagoya Univ. (Japan); Nara Institute of Science and Technology (Japan); Keio Univ. (Japan); Juntendo Univ. (Japan); Duke Univ. (United States);The Univ. of Chicago (United States), pp 27–30

Key Point Detection of Power Tower Based on Improved Yolov5 Changxin Zhao, Yandong Cui, Zushan Ding, and Chuang Cao

Abstract Using UAV to accurately and quickly realize the three-dimensional modeling of power tower is an effective measure to promote the construction of smart grid. A tower target recognition algorithm based on yolov5 is presented to solve the problem of inaccurate tower 3D perception recognition by UAV. By improving the internal structure of the residual block to reduce the impact of steel intersection on the identification results and improve the accuracy of the algorithm, the exponential linear unit function is used as the activation function to accelerate the convergence speed of the network and improve the robustness of the algorithm, so as to realize the rapid and accurate identification of key points of power towers. By comparing the fast R-CNN and EfficientDet target detection methods through experiments, the improved algorithm has improved the recognition accuracy to a certain extent. The model maintains the lightweight characteristics of yolov5 and has a good prospect in application deployment. Keywords Deep learning · Key point detection · Residual network · Tower identification

1 Introduction To ensure the safety of power grid, the State Grid consumes a lot of resources every year to maintain the power lines in order to eliminate hidden dangers and ensure the normal, stable, and safe operation of the lines [1]. UAV power line patrol inspection has gradually become the most effective way of line patrol in the power industry. Through UAV’s independent patrol of live power lines, it can directly obtain highresolution pictures and images, and use modern remote sensing technology and measurement technology for all-round equipment patrol, so as to quickly find equipment faults, carry out operation and maintenance, and provide strong support for C. Zhao · Y. Cui · Z. Ding · C. Cao (B) State Grid Xuzhou Power Supply Company, Xuzhou, Jiangsu, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_70

779

780

C. Zhao et al.

stable operation of power lines [2, 3]. In addition, with the gradual rise of in-depth learning technology and target recognition technology, the acquisition and transmission of aerial images become very convenient. Intelligent patrol inspection can mark aerial images, train in-depth learning models, and then use the models to predict the UAV power patrol inspection images, so as to achieve the purpose of automatic patrol inspection. In the work done by predecessors, some traditional methods are used to carry out supervised classification and unsupervised classification based on pattern recognition, so as to achieve pixel level classification of UAV patrol images [4, 5]. However, the features extracted by these traditional remote sensing image classification methods are relatively shallow, and the classification accuracy is often not very high. Some of them adopt the deep learning method. Zheng [6] and others improved the faster R-CNN model and added an edge detection module to the edge supervised classification network, so that the entire feature extraction network can better learn the edge features of the image, and finally realize the defect detection of composite insulators. In addition, yolo [7] network is used for multi-target recognition of power patrol inspection by training the network. Based on the rotating dense feature pyramid network, migration learning and data enhancement strategies are adopted to improve the speed and accuracy of the algorithm. The yolov5l model is improved for power inspection in the following narration. By marking the key points of the UAV patrol pictures around Xuzhou, and then training, the final model can predict the positions of the key points on the Ganzi tower and Yangjiao tower, so as to achieve the purpose of better power patrol.

2 Key Point Analysis At present, the tower head structures of transmission line towers in China mainly include dry type tower and Yangjiao tower. This paper selects the above two tower types for analysis. The main purpose of key point detection of power towers is to calculate various parameters of unknown towers such as height and floor height, so the two endpoints of ground wire cross arm and upper phase, middle phase and lower phase cross arm of each tower are selected as the points to be measured, totaling 8; In addition, in order to reduce the calculation error of vertical parameters, the intersection of tower leg main material and cross arm of each layer is selected as the point to be measured, a total of 8 points. In the specific annotation process, the open source tool labeling is used for manual annotation. The points are labeled from top to bottom and from left to right. The abbreviations are used to name each point. Some rules are as follows: top left (TL), top left vertex; top left center (TLC). Each tower is marked with 16 key parts and a tower head marking box. The specific location is shown in Fig. 1.

Key Point Detection of Power Tower Based on Improved Yolov5

781

Fig. 1 Location of key points of power tower

3 Yolov5 Algorithm 3.1 Yolov5 Network Architecture The yolov5 network is divided into four parts: At the input, there are mainly the following works: first, data enhancement based on mosaic algorithm, which divides four images into a group, and synthesizes an image through random scaling, random clipping, and random arrangement for subsequent training. The purpose is to enrich data set, reduce training time and the memory requirements of network. The second is adaptive anchor box calculation. Thirdly is the adaptive image slice scaling [8]. The backbone segment network includes focus processing and spatial pyramid pooling (SPP). In the neck segment network, the path aggregation network [9, 10] (PANet) is used for feature fusion. PANet adds a bottom-up information flow path on the basis of FPN [11, 12], shortens the information transmission path, and aims to enhance the accurate underlying positioning information to the entire feature extraction network. The head output is used to complete the output of target detection results. Compared with the two-stage algorithm, the yolov5 model is small in size and occupies less memory. It is not only easy to configure the environment, but also very fast in model training. However, the power tower is composed of metal steel

782

C. Zhao et al.

bars. Each metal steel bar is staggered with many intersections, forming a network structure with a number of internodes. The traditional algorithm is difficult to detect the target point in time. In order to improve the accuracy and speed of the algorithm, an advanced model which combined residual module and activation function based on yolov5 algorithm is proposed.

3.2 Improved Yolov5 Target Detection Algorithm Residual Structure Improvement. Figure 2 is another form of the residual module (bottleneck). The two 3 * 3 convolution cores of the basic form are replaced by one 3 * 3 convolution core and two 1 * 1 convolution cores. The purpose of adding 1 * 1 convolution is to reduce the amount of computation and increase the nonlinearity of the network by compressing the number of channels. Fig. 2 Residual module structure

x Conv2d(64,3*3)

ReLU

Identity(x)

Conv2d(64,3*3) H(x)=F(x)+x (Skip Connection)

ReLU x

Conv2d(64,1*1) ReLU Identity(x)

Conv2d(64,3*3) ReLU Conv2d(64,1*1) H(x)=F(x)+x

(Skip Connection)

ReLU

Key Point Detection of Power Tower Based on Improved Yolov5

783

The functions in the figure are used to adjust the input, most of which are 1 * 1 convolution operations. If the image background is complex and it is difficult to distinguish effectively, multi-layer convolution can also be performed on the residual edge. ELU activation function. The hard swish activation function is applied in the convolution module in the yolov5 network architecture, which can be expressed as: ⎧ x ≤ −3 ⎨0 Hard − Swish(x) = x (1) x ≥3 ⎩ x(x+3) otherwise 6 Leaky ReLU activation function is applied in CSPN module. The further optimization method for leaky ReLU function is called ELU, and its expression is formula 2. ( x x >0 (2) f (x) = α(e x − 1) x ≤ 0 The ELU function effectively combines the advantages of the S-shaped curve function and the linear rectification function. It is linear on the right side of the zero point, which can further alleviate the problem of gradient disappearance; it is nonlinear on the left side of the zero, which makes it more robust to input variations or noise. And because the average output of the ELU function is close to 0, the convergence speed is also improved.

3.3 Construction of Network The algorithm this paper proposed is based on yolov5 network model, taking into account both accuracy and speed, and has outstanding advantages in network lightweight. The improved residual module in the convolution layer ensures that when the network contains enough layers, the problems of model degradation in the traditional neural network will disappear, so that the network can get higher recognition accuracy. At the same time, the ELU activation function is used in the network to replace the linear rectification function with leakage, which can suppress the noise and improve the robustness of the model. The improved algorithm structure is shown in Fig. 3.

784

C. Zhao et al.

Source image

image annotation

Evaluation results

Data

enhancement

Test model performance

Dataset production

Get a trained model

Improved yolov5 algorithm

configuration parameter

Fig. 3 Algorithm flow chart

4 Experimental Results and Analysis Yolov5l is selected as the pre training model, and Adam algorithm is used for training optimization. The image input size is set to 640 * 640, the maximum number of iteration rounds is set to 400, and the number of pictures per training is set to 8; The momentum factor value is set to 0.9; Set the weight attenuation coefficient to 0.0005; The initial learning rate is set to 0.001. The original data used in this experiment are some power tower images of Xuzhou city obtained by UAV low altitude remote sensing technology, with a total of 5000 images. The format of coco data set is adopted. In the experiment, 1000 images of the horn tower and the dry word tower are selected, of which 800 are used as the training set and 200 as the test set. This paper compares the training results of the improved yolov5 network with some mainstream deep learning networks, including faster CNN algorithm and EfficientDet algorithm. The comparison results are shown in Fig. 4. The experimental results show that the map value of this algorithm on the same data set are significantly better than the other two traditional algorithms. The tower recognition accuracy of yolov5 algorithm after improved feature extraction can reach 95.7%, which satisfies high accuracy for tower recognition. Recall rate is used to evaluate the detection coverage of all targets to be detected. Figure 5 shows the recall rates for three different models. It can be seen that this algorithm is significantly better than the other two models and the high recall rate means that the network can detect more targets in the picture. The function of loss function is to measure the distance between neural network prediction information and expected information. The closer the prediction information is to the expected information, the smaller the value of loss function is. The change of loss function during network training is shown in Fig. 6. Table 1 shows the values of various important indicators of the yolov5 algorithm before and after the improvement. From Table 1, we can clearly see that the improved method proposed in this paper improves the map of the model by 11.2%, and the improvement effect is very obvious. In terms of precision and recall, compared with the original method, it has increased by 9.9% and 11.4%, respectively, which proves that the proposed method can effectively improve the detection ability of the key parts of the tower.

Key Point Detection of Power Tower Based on Improved Yolov5 Fig. 4 MAP and precision of different models under the same data set

Fig. 5 Recall rate of different models under the same data set

785

786 Fig. 6 Loss function of different models under the same data set

C. Zhao et al.

Key Point Detection of Power Tower Based on Improved Yolov5 Table 1 Comparison of comprehensive index test results

787

Model

Accuracy (%)

Recall (%)

YOLOv5

85.3

80.9

mAP (%) 84.5

Paper method

94.2

92.3

95.7

Fig. 7 Training results

The figure shows that the improved algorithm has good convergence and fitting effect, and the loss function value is lower, which proves the effectiveness of improvement. Finally, the JSON for tower key point prediction from UAV aerial images is visualized. Some results are shown in Fig. 7. It can be seen that 16 key points on the power tower have been predicted successfully, and the accuracy has reached 95.7%, the prediction accuracy of key point positions is high.

5 Conclusion This paper mainly detects the key points of the power tower in the unmanned power line patrol inspection. On the basis of yolov5, the detection accuracy is improved, the network nonlinearity is increased, and the ELU activation function is used to improve the robustness of the algorithm. Through comparative experiments, it shows that this method can detect the key points of power tower in power line patrol inspection, and the accuracy is high, which can reach 95%. The detection results of tower key points can assist in the calculation of unknown tower specific parameters, three-dimensional reconstruction of power towers and other tasks. The algorithm function is effectively expanded, which will increase the possibility of UAV intelligent power tower line inspection. Acknowledgements This work was supported by the State Grid Xuzhou Power Supply Company, Xuzhou, Jiangsu, China (Research on UAV high-efficiency and fully autonomous power inspection technology based on real-time 3D spatial sensing and intelligent photography technology).

788

C. Zhao et al.

References 1. Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: IEEE conference on computer vision and pattern recognition. IEEE, Hawaii, USA, pp 6517−6525 2. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90 3. Lin TY, Dollar P, Girshick R et al (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 936–944 4. Clevert D-A, Unterthiner T, Hochreiter S (2016) Fast and accurate deep network learning by exponential linear units (ELUs). arXiv preprint arXiv:1511.07289v5 5. Redmon J, Farhadi A (2021) Yolov3: an incremental improvement [EB/OL] 6. Nordeng IE, Hasan A, Olsen D et al (2017) DEBC detection with deep learning. In: Scandinavian conference on image analysis. Springer, Troms, Norway, pp 248−259 7. Bochkovskiy A, Wang CY, Liao HYM (2021) Yolov4: optimal speed and accuracy of object detection [EB/OL] 8. Wei H, Zhang Y, Wang B et al (2020) X-linenet: detecting aircraft in remote sensing images by a pair of intersecting line segments. IEEE Trans Geosci Remote Sens 9. Paszke A, Gross S, Massa F et al (2019) PyTorch: an imperative style, high-performance deep learning library 10. Loshchilov I, Hutter F (2021) SGDR: stochastic gradient descent with warm restarts [EB/OL] 11. Liu Y, Ma Z, Liu X et al (2019) Privacy-preserving object detection for medical images with faster R-CNN. IEEE Trans Inf Forensic Secur (99):1 12. Jaeger PF, Kohl SAA, Bickelhaupt S et al (2020) Retina U-Net: embarrassingly simple exploitation of segmentation supervision for medical object detection. Mach Learn Health Workshop 171–183

Analysis of Regional Logistics Efficiency Based on SE-DEA Model and FCM Algorithm Meijuan Liu

Abstract This paper uses the SE-DEA model to measure the logistics efficiency of eight districts and counties in Xi’an from 2011 to 2018 and analyzes their time series evolution characteristics, combined with the Malmquist index model to decompose the efficiency and dynamic analysis of the logistics of districts and counties. Finally, the FCM algorithm is used to classify the inputs and outputs of Xi’an urban counties. The results show that the average logistics efficiency of Xi’an from 2011 to 2018 was 1.033, the overall efficiency level was higher, and the “W” type change trend was shown. The number of districts and counties with an efficiency value greater than 1 increased from 3 in 2011 to 6 in 2018, and the difference in logistics efficiency between districts and counties increased first and then gradually decreased. The FCM algorithm divides the eight districts and counties into high, medium and low input areas and high and low output areas, and the logistics efficiency of Xi’an has not developed extremely, avoiding the development trend of Matthew effect. Keywords Logistics efficiency · SE-DEA model · Malmquist index model · FCM algorithm

1 Introduction In August 2017, the General Office of the State Council issued the opinions on further promoting logistics cost reduction and efficiency improvement to promote the development of the real economy, pointing out that the logistics industry has become one of the pillar industries in China from the emerging “sunrise industry” and an important part of promoting the high-quality development of China’s economy. Northwest China is one of the important areas for China’s inland economic development, Xi’an,

M. Liu (B) Department of Sciences, Xi’an University of Science and Technology, Xi’an, Shaanxi, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_71

789

790

M. Liu

as the central city and science and technology town in the northwest, since the implementation of the Silk Road Economic Belt has provided an unprecedented opportunity for the development of the logistics industry in Xi’an. In February 2018, the National Development and Reform Commission issued the Guanzhong Plain Urban Agglomeration Development Plan, which pointed out that Xi’an will be built into an international logistics hub city in the Silk Road Economic Belt. So how to scientifically and reasonably evaluate whether logistics resources have been used efficiently is an important problem facing the logistics development process of Xi’an. This paper intends to measure and analyze the logistics efficiency of Xi’an city and put forward suggestions, which has certain theoretical significance for the cost reduction and efficiency of the logistics industry in Xi’an and the promotion of the transformation of logistics enterprises in Xi’an from quantity to efficiency oriented. Logistics efficiency is a measure index of the input resources of the logistics industry into the expected output. In terms of the measurement of logistics efficiency, Tian et al. [1] measured and analyzed the logistics efficiency of the Bohai Economic Circle, it shows that the logistics efficiency of the Bohai Rim Economic Circle is generally high. Liu et al. [2] used the DEA-Malmquist index model to measure the logistics efficiency of Jilin Province and studied the differences in logistics efficiency in various urban areas. In terms of the influencing factors of logistics efficiency, Muhammad et al. [3] used multiple regression analysis to study the impact of flexibility and collaboration in Pakistan’s supply chain capacity on the logistics efficiency of construction projects, and the study showed that flexible supply chain capacity is an important factor in improving logistics efficiency. Chen Y measures and analyzes the logistics efficiency of Shandong Province, mainly based on the perspective of high-quality development. In summary, the research angle of logistics efficiency in the existing literature is mainly concentrated at the provincial micro, economic belt and national macrolevels and lacks in-depth analysis of logistics efficiency and regional differences at the static and dynamic levels. The research content mainly focuses on the measurement and influencing factors of logistics efficiency and lack of research on classification problems. Most of the research methods use the traditional DEA model, ignoring the drawbacks of the DEA model being indistinguishable from the result of the unit being 1. In this paper, we propose to use the SE-DEA model and the Malmquist index model to measure and analyze the logistics efficiency of Xi’an by combining dynamic and static, at the same time classify the input and output of logistics efficiency in combination with FCM algorithm and provide theoretical reference for the development decision of logistics industry in Xi’an [4].

Analysis of Regional Logistics Efficiency Based on SE-DEA Model …

791

2 Research Methods and Data Sources 2.1 SE-DEA Model Since the DEA model cannot distinguish between decision-making units with efficiency values of 1, to solve this drawback, Andersen P et al. proposed the SEDEA model. Suppose a decision-making system has S decision-making units, each decision-making unit has m-type input index and n-type output index, and set, respectively, to represent the kth decision-making unit m-resource input and n-type resource output, then k(k = 1, 2, … n) SE-DEA model of the decision unit: ⎧ ⎪ min θ ⎪ ⎪ s ⎪ Σ ⎪ ⎪ s.t ⎪ λk Yk + n − ≤ θ X j ⎪ ⎪ ⎨ k=1,k/= j s Σ , + ⎪ ⎪ k=1,k/= j λk Yk − n ≥ Y j ⎪ ⎪ ⎪ ⎪ λ ≥ 0, k = 1, 2, . . . s ⎪ k ⎪ ⎪ ⎩ − n ≥ 0, n + ≥ 0

(1)

where is the efficiency value. If the ≥ 1, the decision-making unit is effective, the larger the value of logistics efficiency, the higher the level of logistics resource utilization; If < 1, the decision unit is not effective, indicating that the technology or scale of the decision unit has not reached the optimal state.

2.2 Malmquist Exponential Model Since the SE-DEA model can only compare the efficiency values of each district and county internally and cannot dynamically analyze the logistics efficiency values of the two years before and after, it is necessary to introduce the Malmquist index model. The total factor productivity (TFP) method can comprehensively measure the efficiency of the total input and total output of the logistics industry at various points in time, and the decomposition relationship of the Malmquist index (TFP) is ML = (Sech × Pech) × Tech = Effch × Tech

(2)

TFP = Effch × Tech,

(3)

where Sech is the scale efficiency change index; Pech is the pure technical efficiency change index; Tech is the technological progress change index; Effch is the composite technical efficiency index. When the ML index is equal to 1, it means that the total

792

M. Liu

Table 1 Evaluation system of logistics efficiency indicators in Xi’an The indicator type

Name

The value of the variable

Input metrics

The total investment in fixed assets of the whole society

X1

The indicator type

Name

The value of the variable

Input metrics

Number of employees in transportation, warehousing and postal services

X2

Output metrics

The added value of

Y1

Output metrics

Cargo traffic

Y2

Output metrics

Cargo transport turnover

Y3

efficiency does not change; when the ML index is less than 1, it indicates a decrease in total efficiency.

2.3 Construction of Evaluation Index System There are 12 districts and two counties in Xi’an; this paper mainly studies the logistics efficiency of eight districts and counties in Xi’an and draws on existing research theories [5, 6] to build an index evaluation system on behalf of the logistics industry in the transportation, warehousing and postal industries, which account for more than 85% of the logistics industry. The data are from the China Statistical Yearbook, the China Logistics Statistical Yearbook and the Xi’an Statistical Yearbook. The specific indicator system is given in Table 1.

3 Analysis of Logistics Efficiency Measurement in Xi’an 3.1 The Timing Evolution Characteristics of Logistics Efficiency in Xi’an MaxDEA software was used to calculate the logistics efficiency values of various districts and counties in Xi’an from 2011 to 2018, as given in Table 2, plot the overall change of logistics efficiency values in the city with time, and see Fig. 1. As can be seen from Table 2 and Fig. 1, first of all, the average logistics efficiency of Xi’an from 2011 to 2018 was 1.033, indicating that the overall logistics efficiency of Xi’an in recent years is relatively high. The city’s logistics efficiency value increased from 1.041 to 1.065, an increase of 2.31%. Secondly, the change in logistics efficiency in Xi’an showed four stages, that is, from 2011 to 2013, the logistics efficiency decreased, and the logistics efficiency value reached a minimum

Analysis of Regional Logistics Efficiency Based on SE-DEA Model …

793

Table 2 Logistics efficiency values of districts and counties in Xi’an from 2011 to 2018 Year

Lian Hu

Ba Qiao

Lan tian

Chang’ An

Hu Yi

Wei Yang

Yan Liang

Gao Ling

Mean

2011

1.098

1.309

0.859

1.766

0.887

0.967

0.642

0.802

1.041

2012

0.936

1.211

0.882

1.161

0.973

1.008

0.815

0.764

0.981

2013

0.902

1.168

1.027

0.819

0.968

0.94

0.611

0.869

0.913

2014

0.945

0.703

0.512

0.732

1.116

1.111

1.199

3.089

1.176

2015

1.232

1.151

0.862

1.765

1.562

0.953

1.513

0.364

1.175

2016

1.299

1.042

0.836

0.969

0.947

1.005

1.088

0.582

0.971

2017

1.074

1.004

1.014

0.884

0.946

0.962

0.916

0.738

0.942

2018

1.373

1.1

0.996

1.047

1.009

1.025

1.001

0.969

1.065

Fig. 1 Change of average logistics efficiency in Xi’an by year

of 0.913, down 12.3%, because the smog weather across the country was serious at that time, and Xi’an responded to the national call to strictly control the output of various industries and engaged in logistics industry production without sacrificing the environment [7]; from 2013 to 2014, logistics efficiency increased and peaked, increasing by 28.8%, because during the “Twelfth Five-Year Plan” period, the “Twelfth Five-Year Development Plan for Transportation” issued by the Xi’an Municipal Transportation Bureau stipulates that the transportation advantages of “two horizontal, one vertical, two networks and eighteen lines” should be further exerted, with the focus on promoting the development of modern logistics industry, and encourages leading logistics enterprises to actively transform into modern logistics industry; from 2014 to 2015, the logistics efficiency value of Xi’an is greater than

794

M. Liu

1 and still maintains high efficiency development; From 2015 to 2017, the value of logistics efficiency fell by 19.8%, which is mainly related to the debt crisis that broke out in Europe, because Xi’an is the most inland in China, the debt crisis has a lagging impact on the development of the logistics industry in Xi’an, making the economic recovery of vulnerable emerging markets difficult; from 2017 to 2018, the value of logistics efficiency increased by 13.06%. In summary, the overall development of logistics efficiency in Xi’an shows a “W” type change trend [8].

3.2 Differences in Logistics Efficiency Between Districts and Counties in Xi’an In order to obtain the difference between logistics efficiency in various districts and counties, the logistics efficiency values in 2011, 2015 and 2018 were selected for analysis, as given in Table 3. From Table 3, it can be seen that in 2011, only 3 districts and counties had super efficiency values greater than 1, which increased to five districts and counties in 2015, until 2018, when six districts and counties had super efficiency values greater than 1, indicating that the overall logistics efficiency of Xi’an has maintained a high level of development in recent years. The districts and counties with an efficiency value greater than 1 have reached a relatively ideal state in terms of inputs such as human resources and capital and their output added value, among which the super efficiency value of Chang’an District has reached 1.526, ranking first, among which the logistics efficiency value of Gaoling District is the lowest in the city [9]. From the perspective of the eight districts and counties studied, the difference in efficiency values between districts and counties showed a trend of first expanding and then narrowing. In 2011, the highest efficiency value of Chang’an District reached 1.766, the lowest efficiency value of Yanliang District was 0.642, and the difference between the two was 1.124, indicating that the difference between the regions was significant; in 2015, the difference between Chang’an District with the highest Table 3 Logistics efficiency values of various districts in Xi’an in 2011, 2015 and 2018 District

2011

2015

2018

Mean

Sorting

LianHu

1.098

1.232

1.373

1.234

2

BaQiao

1.309

1.151

1.1

1.187

3

LanTian

0.859

0.862

0.996

0.906

7

Chang’An

1.766

1.765

1.047

1.526

1

Huyi

0.887

1.562

1.009

1.153

4

Weiyang

0.967

0.953

1.025

0.982

6

Yanliang

0.642

1.513

1.001

1.046

5

Gaoling

0.802

0.364

0.969

0.712

8

Analysis of Regional Logistics Efficiency Based on SE-DEA Model …

795

Fig. 2 Difference in logistics efficiency values between districts in Xi’an

efficiency value and the Gaoling District with the lowest efficiency value reached 1.401, showing greater differences between regions; from 2015 to 2018, the difference between the districts and counties was gradually decreasing and stabilizing, until 2018, the difference between Lianhu District with the highest efficiency value and Gaoling District with the lowest efficiency value was only 0.404, and the regional difference gradually decreased. The difference in logistics efficiency values between districts and counties is shown in Fig. 2.

3.3 Dynamic Analysis of Logistics Efficiency in Various Districts of Xi’an In order to further analyze the influencing factors of logistics efficiency changes, according to Formula (2), the efficiency decomposition of logistics in Xi’an city and county is carried out, as given in Table 4, and the trend of logistics efficiency index changes in Xi’an is drawn; see Fig. 3. As can be seen from Table 4 and Fig. 3, the average value of the TFP index from 2011 to 2018 is 1.114, indicating that the logistics efficiency change index in Xi’an is in the rising stage, and the TFP index in 2011–2018 reached a peak of 1.289 in 2013–2014, and then showed a sharp downward trend in 2014–2017. In addition to Tech, Effch, Pech and Sech are relatively consistent with the trend of ML, and all are greater than 1, indicating that the change of the comprehensive technical efficiency index directly affects the growth of the TFP index; from 2017 to 2018, Tech was greater than 1 and Effch was less than 1, and technological progress was obviously

796

M. Liu

Table 4 Decomposition results of Malmquist index model in Xi’an from 2011 to 2018 Period

Effch

Tech

Pech

Sech

TFP

2011–2012

1.456

0.722

1.213

1.174

1.051

2012–2013

0.882

1.035

0.944

0.943

0.913

2013–2014

1.916

0.673

1.494

1.309

1.289

2014–2015

1.452

0.806

1.141

1.299

1.170

2015–2016

1.005

0.958

0.955

1.074

0.963

2016–2017

0.924

1.012

0.971

0.954

0.935

2017–2018

0.894

1.196

0.985

0.909

1.069

Mean

1.218

0.915

1.100

1.095

1.114

Fig. 3 Results and trend of logistics efficiency decomposition in Xi’an

made up for the lack of technical efficiency and thus improved the overall logistics efficiency value, indicating that relying on technological progress can also effectively improve the level of logistics [10].

4 FCM Algorithm Input–Output Analysis Using FCM algorithm, MATLAB software is used to classify the inputs and outputs of Xi’an urban counties, and the classification results are sorted into Tables 5 and 6.

Analysis of Regional Logistics Efficiency Based on SE-DEA Model … Table 5 Classification of logistics inputs in various districts of Xi’an

797

Category

Peculiarity

District

I category

Low input

Lantian, Huyi, Gaoling

II category

Medium input

Lianhu, Baqiao, Yanliang

III category

High input

Weiyang, Chang’an

Table 6 Classification of logistics output in various districts of Xi’an Category

Peculiarity

District

I category

Low output

Gaoling, Huyi, Lantian

II category

High output

Weiyang, Chang’an, Lianhu, Baqiao Yanliang

From Tables 5 and 6, it can be seen that the FCM algorithm divides the eight districts and counties into three types of high, medium and low input areas and high and low output areas, the output of low input districts and counties is relatively low, the corresponding logistics efficiency value is also low, the output of the districts and counties with high input is at a high level, and the logistics efficiency value is high. Judging from the classification results, there is no extreme development of logistics efficiency in Xi’an, and the difference in logistics efficiency values between districts and counties is gradually decreasing, avoiding the development trend of Matthew effect.

5 Conclusions and Recommendations This paper uses the SE-DEA model to measure the logistics efficiency of eight districts and in Xi’an from 2011 to 2018, and the following conclusions are obtained: From 2011 to 2018, the average logistics efficiency of Xi’an was 1.033 and reached a peak in 2014, maintaining a high overall level and showing a “W”-shaped development trend. The difference in efficiency values between districts and counties showed a trend of first expanding and then narrowing and gradually decreased and stabilized after the difference was maximized in 2015. The number of districts and counties with an efficiency value greater than 1 increased from 3 in 2011 to 6 in 2018, and the overall logistics efficiency of the city was at an upward level. After the decomposition of logistics efficiency, in addition to Tech, Effch, Pech and Sech are relatively consistent with the change trend of ML index, they are all greater than 1, then the improvement of technical efficiency level directly affects the city’s logistics efficiency level, and technological progress can also improve logistics efficiency to a certain extent. Judging from the classification results of the FCM algorithm, the logistics output of the districts and counties with low input in the logistics industry is correspondingly low, while the logistics output of the districts and counties with

798

M. Liu

medium and high inputs is also relatively high, and the logistics efficiency of Xi’an has not been extremely developed [11]. In order to better promote the development of the logistics industry in Xi’an and improve the level of logistics efficiency in the city, the measures can be taken: First, the improvement of technical efficiency can affect the city’s logistics efficiency level as a whole, and increasing the optimal allocation and use of logistics structure resources is the key to improving logistics efficiency in the city. Focusing on the use of technology and accelerating technological progress can also improve the logistics efficiency of the city to a certain extent. Second, accelerating the adjustment of the logistics industry structure, promoting the upgrading of the industrial chain, increasing employment opportunities and jobs for citizens, formulating scientific and reasonable investment policies, appropriately reducing the investment in fixed assets, avoiding the waste of logistics resources and properly controlling the scale of logistics are effective ways to improve the logistics efficiency of Xi’an. Acknowledgements This work was funded by the National Natural Science Foundation of China (11201277).

References 1. Tian Q, Liu Y, Li N, Wu Q, Liu M (2020) Study on the efficiency evaluation of logistics industry in the Pan-Ring Bohai economic circle based on DEA. Road Traffic Technol 37:149–158 2. Liu H, Tang J (2019) The research on logistics efficiency and otherness on primary zone of Jilin Province. IOP Conf Ser: Mater Sci Eng 677(5) 3. Muhammad SS, Muhammad AS et al (2019) The impact of supply chain capabilities on logistics efficiency for the construction projects. Civ Eng J 5(6) 4. Chen Y (2021) Under the background of high-quality economic development, the evaluation and promotion of regional logistics capacity in China-take Shandong Province as an example. Bus Econ Res 15:113–116 5. Yu Y, Shen Y, Lian W (2020) Study on the efficiency of Chinese logistics enterprises based on THEA-Malmquist and Tobit models-Empirical evidence from listed companies. Pract Underst Math 50:95–105 6. Zhang H, Yu J (2021) Two-stage efficiency evaluation model for the logistics industry facing the production process. J Tongji Univ Nat Sci Ed 49:591–598 7. Andersen P, Petersen CN (1993) A procedure for ranking efficiency units in data envelopment analysis. Manag Sci 39(10) 8. Gan W, Yao W, Liu Z (2020) Evaluation of the impact of logistics industry on regional economic vitality from the perspective of national logistics hub construction. Logist Technol 39:16–22 9. Cheng C, Mu W (2021) Study on the impact of environmental regulation on the green development of logistics industry—an empirical test from Beijing-Tianjin-Hebei region. Ind Technol Econ 40:107–114 10. Wang W, Kao X (2021) The study of logistics efficiency measurement in Bohai Rim area from the perspective of high-quality development c is based on the three-stage DEA model. Bus Res 4:75–84 11. Cao Y, Li W, Lin H (2020) The path of high-quality development of China’s regional logistics industry is based on the empirical analysis of 31 provinces and municipalities in China. Bus Res 12:66–74

Logistics Picking Path Optimization Based on Improved Ant Colony Algorithm Qi Li

Abstract In today’s society, with the logistics industry playing an important role in economic development, its development speed and efficiency are becoming higher and higher. Therefore, how to effectively reduce costs and improve service quality has become a competitive advantage of enterprises. For all stored procedures in the warehouse, picking process is the longest of all stored procedures. Therefore, based on the improved ant colony algorithm (ACA), this paper studies the logistics picking path optimization. Firstly, this paper studies the traditional ACA, then optimizes it on the basis of the algorithm, and finally tests the robustness of the algorithm. The test results show that the optimized ACA has strong robustness to the random generation of path and the random sampling of the volume of goods to be picked. Keywords Improved ant colony algorithm · Logistics picking · Picking path · Path optimization

1 Introduction With the rapid development of social economy and the continuous improvement of scientific and technological level, the logistics industry accounts for an increasing proportion in China’s national economy. At the same time, people have higher requirements for the quality of cargo transportation [1, 2]. Due to the disadvantages and defects of traditional methods, people begin to pay attention to how logistics enterprises can adapt to the changes of the times and market demands faster and better [3, 4]. Many scholars have done relevant research on the improved ACA. In the logistics process, picking route planning is a very important link, which determines whether the whole distribution route can be carried out efficiently [5, 6]. Since the 1980s, Q. Li (B) Shenyang Urban Construction University, Shenyang 110167, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_72

799

800

Q. Li

some developed countries in the USA and Europe have started a “logistics revolution” integrating various functions and elements of logistics, and logistics activities have begun to move toward systematization and specialization. In the 1990s, a large number of specialized logistics service providers emerged in European and American developed countries and developed rapidly. Thus, the logistics industry has formed and become an important part of the service industry in developed countries. At present, the research on total logistics cost and logistics service level in China is still in its infancy [7, 8]. Even if the enterprise adopts advanced scientific management methods and does not fully understand its total logistics cost and single logistics cost, it cannot bear the logistics cost. In this paper, ACA is used as a research tool to solve this problem, which is of great significance: The most appropriate picking path is solved by improving ACA. The optimal solution is integrated without affecting the driving route of other vehicles.

2 Discussion on Logistics Picking Path Based on Improved Ant Colony Algorithm 2.1 Basic Principle of Ant Colony Algorithm Ant colony algorithm is an evolutionary natural simulation algorithm, which is inspired by the foraging behavior of ants in the wild [9, 10]. The general procedures for ants to find food sources in ant mounds are as follows: The main reason is that ants in ant colonies release chemical “pheromones” at the places they pass during foraging or returning to their nests, maintain indirect asynchronous contact, and the information along the way becomes pheromones, which gradually decreases with the passage of time to some extent [11, 12]. At the same time, the pheromone concentration is also related to the length of the path. The shorter the distance, the higher the pheromone concentration. Within a certain period of time, if a certain path is taken, the more the ants, the higher the pheromone concentration accumulated, and the greater the possibility of being selected by other ants next time. The cycle continues to create feedback effect until all ants are switched to the shortest time. In nature, the process of ant colony foraging is a positive feedback process, and the optimization algorithm of artificial ant colony is a simulation process. From the above analysis, we can see that the similarities between natural ant colony and artificial ant colony are as follows: (1) The route with high pheromone concentration is preferred. (2) In a period of time, the shorter path has higher pheromone concentration than the longer path; (3) The indirect transmission of information is carried out through ants, which will leave some pheromones on the way.

Logistics Picking Path Optimization Based on Improved Ant Colony …

801

The differences are (1) Natural ant colony has no memory function, while artificial ant colony has memory function, so it is necessary to remember which nodes have been visited. (2) The artificial ant colony is not completely blind in choosing the next path, but looks for the shortest path according to certain algorithm rules. Below, this paper makes a simple analysis on some characteristics of ACA. First, it introduces its benefits, including the following: (1) Self organizing algorithm: In the abstract sense, self-organization is a process in which the program is initially disordered and orderly without external intervention. Take the ant colony looking for food as an example to illustrate the above process. If the ant colony is triggered at first because there is no pheromone on the way, it can only look for food by chance. After a period of time, it accumulates a certain amount of pheromone on the food path, and the ant starts to order and arrive with further information. (2) Positive feedback algorithm: It can be seen from the foraging process of ants in nature that ants can finally find the shortest path, which depends on the concentration of pheromones accumulated along the way, and the accumulation of pheromones is the positive feedback of this process. For ACA, there are exactly the same pheromones in the initial area. The ant randomly selects the path, then gives the system a small change, changes the pheromone concentration every other path, and then solves the advantages and disadvantages of the ant structure. The feedback method it uses is to leave more pheromones in a better solution, and more pheromones will attract more ants. This positive feedback process expands the initial difference in the subsequent evolution and finally leads to the evolution of the whole ant colony to the optimal solution. In short, the positive feedback process is an important feature of ACA, which allows the algorithm to evolve continuously. (3) Parallelism: The parallelism of ACA is an obvious advantage over other algorithms. Assuming that the initial state of the ant colony is the same at the start, then the foraging process of each ant is independent and does not interfere with each other, and the ants finally receive it, which is the result of their independent selection. Therefore, the ACA can be imagined as a distributed multiactuator system. At the same time, it starts to find solutions independently at several points in the problem area, which not only increases the reliability of the problem, but also makes the algorithm the same in the search area, greatly reduces the internal search time, and has strong overall search ability. (4) Strong robustness: Robustness is the key to the survival of the system under dangerous and abnormal conditions. Firstly, compared with other algorithms, ACA has low requirements for the initial path, that is, the final solution of ACA does not depend on the selection of the initial path, and it does not need to be manually adjusted to the route during the search. Secondly, the number of parameters that ACA must set is small and simple. As long as the model of ACA

802

Q. Li

is slightly modified, ACA can be used to solve other combinatorial optimization problems. Although ACA has many advantages, it also inevitably has some disadvantages. Now briefly introduce the following disadvantages of ACA: (1) Stagnation is easy to occur, which is limited to the local optimal solution. In the work of the algorithm, after a certain number of iterations, ants will stagnate near some or some local optimal solutions, that is, they can no longer search the solution space, which is very unreasonable. The key to balance this contradiction is whether the selection information has enough influence on the following ants. If the parameter p is too large, the search tends to stagnate; if p is too small, it is likely that the convergence speed is too slow, resulting in the loss of operational importance of ACA. (2) Long search time: For the complexity of ACA, ACA needs a long search time to find the optimal solution, but the search time of ACA can be reduced by distributed computing.

2.2 Improved Ant Colony Algorithm The phenomenon of too many iterations and “premature” solution results is a large error problem in the basic ACA. The improvement of this paper mainly focuses on the above two points. On the other hand, the logistics transportation problem model proposed in this paper is based on VRPTW problem. Customers have higher requirements for delivery time and product quality. Compared with the basic VRP problem, the problem structure and customer requirements are more complex. Based on the basic ACA introduced above, adaptive parameter changes are carried out to improve the transportation efficiency and the quality of the optimal solution in the whole distribution process. (1) Perfect state transition rules The distance from the current point to the nearest node and the pheromone concentration on each path are the main basis for selecting the nearest node in the basic ACA. This is far from meeting the requirements for the accuracy of selection probability in the real problem model. For example, customers in this article usually pay more attention to the punctual arrival of transportation vehicles due to the current pace of life. If the waiting factor is added to the selection rule of the next node, customers with short waiting time are more likely to be selected as the next transportation point. wi jk =

2 kt j − tk

(1)

At the same time, in order to achieve a good balance between the rapid convergence of solutions and the diversity of solutions, the pseudo-random proportion rule is used,

Logistics Picking Path Optimization Based on Improved Ant Colony …

803

where N0 ∈ [0.1] is a random number used to control the transfer rule, and n = 2, n is the number of customers. Combined with the above methods, the state transition rules are improved. For ant K with current node i, the probability of selecting node j as the next visiting target is ( a=

pi j (t) max[ti j ][w(t)]

(2)

(2) The global pheromone update process is improved In this paper, the pheromone update of ACA is the overall update of pheromone, which will be improved on this basis. In order to increase the positive feedback effect of the current optimal solution generated by each iteration on the pheromone concentration of each channel, a new update formula is introduced. When faced with different feasible solutions, the effect will not be affected by its target performance. When an ant completes a cycle, it is K. If all ants have established a feasible path K, compare it with the global optimal path L, where r = min (R | k = 1, 2, … N), so that the iterative transportation mode of each ant is positive and negative. If feedback about the increase or decrease of pheromones is generated, the pheromones of all ants K are globally updated according to the rules in Fig. 1 at each iteration:

Is the number of cars used by K greater than L

All pheromones on K were greatly increased and updated L=R

Is the number of cars in K equal to L

Is the distance K greater than the distance L

A small amount of evaporation of all pheromones on K Fig. 1 Global pheromone update process

All the information on K evaporates massively

804

Q. Li

2.3 Logistics Picking Problem The picking process is based on the customer’s purchase requisition, including product name and quantity. The processor selects the required products from the storage space on the shelf, places them in the designated location, and finally carries out the delivery activities. The purpose of order picking is to quickly and accurately capture the contents of customer orders in a limited time, so as to shorten the lead time from order to receipt and reduce their costs-related operating system. Therefore, how to plan and import a series of appropriate order picking systems and equipment to the logistics center and properly manage the order picking operation is a problem worthy of study. Picking is the main activity of the warehouse. It is an indispensable prerequisite for the development from traditional distribution business to modern warehousing business. It is also the key to the success or failure of modern warehouse operation. It is a very complex task to complete hundreds or even more types of product selection efficiently and accurately in a short time. The sorting process is usually carried out by order sorting, batch sorting, etc. The goods they need come from the storage location they pass by. Usually, the customer’s picking task is completed during each tour. This method is similar to the process of entering the orchard and picking mature fruits from fruit trees. The main features of this method are as follows: 1. Simple operation and high recording accuracy. 2. The picking processes of different customers are independent of each other, and the picking sequence can meet the emergency needs of different customers. 3. After the picking process is completed, the goods required by the order are sorted and can be directly loaded on the delivery truck without landing, which simplifies the work process and improves the operation efficiency. 4. It can better adapt to the changing number of customers and customer orders, adjust the number of pickers at any time, and add operators at any time during peak hours. In this way, it helps to distribute and improve service levels immediately. 5. The requirements for mechanization or automation are not high and are not limited by the degree of equipment. If the order requires two or more products, you can repeat the above process several times until all the products the customer wants are selected. Because this working method is similar to farmers’ sowing, it is often called “sowing method”. The main features of this method are as follows: (1) Because the sorting operation needs to collect ordinary goods first and then sort them to different locations according to different customers, therefore before the sorting process starts, it is necessary to receive a certain number of orders, summarize statistics, and arrange customers to store goods. Therefore, this sorting process is very planned and difficult to implement, and the error rate is relatively high compared with sequential sorting. (2) Because it can pick goods for multiple customers at the same time, it is conducive to organize the centralized distribution of goods, so as to make full use of the loading capacity of vehicles.

Logistics Picking Path Optimization Based on Improved Ant Colony …

805

(3) Since batch sorting cannot be performed separately for a customer, most customer orders require a certain waiting time.

2.4 Solutions to Picking Path Problem With the proposal of warehouse path optimization problem, the method to solve the warehouse picking problem has also been significantly developed. Many factors are considered in optimization. After induction and synthesis: optimization method and approximate / heuristic method. Table 1 shows the solution of picking path.

3 Experiment 3.1 Calculation Steps of Improved Ant Colony Algorithm The improved ACA can be divided into the following steps: (1) Initialization: Either the initial value of the number of iterations NC = 0, initialize the concentration of pheromone C, and set the maximum number of iterations NC_MAX. In addition, the weighted adjacency matrix of the complete graph must be initialized in the form of a = zeros (n, n). The shortest path matrix is expressed as d = zeros (n, n). (2) Judge whether the number of NC iterations is equal to the maximum NC_Max number of iterations. (i) If yes, go directly to the last step (9) and output the result; (ii) Otherwise, go to (3). (3) Exploratory research is carried out, that is, the probability pK (T) is calculated according to the improved ant colony method and the next node after roulette mode is selected. (4) After searching, update the table. (5) Evaluate whether the taboo list is full. (i) When full, turn (8); (ii) If not, turn (6). (6) Judge whether the sum of the load capacity of the elements in the taboo table (i.e., the customer nodes that the vehicle has passed) exceeds the maximum load capacity of the vehicle. (1) If yes, turn (7); (2) If not, turn to (3). (7) Return the vehicle to the distribution center, select the next one and turn (3).

806

Q. Li

Table 1 Pickup path solution comparison Classification

Name

Advantage

Shortcoming

The optimal method

Polynomial exact algorithm

Create at least one exact optimal solution; play a significant role

It shows a slow disadvantage in solving large-scale problems

Mixed-integer linear programming method

Simple hair Internal point method

Dynamic programming Branch and bound algorithm Heuristic method

Heuristic algorithm

Local search algorithm

Neural network methods and artificial intelligence methods

s-shaped inspiration Find the optimal algorithm solution or satisfactory The convex shell solution of the algorithm problem in Alignment-inspired polynomial time; algorithm solve a large number of NP Space filling problems; be algorithm practical; the Basic local search algorithm is easy method to implement and Simulated annealing has small algorithm computational complexity Tab taboo search algorithm

Basic local search algorithms easily fall into the local optimum and have poor robustness; genetic algorithms emphasize the independent use of genetic algorithms and limit the complexity of the problem

Neural network algorithm

The NP difficulties of scheduling are overcome

For large-scale problems, the network model increases dramatically and is detrimental to practical applications

Artificial intelligence method

Get lots of information from special data structures

Large calculation volume and large time-consuming quantity

(8) Update the best path, clear the taboo table, make NC = NC + 1, and turn to (2). (9) Get the best distribution route and output the results.

Logistics Picking Path Optimization Based on Improved Ant Colony …

807

3.2 Implementation Steps of Improved Ant Colony Algorithm The implementation steps of the improved ACA are as follows: Step 1: Set each parameter q, D, C, a, G, and r. The initial pheromone value of the road section between two points is τ MX; set NC_ Max to be the maximum number of iterations and the total number of ants as Mr. Step 2: Set the logistics transportation center as the starting position of ants and add the center to the tab list. Step 3: Set the number of iterations NC = NC + 1, and use the constructive method in turn. Step 4: Calculate the transition probability of ant m according to formula (2), observe the maintenance time window and load limit, select another demand point other than the current solution set, and then put it into the current solution. If not found, return to the aquatic products distribution center when reaching the next node that meets the load limit or time limit of transport vehicles. Step 5: Repeat step (3) until the ants go through all points to get the corresponding cycle starting from the aquatic product distribution center and considering the boundary conditions. Thousands of cycles of each ant correspond to the distribution center to calculate the shortest route K of the distribution route composed of multiple dispatched transport vehicles. Step 6: Global update and adjust r according to the method in Fig. 1. Step 7: Evaluate whether the number of iterations reaches the preset value. If so, stop the transmission. If not, delete the tabu array and continue with step 7.

4 Discussion 4.1 Robustness Analysis of Improved Ant Colony Algorithm Table 2 shows the robustness test data of the improved ACA. Table 2 Run the robustness data n

R

Rmax

Rmin

O

CV

17

526.2

529.7

532.3

2.5

0.0014

21

638.3

632.8

654.5

4.3

0.012

28

805.5

856.6

896.4

2.9

0.0022

35

929.6

958.5

936.7

3.5

0.0074

808

Q. Li R

Rmax

Rmin

CV 0.014

1200 0.012 1000

896.4

929.6

0.012

958.5 936.7

856.6 805.5

0.01

529.7 600

0.008

638.3

0.0074

532.3

526.2

0.006

632.8 400

CV

Data

800

654.5

0.004

200

0.0022

0.002

0.0014 0

0 17

21

28

35

N Fig. 2 Improved robustness contrast of the queen colony algorithm

As can be seen from Fig. 2, although the calculated data fluctuates greatly, the result is acceptable for the problem that the storage site and the volume of goods to be picked are random variables. From the discrete coefficient CV, the maximum value of the four examples is 0.7%, and the results are satisfactory, which shows that the optimized ACA has strong robustness to the random generation of path and the random sampling of the volume of goods to be picked.

5 Conclusion With the rapid development of social economy, logistics industry has become one of the important industries in the national economy and is playing a more and more important role in China’s economic development. The picking path optimization problem is the most critical, complex, difficult, and challenging topic that affects the production efficiency and cost of enterprises. Firstly, this paper introduces the research status of logistics picking route planning and VRP algorithm at home and abroad. Secondly, it analyzes the related theories of ACA and designs the solution model combined with practical cases. Finally, the mathematical model is established by using the improved ant colony method to determine the optimal solution aggregation point and the selection method of path strategy.

Logistics Picking Path Optimization Based on Improved Ant Colony …

809

References 1. Zhou X, Yang T, Liu X et al (2017) Intelligent logistics delivery path optimization method based on improved ACA. J Inner Mongolia Norm Univ (Chin Version Nat Sci), 046(006):888–892 2. Zhao B, Gui H, Li H et al (2020) Cold chain logistics path optimization via improved multiobjective ant colony algorithm. IEEE Access 8:142977–142995 3. Zhang L, Gao Y, Sun Y et al (2019) Application on cold chain logistics routing optimization based on improved genetic algorithm. Autom Control Comput Sci 53(2):169–180 4. Wen S, Zeng X, Liu Y (2017) Research on path optimization for tripod robot based on ACA. Mach Electron 035(003):77–80 5. Wu F (2021) Contactless distribution path optimization based on improved ant colony algorithm. Math Probl Eng 2021(7):1–11 6. Zhang (2017) Research on optimizing the distribution path of agricultural Cold Chain Logistics in green consumption era. J E-commer: Chin Engl 008(001):13–17 7. Dang FL, Wu CX, Wu Y et al (2019) Cost-based multi-parameter logistics routing path optimization algorithm. Math Biosci Eng 16(6):6975–6989 8. Li Z, Li Y, Lu W et al (2020) Crowdsourcing logistics pricing optimization model based on DBSCAN clustering algorithm. IEEE Access PP(99):1 9. Gong Y, WuM, Xu X et al (2017) Research on path optimization of hole group machining based on improved genetic algorithm. Modular Mach Tools Autom Mach Technol 000(011):52–56 10. Hou P, Liu J, Gao W (2019) Optimization of laser cutting path based on improved ACA. Electromech Eng 036(006):653–657 11. Wu C, Song CK, Yao J (2018) Study on optimization of logistics distribution route based on improved adaptive genetic algorithm. Comput Meas Control 026(002):236–240 12. Jin S, Shan LV, Wu Y et al (2017) Method of logistics distribution routing optimization based on improved genetic algorithm. Comput Dig Eng 045(004):629–631

Vehicle Automatic Braking System Based on Multi-target Acquisition Algorithm Chengli Pang

Abstract With the development of the automotive industry and the advancement of artificial intelligence technology, the human vision of driverless cars is gradually becoming a reality. However, the current level of self-driving technology is immature, and the self-driving car must be able to handle emergency situations when it leaves the laboratory and enters the bus. The main purpose of this paper is to study the design and implementation of the automatic braking system of automobiles based on the multiobjective capture algorithm. Aiming at the typical dangerous situation of high-speed collision avoidance, this paper proposes a design scheme of a collision avoidance system for autonomous vehicles and studies its important basic theories and key technical issues involved in motion control and decision-making. The experiment found that in the case of emergency braking, 95% of drivers can achieve an average braking efficiency of 0.49 g, and the maximum reduction can reach 0.87 g. The average deceleration of the driver is 0.32 g, and the maximum deceleration is 0.63 g. The results show that it is often difficult for the driver to fully utilize the braking force of the vehicle. Keywords Multi-target acquisition · Target recognition · Vehicle braking system · Automatic emergency braking

1 Introduction In recent years, advanced driver assistance systems (ADAS) have developed rapidly and are widely used in automobiles and have also become a global research focus, especially the automatic emergency braking system (AEB) with collision warning function. Automatic emergency braking because the AEB system works in the emergency and dangerous situation before the accident, and the AEB system has a great C. Pang (B) School of Automotive and Marine Engineering, Dalian Vocational &Technical College (Dalian Open University), Dalian 116037, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_73

811

812

C. Pang

impact on the driver’s normal road judgment, driver’s comfort, and accident safety [1, 2]. However, the current AEB systems still have many deficiencies in performance, lack of control, and attack functions. In a related study, Sakhno et al. considered the possibility of identifying the braking pattern of a vehicle combination with a partially filled fuel tank by an automatic control system [3]. The changes of the normal reaction force of the support facing the combined axis of the vehicle during the braking process of the semi-trailer with semi-full tank and the equivalent rigid fixed load are compared and analyzed. The analysis shows that the load on the axles of the vehicle combination varies linearly and is proportional to the retarder. Haus believes that automatic emergency braking (AEB) can help prevent and/or mitigate many vehicle-bike collisions [4]. To elucidate vehicle-bike crashes and examine relevant factors to estimate the effectiveness of AEB, an in-depth crash investigation and validation were performed using the crash research dataset. This paper mainly studies the design and implementation of the automatic braking system of automobiles based on the multi-objective acquisition algorithm. Aiming at the typical dangerous situation of high-speed collision avoidance, this paper proposes a design scheme of a collision avoidance system for autonomous vehicles and studies its important basic theories and key technical issues involved in motion control and decision-making. The AEB system realizes its function by controlling the longitudinal dynamics of the vehicle, so the design of the control system is the key to the research. According to the functions to be realized by the AEB system, its control system must satisfy fast response, high accuracy, and strong robustness. Combined with the AEB system control scheme, based on the hierarchical early warning/braking strategy, the AEB control system is designed in layers, and the upper and lower controllers jointly realize the precise following of the expected acceleration of the vehicle in the complex and changeable driving environment, so as to ensure the realization of the early warning and braking functions of the AEB system [5].

2 Design Research 2.1 Overall Architecture of AEB System Automatic emergency braking has three meanings, in which “automatic” means that the system can actively brake the vehicle through its own control module without the driver’s active intervention; “emergency” means that the system is only in danger of collision. It will intervene and will not affect the normal driving of the driver; “braking” means that the system only brakes the vehicle to reduce or avoid accidents, so this paper only studies the longitudinal motion of the vehicle [6]. The realization of the automatic emergency braking function requires the cooperation of multiple modules. Before designing the control scheme of the AEB system, it is first necessary to clarify the functions of each sub-module and the connection

Vehicle Automatic Braking System Based on Multi-target Acquisition …

813

AEB system

Information collection module Target Vehicle 1 Information radar

Target Vehicle 2 Information ... target vehicle n information

control module

executive body

Most dangerous target selection module Most Dangerous Target Information

car information

engine

brake

Warning/Brake Module

Fig. 1 Overall structure of AEB system

between them and the AEB system. The overall structure of the system is shown in Fig. 1.

2.2 Improvement Design of Control Strategy of AEB System As an advanced driver assistance system, the control strategy of AEB system involves the real-time judgment of driving safety status and collision risk, and the decisionmaking control of issuing early warning signals, applying braking signals and braking intensity. The overall requirements of the control strategy of the AEB system need to meet the following conditions as far as possible: (1) It can ensure safety as much as possible in various types of traffic situations and avoid the phenomenon of “missing reports”; (2) there are various methods (indicator, sound, and vibration sensor) before automatic emergency braking. Early warning and reminder function, with multi-level (first-level and secondlevel) braking mode during emergency braking; (3) the priority of the driver’s operation is higher than the active intervention of the AEB system; (4) avoid “false alarms” and try to reduce the issue of invalid early warning and interfere with the normal driving operation of the driver; (5) it is necessary to comprehensively consider performance factors such as safety, stability, comfort, and accuracy of working timing [7, 8]. In order to realize the above-mentioned general requirements of the AEB system, the control strategies of graded early warning and graded braking are applied to the actual control system of the AEB system. For the control strategy of the AEB system’s graded early warning, three different early warning methods are proposed, which use the warning light to warn visually, the sound to warn from the sound, and

814

C. Pang

driving direction

Collision Warning Phase partial braking phase The driver can avoid a collision by braking

full braking phase

The car ahead is slow

high chance of collision

collision occurs

Fig. 2 Schematic diagram of the functionality of the AEB system

the seat belt vibration to warn from the body sense, which can provide the driver with a longer response. Time but does not cause serious interference to the driver, avoid distracting the driver’s attention, and fully and effectively play the early warning and reminder function of the AEB system. The function of the AEB system is shown in Fig. 2. The performance requirements of the AEB system are the safety of collision avoidance, the comfort of riding, the accuracy of target perception, the rapidity of braking response, and the robustness of system control.

3 Experimental Study 3.1 Determination of the Intervention Method of the AEB System In order to better ensure the driver’s active control and provide more complete functions, this paper adds an anti-collision warning function to the function of automatic braking. If only the first-level warning is used, the sudden warning sound will distract the driver’s attention in a crisis situation, and the warning will be issued when the danger level is already high, leaving the driver with relatively short reaction time, and it is often difficult to hear the sound. Make reasonable judgments and responses within a short period of time after the warning [9]. If the braking intensity of the same magnitude is used in non-emergency conditions and emergency conditions, it is obviously impossible to satisfy both driving safety and riding comfort and may also affect traffic efficiency, and it is not in line with the driving habits of most drivers. Therefore, in the selection process of the intervention mode of the AEB system, the two-level warning strategy of warning light warning and sound warning and the two-level braking strategy of partial braking and full braking are adopted. The traffic environment where the vehicle is located is divided into 5 grades (I–V) according to the degree of danger, as shown in Table 1.

Vehicle Automatic Braking System Based on Multi-target Acquisition …

815

Table 1 Classification of the danger level of the vehicle Hazard level

Dangerous state

AEB system intervention method

I

Relatively safe state

No intervention required

II

Less dangerous

Level 1 warning (warning light)

III

High risk

Secondary early warning (warning light + sound)

IV

Very dangerous

Primary braking (partial braking)

V

Very dangerous

Secondary braking (full braking)

The relative speed and distance between the two vehicles Graded early warning

Staged braking

first-level warning Level 2 Early Warning

Safe Time Model

safe distance model first level braking

secondary braking

Fig. 3 Overall scheme of graded warning/braking

Figure 3 shows the overall scheme of the graded early warning/braking of the AEB system.

3.2 AEB System Target Classification Based on the measurement information of the radar system, the selection of the most dangerous target vehicle in the traffic environment is studied under the condition of straight road traffic. According to the different types of targets, the targets are mainly divided into three categories: cars, two-wheeled vehicles, and pedestrians. This paper mainly considers the case where the target is a car. Therefore, according to the relative positional relationship between the target car and the vehicle, as shown in Fig. 4, the goals of the AEB system can be grouped into the following categories: (1) The target vehicle driving normally in this lane; (2) The target vehicle driving normally in the adjacent lane in the same direction; (3) The target vehicle that is changing lanes in the adjacent lane in the same direction; (4) The target vehicle driving normally in the opposite lane.

816

C. Pang

Fig. 4 Schematic diagram of vehicle driving environment on straight road

4 2

1 3

3.3 The Selection Principle of the Most Dangerous Target Vehicle According to the target classification, a series of rules are now set to determine the most dangerous target screening range. As shown in Fig. 5, let the lane width be W and the vehicle width be B. The angle between the center line of the i-th target vehicle detected by the radar and the center line of the vehicle is θ i, and the relative distance from the vehicle is dri. Therefore, the coordinates of the i-th target vehicle are set as Ti (dri, θ i). (1) When Eq. (1) is satisfied, that is, the target vehicle is driving within the lane, and this target is a dangerous target, such as targets No. 2 and No. 3 in Fig. 5 dri sin θi ≤

W 2

(1)

(2) When the target vehicle is driving in the middle of the adjacent lane, the vehicle will not pose a safety threat, and when (2) is satisfied, the vehicle has a tendency to change lanes to the current lane. It is a potentially dangerous target, such as target No. 1 in Fig. 5 W B < dri sin θi ≤ W − 2 2 Fig. 5 Schematic diagram of target vehicle classification on a straight road

(2)

4

W

B

θ θ

dr3

2

Vehicle Automatic Braking System Based on Multi-target Acquisition …

817

(3) The target No. 4 in Fig. 5 is a non-hazardous target, which satisfies Eq. (3) dri sin θi ≥ W −

B 2

(3)

4 Experiment Analysis 4.1 Determination of Braking Intensity The braking intensity z is defined as follows: z·g=

du dt

(4)

In the formula, du/dt is the longitudinal acceleration of the vehicle; g is the acceleration of gravity, taking 9.8 m/s2 . Braking intensity grading enables the system to select the braking intensity according to different relative vehicle speeds. When the relative speed of the two vehicles is small, the system adopts the firstlevel braking. At this time, the braking intensity is low, but it can also avoid collision, which not only ensures the driving safety, but also improves the riding comfort of the driver and passengers. When the relative speed of the two vehicles is very large, the system will adopt two-level braking. At this time, the braking intensity is high, and the system mainly ensures the driving safety. When the relative speed of the two vehicles is large, the system will switch from the first-level braking to the secondlevel braking at a certain moment, and the corresponding braking intensity will be switched from low-level to high-level, compared to the traditional AEB system. The braking intensity is directly switched from 0. At the advanced level, the frustration of the vehicle will be reduced, improving comfort while ensuring driving safety [10].

4.2 Partial Braking The driver’s braking level is an important factor that affects the selection of the braking strength of the AEB system. This paper does not consider the impact of the driver’s individual differences on the system in the research process. The vehicle deceleration values of more than 80 drivers during the emergency braking process were randomly selected, and then the deceleration values and average values of the drivers during the emergency braking process were analyzed. It is obtained as shown in Table 2. As can be seen from Fig. 6, in the case of emergency braking, 95% of drivers can achieve an average braking efficiency of 0.49 g, and the maximum reduction

818

C. Pang

Table 2 Driver’s braking level Brake deceleration

Brake deceleration percentage 5% (g)

25% (g)

75% (g)

Mean value (g) 95% (g)

aaverage

− 0.11

− 0.26

− 0.45

− 0.49

− 0.32

amax

− 0.38

− 0.46

− 0.79

− 0.87

− 0.63

aaverage

amax

0 -0.1

5%

25%

75%

95%

mean value

-0.2

Value

-0.3 -0.4 -0.5 -0.6 -0.7 -0.8 -0.9 -1

Percentage

Fig. 6 Analysis of driver’s braking level

can reach 0.87 g. The average deceleration of the driver is 0.32 g, and the maximum deceleration is 0.63 g. The results show that it is often difficult for the driver to fully utilize the braking force of the vehicle [11].

4.3 Full Braking In addition to the influence of the driver’s driving habits, the braking intensity during the driving process of the vehicle, the adhesion coefficient of the road surface affects the actual maximum deceleration that the vehicle can achieve. During the braking process of the car, the relationship between the ground breaking force F Xb , the braking force F μ , and the adhesion force F φ is shown in Fig. 7. It can be seen from the figure that the ground adhesion F φ has Fφ = Fz · φ

(5)

Vehicle Automatic Braking System Based on Multi-target Acquisition … Fig. 7 Relationship between ground breaking force, brake braking force, and road adhesion during braking

819

FXb,Fμ,Fφ

Fμ

FXbmax=Fφ Fφ

p

Within the ground adhesion limits are FX b = Fμ ≤ Fφ

(6)

When F Xb = F μ = F ϕ , with the increase of the brake pedal force, the brake braking force F μ will continue to increase, but the ground breaking force F Xb will always be equal to the ground adhesion force F ϕ and no longer rise. ab max = φ · g

(7)

Considering the change of the ground adhesion coefficient in rainy and snowy weather, the road surface is divided into four conditions: dry, wet, icy, and snowy (Table 3). Different road conditions have different maximum braking decelerations. Taking dry asphalt road as an example, the graded braking strategy is designed and the braking intensity at full braking is 0.8 [12] (in Fig. 8). Table 3 Adhesion coefficient values under different road conditions

Pavement

Value

Dry

0.7–0.8

Damp

0.65–0.7

Ice

0.2–0.25

Snow

0.3–0.35

820

C. Pang

0.9 0.8 0.7

Value

0.6 0.5 0.4 0.3 0.2 0.1 0 dry

damp

Ice

snow

Pavement Fig. 8 Analysis of adhesion coefficient values under different road conditions

5 Conclusions The AEB system is an important means to ensure the safety of vehicles, and the core of its development is the control strategy. This paper studies the control strategy of automatic emergency braking system with both safety and comfort, considering the situation of vehicles in adjacent lanes changing lanes for driving conditions on straight roads. In order to ensure the accuracy of the control of the AEB system, according to the situation of vehicles in adjacent lanes changing lanes, using the relative position relationship between the target vehicle and the vehicle, the most dangerous target vehicle selection algorithm is proposed, and the most dangerous target vehicle of the AEB system is judged and selected in real time. Secondly, the driver’s reaction time to different types of early warning signals is analyzed, and a hierarchical early warning strategy based on the safety time model is proposed. In order to achieve the desired acceleration planned by the hierarchical braking strategy, a vehicle inverse longitudinal dynamics model is established to achieve vehicle speed maintenance and vehicle deceleration. Taking into account the change of road adhesion conditions and the influence of front and rear axle load transfer factors on the distribution of the four wheel cylinder pressures, a braking force distribution model is established to enable the car to automatically select the optimal slip rate on different road surfaces.

Vehicle Automatic Braking System Based on Multi-target Acquisition …

821

References 1. Odachowska E, Uci´nska M, Kruszewski M et al (2021) Psychological factors of the transfer of control in an automated vehicle. Open Eng 11(1):419–424 2. Al-Quran M, Mayyas AR (2021) A nonlinear tire blowout stabilizer based on a novel integral terminal sliding mode controller. IEEE Access PP(99):1 3. Sakhno V, Popelysh D, Tomchuk S (2020) Automatic determination of the partially filled tank vehicle combination braking mode. Avtoshliakhovyk Ukrayiny 264(4):33–39 4. Haus SH, Anderson RM, Sherony R et al (2021) Potential effectiveness of bicycle-automatic emergency braking using the Washtenaw Area transportation study data set. Transp Res Rec 2675(9):265–270 5. Makki AA, Nguyen TT, Ren J et al (2020) Estimating road traffic capacity. IEEE Access PP(99):1 6. Kolla E, Ondru J, Gogola M et al (2020) Braking characteristics of the specified modern electric vehicle during intensive braking. Adv Sci Technol—Res J 14(3):125–134 7. Rajguru E (2021) Implementation paper of traffic signal detection and recognition using deep learning. Turk J Comput Math Educ (TURCOMAT) 12(1S):212–219 8. Vaiyapuri T, Mohanty SN, Sivaram M et al (2021) Automatic vehicle license plate recognition using optimal deep learning model. Comput, Mater Continua 67(2):1881–1897 9. Rizik A, Tavanti E, Chible H et al (2021) Cost-efficient FMCW radar for multi-target classification in security gate monitoring. IEEE Sens J PP(99):1 10. Ahmed H, Lee JR (2020) Development of autonomous target recognition and scanning technology for pulse-echo ultrasonic propagation imager. Struct Health Monit 19(4):1064–1074 11. Stumpfe D, Hoch A, Bajorath J (2021) Introducing the metacore concept for multi-target ligand design. RSC Med Chem 12(4):628–635 12. Akande DO, Salleh M (2020) A multi-objective target-oriented cooperative MAC protocol for wireless ad-hoc networks with energy harvesting. IEEE Access PP(99):1

Intelligent Fault Diagnosis of Electronic Engineering Equipment System Based on Machine Learning Algorithm Yan Yang

Abstract With the development of microelectronics technology, computer technology, and new application technology, the design of electronic system becomes more and more complex and complex. With the frequent use of electronic systems, many parts will have multiple failures, thus reducing or losing the default performance of the system, and even causing major accidents. This technique has been widely used in the field of error detection due to its strong production process, close to arbitrary operation, training ability, and self-adaptation ability. This paper proposes two new intelligent diagnosis methods by using the method of mechanical learning sparse autoencoder and wavelet transform, and the fault diagnosis of electronic engineering equipment system. At the same time, the stochastic resonance model is improved to make the extracted fault features more obvious, and the effectiveness of the newly proposed algorithm is verified through the fault diagnosis example of the electronic engineering equipment system. The experiment proved that in the 10,000 simulated fault detection, the machine learning algorithm with resonance model of intelligent fault diagnosis system diagnosis accuracy and diagnosis time is much higher than other distribution models. Keywords Machine learning · Electronic engineering equipment · Intelligent system · Fault diagnosis

1 Introduction To accommodate modern science and technology and construction, equipment and applications are evolving into a highly integrated culture. With the increasing complexity of machine equipment, it not only increases the possibility of hardware failures, but also distinguishes these types of failures. Second, research materials are becoming more difficult. Finally, for large design and automated devices, equipment Y. Yang (B) Michigan State University, East Lansing 48823, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_74

823

824

Y. Yang

failures can lead to many catastrophic consequences. However, the quality of the machine and equipment can be improved through perfect technology and functional technology, so as to ensure the normal operation of the machine. However, the above steps do not fully guarantee that the operating system will not fail during operation. Therefore, how to improve the fault detection ability of electronic engineering equipment to make its normal work has become one of the development directions of the industry [1, 2]. In electronic engineering equipment based on machine learning algorithm system intelligent fault diagnosis research, many scholars to study, and achieve good results, such as Tayyab S M et al. use the sensor through 1 MHz sampling frequency to collect electronic engineering equipment surface acoustic signal, combined with the time– frequency analysis to detect the running status of electronic engineering equipment [3]. Lu N published the first international academic monograph on the progress of FDD research, and subsequently established the fault diagnosis and Safety Technical Committee [4] for the IFAC Technology Process. This paper mainly studies the intelligent fault diagnosis of electronic engineering equipment, puts forward two new intelligent diagnosis methods based on machine learning sparse autoencoder combined with wavelet transform and random resonance, and improves the stochastic resonance model, and proposes a new method of nonlinear coupled bistable stochastic resonance. For the problems of multiple modification of traditional machine learning network and long training time, this paper uses deep training equipment to diagnose electronic device errors. Error, but their image size is also smaller than the directly generated 2 D images, which is not a normal scan for the deep trainer. Also, you need more time and then analyze and map the world of deep dimensions. Machine learning in electronic devices is checked experimentally.

2 Research on Intelligent Fault Diagnosis of Electronic Engineering Equipment System Based on Machine Learning Algorithm 2.1 Data Preprocessing Data mining is to process the collected error information, including: data cleaning, data integration, data conversion, data reduction, etc. These data processing processes will improve the quality of the data, improve the readability of the machine, and facilitate unification and computational analysis. After the initial process, many data centers form the basis of central storage and access. This information includes configuration data, performance data, alert event data, transaction tracking logs, machine training standards, and model training data. Data production requires the cooperation of business and technical personnel with [5, 6].

Intelligent Fault Diagnosis of Electronic Engineering Equipment …

825

The process of data recovery in this system can be carried out through external monitoring, full value loss (− 1, average example), conversion and improvement of conversion format, conversion coding (replication), switching operation, and continuous value adjustment.

2.2 Application of Machine Learning in Intelligent Diagnosis of Electronic Engineering Equipment System Machine learning is also called deep learning. Depth in deep degree learning refers to the number of network layers in the learned neural network. In general, the more number of layers in the network when external conditions allow, the more likely the network can mine data. However, it is not that the deeper the layers of the network, the better. When the deeper the network layers, the network will be complicated, the more complex the calculation of the network, the longer the training time of the network, and the possibility of error accumulation and low training efficiency. At present, there is no specific conclusion on the specific number of layers needed for deep learning neural network. This paper selects networks with two hidden layers, and the experimental results show that this model satisfies the informative feature extraction problem while reducing the computational complexity. The number of nodes in the hidden layer plays a very important role in the calculation process of forward propagation and back propagation. It is not only related to the performance of deep learning neural network, but also its number may be the key to determine whether the network overfitting phenomenon is produced. Although it is very important, but there is not a scientific and effective method in theory, through a large number of literature review, about the number of hidden nodes calculation formula is in the case of enough samples, and because of the calculation formula, according to different calculation formula number of nodes is different, therefore, a large number of models different hidden layer node number of experimental simulation [7, 8].

2.3 Fault System Architecture Design The basic error analysis system architecture based on machine learning consists of three parts: big data platform layer, artificial intelligence and machine learning, visual display, and alarm: 1. Big data platform level. The big data platform level mainly has the following characteristics. (1) Obtain the NoSQL-based cluster scheme, acquire the storage capacity technology, create a unified virtual storage, and create a global unified domain

826

Y. Yang

name. It can support three levels: local node, server node, and server capacity. Each cluster storage system supports out-of-band data storage recovery capability (data indexing, etc.) to improve big data recovery performance. (2) The relational database complex adopts the traditional relational system, which first maintains the basic knowledge of the region, the initial data required by the system, the functional description, the calculation and the results that must be displayed on the page k. 2. Artificial intelligence and educational equipment. Using artificial intelligence and machine learning techniques, the early detection error detection system is gradually improved by integrating error identification libraries, algorithms, building templates, and myth optimization. First, follow the following steps: transfer the bank alarm data to the big data column. The data processing process includes cleaning, modifying, and merging. The basic statistical results will be stored in the database, and finally the calculator will transmit the data and complete the test plan. The template is stored as a duplicate pattern. 3. Optical display and automatic alarm. This is the process of presenting abstract data information with relevant vision and projections and combining artificial intelligence and machine learning techniques to complete automatic alerts. When an error is detected, it automatically connects to the corresponding operator. In this case, the error event will activate the problematic operation and notify the automatic monitoring process and related responsibility [9, 10], guided by the permission exercise program.

2.4 The Basic Algorithm Using the support vector (SVM) to sort data is finding the best level to meet the adaptation requirements to become part of the course and dividing the points in the course as flat as possible, so the distance between the vectors near the hyperplane and the hyperplane, and the vacuum area on both sides is much larger. With SVM for classification, the basic process is as follows: D = {(xi , yi )|i = 1, 2, · · · , l } x ∈ R l , y ∈ {+1, −1} w · x − b = 0 Assuming that the training set can be separated by a hyperplane without error, this hyperplane can be obtained from the following quadratic program: 1 1 Φ(w) = min ||w||2 = min (w · w) w·b 2 w·b 2 yi [(w · xi ) + b] − 1 ≥ 0, i = 1, . . . , l, That is, take the w and the threshold b.

(1)

Intelligent Fault Diagnosis of Electronic Engineering Equipment …

827

If the training set data is linearly not separable, then when constructing the optimal hyperplane, non-negative variables can be introduced to solve the quadratic programming [11, 12]: ξi ≥ 0 ) ( l Σ 1 min (w · w) + C ξi w·b·ξ 2 i=1

(2)

2.5 Parameter Optimization The SVM training needs to select the kernel function as well as the parameters of the kernel function. The kernel function as well as the parameters of the kernel function can directly affect the classification effect, so it is crucial to choose the appropriate kernel function and find the optimal parameters. Among these four commonly used nuclear functions, linear nuclear function can be regarded as a special case of radial basis nuclear function. Many polynomial nuclear function parameters are not conducive to regulation. Sigmoid kernel function is similar to radial basis nuclear function in many aspects. Therefore, radial basis nuclear function is usually selected as the nuclear function of SVM. In terms of kernel function optimization, there are two parameters to be adjusted: c and g. Where c represents the penalized parameter, and g is the abbreviation of the kernel function parameter gamma. Usually, according to the understanding and experience of practical problems, the parameters of the kernel function are set and the classification effect is tested by experiments, or the optimal parameters are found through the optimization algorithm. In this problem, genetic algorithm (GA) is used to optimize SVM parameters. Genetic algorithm uses the characteristics of biological genetics, selects the individual through the idea of survival of the fittest in the biological field, and obtains the new individual through crossover and variation, making the new population more adaptive than the previous population, with the characteristics of self-study habits and comprehensiveness. The optimization of SVM parameters based on genetic algorithm includes the following steps: (1) Encode the SVM parameters and generate the initial population; (2) Decoding the chromosomes of all the individuals in the population to form a SVM parameter model; (3) Use the training data set to train the parameter model and get the trained SVM classifier; (4) The performance of the SVM classifier was evaluated by the cross-validation method to obtain the fitness of each individual in the population;

828

Y. Yang

(5) Determine whether the stop optimization condition is met (the evolutionary algebra reaches the upper limit, or the optimal solution is generated). If satisfied, the preservation results stop optimizing; if not, select, cross, and calculate individuals in the population to form a new generation of population and move to step (2).

2.6 Insulator Detection Based on Multiscale Convolutional Neural Networks An insulator detection method in a substation is proposed based on a multiscale convolutional neural network based on its distribution and scale characteristics in substations. Insulator is an important equipment in the power system, not only supporting and connecting the role, but also playing the insulation role. Poor insulation is the main cause of accidents in the high-voltage power grid, and the insulator is an important equipment for the insulation function in the transmission lines. Insulators are widely distributed in various components of transmission lines, and their use is very large. Although the structure is simple, their role and safety are extremely important. When the insulator has potential safety risks, the flashover may occur, resulting in a large area of power failure accidents, seriously affecting the industrial operation and normal life. Traditional machine learning methods usually require manual selection of object features, so they usually rely on experience to acquire features. At the same time, it is usually difficult to guarantee the stability of the detection when the image that needs to be processed is complex. The insulator image not only has a complex background, but also makes the traditional feature extraction method unable to achieve good results due to the mutual occlusion between the transmission lines and the supports and the huge difference in the insulator scale caused by the shooting distance and Angle. Convolutional neural network (CNN) can fully learn the features of the object to be identified in the original image, through the alternate use of the unique convolutional layer and sampling layer, and store the features in the network, and finally judge the image category through the terminal classifier. Due to its strong self-learning ability, it is widely used in various target detection, and has achieved good results in many target classification tasks. Traditional CNN can learn a large number of images to be measured and detect the characteristics of a large number of images. Although the training time is long, once the network training is over, the network not only stores a large number of picture features in the training set, but also only requires one-way operation in the test process, with short detection time, high recognition accuracy and strong anti-interference ability. Nevertheless, the traditional CNN method still has limitations: a large number of insulators are distributed in the substation, usually more than ten in the same image;

Intelligent Fault Diagnosis of Electronic Engineering Equipment …

829

the insulators distort widely in the image, and the largest insulator in the same image may be 2–4 times that of the minimum insulator; the pipeline in the image is complex, and the equipment blocks each other seriously. Therefore, it is difficult to identify and locate the insulators accurately by the traditional CNN method. According to the specific distribution of insulator images and the shortcomings of traditional CNN methods, this paper proposes an insulator detection method based on multiscale CNNs, which constructs a network containing three different scale CNNs with different scales, structure and training sets, so each network has different detection capabilities and sensitivity to different objects in the image. Finally, according to the characteristics of the three networks, the detection results of the three networks are integrated, so as to obtain a better and more accurate detection results compared with the ordinary CNN.

3 Research and Design Experiment of Intelligent Fault Diagnosis of Electronic Engineering Equipment System Based on Machine Learning Algorithm 3.1 Intelligent Fault Diagnosis Model Based on the characteristics of the vector support and advanced electronic equipment. The document divides the entire error analysis model into four separate sections, linked separately. After using independent units, the analysis can be conducted successively, and some modules can be switched according to the needs of the diagnostic test to improve the accuracy of the error diagnosis. Master is a data source unit that needs to minimize the effect of the absolute value on the error model for different fields in the SVM sample. The second feature is to understand the training module, creating error patterns and recording the optimal hyperlevel of different types of errors. The third feature is the error check section, where you enter a new electronic application example in the template to detect electronic system errors. The fourth feature is the new error module, namely, when new forms and error positions are displayed on the system, they can be inserted into the template to get a better new superior, thus improving the error detection and detection capabilities.

3.2 Actual Operation Test of the System Different classification models are constructed to compare the fault identification accuracy experiments to further verify the effectiveness of this method. KNN algorithm, which is one of the most commonly used methods in data mining classification technology, takes the K value to 7, the ratio of training data and test data is 1:1, and respectively classifies the original time-domain data of bearing fault and the spectrum

830

Y. Yang

data after random resonance processing and also includes fault diagnosis identification using a random forest algorithm with a decision tree order of 150. The raw time-domain data is then processed and classified using the sparse autoencoder, and finally compared with the sparse autoencoder, stochastic resonance, and softmax classifier models. The sparse autoencoder has a model structure of 500-300-100-6, a learning rate of 0.1 and a dropout of 0.1, calculating the average of the accuracy of 10 trials.

3.3 Experimental Data Before training the convolutional neural network, we first need to make a database, which contains the following requirements when making the database: (1) the number of positive and negative samples is as large as possible, and the ratio is as 1:1 as possible. Because convolutional neural networks require a large number of different data to optimize the network, increasing the number of samples can improve the richness of network learning features. Alternatively, the convolutional neural networks will optimize the network starting from the random initial states, so that the similar positive and negative samples can reduce the effect of the random classification on the overall accuracy. (2) The sample contains as many more changes as possible. The richer the samples in the training set, the more features the convolutional neural network can learn, and the stronger the robustness when the image changes. Therefore, it is necessary to mine the image as fully as possible to improve the richness of samples. For example, the image block of the positive sample needs to contain the insulator center, the insulator highlight region, and the insulator edge in different backgrounds. Negative samples need to include sky, cable, bracket, fence and control box, etc. (3) Due to the existence of the three scales, we need to consider the different information contained by the image blocks at the different sizes when making the training samples.

4 Experimental Research and Analysis of Intelligent Fault Diagnosis of Electronic Engineering Equipment System Based on Machine Learning Algorithm 4.1 Comparison Tests of Different Classification Models In this paper, various algorithms are adopted to simulate the comparison experiments and conduct two sets of comparison experiments. Five classification models are mainly used to diagnose 10,000 simulated faults. The experimental results are recorded as described in Table 1.

Intelligent Fault Diagnosis of Electronic Engineering Equipment …

831

Table 1 Fault diagnosis results of different classifier models KNN

KNN + SR

RF

DAE

DAE + SR + Softmax

Test1

29.166

66.632

88.763

85.723

99.813

Test2

30.147

62.124

85.564

87.235

99.504

120 88.763 85.564

100

per cent

80

87.235 85.723

99.504 99.813

66.632 62.124

60 40

30.147 29.166

20 0

algorithm Test1

Test2

Fig. 1 Fault diagnosis results of different classifier models

According to the analysis of Fig. 1 results, models based on improved stochastic resonance, sparse autoencoder, and softmax classifier are better at identifying this data than other traditional models. Judging from the effect of KNN classification effect and the classification effect of KNN and stochastic resonance, the accuracy of the model with stochastic resonance is greatly improved.

4.2 Simog Diagnosis Time Comparison In the two sets of comparison experiments, the different classification models will record the time of the simulated fault diagnosis. The data are shown in Table 2. As can be seen from Fig. 2, the classification models with stochastic resonance are the fastest to diagnose simulated faults. Combined with the above added stochastic resonance model, the fault recognition rate is as high as 99%, which is much higher than the other classification models. It can be seen that the accuracy and diagnosis time of this model are far better than other classification models.

832

Y. Yang

Table 2 Comparison of simulation diagnosis time of different classifier models KNN

KNN + SR

RF

DAE

DAE + SR + Softmax

Test1

7.8

3.5

2.2

2.6

0.7

Test2

6.9

3.7

2.4

2.4

0.5

9 8 7

time

6 5 4 3 2 1 0

algorithm Test1

Test2

Fig. 2 Comparison of simulation diagnosis time of different classifier models

5 Conclusions After the continuous development of machine learning in recent years, researchers and scholars at home and abroad have widely applied machine learning methods in the fault feature extraction and identification and diagnosis of electronic engineering equipment and have become an important research direction in the field of artificial intelligence and machine learning. Using electronic engineering equipment, this paper proposes two new intelligent fault diagnosis methods for the problem that low efficiency, high misdiagnosis rate, and a large number of redundant features can reduce the classification efficiency of the classifier. The effectiveness of the proposed diagnostic method is verified by theoretical analysis and diagnostic examples.

Intelligent Fault Diagnosis of Electronic Engineering Equipment …

833

References 1. Huang H, Fan Q, Wei J et al (2019) An intelligent fault identification method of rolling bearings based on SVM optimized by improved GWO. Syst Sci Control Eng Open Access J 7(1):289– 303 2. Mayadevi N, Mini VP, Hari Kumar R et al (2020) Fuzzy-based intelligent algorithm for diagnosis of drive faults in induction motor drive system. Arab J Sci Eng 45(3):1385–1395 3. Samara S, Natsheh E (2020) Intelligent PV panels fault diagnosis method based on NARX network and linguistic fuzzy rule-based systems. Sustainability 12(5):2011 4. Zaher AA, Hummes D, Hussain GA (2019) Intelligent nonmodel-based fault diagnosis of electric motors using current signature analysis. J Phys Conf Ser 1391(1):012065:1–11 5. Khoudry E, Belfqih A, Ouaderhman T et al (2020) A real-time fault diagnosis system for highspeed power system protection based on machine learning algorithms. Int J Electr Comput Eng 10(6):6122 6. Aziz M, Ahmad T (2019) Cluster analysis-based approach features selection on machine learning for detecting intrusion. Int J Intell Eng Syst 12(4):233–243 7. Tkachenko AL, Denisova LA (2022) Designing an information system for the electronic document management of a university: automatic classification of documents. J Phys: Conf Ser 2182(1):012035 8. Goyal D, Dhami SS, Pabla BS (2020) Non-contact fault diagnosis of bearings in machine learning environment. IEEE Sens J 20(99):4816–4823 9. Gültekin Ö, Çinar E, Zkan K et al (2022) novel deep learning approach for intelligent fault diagnosis applications based on time-frequency images. Neural Comput Appl 34(6):4803–4812 10. Nakamura H, Mizuno Y (2022) Diagnosis for slight bearing fault in induction motor based on combination of selective features and machine learning. Energies, 15(2):453 11. Guo Q (2018) Research on the application of improved shuffled frog leaping algorithm in mechanical fault diagnosis. Acad J Manuf Eng 16(2):137–142 12. Huo J, Lin D, Qi W (2020) Intelligent fault diagnosis method of mechanical equipment based on fuzzy pattern recognition. J Intell Fuzzy Syst 38(12):1–8

Content-Based Visual Information Retrieval Technique Using Adaptive Deep Learning Algorithms: A Review Gaurav Singh and Hemant Kumar Soni

Abstract Because of the availability of IoT and the affordability of image sensors, several picture databases have been developed for use in a variety of contexts. The need to provide effective picture retrieval search techniques that satisfied the requirement of users is increased by these image databases. The semantic break among low-level characteristics and visuals of human judgments has received a lot of attention and effort in the quest to enhance content-based picture retrieval methods. This study evaluates, analyses, and contrasts the most recent state-of-the-art CBVIR field approaches during the previous six years in the light of the expanding body of research in this area. A performance evaluation, contemporary low-level feature abstraction techniques, machine learning algorithms, similarity measurements, plus an introduction to the CBVIR framework are also included in this study to serve as further inspiration. Keywords CNN · Content-based image retrieval · Content-based visual information retrieval

1 Introduction Content digital expertise has radically augmented the extent of multimedia system content developed currently every day. The increasing access to large volumes of multimedia data that is accessible raises issues with managing massive amounts of content. The necessity for enhanced retrieval and preserving visual content has been acknowledged over a decade ago [1]. Content-based visual information recovery (CBVIR) designates the image retrieval technique that was chosen from a wide collection depending on visual attributes, such as shape, colour, and pattern, that are pulled directly from the video or image content. Nevertheless, structural details like G. Singh (B) · H. K. Soni Amity School of Engineering and Technology, Amity University, Gwalior, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_75

835

836

G. Singh and H. K. Soni

title, duration, recording type, etc., as well as metadata data like keywords from hand annotation. Although textual annotations on images can be easily retrieved with current search technology, this requires humans to annotate each video or image clip in a database, which is problematic for content with a lot of content. As the quantity of digital data increases like images, audio, and data allied to videos is increasing, it is necessary to build up a method to search various forms of information from the large assemblage. A total of three types of ways have been put to benefit retrieving images: text-based, content-based, and semantic-based. Yahoo and Google-like search engines use text-based retrieval systems [1]. To formulate it feasible to search for and get related videos from the larger database, one must analyse its content, so the technique called content-based video retrieval (CBVR) is developed. Content-based video retrieval (CBVR) system involves retrieving the relevant videos based on the request from the database image or clip quality. The term “content” accredits the content of the query image or video that is colour, texture, shape, or any other information present on it. Deprived of the ability to inspect video searches must base on content user-supplied frames [2]. The various visual colour is an image’s information, texture, shape, faces, etc. (Fig. 1). Generally, the CBVR system extracts the features using a variety of techniques, including text-based, audio-based, content-based, and metadata-based (title, type, and changed date). A text-based technique that uses text from OCR to extract the subtitles. Different voice recognition and speech-to-keyword extraction algorithms are used in the audio-based method. The last way is the content-based method, which combines all the others. The fundamental principle behind video retrieval involves. 1.1. Frames: Video is divided into frames (images).

Fig. 1 Image retrieval

Content-Based Visual Information Retrieval Technique Using Adaptive …

837

1.2 Feature Extraction: Various approaches are used to extract the image’s characteristics. 1.3 Matching: In the third step, these acquired features are matched from the database video. CBVR involves two important processes: An image’s information is described through colour and the property comparable process. There are numerous techniques for property extraction like scale-invariant feature transform (SIFT), speeded up robust feature (SURF), linear binary pattern (LBP), linear ternary pattern (LTP), and linear quinary pattern (LQP). There are so many machine methods and techniques for recognition. The machine learning task of deriving a characteristic of the labelled activity data is referred to as supervised learning. The exercise dataset is made up of a compilation of training scenarios. Each example of supervised learning is poised of such an input vector and the outcome value is also named as the signal of supervisory. The supervised algorithm examines the exercise data and generates an inferred function that may be applied to fresh illustration mapping. The unsupervised learning algorithm is inferring a function to designate hidden construction from “unlabelled” data, a classification or labelling is not comprised of observations. The initial step of CBVR is to break the video into frames. These frames are building blocks of video. The frames that are bagged together are termed shots. Every single shot contains hundreds of frames, these shots combine to form large videos. There is relatively little difference between two successive frames of the same scene [2]. A video is made by putting together a series of pictures with the help of composition operators. Video segmentation aims to extract structural primitives, which entails recognizing temporal boundaries between scenes and shots as shown in Fig. 2. Still, pictures recorded from the video are known as keyframes, and these frames best depict the content of shots. When properly extracted, keyframes serve as a visual abstract of video information and are extremely beneficial for quick video viewing.

Fig. 2 Video segmentation

838

G. Singh and H. K. Soni

After extracting frames from the movie, we must extract various attributes from them. Extracting functions from frames is a time-consuming process. As the number of features to be extracted increases, the time of extraction also increases. There are two types of features: qualities that are both universal and domain-specific. Shape, texture, and colour are examples of general qualities that are not application-specific. So, there are 3 characteristics: (a) Global features. (b) Local features. (c) Pixel level. The ability to get relevant movies from the database depends on the ability to measure similarity. A query image is hooked on a CBVR system that searches the database for comparable videos. In content-based video retrieval, distance metric, often known as similarity measure, is a crucial component. The Euclidean distance between the database feature vector and the query picture feature vector is computed to retrieve movies from the database. If the Euclidean distance is lower, the requested frame is more comparable to the database frame; that is, the closer the query picture resembles the database image frame. Different types of classifiers categorize a feature vector based on its attributes. The nearest neighbour classifier is the most often used classifier. This classifier employs a variety of distance metrics to measure the span between invited images and collected images in the database. The various distance metrics are Euclidean distance, squared chord, chi-square, Manhattan, divergence, wave hedges, etc. [3]. 1.1 Query by Object: Object events in video databases are observed, and the positions of the items decide the query’s performance [4]. 1.2 Query by Text: In content-based image retrieval, sample images are used as searches to locate videos that are connected in a video database (query by example), although the main drawback is that context info from the video being examined is not worn. It is solely focused on the details of one’s appearance. Moreover, using an example picture, and locating a video clip for an interesting topic may become too difficult. Textual query claims to be a superior way for querying in video databases since it has a more intuitive interface [5]. 1.3 Query by Example: If the query’s visual properties are used for CBVR, a query by example is preferable. 1.4 Query by Shot: Some applications make use of the whole of the captured video as the call. This may be a safer solution, however, it arrives at a high computational cost [6]. 1.5 Query by Clip: Since a shot does not represent enough detail about the entire context, a clip should be a resort to improve video retrieval efficiency as opposed to how a shot is used. Both clips that have a higher degree of affinity or relevance to the question clip are recovered [7].

Content-Based Visual Information Retrieval Technique Using Adaptive …

839

1.6 Query by Faces: Faces may also be used as a test to find a video section with frames labelled for a certain form based on faces [8]. Identification of people based on their unique physical, mental, and behavioural qualities is the goal of biometric security. We refer to a person’s physical characteristics as things like their fingerprints, DNA, ability to recognize their iris, hand geometry, and facial features. Voice, movement, and typing rhythm are examples of behavioural features, together with a digital signature. Based on these characteristics, biometric security solutions are being researched and implemented in a growing number of applications today. For security reasons, the major goal of these applications is to identify and detect humans. To assist government and law enforcement authorities, surveillance cameras were used to detect traffic flow, threats, and criminal activity. Surveillance cameras are critical in addressing security concerns, they are generally used in public spaces to deter or prosecute incidents and to identify unknown individuals to make the area safer. The primary issue with surveillance photographs is their poor quality. This makes face identification, classification, and recovery from security camera videos more difficult, but not absurd. The quality of surveillance cameras is poor for a variety of reasons, including target orientation, the usage of low-resolution cameras, and environmental factors. Rainfall, fog, and snow are also examples of environmental influences. These elements mask target information and generate noise, impairing image detection and retrieval [9]. This study aims to suggest a method for biometric security video retrieval that is content-based. A CBIR framework searches the database for related videos based on the virtue of the query image or clip. After converting the video-to-video frames, the faces are identified and cropped using the Viola-Jones algorithm. The cropped faces are then subjected to a facial recognition algorithm, and feature vectors are computed and processed in excel sheets. The query image yielded the information for the characteristics listed above and linked to the function in the feature library. Finally, a resemblance measure is used to extract the best corresponding frames from the images, which are then combined to create a new video. CBIR [10], to get a better of the complications of text-based image retrieval, obtaining from image annotation by hand, which is relying on subjective human interpretation and annotation time and labour requirements. CBIR relies on the visual content of photos to extract those that are pertinent to a query image from a large assemblage [11]. Over the last two decades, CBIR, which finds an image database dependent on the graphic representation of query images, has become an increasingly active research field [12]. Despite rapid advancements in CBIR technology, it remains a difficult challenge to solve today. CBIR compares the resemblance of visual characteristics derived from the query image and the database images. As a result, CBIR is extracting relevant and successful visual features to accurately represent image information [12].

840

G. Singh and H. K. Soni

These characteristics are expected to be insensitive to geometrical transformations, lighting conditions, and so on. Since the early 1990s, various graphics some characteristics have been created to translate images and visual data into a vector representation. Hundreds of methods for quick and stable CBIR have been suggested [13]. In general, the bulk of contemporary image retrieval approaches aim to image retrieval efficiency in three ways: (a) Developing discriminative image functions, (b) Developing effective resemblance inference systems, (c) Dealing with large-scale problems. The meaningful material from raw data to close the supposed semantic gap is an issue linked with CBIR [14]. The semantic gap is the distinction between lowlevel visual representations and their higher-level concepts. While previous research focused on rudimentary aspects such as colour, texture, and form to characterize image content, more recent research has focused on developing semantically richer image representations. Those that employ Fisher vector descriptors, or vectors of locally aggregated descriptors, are among the most effective (VLAD) and can use local descriptors along with bag-of-words models, like the scale-invariant feature transform (SIFT) [15]. CBIR techniques use some hand-crafted low-level features, such as scale-invariant features transforms, to extract representative picture features (SIFT) [16] and boost-up hefty features (SURF) [17] descriptors. General order fewer quantization approaches, such as a vector of locally aggregated descriptors, are commonly used to encode these lineaments (VLAD) [18]. By capturing local properties of picture objects, such as edges and corners, the final image representations have demonstrated a great capability of retaining the local patterns of image contents [19]. So, they are applicative for image retrieval tasks and universally worn for matching local object patterns. Various new researchers announce deep learning algorithms [20] in contrast to the above-mentioned shallow techniques for a broad range of computer vision applications, such as image retrieval [21]. The major speculates trailing their success are accessible of big annotated datasets, and the GPU’s computational power. DCNN [22], for visual information analysis, is thought to be the more efficient deep learning architecture. Recent breakthroughs in deep learning, in particular, offer a way to close the well-known semantic gap in CBIR [22]. CNN can learn such abstract representations subjectively for a wide range of vision and recognition operations by using a deep learning approach on different layers of convolutional filters. Much recent research has shown that when correctly trained on large and diverse picture datasets like ImageNet, CNN-based generic features may be successfully used for a diversity of visual understanding tasks. Furthermore, by employing domain-specific training data and conducting proper fine-tuning on CNNs, it is possible to make noticeable performance gains in common vision tasks [23], including item localization and image extraction for instance.

Content-Based Visual Information Retrieval Technique Using Adaptive …

841

Deep CNNs have as a result of their excellent results on a variety of visual understanding tasks, such as image classification, face recognition, digit recognition, pose prediction, and object and pedestrian detection, image classification has emerged as one of the most promising research areas in computer vision in recent years [24].

2 Literature Work A new taxonomy of a hybrid design named ELM, i.e. extreme learning machine was developed to improve a CBIR framework to enhance the accuracy issues of initial image retrieval schemes [25]. The primary objective of the system is to have less accuracy over time. Regarding this, the Wang database is used for texture extraction, colour, shape and edge, features of the local binary pattern (LBP), colour moments, Canny edges, and props. For security purposes, in many biometric applications, face recognition is frequently employed [26]. Pose variations and different illumination conditions create common problems in recognizing a face. The study’s goal is to determine if a given facial input belongs to a person who has been registered in the database. In the AT&T database, the histogram of oriented gradients (HOG) approach was used to evaluate the algorithm’s efficiency in terms of performance. The HOG descriptor’s feature vectors were used to train a support vector machine, and the outputs are checked against a test input. The results of the proposed technique reveal that there are fewer false positives and that detection accuracy is increased. An advance bilinear CNN-based model was given by Alzu’bi et al. [16]. The activation of convolutional layers was directly employed to extract visual features at various picture sizes and locations. DCNNs were used to set up the network architecture, which was appropriately pre-trained on a large generic picture dataset before being fine-tuned for the content-based image retrieval job. The experimental findings obtained on three typical benchmarking picture datasets proved the proposed framework’s exceptional performance in learning and extracting complicated features for the CBIR task leaving out prior knowledge of image semantic metadata. For example, consider utilizing a 16-length picture vector, On Oxford 5 K, the precision of 95.7% map retrieval was attained, compared to 88.6% on Oxford 105 K, outperforming the best results recorded by state-of-the-art approaches. Overall, the needed extraction time for picture features and the memory capacity required for storage were significantly reduced. This paper demonstrates a framework for extracting images that is trustworthy by integrating low-level characteristics from DDBTC, dot-diffused block truncation coding, and high-level features from the CNN system [27]. The vector quantization indexed histogram was used to create the low-level characteristics, such as texture and colour, from the DDBTC bitmap, maximum, and minimum quantizers. On the other hand, advanced CNN features could accurately replicate human perception. The extended deep learning two-layer codebook features were created utilizing the suggested two-layer codebook, dimension reduction, and similarity reweighting to

842

G. Singh and H. K. Soni

increase the overall retrieval rate. These features combined the DDBTC and CNN features. Several datasets were examined using the average recall and precision rate (ARR) measures. The analysis findings show that the suggested plans could achieve outstanding. This work suggested a new feature learning technique based on non-negative sparse for generating a holistic image representation from low-level local features [28]. More precisely, a revamped spectral clustering technique was introduced to build a non-negative visual dictionary based on local characteristics extracted from training videos. Non-negative locality-constrained linear coding (LLC) was proposed as a framework for sparsifying the widely used locality-constrained linear coding method that is more valid and interpretable for the representation of a characteristic. Additionally, a new feature pooling technique dubbed k Max Sum pooling is introduced to mitigate the knowledge loss caused by max-pooling or sum pooling strategy, which produced a picture representation that is more holistic and could be viewed as a generalization of the sum-maximum and maximum-sum-maximum pooling methods. The findings of the image extraction from two public image databases demonstrated the efficacy of the proposed strategy. The big notion of CNNs and the significance of the learner were offered as a new distance depiction. The system automatically determines what the image features are in off-site correspondence to best address client difficulties [29]. Another picture dataset was able to find related images to such a query image thanks to CBIR systems. The most widely used CBIR technique must be Google’s search-by-image feature. Tzelepi and Texas have proposed a retraining idea for learning further powerful convolutional representations for content-based image retrieval [30]. A deep CNN model was employed max-pooling was used to obtain the network, and it was then tweaked and kept to provide more efficient compact picture descriptors, which improved both retrieval performance and memory needs while depending on the available data. This technique included three main model retraining procedures. That is if no other information beyond the dataset itself was available, the fully unsupervised retraining, the retraining with relevance information if the training dataset’s labels were accessible, and the relevance input-based retraining if user feedback was available. The suggested method outperformed earlier CNN-based retrieval strategies as well as conventional hand-crafted feature-based approaches in all three publically available image retrieval datasets, indicating that it is effective in learning more effective representations for the retrieval problem. An efficient method for retrieving AN picture using deep belief CNN feature representation has been proposed by the authors. The input dataset has initially undergone preprocessing [31]. During the preprocessing stage, the image noise was removed using the median filter (MF) and scaled. Using a deep belief convolutional neural network, features were represented (DB-CNN). Now, binary coding has replaced the picture feature. Then, using the modified-Hamming distance, the similarity measurement was calculated. The photos were then retrieved with an emphasis on the similarity values. The test results demonstrated that the proposed.

Content-Based Visual Information Retrieval Technique Using Adaptive …

843

Wei et al. Via qualitative and quantitative studies, the author attempted to elucidate the basic effect of visual saliency in CBIR [32]. To accomplish this, the fixation density maps of images were created using an eye-tracking apparatus from a commonly used CBIR dataset. These ground-truth saliency maps were then used to quantify the effect of visual saliency on the CBIR task by investigating several possible methods for integrating saliency cues into the retrieval phase. It was discovered that visual saliency was also helpful for the CBIR mission and that the optimal saliency-involved scheme could vary among different image retrieval models. The primary objective was to derive discriminative visual characteristics that were intimately tied to semantic attributes. By redirecting feature extraction to the most significant visual content that a human may perceive, the auxiliary stream was created to support the mainstream. Image resemblance could be computed like how people do by reserving prominent content and deleting unimportant sections by combining these two streams into main and auxiliary CNNs (MAC). Extensive testing revealed that the suggested model excelled wonderfully in picture extraction on four datasets that were made available to the public. A workable algorithm has been proposed by the authors. The DBNN deep learning technique was utilized to extract the features and categorize the data due to the vast amount of data gathered and the fact that it was a novel study topic (Saritha and others, 2019). The authors recommended using LBP in a content-based image recovery method. To find reliable linked features, searching on a certain feature premise must be used [33]. The potential for recovery is constrained for complicated feature images. In this way, multi-feature combination recovery research has drawn more and more attention. Convolutional neural networks (CNN) were the core of Simran et al. simple’s effective deep learning system, which also included feature extraction and classification for quick picture retrieval [34]. Several thorough observational studies employing picture databases for various CBIR tasks produced some encouraging results that could help CBIR become more effective. In 2018, Saritha et al. [35] created a brand-new CBIR technique based on deep learning. The deep belief network (DBN) approach was used to extract and categorize the input features, which produced a significant amount of image data. Thus, it has been established by the simulation results that the proposed strategy has a “large positive divergence towards its performance.”

3 Result Analysis In this section, a detailed comparative analysis of different research papers regarding content-based visual information retrieval will be shown in Table 1.

844

G. Singh and H. K. Soni

Table 1 Comparative result analysis of various research papers related to CBVIR Author name [citation]

Method

Features

Challenges

Alzubi et al. [9]

CNN

• The time needed for the • It cannot perform better extraction of image features is with real-time data less • The memory size used for the storage is less

Liu et al. [23]

DL-TLCF • It is a strong and extremely effective contender for image retrieval applications • The overall retrieval rate is enhanced

• It does not perform well with a large number of data

Kumar et al. [25]

DB-CNN

• The median filter eliminates the image noise • It demonstrates improvement in terms of accuracy, recall, time, precision, etc

• In Indian civilization, AV illness is substantially less prevalent

Xu et al. [29]

NNLLC

• More discriminative • It does not combine with a information is preserved hierarchical deep feature • It generates interpretable and learning framework for more meaningful sparse codes narrowing the semantic for feature representation gap

Wei et al. [7]

CNN

• The ability to capture the • It does not embed the inherent query intention is hashing operations into the enhanced network for making it • Better performance is achieved faster on retrieving the snow/rain images and low-quality/ low-resolution images

Simran et al. [17] CNN

• Superior effectiveness is attained via the acquisition of various appropriate images • The effective image representation of images is learned via CNN

Tzelepi and Tefas CNN [21]

• It applies to all types of • It does not extend the deep CNN-oriented image retrieval learning model for techniques simulating the entire • It achieves better performance behaviour of the system through the usage of successive retraining processes or a single retraining technique

Saritha et al. [35]

• It offers a better classification for handling effective content extraction • A huge positive deviation is retrieved in its performance

DBN

• Various discriminative features are not extracted

• It is not used with real-time extraction

(continued)

Content-Based Visual Information Retrieval Technique Using Adaptive …

845

Table 1 (continued) Author name [citation]

Method

Mst Alema CNN & Khatun et al. [36] LSTM

Features

Challenges

• It offers a better accuracy result • It uses statistical machine learning techniques to detect human activity

• LSTM Performance is low at F1-score and accuracy

4 Conclusion and Future Scope The enormous number of picture databases and the lack of an effective text-based image retrieval technique have driven the need to develop an effective image retrieval mechanism based on image content. This report provided a review of the literature on various research conducted in the CBIR field during the last six years. The stages of the overall CBIR framework and the most recent methods for closing the semantic gap were also covered in this study. This research focused on numerous areas that may aid in developing a unique CBIR and emphasized some of the most significant concerns that have an impact on CBIR performance. But creating an algorithm with high retrieval accuracy and low processing cost is a difficult issue. Because CBIR algorithms begin with basic low-level data, the issue for any CBIR algorithm is the semantic gap between the high-level meaning of the picture and the visual attributes. Numerous efforts have been made in this sector to close this gap. Utilizing a variety of innovative characteristics as well as feature fusion, the CBIR algorithm was created. These traits are developed and examined through experimental design. The body of literature demonstrates the enormous work scholars have put into this area. Based on the feature extraction techniques, CBIR algorithms may be split into two groups: global feature extraction and local feature extraction. Low-level features include both global and local characteristics. In conclusion, there is a strong need for an algorithm that closes the semantic gap. The following should be taken into account when designing the algorithm: First, because they affect how effectively the CBIR performs, the algorithm must take feature extraction and similarity measure into account. Second, additional features may be extracted to improve the CBIR’s accuracy while maintaining the computational cost, which is regarded as a crucial component in real-time applications. Third, combining local and global features will result in a more balanced design since local features are faster at feature extraction and similarity testing than global features and are more resistant to scale, translation, and rotation changes. Fourth, deep learning algorithms may be applied at various CBIR phases to improve system accuracy, but their computational costs need to be given greater consideration. Finally, there is a trade-off between computational expense and system accuracy. A solid framework for learning a classification-based model may be created by optimizing the feature representation in terms of feature dimensions, which will prevent issues like overfitting. Deep neural networks have

846

G. Singh and H. K. Soni

been used in current CBIR research with strong results on many datasets, outperforming handmade features with the caveat that the network must be fine-tuned. The primary needs for any deep network are vast picture collections and powerful computing power. The management of a sizable picture collection for the supervised training of a deep network is a challenging and time-consuming process. As a result, one of the potential methods for assessing the performance of a deep network on a large-scale unlabelled dataset in unsupervised learning mode for future research direction in the area.

References 1. Su JH, Huang YT, Yeh HH, Tseng VS (2010) Effective content-based video retrieval using pattern-indexing and matching techniques. Expert Syst Appl 37(7):5068–5085. https://doi.org/ 10.1016/j.eswa.2009.12.003 2. Singh N, Singh K, Sinha AK (2012) A novel approach for content based image retrieval. Procedia Technol 4:245–250. https://doi.org/10.1016/j.protcy.2012.05.037 3. Ajam A, Forghani M, Alyannezhadi MM, Qazanfari H, Amiri Z (2019) Content-based image retrieval using color difference histogram in image Textures. In: 5th Iranian conference on signal processing and intelligent systems, ICSPIS 2019 46:188–198. https://doi.org/10.1109/ ICSPIS48872.2019.9066062 4. Shao L, Jones S, Li X (2014) Efficient search and localization of human actions in video databases. IEEE Trans Circuits Syst Video Technol 24(3):504–512. https://doi.org/10.1109/ TCSVT.2013.2276700 5. Wan S, Jin P, Yue L (2011) An effective image retrieval technique based on color perception. In: Proceedings—6th international conference on image and graphics, ICIG 2011, June, 1017– 1022. https://doi.org/10.1109/ICIG.2011.165 6. Christiyana CC, Rani JMS, Rajamani V (2019) Relative retrieval efficiency analysis of local binary pattern variants in color images. Int J Sci Technol Res 8(8):1299–1305 7. Ramya R, Kalaiselvan C (2016) Feature extraction in content based image retrieval. Int J Bus Intell 5(1):96–100. https://doi.org/10.20894/ijbi.105.005.001.023 8. Dyana A, Das S (2010) MST-CSS (multi-spectro-temporal curvature scale space), a novel spatio-temporal representation for content-based video retrieval. IEEE Trans Circuits Syst Video Technol 20(8):1080–1094. https://doi.org/10.1109/TCSVT.2010.2051367 9. Manjula K, Monisha A, Reshma K, Swetha P, Vijayarekha K (2016) Content based image retrieval systems: a review. Res J Pharm, Biol Chem Sci 7(6):1915–1921. https://doi.org/10. 14445/22312803/ijctt-v34p123 10. Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380. https:// doi.org/10.1109/34.895972 11. Tunga S, Jayadevappa D, Gururaj C (2015) A comparative study of content based image retrieval trends and approaches. Int J Image Process (IJIP) 9(3):127 12. Gandhani S, Singhal N (2015) Content based image retrieval: survey and comparison of CBIR system based on combined features. Int J Signal Process, Image Process Pattern Recogn 8(11):417–422. https://doi.org/10.14257/ijsip.2015.8.11.37 13. Liu GH, Yang JY, Li ZY (2015) Content-based image retrieval using computational visual attention model. Pattern Recogn 48(8):2554–2566. https://doi.org/10.1016/j.patcog.2015. 02.005 14. Liu Y, Zhang D, Lu G, Ma WY (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recogn 40(1):262–282. https://doi.org/10.1016/j.patcog.2006.04.045

Content-Based Visual Information Retrieval Technique Using Adaptive …

847

15. Wu P (2013) Institutional Knowledge at Singapore Management University Online multimodal distance metric learning with application to image retrieval. IJCAI 454–467 16. Alzu’bi A, Amira A, Ramzan N (2017) Content-based image retrieval with compact deep convolutional features. Neurocomputing 249:95–105. https://doi.org/10.1016/j.neucom.2017. 03.072 17. Rui Y, Huang TS, Ortega M, Mehrotra S (1998) Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans Circuits Syst Video Technol 8(5):644–655. https:// doi.org/10.1109/76.718510 18. Lin CH, Chen RT, Chan YK (2009) A smart content-based image retrieval system based on color and texture feature. Image Vis Comput 27(6):658–665. https://doi.org/10.1016/j.imavis. 2008.07.004 19. Jhanwar N, Chaudhuri S, Seetharaman G, Zavidovique B (2004) Content based image retrieval using motif cooccurrence matrix. Image Vis Comput 22(14):1211–1220. https://doi.org/10. 1016/j.imavis.2004.03.026 20. Ahmed K, El-Henawy I (2014) Content-based image retrieval using multiresolution analysis of shape-based classified images 1(1):1–8. http://www.gpcpublishing.org/index.php/gjct/art icle/download/1/pdf%0A; https://gpcpublishing.org/index.php/gjct/article/view/1%0A; https:/ /lens.org/029-317-820-386-390 21. Saadatmand-Tarzjan M, Moghaddam HA (2007) A novel evolutionary approach for optimizing content-based image indexing algorithms. IEEE Trans Syst Man Cybern B Cybern 37(1):139– 153. https://doi.org/10.1109/TSMCB.2006.880137 22. Guo JM, Prasetyo H, Chen JH (2015) Content-based image retrieval using error diffusion block truncation coding features. IEEE Trans Circuits Syst Video Technol 25(3):466–481. https:// doi.org/10.1109/TCSVT.2014.2358011 23. Murala S, Maheshwari RP, Balasubramanian R (2012) Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans Image Process 21(5):2874–2886. https://doi.org/10.1109/TIP.2012.2188809 24. Banerjee I, Kurtz C, Devorah AE, Do B, Rubin DL, Beaulieu CF (2018) Relevance feedback for enhancing content based image retrieval and automatic prediction of semantic image features: application to bone tumor radiographs. J Biomed Inform 84(July):123–135. https://doi.org/10. 1016/j.jbi.2018.07.002 25. Omar M, Ahmad K, Rizvi MA (2015) Content based image retrieval system, 345–362. https:/ /doi.org/10.4018/978-1-4666-8853-7.ch017 26. Julina JKJ, Sree Sharmila T (2017) Facial recognition using histogram of gradients and support vector machines. In: International conference on computer, communication, and signal processing: special focus on IoT, ICCCSP 2017, November. https://doi.org/10.1109/ICCCSP. 2017.7944082 27. Liu P, Guo JM, Wu CY, Cai D (2017) Fusion of deep learning and compressed domain features for content-based image retrieval. IEEE Trans Image Process 26(12):5706–5717. https://doi. org/10.1109/TIP.2017.2736343 28. Xu W, Wu S, Joo M, Zheng C, Qiu Y (2017) New non-negative sparse feature learning approach for content-based image retrieval. In IET image processing, 11(9):724–733. https://doi.org/10. 1049/iet-ipr.2016.0726 29. Pyykkö J, Głowacka D (2017) Interactive content-based image retrieval with deep neural networks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9961 LNCS, 77–88. https://doi.org/ 10.1007/978-3-319-57753-1_7 30. Tzelepi M, Tefas A (2018) Deep convolutional learning for content based image retrieval. Neurocomputing 275:2467–2478. https://doi.org/10.1016/j.neucom.2017.11.022 31. Sundararajan SK, Sankaragomathi B, Priya DS (2019) Deep belief CNN feature representation based content based image retrieval for medical images. J Med Syst 43(6). https://doi.org/10. 1007/s10916-019-1305-6 32. Wei S, Liao L, Li J, Zheng Q, Yang F, Zhao Y (2019) Saliency inside: learning attentive CNNs for content-based image retrieval. IEEE Trans Image Process 28(9):4580–4593. https://doi. org/10.1109/TIP.2019.2913513

848

G. Singh and H. K. Soni

33. Wei Z, Liu GH (2020) Image retrieval using the intensity variation descriptor. Math Prob Eng 2020. https://doi.org/10.1155/2020/6283987 34. Simran A, Shijin Kumar P, Bachu S (2021) Content based image retrieval using deep learning convolutional neural network. IOP Conf Ser: Mater Sci Eng 1084(1):012026. https://doi.org/ 10.1088/1757-899x/1084/1/012026 35. Saritha RR, Paul V, Kumar PG (2019) Content based image retrieval using deep learning process. Clust Comput 22:4187–4200. https://doi.org/10.1007/s10586-018-1731-0 36. Bijalwan V, Semwal VB, Singh G, Mandal TK (2022) HDL-PSR: modelling spatio-temporal features using hybrid deep learning approach for post-stroke rehabilitation. Neural Process Lett, pp 1–20

The Development Trend of Intelligent Logistics Based on Machine Vision Xin Zhang and Pengmin Jia

Abstract After half a century of development, artificial intelligence technology has been preliminarily mature, and it has ushered in a stage of rapid development in the twenty-first century. Artificial intelligence has injected new impetus into health care, the Internet, manufacturing, and logistics, which have given these industries an opportunity for rapid development. Especially in the logistics industry, automatic vehicles, AGV, and intelligent picking have applied a large number of artificial intelligence technologies, bringing more changes to all aspects of logistics, improving efficiency, and reducing costs. In addition, AI will play a role in more aspects of logistics in the future, especially in data mining, machine vision, machine deep learning, and natural speech processing to open up more efficient ways to make “data-driven logistics” a reality. This paper shows the application and reform of artificial intelligence to the logistics industry from two dimensions, namely the application of artificial intelligence existing technology in the field of logistics and the promotion of the development of artificial intelligence to the development of logistics and the resulting development trend of intelligent logistics. Keywords AI · Logistics · Development trend

1 Introduction Artificial intelligence is a new science and technology, mainly used to simulate and extend human intelligence, giving machines or systems the ability to learn by themselves. To put it simply, artificial intelligence is to realize human intelligence and serve human beings through computer technology. In the current work scene of human beings, simple and repetitive work will be replaced by artificial intelligence X. Zhang (B) Chongqing Vocational Institute of Engineering, Chongqing, China e-mail: [email protected] P. Jia Chongqing Preschool Education College, Chongqing, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_76

849

850

X. Zhang and P. Jia

in the future, promoting the transformation of all walks of life. Logistics, though an emerging industry, will also be one of the biggest beneficiaries of AI. Intelligent, unmanned and informationized logistics equipment will become the norm and will further promote the improvement of production efficiency and cost reduction in the logistics industry [1–7].

2 The Application Ability of Artificial Intelligence Technology 2.1 Perception Humans receive stimuli from the environment through their senses (skin, eyes, ears, etc.), and produce touch, vision, hearing, smell, taste, etc., and then interact with the environment through speech or action. Artificial intelligence technology helps agents learn to acquire human-like perceptual abilities and eventually complete related tasks. Perceptive ability can be summarized into six abilities: seeing, listening, speaking, reading, writing, and feeling. Among them, “reading”: natural language processing, speech to text and so on. “Writing”: machine translation, natural language generation, etc. “Speaking”: speech generation, text conversion to speech, etc.

2.2 Cognitive Ability Cognitive refers to the human, judgment, and analysis of psychological activity by learning to understand the message, the process of acquiring knowledge and ability, to the imitation of human cognition and learning is also the focus of the current artificial intelligence research field, mainly includes: [8] the ability to predict, such as equipment life prediction based on artificial intelligence, intelligent natural disaster prediction, and prevention. Judgment ability: for example, AI can play Go, selfdriving car, intelligent search, intelligent control, game, etc. [9]. Learning ability: such as machine learning, deep learning, reinforcement learning, and other learning methods. In terms of cognitive ability, there is still a gap between ARTIFICIAL intelligence and human beings in many areas, but in some sub-areas, artificial intelligence can already achieve human intelligence, such as playing go, automatic driving, and other fields.

The Development Trend of Intelligent Logistics Based on Machine Vision

851

2.3 Creativity Creativity refers to the ability of humans to generate new ideas, approaches and create new things, combining human knowledge, intelligence, and other factors, far ahead of artificial intelligence.

2.4 Intelligent Intelligence is the human ability to search for truth and distinguish right from wrong, in areas such as values and self-awareness. This is an area where artificial intelligence has yet to tread, and where humans are most difficult to imitate.

3 The Application of Artificial Intelligence in the Field of Logistics Different typical logistics industry scenarios have different characteristics and require different technologies, so the application of technologies should be determined according to the actual needs. The following describes the possible application of artificial intelligence technologies in some typical logistics scenarios (see Fig. 1).

Fig. 1 T Dengine timing data processing platform. Source Author’s own photograph

852

X. Zhang and P. Jia

3.1 Supplier Management Supplier is the supplier of producer or consumer, in the supply activities, scientific and efficient is the consistent pursuit of both sides. In terms of receiving goods, quality inspection and finance, it is the breakthrough of intelligent logistics and is also a common means to improve efficiency and reduce costs.

3.2 Warehouse Management Warehousing, warehousing, and storage are routine operations of warehouse management, and also important links of intelligent transformation of logistics. The application of intelligent equipment such as automatic storage equipment and logistics robot will play a subversive role in the development of logistics industry [10]. (1) Intelligent storage devices: There are many kinds of storage devices with different functions. Common are high–rise shuttle, stacker, and MiniLoad. In order to improve the turnover efficiency and utilization of these equipment, intelligent operation is the key, and machine learning, computer vision, and automatic control technology are often applied to these equipment [11]. Expert system and big data analysis are often used in the scientific planning and implementation of storage equipment to improve the effect of system planning. As for the maintenance and maintenance of equipment, the big data prediction technology can accurately control the status of equipment, take measures in advance, and prevent future problems. (2) Intelligent sorting system: common equipment includes intelligent sorting vehicle, conveyor belt, AGV, and information flow in the transmission process. To make these devices more secure and efficient, machine vision and path optimization will give them more intelligence [12]. In order selection, big data analysis will split and merge orders, so that machines and personnel form a linkage, to improve efficiency.

3.3 Transportation Management Transportation management has two aspects of content, and transportation process information management and transportation equipment management. For road transportation, autonomous driving technology will make road transportation more efficient and safe, freeing manpower to improve the efficiency of road transportation. Specifically, emergency dispatch and information tracking, as well as subsequent loading, unloading and inventory taking, will make AI more efficient than humans. Through big data analysis, artificial intelligence can develop a reliable and timely scheme to detect vehicle status, preset alarm system, and reduce the probability of vehicle failure.

The Development Trend of Intelligent Logistics Based on Machine Vision

853

3.4 Distribution Management The logistics distribution system is applied to tablet computer to replace traditional industrial PDA as data terminal, which greatly reduces the operation difficulty of equipment. Front-line employees only need to click on the tablet computer to select the order, load, send, a total of 2–3 simple click operations can be the post’s calling information to the logistics system [13]. The logistics distribution order making system in the logistics distribution area will refresh the material requirement of the final assembly in real time, and make voice reminder when the distribution order is not printed. One-point trigger, multi-point response and one-set distribution: according to the material consumption rule of the assembly production line, a production line is divided into several line segments. At the same time, combined with the business model, the production material on-demand pull distribution mode is implemented, realizing one-point trigger, multi-point response, and one-set distribution.

4 AI+ Logistics Era New Trend 4.1 AI Is Evolving from “Perceptual Intelligence” to “Cognitive Intelligence” In terms of development stages, AI includes logical intelligence, perceptual intelligence and cognitive intelligence. It is widely believed that AI is evolving from “perceptual intelligence” to “cognitive intelligence”. In other words, the machine has reached or even surpassed the human level in perceptual intelligence such as “listening, speaking and seeing”; however, cognitive intelligence, which requires external knowledge, logical reasoning, or knowledge domain transfer, is still in its initial stage. Perceptual intelligence is a machine with visual, auditory, tactile and other perceptual capabilities to structure multiple data and communicate and interact in a way familiar to humans. According to Wan Gang, “Cognitive intelligence, on the other hand, draws inspiration from brain-like research and cognitive science, combining cross-domain knowledge mapping, causal reasoning and continuous learning to give machines human-like thinking logic and cognitive abilities, especially the ability to understand, summarize and apply knowledge [14]”. Taking new energy smart cars as an example, he explained the realistic picture of the transformation of “perceptual intelligence” to “cognitive intelligence”: in addition to applying the intelligence of system perception to realize the perception and processing of the surrounding environment, the new generation of smart cars must also realize the transformation of “cognitive intelligence” through vehicle-network collaboration, vehicle–road. In addition to the application of system-aware intelligence to perceive and process the surrounding environment, the new generation of intelligent vehicles must also achieve safer, more convenient and efficient intelligent services through vehicle-net collaboration, vehicle–road collaboration, and

854

X. Zhang and P. Jia

even integrated processing of super-aware factors such as geography, traffic, intersections, signals, weather, and other real-time information. In the past decade, the development of China’s AI technology has gone through three value transformation stages: (1) basic science research to achieve a single-point algorithm breakthrough; (2) second, ground applications to promote algorithm boundary expansion; and third, infrastructure construction to lower the threshold of algorithm production and solve problems in various industries [15].

4.2 AI Algorithms Change Products from “Good” to “Better” AI algorithms can make devices and systems smarter. With the help of AI algorithms, autonomous mobile robots (AMRs) can “imagine” missing information without losing it and with greater environmental robustness, shape, and other information and can even judge its goodness. With AI optimized scheduling, different types of intelligent devices can achieve efficient collaboration. From the perspective of the underlying logic and future trends of the entire logistics industry, logistics is one of the best application scenarios for AI. AI technology, which integrates driverless, robotics, image and video recognition, big data, predictive analysis and other information technology, provides powerful technical support for logistics enterprises in their transformation to intelligent logistics enterprises, thus becoming a “new engine” for the transformation and upgrading of the logistics industry. For logistics enterprises, adjusting the direction of timely compliance with today’s trend of intelligent development is a must. Vigorously promote the transformation of logistics infrastructure and production tools to intelligence, realize the construction of supply chain logistics operation process in the direction of intelligence, and at the same time realize the sharing of supply chain resources, sound system standards and increase the training of AI professionals to promote the mutual integration and synergistic development of AI and supply chain logistics.

4.3 AI Algorithm Is Integrated into the System to Redefine Software and Hardware In the past, algorithms were an adjunct to product software, often built after the hardware was designed. In the process of developing innovative intelligent products, Megvii found that the ease of use and characteristics of products are largely determined by algorithms. Therefore, algorithms need to be taken into account at the product definition, design, and development stages. Megvii believes that AI algorithms have become a “core component” of its products, and has begun to redefine hardware and software to transform products from “impossible” to “possible” [16].

The Development Trend of Intelligent Logistics Based on Machine Vision

855

For example, robot arm grasping, SLAM navigation, multi-device collaboration, operations optimization, and other algorithms are of great value to logistics products.

4.4 AI Logistics Has Three Levels of Value: Single Intelligence, Group Intelligence, and Business Intelligence Under the joint action of technology, market, society, national policy, and other factors, digital-intelligence upgrading becomes the inevitable choice for enterprises to achieve cost reduction and efficiency increase (see Fig. 2). According to Megvii, current users have three requirements for intelligent logistics. Single unit intelligence mainly solves the efficiency problem at the terminal device layer. AI algorithms will redefine the capability boundaries of logistics equipment. Taking robots as an example, the AI algorithm can make AMR not get lost even when 50% of the scene is blocked and realize autonomous obstacle avoidance and highprecision navigation. In terms of terminal perception, product quality inspection and drug review can be carried out through AI vision scheme, which can greatly improve the operation efficiency and accuracy of production scenes. Swarm intelligence mainly solves the efficiency problem of workflow layer. At present, robots (AGV/AMR), unmanned forklifts, shuttles, stackers, mechanical arms, sorting machines, and other intelligent equipment are increasing, and it is inevitable that various types of equipment work together. AI will play a huge role in ensuring effective teamwork across groups. Business intelligence mainly solves the efficiency problem of information dimension. Big data analytics can better guide decisions and make the whole business smarter [17]. Big data needs to be combined with AI and IOT to automate and intelligentize the whole process from intelligent perception, analysis, and decision Fig. 2 Users’ three-layer demand for intelligent logistics. Source Author’s own photograph

856

X. Zhang and P. Jia

Fig. 3 AI+ logistics development history. Source Author’s own photograph

making to execution. Business intelligence involves longer chains and links that the industry is still exploring.

4.5 The New Era of AI+ Logistics Has Arrived The year 2020 can be regarded as the first year of AI logistics. Robot companies and technology companies have entered this track one after another, and the impact of the epidemic has further accelerated the integration of AI and logistics. In the past two years, the number of companies laying out the track has multiplied; some enterprises that have used AI technology and products have also initially tasted the benefits. In various scenarios of logistics, AI is a dimension reduction blow compared with traditional technology in data collection and processing. “We have every reason to believe that in the next 20 years, AI will be as deeply integrated with all industries as information technology was in the previous 20 years, and bring more far-reaching industrial changes”, xu said (see Fig. 3).

4.6 AI Creates a Digital Workforce for the World State-owned Guangzhou Logistics Center is one of the cases that has achieved initial results by adopting AI technology and solutions. After adopting Megvii 3A Smart logistics solution (AS/RS + AMR + AI) to carry out the digital-intelligence upgrade, the overall efficiency of the logistics center is increased by 25%, the average daily operation completion time is advanced by 2 h, and the cost will be saved by tens of millions of yuan in the next five years. At the same time, the labor intensity of warehouse staff is greatly reduced: each person had to move 30,000 kg of goods

The Development Trend of Intelligent Logistics Based on Machine Vision

857

every day, but now they are handed to AI mechanical arm for stacking; in the past, each person had to walk 30,000 steps every day, but now AMR is responsible for picking goods to people and goods to robots. The warehouse, which used to have to look for able-bodied young men, is no longer subject to such restrictions [18]. “AI can not only help companies reduce costs and increase efficiency, but more importantly, create a digital workforce for the world,” said Qingcai Xu of Megvii. Labor shortages are growing as many countries around the world face falling birth rates, an aging population and a growing number of young people unwilling to work in the workplace. The digital workforce created by AI technology can fill in the gaps and interact well with people. For example, in some chemical raw material warehouses, there is much dust, which is not conducive to the health of operators. Now robots are used for automatic handling and storage, and people only need to supervise operations in the central control room and deal with abnormal situations [19]. In 2022, facing the important opportunity of “AI+ logistics”, Megvii robot will continue to deeply cultivate key industries such as medicine, shoes and clothing, new energy and intelligent manufacturing, and work with users and partners to create more benchmark cases. Continue to polish the integration of software and hardware of AIOT product system, including AI redefined logistics equipment, software platform and solution; we will empower “AI+ logistics” products to more domestic and overseas partners to build a win–win “AI+ logistics” ecosystem. As Qingcai Xu said, “There may be a long way to go, but together we can get there”.

5 Conclusions As an important development direction in the new century, intelligent logistics has a pivotal position in the field of science and technology and life. With the continuous development of Internet information technology, which provides a large number of advanced technologies, and machine vision systems as an important research object at home and abroad, this has important applications in the field of logistics. In summary, logistics systems under artificial intelligence have great potential for development. Only by making full use of the advantages of AI and combining the original resources to provide maximum convenience for users can a new logistics picture be opened. Therefore, in the background of the intelligent era, the future development of AIbased technology core warehouse robot, first, the government should play a function to integrate the industry market, so that it can obtain more funds as well as technical support; second, combined with network communication technology, focus on AI frontier technology research, committed to breakthrough robot common control software platform, path planning, safety control, and other core technologies; finally, sound robot improves the industry standards, testing, and certification system. The development of the industry requires a set of standards in line with the development rules of the industry to ensure the research and development environment of AI technology. There are more than billions of connected terminals worldwide, and in the

858

X. Zhang and P. Jia

future, based on the massive connectivity of the network, enterprises are expected to realize the decentralization model, distribute the intelligent processing capability to the massive terminals that constitute the wireless edge, and provide rich data resources for AI systems, so that AI has more entry points in the logistics field and truly enable the technology to empower the logistics industry.

References 1. Hulsen T, de Vlieg J, Alkema W (2008) BioVenn—a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genomics 9:488 2. Tang Z (2022) Application of de-noising automatic coding method in freight volume prediction under intelligent logistics. Int J Grid Util Comput 13(1) 3. Miao J, Lan S (2021) Application of visual sensing image processing technology under digital twins to the intelligent logistics system. Adv Civ Eng 2021 4. Han X (2021) Intelligent logistics tracking system based on wireless sensor network. Int J Front Eng Technol 3.0.10.0 5. Pan C, Liu M (2021) Optimization of intelligent logistics supply chain management system based on wireless sensor network and RFID technology. J Sens 2021 6. Gao D (2021) Design and development of intelligent logistics tracking system based on computer algorithm. J Phys: Conf Ser 2074(1) 7. Li Q (2021) Inventory method of intelligent logistics warehouse based on artificial intelligence. J Phys: Conf Ser 2037(1) 8. La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner, H., Petukhov V, Lidschreiber K, Kastriti ME, Lönnerberg P, Furlan A et al (2018). RNA velocity of single cells. Nature 560:494– 498 9. Levine DS, Haggitt RC (1989) Normal histology of the colon. Am J Surg Pathol 13:966–984 10. Liu Y, Yang M, Deng Y, Su G, Enninful A, Guo CC, Tebaldi T, Zhang D, Kim D, Bai Z et al (2020) High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183:1665–1681 11. Marx V (2021) Method of the Year: spatially resolved transcriptomics. Nat Methods 18:9–14 12. Mazzarini M, Falchi M, Bani D, Migliaccio AR (2021) Evolution and new frontiers of histology in bio-medical research. Microsc Res Tech 84:217–237 13. Mercier JF, Slater GW (2005) Solid phase DNA amplification: a Brownian dynamics study of crowding effects. Biophys J 89:32–42 14. Nestorowa S, Hamey FK, Pijuan Sala B, Diamanti E, Shepherd M, Laurenti E, Wilson NK, Kent DG, Göttgens B (2016) A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128:e20–e31 15. Park SR, Cho CS, Xi J, Kang HM, Lee JH (2021) Holistic characterization of single-hepatocyte transcriptome responses to high-fat diet. Am J Physiol Endocrinol Metab 320:E244–E258 16. Pelaseyed T, Bergström JH, Gustafsson JK, Ermund A, Birchenough GM, Schütte A, van der Post S, Svensson F, Rodríguez-Piñeiro AM, Nyström EE et al (2014) The mucus and mucins of the goblet cells and enterocytes provide the first defense line of the gastrointestinal tract and interact with the immune system. Immunol Rev 260:8–20. Ravi RK, Walton K, Khosroheidari M (2018) MiSeq: a next generation sequencing platform for genomic analysis. Methods Mol Biol 1706:223–232 17. Ro SH, Xue X, Ramakrishnan SK, Cho CS, Namkoong S, Jang I, Semple IA, Ho A, Park HW, Shah YM, Lee JH (2016) Tumor suppressive role of sestrin2 during colitis and colon carcinogenesis. eLife 5:e12204

The Development Trend of Intelligent Logistics Based on Machine Vision

859

18. Chen X, Chen R, Yang C (2021) Research to key success factors of intelligent logistics based on IoT technology. J Supercomput (prepublish) 19. Lei N (2022) Intelligent logistics scheduling model and algorithm based on Internet of Things technology. Alexandria Eng J 61(1)

Optimization Algorithm of Building Drainage Pipe System Based on Isotope Tracking Method Xiaoting Liu, Ou Luo, Xialing Huang, and Hao Zhu

Abstract The sustainable development of urban construction, optimizing the construction of drainage pipe system is the premise to ensure the sustainable development of water ecological environment. The purpose of this paper is to study the optimization algorithm of building drainage system based on isotope tracking method. Taking the groundwater in the B area of M city as the research object, using hydrochemistry and isotope tracer technology, the migration and transformation laws of main anions and cations in the multi-media of the water system were studied, and the understanding of the water environment problems in the M city area was deepened. The optimization of the building drainage system has laid a certain scientific basis. The results of the research on the Sr isotope in the groundwater in M city and surrounding areas show that: M city and surrounding groundwater have different recharge sources, and their isotopic characteristics have great changes. Drainage pipeline system optimization design, the sewage interception main pipes in City M are divided into two major systems: south and north. The total length of the sewage interception main pipes is 10,365 m. Keywords Isotope tracking · Building drainage · Drainage system · Optimization algorithm

1 Introduction With the development of building a moderately prosperous society in an all-round way in my country, residents’ living standards, environmental awareness and pursuit of high-quality and high-quality living environment have improved simultaneously, and high-rise buildings have also appeared in large numbers of two families or even more families sharing corridors, gradually transforming into one with two ladders, it X. Liu (B) · O. Luo · X. Huang · H. Zhu Hubei Electric Power Survey and Design Institute Co. LTD., Wuhan 430000, Hubei, China e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_77

861

862

X. Liu et al.

is more affluent, comfortable, and private [1]. However, in terms of indoor drainage, most of the houses still follow the traditional indoor drainage method, that is, the drainage riser passes through the lower floor, the drainage transverse branch pipes are hung under the roof of the lower floor, and various types of upper floors are collected along the walls. The drainage of sanitary appliances includes wash basins, water heaters, bathtubs, flushing tanks, toilets, washing machines, floor drains, etc [2, 3]. Isotope tracing technology is now widely used as a tracing and labeling tool in the fields of geology and hydrology, biology, medicine, chemistry, and the environment. The principle is to use the elemental labeling method to label various compounds and their isotopic compounds by the difference in mass number and chemical properties to achieve the purpose of tracing, monitoring, identification, analysis, and processing. With the continuous innovation and development of nuclear magnetic resonance, online mass spectrometry and ion exchange selection technologies, isotope tracing technology is used as the main body, combined with various scientific and technological means to study the change of precursor substances to products and then deduce the transfer pathway of precursor substances in the labeling pathway and the reaction transformation of various substances, with a view to the transfer and circulation of elements in the environmental ecosystem, the metabolism of compounds in the body and metabolic changes, the action of drugs on neurology, and the development of the chemical properties. The aim of this study is to provide insight into the cycling of elements in environmental ecosystems, the metabolic patterns and changes in the metabolism of biological compounds in vivo, and the effects of the release of elements on neurotransmission in the synaptic response to drugs. Independence, integrity, freedom, economy, and health are the development directions of future building drainage system [4]. Campbell D describes the empirical investigation and development of the new Drainage Research Group Drainage Test Solids (DRG) for the study of building drainage, waste, and ventilation systems. This research is timely given the changing (is general reduction) contemporary emissions and energy through water conservation initiatives such as WaterWise. Modern materials and analytical techniques provide opportunities to update NBS entities and develop modern research alternatives. Laboratory test results for DRG solids were superior to those for NBS solids [5]. Otsuka M experimentally examines how the drainage performance of a condominium residential drain system is affected by installing a flush toilet system for nursing in an optional location. Evaluated the drainage performance of high-rise building drainage systems when toilets and other fixtures were connected to the drainage system through horizontal branches and loads were applied from each fixture, and provided basic data for planning and development [6]. Therefore, the improvement and optimization of the traditional drainage system is extremely urgent, and it is particularly important to strengthen the improvement and optimization of the building drainage system [7, 8]. This document understands the physical conditions and hydrological characteristics of M city by consulting relevant materials and field research and analyzes the current situation of the sewer system in M city combined with the isotope tracking method and puts forward the existing problems. It is determined that the sewer system

Optimization Algorithm of Building Drainage Pipe System Based …

863

of M city is a rainwater and sewage diversion system. For the optimal design of the diversion system, it is mainly carried out from the selection of design parameters and the plane layout of the pipe network. Through comparative analysis, the optimization technology of the sewer system and channel is proposed. In addition, for the optimization of the rainwater system, the utilization of rainwater resources is specially proposed. Reasonably estimate the amount of sewage in M·City, and determine a reasonable scale of sewage treatment and a reasonable quality of incoming and outgoing water. Select the optimized wastewater treatment process by comparing the options.

2 Research on Optimization Algorithm of Building Drainage Pipe System Based on Isotope Tracking Method 2.1 Problems with the Construction of Drainage Pipes Compared with the increasing amount of sewage discharge, the construction of urban drainage in China is in an awkward situation. On the one hand, the construction funds of urban infrastructure mainly come from the state appropriation and local finance, which is limited by the financial constraints of the government, and it is impossible to meet the needs of all parties in terms of investment, so the construction of urban infrastructure is faced with a serious shortage of construction funds. On the other hand, the orientation, allocation, scale, and management of investment funds still lack systematization, science, and rationality to a large extent, and the results of some investment projects are not satisfactory. The traditional drainage design process relies mainly on the experience of designers and manual access to icons, which results in a lot of repetitive work, long engineering design time, low design efficiency, and the choice of a limited number of design options for comparison, which may result in excessive investment in drainage, wasting valuable funds and time.

2.2 Isotope Tracking Method The application of geochemical tracing method to the study of sea (salt) water intrusion has become another important technical means [9]. The tracer method of geochemistry is devoted to studying the material (element) migration and transformation process of elements within or between the various layers of the earth. It can study the groundwater body itself and is not restricted by the number of aquifers and the shape of the region and can directly and truly reflect the groundwater migration path and element changes [10, 11].

864

X. Liu et al.

As an effective geochemical tracer, strontium has been widely used in research on crustal weathering, paleoenvironment and paleoclimate. Since the last century, strontium isotope geochemistry has become a frontier field of hydrogeochemical research due to the improvement of analytical testing level and the improvement of geochemical models [12, 13]. Strontium content and element ratio can be used as an auxiliary method for strontium isotope tracer to study groundwater salinity and migration processes [14]. In coastal areas, when the source of surface runoff is rainwater input from the ocean atmosphere, the strontium isotope ratios of seawater and surface runoff are almost identical, and it is difficult for the isotope to identify its source. However, their strontium content differs by a factor of 100–1000, and the change in strontium content can distinguish their different sources [15, 16]. The tracer method of strontium content and element ratio also plays an important role in endmember identification and migration pathway research during groundwater mixing. The comprehensive application of tracer methods such as strontium isotope, strontium content, and element ratio will make it easier to realize the goal of revealing the mechanism of groundwater salinity [17].

2.3 Optimization Design of Drainage Pipes (1) Utilization of rainwater resources Principles of resource utilization: We should choose solutions for urban rainwater resource utilization according to local conditions, and at the same time, follow the following principles: high efficiency, low cost, and overall consideration in the near and long term to avoid repeated reconstruction; and pollution control, ecological landscape, urban combined with road sprinkler to reduce dust and reduce air TSP concentration [18]. Key Strategies: Collect and store rainwater for subsequent use that is easy to treat to meet water needs, with storage and utilization of natural resources and processes as primary methods. Surface water storage system: The collected rainwater is clean and can be reused after simple treatment: flushing toilets, irrigating green spaces or watering structures, etc. Land bank: Deeply dig reservoirs in green space to improve water environment, improve urban environment, store rainwater, and increase groundwater; waste management technology based on the principle of capillary soil penetration to the soil surface. Treatment options include approved bricks, inlet pipes, inlet wells, etc. (2) Sectional form of sewage pipeline The section form of the sewage pipeline should be economical and easy to maintain and meet the static conditions and hydraulic requirements. In terms of statics, the pipeline can withstand large loads, is stable and strong. Common forms of pipeline

Optimization Algorithm of Building Drainage Pipe System Based …

865

section: round, rectangular, trapezoidal, rectangular with low flow groove, rectangular with arc flow groove and ellipse, etc. Circular pipelines have simple structure, large flow capacity and excellent hydraulic conditions, and are the most commonly used cross-sectional forms in water conveyance canal engineering.

3 Investigation and Research on Optimization Algorithm of Building Drainage Pipe System Based on Isotope Tracking Method 3.1 Overview of M City The pH value of the deep groundwater in City M varies from 7.59 to 8.42, which is a weakly alkaline environment. The electrical conductivity (EC) is a reflection of the strength of the electrolyte in the water, and it is the ratio of F to the salt content. The EC of the groundwater water body varies from 760 to 2690 s cm−1 , with an average value of 1410 s cm−1 . I aquifer, the water EC gradually increased. The degree of salinity is a reflection of the total dissolved solids (TDS) in water, and the main components are soluble salt ions.

3.2 Analysis and Determination Anion and cation analyses were performed in the State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences. Anion (CI, SO4− , NO3 ) was completed by ICS-90 high performance ion chromatograph from Dionex Company of the United States. The cations (Ca2+ , Na+ , Mg2+ , K+ ) were measured by VistaMPS type inductively coupled plasma optical emission spectrometry (ICP-OES) from Varian Company in the USA. The analytical errors of anion and cation are within + 10%. Strontium isotope samples were separated and purified in a 1000-level ultra-clean laboratory; an appropriate amount of samples were taken and placed in a clean beaker and evaporated to dryness, and 2N HCl was used to dissolve the samples that had removed the organic matter, and the regenerated resin (Dowex 50, 200–400 mesh) column enriched Set, 2N HCl will be separated and purified from other main elements, and then the strontium isotope ratio will be tested by MC-ICP-MS.

866

X. Liu et al.

3.3 Determination of Hydraulic Calculation Parameters of Rainwater Pipelines The rainwater flow formula of M city adopts Q = ψ · F · q.

(1)

Using the rainstorm intensity formula, the formula is in the form of q=

2054(10 + 0.761gp) (l/s ha). (t + 7)0.87

(2)

In the formula, P is the design return period, according to the importance of the terrain characteristics and construction nature of M City, take P = 1 year; ψ—comprehensive ground runoff coefficient, according to regional rainfall and ground conditions, take ψ = 0.5; t—rainfall duration (min), t = t 1 + mt 2 ; t 1 —the ground water collection time is 5–15 min; t 2 —popular time in the tube (min); m—reduction coefficient, take 2 for dark pipe. The rainwater design flow of each pipe section is calculated according to the above formula to determine the pipe diameter.

4 Analysis and Research on Optimization Algorithm of Building Drainage Pipe System Based on Isotope Tracking Method 4.1 Strontium Isotopes of Groundwater in M City and Surrounding Areas In the geochemical system, the solution produced by weathering in silicate areas usually has the characteristics of low strontium concentration and high strontium isotope ratio: > 0.8; the groundwater in carbonate areas usually has the characteristics of high strontium concentration and low strontium isotope: < 0.8; the strontium isotope ratio in modern seawater is 0.79. The changes of strontium isotopes in groundwater in different aquifers or different regions of the same aquifer in M city and surrounding areas are very obvious, as shown in Fig. 1. Since the Cl element in the groundwater of the above sampling points is much smaller than that in the seawater, it can be judged that the above sampling points are less affected by sea/salt

Optimization Algorithm of Building Drainage Pipe System Based …

867

water intrusion. From the strontium isotope analysis, the strontium isotope ratios of C, E, F, J and L are less than 0.8, as shown in Table 1. Groundwater recharge in this region is mainly controlled by carbonates. The strontium isotope ratios of the D, G, H, I and K sites are all greater than 0.8, and the groundwater recharge in this area is mainly controlled by silicates. Among them, the groundwater age of the G sampling point is shorter, its isotope ratio is the highest 0.8212, and its silicate characteristics are more obvious. From the chloride ion concentration and strontium isotope characteristics of groundwater in M city and surrounding areas, it can be judged that the groundwater in this area is less affected by salt water intrusion. The reuse of urban sewage is a necessary means to solve the shortage of urban water resources, and it is also a requirement for sustainable development. As long depth(m)

C1(mg/L)

350 300

value

250 200 150 100 50 0

C

D

E

F

G

H

I

J

K

L

Sampling point

Fig. 1 Sr isotopic content of groundwater in the plain area of M city

Table 1 Sr isotope content of groundwater in the plain area of M city

Sampling point

Depth (m)

C1 (mg/L)

Strontium isotope ratio

C

107

56

0.7632

D

122

88

0.8011

E

213

76

0.7716

F

175

43

0.7882

G

156

18

0.8212

H

254

35

0.8091

I

115

22

0.8064

J

311

46

0.7912

K

187

99

0.8115

L

125

87

0.7944

868

X. Liu et al.

as the quality of the reclaimed water generated after treatment can reach a certain standard, it can be widely used in building drainage pipe systems. Making full use of brackish water and fresh brackish water has also become an effective way to solve the shortage of water resources. At present, the utilization of salt water has mature technologies and techniques in industry and agriculture. Under the premise of ensuring the control of seawater intrusion, the exploitation of underground salt water and brackish water cannot only alleviate the tension of water use, but also reduce the evaporation of shallow groundwater.

4.2 Optimization Design Results of Drainage Piping System The main sewage interception pipes in M city are divided into two major systems: south and north. The end design flow of the southern sewage interception main pipe is Q = 1.221 m3 /s (including the rainwater collected by this pipe section), the end pipe diameter is DN1200 mm, the flow rate is 0.869 m/s, and the pipe depth is 7.54 m. The design flow rate at the end of the northern sewage interception main pipe is Q = 0.683 m3 /s (including the amount of rainwater collected by this pipe section). After the overflow, the end pipe diameter is DN800mm, the flow rate is 0.992 m/s, and the buried depth of the pipe is 4.76 m, as shown in Fig. 2 shown. The design flow at the end of the main sewage interception main pipe is Q = 1.25 m3 /s (no rainwater enters this pipe section), the diameter of the end pipe is DN1200 mm, the flow rate is 1.114 m/s, and the buried depth of the pipe is 6.48 m. The total length of the sewage interception main pipe is 10,365 m, and the pipe diameter is DN600–1200 mm. A total of 21 overflow wells and 10 water outlets were newly set up on the sewage interception main pipe, as shown in Table 2.

5 Conclusions As an important part of construction equipment engineering, building drainage system directly affects human health, safety and reliability of buildings, project cost, and social benefits. Therefore, the optimization research of the building drainage system cannot only improve the safety and reliability of the drainage system, but also achieve the purpose of saving water and energy, and can provide a reference for the formulation of relevant norms. In this paper, in the process of quantitatively studying water movement using strontium isotope tracer technology, the error of sampling and measurement and the error of model input parameters will affect the results. Based on the previous research methods and results, this thesis systematically investigates the optimization algorithm of building drainage system based on isotope tracking method, but due to the complexity of strontium structure and the diversity

Optimization Algorithm of Building Drainage Pipe System Based …

869

8 7 6

value

5 4 3 2 1 0

end design flow(m3/s) flow rate(m/s) pipeline depth(m)

Southern Sewage Interception Main Pipe 1.221 0.869 7.54

Northern sewage interception main pipe 0.683 0.992 4.76

Sewage interception main trunk 1.25 1.114 6.48

Interceptor trunk Fig. 2 Results of optimized design of drainage piping system Table 2 Optimization design results Interceptor trunk

End design flow (m3 /s)

Flow rate (m/s)

Pipeline depth (m)

Southern sewage interception main pipe

1.221

0.869

7.54

Northern sewage interception main pipe

0.683

0.992

4.76

Sewage interception main trunk

1.25

1.114

6.48

870

X. Liu et al.

of strontium reactions, the authors believe that further research is needed in the following areas: (1) Study more reactions of strontium with water, which can better verify the generality of the experiment; (2) To develop a chemical model of the reaction between hydrogen and oxygen atoms in water, oxygen, and strontium and to better trace the pathways of origin of the products; (3) Further research is needed on how to reasonably quantify the margin of error and reduce it. At the same time, strontium isotope tracing techniques should be reasonably compared with other technical means to ensure the accuracy and reasonableness of the study.

References 1. Joyce J, Chang N B, Harji R et al (2018) Coupling infrastructure resilience and flood risk assessment via copulas analyses for a coastal green-grey-blue drainage system under extreme weather events. Environ Model Softw 100:82–103 2. Kiran P, Singh JP (2021) Simulation of soil salinity using DRAINMOD-S model under subsurface drainage system in arid and semi-arid regions of Punjab, India. J Agric Eng 58(1):73–89 3. Jówiakowski K, Marzec M, Listosz A et al (2021) The influence of household wastewater treatment plants with drainage system on the quality of groundwater in the Lublin Province, Poland. J Ecol Eng 22(3):18–39 4. Matsui A (2021) Fish community in a fish-retreat ditch created in a paddy irrigation and drainage system near the sea. Jpn J Conserv Ecol 26(1):165–175 5. Campbell D (2018) Empirical derivation of flow parameters describing the behaviour of a novel artificial test solid for building drainage systems. Build Serv Eng Res Technol 39(1):38–49 6. Otsuka M, Motomura Y, Araki Y (2019) Study on application of pumping toilet system for nursing care to drainage stack system of high rise apartment house. AIJ J Technol Des 25(59):243–248 7. Awwad M (2021) Studying the effects of roads geometry and design parameters on the pavement drainage system. Civ Eng J 7(1):49–58 8. Khaing A, Tashiro T (2020) Factors controlling the Pluvial flooding in the downtown area of Yangon, Myanmar with the old storm drainage system. J Jpn Soc Civ Eng Ser B1 (Hydraul Eng) 76(2):I_541–I_546. 9. Kim GB, Won KS, Kim SG (2020) Pumped well drainage system effects on mitigating barrageinduced inundation problems in low-lying plains. J Irrig Drainage Eng 146(1):05019012.1– 05019012.9 10. Wrzesi´nski G (2020) Hazards resulting from improper building drainage system by the use of BIM. Acta Scientiarum Polonorum—Architectura Budownictwo 19(2):21–29 11. Bubela A (2020) Comprehensive assessment of the quality condition of the road with drainage system. Technol Audit Prod Reserves 4(2(54)):20–26 12. Trost G, Robl J, Hergarten S et al (2020) The destiny of orogen-parallel streams in the Eastern Alps: the Salzach-Enns drainage system. Earth Surf Dyn 8(1):69–85 13. Mbanaso FU, Charlesworth SM, Coupe SJ et al (2020) State of a sustainable drainage system at end-of-life: assessment of potential water pollution by leached metals from recycled pervious pavement materials when used as secondary aggregate. Environ Sci Pollut Res 27(5):4630– 4639

Optimization Algorithm of Building Drainage Pipe System Based …

871

14. Adhikari N, Davidson PC, Cooke RA et al (2020) Drainmod-linked interface for evaluating drainage system response to climate scenarios. Appl Eng Agric 36(3):303–319 15. Elias Z, Sissakian VK, Al-Ansari N (2020) Evaluation of the drainage system of Zagros Basin (Greater Zab River, northern Iraq) and insights into tectonic geomorphology. Arab J Geosci 13(22):1–12 16. Yoo HM, Chung TH, Lee SY et al (2019) A study on applicability of tracking for air pollutant materials sources: characteristics comparison of carbon dioxide isotope with combustion engines. J Korea Soc Waste Manag 36(7):644–651 17. Martino JC, Doubleday ZA, Gillanders BM (2019) Metabolic effects on carbon isotope biomarkers in fish. Ecol Indic 97:10–16 18. Nicholson AD, Archer DE, Garishvili I et al (2018) Characterization of gamma-ray background outside of the high flux isotope reactor. J Radioanal Nucl Chem 318(1):361–367

Optimization of Injection Moulding Process Parameters Using Hybridization Approach Md. Mofid Alam, Padmakar Pachorkar, Gurjeet Singh, Neeraj Agarwal, Rakesh Yadav, Jagdish Prasad, and Ashish Srivastava

Abstract Due to the production of plastic products with complex shapes, the plastic injection moulding manufacturing method has recently received a lot of attention. In order to improve manufacturing performance, the cycle time for the injection moulding process has been reduced, the defect known as dimensional warpage that results from temperature differences inside the mould has been reduced, and the mechanical tensile strength of the material has been increased so that it cannot warp easily. Warpage is a dimensional flaw of the product; hence, a longer cycle time is needed. We risk a decline in production if we do this. Additionally, a superior product should have a higher tensile strength. A suitable experimental design is prepared using the Taguchi orthogonal array to determine the trade-off analysis between quality and productivity. The Taguchi orthogonal array is used to select an appropriate experimental design, and utilizing the experimental data, the entropy measurement method is then used to look into the choice of response behaviour. The utility idea is then used to determine how the processing parameters connect to the various process. Keywords Cycle time · Injection moulding process · Tensile strength · Warpage

1 Introduction The plastics industry is the fourth largest manufacturing sector in the India, with $321 billion in annual shipments and more than 1.5 million direct employees, as per report of the Society of the Plastics Industry (SPI). Plastics have been the most Md. M. Alam Department of Mechanical Engineering, IES College of Technology, Bhopal 462003, India P. Pachorkar · G. Singh (B) · N. Agarwal Department of Mechanical Engineering, IES University, Bhopal 462003, India e-mail: [email protected] R. Yadav · J. Prasad · A. Srivastava Mechanical Engineering Department, Sagar Institute of Science, Technology and Research, Bhopal 462003, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_78

873

874

Md. M. Alam et al.

widely used material in the country since 1976, outpacing the volume of steel, copper, and aluminium combined. More than 33% of all polymeric materials handled by all polymer processing techniques go through injection moulding, which is a popular method for mass making precise discrete plastic parts with complex shapes. Different materials used in the injection moulding process have unique thermomechanical, rheological, and flow characteristics. Different pressure and temperature processing machine conditions are to blame for the change in the material’s properties. The processing of the material inside the barrel and the injection moulding machine’s mould conditions determines the machine’s conditions. The steps in the injection moulding machine’s process are plastification, injection, holding, cooling, and finally ejection. The time it takes from the injection of the melt into the cavity to the ejection of the final part is the most important factor in determining how much it will cost to manufacture a plastic item. It is referred to as the cycle time. As a result, every step is dependent on the injection moulding process’ cycle time. Cycle time can be divided into four stages: Injection, holding, cooling, and ejection are listed in that order. When the granules enter the barrel from the hopper and are transformed into liquid form, the material is first forced. The duration between the introduction of raw materials into the cavity and the ejection of the finished product is the most significant factor in determining the cost to make a plastic item. The cycle time is the term for it. As a result, the cycle time of the injection moulding process determines the outcome of each phase. Injection, holding, cooling, and ejection are the four processes that make up a cycle time, given in that order. The injection phase is the time when grains enter a barrel from a hopper and change into a liquid condition before being pressed into a mould. The filler material stretches and contracts inside the mould. Attempt to pour melted material into the hollow area of the mould as well. The time it takes for a substance to go from liquid to solid is called the cooling time of the substance. This includes material compensation when the material shrinks and is compensated by other materials. The mould open/ close time is the time the material takes to come out of the mould and harden. The time from solidification to the opening of the mould gate plate is called ejection time (Fig. 1). Throughout the process conditions, the operator is constantly trying to find the optimum settings to find the optimum set combination to process the material on the machine. It is a trial and error process. This distorts the shape of the material. There are also differences between CAD models and prototypes. This Fig. 1 Cycle time of the process

Plastification Injection Holding Cooling Ejection

Optimization of Injection Moulding Process Parameters Using …

875

error is therefore called product warpage. It is a dimensional error. The cause of warping is unevenness that sticks to the inside of the fabric. Occurs when the part ejection temperature is too high, the injection fill factor is low, the material flows too long in the mould, the gate is insufficient, or the gate is changed. Process parameters are responsible for this. Warpage is caused by flow imbalances in the material and thus exceeds the material’s capacity. Therefore, simultaneous investigation of these response factors is necessary for the finished product in order to improve the production speed and quality of the product. Several studies have been performed in the past on individual reactions of materials using different process parameters, some of which studied these factors simultaneously. Some of them are listed below. Daiyan et al. use the Taguchi method to investigate the tensile strength of welds using melt temperature, mould temperature, and holding pressure. In this study, it was found that as the melt temperature increased, the flowability increased, and the tensile strength of the joint increased [1]. Chen et al. investigated the effects of strain using process parameters such as melt temperature, injection pressure, and packing pressure using Taguchi hybridization and desirability function analysis. In this study, melting temperature and packing pressure significantly affect warpage [2]. Hazwan et al. use the central composition response song method to investigate the effects of protection, cooling time, refrigerant temperature, and melting temperature of process parameters to warp. The contribution of melting temperature to warpage is large [3]. Ozcelik etc. studies Warpage using packing pressure and thickness. Their Research describes that packaging pressure contributes significantly to the warpage [4]. Riaz Ahamed et al. used injection pressure, melt temperature, and cooling time to study the tensile strength of specimens. Melting temperature increases the tensile strength of the sample [5]. Özcelik et al. investigate tensile strength and impact strength using injection pressure, packing pressure, and melt temperature. Increasing the melt temperature increases the tensile strength of the welded and unwelded samples [6]. Toilet Chen et al. examine product weight using the process parameters packing pressure, injection speed, and injection time. Increasing the filling pressure increases the weight of the sample and increases the tensile strength of the sample [7]. Villarreal-Marroquín et al. investigated shrinkage and cycle time using the process parameters packing pressure and melt temperature according to the Taguchi method. Increasing the melt temperature reduces warpage and increases cycle time [8]. Villarreal et al. used the process parameters packing pressure, mould temperature, and resin temperature with numerical methods to investigate cycle time and distortion. Increasing melt temperature reduces cycle time and reduces warpage [9]. Ying et al. used backpropagation and genetic algorithm approaches to investigate mould warpage and clamp force utilizing packing pressure, packing time, cooling time, melt temperature, and mould temperature. Strain reduces and clamping force rises as pack pressure does [10]. Ku et al. investigated the tensile characteristics of polymers reinforced with natural fibres. The adhesion between the matrix and the fibres determines the tensile strength of thermosets and thermoplastics, respectively [11]. This white paper’s goal is to provide a sample method for managing multiple responses. A weight limit that can offer the best solution is necessary for this. Finding boundaries with ideal conditions is the

876 Table 1 Specification of injection moulding machine

Md. M. Alam et al.

Tonnage restraint

80 tonne

Dimensions of the screw

45 mm

Screw L/D

20:1–24:1

Injection force

1300 bar

Max. dayiight

650 mm

Min mould height

250 mm

Platen size

660 × 560 mm

The distance between the tie bars

Tie bar less

Stroke of the ejector

100 mm

aim of multi-response optimization. Consequently, the Taguchi loss function depicts the loss of the actual value, a process’ deviation from its intended value. The product quality was defended using a loss function. To support the product quality parameters, the design incorporates both random and systematic fluctuations in the process.

2 Injection Moulding Machine and Material Selection for Study 2.1 Injection Moulding Machine Specification Injection moulding machine is hydraulically operated machine which has following specification listed in the Table 1.

2.2 Material Selection PP was chosen as the material because of its heat resistance, chemical inertness, low density, high stiffness, and recyclable properties. The polyproplyene had been taken from Haldia Petrochemicals Pvt Ltd India of M103 homopolymer of polyproplyene which had been listed in the Table 2 which displays the material properties.

2.3 Selection of Process Parameters Several process parameters influence the injection moulding process’s behaviour in this case. However, optimal process parameters for multi-response optimization studies of injection moulding processes are chosen based on a literature review. The reactive effects of warpage, tensile strength, and cycle time are investigated using

Optimization of Injection Moulding Process Parameters Using … Table 2 Properties of PP

Table 3 Process parameters and their levels

877

Material properties

Unit

Value

Density at 23 °C

g/cm3

0.90

Melt flow index (2.16 kg and 230 °C)

g/10 min

3

Tensile strength at yield

MPa

32

Tensile elongation at yield

%

7

Flexural modulus

MPa

1300

Izod impact strength (Notch, 23 °C)

J/m

35

Vicat softening point (10 N)

°C

153

Heat deflection temperature(0.46 N/m2 )

°C

93

Barrel temperature

°C

190–250

Process parameters

Level 1

Level 2

Level 3

Packing pressure (PP)

12

18

24

Injection pressure (IP)

20

25

30

Melt temperature (MT)

230

235

240

Packing time (PT)

4

8

12

Cooling time (CT)

30

60

90

injection pressure, melt temperature, packing pressure, packing time, and cooling time. Table 3 displays the process parameter ranges and spans.

2.4 Study of the Response Based on a literature review, cycle time, warpage, and tensile strength were found to be the most influential answers in studies affecting the quality and productivity of injection moulding processes. The injection moulding process is used to manufacture plastic products with complex shapes. These reactions, therefore, have a strong influence on the process. Warpage is dimensional error, cycle time reduces productivity, and plastic products require good tensile strength. Tensile specimens are prepared according to the ASTM D 638 standard. These responses are selected for the survey.

878

Md. M. Alam et al.

3 Experimental Studies 3.1 Experimental Design Studying these reactions requires a suitable design. The Taguchi method provides a suitable design for experimental studies. We minimized the number of experiments by choosing an appropriate orthogonal arrangement. Orthogonal arrays are used for the effects of multiple regulators for industrial experiments. The popular Taguchi experimental orthogonal table contributes to the experiment as follows: 1. A model of the engineering design process 2. Robust design principles 3. Efforts to bring quality upstream into the design process. The advantages of orthogonal tables are 4. Conclusions that are valid across the range spanned by the control factors and their settings 5. Significant savings in experimental effort 6. Ease of analysis An orthogonal array is a two-dimensional array that: m has rows, and n columns. Each entry in the array is k size “symbols” often referred to as {0, 1, 2, 3…………k−1} and {1, 2, 3, ………….k} consists of It must be a positive integer t ≤ x to define the final base size of the intensity of the array. An N-by-y array of z symbols that is OA of intensity x such that any subset of t columns, considered individually, contains each of the possible k-by-t ordered rows the same number of times for a single request. You must meet your needs.

3.2 Experimental Design A suitable experimental design is selected according to 5 factors and 3 levels. The L-27 orthogonal array has been selected for the experimental purposes shown in the table (Fig. 2).

Fig. 2 a Injection moulding machine b Mould of the specimen

Optimization of Injection Moulding Process Parameters Using …

879

Table 4 Specification of tensile testing machine Type of machine

Screw driven

Maximum capacity

100KN

Minimum load can be tested

1 gm

Type of test can be conduct

3 point bend, 4 point bend, loading and unloading, and oil dip up to tensile 400 °C silicon oil

Cross head speed

Max-40 mm/min Min-0.005 mm/min

Cross head displacement

Max-700 mm

3.3 Data Analysis Based on Experiments Different values of responses are collected based on the experimental table. Cycle time is the sum of all manual load times on the injection moulding machine. Strain is measured using a dial gauge and Vernier callipers. The tensile strength of each sample is tested with an Instron -1195 tensile tester screw driven by IIT Kanpur Mechanical Testing ACMS Lab (Tables 4 and 5; Fig. 3).

3.4 Utility Concept If X i is the measure of effectiveness of an attribute (or quality characteristics) I and there are n attributes evaluating the outcome space, then the joint utility function can be expressed as [13, 14]. U (X 1 , X 2 , X 3 , X 4 . . . X n ) = f (U (X 1 ), U (X 2 ), U (X 3 ) . . . U (X n )

(1)

U i (X i ) is the ith attribute value. If the attributes are independent, the utility function is given as U (X 1 , X 2 , X 3 , X 4 . . .) =

n Σ

Ui (X i )

(2)

r =0

The attributes may be assigned weights depending upon the relative importance or priorities of the characteristics. The overall utility function after assigning weights to the attributes can be expressed as U (X 1 , X 2 , X 3 , X 4 . . . X n ) =

n Σ r =0

Wi Ui (X i )

(3)

880

Md. M. Alam et al.

Table 5 Process parameters and response Melt Injection Packing Packing Cooling Cycle Warpage Tensile temperature pressure(IP) pressure(PP) time(PT) time(CT) time strength (MT) (MPa) 230

20

12

4

30

59

0.02

31.66916

230

20

12

4

60

89

0.85

35.00148

230

20

12

4

90

119

0.06

34.00146

230

25

18

8

30

63

0.31

33.05103

230

25

18

8

60

93

0.47

35.94043

230

25

18

8

90

123

0.04

35.73115

230

30

24

12

30

67

0.47

38.12056

230

30

24

12

60

97

0.55

37.93009

230

30

24

12

90

127

0.16

36.00087

235

20

18

12

30

67

0.11

34.86252

235

20

18

12

60

97

0.05

35.10164

235

20

18

12

90

127

0.17

35.92237

235

25

24

4

30

59

0.4

35.16108

235

25

24

4

60

89

0.14

34.65285

235

25

24

4

90

119

0.29

35.3011

235

30

12

8

30

63

0.34

36.5562

235

30

12

8

60

93

0.62

36.45256

235

30

12

8

90

123

0.68

35.9711

235

20

24

8

30

63

0.89

37.39918

240

20

24

8

60

93

0.03

35.78657

240

20

24

8

90

123

0.63

36.05893

240

25

12

12

30

67

0.26

37.40028

240

25

12

12

60

97

0.11

35.78012

240

25

12

12

90

127

0.48

33.5895

240

30

18

4

30

59

0.89

35.17538

240

30

18

4

60

89

0.35

34.83213

240

30

18

4

90

119

0.26

37.13157

W i is the weight assigned to the attribute I in this case. The total of the weights for all of the attributes must equal 1. For each quality characteristic, a preference scale is created to determine its utility value. The just acceptable and best values of the quality characteristic are assigned arbitrary numerical values (preference numbers) of 0 and 9, respectively. On a logarithmic scale, the preference number Pi is expressed as follows: Pi = A log

Xi ' Xi

(4)

Optimization of Injection Moulding Process Parameters Using …

881

Fig. 3 a Specimen on tensile machine Instron-1195 b warpage measurement

X i = value of any quality characteristic or attribute i X i = just an acceptable value of quality characteristic or attribute i. A is a constant. The value A can be found by the condition that if X i = X ∗ (where X ∗ is the optimal or best value), then Pi = 9. Therefore, A=

9 log

Xi X∗

(5)

Expression for the overall utility index follows is as follows: Ur =

n Σ

Wi Pi

(6)

r =0

Subject to the condition: n Σ

wi = 1

(7)

i=1

The various quality attribute types proposed by Taguchi, namely higher-the-better (HB), nominal-the-best (NB), and lower-the-better (LB), are useful for higher-thebetter type selection. Its quality becomes function, it has the best winning quality characteristics. The quality characteristics considered in the evaluation are automatically optimized (maximized or minimized). But here, the quality function allows us to maximize the quality characteristics. In this proposed approach, an overall utility index is calculated based on the utility value of each response. The overall objective function can be optimized by converting all responses into a single response optimization.

882

Md. M. Alam et al.

3.5 Gravimetry Using Entropy Measurement Method Analysis Wen et al. (1998) proposed a gravimetric method for determining weight that is based on the discrete nature of entropy. The steps in this method are as follows: 1. Let the R be a factor of the grey rational factor the sequence can be denoted as the ( Ri = Ri (1) Ri (2) . . . Rm(n)

(8)

2. Computation of summation of each response attributes for all sequence: m Σ

Dk =

Ri (k)

(9)

i=1

3. Normalization coefficient K is calculated as K =

(e.5

1 −1)n

(10)

4. Find the entropy of each response attribute by the following way by ek : ek = K

m Σ

we ( z i )

(11)

i=1

we z i = z i e(1−zi ) +(1 − z i ) e zi −1

(12)

where z i = RDi (k) k 5. Computation of the total entropy value E E=

n Σ

ek

(13)

k=1

6. Determination of the relative weight factor λk : λk =

1 (1 − ek ) (n − E)

(14)

7. The normalized weight of each factor can be calculated as λk βk = Σn i=1

λi

(15)

Optimization of Injection Moulding Process Parameters Using …

883

Table 6 Overall utility function value U cycle time

Uwarp

U tensile strength

Overall utility function value

S/N ratio

56.6352

0.95469

70.07033

36.23408

31.1823

58.7756

1.370653

107.544

44.03159

32.8753

89.8954

7.418608

120.9182

61.7382

35.8111

70.6167

3.671899

78.41569

44.60985

32.9886

65.7312

2.722438

119.3898

49.58338

33.9067

94.6553

8.343668

149.3152

69.18975

36.8008

65.7312

2.722438

112.5194

48.34671

33.6873

51.7229

0

150.8741

48.36371

33.6904

78.3811

5.180866

160.7922

63.2548

36.0219

82.7797

6.035721

90.3872

52.74439

34.4435

92.0357

7.83457

114.7615

61.68223

35.8032

77.6694

5.042552

158.9747

62.57776

35.9284

67.6244

3.090368

86.15581

44.532

32.9734

55.3295

0.700931

104.788

41.84133

32.4321

71.3996

3.824054

137.4307

55.61745

34.9042

69.5323

3.461151

97.33754

47.48267

33.5307

62.4795

2.090498

124.796

48.9579

33.7965

61.3951

1.87975

154.0189

53.68491

34.597

58.2357

1.265739

102.8753

42.92582

32.6544

98.0325

9.000008

117.8629

65.18864

36.2834

62.2917

2.053994

155.8527

54.45577

34.7209

72.6815

4.07319

106.8738

50.74744

34.1083

82.7797

6.035721

121.5012

58.34491

35.3201

65.4840

2.674405

122.2595

49.97842

33.9757

58.2357

1.265739

86.23005

39.92967

32.0259

69.1920

3.395017

106.1892

48.90867

33.7877

72.6815

4.07319

175.7699

63.14874

36.0073

The corresponding weight for each factor warpage, cycle time, and tensile strength is find by .041, 0.41, and 0.18 (Table 6).

4 Result and Discussion The following process steps are considered to calculate the optimal process settings for cycle time, strain, and tensile strength for the injection moulding process. 1. Obtain the experimental data using Taguchi’s design of experiments.

884

Md. M. Alam et al.

Table 7 Optimal combination of process parameters Melt temperature(MT)

Injection pressure(IP)

Packing pressure(PP)

Packing time(PT)

Cooling time(CT)

240

20

18

12

90

2. 3. 4. 5.

Normalize the experimental values. Use the utility function to determine the utility of the answer. Weights are determined using entropy. Computation of the total utility function with the utility values from the weight function. 6. Calculation of signal-to-noise ratio. 7. Select the optimal settings for the process parameters of the injection moulding process.

5 Confirmation Test As we see that melt temperature, injection pressure, packing pressure, packing time, and cooling time have the following combination which is presented in the Table. There we can see the optimal condition of the process parameters. After running the confirmation experiment, we found that this satisfied the optimum condition of warpage, cycle time, and tensile strength. After experiment validation, the value of the cycle time comes 68 s, warpage comes 0.02 mm, and tensile strength of specimen comes 37.94 MPa, Which is significant improvement in the defect minimization and process optimization (Table 7).

6 Conclusion From the experiment, we see that this experimental setting decreases the warpage and increases the yield tensile strength which is also cycle time is decrease to maximum cycle time. By this, we can increase productivity because cycle time minimization can increase the productivity. Also warpage has dimensional defect which is more concern. So by this model, we can minimize it up to a large extent. Also material will not cross the maximum yield tensile strength so that warpage can be minimum occur in the plastic product. This is very good approach for the cost and mass production point of view. This approach is the powerful tool for the multi-response optimization of manufacturing process. This approach can be recommended for the off line quality control of the product. Acknowledgements This work has been accomplished with the assistance of CIPET Jaipur and IIT Kanpur. In this project, Mr. Alok Sahu, Chief Manager and his staff CIPET Jaipur support a lot.

Optimization of Injection Moulding Process Parameters Using …

885

References 1. Daiyan H et al. (2010) Low-speed influence reaction of infusion shaped polypropylene platessection 2: Impacts of embellishment conditions, striker calculation, clipping, surface, weld line and paint. Polym Testing 29.7:894–901 2. Chen C-P et al. (2009) Reenactment and exploratory concentrate in deciding infusion shaping cycle boundaries for flimsy shell plastic parts by means of plan of trials examination. Master Frameworks with Appl 36.7:10752–10759 3. Hazwan MHM et al. (2017) Warpage streamlining on the formed part with conformal cooling channels utilizing reaction surface technique (RSM) and glowworm multitude enhancement (GSO). MATEC Web of Gatherings, EDP Sciences, vol 97. 4. Ozcelik B, Sonat I (2009) Warpage and primary examination of slender shell plastic in the plastic infusion shaping. Mater Plan 30(2):367–375 5. Ahamed AR, Dawood AK, Karthikeyan R (2013) Planning and upgrading the boundaries which influence the embellishment cycle utilizing plan of examination. Global Diary of Explor Mech Design 1.2:116–22 6. Ozcelik B (2011) Streamlining of infusion boundaries for mechanical properties of examples with weld line of polypropylene utilizing Taguchi strategy. Worldwide Correspondences in Intensity and Mass Exchange 38(8):1067–1072 7. Chen W-J et al. (2009) Process boundary enhancement for MIMO plastic infusion shaping through delicate figuring. Master Frameworks with Appl 36.2: 1114–1122 8. Villarreal-Marroquín MG, Cabrera-Ríos M, Castro JM (2011) A multicriteria reenactment enhancement technique for infusion shaping. Diary of Polym Eng 31.5:397–407 9. Villarreal MG et al. (2008) Reenactment advancement applied to infusion shaping. In: Procedures of the 40th gathering on winter recreation. Winter Reenactment Gathering 10. Fei, Ng J, Mehat NM, Kamaruddin S (2013) Functional uses of Taguchi technique for streamlining of handling boundaries for plastic infusion shaping: a review survey. ISRN Modern designing 11. Ku H et al (2011) A survey on the tractable properties of regular fiber supported polymer composites. Compos Part B: Design 42(4):856–873 12. Chen Z, Turng L-S (2005) A survey of current improvements in cycle and quality control for infusion shaping. Adv Polym Innov 24(3):165–182 13. Walia RS, Shan HS, Kumar P (2006) Multi-response optimization of CFAAFM process through Taguchi method and utility concept. Mater Manuf Processes 21(8):907–914 14. Routara BC, Mohanty SD, Datta S, Bandyopadhyay A, Mahapatra SS (2010) Optimization in CNC end milling of UNS C34000 medimum leaded brass with multiple surface roughnesses characteristics. Sadhana 35:619–629

Structure and Electric Properties of Ba (Ti, Zr)O3 Thin Films Using Sol–gel Method Ling Huang, Bo Zheng, and Qian Wang

Abstract BaTiO3 (BT) and Ba(Zr0.15 Ti0.85 )O3 (BZT15) thin films grown on Pt/ Ti/SiO2 /Si(100) substrates were made up of Sol–gel method. The BZT thin films with x = 0.15 exhibited enhanced dielectric and ferroelectric properties. The large remanent polarization, high dielectric constant, and low dielectric loss of the material are 10.9 μC/cm2 , 1004, and 0.03, respectively, for the BZT15 films. The BZT15 film exhibited 49% high tunability at 275 kV/cm electric field and 1 MHz measurement frequency. These results indicate that we have successfully deposited high quality and potential materials for dielectric applications. Keywords BZT thin films · Sol–gel method · Dielectric properties · Tunability

1 Introduction BaTiO3 (BT)-based solid solution is a potential dielectric material for capacitors and tunable microwave devices [1, 2]. However, high dielectric losses limit their application in tunable microwave devices. BaZrx Ti1−x O3 (BZT) is widely concerned because of its high dielectric constant, low loss and good adjustability [3, 4]. This is because the stability of Zr4+ (0.087 nm) is more stability than Ti4+ (0.068 nm), allowing electrons to jump between Ti4+ and Ti3+ ions [5]. The polymorphic transition of BZT is gradually approached with the addition of Zr, and polymerization occurs at room temperature with a composition of 0.15 [6, 7]. The coexistence of many ferroelectric phases has good ferroelectricity and piezoelectricity. Most scholars focus on the composition of x = 0.2 [8, 9]. In addition, compared with BZT ceramics, there is still a big difference in its performance. Kim et al. [10] reported a BZT film with the εr of 350. Nguyen [4] reported the growth of La-doped BZT films on LaNiO3 -buffered L. Huang (B) · Q. Wang School of Science, XiJing Univeristy, Xi, an 710123, China e-mail: [email protected] L. Huang · B. Zheng College of Chemistry and Materials, Weinan Normal University, Weinan 714000, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_79

887

888

L. Huang et al.

Ca2 Nb3 O10 -nanosheet/Si substrates with the εr of 360. Therefore, we need to study and optimize the process technology suitable for semiconductor devices. Films can be formed by physical and chemical methods [11]. The chemical process is split into vapor evaporation and sputtering, and the chemical process is split into gas phase and liquid phase. Although high-density films can be obtained using physical methods, their composition cannot be precisely controlled. The Sol–gel process of membranes has many advantages, such as: good stoichiometric control, easy component adjustment, convenient use, low cost, and easy preparation of large-area membranes [11]. The structure and properties of BT and BaZr0.15 Ti0.85 O3 (BZT15) thin films were studied more stably with Sol–gel method.

2 Experimental Procedure BT and BZT15 films were worked up Pt/Ti/SiO2 /Si(100) substrate by Sol–gel technique. Figure 1 shows the preparation process of BT and BZT precursor solutions and membranes. Barium acetate (CH3 COO)2 ), TiC16 H36 O4 (tetrabutyl titanate), Zr (CH3 COOH)2 ), C3 H8 O2 and other co-solvents. Ba(CH3 COO)2 is initially dissolved in heated acetic acid in a predetermined ratio. At room temperature, add TiC16 H36 O4 , Zr (CH3 )4 . Under the above conditions, add 2-methoxyethanol to ensure that its concentration is not greater than 0.2 M. Leaving this precursor solution for 48 h later, it was rotated at 4200 rpm, and then Pt/Ti/SiO2 was deposited on the Si(100) substrate. Then, each coating was heat-treated at 400 °C for 30 min. After repeated coating and heat treatment, all performance indicators reached 370 nm. BT and BZT15 are heat-treated under oxygen conditions. Thin films were constructed using Cu Kα rays (λ = 1.54056, Eindhoven, Netherlands). The cross-section observation of BZT films was carried out with SEM, Sigma500. In the measurement of electrical properties, the film was made into a metal-ferroelectric-metal interlayer, as shown in Fig. 2. A Pt film was coated on the surface of the film to form a round tip contact with a diameter of 0.3 cm. The BT/ BZT films were investigated by magnetron sputtering. The dielectric constant was measured using a high-precision LCR device (USA) from 40 to 1 MHz. Ferroelectric properties were measured by precision radiation workstation.

3 Results and Discussions As shown in Fig. 3, in order to accurately grasp the changes of the sol during various stages of the heating process and determine the heat treatment process conditions of the membrane, thermogravimetric/differential thermal analysis (TG/DTA) tests were conducted on the precursor solution. The figure shows that the weight loss curve of TG/DTA can fall into four stages. In the first period, from room temperature to 317 °C, there is a small endothermic peak at 286 °C, and the thermal weight loss is small

Structure and Electric Properties of Ba (Ti, Zr)O3 Thin Films Using …

889

Fig. 1 Preparation of precursor solution and membrane of BT and BZT15

Fig. 2 Configuration of a MFM structure

(about 3.84%), which corresponds to the evaporation of organic solvent and water. In the next period, there are several small exothermic peaks between 322 and 606 °C, with the maximum thermal weight loss (23.9%), which corresponds to a large amount of decomposition and volatilization of organic matter. The third stage is between 606 °C and 739 °C, there are still some exothermic and endothermic peaks,

890

L. Huang et al.

Fig. 3 TGA/DTA analysis of BZT15 precursor solution

and the thermal weight loss is 9.93%, which mainly corresponds to the decomposition and combustion of residual volatile substances and the formation of perovskite structure phases. The fourth stage is when the temperature is above 736 °C, there is no exothermic peak and endothermic peak and no obvious weight loss, indicating that the material has been completely crystallized at this stage. Therefore, the crystallization temperature of the film can be selected to be above 739 °C. To further determine the heat treatment temperature of the film, we performed an XRD analysis of the film at discrepancy heat treatment temperatures, as seen from Fig. 4. The film has been crystallized into a perovskite structure when the heat treatment temperature was 700 °C. The crystallinity of the film was gradually increased as the heat treatment temperature increased to 800 °C. With the further increase of heat treatment temperature, the diffraction peak intensity of the film changed significantly. The results show that the film at 800 °C has completely crystallized and formed a perovskite structure. Considering the economy and previous studies, 800°C was chosen as the final heat treatment temperature. Figure 5a shows the XRD spectra of BT and BZT15 films made by Sol–gel method on Pt/Ti/SiO2 /Si(100) substrates. These films are purity perovskite crystals with a random orientation. Films between 2θ –43.5°–46.5° are shown in Fig. 5b. Although the diffraction peaks of (002)/(200) are not clearly split, the peak width of the BZT15 film is much broader than that of the BT film. It has been reported that multi-phase mixing occurs when Zr doping is 0.15, which leads to this phenomenon [6–8]. The structure of BZT material is complex, and the shortening of its associated length makes its structural analysis difficult [12, 13]. Figure 6 is the sectional SEM micrograph of BZT15 thin film prepared at 800 °C on Pt/Ti/SiO2 /Si (100) substrate. The research reveals that on Pt/Ti/SiO2 /Si (100) substrate, the thin films were made by Sol–gel method have dense and smooth surface

Structure and Electric Properties of Ba (Ti, Zr)O3 Thin Films Using …

891

Fig. 4 XRD patterns of BZT15 films at different heat treatment temperatures

Fig. 5 XRD patterns of Sol–gel deposited BT and BZT15 thin films a 20–60°, b 45°

morphology. The thickness of BZT15 film is 370 nm from the scanning electron microscope section. The thickness of the film is getting command of adjusting amount of coatings. The average thickness of each coating was about 60 nm. Due to prepared with the same preparation conditions, all of the films have the same thickness. Figure 7 reveals the dielectric constant (εr ) and dielectric loss (tanδ) of BT and BZT15 films at room temperature. εr shows that the film has typical dielectric dispersion. The rising trend of tanδ shows a significant change in the higher frequency domain.

892

L. Huang et al.

Fig. 6 SEM morphology of BZT15 thin films on Pt/Ti/ SiO2/Si (100) substrate

Fig. 7 BT and BZT15 thin film εr R and tan tanδ Change rule with frequency

As everyone knows that the increase of dielectric loss can be caused by the contact resistance between the detector and electrode or the resonance caused by high dielectric constant. We know that the increase of dielectric loss can be caused by the contact resistance between the detector and electrode or the resonance caused by high dielectric constant. Other ferroelectric thin films have similar dispersion phenomena [14]. The εr value of pure BT thin films is 444, and the tanδ is 0.05. Doping Zr can significantly improve the dielectric properties of BT films. When x = 0.15, the εr of the Zr-doped BT film is 1004 and the tanδ is 0.03. This εr value is much larger than the previous BZT films [4, 10]. As shown in Fig. 8, the ferroelectric properties of BT and BZT15 films are assessed by the electric field (P-E) hysteresis loop at normal temperature. The hysteresis loops appear clearly on each film. The result is that the ferroelectric properties of BT films can only be obtained at lower pressures and are poor. The residual polarization (Pr ) value of the BT film was 2.6 μC/cm2 , while the coercive field (E C ) was 20 kV/ cm2 . The Pr value of the BZT15 film obtained with the P-E hysteresis loop was 10.9 μg/cm2 and the EC was 130 kV/cm2 . The results show that the ferroelectric

Structure and Electric Properties of Ba (Ti, Zr)O3 Thin Films Using …

893

performance can be significantly improved when the Zr-doped BT film is x = 0.15. As with other BZT and KNN materials, the increased ferroelectric properties are due to the coexistence of phases [6, 14]. The results show that the applied electric field has a strong correlation with the relative permittivity. Figure 9 reveals the fact between the dielectric constants and the electric field (ε-E) of BT and BZT15 thin films at room temperature with a frequency of 1 MHz. It can be see that the ε-E curve shows a typical butterfly curve, but it’s not very symmetrical. The studies show that the interfacial interaction between film and substrate is obviously different in the direction of positive and negative polarity [15]. Calculate the adjustability (K) of the dielectric constant with the following formula Tunability % =

Fig. 8 Room temperature P-E loops of BT and BZT15 thin films

Fig. 9 Relationship between dielectric constant and electric field of BT and BZT15 films

ε(E 0 ) − ε(E) × 100% ε(E 0 )

(1)

894

L. Huang et al.

E 0 is the initial electric field, usually E 0 = 0 kV/cm, E is the electric field when the calculation is adjustable [15]. The BT has a tunability of around 14.3% at a loading field of 13 kV/cm and a measurement frequency of 1 MHz. Zr doping can improve the tunability of the BT films. The BZT15 thin films showed a high tuning performance of 49.5% when the applied electric field was 275 kV/cm and the measurement frequency was 1 MHz. This result is better than previously reported about the tunability of BZT film deposited on Pt/Ti/SiO2 /Si(100) substrate [4, 15].

4 Conclusions In this paper, BT and BZT15 films were made by a Pt/Ti/Sio2 -si/100-i substrate and investigated for their properties. The final heat treatment temperature was determined to be 800 °C by TG/DTA, XRD, etc. The electrical and structure properties of the thin films are studied. The thin films are pure perovskite crystals with a random orientation. At x = 0.15, the dielectric properties and ferroelectric properties of the BZT films are improved. The experimental results reveal that the BZT15 films have remnant polarization factors of 10.9 microC/cm2 , 1004 and 0.03. The BZT15 film is well tuned at 275 kV/cm and 1 MHz. Its dielectric properties have been significantly improved, which shows that it has the potential to become a new type of dielectric application material. Acknowledgements The work was funded by the Shaanxi Provincial Natural Science Foundation of China (No. 2019JQ-922), the Scientific Research Program Funded by Shaanxi Provincial Education Department (No. 19JK0908), and Weinan Normal University Doctoral Research Launch Fund (No. 2020RC06).

References 1. Haertling GH (1999) Ferroelectric ceramics: history and technology. J Am Ceram Soc 82:797– 818 2. Khandelwal P, Ahlawat N, Kundu RS et al (2020) Stuctural and dielectric study of Cr doped BZT ceramics. AIP Conf Proc 2265:030418 3. Nagata T, Kumaragurubaran S, Takahashi K et al. (2022) Combinatorial synthesis and interface analysis for development of high dielectric constant thin films. IOP Publishing Ltd 4. Nguyen MD (2021) Ultrahigh energy-storage performance in lead-free BZT thin-films by tuning relaxor behavior. Mater Res Bull 133:111072 5. Mahajan S, Haridas D, Ali ST et al. (2014) Investigation of conduction and relaxation phenomena in BaZrxTi1-xO3 (x = 0.05) by impedance spectroscopy. Physica B 451:114–119 6. Li W, Zhou QG, Hao JG et al (2012) Effect of LaNiO3 buffer layer on ferroelectric properties of Ba(Zr, Ti)O3 thin films. Integr Ferroelectr 140:116–122 7. Dobal PS, Dixit A, Katiyar RS (2001) Micro-Raman scattering and dielectric investigations of phase transition behavior in the BaTiO3 –BaZrO3 system. J Appl Phys 89:8085 8. Chen HW, Yang CR, Zhang JH et al. (2009) Electrical behavior of BaZr0.1Ti0.9O3 and BaZr0.2Ti0.8O3 thin films. Appl Surface Sci 255:4585–4589

Structure and Electric Properties of Ba (Ti, Zr)O3 Thin Films Using …

895

9. Tang XG, Wang J, Wang XX et al. (2004) Effects of grain size on the dielectric properties and tunabilities of sol–gel derived Ba(Zr0.2Ti0.8)O3 ceramics. Solid State Commun 131:163–168 10. Kim JW, Shima H, Nishida K et al (2014) Structural and tunable characteristics of Ba(ZrxTi1x)O3 films prepared by RF-magnetron sputtering using a metal target. J Korean Phys Soc 65:275–280 11. Li TG, Huang DX, Shang HJ et al. (2022) Progress in preparation of YBCO thin films by metal organic deposition. Mater Rev 36(2):24–34 12. Mahesh MLV, Prasad VVB, James AR (2014) Enhanced dielectric and ferroelectric properties of lead-free Ba(Zr0.15Ti0.85)O3 ceramics compacted by cold isostatic pressing. J Alloys and Compounds 611:43–49 13. Miao S, Pokorny J, Pasha UM et al (2009) Polar order and diffuse scatter in Ba(Ti1-xZrx)O3 ceramics. J Appl Physiol 106:114111–114116 14. Dash S, Pradhan DK, Kumari S et al (2021) Enhanced ferroelectric and piezoelectric properties of BCT-BZT at the morphotropic phase boundary driven by the coexistence of phases with different symmetries. Phys Rev B 22:104 15. Zhai JW, Yao X, Shen J et al (2004) Structural and dielectric properties of Ba(ZrxTi1-x)O3 thin films prepared by the sol–gel process. J Phys D Appl Phys 37:748–752

Smart Agriculture Monitoring System to Prevent Water Wastage Alapati Akash, Muthyala Lalith Krishna, Bommaganti Chandana, Kottnana Janakiram , and P. Joshua Reginald

Abstract Historically, agriculture has been handled manually. It is important that agriculture follows the global trend toward new technology and applications. Internet of Things (IoT) is a significant aspect of smart agriculture. Data about agricultural areas can be gathered using IoT sensors. We have suggested a smart agriculture and IoT system that uses automation. Using wireless sensor networks, this IoT-based farm monitoring system produces reports by several sensors located at various nodes and sends it via wireless transmission. A DC motor, temperature, moisture, and water level sensors, as well as the Blynk app, are all parts of the NodeMCU-powered IoT system for smart agriculture. The IoT-based farm monitoring system begins up with a check of the moisture level, humidity, and water level. It sends an SMS alerting the phone of the levels. Sensors detect a dip in the water level and promptly activate water pump. The standard technique of farming is replaced by smart agriculture with IoT, which is more user-friendly, affordable for farmers, and lowers crop and water waste. Keywords NodeMCU · Internet of things (IoT) · Agriculture · Crop · Blynk app

1 Introduction The Internet of Things (IoT) is made up of objects with unique identities that are linked to the internet. IoT enables these objects to engage and share information while carrying out useful activities that work toward machine-desired outcome. The A. Akash · M. L. Krishna · B. Chandana · K. Janakiram (B) · P. Joshua Reginald Vignan’s Foundation for Science, Technology and Research, Vadlamudi Village, Guntur, Andhra Pradesh, India e-mail: [email protected] A. Akash e-mail: [email protected] P. Joshua Reginald e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_80

897

898

A. Akash et al.

traits of the IoT are dynamic, flexible and the ability to configure. The communication protocols that are suitable, peculiar in identity and assembled as a part of the information network [1]. In this article, the IoT-based agriculture monitoring system begins up with a check of the moisture, humidity, and water level. The levels are notified through SMS to the phone. Sensors monitor the water level and activate the water pump as soon as it dips [2].

1.1 Block Diagram There are six levels in IoT. They are IoT Level-1 (Home Automation), IoT Level-2 (Smart Irrigation), IoT Level-3 (Tracking package handling), IoT Level4 (Noise monitoring), IoT Level-5 (Forest fire detection), and IoT Level-6 (Weather monitoring) (Fig. 1). In a Level-1 Internet of Things system, a single node or device plays the role as the application host, conducts sensing and actuation, saves data, and does detailed study. In IoT system, Level-2 contains a node that handles local analysis, sensing, and actuation [3]. Applications are often cloud-based, and data are saved there. Although the amount of data involved is large, Level-2 IoT devices are appropriate for the solutions where a major study needed is not computationally demanding and can be locally completed. In a Level-3 IoT, one node is present. The cloud is where

Fig. 1 Block diagram of IoT

Smart Agriculture Monitoring System to Prevent Water Wastage

899

data are stored, processed, and used for applications. IoT devices are appropriate for problems with lots of data and demanding processing needs for analysis [4]. Numerous nodes in a Level-4 Internet of Things system execute local analysis. Both the application and the data are hosted on the cloud. The observer nodes at Level-4 are both locally located and cloud-based, and they may subscribe to and receive data that IoT devices have sent to the cloud. Systems at Level-4 of the IoT are appropriate for solutions when several nodes are needed, the volume of data is large, and the analytical needs are computationally demanding. Several end nodes and one coordination node form a Level-5 Internet of Things system. The end nodes conduct actuation or sensing [1]. Data from different end nodes are gathered and sent to cloud via the coordinator node. The cloud is where data are stored, processed, and used for applications. Wireless sensor network-based solutions with large amounts of data and computationally intense analytical needs are best suited for Level-5 IoT systems. A Level-6 IoT system’s several independent end nodes carry out sensing and actuation while sending data to the cloud. Cloud-based applications and data storage are both used. The cloud database is used by the analytics component to evaluate the data and store the findings. With the cloud-based application, the outcomes are displayed. All the end nodes’ statuses are known to the centralized controller, which also delivers control instructions to the nodes. In general, most farmers encounter more difficulties when cultivating in their agricultural lands. Additionally, it has been noted that farmers continue to rely on traditional agricultural practices [5, 6]. It is limiting crop output. They can therefore develop by utilizing current technologies to maximize crop yield, which can be done at low cost with increased productivity. A lot of research has been done on sensor-based irrigation systems. These sensors transmit real-time values to the microcontroller, which then transmits those values to the mobile using serial connection [7]. The system recommends an affordable and simple automatic irrigation system powered by NodeMCU (ESP8266) that can be remotely controlled by an Android smartphone. The user interface of the Android smart phone displays the data that were received from the NodeMCU (ESP8266) [8]. Agricultural modernization may result from the integration of traditional practices with cutting-edge technologies like wireless sensor networks and the Internet of Things. The Wireless Sensor Network is a network of sensors that gathers data from various kinds of sensors and transmits it wirelessly to the main server. In the Table 1, specifications of sensors are mentioned, and by taking all these parameters into consideration, smart agriculture has been performed.

900

A. Akash et al.

Table 1 Sensor’s specifications Sensors/ parameters

Resolution

Operating voltage

Soil moisture

0.4%

3 mA @ 5 VDC

DHT 11

Temperature and humidity both are 3–5 V 16-bit

Operating temperature –40 to + 60 °C 0 –50 °C (temperature) 20–90% (humidity)

2 Literature Review Controlling drip irrigation with a phone is discussed in this paper. For detection, they employ a variety of sensors, including those for humidity, temperature, and light. The sensor uses serial communication to transmit real-time data to the microcontroller and LCD display. In this method, a mobile phone is used for remote monitoring, and the mobile receives commands over the network via the Blynk app, which then turns on the motor automatically. In this paper, Akhund [8] provide a cheap intelligent irrigation system for agriculture. With the integration of cloud, fog, and IoT networks, farming is now managed and monitored remotely and automatically. With the help of a hardware sensor and microcontroller, prototype can measure the temperature, humidity, and water level. To decide whether to switch on or off the motor, they engage several sensors to obtain various readings and values. To describe the system, they offer the fundamental algorithm and flowchart. IoT application-based automated system develops automatic irrigation system and reminds us via mobile SMS with the parameters of the field remotely. In this paper, Sheham et al. [9], an autonomous system is created for a farm that uses IoT technology to run. The goal of the suggested system is to integrate cuttingedge technology in the established irrigation system. Moreover, the suggested method comprises temperature-based fan control systems, intelligent tracking systems, fire and gas detection systems, feed and water level indication control systems, health monitoring systems, and cow dung cleaning mechanisms. Finally, various sensors will gather all data, which will then have managed by an Arduino Nano microcontroller that has IoT characteristics. As a result, users may manage their fields from anywhere and receive mobile real-time data from the farms. Additionally, this suggested approach is both feasible and eco-friendly. Kumar et al. [2] article analyzes an Internet of Things (IoT) application for sustainable farming. It uses a soil moisture sensor module to detect soil moisture and an ESP-8266 Wi-Fi module to communicate over the internet to an Arduino Uno R3 that then switches on a submersible motor pump (motor driver-289D). The computer initiates the communication request on the specified web page of the ThinkSpeak app from the server, which also visually shows the real-time sensor data and the pump status. The prototype was constructed and tested to detect the soil moisture levels in real-time, communicate that information to the Arduino through serial connection over the ESP-8266 Wi-Fi module, and then plot those estimations on the newly formed ThinkSpeak channel.

Smart Agriculture Monitoring System to Prevent Water Wastage

901

Fig. 2 Existing system

3 Functional Analyses 3.1 Existing System In this existing system, they used 8051 microcontroller for the mechanism of motor on/off. Soil moisture sensor is used to determine the water content present in the soil with GSM Modem (Fig. 2).

3.2 Proposed System See Fig. 3.

Fig. 3 Proposed system

902

A. Akash et al.

4 Working Testing and verifying that each electronic component is connected to the proper power source. Then, insert the pipe and soil sensor. Install the Blynk app, then press the hardware’s Wi-Fi connection button. A list of Wi-Fi devices available for connection will appear. The [10] smart agriculture system app will show the real-time soil sensor data following a successful connection. The values of the soil moisture sensor only depend on the soil’s resistivity. The sensor’s value at the start of a wet condition is 0. The measured value is transmitted to the microcontroller through NodeMCU. In this situation, the motor pump turns off. On dry soil, 1023 is the maximum threshold value. The microcontroller activates the relay and turns on the motor when the sensor-sensed value meets the threshold value. The motor pump turns on and off automatically when plants receive enough [11] water. If the soil moisture is less than 50%, it indicates that the soil is dry and needs water, which will be delivered to it automatically. Once there is an enough water, the supply will stop. By hitting the spray on/off button, you may also use the app to spray pesticides or fertilizers. To do so, you must create an additional setup using pipe to spray fertilizer and pesticides.

5 Result and Analysis The main objective of this initiative is to bring cutting-edge technology to sectors like agriculture that are vital to society. Utilizing IoT technology, this system streamlines farm monitoring. In the present agricultural context, the benefits mentioned above, such as saving labor and water, are particularly necessary (Fig. 4). So, it makes sense to use sensor networks to irrigate agricultural areas. IoT data are delivered to the customer by way of the cloud. This makes it simple to identify any changes in the crop and allows for early study. The below figure shows the hardware prototype. Fig. 4 Circuit connection

Smart Agriculture Monitoring System to Prevent Water Wastage

903

Fig. 5 Clear view of LCD output from the circuit

As shown in Fig. 5, the LCD is interfaced with the above circuit. Due to farmers need not to check the above readings in mobile.The data are directly displayed on the LCD screen. From the Fig. 6, the microcontroller communicates the collection of data from the sensors, which is presented as a notification in the Blynk app. The aforementioned notification offers a precise time and date display in addition to automatically turning on and off the motor based on sensor data. Here, in the above Table 2, parameters of temperature and humidity with respect to the time for 6 months have been recorded. Here, the data are acquired on humidity and temperature in the nearby locations from Figs. 6 and 7. It is represented in Fig. 8 graphical view. It is the data and analysis for six months. In the illustration above, the temperature first dropped from 32 to 26.8 °C and raises to 31 °C after 2 weeks and steadily dropping in the third week and so on. Humidity also follows the same manner.

Fig. 6 Notifications in the Blynk app

904 Table 2 Result analysis

A. Akash et al.

Time/parameters

Temperature (Celsius)

Humidity (%)

Day 1

32

71

Day 3

26

76

Week 1

27.5

80

Week 2

30

86

Week 3

28

84

1 Month

33.5

84

2 Months

32

74

4 Months

37

85

6 Months

27

81

Fig. 7 Blynk display of temperature and humidity

6 Conclusion and Future Scope IoT will improve intelligent farming. The technology can monitor and manage the irrigation system by forecasting the humidity and soil moisture levels. IoT helps to improve time management, water management, crop monitoring, soil management, and pesticide and insecticide control in several areas of farming. Additionally, this approach reduces the amount of labor required by humans, streamlines farming practices, and promotes smart farming. In addition to the benefits this system offers, smart farming can expand the farmer’s market with a single touch and less effort. The main disadvantage in this article is given the limited availability of internet in rural

Smart Agriculture Monitoring System to Prevent Water Wastage

905

Fig. 8 Graphical view of temperature and humidity

regions, it should have greater connection, and also the farmers need to understand and have to learn to utilize the technology. The project provides a broad scope for extending the system, making it more userfriendly, and adding new functions like: by integrating a webcam, this information can be uploaded into a database. For those who are less literate, a speech-based option can be added to the system. The integration of Global Positioning System (GPS) can provide the farmer’s precise location as well as more precise weather reports for agricultural fields and gardens. To assist farmers who can only speak their native tongue, a regional language function could be implemented.

References 1. Shoba MS et al. (2022) Survey on IoT based E-farming technology enabled farming. In: 2022 international conference on sustainable computing and data communication systems (ICSCDS). IEEE 2. Nanda KK, Pillai AV, Badri Narayanan MK (2021) Smart agriculture using IoT. Mater Today: Proc 3. Behura A, Satpathy S, Mohanty SN, Chatterjee JM (2022) Internet of things: basic concepts and decorum of smart services. In: Internet of things and its applications, Springer, Cham, pp 3–36 4. Ritika S et al. (2020) A research paper on smart agriculture using IoT. Int Res J Eng Technol (IRJET) 7.07:2708–2710 5. Muhammad A et al. (2019) Internet-of-things (IoT)-based smart agriculture: Toward making the fields talk. IEEE Access 7:129551–129583 6. Ramachandran V, Ramalakshmi R, Srinivasan S (2018) An automated irrigation system for smart agriculture using the internet of things. In: 2018 15th international conference on control, automation, robotics and vision (ICARCV). IEEE 7. Jash D, Patel T, Bharti SK (2019) Smart farming using IoT, a solution for optimally monitoring farming conditions. Proc Comput Sci 160:746–751 8. Tajim Md A et al. (2022) Iot-based low-cost automated irrigation system for smart farming. In: Intelligent sustainable systems. Springer, Singapore, pp 83–91

906

A. Akash et al.

9. Sheham MDNH et al. (2022) Design of an IoT-based smart farming system: IoT-based smart farming system. In: Proceedings of the 2nd international conference on computing advancements 10. Hossain NMR et al. (2021) An IoT based smart irrigation system. In: 2021 2nd international conference on robotics, electrical and signal processing techniques (ICREST). IEEE 11. Prakash A et al. (2021) Reverse pumping based smart agricultural system. In: 2021 7th international conference on electrical energy systems (ICEES). IEEE

Monolithic Integration of Cu(In,Ga)Se2 Thin Film Solar Modules by all Nanosecond Laser Scribing Amol Badgujar , Bhushan Nandwalkar , and Sanjay Dhage

Abstract Cu(In,Ga)Se2 (CIGSe) thin film solar cell (TFSC) is an emerging photovoltaic technology with lab-scale device efficiency surpassing 23% and monolithically integrated module efficiency ranging from 17–19%; it is anticipated to meet escalating global electricity demand. The division of a large photovoltaic cell into serially interconnected smaller devices is known as monolithic integration. To reduce shunting losses, a monolithic integration configuration of CIGSe TFSC comprising stacks of Al:ZnO/i:ZnO/CdS/CIGSe/Mo/Glass is adapted, often by combination of laser-mechanical scribing operations during the device fabrication process. The traditional mechanical scribing procedure, which engages sharp ceramic tips, is sluggish (< 0.2 m/s) and produces broader scribing widths (> 100 µm). The module’s scribing area is a dead zone and a loss of active photovoltaic region that must be minimized. Given this, we report rapid (1 m/s) nanosecond pulsed fiber laser-driven micro-patterning of CdS/CIGSe/Mo/Glass (P2 scribing) and Al:ZnO/i:ZnO/CdS/ CIGSe/Mo/Glass (P3 scribing) stacks, which replaces typical sub-optimal mechanical scribing. The electrical, morphological and compositional analysis of scribed structures confirmed a significant reduction in scribe widths (< 50 µm) using a laser with 1064 nm wavelength and pulse width 25 ns, a commonly utilized configuration for scribing of Mo thin film electrodes. The process eventually reduces the dead zone and increases the overall active area in the module. Keywords Scribing · CIGSe · Thin films · Solar modules · Laser · Nanosecond

A. Badgujar · B. Nandwalkar SVKM’s Institute of Technology, Dhule, Maharashtra 424001, India A. Badgujar (B) · S. Dhage International Advanced Research Centre for Powder Metallurgy and New Materials (ARCI), Hyderabad 500005, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_81

907

908

A. Badgujar et al.

1 Introduction Cu(In,Ga)Se2 (CIGSe) thin film solar cells (TFSC) have emerged as best in the thin film category, with laboratory-size cell efficiencies exceeding 23% and module efficiencies ranging from 17–19% having already been demonstrated [1]. CIGSe can realize commercially viable, cheap flexible and efficient solar modules. Monolithic integration [2] for series interconnection is one of the attractive features of CIGSe TFSC technology, unlike silicon wafer-based solar cells, where the series connection is generally achieved by stringing cells externally [3]. Monolithic integration divides a large device cell into serially interconnected tiny area cells. The series connection is essential as it reduces the loss of power in heat during electric energy transmission from cell to cell. The reduced loss of power corresponds to a decrease in short circuit current because of an increase in the module’s open circuit voltage. CIGSe TFSC, which classically involves a multi-layered stack of Al:ZnO/i:ZnO/CdS/CIGSe/Mo on a glass substrate, monolithic integration comprises three different types of scribing (commonly known as P1, P2 and P3) as shown in Fig. 1. Electrical isolation of Molybdenum (Mo/Glass) is called P1 scribing and is generally processed using a laser [4]. Mechanical patterning of CdS/CIGS up to Mo, followed by i:ZnO/Al:ZnO coating, allows series contact between Mo and bilayer i:ZnO/Al:ZnO, known as P2 scribing. Electrical isolation of the i:ZnO/Al:ZnO layer is known as P3 scribing and is generally done by mechanical means. In our earlier work [5], we reported the development of a monolithically integrated CIGSe thin film solar mini-module by employing laser scribing for P1 and mechanical scribing for P2 and P3 patterning, respectively. The mini-module exhibited power conversion efficiency of 5% compared to lab-scale efficiency of 12.9%.

Fig. 1 Schematic of CIGSe thin film solar module with monolithic interconnects by P1, P2 and P3 scribing

Monolithic Integration of Cu(In,Ga)Se2 Thin Film Solar Modules by all …

909

The significant loss of efficiency could be attributed to broad scribe channels obtained by mechanical needle-based scribing. The total area occupied by the above three scribes and the spacing among them is a dead zone for photovoltaic conversion. It needs to be restricted to a minimum. Therefore, a well-optimized monolithic integration with a minimum dead zone is desirable for manufacturing efficient CIGSe solar cell modules. The optimal laser process usually yields patterns with a narrower width and is desirable. P2 and P3 scribing processes have been reported using picosecond and femtosecond lasers [6–8]. P1 scribing [9] using nanosecond lasers is widely accepted by industry due to its low cost compared to picosecond/femtosecond lasers [10–12]. The use of different lasers types and processes for P1, P2 and P3 adversely affects total capital cost; therefore, the possibility of utilizing the same laser source for all P1, P2 and P3 scribing with inconsequential changes in process parameters would be attractive for thin film solar cell manufacturers as well as promising for further reducing cost of TFSC manufacturing [13]. This work presents the systematic optimization of micro-patterning parameters for P2 and P3 scribing thin film structures employing nanosecond laser ablation using Nd: YAG laser source 1064 nm with a pulse width of 25 ns is presently being used for P1 scribing. The overall dead zone comprising of P1, P2 and P3 scribe are reduced substantially by employing optimized laser parameters.

2 Methodology In the present work, scribing of Mo/Glass, CdS/CIGSe/Mo/Glass and Al:ZnO/i:ZnO/ CdS/CIGSe/Mo/Glass samples of size 300× 300 mm were done employing varied laser and mechanical scribing conditions. The sample preparation of Mo/Glass, CdS/CIGSe/Mo/Glass and Al:ZnO/i:ZnO/CdS/CIGSe/Mo/Glass is discussed in our previous publications [14–18] in detail. A scriber system (Presto, LPKF Solarquipment, Germany) with a 1064 nm Pulsed Nd:YAG laser source and suitable mechanical scribing head with silicon carbide tip was employed. On substrates up to 6 mm in thickness, the scribing system can be operated at axis speeds of up to 2 m/s. As summarized in Table 1, an optimal laser scribing process was obtained by altering the laser energy from 0.1 to 0.75 J/cm2 while keeping the other laser parameters constant. Table 1 Laser parameters employed for P1, P2 and P3 scribing processes accomplished in present work

Laser parameter

Range/value

Spot size

40 µm

Laser energy

0.1–0.75 J/cm2

Pulse duration

25 ns

Pulse repetition frequency

45 kHz

Table scanning speed

0.1–1 m/s

910

A. Badgujar et al.

Electrical isolation was confirmed by measuring electrical resistance using a multimeter (Meco: 45 CF). The surface morphology of the scribed structure and compositions over the scribed channel were studied using a FESEM (S-4300 SE/N, Hitachi, Japan) equipped with EDS unit.

3 Result and Verification In this study, scribing patterns were obtained by the waveform of 1064 nm using a single pulse width of 25 ns for various laser energy fluence and characterized for electrical isolation as well as scribe width was measured of the obtained scribing, summarized in Table 2.

3.1 P1 Scribing P1 scribing by selective removal of Mo/Glass thin films is obtained by glass side ablation. Here laser pulse width, pulse power and pulse repetition frequency were tuned to obtain heat-affected zone (HAZ) free scribe channels yielding essential electrical isolations of order of 20 MΩ. The ablation mechanism is lift-off comprising of absorption of laser energy at the interface of Mo and Glass, partial melting and vaporization followed by thermal straining and breakdown, discussed in detail in our previous publication [19]. The surface profiling of corresponding scribe channel obtained using optimized process parameters (25 ns, 0.25 J/cm2 ) is shown in Fig. 2a. EDS line profile over scribed channel confirms the complete removal of Mo, whereas the exposure of Si from glass substrate is realized in Fig. 2b. Here, the mechanism of ablation is mechanical stress-assisted selective breakdown of Mo thin films to obtain clean HAZ free scribe channel of a width of 53 µm. Table 2 Summary of characteristics of optimal P1, P2 and P3 scribing processes accomplished in present work Process

Sample

Scribing approach Laser

Scribe width (µm)

Electrical isolation (MΩ)

P1

Mo/Glass

53

< 20

P2

CdS/CIGSe/Mo/Glass Mechanical

90

0.51

P2

CdS/CIGSe/Mo/Glass Laser

45

0.54

P3

Al:ZnO/i:ZnO/CdS/ CIGSe/Mo/Glass

Mechanical

205

0.53

P3

Al:ZnO/i:ZnO/CdS/ CIGSe/Mo/Glass

Laser

63

0.61

Monolithic Integration of Cu(In,Ga)Se2 Thin Film Solar Modules by all …

911

Fig. 2 a FESEM of obtained P1 scribed Mo/Glass and b EDS line scan over P1 scribed Mo/Glass structures for optimal laser energy fluence of 0.25 J/cm2

3.2 P2 Scribing P2 scribing by mechanically scratching a thin film by a ceramic or diamond tip is state of art but is limited by high scribing width (> 80–90 µm) and time-dependent wear tear of scribing tool, affecting module performance and throughput for commercialization. The laser beam energy was varied from 0.1 to 0.75 J/cm2 to instigate the necessary ablation energy. The samples could not be entirely scribed at lower laser energy (0.1–0.4 J/cm2 ), and essential electrical isolation was not attained due to impartial scribing. Optimized scribe channels of width 45 µm for the length of 300 mm were realized repeatedly with nanosecond laser processing of CIGSe thin films without damaging underlying Mo back contact using laser energy of 0.5 J/ cm2 . Here, selective removal of CIGSe was attributed to indirect ablation where the difference in thermal expansion was caused by laser energy shock basis breakdown rather than melting and evaporation, resulting in undamaged underlying Mo for interconnection. FESEM micrographs, along with Mo composition across scribe channels obtained by mechanical and laser means, are shown in Fig. 3. Upon comparison of laser and mechanical scribing, the results confirm that the electrical isolation achieved of laser-scribed samples was equivalent to that of mechanical scribing. It is to note that the underlying Mo contacts were damaged for a higher laser energy of 0.6 J/cm2 onwards and substantial HAZ was also observed.

3.3 P3 Scribing Similar to P2 scribing, mechanical means of selective removal of Al:ZnO layer from multilayer stacking of Al:ZnO/i:ZnO/CdS/CIGSe/Mo/Glass is state of the art and has certain limitations of high scribe width, poor reliability, wear and tear of mechanical needle. Therefore, we attempted nanosecond laser ablation of Al:ZnO/i:ZnO/CdS/ CIGSe/Mo/Glass stack with laser energy (0.1–0.75 J/cm2 ) as same as P2 scribing.

912

A. Badgujar et al.

As Al:ZnO is transparent to selected laser wavelength of 1064 nm, indirect ablation induced breakdown of CIGSe layers was attempted thereby removing Al:ZnO stacked on CdS/CIGSe layer. This is consistent with previous report [12] employing nanosecond laser for P3 scribing. The scribing was ineffective with no or partial removal of Al:ZnO layer using laser fluence up to 0.5 J/cm2 , and the electrical isolation was not satisfactory. The electrical isolation was satisfactory and nearly comparable to the scribing obtained by the mechanical method for laser fluence of 0.55 J/cm2 . The P3 scribed width using 0.55 J/cm2 was observed to be 63 µm which is considerably low compared to corresponding mechanical scribing. FESEM micrographs, along with Mo composition across scribe channels obtained by mechanical and laser means, are shown in Fig. 4. The underlying Mo layer was damaged for higher laser fluence of 0.75 J/cm2 onwards. Though satisfactory electrical isolation was obtained using nanosecond laser-driven scribes, substantial amount of debris is clearly realized from FESEM images and needs to be minimized for enhancing reliability of scribing process (Fig. 4). The P1, P2 and P3 processes were optimized for narrow scribe width and desired electrical isolation using the same nanosecond laser source with pulse width of 25 ns. The small window of laser fluence for P2 and P3 scribing is identified. The overall dead zone comprising P1, P2 and P3 is narrowed considerably compared to a slower mechanical process. This work presented all three scribing steps using a

Fig. 3 FESEM of P2 scribed CdS/CIGSe/Mo/Glass obtained by a mechanical needle b laser and EDS line scan over P2 scribed structures obtained by c mechanical needle d laser

Monolithic Integration of Cu(In,Ga)Se2 Thin Film Solar Modules by all …

913

Fig. 4 FESEM of P3 scribed Al:ZnO/i:ZnO/CdS/CIGS/Mo/Glass obtained by a mechanical needle b laser and EDS line scan over P3 scribed structures obtained by c mechanical needle d laser

nanosecond laser which is expected to improve the active solar device area and aids the development of economical fabrication of CIGSe TFSC modules. All nanosecond laser scribing strategy may not be useful for CIGSe TFSC prepared in superstrate configurations and needs further studies in detail.

914

A. Badgujar et al.

4 Conclusion P1 scribing by laser and P2/P3 scribing by the mechanical needle is state of the art to achieve monolithically integrated CIGSe TFSC modules. P2 and P3 scribing was accomplished using a pulsed nanosecond laser with a pulse width of 25 ns employing indirect ablation of the active layer. The scribe width reduction was more than 100% for laser processes compared to mechanical scribing with reliable electrical isolation essential for monolithic integration. P1, P2 and P3 patterning all by nanosecond laser ablation using a pulse width of 25 ns is achieved which substantially reduce the dead zone in the module.

References 1. Green M, Dunlop E, Hohl-Ebinger J, Yoshita M, Kopidakis N, Hao X (2021) Solar cell efficiency tables (version 57). Prog Photovoltaics Res Appl 29:3–15. https://doi.org/10.1002/pip. 3371 2. Neugebohrn N, Haas S, Gerber A, Grimm M, Nissel J, Liebers R, Turan B, Gehrke K, Vehse M (2022) Flexible design of building integrated thin-film photovoltaics. Prog Photovoltaics Res Appl 30:1188–1197. https://doi.org/10.1002/pip.3568 3. Satpathy R, Pamuru V (2021) Manufacturing of crystalline silicon solar PV modules. In: Solar PV Power. Elsevier, pp 135–241. https://doi.org/10.1016/B978-0-12-817626-9.00005-8 4. Ishizuka S, Kamikawa Y, Nishinaga J (2022) Lightweight and flexible Cu(In,Ga)Se2 solar minimodules: toward 20% photovoltaic efficiency and beyond. npj Flexible Electronics 6:90. https://doi.org/10.1038/s41528-022-00224-1 5. Dhage SR, Yadav BS, Jha GK, Badgujar AC (2021) 12.95% efficient Cu(In,Ga)Se2 solar cells by single-step atmospheric selenization, scaled to monolithically integrated modules. ACS Appl Energy Mater 4:286–294. https://doi.org/10.1021/acsaem.0c02254 6. Geˇcys P, Markauskas E, Žemaitis A, Raˇciukaitis G (2016) Variation of P2 series interconnects electrical conductivity in the CIGS solar cells by picosecond laser-induced modification. Sol Energy 132:493–502 7. Kessler F, Hariskos D, Spiering S, Lotter E, Huber HP, Wuerz R (2020) CIGS thin film photovoltaic—approaches and challenges. Presented at the (2020). https://doi.org/10.1007/978-3030-22864-4_9 8. Geˇcys P, Markauskas E, Nishiwaki S, Buecheler S, de Loor R, Burn A, Romano V, Raˇciukaitis G (2017) CIGS thin-film solar module processing: case of high-speed laser scribing. Sci Rep 7:40502. https://doi.org/10.1038/srep40502 9. Suryavanshi PS, Panchal CJ (2022) Nanosecond pulse fiber laser patterning of bilayer molybdenum thin film on 2-sq inch soda-lime glass substrate for CIGS thin film solar cell applications. J Opt. https://doi.org/10.1007/s12596-022-00861-9 10. Burn A, Heger C, Buecheler S, Nishiwaki S, Bremaud D, Ziltener R, Krainer L, Spuehler G, Romano V (2016) High throughput P2 laser scribing of Cu(In,Ga)Se2 thin-film solar cells. Presented at the March 14 (2016). https://doi.org/10.1117/12.2212496 11. Heise G, Börner A, Dickmann M, Englmaier M, Heiss A, Kemnitzer M, Konrad J, Moser R, Palm J, Vogt H, Huber HP (2015) Demonstration of the monolithic interconnection on CIS solar cells by picosecond laser structuring on 30 by 30 cm2 modules. Prog Photovoltaics Res Appl 23:1291–1304. https://doi.org/10.1002/pip.2552 12. Markauskas E, Zubauskas L, Raˇciukaitis G, Geˇcys P (2020) Damage-free patterning of thermally sensitive CIGS thin-film solar cells: can nanosecond pulses outperform ultrashort laser pulses? Sol Energy 202:514–521. https://doi.org/10.1016/j.solener.2020.03.112

Monolithic Integration of Cu(In,Ga)Se2 Thin Film Solar Modules by all …

915

13. Valtonen V-M, Roozbahani H, Alizadeh M, Handroos H, Salminen A (2022) Real-time monitoring and defect detection of laser scribing process of CIGS solar panels utilizing photodiodes. IEEE Access 10:29443–29450. https://doi.org/10.1109/ACCESS.2022.3158355 14. Badgujar AC, Dhage SR, Joshi SV (2015) Process parameter impact on properties of sputtered large-area Mo bilayers for CIGS thin film solar cell applications. Thin Solid Films 589. https:/ /doi.org/10.1016/j.tsf.2015.04.046 15. Yadav BS, Badgujar AC, Dhage SR (2017) Effect of various surface treatments on adhesion strength of magnetron sputtered bi-layer Molybdenum thin films on soda lime glass substrate. Solar Energy 157 16. Dhage SR, Badgujar AC (2018) Transparent conducting Al:ZnO thin films on large area by efficient cylindrical rotating DC magnetron sputtering. J Alloys Compd 763. https://doi.org/ 10.1016/j.jallcom.2018.05.234 17. Badgujar AC, Yadav BS, Jha GK, Dhage SR (2022) Room temperature sputtered aluminumdoped ZnO thin film transparent electrode for application in solar cells and for low-band-gap optoelectronic devices. ACS Omega 7. https://doi.org/10.1021/acsomega.2c00830 18. Dhage SR, Yadav BS, Jha GK, Badgujar AC (2021) 12.95% Efficient Cu(In,Ga)Se2 solar cells by single-step atmospheric selenization, scaled to monolithically integrated modules. ACS Appl Energy Mater 4. https://doi.org/10.1021/acsaem.0c02254 19. Badgujar AC, Joshi SV, Dhage SR (2018) Process parameter impact on selective laser ablation of bilayer molybdenum thin films for CIGS solar cell applications. Mater Focus 7:556–562. https://doi.org/10.1166/mat.2018.1540

Parametric Optimization in Nd:YAG Laser Micro-drilling of Carbon Black/Epoxy Composite Utilizing GRA and Response Surface Methodology Lipsamayee Mishra, Trupti Ranjan Mahapatra, Soumya Ranjan Parimanik, Sushmita Dash, and Debadutta Mishra

Abstract Quality enhancement of micro-hole in laser drilling of thermoset-based composites is a prime focus of the researchers in recent times. When lasers are used to drill holes in polymer composites, the heat-affected zone and taper are the most important quality parameters. Dispersing thermally conductive fillers across polymer composites to increase their heat transmission properties during laser drilling is one of the potential ways that can be used to reduce the heat-affected zone. In this study, an effort has been made to investigate the taper and the heat-affected zone during pulsed Nd:YAG laser micro-drilling on a 3 mm thick epoxy-based nanocomposite specimen with optimum 6 wt.% of carbon black and further optimize them using gray relational analysis and response surface methodology. Input variables such as cutting speed, lamp current, pulse frequency, and air pressure have been experimentally changed for 27 combinations that are developed with the help of Box–Behnken design. A confirmatory test has been carried out with the optimal parameter setting thus obtained to validate the optimum results. Keywords Nd:YAG laser micro-drilling · Carbon black · Gray relational analysis · Response surface methodology

L. Mishra · T. R. Mahapatra (B) · S. R. Parimanik · D. Mishra Production Engineering Department, Veer Surendra University of Technology, Burla, Odisha, India e-mail: [email protected] S. Dash Department of Mechanical Engineering, GITA Autonomous College, Bhubaneswar, Odisha, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_82

917

918

L. Mishra et al.

1 Introduction Due to the advantages of high cut quality and cost-effectiveness, laser machining has various uses in many industrial manufacturing processes. The focused laser beam melts the material that needs to be machined. The assist gas is also used to blow away the melt, creating a kerf as it flows coaxially with the laser beam. To reduce cutting costs by increasing cutting speed, it is essential to choose a suitable gas type or a mixture of gases having a specific mixing proportion [1]. Polymer nanocomposites (PNCs), where at least one of the filler phase’s dimensions is smaller than 100 nm, are a relatively new type of nanoscale materials [2]. The possibility to investigate new behavior and functionalities beyond those of standard materials is provided by polymer nanocomposites [3]. Due to smaller interparticle distances, the incorporation of nanofillers has a significant impact on the properties of composites [4]. Even though there has been a lot of research on laser drilling of polymer composites (PMCs), there is a wide window open to understand the current state of laser drilling in general and the PMCs in particular. Tamrin et al. [5] studied the optimized joint characteristics in CO2 laser cutting of dissimilar material employing gray relational analysis. Jain et al. [6] implemented the multi-objective genetic algorithm (MOGA) based on response surface methodology (RSM) to attain maximum circularity with minimum HAZ in basalt–glass hybrid composite using the laser. Liu et al. [7] proposed the feasibility of producing high-quality micro-hole via experimental investigation and optimization in 2.5D Cf/SiC composite. A study made by Mishra et al. [8] proposed a model comprising the Artificial Neural Network (ANN) and finite element method (FEM) for laser beam percussion drilling. Kumar and Gururaja [9] investigated the influence of laser frequency and line energy on the output responses like the taper, HAZ, metal composite interface damage (MCI), circularity, and surface roughness on a TI/CFRP/TI laminate using CO2 laser. From the review of literature and to the best of author’s knowledge, the investigation on the laser drilling of PMCs containing carbon black as reinforcement material is yet to be reported. Therefore, the present research aims to (i) prepare a novel polymer composite having carbon black as reinforcing material, (ii) perform laser micro-drilling on such composite to avoid chipping, cracking, and delamination, (iii) implement RSM and GRA to determine the optimum input characteristics to minimize the taper and HAZ during laser micro-drilling.

2 Methods, Material, and Experimental Procedure The methodology used for this research paper has been segregated into four stages. The first step involves the fabrication of the composite specimen containing optimum percentage of carbon black nanomaterial as reinforcement and epoxy as matrix via compression molding method. In the experimentation phase, drilling operations on the currently prepared nanocomposite have been carried out using a variety of input

Parametric Optimization in Nd:YAG Laser Micro-drilling of Carbon …

919

parameters. The diameters corresponding to inlet and exit of the hole and the HAZ have been measured using scanning electron microscope (SEM). The data from the experiments have been normalized, and the gray relational grades (GRG) are computed for multi-objective optimization in the third phase. In the last phase, the RSM has been implemented for multiple performance optimization and the results obtained are compared with those attained through the GRA. Validation of the results through the confirmatory experiments is also performed at this stage.

2.1 Workpiece Material The polymeric composite specimen has been fabricated using filler material as carbon black (6 wt.%) and diglycidyl ether of bisphenol (DGEBA), which is a commercial epoxy resin favorably used in industrial applications (marketed as Araldite GY 257) used as the matrix material. The average diameter of carbon black (VULCAN XC72R) used is 20–60 nm as observed from the Transmission Electron Microscopy (TEM) analysis.

2.2 Laser Beam Machining Unit In this research, a pulsed CNC Nd:YAG laser machine that includes a variety of subsystems, namely the power supply unit (PSU), a laser source, radio frequency Q-switch driver unit, a CNC controller unit (CCU), to facilitate X, Y, and Z axis movement, a beam delivery unit (BDU), a cooling unit (CU), and a compressed air supply unit, has been used for the conduction of the micro-drilling experiments.

2.3 Design of Experiment A suitable design of experiments is accomplished utilizing Box–Behnken design based on the RSM. A total of four input parameters with three levels each are chosen. Table 1 shows the laser parameters and their levels. Thus, 27 numbers of trials have been designed to determine optimal outcomes for the taper and the HAZ in a 3 mm thick of presently prepared nanocomposite material. After drilling the material, the taper and the HAZ have been measured using a HITACHI make SU3500 SEM. The measured values for the performance measuring indices are depicted in Table 2.

920

L. Mishra et al.

Table 1 Laser micro-drilling control parameters and their levels Symbol

Cutting parameters

Low

Medium

High

X1

Cutting speed (m/s)

30

90

150

X2

Lamp current (amp)

22

24

26

X3

Pulse frequency (kHz)

3

9

15

X4

Air pressure (kg/cm2 )

1

3

5

Table 2 DOE and responses for the laser micro-drilling of currently prepared nanocomposites S. No.

Cutting speed (m/s)

Lamp current (amp)

Pulse frequency (kHz)

Air pressure (kg/cm2 )

Taper (degree)

HAZ (µm)

1

90

24

3

1

3.0680

546.3052

2

150

26

9

3

4.1239

531.9072

3

150

24

3

3

3.3626

550.4902

4

90

22

3

3

3.4862

557.5487

5

90

22

9

5

4.9788

533.6896

6

90

22

15

3

3.5663

487.4769

7

30

24

9

1

3.3985

552.4674

8

90

24

9

3

4.9162

534.3895

9

90

24

15

5

3.4466

481.7331

10

90

26

9

1

6.5596

662.6323

11

90

24

9

3

5.1086

615.1727

12

90

26

9

5

5.5109

543.9106

13

150

22

9

3

5.6626

531.6423

14

90

24

9

3

5.1269

624.7468

15

150

24

15

3

2.8910

578.0156

16

30

24

15

3

2.2467

461.5251

17

90

22

9

1

5.8038

533.9348

18

90

26

15

3

2.8839

509.4197

19

150

24

9

1

4.0235

533.1036

20

90

24

3

5

4.5906

530.7595

21

30

22

9

3

3.9101

500.2653

22

90

24

15

1

3.2123

508.2213

23

30

24

9

5

3.9341

434.7473

24

150

24

9

5

4.5997

530.5178

25

90

26

3

3

3.7648

519.2559

26

30

24

3

3

2.0732

458.4032

27

30

26

9

3

3.6604

443.3365

Parametric Optimization in Nd:YAG Laser Micro-drilling of Carbon …

921

2.4 GRA Method The GRA is a powerful tool that is commonly utilized for the optimization of multiple performance characteristics [10]. The method involves the normalization of the experimental data between 0 and 1 [11] according to the “Nominal-the-best”, “Lowerthe-better”, and “Higher-the-better” criteria. Here, the lower-the-better approach has been adopted to minimize the taper and the HAZ, and the normalization of the data is achieved by following Eq. (1). Yin =

max(E in ) − E in . max(E in ) − min(E in )

(1)

Yin is the value after gray relational normalization. Subsequently, the gray relational coefficient (GRC) is calculated using Eq. (2) ϕin =

Δmin + σ Δmax . Δin + σ Δmax

(2)

Here, ϕin is the GRC, σ is the identification coefficient, and Δmin Δmax are the smallest and largest values of the absolute difference. Finally, the gray relational grade (GRG) values (χi ) are computed for the total number of number of responses (z) with the help of Eq. (3). χi =

1Σ ϕin . z

(3)

3 Analysis of Experimental Results The GRC and GRG have been calculated for the taper and the HAZ and are shown in Table 3. The GRG values corresponding to each experiment are also shown in Fig. 1. The input variables used in experiment number 23 (X 1 = 30 m/s, X 2 = 24 amp, X 3 = 9 kHz, X 4 = 5 kg/cm2 ) result the highest GRG value (0.77328) and are close to the idealized value of 1. The ANOVA result for the obtained GRG values is depicted in Table 4. Based on the p values, it is inferred that the cutting speed is the most significantly contributing for the improvement of GRG. Figure 2 displays the pareto chart of the standardized effects describing the significant link between the individual and the combination of input parameters with the GRG.

922

L. Mishra et al.

Table 3 Data preprocessing, GRC, and GRG S. No.

Taper

HAZ

Normalization

Deviational sequence

GRC

Taper

Taper

Taper

HAZ

HAZ

GRG HAZ

1

3.0680

546.3052

0.7783

0.5105

0.2217

0.4895

0.6928

0.5053

0.5990

2

4.1239

531.9072

0.5429

0.5736

0.4571

0.4264

0.5224

0.5397

0.5311

3

3.3626

550.4902

0.7126

0.4921

0.2874

0.5079

0.6350

0.4961

0.5655

4

3.4862

557.5487

0.6850

0.4611

0.3150

0.5389

0.6135

0.4813

0.5474

5

4.9788

533.6896

0.3523

0.5658

0.6477

0.4342

0.4357

0.5352

0.4855

6

3.5663

487.4769

0.6672

0.7686

0.3328

0.2314

0.6004

0.6836

0.6420

7

3.3985

552.4674

0.7046

0.4834

0.2954

0.5166

0.6286

0.4918

0.5602

8

4.9162

534.3895

0.3663

0.5628

0.6337

0.4372

0.4410

0.5335

0.4873

9

3.4466

481.7331

0.6939

0.7938

0.3061

0.2062

0.6203

0.7080

0.6641

10

6.5596

662.6323

0.0000

0.0000

1.0000

1.0000

0.3333

0.3333

0.3333

11

5.1086

615.1727

0.3234

0.2083

0.6766

0.7917

0.4250

0.3871

0.4060

12

5.5109

543.9106

0.2337

0.5210

0.7663

0.4790

0.3949

0.5107

0.4528

13

5.6626

531.6423

0.1999

0.5748

0.8001

0.4252

0.3846

0.5404

0.4625

14

5.1269

624.7468

0.3193

0.1662

0.6807

0.8338

0.4235

0.3749

0.3992

15

2.8910

578.0156

0.8177

0.3713

0.1823

0.6287

0.7328

0.4430

0.5879

16

2.2467

461.5251

0.9613

0.8825

0.0387

0.1175

0.9282

0.8097

0.8690

17

5.8038

533.9348

0.1685

0.5647

0.8315

0.4353

0.3755

0.5346

0.4551

18

2.8839

509.4197

0.8193

0.6723

0.1807

0.3277

0.7345

0.6041

0.6693

19

4.0235

533.1036

0.5653

0.5684

0.4347

0.4316

0.5349

0.5367

0.5358

20

4.5906

530.7595

0.4389

0.5787

0.5611

0.4213

0.4712

0.5427

0.5070

21

3.9101

500.2653

0.5906

0.7125

0.4094

0.2875

0.5498

0.6349

0.5924

22

3.2123

508.2213

0.7461

0.6776

0.2539

0.3224

0.6632

0.6080

0.6356

23

3.9341

434.7473

0.5852

1.0000

0.4148

0.0000

0.5466

1.0000

0.7733

24

4.5997

530.5178

0.4369

0.5797

0.5631

0.4203

0.4703

0.5433

0.5068

25

3.7648

519.2559

0.6229

0.6292

0.3771

0.3708

0.5701

0.5742

0.5721

26

2.0732

458.4032

1.0000

0.8962

0.0000

0.1038

1.0000

0.8281

0.9140

27

3.6604

443.3365

0.6462

0.9623

0.3538

0.0377

0.5856

0.9299

0.7578

3.1 Multiple Response Optimization Using RSM The RSM is an assortment of mathematical and statistical techniques helpful for modeling and analysis of the relationship between several explanatory variables with one or more response variables [12]. Using the RSM, the optimization for the process parameters for individual as well as simultaneous minimization of the taper and the HAZ has been performed by tuning the parameters about their target values to get the optimal parameter settings. The desirability of the optimization parameters

Parametric Optimization in Nd:YAG Laser Micro-drilling of Carbon … Fig. 1 GRG values corresponding to each experiment

923

0.9 0.8

GRG

0.7 0.6 0.5 0.4 0.3 0

5

10

15

20

25

30

Experiment Number

Table 4 Analysis of variance for GRG Source

DF

Adj SS

Adj MS

F-value

P-value

Model

4

0.154393

0.038598

2.53

0.069

Linear

4

0.154393

0.038598

2.53

0.069

X1

1

0.135886

0.135886

8.92

0.007

X2

1

0.001443

0.001443

0.09

0.761

X3

1

0.010971

0.010971

0.72

0.405

X4

1

0.006092

0.006092

0.40

0.534

Error

22

0.335116

0.015233

Lack-of-fit

20

0.330315

0.016516

6.88

0.134

Pure error

2

0.004801

0.002400

26

0.489508

Total

has furthermore evaluated to illustrate the feasibility of the model, i.e., to analyze if all the parameters are inside their working range or not [13]. The single objective optimization (minimize) plots for the taper and HAZ are shown in Fig. 3a and b, respectively. The desirability values in both cases are obtained as 1, thus verifying that each parameter falls inside their working length. The predicted value for the minimum taper is 1.4683 degree with the parameter setting (X 1 = 30 m/s, X 2 = 22 amp, X 3 = 3 kHz, X 4 = 1 kg/cm2 ). The predicted value for minimum HAZ is 322.4686 µm with the parameter setting (X 1 = 30 m/s, X 2 = 26 amp, X 3 = 15 kHz, X 4 = 5 kg/cm2 ). Figure 4 displays the multi-objective optimization plot for the minimization of the taper and the HAZ simultaneously. A composite desirability value 1 is attained. It is observed that for the combination of parameters at (X 1 = 30 m/s, X 2 = 24.1818 amp, X 3 = 15 kHz, X 4 = 5 kg/cm2 ), minimum taper and HAZ are predicted as 1.8620 degree and 366.5823 µm, respectively.

924

L. Mishra et al.

Fig. 2 Pareto charts of the standardized effects for the obtained GRG values

Fig. 3 Optimization plot for taper and HAZ

3.2 Confirmatory Test To validate the results obtained by the GRA approach and RSM-based desirability approach, confirmation tests are performed. Experiments are conducted by using the optimal parameters’ setting obtained via the GRA and RSM, and the corresponding responses along with the predicted values are provided in Table 5. The confirmation results obtained from SEM for the GRA and the RSM are shown in Fig. 5a and b. It is observed that the percentage error in the case of the RSM is less than 10% (8.65% for taper and 9.88% for HAZ) demonstrating the acceptability of the optimum parameter settings thus obtained. Moreover, improvement in the taper (48%) and the HAZ (9.88%) values are attained by the implementation of the RSM with respect to those obtained via the GRA.

Parametric Optimization in Nd:YAG Laser Micro-drilling of Carbon …

925

Fig. 4 Optimization plot for all responses Table 5 Experimental validation Method

Optimum setting X1

X2

X3

X4

Taper (degree)

HAZ (µm)

Predicted

Predicted

Experimental

GRA

30

24

9

5

–

3.9341

RSM

30

24.1818

15

5

1.862

2.0384

– 366.5823

Experimental 434.7473 406.7528

Fig. 5 SEM image of the hole obtained from confirmatory test for GRA and RSM

926

L. Mishra et al.

4 Conclusion The pulsed Nd:YAG laser micro-drilling is performed on the epoxy-based nanocomposite incorporating carbon black nanofillers. Experiments are performed according to the Box–Behnken design of experiment, and the taper and the HAZ of the drilled hole are acquired through SEM. The GRA and the RSM have been implemented to obtain the optimal parameter setting for simultaneously minimizing taper and the HAZ, and the results are compared by conducting confirmatory tests. From the GRA, the cutting speed is found to be significantly influencing both the responses. The RSM is observed to be outperforming the GRA in terms of optimizing the taper and HAZ simultaneously with less than 10% error. The optimal parameter setting for minimal taper and HAZ is obtained as cutting speed 30 m/s, lamp current 24.1818amp, pulse frequency 15 kHz, and assisted air pressure 5 kg/cm2 . The confirmation test also indicated improvements in tapper and HAZ by 48 and 9.88% through the RSM as compared to the GRA.

References 1. Chakraborty DGS (2015) A study on the optimization performance of fireworks and cuckoo search algorithms in laser machining processes. J The Institut Engineers (India): Series C 96(3):215–229 2. Gauri SK, Pal S (2010) Comparison of performances of five prospective approaches for the multi-response optimization. Int J Adv Manuf Technol 48(9–12):1205–1220 3. Reddy VV, Mandava RK, Rao V, Mandava S (2022) Optimization of dry sliding wear parameters of Al 7075 MMC’s using Taguchi method. Mater Today Proc 62:6684–6688 4. Mishra L, Mahapatra TR, Mishra D (2022) Performance evaluation and sustainability assessment in laser micro-drilling of carbon nanotube—reinforced polymer matrix composite using MOORA and whale optimization algorithm. Process Integra Optim For Sustain 6:603–620 5. Tamrin KF, Nukman Y, Sheikh NA, Harizam MZ (2014) Determination of optimum parameters using grey relational analysis for multi-performance characteristics in CO2 laser joining of dissimilar materials. Opt Lasers Eng 57:40–47 6. Jain A, Singh B, Shrivastava Y (2019) Reducing the heat-affected zone during the laser beam drilling of basalt-glass hybrid composite. Compos Part B: Eng 176:107294 7. Liu C, Zhang X, Gao L, Jiang X, Li C, Yang T (2021) Feasibility of micro-hole machining in fiber laser trepan drilling of 2. 5D Cf/SiC composite:experimental investigation and optimization. Optik (Stuttg) 242:167186 8. Mishra DR, Bajaj A, Bisht R (2020) Optimization of multiple kerf quality characteristics for cutting operation on carbon–basalt–Kevlar29 hybrid composite material using pulsed Nd:YAG laser using GRA. CIRP J Manuf Sci Technol 30:174–183 9. Kumar D, Gururaja S (2020) Investigation of hole quality in drilled Ti/CFRP/Ti laminates using CO2 laser. Optics and Laser Technol 126:106130 10. Reddy S, Reddy KV, Mandava RK (2022) Optimization of turning process parameters using entropy-gra and dear methods. recent advances in industrial production. Lecture Notes in Mechanical Engineering. Springer, Singapore 11. Panda S, Mishra D, Biswal BB (2011) Determination of optimum parameters with multiperformance characteristics in laser drilling-a grey relational analysis approach. Int J Adv Manuf Technol 54(9–12):957–967

Parametric Optimization in Nd:YAG Laser Micro-drilling of Carbon …

927

12. ChittaranjanDas C (2016) Response surface methodology and desirability approach to optimize EDM parameters. Int J Hybrid Inform Technol 9(4):393–406 13. Mandava RK (2022) Wear and frictional behavior of Al 7075/FA/SiC hybrid MMC’s using response surface methodology. pp 1–14

Static Structural Analysis of Below Knee Prosthesis Using Fea Prabhakar Nandivada, Jagana Nikhil, I. Arun Kumar, and Ravi Kumar Mandava

Abstract The main aim of this article is to conduct the static structural analysis of below knee prosthetic leg using finite element analysis (FEA). The structure of below knee prosthetic joint consists of six main components: Socket, Pylon/Shank, Frontal, Hind Calcaneus, Feet joint, Universal joint. Initially, the below knee prosthetic leg was modeled using Creo software. Furthermore, an ANSYS Workbench 2022 R2 was used to perform a finite element analysis of below knee joint model. Static structural analysis is carried out with different loads of magnitudes 50N, 60N, 70N, 80N, 90N which was applied on the inner surface of the socket. The result shows that the finite element model imparting the induced stress distribution in the prosthesis to ensure its safe performance in the fatigue life has been developed. Keywords Static structural analysis · FEA · Knee prosthesis · Von Mises stresses

1 Introduction Prosthetic leg is an artificial device that replaces a missing leg. Due to various illnesses, chronic medical conditions, and accidents, many people have lost their natural limbs. Depending on the criteria and level of disability, the prevalence of disability in SouthEast Asia ranges from 1.5 to 21.3% of the total population [1]. Lower limb amputations do not involve any complexity, while above-knee amputations result in the loss of a person’s most complicated joint, the knee [2] which poses stability, toe clearance, and load-sharing challenges to prosthetic designers. It permits movement between the femur, tibia, and patella and is the first joint of the P. Nandivada (B) · J. Nikhil · I. A. Kumar Department of Mechanical Engineering, Maulana Azad National Institute of Technology Bhopal, Bhopal 462003, India e-mail: [email protected] R. K. Mandava Department of Mechanical Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kurnool, Kurnool 518007, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_83

929

930

P. Nandivada et al.

lower limb [3]. Despite the rising prevalence of disability worldwide, its assessment, management, and prevention have received little attention for a variety of reasons [1]. Prostheses that are currently accessible are created to be so cosmetically accurate that barely anyone can even tell that it is not the original one [4]. From the basic pendulum knees of the 1600 s to those controlled by rubber bands and springs, pneumatic or hydraulic components, prosthetic knees have seen significant development through time. Some knee units now feature sophisticated motion control modified by microprocessors. Not everyone can go for the newest or most advanced prosthetics. Amputation is one of the most ancient of all surgical treatments, its history dating back as far as the sixteenth century. Ambroise Pare´ was the first to use ligatures to control bleeding after amputation and also designed relatively sophisticated prostheses [5]. Some amputees place a higher priority on stability and safety than functional capability. In addition to the design’s consideration of stability and user friendliness, safety is a key factor. For safe ambulation, the prosthetic knee demands the highest level of control [6]. The use of FEM is a very powerful tool for structural investigation. Application of FEM is an efficient tool for structural analysis and thus saving time and money of manual testing in laboratories [2]. Stress analysis is a good way to figure out the stresses and strains on any given material, and those materials are subjected to any particular load and forces in any direction [7]. By examining the factor that could contribute to the failure and damage of a given model, stress analysis is used to keep that structure functional and maintain its structure. Any geometrically specified structure that consists load is subjected to stress analysis in order to examine the material’s characteristics there. When used for the activities for which it was intended, a prosthetic design that achieves an ideal interface stress distribution will be safe to use and comfortable to wear for extended periods of time. On the other hand, a design that causes an incorrect stress distribution may cause pain or discomfort that, in the worst instance, could necessitate re-amputation at a higher anatomical level and result in blisters, cysts, or ulcers in the remaining limb [8]. Due to the requirement of transmitting strong compressive and shear stresses between the prosthesis and residual limb, there is a significant occurrence of skin problems [9]. The main objective of this work is to check the induced stresses and strains in the prosthesis due to various loads using finite element method. In the current research work, authors have developed a unique model for below knee prosthesis using Creo software. Further, finite element method was used to conduct static structural analysis on the developed prosthesis using Ansys 2022R2. Directional deformation in X, Y, and Z axes, total deformation, Von Mises stress, and equivalent elastic strain were successfully obtained. We recorded how this data varied under different loads of magnitudes 50, 60, 70, 80 and 90N. Mesh independence results of generated Von-Mises stress are computed by changing the resolution of meshed model.

Static Structural Analysis of Below Knee Prosthesis Using Fea Table 1 Physical properties of aluminum

931

Physical property

Value

Density (kg/m3 )

2689

Modulus of elasticity (GPa)

70

Poisson’s ratio

0.33

Shear modulus (GPa)

26.316

Bulk modulus (GPa)

68.672

2 Methodology In this below knee prosthesis analysis, aluminum is used for every component, and the physical properties of this material are listed in Table 1. A strong numerical technique called finite element (FE) can be used to address issues including complex geometry, material properties, and loading [10]. When compared to experimental observations, the speed with which complex circumstances can be parametrically analyzed is FE analysi’s most potent capability. Without having to construct prostheses, it is preferable to methodically explore the impact of numerous parameters [11]. The stress distributions on a prosthetic knee implant or knee joint replacement are analyzed using FEM. In order to create more durable knee implants, a better understanding of the stress contour is necessary [12]. The original design and 3D Creo parametric geometry model were developed and imported to Ansys Design Modeler as shown in Fig. 1. It consists of six components which are socket, pylon/shank, feet joint, hind calcaneus, frontal, and universal joint. Apart from the imported geometry, all the remaining procedures are carried out in Ansys Mechanical. After assigning the desired material to the prosthesis, it is required to modify the contacts between various parts. In this design, we used two types of linear contacts: Bonded and no separation contacts. Then, mesh was generated using automatic method and the information regarding number of nodes and elements in the meshed model is given in Table 2. Then, loading and boundary conditions were given in static structural part of outline. The surfaces which come in contact with ground were fixed as shown in Fig. 2. In this analysis, five different loads are used which are of magnitudes 50, 60, 70, 80 and 90N, and these loads are applied individually on inner surface of socket in vertically downward direction. Once these inputs were given, the solution is obtained by solving the problem.

3 Results and Verification Number of parameters related to the strength of model can be obtained through this Ansys software. Some of them are deformations, equivalent stresses and strains, principal stresses and strains, strain energy, shear stresses, and strains. In this study,

932

P. Nandivada et al.

Fig. 1 Below knee prosthesis model Table 2 Mesh information

Number of nodes:

18,003

Number of elements:

8799

Mesher used:

Standard mesh

Smoothing:

Medium

Element size:

Default

Mesh defeaturing:

Yes

Resolution:

2

Fig. 2 Surfaces which were fixed

Static Structural Analysis of Below Knee Prosthesis Using Fea

933

Von Mises stresses and strains, total deformation, directional deformations in X, Y, and Z axes are generated for the model.

3.1 Directional Deformation The displacement of the prosthesis in a certain axis or defined direction is referred to as directional deformation. Directional deformation in all the three axes is calculated in this analysis. From the results, it is observed that irrespective of the chosen axes and loads, maximum deformation occurs at foot region. Directional deformation in X, Y and Z axes for a load of 50N is shown in Figs. 3, 4 and 5, respectively. To compare the maximum directional deformations in each axis generated in prosthesis for different loads, a line graph was plotted as shown in Fig. 6. Loads are assigned to X-axis, whereas deformations in mm are given along y-axis. From

Fig. 3 Directional deformation in Z-Axis at a load of 50 N

Fig. 4 Directional deformation in X-Axis at a load of 50N

934

P. Nandivada et al.

Fig. 5 Directional deformation in Y-Axis at a load of 50N

Deformation in mm

0.025 0.02 0.015 0.01 0.005 0 50

60

70

Directional Deformation in X-Axis

80

90

Directional Deformation in Y-Axis

Directional Deformation in Z-Axis

Fig. 6 Graph shows the directional deformations against loads

the graph, it is observed that the deformations are increasing with increase in load linearly. It can also be said that the maximum deformation in the model for every load is occurring in X-axis because the geometry of the prosthesis is not symmetrical, so the load applied was distributed more along the X-axis at standing position. The maximum deformation in prosthesis is occurring in X-axis direction when the load is 90N, whereas the minimum deformation is occurring in Z-axis direction when the load is 50N.

3.2 Total Deformation Total deformation is defined as the square root of sum of squares of directional deformation in X, Y, Z axes which was shown in Fig. 7. Total deformation induced in

Static Structural Analysis of Below Knee Prosthesis Using Fea

935

the prosthesis is maximum when the load is 90N and its value is 1.32 mm. Maximum deformation is occurring in the upper half of socket for all the loads. Average total deformation in the prosthesis for different loads is compared using a bar graph as shown in Fig. 8. The values of average total deformation have gradually increased with the increase in magnitude of load.

Fig. 7 Total deformation of the prosthesis at a load of 90N

Average total deformation 0.2 0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 50

60

70 Average total deformation

Fig. 8 Bar graph shows the average total deformation against load

80

90

936

P. Nandivada et al.

Maximum Equivalent stress

60 50 40 30 20 10 0 50

60

70

80

90

Maximum Equivalent stress (in Mpa) Fig. 9 Bar graph shows the maximum equivalent stress against load

3.3 Equivalent Stresses (Von mises stresses) The results of equivalent stresses induced in the prosthesis depict that these stresses are considerable around universal joint. Except the universal joint, stresses induced in the model are negligible. Therefore, the maximum stress is induced in universal joint, and data related to maximum stress induced in prosthesis is given in Fig. 9. There is a chance that results might vary with number of nodes and elements in the meshing model which means that stresses and deformations can change with the resolution given to the mesh. Figure 10 shows how Von-Mises stresses vary with number of nodes in the mesh, and this data is provided for all the applied loads. The number of nodes and elements in the mesh are changed by changing mesh resolution. As shown, equivalent stresses are higher when the applied load is 90N and minimum when it is 50N.

3.4 Equivalent Elastic Strain Similar to the outcome of equivalent stresses, equivalent strains were also only appreciable around universal joint. The maximum elastic strain is produced when the load is 90N that is 0.00082459. Figure 11 depicts the values of maximum equivalent elastic strain for different loads. Since strain is directly proportional to change in length (deformation), strain with increases with increase in deformation. As deformation has increased with the load, equivalent elastic strain also increases.

Static Structural Analysis of Below Knee Prosthesis Using Fea

937

Mesh Independence Study 120

Von-Mises Stress

100 80 60 40 20 0 18003

21566

39306

56535

Number of nodes 50N

60N

70N

80N

90N

Fig. 10 Mesh independence study of generated Von Mises stresses Maximum Equivalent Elastic Strain 0.0009 0.0008 0.0007 0.0006 0.0005 0.0004 0.0003 0.0002 0.0001 0 50

60

70

80

90

Maximum Equivalent Elastic Strain( mm/mm )

Fig. 11 Bar graph shows the maximum equivalent elastic strain against loads

4 Conclusions The CAD model was developed by using Creo Parametric and then imported to ANSYS Workbench 2022 R2 which was used to perform the static structural analysis of this below knee prosthesis model. The five different loads 50N, 60N, 70N, 80N, 90N applied, and the results obtained total deformations, directional deformations in X, Y, Z axes, von-misses stresses, and equivalent elastic strains for each load. All the results have recorded at maximum values when the load is 90N. For all the applied loads, directional deformation in X axis is greater when compared to Y and Z axes.

938

P. Nandivada et al.

The total deformation is maximum in the upper half of socket, i.e., 1.32 mm when the load is 90N, and it is minimum in the lower half of foot part of the prosthesis. Von misses stresses developed in the model are maximum in universal joint region for each and every applied load, and the maximum value obtained is 56.093 MPa when the load is 90N. Even though the load is applied on the inner of socket, the stress developed in the socket is negligible. Finally, the maximum equivalent elastic strain generated in the model came out to be 0.000824. It is suggested that for universal joint that possess the highest elastic strain and stress, a titanium alloy can be used to increase fatigue life.

References 1. Pooja GD, Sangeeta L (2013) Prevalence and aetiology of amputation in Kolkata, India: a retrospective analysis. Hong Kong Physiotherapy J 31.1:36–40 2. Chauhan SS, Bhaduri SC (2020) Structural analysis of a Four-bar linkage mechanism of Prosthetic knee joint using finite element method. pp 209–215 3. Sundararaj S, Subramaniyan GV (2021) Structural design and economic analysis of prosthetic leg for below and above knee amputation. Mater Today: Proc 37:3450–3460 4. Debta S, Kumar K (2018) Static structural analysis of a powered ankle foot prosthesis mechanism. Mater Today: Proc 5(5):11616–11621 5. Kim YC et al (1996) Statistical analysis of amputations and trends in Korea. Prosthet Orthot Int 20(2):88–95 6. Dabiri Y et al. (2010) Comparison of passive and active prosthetic knee joint kinematics during swing phase of gait. In: 2010 17th Iranian conference of biomedical engineering (ICBME). IEEE 7. Kumar M, Pavan V, Bhanulatha I (2020) Design and structural analysis of knee implants using different materials. Int J 5.7 8. Zachariah SG, Sanders JE (1996) Interface mechanics in lower-limb external prosthetics: a review of finite element models. IEEE Trans Rehabil Eng 4(4):288–302 9. Dickinson AS, Steer JW, Worsley PR (2017) Finite element analysis of the amputated lower limb: a systematic review and recommendations. Med Eng Phys 43:1–18 10. Behforootan S et al. (2017) Finite element modelling of the foot for clinical application: a systematic review. Med Eng Phys 39:1–11 11. Zhang M, Mak AFT, Roberts VC (1998) Finite element modelling of a residual lower-limb in a prosthetic socket: a survey of the development in the first decade. Med Eng Phys 20.5:360–373 12. Lapapong S et al. (2014) Finite element modeling and validation of a four-bar linkage prosthetic knee under static and cyclic strength tests. J Assistive Rehabilitative Therapeutic Technol 2.1:23211

Toward Machinability Improvement of AISI 4340 Using CVD Multilayer TiN-Coated Carbide Insert Through MQL: A Case Study Ashok Kumar Sahoo, Ramanuj Kumar, Amlana Panda, Purna Chandra Mishra, and Tanmaya Mohanty

Abstract Sustainability is the approach to obtain overall efficiency in manufacturing by achieving economic, ecological and societal benefits. This experimental research presents a concise comparative investigation on machinability characteristics of highstrength grade hardened steel (AISI 4340) through dry and MQL environments for sustainability. Surface characteristics and machinability studies such as white layer, surface topology, chip morphology, surface roughness and tool wear/tool life were investigated under both the environments. Tool life under MQL is 32.3% higher compared to dry condition at cutting speed of 50 m/min, feed rate of 0.08 mm/ rev and depth of cut of 0.1 mm, respectively. In another setting of cutting speed of 100 m/min, feed rate of 0.04 mm/rev and depth of cut of 0.1 mm, tool life under MQL condition is 39.6% more than dry condition. Phenomenon like abrasion, diffusion, notching, chipping and built-up edge is reported as principal wear mechanism. Detailed investigation is still needed for sustainable machining using nano-assisted MQL environments. Keywords Sustainability · Hard turning · MQL · Surface characteristics · Machinability · Tool life · Surface roughness

1 Introduction Reduction of manufacturing cost with good surface quality is the major impact on sustainability. This is essentially required in shop floor toward achieving ecological, economical and social benefits for machinability improvements in hard machining.

A. K. Sahoo (B) · R. Kumar · A. Panda · P. Chandra Mishra · T. Mohanty School of Mechanical Engineering, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Bhubaneswar, Odisha 751024, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_84

939

940

A. K. Sahoo et al.

AISI 4340 high-strength steel is particularly used in aircraft landing gear. Highstrength AISI 4340 steel finds wider application in aerospace and automobile industries such as gears, shafts, bearings and cams at hardened state [1, 2]. Very minimal quantity of coolant is sprayed over the machining zone for evacuation of heat and induced better performances through minimum quantity lubrication (MQL) [3]. Dhar et al. [4] examined improved machining performance of MQL due to minimization of cutting temperature. Residual stresses are due to the thermomechanical loading and microstructural effects (white layer and dark layer) developed during machining where some stress remained in the work surface after loading is removed. Together, Sharma and Pandey [5] revealed during turning experimentation that cutting speed and feed rate have the significant effect on residual stress. Development of white layer may be attributed to predominantly thermally or mechanically initiate mechanism. White layer formation takes place as a result of rapid heating and cutting temperature (thermally) or due to rigorous plastic deformation (mechanically) and is of vital aspect hard machining study as it affects the cutting insert and quality of surface. Griffiths [6] stated that white layer’s generation in various manufacturing processes is primarily due to three mechanisms (i) phase transformation because of rapid heating followed by a succeeding rapid cooling, (ii) scrupulous plastic deformation ensuing in a fine grain structure and (iii) chemical surface reaction with the surroundings. Arfaoui et al. [7] found that variation in feed increases the white layer thickness and increase in cutting speedup to a limit enhances the temperature as a result of which white layer thickness also increases, but at maximum speed contact time decreases lowering the white layer thickness. Ramesh and Melkote [8] explained that increase in white layer width decreases the compressive residual stresses. They also concluded that high temperature associated the white layer development decreases compressive stresses. Speich and Leslie [9] presented a study of discrete change in microstructure during the process of tempering. In another study, Lee and Su [10] studied that holding time and temperature in tempering significantly affects microstructure and mechanical property of AISI 4340 steel and carbide precipitations are formed at diverse tempering temperature. Suresh et al. [11] demonstrated that wear at tool surface influences the surface roughness, microhardness deviation, phase transformation and residual stress in both the surface as well as subsurface. Sahoo et al. [12] developed the mathematical model for surface roughness through response surface methodology and artificial neural network during machining AISI 1040 steel. ANN predicted the responses very well compared to RSM and optimization of process parameters has been obtained through 3D surface plots. From the literature studied, generation of heat occurs owing to the friction and plastic deformation involving tool and work specimen surface which definitely lowers cutting force and favors for hard machining. However, achievable tool life and surface finish suffer a lot. Further, chip sticks to the tool and work surface deteriorating surface quality accelerates tool wear with built-up-edge (BUE) formation if machining is undergone without lubrication and cooling application. In another way, flood cooling application is normally avoided due to rise of cutting force and thermal stress that leads to catastrophic failure and fracture of tool tip. In stipulations to accomplish improved surface quality and minimization of tool wear, MQL

Toward Machinability Improvement of AISI 4340 Using CVD …

941

is an intermediate methodology between dry and flood cooling to get the benefits of sustainable machining. In this context, the present study focuses on surface characteristics (average surface roughness and residual stress) and machinability (flank wear, chip characteristics, i.e., white layer, chip shape and color and tool life) through MQL-enabled hard machining of high-strength AISI 4340 steel and is compared the performances under dry environment.

2 Materials and Methods The experiments were conceded on a CNC lathe with Fanuc controller under dry and MQL environments. Heat-treated cylindrical shape (AISI 4340 steel) of dimension 40 × 180 mm long of 50 ± 1 HRC was utilized as workpiece for experimentation. The cutting conditions selected for the study were based on recommendation of tool manufacture and previous researchers, i.e., cutting speed (v) of 50–200 m/min: v1 (50 m/ min)–v2 (100 m/min)–v3 (150 m/min)–v4 (200 m/min), feed of 0.04–0.12 mm/rev: f1 (0.04 mm/rev)–f2 (0.08 mm/rev)–f3 (0.12 mm/rev)–f4 (0.16 mm/rev) and depth of cut of 0.1–0.4 mm: d1 (0.1 mm)–d2 (0.3 mm)–d3 (0.4 mm), respectively [11–13]. In this experimental investigation, the cutting insert used was coated carbide (TiN/TiCN/ Al2 O3 /TiN) TN-7105 deposited by CVD having ISO geometry CNMG120408, and it was tightly clamped in a PCLNR2525 M12 tool holder. Minimum quantity lubrication setup used for experimentation was of DROPSA made in Italy, and its mechanism is shown in Fig. 1.

Fig. 1 Schematic of MQL setup and its mechanism

942

A. K. Sahoo et al.

The working lubricant used in this MQL unit was mineral oil, i.e., iron–aluminum lubricant (LRT 30) with pressure of 5 bars and 50 ml/hr flow rate [4, 14, 15]. LRT 30 iron–aluminum oil has favorable properties like environment friendly (air–oil system does not produce oil mist), non-toxic, insoluble in water, excellent antifriction and anti-welding with cooling properties, etc. It does not produce stain mark, smoke or skin allergy and advantage of low lubricant consumption. Flank wear (VBc) was measured through Olympus microscope, STM 6 with image analyzer with nose flank width of 0.2 mm criteria. EDS-SEM analysis of tool tip before and after machining has been performed (HITACHI make SU3500 with INCA software by OXFORD Instruments) along with white layer measurement. Measurement of average surface roughness (Ra) was performed by Taylor Hobson roughness tester. Residual stress measurement of turned workpiece was conducted through X-ray diffraction method based on goniometry. Microhardness (HV) of cutting insert was measured and found to be 2193 HV which ensures higher resistance ability against wear through Vickers microhardness tester (Zwick/Roell, ZHV30) with 1 kg load (HV1). The cutting temperature was measured with the help of FLUKE Ti-32 infrared thermal imaging camera under dry and MQL conditions with same settings. The comparative experimental results (VBc and Ra) are visualized in Figs. 2 and 3, respectively, for dry and MQL.

Fig. 2 Comparison of VBc

Toward Machinability Improvement of AISI 4340 Using CVD …

943

Fig. 3 Comparison of Ra

3 Results and Discussion 3.1 Surface Characteristics Assessment Surface integrity affects the working phenomena of the manufactured components toward fatigue as well stress concentration. It includes the surface-related study of residual stress, surface roughness, surface topology and while layer formation etc. [16]. This section emphasizes on assessment of residual stress, surface topology, surface roughness, white layer formation and cutting temperature. Residual stress has been measured at surface in the feed (axial) direction at cutting speed (200 m/min), feed (0.16 mm/rev) with depth of cut of 0.1 mm under dry and MQL environment. According to Maranhão and Davim [17], residual stress is more prominent at elevated speed and feed, whereas depth of cut is not significant. In dry machining, compressive residual stress is encountered, i.e., −605.8 ± 13.9 MPa, whereas in MQL-assisted machining, tensile residual stress, i.e., + 151.4 ± 6.3 MPa is recorded by X-ray diffraction. These outcomes point to a mutual way of mechanical and thermal aspects on constituents of residual stresses (tensile and/or compressive) on the superficial surface layer after turning hardened AISI 4340 steel that remnants consistent with the previous researchers [18, 19]. In dry condition, axial compressive residual stress has developed at higher cutting speed–feed combination and may be due to the increase of cutting temperature that softens work material and led to lower the cutting force and residual stresses (negative in nature). Under MQL condition with application of lubrication with intensive cooling and penetration of cutting fluid, friction and temperature reduce significantly due to rapid evacuation of heat. As a result, work

944

A. K. Sahoo et al.

hardening takes place that caused tensile axial residual stress as confirmed by X-ray diffraction measurements. Flank wear has significant effects on residual stress. In the present study, high compressive residual stress (−605.8 ± 13.9 MPa) obtained under dry cutting results higher flank wear, i.e., 0.232 mm and surface roughness, i.e., 0.98 micron due to increase of cutting temperature. But at same parametric combination under MQL environment, lower flank wear rate, i.e., 0.195 mm and surface roughness 0.9 micron are achieved. In unmachined heat-treated specimen, the microstructure for Run 6 reveals the presence of different types of phases such as ferrite, carbides and martensites. The black color structure relates to carbide, white colored indicates to the ferrite and gray colored structure indicates to martensite. Primarily, steel with high hardness range consists of hard carbide which is the key determining aspect behind tool wear that creates grooves on flank surface by mechanism of abrasion, while martensite structure is primarily observed with steel constituting of approximately 0.4% carbon content which is similar to the carbon content (0.380%) of test specimen machined under present study. After finish hard turning operation at cutting speed (200 m/min), feed (0.16 mm/rev) with depth of cut of 0.1 mm under dry and MQL, a noticeable change in microstructure. During hard machining, a complex structure has been formed at temperature range between 200 and 350 °C which is known as tempered martensite [9, 10]. The tempered martensite is embedded with ferrite matrix. It is also observed that in MQL machining, tempered martensite is distributed evenly as compared to dry machining. The entire set of trails under MQL-assisted hard turning reveals that turning at maximum feed will result in more widespread groove lines, while fine groove lines are formed with machining at lower feed. The highly compressed air–oil mixed lubricant in MQL helped in reducing the friction and contact stresses that lowers the cutting force and yields finer groove lines and better surface quality. The complete elimination of ridges between the feed lines was noticed under MQL condition. Thus, under MQL-assisted machining, superior surface finish was reported along with the elimination of material side flow and ridges, chipping and BUE formation compared to dry machining. Experimental observations revealed that surface roughness obtained is quite less and below the criteria of 1.6 microns in all trials even at higher cutting speed–feed combinations under both environments as shown in Figs. 2 and 3. Under MQL condition, surface roughness is noticed to be within 1 µm indicating better surface quality compared to dry machining and may be comparable with cylindrical grinding. MQL expedites better cooling and lubrication in machining zone and improved surface quality performance compared to dry cutting environment. In this present experimental investigation, white layer on chip was noticed when machining was carried in dry condition (Fig. 4a) rather than machining under MQL condition (Fig. 4b) at similar parametric combinations. Higher cutting temperature may be the cause for white layer formation under dry machining [7, 9]. The white layer formation is mainly attributed to phase transformation to form austenite. Moreover, the formation of the white layer is believed to be contributing more from the mechanical effect preferably the thermal effect of turning.

Toward Machinability Improvement of AISI 4340 Using CVD …

945

Fig. 4 Chip micrograph a dry, b MQL for white layer analysis for run 4

3.2 Flank Wear and Chip Morphology Assessment In majority of test run, growth of flank wear is less under MQL compared to dry condition as observed from Fig. 3. Abrasion phenomenon was observed to be the principal wear mechanism under both environments [17]. Chipping phenomena at tool tip were prominent under dry circumstances at Run 3 and Run 4 at higher cutting speed–feed condition, whereas under MQL circumstances, abrasion with micro-chipping was observed at Run 4. Scanning electron microscopy image of cutting insert Fig. 5a under dry condition at Run 4 shows the degradation of cutting edge due to chipping, whereas abrasion followed by micro-chipping at tool tip was observed under MQL condition (Fig. 5b). Reduction of flank wear and severe chipping under MQL-assisted hard turning may be attributed due to superior performance of MQL fluid (highly compressed air and oil) in evacuation of heat at the cutting zone. The outcome of EDS and SEM (Table 1 and Fig. 7) shows that percentage of C has increased highly after machining in both the cutting environments that promotes adhesion and/or micro-diffusion of carbon particles from workpiece to the tool

Fig. 5 Tool tip SEM image at run 4 a, dry b MQL

946

A. K. Sahoo et al.

surface. After machining under both cutting situations, elemental % of Ti reduced substantially due to diffusion mechanism active under high temperature and stress. However, % of Ti is more in case of MQL condition. Similarly, element N diffused to the workpiece [20]. A similar phenomenon was noticed in case of increase in percentage O after machining in both the cutting conditions [21]. Presence of Fe was noticed under both the conditions due to diffusion. However, % of Fe (8.86) is higher in dry condition compared to MQL condition (1.86). Element tungsten is exposed in both cases but at higher rate under dry condition (14.72) indicating predominant diffusion mechanism along with abrasion. Hard turning on hardened alloy steel AISI 4340 with TIN-coated carbide insert shows different shapes of chips like helical, ribbon and long snarl chips with sawtooth chip which are presented in Fig. 6. Hard turning under dry circumstances generates high amount of heat, which enhances the overall temperature at the cutting zone that produces metallic blue and blue color chip in majority of runs [21]. Serration takes place in chip which involves side-wise flow and shear cracks due to plastic deformation at elevated temperature. Table 1 Elemental compositions of TiN-coated carbide insert for unmachined, dry and MQL (Run 4) Dry turning

Unmachined

MQL-assisted turning

Element

Weight %

Atomic %

Element

Weight %

Atomic %

Element

Weight %

Atomic%

CK

10.38

17.09

CK

41.17

61.07

CK

44.8

58.76 35.75

NK

45.63

64.42

OK

28.00

31.19

OK

35.67

Al K

1.00

0.73

Al K

1.69

1.12

Na K

0.87

0.59

Ti K

42.99

17.75

Si K

1.29

0.82

Al K

1.48

0.59

Total

100

Ti K

3.53

1.31

Si K

1.66

0.93

Mn K

0.77

0.25

SK

0.41

0.20

Fe K

8.83

2.82

Cl K

0.50

0.22

WM

14.72

1.43

Total

100

K

0.36

0.15

Ti K

5.32

1.75

Mn K

1.23

0.35

Fe K

1.86

0.52

WM

5.88

0.50

Total

100

Toward Machinability Improvement of AISI 4340 Using CVD …

Fig. 6 Chip shapes a–d dry, e–h MQL

Fig. 7 SEM and EDS of insert before machining a–d, dry b–e and MQL c–f

947

948

A. K. Sahoo et al.

3.3 Cutting Tool Life Assessment in Two Different Machining Conditions The cutting tool life assessment is performed under dry and MQL machining environments using the following two cutting conditions: Case (i) cutting speed = 50 m/ min; tool feed: 0.08 mm/rev and depth of cut: 0.1 mm, Case (ii) cutting speed = 100 m/min; tool feed: 0.04 mm/rev and depth of cut: 0.1 mm. Tool life (in machining time, min) in both machining conditions is estimated based on flank wear (VBc) limiting criteria = 0.3 mm and compared for the both machining conditions. Along with it, auxiliary flank wear (Vs) and surface roughness (Ra) are measured with machining time, and the results are compared for both cooling environments. The development of flank wear is steadily enhancing with the machining time with three distinct zones, i.e., initial wear, steady wear and rapid wear. Abrasion and diffusion phenomena are reported to be the chief mechanism. Chipping, built-up edge and notching have been noticed on flank face of the insert tip during evaluation of tool life of the insert. Wear phenomena such as abrasion, built-up edge (BUE) and notching were significantly reduced under MQL and delayed compared to dry condition due to application of highly compressed mixture of air and oil. Especially, chipping phenomena are completely absent in case of MQL-assisted machining. At machining time 5.18 min of case-1 under dry condition, BUE formation was observed and absent under MQL surroundings. From 20.72 min onward in dry cutting condition, severe abrasive marks have been noticed, whereas similar phenomena begin from 39.79 min onward for MQL-assisted machining. At 67.06 min under dry condition, flank wear surpasses 0.2 mm criteria, whereas under MQL condition, flank wear exceeds 0.2 mm criteria at 88.7 min. Thus, in input variable settings of case-1, 67.06 min of tool life is noticed under dry, while 88.7 min is for MQL, i.e., 32.3% greater than dry condition. Similarly, in another parameter setting of case-2, flank wear exceeds criteria of 0.2 mm at 40.17 min under dry condition, whereas it exceeds at 56.09 min under MQL-assisted machining condition. Thus, tool life under MQL is 39.6% higher compared to dry and may be attributed toward the utilization of highly compressed air and oil (lubricant) sprayed through precise nozzle at machining zone which minimizes the friction and overall cutting temperature. Thus, MQL contributes improved results compared to dry machining in terms of machinability assessment (Table 2).

4 Conclusions From the current experimental investigation, following observations were made. . Compressive residual stress (−605.8 ± 13.9 MPa) obtained under dry cutting at higher cutting speed (200 m/min) and feed rate (0.16 mm/rev) results higher flank wear (0.232 mm) and surface roughness (0.98 micron). But at same cutting condition under MQL, tensile residual stress (+151.4 ± 6.3 MPa) obtained with

Toward Machinability Improvement of AISI 4340 Using CVD …

949

Table 2 Tool life assessment Case 1: Parametric condition (d: 0.1 mm, f: 0.08 mm/rev, v: 50 m/min)

Case 2: Parametric condition (d: 0.1 mm, f: 0.04 mm/rev, v: 100 m/min)

Machining time

Machining time

VBc (Dry) mm

Machining time

VBc (MQL), mm

Dry

Machining time

MQL

5.18

0.124

4.89

0.091

5.16

0.157

5.06

0.091

20.72

0.151

19.81

0.108

15.18

0.167

15.33

0.11

41.35

0.181

39.79

0.132

30.19

0.177

30.5

0.122

67.06

0.201

69.4

0.187

40.17

0.202

40.6

0.132

88.7

0.202

56.09

0.202

. .

.

.

lower flank wear, i.e., 0.195 mm and surface roughness (0.9 micron). In MQL, tempered martensite is distributed evenly as compared to dry machining. Compressed air–oil mixture lubricant of MQL has helped in reducing the friction and contact stresses that yields finer surface finish with low tool wear rate compared to dry turning. Growth of flank wear is lower under MQL compared to dry condition. Abrasion, chipping and diffusion were prominent mechanism of wear in the studied range. MQL-assisted hard turning induces better surface quality compared to dry condition due to better penetration of droplets in the machining zone that minimizes the contact length by curling of chips and improved surface quality. Due to formation of white layer, growth of flank wear (0.232 mm at Run 4) under dry machining is comparable at higher side than MQL machining (0.193 mm at Run 4). Dry machining also reveals higher cutting temperature ranges of 92.9– 220.4 °C, whereas 60.3–149.8 °C has been recorded under MQL machining condition. Hard turning under dry and MQL-assisted condition experiences metallic, metallic blue and blue color chips with saw tool appearance. Tool life under MQL condition is 32.3 and 39.6% greater than dry condition under different cases of parametric studied range. It may be attributed due to the utilization of highly compressed air and oil (lubricant) sprayed through precise nozzle at machining zone which minimizes the friction and overall cutting temperature.

MQL hard machining outperformed with favorable change in work-tool and chiptool interaction compared to dry and thus may be adopted in machining practice of shop floor for sustainable hard machining to achieve ecological, economical and social benefits. Acknowledgements This research has been financially supported by Science and Engineering Research Board (SERB), DST, New Delhi, India (Grant No. SB/S3/MMER/0054/2013), and authors express their sincere thanks and gratitude. This paper is dedicated to the late Mr. Rabin Kumar Das for his contribution in conducting all experiments and various characterizations of cutting tools and workpiece.

950

A. K. Sahoo et al.

References 1. Rashid WB, Goel S, Davim JP, Joshi SN (2016) Parametric design optimization of hard turning of AISI 4340 steel (69 HRC). Int J Adv Manuf Technol 82(1):451–462 2. Rashid WB, Goel S, Luo X, Ritchie JM (2013) An experimental investigation for the improvement of attainable surface roughness during hard turning process. Proc IMechE Part B: J Eng Manuf 227(2):338–342 3. Sen B, Mia M, Krolczyk GM, Mandal UK, Mondal SP (2021) Eco-friendly cutting fluids in minimum quantity lubrication assisted machining: a review on the perception of sustainable manufacturing. Int J Prec Eng Manuf-Green Technol 8(1):249–280 4. Dhar NR, Kamruzzaman M, Ahmed M (2006) Effect of minimum quantity lubrication (MQL) on tool wear and surface roughness in turning AISI-4340 steel. J Mater Process Technol 172(2):299–304 5. Sharma V, Pandey PM (2018) Experimental investigations and statistical modeling of surface roughness during ultrasonic-assisted turning with self-lubricating cutting inserts. Proc of the Inst of Mech Eng Part E: J Process Mech Eng 232(6):709–722 6. Griffiths BJ (1987) Mechanisms of white layer generation with reference to machining and deformation processes. J Tribol 109:525–530 7. Arfaoui S, Zemzemi F, Tourki Z (2018) A numerical-analytical approach to predict white and dark layer thickness of hard machining. Mat Manuf Process 33(6):661–669 8. Ramesh A, Melkote SN (2008) Modeling of white layer formation under thermally dominant conditions in orthogonal machining of hardened AISI 52100 steel. Int J Mach Tools Manuf 48:402–414 9. Spiech GR, Leslie WC (1972) Tempering of steel. Metall Trans 3(5):1043–1054 10. Lee WG, Su TT (1999) Mechanical properties and microstructural features of AISI 4340 high-strength alloy steel under quenched and tempered conditions. J Mat Process Technol 87:198–206 11. Suresh R, Basavarajappa S, Gaitonde VN, Samuel GL (2012) Machinability investigations on hardened AISI 4340 steel using coated carbide insert. Int J Ref Metals Hard Mat 33:75–86 12. Sahoo A, Rout AK, Das D (2015) Response surface and artificial neural network prediction model and optimization for surface roughness in machining Int. J Ind Engg Comput 6(2):229– 240 13. Lima JG, Avila RF, Abrao AM, Faustino M, Davim JP (2005) Hard turning AISI 4340 high strength low alloy steel and AISI D2 cold work steel. J Mat Process Technol 169:388–395 14. Dhar NR, Kamaruzzaman M, Ahmed M (2006) Effect of minimum quantity lubrication (MQL) on tool wear and surface roughness in turning AISI-4340 steel. J Mat Process Technol 172:299– 304 15. Gaitonde VN, Karnik SR, Davim JP (2008) Selection of optimal MQL and cutting conditions for enhancing machinability in turning of brass. J Mat Process Technol 204:459–464 16. Davim JP (Ed) In: Surface integrity in machining (Vol. 1848828742). London, Springer 17. Maranhão C, Davim JP (2012) The role of flow stress and friction coefficient in fem analysis of machining: a review. Rev Adv Mater Sci 30:267–272 18. Leppert T, Peng RL (2012) Residual stresses in surface layer after dry and MQL turning of AISI 316L steel. Prod Engg 6:367–374 19. Outeiro JC, Dia AM, Lebrun JL, Astakhov VP (2002) Machining residual stresses in AISI 316L steel and their correlation with the cutting parameters, machining. Sci Technol 6:251–270 20. Sobiyi J, Sigalas I (2015) Chip formation characterisation and Tem investigation of worn PcBN tool during hard turning. Machining Sci Technol 19:479–498 21. Konig W, Klinger M, Link R (1990) Machining hard materials with geometrically defined cutting edges—field of applications and limitations. CIRP Annals-Manuf Technol 39:61–64

Experimental Explorations on Mechanical Performance of Waste Marble Dust Powder and Banana Fibre Strengthened Hybrid Bio-composites J Joshua Gnana Sekaran , G. Gokilakrishnan , G. M. Pradeep , R. Saravanan , and R. Girimurugan

Abstract The reinforcing of natural fibre and fillers in polymer resin is the most recent development in the production of sustainable composites. Banana fibre and marble dust powder (MDP), two naturally occurring ingredients, are utilized to improve the characteristics of polymers. The current study looked into how banana fibre and MDP affected epoxy resin. MDP-filled (0, 2, 4, 6, and 8 wt%) banana fibre (25 wt%) with epoxy resin (75, 73, 71, 69, and 67 wt%) is produced using the hand lay-up technique, and its mechanical behaviour is examined. The tensile, flexural, and impact test specimens were made and put through testing in accordance with ASTM specifications. The Vickers hardness of epoxy increased each condition of MDP’s reinforcement and up to 6 wt% of MDP’s tensile, flexural, and impact characteristics. Tensile strength for unfilled polymer composites is roughly 56.21 MPa, however, when MDP is added, the strength rises (76.51 MPa). While, when initially compared with specimens, the tensile modulus of the MDP-filled composite increases J. Joshua Gnana Sekaran Department of Mechanical Engineering, CSI College of Engineering, Ooty, Tamilnadu 643215, India G. Gokilakrishnan Department of Mechanical Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu 641 202, India G. M. Pradeep Department of Mechanical Engineering, Velammal Institute of Technology, Chennai, Tamilnadu 601 204, India R. Saravanan Department of Mechanical Engineering, J J College of Engineering and Technology, Trichy, Tamilnadu 620 009, India R. Girimurugan (B) Department of Mechanical Engineering, Nandha College of Technology, Perundurai, Tamilnadu 638 052, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_85

951

952

J. Joshua Gnana Sekaran et al.

in a linear fashion up to 8 wt% (2.61 GPa) (0, 2, 4, and 6 wt%). Flexural strength (96.28 MPa) improves up to 6 wt% when MDP is added to the composite, whereas flexural modulus (4.31 GPa) improves up to 8 wt%. The impact energy and Vickers hardness number (VHN) values are found to be 84 VHN and 3.6 J, respectively, for 6 and 8 wt.% MDP-filled composites. Keywords Marble dust powder · Banana fibre · Mechanical performance · Bio-composites

1 Introduction Modern natural fibres that are recyclable and eco-friendly have exceeded the popularity of synthetic fibres, which are frequently employed in the fashion sector. Scientists are drawn to natural fibres because of their biodegradability. Compared to synthetic fibres, which mostly consist of glass, carbon, and plastic fibres, it offers a significant advantage. Natural fibre use in composite materials for industrial purposes has increased recently [1]. With consumer’s increased acceptance of new and creative items, there has been an increase in demand for these goods during the previous few years [2]. Natural fibres offer a variety of benefits, such as their high specific strength, significant reduced density, wide availability, exceptional thermal insulation, major durability, great electrical resistance, and considerable durability [3]. Due to improved specific strength and modulus, as well as their accessibility and light weight, fibre-reinforced polymer composites have seen a substantial increase in demand over the past few years. In contrast to artificially manufactured fibrereinforced composite constructions, natural fibre-reinforced polymer composites are superior [4]. After bananas are harvested, banana fibre frequently goes to waste. As a result, banana fibres are easily and cheaply available in sufficient quantities for mass manufacture. Previous studies showed that banana fibre and epoxy resin provide greater reinforcement when preparing composites [5]. The combination of fibre and filler reinforcement is hardly ever published, despite the fact that the majority of research on fibre-reinforced composites concentrates primarily on the type and quantity of fibrous reinforcements. In prior studies, fillers such as alumina, glass fibre, wollastonite, SiC, and silica were added to polymer matrixes, improving the fracture toughness in comparison with the matrix material [6]. Scientists began working on their incorporation into polymer composites since environmental issues have been getting worse along with a rise in industrial/agricultural waste [7]. Additionally, a significant proportion of ceramic components like alumina, silicon carbide, and silica is present in industrial wastes and is already used in composite materials [8]. Utilizing garbage not only aids in solving their disposal problems but also provides a good, cost-free alternative to the filler materials that are typically used. Kumar et al. [9] examined two body abrasive wear parameters as well as the mechanical properties of cenosphere-filled (0, 2, and 4 wt%) carbon-epoxy composites. The 4 wt% cenosphere-filled composite had the maximum impact strength (1126.15 J/m),

Experimental Explorations on Mechanical Performance …

953

while the unfilled composite had the maximum flexural strength (751 MPa). Kulkarni et al. [10] examined the treated and untreated fly ash filled banana fibre-reinforced polypropylene hybrid composite’s mechanical characteristics. The highest mechanical characteristics were noted at 20 wt% fly ash. Erkli˘g et al. [11] examined the mechanical characteristics of different industrial fillers used in polyester composites, such as SiC, fly ash, and sewage sludge ash. When compared to unfilled composite, particle loaded composites have much better mechanical characteristics. In comparison with other fillers, the findings showed good agreement with 5 wt% filled sewage sludge ash. To ascertain the effectiveness of granite filled glass fibre composites for use in wind turbine blades, experimental research was conducted by Pawar et al. [12]. With a fibre loading of 40% by weight, the flexural and tensile strengths were 361 and 320 MPa, respectively. When granite powder (8–24 wt%) is added to composite materials, the hardness and impact strength increase. At a given amount of leftover marble dust (15 wt%), the mechanical and physical characteristics of composites improved when bagasse fibre weight was increased to 7.5% [13]. The addition of marble dust to fibre-reinforced polymer composites may enhance their mechanical characteristics since it contains a considerable proportion of ceramic components. In order to produce composites that are stronger, lighter, and ultimately more inexpensive, this study aims to find out whether marble dust powder (MDP) may be employed as a filler in fibre-reinforced polymers. As was made abundantly obvious earlier, utilizing banana fibres and marble dust in polymer composites has many advantages. Their blending into a single composite may also improve the polymer’s inherent qualities, resulting in stiff, sustainable, and composites with a larger range of potential uses. According to the research, banana fibre considerably enhances the mechanical properties of polymers at a reasonable cost and is as strong as glass fibre. Additionally, MDP is a hard particle that is easily accessible and inexpensive, giving the matrix material hardness in addition to a large increase in strength. A unique material that can be utilized in low-load structural applications might be created by combining the two reinforcements. The initiative aims to build a short basalt fibre epoxy composite to disperse MDP. Physical (density and void content), mechanical, and other distinct characteristics of the created hybrid composite were noted (tensile, flexural, impact, and hardness behaviour).

2 Materials and Methods The matrix materials were made of epoxy resin (LY556) and hardener (HY991) (Fig. 1a) from Covai Seenu and Company in Coimbatore, Tamil Nadu, India. The marble powder filling components were physically bonded with the epoxy resin and curing agent in a 10:1 ratio to create a strong bond. The necessary quantity of leftover marble dust powder (Fig. 1b) was purchased from nearby marble processing businesses. Initially, the collected waste marble dust powder was cleaned by normal water and then by distilled water to remove the foreign particles in it. The well-cleaned waste

954

J. Joshua Gnana Sekaran et al.

Fig. 1 a Epoxy resin and hardener, b marble dust powder, c long banana fibre

marble dust powder was allowed for manual sieving process to get the fine particles in the size range of 80–100 microns. The leftover banana fibres (Fig. 1c), which are used as filler materials, were gathered in the shape of long fibre from a nearby fibre market. The waste banana fibres were first cleaned with ordinary water and then rinsed one more with hot water to completely eliminate any dust particles. It was let to air dry for six hours in the sun after washing. Through this method, the necessary quantity of tiny banana fibre particles was obtained. The composite plates utilized in this investigation were made using a compression-moulding machine, marble powder, epoxy resin, and chopped banana fibre. The initial stage of the composite plates begins with the installation of chopped banana fibre within the compression-moulding machine. On top of the banana fibre mat, there are marble powder particles. Following this setup, the appropriate quantity of banana fibre was evenly distributed across the surface of the marble powder. A particular thickness of epoxy resin/hardener solutions has been applied to the surface of the surface with dispersed banana fibre particles. Following the application of the epoxy resin/hardener solution, the marble powder was reinstalled on the same surface along with the dispersion of the banana fibre. The epoxy resin, hardener, marble fibre, banana fibre, and a specified amount of hydraulic pressure allowed the compression-moulding machine to operate at a temperature of 110 °C for 45 min. The necessary composite plates could then be obtained after 45 min of processing, allowing for cooling to room temperature. The composite plate can next go through water jet machining to remove the required composite samples from the composite mate in accordance with ASTM specifications after cooling. The procedures are the same for the remaining compositions. Figure 2a, b and c illustrates the appropriately prepared composite samples for flexural, tensile, and impact testing in line with ASTM standards. The mechanical behaviour of the composites has been identified using tensile, flexural, and impact testing on well-constructed composite specimens. The tensile qualities of a specimen with dimensions of 150,206 mm were determined using ASTM D 3039, while the flexural properties of a specimen with dimensions of 125 × 20 × 6 mm were assessed using ASTM D790-07. Tensile and flexural tests have been carried out with the aid of a universal testing apparatus with a crosshead speed of 2 mm/min (Fig. 3a). The ASTM E23 standard was followed to evaluate the impact

Experimental Explorations on Mechanical Performance …

955

Fig. 2 Specimens for a tension, b Flexural, and c Impact test

properties of a sample measuring 55 mm by 10 mm by 10 mm with a 45°-cut notch that was 2 mm deep using an impact testing equipment (Fig. 3b). In accordance with ASTM E-92 specification, the Vickers hardness machine (Fig. 3c) and a diamond-shaped indenter with a 136° angle were used to assess the hardness of composite materials (Table 1).

3 Results and Discussions 3.1 Density As shown in Fig. 4, when high-density MDP was used in place of low-density epoxy, the inclusion of MDP enhanced the densities of the composites both experimentally and theoretically. The theoretical density ranged from 1.401 g/cm3 (0 wt% of MDP) to 1.468 g/cm3 (8 wt% of MDP), while the experimental density ranged from 1.387 g/

Fig. 3 a Universal testing machine, b Vickers hardness tester, c impact testing machine

956 Table 1 Composition details in Wt. %

J. Joshua Gnana Sekaran et al.

Samples

Epoxy resin

Banana fibre

Waste marble dust

BM1

75

25

0

BM2

73

25

2

BM3

71

25

4

BM4

69

25

6

BM5

67

25

8

Fig. 4 Theoretical, actual density, and voids of the developed composites

cm3 (0 wt% of WMP) to 1.433 g/cm3 (8 wt% of MDP). Similarly, as seen in Fig. 4, the composite’s void % showed a rising tendency. This happens because the MDP’s wettability in the epoxy resin is insufficient and because air is trapped in the composite as it solidifies [14]. However, the vacancy in the composite is within the limiting range, demonstrating the method’s feasibility. Because the maximum percentage of voids found in the composites, 2.38% at 8 wt% of MDP [15], was within the limiting range, they were rigorously cemented to remove air bubbles.

3.2 Mechanical Properties The tensile strength of the composite specimens generated is shown in Fig. 5. Tensile strength was determined to be 56.21 MPa at 0% weight MDP; adding 2% weight MDP increased tensile strength by 15%. The wt% of MDP reached 6, or 76.51 MPa, at which point the tensile strength kept rising. The inclusion of marble powder and the fortification of strong banana fibre are the two key factors behind the improvement [16, 17]. The banana fibre’s intermetallic bonding with epoxy gave the composite its strength, while the MDP served as a link between the banana and the MDP. The accumulation of MDP particles, which leads to a larger stress concentration at the broken portion, only slightly reduced the tensile strength at 8 wt% of MDP, though.

Experimental Explorations on Mechanical Performance …

957

Fig. 5 Vickers hardness, tensile strength, and flexural strength of the produced composites

The tensile modulus improved from 1.98 GPa at 0 wt% MDP to 2.61 GPa at 8 wt% MDP as a result of the improvement in tensile strength. The addition of MDP increased the quantity of voids that formed, which led to the creation of a dislocation cloud when the specimen was loaded [18]. As shown in Fig. 5, the composite’s sample’s flexural strength rose at a higher rate and magnitude than the tensile strength did. Observations showed that at 0 wt% MDP and 8 wt% MDP, respectively, flexural strengths of 73.24 and 90.21 MPa were noted. The highest flexural strength of 96.28 MPa was achieved, nevertheless, at 6 wt% MDP. The same results were reported by Nayak et al. [19]. The shear behaviour MDP supplied in the composite was the reason of the increase in flexural strength. When the specimen was loaded, the MDP particles underwent lateral deformation and passed the shear stresses to the banana fibre. Even more lateral distortion resulted from this, which raised the applied load and delayed fracture time [20]. Figure 6 also depicts the flexural modulus, which increases from 3.32 GPa at 0 wt% MDP to 4.31 GPa at 8 wt% MDP. The MDP addition may have aided the shear stress transmission between the MDP, banana, and epoxy, increasing flexural strength, given that the lateral deformation of the composites influences the flexural modulus. Figure 6 displays the composite’s impact energy uptake before to fracture as well as the sample’s Vickers hardness rating. The impact energy was seen to be 2.3 J at 0 wt% MDP, however, after accounting for MDP, it was discovered to be 3.6 J at wt% MDP. Due to the great load bearing capacity of both banana and MDP, this occurred. The banana fibre and epoxy resin were connected by MDP, which promoted the transfer of stress and caused significant deformation prior to fracture [21, 22]. Additionally, the inclusion of MDP in the composite caused a significant degree of dislocation development, which increased energy absorption. However, the aggregation of hard MDP particles at a single cross section that causes the emergence of an early crack may be responsible for the decrease in impact energy at 8 wt% MDP. According to Fig. 5, which depicts the composite’s Vickers hardness, the addition of MDP to the banana fibre epoxy composite is highly successful. The Vickers hardness number rises from 51 VHN at 0 wt% MDP to 84 VHN at 8 wt% because MDP are

958

J. Joshua Gnana Sekaran et al.

Fig. 6 Impact energy, flexural modulus, and tensile modulus of the produced composites

the low-density hard particles, and they predominantly aggregated at the surface during solidification. According to past reports [23–26], these hard particles offered a significant level of resistance when in contact with the indenter, increasing the Vickers hardness number.

4 Conclusions The creation, production, and testing of epoxy composite materials with fixed banana fibre (25 wt%) and variable marble dust filler (0, 2, 4, 6, and 8 wt%) were conducted in this research. The study’s findings allow us to draw the following conclusions: The attributes of marble dust, such as its tensile strength, flexural strength, modulus of elasticity, hardness, and impact strength, are frequently raised along with its quantity. This may be the case since the banana fibres were intermetallically bonded to the epoxy to strengthen the composite and the MDP functioned as a bridging material between the banana and the MDP. In general, the BM4 combination had the best outcomes. Due to the accumulation of MDP particles creating a larger stress concentration at the cracked area, a little loss in characteristics was seen at 8 wt. MDP. A low failure rate for the final product can be achieved by using the mechanical characteristic to enhance the production process. Future research on epoxy composite materials reinforced with banana fibre, particularly for medium load structural applications, may be guided by the findings of this test.

Experimental Explorations on Mechanical Performance …

959

References 1. Siva R et al. (2020) Mechanical behavior of sisal and banana fiber reinforced hybrid epoxy composites. Mater Today: Proc 2. Amiandamhen SO et al (2020) Natural fibre modification and its influence on fibre-matrix interfacial properties in biocomposite materials. Fibers Polym 21(4):677–689 3. Siva R et al (2019) Processing and evaluation of mechanical properties of sisal and bamboo chemically treated hybrid composite. In: Chandrasekhar U, Yang LJ, Gowthaman S (eds) Innovative design, analysis and development practices in aerospace and automotive engineering (I-DAD 2018). Lecture Notes in Mechanical Engineering. Springer, Singapore, pp 435–441 4. Mayandi K et al (2015) Mechanical performance of cissus quadrangularis/polyester composite. Mater Today Commun 4:222–232 5. Sapuan SM et al (2006) Mechanical properties of woven banana fibre reinforced epoxy composites. Mater Des 27(8):689–693 6. Yilmaz C et al (2007) The reinforcement effect of nano and microfillers on fracture toughness of two provisional resin materials. Mater Des 28:2063–2070 7. Singh T et al. (2019) Application of waste tire rubber particles in non-asbestos organic brake friction composite materials. Mater Res Expr 6:035703 8. Singh T et al (2016) Optimization of tribological properties of cement kiln dust- filled brake pad using grey relation analysis. Mater Des 89:1335–1342 9. Kumar SMV et al. (2019) Mechanical and abrasive wear behavior of cenosphere filled carbon reinforced epoxy composites using Taguchi-Grey relational analysis. Mater Res Expr 6:015307 10. Kulkarni MB et al. (2019) Structure, mechanical and thermal properties of polypropylene based hybrid composites with banana fiber and fly ash. Mater Res Expr 6:075318 11. Erkli˘g A et al. (2016) A comparative study on industrial waste fillers affecting mechanical properties of polymer-matrix composites. Mater Res Expr 3:105302 12. Pawar MJ et al (2017) Experimental investigation and numerical simulation of granite powder filled polymer composites for wind turbine blade: a comparative analysis. Polym Compos 38:1335–1352 13. Singh T (2021) Abrasive wear and dynamic–mechanical behavior of marble dust filled bagasse fiber reinforced hybrid polymer composites. Polym Compos 42:2817–2828 14. Girimurugan R et al (2020) Nano materials with fiber reinforced polylactic acid matrix composite: a review. Solid State Technol 63(5):10730–10745 15. El-Sherbiny S et al (2015) Preparation and modification of nano calcium carbonate filler from waste marble dust and commercial limestone for papermaking wet end application. Powder Technol 279:290–300 16. Jayaraman R et al (2022) Improvement on tensile properties of epoxy resin matrix sugarcane fiber and tamarind seed powder reinforced hybrid bio-composites. ECS Trans 107(1):7265 17. Asyraf M et al (2022) Mechanical properties of oil palm fibre-reinforced polymer composites: a review. J Mater Res Technol 17:33–65 18. Thiruvasagam R et al (2019) Art of review-fiber reinforced polymer composites. J Inform Comput Sci 9(11):461–468 19. Nayak SK et al. (2021) Development and characterization of polymer-based composites filled with micro-sized waste marble dust. Polym Polym Compos 29:497–508 20. Choudhary M et al (2019) Waste marble dust-filled glass fiber-reinforced polymer composite part I: physical, thermomechanical, and erosive wear properties. Polym Compos 40:4113–4124 21. Alias AH et al (2021) Hybridization of MMT/lignocellulosic fiber reinforced polymer nanocomposites for structural applications: a review. Coatings 11:1355 22. Ilyas RA et al (2022) Natural fiber-reinforced polycaprolactone green and hybrid biocomposites for various advanced applications. Polymers 14:182 23. Jain M et al (2016) Morphology and mechanical properties of sisal fiber and nano cellulose green rubber composite: a comparative study. Int J Plast Technol 20:378–400

960

J. Joshua Gnana Sekaran et al.

24. Sumit Nijjar P et al (2022) Overview on the latest trend and development on mechanical, tribological and microstructural properties of natural fibre polymer composites. Mater Today: Proc 63:663–672 25. Mishra RR et al. (2022) Research progress on nano-metal matrix composite (NMMC) fabrication method: a comprehensive review. Mater Today: Proc 56(4):2104–2109 26. Rathore A et al. (2017) Hybrid cellulose bionanocomposites from banana and jute fibre: a review of preparation, properties and applications. Mater Today: Proc 4(2)Part A:3942–3951

Effect of ZrO2 Nanofluid Concentrations in Hard Turning of AISI D2 Steel Saswat Khatai, Ashok Kumar Sahoo, Ramanuj Kumar, and Amlana Panda

Abstract Due to the challenging issues like high heat generation concerned with hardened steel machining, therefore cutting fluid has been used for removing the more higher cutting temperature. Furthermore, use of nanolubrication system makes the cutting environment more sustainable. The present work represents the impacts of cutting parameters such as cutting speed, feed rate, depth of cut and LRT 30 mineral oil-based ZrO2 nanofluid concentrations in hard turning of AISI D2 steel. The ZrO2 nanofluid was first time implemented for cooling purpose in hard turning application. The performance was examined by taking average surface roughness, tool flank wear, cutting power and cutting temperature results. Experimental results found that the 0.20% nanofluid concentration was the better choice among all adopted weight concentrations (0.05%, 0.2% and 0.5%) of nanofluid. Abrasion, cutting edge chipping and adhesion were found to be the principal wear mode. Also, acceptable range of surface roughness (0.498–0.665 micron) was seen in the entire investigations. Keywords Hard turning · ZrO2 · CVD · Nanofluid · Cutting power

1 Introduction The demands for the most cost-effective and environmentally friendly manufacturing processes are developing, that have sparked the rapid commercial growth of various installed coatings on tool substrate and supplanted the most popular flood cooling environment [1]. Due to superior cutting tool material and advanced coating layer machining of hard to cut materials with presence of suitable cooling and lubrication technique have gained popularity in manufacturing industry and replaced the highenergy and cost-intensive grinding processes. Heat-treated materials having hardness between 45 and 68 HRC are processed by geometrically defined single-point cutting S. Khatai · A. K. Sahoo (B) · R. Kumar · A. Panda School of Mechanical Engineering, Kalinga Institute of Industrial Technology (KIIT), Deemed to Be University, Bhubaneswar, Odisha 24, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_86

961

962

S. Khatai et al.

tools (CBN, PCBN, ceramic and coated carbide tool) [2]. Due to their superior hardness values, resistance to wear, and balanced chemical properties of the coatings, that offers substantial advantages in terms of tool life and advance manufacturing process. The easiest way to do hard turning operations is to machine in a dry environment because the largest amount of heat is generated, which helps upper surface to be soften of hardened steel. However, the amount of heat produced at the tool tip and workpiece contact zone needs to be regulated in order to reduce the tool wear and protect the workpiece’s surface from undesirable residual stress that might cause change in the microstructure of the work material [3, 4]. To solve this major problem during hard machining process, green and sustainable cooling and lubrication techniques have been introduced which help to reduce the cutting zone temperature and provide better lubrication facility during hard machining process. Nowadays, MQL and NFMQL have gained immense popularity among researchers and industry, due to their superior heat absorption capability and greater lubricious properties. Abrasion, attrition, adhesion, chipping and the built-up edge (BUE) are the common phenomena which affect the machinability performance during hard machining [5, 6]. To overcome from this type of difficulties during machining, novel lubrication techniques like MQL and NFMQL have been introduced to boost overall machinability performance by dropping the cutting zone temperature between cutting zone and workpiece during machining operation. These techniques helped to reduce overall machining costs by preventing the overuse of lubricant and helped to create an environmentally friendly machining environment, which replaced the traditional and health hazardous flood/wet cooling environment [7, 8]. Nowadays, machining under nanofluid environment has gained immense popularity among researchers and industries as a green and sustainable environment and a successful alternate solution for conventional cutting fluids which are difficult to dispose and hazardous for the operator’s health. Nanofluid is a homogeneous mixture of metallic or non-metallic nano-sized particles (< 100 nm) and cutting fluids, which improves the heat transfer capability of the base cutting fluid. The solid lubricants in the nanofluid also contribute to a thicker oil layer between the cutting insert and the workpiece. As a result, they improve the coolant’s tribological qualities and lubricating performance [9]. Nanofluids are applied by minimum quantity lubrication (MQL) technique in which cutting fluid and compressed air are mixed in a chamber and delivered to the cutting zone through a single or multi-nozzle. From literature study, it is evident that nanoparticles have good thermal conductivity value which improves the overall efficiency of cutting fluid after dispersed in the base fluid homogeneously [10, 11]. Najiha et al. [9] employed TiO2 nanofluid by MQL technique and reported that due to the better thermal characteristics of nanofluid, cutting edge fracture and chipping were significantly reduced as compared to MQL condition. Duc et al. [12] analyzed the effect of Al2 O3 and MoS2 dispersed in soyabean oil and water emulsion in hard part turning of 90CrSi steel. It was observed that nanofluid concentration plays a vital role in cutting performance. Muthuswamy et al. [13] conducted machining operation on AISI 304 steel under the presence of ethyl glycol-based TiO2 nanofluid environment. Due to the lubrication properties of nanofluid friction among

Effect of ZrO2 Nanofluid Concentrations in Hard Turning of AISI D2 Steel

963

the cutting tool tip and the workpiece reduced so that the tool life enhances during machining. Sharma et al. [14] analyzed multi-wall carbon nanotubes (MWNCTs)-based nanofluid while hard part turning of AISI D2 steel and compared the result with MQL environment. It was reported that due to higher thermal conductivity properties of nanofluid, less tool wear, reduced cutting temperature and improved surface quality were noticed under MWCNTs nanofluid environment than only MQL environment. Duc et al. [15] worked with different concentrations of Al2 O3 -based nanofluid and compared the result while hard milling of 60Si2 Mn steel. It was observed that while concentration of nanofluid increases from 0.5% to 1.5%, tool life increases by 43.8%. Uysala et al. [16] investigated the use of a vegetable cutting fluid-based MoS2 nanofluid (1.0 wt.%) in different flow rates while milling martensitic stainless steel. According to the result nano-MQL milling at a MQL flow rate of 40 mL/h, minimum tool wear and lower surface roughness values were attained. Hegab et al. [17] conducted turning operation on Ti–6Al–4V alloy in presence of multi-walled carbon nanotubes (MWCNTs) dispersed in vegetable oil. Because of the superior cooling and lubrication properties, surface quality has been increased and the tool wear decreased significantly. It was observed that concentration of nanofluid and feed rate are the major factor for affecting surface quality. Das et al. [18] compared the result of different nanofluids in hard turning of AISI 4340 steel and concluded that CuO exhibited better result in terms of surface quality, machining force and tool wear than Al2 O3 , Fe2 O3 and ZnO nanofluid. Khajehzadeh et al. [19] investigated the effect of nanofluid on the tool chip contact area in hard part turning of AISI 4340 steel. It was noticed that TiO2 nanofluid is able to reduce tool chip contact area more prominently than dry cutting. When the nanoparticle concentration increases in the base fluid, a reduction was observed in tool chip contact area significantly. Tuanet et al. [20] compared the result of moS2 nanofluid and plane MQL condition while hard turning of 90CrSi low alloy steel. From the experimental analysis, it was noticed that MoS2 nanofluid works better than plane MQL in aspects of surface quality, due to its superior lubrication properties. Khalil et al. [21] conducted turning procedure on AISI 1040 steel in presence of Al2 O3 nanofluid due to the superior heat-carrying capacity of Al2 O3 nanofluid, and cutting zone temperature reduced, so that tool wear was reduced, and tool life increased significantly. Many researchers have conducted experiments using different types of nanofluid. However, very few experimental works have been demonstrated using ZrO2 nanofluid as a coolant in nanofluid MQL machining condition. Therefore, the objective of the research is to find out the influence of ZrO2 nanofluid concentrations in tuning of heat-treated AISI D2 steel.

964

S. Khatai et al.

2 Materials and Methods A cylinder-shaped workpiece having length and diameter 200 mm and 50 mm, respectively, was utilized as the working material in this experiment. AISI D2 tool steel has high chromium and high carbon element properties, which make this steel very complex to cut. AISI D2 steel was selected for the purpose of investigating its machinability because of its widespread utilization in the dye and mold manufacturing industries, automotive enterprises, etc. A heat treatment method, followed by air quenching, elevated the specimen’s hardness to (55 ± 1) HRC. Machining length was fixed as 150 mm for all the experiments. A high speed, medium duty Jyoti make DX 200-4A-CNC lathe machine tool with a power rating of 7.5 kW and a variable spindle speed range of 50–4000 r/min was the equipment utilized for the hard turning operation. For the finishing operation, WIDIA make CVD (Al2 O3 TiCN-TiN) multilayer-coated carbide insert (CNMG120408FW-WK05CT grade) and PCLNR 2525M12 as tool holder were used in this present work. The tool wear and chip images were analyzed using by an Olympus STM 6 optical microscope. The surface finishing of machined cylindrical surface was obtained using Mitutoyo make Surftest SV-2100 surface measuring instrument. For cutting power three-phase multifunction bidirectional power cum energy meter-EMT34 was used. To investigate the temperature degenerated during machining at the cutting zone, FLIR make T540 professional thermal camera has been utilized. For the advance characterization of tool wear and chip morphology has been carried out by ZEISS make Gemini SEM 450 scanning electron microscope, and the reports are analyzed by the EDS or EDAX software. Initially, commercially available ZrO2 nanoparticle of size 30–50 nm was purchased from AD-Nano Technologies Pvt. Ltd. Before preparation of the nanofluid, the ZrO2 nanoparticles are preheated for 3 h to remove the moisture from the nanoparticle. Then, Zro2 nanoparticles are dispersed into LRT 30 mineral oil. In this current work, three concentrations of nanofluids (0.05%wt. 0.2%wt and 0.5%wt) have been prepared. Nanofluids are prepared by two-step verification method in which after adding nanoparticles to the base oil, 0.5%, 0.2% and 0.5% weight concentrations of nanofluids are mixed in a magnetic stirrer for 8 h, 12 h and 20 h, respectively. When the particles are dispersed in the base oil completely, again 0.5%, 0.2% and 0.5% weight concentrations of nanofluids are kept for ultrasonication for 4 h, 6 h and 9 h, respectively, to minimize the agglomeration of nanoparticles. After the homogeneous dispersion of all nanofluids, in each concertation, two experiments are performed to check the machinability performance of AISI D2 steel. All the experimental aspects and cutting conditions are displayed in Table 1, and the schematic diagram of MQL setup and its mechanism are displayed in Fig. 1.

Effect of ZrO2 Nanofluid Concentrations in Hard Turning of AISI D2 Steel Table 1 Experimental aspects and cutting conditions

965

Material

AISI D2 steel

Hardness

55 ± 1 HRC

Cutting tool

CNMG120408FW-WK05CT grade

Tool holder

PCLNR 2525M12

Nanoparticle

ZrO2

Particle size

30–50 nm

Base oil

LRT 30 Mineral Oil

Concentration of nanoparticle (% wt.)

0.05, 0.2, 0.5

Cutting parameters

Level 1: 0.05 (mm/rev), 0.1 (mm), 80 (m/min) Level 2: 0.15 (mm/rev), 0.3 (mm), 200 (m/min)

Cutting environment

Nanofluid MQL

Fig. 1 Schematic diagram of MQL setup and its mechanism

966

S. Khatai et al.

3 Results and Discussion Nanofluids are mainly used during cutting processes to enhance heat transfer capabilities and tribological characteristics. ZrO2 is used as the nanoparticle in this study, and it is combined with LRT 30 at different concentrations to evaluate the machinability of CNC hard turning (surface roughness, power, tool wear and cutting temperature). Applying a lubrication system will reduce the coefficient of friction at the tool tip and workpiece contact zone, culminating in lower tool wear and better surface quality. The appropriate use of nanofluid concentration is a very significant factor related to tribological characteristics and machining performance [15]. In hard machining process, surface roughness is an important factor of surface integrity. Moreover, the surface roughness is an effective method for assessing a machined surface quality. Herein, Ra was chosen as the roughness variable in the current experiment. Figure 2a shows a comparison graph of surface roughness result with respect to the variations of nanoparticle concentration at two different cutting parameter levels The lowest value of 0.498 µm was obtained with 0.2% concentration of nanofluid with 200 m/min of speed due to superior thermal conductivity and optimal viscosity of nanofluid which exhibited relatively lower friction and temperature in machining. It is essential to thoroughly assess tool wear because it affects other machining output like tool life, cutting temperature and quality of machined surface. In this current study, the influence of three cutting parameters and nanofluid weight concentrations (0.05%, 0.2% and 0.5%) on wear was examined. Figure 2b shows a comparison graph of flank wear result with respect to the variations of nanoparticle concentration at two different cutting parameter levels. After evaluation, it has been observed that highest tool wear was obtained at highest speed (250 m/min) with 0.5% concentration of nanofluid. At concentration 0.2%, lowest tool wear value was observed in both cutting speed levels (80 as well as 250 m/min). In each concentration, flank wear rises when the speed increases from 80 to 200 m/min. Flank wear images (Fig. 3) and SEM micrographs (Fig. 4) are demonstrating wear characterizations under two different cutting levels and three different concentrations of nanofluid. The wear mechanisms that are apparent are adhesion, chipping and abrasion. More regular wear was pronounced, especially at a high cutting speed value of 200 m/min. The temperature and friction increase at the tool–workpiece contact are related to the growth of tool wear [22]. The cutting temperature is an immensely important parameter that influences tool life and dimensional accuracy. Because of the increase in friction whenever cutting speed rises, the temperature increases. Figure 2c shows a comparison graph of cutting power result with respect to the variations of nanoparticle concentration at two different cutting parameter levels. At higher speed and feed rate combination with more nanofluid concentration (0.5%), the cutting temperature observed to be higher. Compared to other nanofluid concentration, 0.2% nanofluid concentration exhibited better result in aspects of cutting temperature and the temperature which varies in Level 1 and Level 2 was 81.4 °C–133.6 °C. Cutting temperature images

Effect of ZrO2 Nanofluid Concentrations in Hard Turning of AISI D2 Steel

967

Fig. 2 Graphical representation of experimental results a surface roughness b flank wear c temperature d cutting power at 0.05%, 0.2 and 0.5% wt. concentration of ZrO2 nanofluid

Fig. 3 Flank wear images at level 1 in a 0.5% b 0.2% c 0.5% wt. concentration of ZrO2 nanofluid and at level 2 in a 0.5% b 0.2% c 0.5% wt. concentration of ZrO2 nanofluid

968

S. Khatai et al.

Fig. 4 SEM micrograph images at level 1 in a 0.5% b 0.2% c 0.5% wt. concentration of ZrO2 nanofluid and at level 2 in a 0.5% b 0.2% c 0.5% wt. concentration of ZrO2 nanofluid

taken by thermal camera in different concentrations and cutting level are represented in Fig. 5. The less cutting power obtained in NFMQL technique as it produces less heat in the tool chip interface region. Moreover, increasing the concentration of nanoparticle could lead to higher friction. Highest cutting power was observed at the Level 2 condition under 0.5% nanofluid concentration. Lowest cutting power was observed

Fig. 5 Cutting temperature images at level 1 in a 0.5% b 0.2% c 0.5% wt. concentration of ZrO2 nanofluid and at level 2 in a 0.5% b 0.2% c 0.5% wt. concentration of ZrO2 nanofluid

Effect of ZrO2 Nanofluid Concentrations in Hard Turning of AISI D2 Steel

969

at Level 1 under 0.2% nanofluid concentration. Figure 2d shows a comparison graph of cutting power with respect to the variations of nanoparticle concentration at two different cutting parameter levels.

Fig. 6 Optical microscope chip images at level 1 in a 0.5% b 0.2% c 0.5% wt. concentration of ZrO2 nanofluid and at level 2 in a 0.5% b 0.2% c 0.5% wt. concentration of ZrO2 nanofluid

Fig. 7 SEM micrograph of chip images at level 1 in a 0.5% b 0.2% c 0.5% wt. concentration of ZrO2 nanofluid and at level 2 in a 0.5% b 0.2% c 0.5% wt. concentration of ZrO2 nanofluid

970

S. Khatai et al.

Surface morphology, service life of cutting tool and cutting temperature are the machinability characteristics that are influenced by chips and their morphological characteristics. Furthermore, at the cutting tool’s edge, the material is both heated and deformed plastically. The procedure itself repeats in a cyclic manner after chip segment has slipped with other new crack formation. The chip segment results in saw-tooth chip formation [23]. As cutting speed increases, serrated type saw-tooth chips are rendered, and intense serrations are seen on the top chip surface which can be demonstrated in above Figs. 6 and 7. The two major factors that contributed to the formation of the saw tooth during machining are shear localization and plastic deformation [24].

4 Conclusions The MQL technique combined with ZrO2 nanofluids enhances the cutting performance of the coated carbide inserts. The presence of ZrO2 nanoparticles appears to improve the thermal conductivity and lubricating properties of the base fluid. In order to achieve the intended outcomes, the path for MQL variables employing nanofluids is explored based on the experimental findings. To obtain the most suitable nanofluids, the concentration of nanoparticles was varied from 0.05% to 0.20% to 0.5%. However, to get improved machinability, 0.20% nanofluid concentration has been preferred. Abrasion, chipping and adhesion are the main wear seen on the cutting edge. Serration chips are found in each investigation. The effect of the concentrations and size of the nanoparticles and their interactions with various types of fluids on tribological and heat transport capabilities in hard machining need more research to explore. When machining hardened AISI D2 grade tool steel, nanofluid can be effectively used as a metal working fluid to achieve the desired machining characteristics within a defined range of process parameters. Acknowledgements Authors are grateful for the financial support by All India Council for Technical Education (AICTE) under Research Promotion Scheme (RPS) project, New Delhi, India (File No: 8-87/FDC/RPS (POLICY-1)/2019–20).

References 1. Diniz AE, Oliveira AJ (2004) Optimizing the use of dry cutting in rough turning steel operations. Int J Mach Tools Manuf 44:1061–1067 2. Konig W, Berktold A, Koch KF (1993) Turning versus grinding—a comparison of surface integrity aspects and attainable accuracies. CIRP Ann 42:39–43 3. Khatai S, Kumar R, Sahoo AK (2021) Hard turning assessment on en31 steel in dry and wet cooling environments using grey-fuzzy hybrid optimization approach. Int J Mod Manuf Technol 13(2):55–62 4. Tönshoff HK, Arendt C, Amor RB (2000) Cutting of hardened steel. CIRP Ann 49(2):547–566

Effect of ZrO2 Nanofluid Concentrations in Hard Turning of AISI D2 Steel

971

5. Anand A, Behera AK, Das SR (2019) An overview on economic machining of hardened steels by hard turning and its process variables. Manuf Rev 6:4 6. Khatai S, Kumar R, Sahoo AK, Panda A (2022) Investigation on tool wear and chip morphology in hard turning of EN 31 steel using AlTiN-PVD coated carbide cutting tool. Mater Today Proc 59:1810–1816 7. Vogli E, Tillmann W, Selvadurai-Lassl U, Fischer G, Herper J (2011) Influence of Ti/ TiAlN-multilayer designs on their residual stresses and mechanical properties. Appl Surf Sci 257:8550–8557 8. Khatai S, Sahoo AK, Kumar R, Panda A (2022) On machining behaviour of various cutting inserts: a review on hardened steel. Mater Today Proc 62(6):3485–3492 9. Najiha MS, Rahman MM (2016) Experimental investigation of flank wear in end milling of aluminum alloy with water-based TiO2 nanofluid lubricant in minimum quantity lubrication technique. Int J Adv Manuf Technol 86:2527–2537 10. Yıldırım CV (2020) Investigation of hard turning performance of eco-friendly cooling strategies: cryogenic cooling and nanofluid based MQL. Tribol Int 144 11. Khatai S, Kumar R, Sahoo AK, Panda A, Das D (2020) Metal-oxide based nanofluid application in turning and grinding processes: a comprehensive review. Mater Today Proc 26:1707–1713 12. Duc TM, Long TT, Chien TQ (2019) Performance evaluation of MQL parameters using Al2 O3 and MoS2 nanofluids in hard turning 90CrSi steel. Lubricants 7:40 13. Muthusamy Y, Kadirgama K, Rahman MM, Ramasamy D, Sharma KV (2016) Wear analysis when machining AISI 304 with Ethylene Glycol/TiO2 nanoparticle-based coolant. Int J Adv Manuf Technol 82:327–340 14. Sharma P, Sidhu BS, Sharma J (2015) Investigation of effects of nanofluids on turning of AISI D2 steel using minimum quantity lubrication. J Clean Prod 108:72–79 15. Duc TM, Long TT, Dong PQ (2019) Effect of the alumina nanofluid concentration on minimum quantity lubrication hard machining for sustainable production. Proc Inst Mech Eng Part C J Mech Eng Sci 233(17):5977–5988 16. Uysala A, Demirena F, Altana E (2015) Applying minimum quantity lubrication (MQL) method on milling of martensitic stainless steel by using nano MoS2 reinforced vegetable cutting fluid. Proc Soc Behav Sci 195:2742–2747 17. Hegab H, Kishawy HA, Gadallah MH, Umer U, Deiab I (2018) On machining of Ti–6Al–4V using multi-walled carbon nanotubes-based nano-fluid under minimum quantity lubrication. Int J Adv Manuf Technol 97(5):1593–1603 18. Das A, Patel SK, Arakha M, Dey A, Biswal BB (2021) Processing of hardened steel by MQL technique using nano cutting fluids. Mater Manuf Process 36:316–328 19. Khajehzadeh M, Moradpour J, Razfar MR (2018) Influence of nanofluids application on contact length during hard turning. Mater Manuf Process 34(1):30–38 20. Tuan NM, Ngoc B, Thu TL, Long TT (2021) Investigation of the effects of nanoparticle concentration and cutting parameters on surface roughness in MQL hard turning using MoS2 nanofluid. Fluids 6:398 21. Khalil ANM, Ali MAM, Azmi AI (2015) Effect of Al2 O3 nanolubricant with SDBS on tool wear during turning process of AISI 1050 with minimal quantity lubricant. Proc Manuf 2:130–134 22. Khan MMA, Dhar NR (2006) Performance evaluation of minimum quantity lubrication by vegetable oil in terms of cutting force, cutting zone temperature, tool wear, job dimension and surface finish in turning AISI-1060 steel. J Zhejiang Univ-Sci A 7(11):1790–1799 23. Sobiyi K, Sigalas I (2015) Chip formation characterisation and Tem investigation of worn PCBN tool during hard turning. Mach Sci Technol 19(3):479–498 24. Das A, Das SR, Patel SK, Biswal BB (2020) Effect of MQL and nanofluid on the machinability aspects of hardened alloy steel. Mach Sci Technol 24(2):291–320

Study on Thermal and Electrical Conduction Properties of Nano Zinc Particle-Reinforced Polyester-Graded Composites Archana Nigrawal, Arun kumar Sharma, and Fozia Z. Haque

Abstract In this paper, development and D.C. conductivity behavior of nano zinc particle-reinforced polyester gradient composite are discussed. Nano zinc particlereinforced polyester composites having 5 wt.% of nano zinc particle and polyester resin were developed. D.C. conductivity calculation was performed on the gradient composites by using an Electrometer apparatus from 25 °C–170 °C. It was concluded that D.C. conduction decreases on enhancement of distance in the route of centrifugation force, which confirmed the configuration of gradient structure of the composites. D.C. conductivity increased on improving in nano zinc particle concentration. Calculation of activation energy values was calculated by using the Arrhenius equation for specimens 1, 2, 3, and 4 is 0.41, 0.48, 0.72, and 0.84 eV, respectively, which exhibit ionic conduction. Keywords Nano zinc particle · Polyester · Gradient · D.C. conductivity · Composites

1 Introduction Polymer composites are very useful because of their applications in photovoltaic and solar cells in semiconductors, gas sensors, and optical recording [1–7]. Conductivity of the free charge carriers in the applied electric field can be determined by D.C. conductivity. The addition of metallic filler with polymer results in an increase in both electrical and thermal conductivity of the composites. Zinc oxide, an inert mix, is white fine particle that is unsolvable in water and extensively used as a filler in various materials and goods including ceramics, rubber, plastics, glass, cement, A. Nigrawal (B) · F. Z. Haque Optical Nanomaterials Lab, Department of Physics, Maulana Azad National Institute of Technology, Bhopal 462 003, India e-mail: [email protected] A. Sharma Laxmipati Group of Institutions List, Bhopal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_87

973

974

A. Nigrawal et al.

ointments, adhesives, sealants, etc. The ZnO nanoparticle is extremely gifted nanomaterial which is used in polymeric-based composite functions. It has probable good electronic property that is mainly functional in electronic devices, and it has a direct and broadband gap which can contribute a vital role in energy interface and conversion. It is having direct band gap approximately to the ultraviolet spectrum which is a benefit that could be applicable for photo-electronic behavior, because of the properties researchers are attracted toward nano zinc particles [8–10]. The conductivity of the polymeric composites does not amplify on rising the additive concentration, in fact at a vital concentration named as percolation threshold. Percolation threshold is a point where conductivity values change from an insulating material range to the semiconductive or metallic sort [8]. The electrical property of metal filled polymers can be calculated by the internal thickness, warmth, and potency of the electric field ascribed to electron tunnel. The temperature reliance of the electrical conduction of ZnO film is reported in the literature [9]. The conduction method in the films was established to be dissimilar at various temperature regions. At elevated temperature region, the activated kind of conduction was dominating while in the minor temperature range Mott’s variable range hopping method was dominating. Present work describes about gradient composites developed by using nano zinc particles, their D.C. conductivity property and an attempt has been made to explore the gradual variations in the thermal and electrical properties of nano zinc particle-filled polyester composites. Materials and Methods Nano zinc particle used in this study was obtained from Merck India. Nano zinc particle-filled polyester gradient composite was prepared by utilizing centrifugation technique. X-direction, centrifugation force was active in this process. Composites are developed by using nano zinc particle-reinforced blend having 5 wt.% of nano zinc fine particles. Nano zinc fine particles were combined with polyester resin and hardener polyester resin having 1.5% accelerator and 1.5 wt.% hardeners and stimulated previous to the addition of the reinforcing material. Whole mix up was again methodically stimulated by means of a glass bar. Particulars of the setup and process of graded structures production are same which are described in previous patent (Chand and Hashmi) [11]. The whole combination was transferred in the mold. Then, specimen-filled mold was rotate at 900 + 70 RPM with distance kept at 150 mm. Specimens be detached from the mold following post-curative at 28 °C up to 48 h. Specimens were sliced from the molded pin after that composites were layered by atmosphere drying kind silver paint before the measurements. Density of sliced test pellets was calculated by using an accuracy balance. Specimens were named as PZG 1, PZG 2, PZG 3, and PZG 4.

Study on Thermal and Electrical Conduction Properties of Nano Zinc …

975

2 Electrical Measurements Electrical property analysis of the composites was done by using a Keithley electrometer model 610C which is a versatile instrument for measuring D.C. voltages, current, and resistance of the prepared samples. An important attribute of this device is that it permits accurate measurement with less adjustments, and also, it can determine very low voltage values also with accuracy. In this paper, resistance (R) values of the specimens were measured by in the temperature range 25 24°C to 170 °C through an invariable heat pace at ± 1 °C/min. Thermal Analysis: DSC curves of the prepared composites were taken by differential scanning calorimeter in which 3 mg of the sample was taken to determine peak temperature and onset end set temperature values with constant rate of 5 °C at temperature range 25–350 °C. Results and Discussion Density of nano composite specimens is listed in Table 1. This table shows that on the rise of distance from periphery the mass of the samples decreases. Which is mainly because of reduction in nano zinc concentration. Figure 1 shows the change in D.C. conductivity and temperature for nano zinc particle-filled polyester specimen PZG 1. There is an abrupt rise in D.C. conductivity later than 65 °C. The plot shows a pointed increase in the D.C. conductivity value from 96 °C and goes on increase till 150 °C. Figure 2 shows D.C. conductivity change with temperature for specimen PZG 2. It can be seen that up to 50 °C no change in D.C. conductivity occurred, later on at 70 °C an abrupt change in D.C. conductivity occurred. It can be noted that the peak of abrupt raise in D.C. conductivity shift to the high temperature value. Figure 3 shows D.C. conductivity change with temperature for specimen PZG 3. This graph depicts an alteration in D.C. conductivity with respect to the temperature for specimen PZG 3. At low temperature, the electrons do not have sufficient energy to jump. They conduct by hopping from one level to another. Therefore, the free electron band conduction is not dominating in the low temperature range. Nearest-neighbor hopping factor and variable range hopping could be able to describe conductivity at the lower temperature region. Figure 4 shows D.C. conductivity change with temperature for specimen PZG 4. D.C. conductivity increase after 100 °C then at 110 °C there is a sudden increase in D.C. conductivity. Table 1 Density values

Specimen No

Density(ρ) (g/cc)

PZG 1

1.08278

PZG 2

1.02478

PZG 3

1.10280

PZG 4

1.11321

976

A. Nigrawal et al.

PZG1

6E-09

D.C. conductivity(S/cm)

5E-09 4E-09 3E-09 2E-09 1E-09 0 0

20

40

60 80 100 Temperature (°C)

120

140

160

120

140

160

Fig. 1 D.C. conductivity of PZG 1

PZG 2

7E-09

D.C.Conductivity (S/cm)

6E-09 5E-09 4E-09 3E-09 2E-09 1E-09 0 0

20

40

60 80 100 Temperature (°C)

Fig. 2 D.C. conductivity of PZG 2

It can be noted that D.C. conductivity rapidly increases subsequent to 70 °C and sharp enhancement in D.C. conductivity starts from 100 °C. At 110 °C a peak can be noted. The sudden reduction in electrical conduction values of the polymeric composite during its heating is mainly because of the thermal expansion coefficient of the plastic material with nano particle concentration. It was reported that the resistivity decreases with the rise in temperature showing the semi-conducting actions. The conductivity depends on filler phase concentration showed a percolation change

Study on Thermal and Electrical Conduction Properties of Nano Zinc …

977

PZG3

1.8E-09

D.C. conductivity(S/cm)

1.6E-09 1.4E-09 1.2E-09 1E-09 8E-10 6E-10 4E-10 2E-10 0 0

20

40

60 80 100 Temperature(°C)

120

140

160

120

140

160

Fig. 3 D.C. conductivity of PZG 3

4E-09

PZG4

D.C.conductivity (S/cm)

3.5E-09 3E-09 2.5E-09 2E-09 1.5E-09 1E-09 5E-10 0 0

20

40

60 80 100 Temperature (°C)

Fig. 4 D.C. conductivity of PZG 4

behavior, when the thickness decreases the threshold content increased. The conductivity of metal-filled polymers in low assortment of filler concentration close to the percolation threshold was studied and conduction behavior was determined by the tunneling through the particle interlayer [13]. It was reported [13] that in case of carbonyl nickel-filled epoxy resin-based composites abrupt decline in conductivity at a particular temperature is similar to dissipated heat in the composite and is equivalent to a decline in the filler ratio, thus causing an alteration in the conduction behavior.

978

A. Nigrawal et al.

Table 2 Activation energy

Specimen

Activation energy (eV)

PZG 1

0.41

PZG 2

0.48

PZG 3

0.72

PZG 4

0.84

Table 3 DSC data of PZG 1–PZG 4 S. No.

Specimen title

Rate of heating (°C/min)

Peak temperature (°C)

End set temperature (°C)

1

PZG 1

10

55.4

115.7

2

PZG 2

10

56.9

222.7

3

PZG 3

10

63.3

234.7

4

PZG 4

10

67.7

317.4

Activation energy of the nano zinc particle-filled polyester gradient composites is listed in Table 2. Activation energy in the case of PZG 1 is 0.41 eV, which is least. It depicts that electronic conduction is dominant in PZG 1, this is due to the highest nano zinc particle content present in specimen PZG 1. Increase in activation energy value from 0.41 eV to 0.84 eV occurred in specimen PZG 4. Increase in activation energy is due to the decrease in nano zinc particle content. This augment in activation energy signifies an amplification in binding forces, that resisted dipole direction and increase the activation energy. The activation energy is dependent on the donor carrier content and the impurity energy levels. An increase in donor carrier concentration caused the rise in the Fermi level in the energy gap and as a result activation energy decreases. It was investigated that electrical conductivity of the composites showed S-shaped dependence mainly in three regions named as dielectric, transition, and conductive and the specimens with low filler content were nonconductive. It was reported that the unexpected reduction in conduction value of the polymeric composite with additive ratio throughout its heat is concerned [14] by means of the difference in the thermal expansion coefficients of the polymer and the filler, which cause a bond breakage among the component part of the percolation cluster. Glass transition temperature of the specimens were recorded as 55.4 °C, 56.9 °C, 63.3 °C, and 67.7 °C for PZG 1, PZG 2, PZG 3, and PZG 4, respectively (Table 3).

3 Conclusions It was observed that rise in D.C. conductivity value occurred from specimen 4 to specimen 1, depicting the establishment of gradient structure. On increasing the nano zinc particle content, the D.C. conductivity increased. Various peak points can

Study on Thermal and Electrical Conduction Properties of Nano Zinc …

979

be seen in the plot of D.C. conductivity of different specimens. Peak temperature shifts to minor side on increase in nano zinc content. Reduction in values of activation energy occurred on increasing the nano zinc particle content in the specimens. Acknowledgements Financial support to Dr. Archana Nigrawal by the DST Project (DST/WOS-B/ AFE-5/2021) is gratefully acknowledged.

References 1. Friend RH, Gymer RW, Holmes AB, Burroughes JH, Marks RN, Taliani C, Bradley DDC, DosSantos DA, Bredas JL, Logdlund M, Salneck WR (1999) Nature 397:121–131 2. Nigrawal A, Chand N (2010) Preparation and dielectric behaviour on carbon soot filled polyester graded composites. Mater Des 31:3672–3676 3. Saravanan R, Malyadri T, Rao MS, Sunkara N (2019) Synthesize and characterization of maleic acid treated banana fiber composites. Mater Today Proc 18:5382–5387 4. El-Nahass MM, Zeyada HM, Aziz MS, El-Ghamaz NA (2005) Solid-State Electron 49:1314– 1317 5. Burr PM, Jeffrey PD, Benjamin JD, Uren MJ (1987) Thin Solid Films 151:L111 6. Sachdeva RA, Bhattacharya B, Singh V, Singh A, Tomar SK, Singh PK (2018) Highperformance polymers 30(8):949–956 7. Gu D, Chin Q, Tang X, Gan F, Liu K, Xu H (1995) Opt Commun 121:125–135 8. Qin HB, Luan XH, Feng C, Yang DG, Zhang GQ (2017) Mechanical, thermodynamic and electronic, properties of Wurtzite and Zinc-Blende GaN crystals. Materials 10:1419 9. Smijs T, Pavel S (2011) Titanium dioxide and zinc oxide nanoparticles in sunscreens: focus on their safety and effectiveness. Nanotechnol Sci Appl 4:95–112 10. Ilyas RA, Sapuan SM, Ishak M (2017) R, Isolation and characterization of nanocrystalline cellulose from sugar palm fibres (arenga pinnata). Carbohyd Polym 181:1038 11. Chand N, Hashmi SAR (2003) Filed Indian patent a novel process of making flow based gradient polymeric composites 438 12. Nigrawal A, Prajapati SC, Chand N (2012) Mechanical and thermal properties of nano-cellulose obtained from sisal fiber reinforced polyvinyl alcohol (PVA) bio-composites. J Sci Res Rev 1:40–50 13. Wang H, Qiu X, Liu W, Yang D (2017) Facile preparation of well-combined lignin-based carbon/ZnO hybrid composite with excellent photocatalytic activity. Appl Surf Sci 426:206– 216 14. Kolosova NN, Boitsov KA (1979) Fiz Tverd Tela 21:2314

An Experimental Investigation on Overlapped Multipass Laser Transformation Hardening of Ti–6Al–4V Titanium Alloy Using Nd:YAG Laser Duradundi Sawant Badkar

Abstract In this research article, a 2 mm thick Ti–6Al–4V titanium alloy sheet was exposed to an experimental investigation of the overlapped multipass laser transformation hardening (LTH) for the specific instance of a uniformly intense, CW spherical beam travelling at a constant speed utilizing a 2 kW Nd:YAG laser. Two sets of laser process parameters were selected and optimized through experiments: (1) High laser process parameter (HLPP), L p = 800 W, S s = 3000 mm/min, F p = −10 mm, with heat input 180 J/cm, and (2) Low laser process parameter (LLPP), L p = 600 W, S s = 2000 mm/min, F p = −10 mm, with heat input, H i 160 J/cm and 180 J/cm, respectively, both with the same F p = −10 mm. The Vickers microhardness of a Ti–6Al–4V titanium alloy as received is 328 HV. The maximum values of Vickers microhardness were found in the all the regions of hardened bead than in the bulk material, respectively. The higher values of 450 HV and 445 HV were investigated on the top surface for both higher and lower laser processing parameters, respectively. A significant improvement in corrosion and wear resistance of the laser hardened surface of Ti–6Al–4V in contrast to parent material can be attributed to the excellence properties of the dissolution of minute quantities of carbon, nitrogen and oxygen during the formation of hard martensitic α , (or transformed β). Keywords Ti–6Al–4V · Laser transformation hardening · Nd:YAG laser · Overlapped · Vickers microhardness · Scanning electron microscopy

Nomenclature Lp Ss

Laser Power Scanning Speed

D. S. Badkar (B) Faculty of Engineering, Fabtech Technical Campus, College of Engineering and Research, Sangola-413307, Affiliated to Dr. Babasaheb Ambedkar Technological University (DBATU), Lonere, Satara, Maharashtra 415011, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_88

981

982

Fp Ts Fz F z -H az H az Bm Hi Ord hd F z of Ord

D. S. Badkar

Focused Position Top of the Surface Hardened or Fusion Zone Interface of Fusion and Hardened Zone Heat Affected Zone Base Metal (Bm ) Heat Input, Overlapped Region Depth Hardened Depth Fusion/Hardened Zone of the Overlapped Region Depth

1 Introduction A typical two-phase alpha–beta titanium alloy Ti–6Al–4V grade V is used extensively since its inception in the early 1950s in the thermal energy, chemical processing, biomedical, aerospace, and marine industries [1–4]. Titanium and its alloys are also find widespread application in many manufacturing and production industries due to their outstanding mechanical, thermal and physical thermal properties, such as high tensile strength-weight ratio, high tensile strength-density ratio, more corrosion resistance, crack resistance, and their ability to combat modestly an elevated temperatures without creeping [5]. The titanium alloy (Grade 5) employed in this research paper is Ti–6Al–4V, which is extensively utilized in the production of aircraft and airframe engine parts, medicinal devices, ships, chemically processing equipments, automobiles, marine, and power generation equipment. This ASTM grade 5 is lightweight, exhibits favourable creep and fracture toughness properties, and is weldable and fabricatable [6]. The Nd:YAG laser has substantially superior absorptivity by highly reflective materials and metals than the CO2 laser due to its shorter radiation wavelength of 1.064 m. Additionally, it enables the laser beam to be concentrated at a point on the workpiece with a smaller diameter and enhancing energy concentration [7]. The various investigations have been carried to aid in the review of literature on ferrous and nonferrous materials, particularly steel materials, which are subject to laser transformation hardening processes. Yao et al., examined the tempering in overlapping tracks by laser overlapping quenching and surface heating of 45, 9Cr2Mo, and W18Cr4V steels. The final findings demonstrated that the action power needed for carbon dissemination in steels plays a significant character in impeding carbide decomposition and that cooling rate has been restricted to impact on lowering temper softening during laser-overlapping by laser transformation hardening [8]. By using overlapping laser beams, Babic et al. successfully experimented with robot laser surface transformation hardening of materials meeting DIN standards 1.7225 and 1.2379. They were able to obtain a sizable advancement throughout the hardness of the melting surface layers as a result, which greatly improves the corrosion and wear

An Experimental Investigation on Overlapped Multipass Laser …

983

resistance of such changed items. Experimental findings revealed that substances of DIN specifications 1.7225 and 1.2379 are incredibly suitable for laser surface heat treatment since the substance’s durability and wearing resistance properties were significantly increased after laser heat treatment [9]. Badkar et al. used a 2-kW continuous wave Nd:YAG laser to establish and improve the laser transformation hardened processing parameters of the laser hardened bead geometry integrity of conventionally pure titanium sheets of 1.6 mm thickness [10]. Using a 400 W average power-pulsed mode Nd:YAG laser, Ghorbani et al. examined the liquid phase surface engineering of Ti–6Al–4V titanium alloy and selected the best laser heat surface modification process parameters based on the quality characteristics of the laser-treated hardened surface zones. According to the measured microhardness values, the substrate material’s hardness would rise by around double (600–630 HV0.2) after surface melting (300–350 HV0.2) [11]. A high-power laser diode (HPDL) and a CO2 laser, respectively, were used in study by Li et al., on the laser transformation hardening of AISI1045 steel. They looked at the effects of the laser process variables, beam power, and scanning speed on the depths and microhardness of AISI1045 steel. The findings of HPDL and CO2 laser surface hardening revealed significant variances among the hardened specimens in in addition to a substantial increase in laser hardened surface [12]. Isaac Adebiyi et al. revealed a considerable improvement in hardness when examining the optimizing the process properties of SiC laser hardened on titanium alloys: 254.5 Hv0.3 in the received samples, 1372.7 Hv0.3 in the pure SiC-coated samples, and 1923.5 Hv0.3 in the cermet-coated samples [13]. Badkar et al., conducted an experimental bead on trials of the laser transformation hardening of commercially pure titanium sheet of thickness 1.6 mm, relatively close to ASTM Grade 3 in chemical composition using a continuous wave, 2 kW Nd:YAG laser, by employing Box-Behnken design matrix and established linear and quadratic polynomial equations derived from the hardened bead geometry and heat input were predicted. The outcomes showed that, within the limitations provided by the hardening parameters employed, the suggested models correctly anticipated the responses. The best hardening conditions for the necessary criterion can be found by using regression equations [14]. After conducting mechanical and microstructural research, Hahn et al. created a kinetic model of the hardness improvement by employing the direct laser diodes process variables of laser beam energy and traverse speed. This kinetic model was improved to forecast the variations in martensitic α , percentage that happen with self-quenching and the volume of α that converts to β following heat treatment [15]. Yan and Liu carried out an experimental and hypothetical investigation on the laser surface transformation hardening of the hardened depth of the laser phase transformation hardened bead geometry of ductile cast iron QT600-3 material applying a CW 1.5 kW CO2 laser source. The experimental investigation revealed good agreement between the experimental and theoretical findings [16]. By employing a fibre laser for laser transformation hardening, Qiu et al., studied the microstructure and microhardness of several carbon steels, including rolled steel, steel that had been quenched and tempered, and steel that had been annealed with alloy. Based on the findings, measured microhardness was between 100 and 250

984

D. S. Badkar

HV which is higher than measured macrohardness [17]. Using AISI H13 hardened tool steel as the base metal, Ciurana et al. conducted a number of planned experiments using a pulsed Nd:YAG laser. By using artificial neural networks (ANN), they were able to simulate the relationship between the laser processing variables and the hardness quality characteristics of AISI H13 hardened tool steel [18]. Yang et al. performed an experimental laser transformation hardening bead on trials on ASSAB DF2 (equivalent to AISI 01) tool steel using only a continuous wave CO2 laser with a defocused laser beam, The final findings demonstrated that hardened depth rises with reducing power input and reduces with increased traversal speed [19]. De Formanoir et al., carried out an experimental study of work hardening to improve the mechanical properties of Ti–6Al–4V. Results indicated an improvement in ductility and strength which in turn indicated unusual laser heating characteristics [20]. Badkar et al., developed the models based on RSM and ANN in order to evaluate and determine the effect of processing parameters of beam unalloyed titanium c on heat flux and tensile strength. The report includes data on experimental, model creation, test, and validating as well as the impacts of laser beam processing parameters on heat input and output in addition to comparing the effectiveness of RSM and ANN models [21]. Badkar et al., used the Taguchi technique and the utility principle in their research on the laser transformation hardening of commercially pure titanium using a CW 2-kW Nd:YAG laser. In this study, an optimal set of laser process parameters was estimated using the benefit principle and the Taguchi method of orthogonal arrays. The appropriate answers for the laser processing settings were found through research. Utilizing confirmation experiments with the ideal amounts of laser process variables, the efficacy of such a Taguchi optimization method was evaluated [22]. Badkar et al., carried out experimental bead-on trials of the laser transformation hardening of unalloyed titanium sheets by utilizing Nd:YAG laser. By applying the DOE approach, the experimental procedures were enhanced. The experimental results were produced using a thorough central composite design matrix comprising three components and five levels. The findings demonstrate that the models adequately and successfully explained the shape of the hardened bead surface and the heat input. The created models were used to study the primary and interaction effects of the laser process input factors on the heat input and laser-hardened bead profile responses [14]. Using a 2 kW CW Nd:YAG laser, Badkar et al., conducted bead-on testing on laser phase surface modification of unalloyed pure titanium. The findings indicate that the suggested models accurately predicted the responses within the bounds of the used laser hardening parameters. It is suggested that regression equations can be used to determine the ideal hardening settings for specified criteria [23]. Badkar et al., used a 2 kW CW solid-state Nd:YAG laser supply in their study to investigate how different laser beam characteristics affected responses such as the hardened depth and hardened bead width of the laser hardened bead profile and the wide swath of the laser scanned sheet [24]. The major goal of carrying out experimental research on multipass overlapping laser transformation hardening of Ti–6Al–4V titanium alloy is to achieve the largest laser hardened zone width with the least amount of heat input. Several tracks or

An Experimental Investigation on Overlapped Multipass Laser …

985

multiple passes overlapping bead on runs trials were typically achieved in laser transformation hardening to shield the zone with broader hardened bead widths than the maximum laser beam size from an arrangement can produce (LTH).

2 Experimental Methodology 2.1 Principle of Laser Transformation Hardening (LTH) A schematic illustration of a laser device for substance surface modification is shown in Fig. 1. The fundamental experimental work was carried out employing a 2 kW continuous wave and a 1.06 m Nd:YAG lasers sources from GSI Lumonics, as shown in Fig. 2. The working chamber’s experimental setup for the laser head and shielding is shown in Fig. 3. To process applications, the work centre rotates as well as moves in the x, y, and other directions. Figure 4 depicts the overlapped multipass laser hardened bead on trials as received and evaluated with the selected laser process variables for continuous wave spherical beam type. Table 1 shows the Ti–6Al–4V, ASTM Annealed Grade 5, is more likely to be the chemical composition. The test piece is thoroughly cleaned with acetone and saturated in a solution of HNO3 , HF, and H2 O (27 vol. % HNO3 , 3 vol. % HF, balance H2 O) before carrying out the experimental bead on tests on the base material.

2.2 Specimen Preparation Standard metallography was performed on each transversely sectioned specimen. Figure 5 depicts an experimental setup for microhardness testing as well as the Vickers microhardness method. The laser-hardened bead profile parameters’ measured ‘responses’ were recorded. Vickers microhardness is determined by multiplying the load (in kgf) by the area (in mm2 ) of the indentation.

HVN =

2FSin(136◦ )/2 d2

(1)

1.854F d2

(2)

HVN =

HVN = Vickers microhardness. F = Load in kgf, d = Arithmetic mean of the two diagonals, d1 and d2 in mm.

986

Fig. 1 Principle of laser phase transformation hardening schematic diagram Fig. 2 WRI employs a solid-state Nd:YAG laser source for experimental purposes [25]

D. S. Badkar

An Experimental Investigation on Overlapped Multipass Laser …

987

Fig. 3 Experimental set-up illustrates the laser beam head and shielding configurations in the working chamber [25]

Fig. 4 Overlapped multipass laser hardened bead on trials of Ti–6Al–4V titanium alloy sheet of 2 mm thickness as received

V

3.5–4.5%

Al

5.5–6.76%

Element

Weight %

Table 1 Chemical composition of Ti–6Al–4V < 0.08%

C < 0.25%

Fe < 0.20%

O < 0.05%

N2

< 0.08%

C

< 0.004%

H2

Bal

Ti

988 D. S. Badkar

An Experimental Investigation on Overlapped Multipass Laser …

989

Fig. 5 Vickers microhardness experimental setup and technique [25]

3 Results and Discussion 3.1 Microhardness Analysis Microhardness values for the two chosen process parameters were determined, 1. LLPP, L p = 600 W, S s = 2000 mm/min, F p = −10 mm, and H i = 180 J/cm, and 1. HLPP, L p = 800 W, S s = 3000 mm/min, F p = −10 mm, and H i = 160 J/cm, respectively. It has been determined that the primary and major objective of laser surface modification of laser phase transformation hardening (LTH) is to improve the base material’s laser-hardened surface microhardness. Microhardness was tested on T s , in the F z , at the F z -H az , in the Bm , in the F z of Ord , and in the centre lines of overlapped path between full hd and Ord of the hardened bead profile as shown in Fig. 6. The maximum, minimum, range, and average values of VHN on T s and in various regions are shown in Table 2. VHN values were estimated on T s and hardened zone with respect to the various regions and the parent material. Figure 7 plots the laser-hardened surface depth versus the VHN as determined by the zones.

Fig. 6 Vickers microhardness values measured in various zones (VHN)

990

D. S. Badkar

Table 2 Vickers micro-hardness (VHN) measurements for two laser processing parameters were taken at the top surface and in all the zones of hardened bead profile Ts

Process Parameters L p = 600 W S s = 2000 mm/ min F p = −10 mm L p = 800 W S s = 3000 mm/ min F p = −10 mm

Fz

F z -H az

H az

Bm

F z of Ord

OPBCL of H d -Ord

Maximum

445

438

407

406

334

436

407

Minimum

438

408

407

395

328

408

398

Range

7.0

30

0.0

11

6.0

26

9.0

Average

443

422

407

400

330

424

403

Maximum

450

441

422

418

334

438

422

Minimum

442

426

422

409

328

426

410

Range

8.0

15

0.0

9.0

6.0

12

12

Average

446

434

422

413

331

432

415

Fig. 7 Vickers microhardness values VHN are shown in relation to the depth of the laser-hardened surface

3.2 Microhardness Analysis for LLPP, Lp = 600 W, Ss = 2000 mm/min, Fp = −10 mm with Higher Hi = 180 J/cm Vickers microhardness values (VHN) for two laser process settings are plotted against the hardened bead geometry. Table 2 shows the top surface and other zones’ maximum, minimum, and average VHN measurements. The major goal of LTH is to enhance the VHN values of the laser hardened bead profile from the bulk material value of 328HV to higher values of 445HV, 438HV on T s , and 438HV in the fusion or hardened zone, respectively (from Table 2). The maximum, minimum and average

An Experimental Investigation on Overlapped Multipass Laser …

991

values of VHN observed on the T s , were 445HV, 438HV, and 444HV, respectively. The VHN hardness values obtained on the T s were the highest, continued relatively uniformly throughout, and gradually decreased in the deeper regions of the hardened bead geometry. As we proceed farther away from the top surface in the hardened surface area, the amount of martensitic T i , phase present rises, causing the microhardness at the F z -H az contact to decrease to 407HV. Figure 6 shows that the hardness ranges for the F z were 439HV to 409HV, 395HV to 396HV in the H az , and 328HV to 328HV for the parent material. The hardened zone, or F z , was confirmed to have a microhardness of roughly 438HV to 408HV in compared toward the parent metal along the depths of a laser hardened bead profile, which has been observed to have microhardness of 334HV to 328HV. This is brought on by the significant difference in α and β phases present. The variability of phases layouts can be attributed for an excessively significant variation in the hardened or fusion zone. The H az ’s hardness ratings ranged from 395 to 406HV (towards F z ) (towards Bm ). The hardness value in H az continues to drop as we travel from F z to Bm . This is because primary has increased farther away from the F z or closer to the Bm , whereas areas evidence of α ,, (transformed β) have reduced and closer to the F z . From Fig. 7, it can been seen that the Bm , which ranges in hardness from 334 to 328HV, as we get closer to the conclusion of the H az . The basic parent metal has a high hardness value of 334HV > 328HV closer to the H az . This is a result of the presence of tiny, minute acicular α , particles, which are either transformed β or martensitic elements that have been formed after laser hardening. Figure 7 and Table 2 show that the VHN value inside the F z of Ord ranged from 436 to 408HV, and that these hardness values are comparable to those from 439 to 409HV in the F z . The microhardness along the overlapped path of the centre lines (OPBCL) of H d and Ord ranged from 408 to 398HV and are comparable to the hardness values attained at the F z –H az is 408HV and from 407 to 396HV in the H az which is indicated in Fig. 6 by an arrow.

3.3 Microhardness Analysis for HLPP, Lp = 800 W, Ss = 3000 mm/min, Fp = −10 mm with Higher Hi = 160 J/cm The primary objective of this analysis’s laser transformation hardening is to use a CW spherical beam to enhance the microhardness from 327HV of parent metal that was as received to higher values of 451HV, 442HV on the T s and varied 441HV in the F z . It has been demonstrated from Fig. 7 that a high level of microhardness was found on the T s is 450HV. The hardness values on T s , obtained were 450HV, 442HV, and 446HV, respectively. Similar to the previous instance, it is discovered that the VHN values obtained on the T s are the maximum, practically remain constant throughout, and progressively drop in the deeper portions of the hardened region. As we move further away from the top of the hardened bead region, or as the martensitic T i phase content increases, the VHN value is lowered up to 423HV at the interface of

992

D. S. Badkar

F z -H az . In the H az , the hardness values ranged from 419HV (nearer to F z ) to 409HV (nearer to Bm ). In the H az , the hardness value also drops as we travel from F z to Bm . From 418HV (towards FZ) to 409HV, the H az ’s hardness levels ranged (towards BM). The hardness value similarly lowers as we travel from F z to Bm in the H az . This is caused by a decrease in areas with traces of α , (transformed β) closer to the F z and an improve and progress in areas with traces of primary α , far away from the F z or closer to the Bm . As we proceed further into the H az , we encountered Bm , and the VHN within the Bm ranged from 333 to 327HV. The VHN values observed is higher near the H az which is 333HV > 327HV that is as we move further away from the hardened region. This is caused by the presence of tiny, microscopic equiaxed particles of α , , also known as hardened martensitic or transformed β agglomerated particles. Both Fig. 7 and Table 2 show that the VHN values in the hardened surface of the overlapped region (F z of Ord ) varying from 439 to 427HV, which is comparable to the VHN values observed in the fusion zone of full hardened depth, which usually ranges from 441 to 426HV. The VHN measurements along the meeting path the centres lines of H d and Ord (the measured path is shown in Fig. 6 by an arrow) ranged from 423 to 411HV, which is comparable to the VHN values obtained at the interface F z -H az of 423HV and from 419 to 410HV in the H az . In comparison to LLPP, the averaged hardened zone of the hardened bead profile area was found to be less than 331 microns, or roughly 233 microns. The efficiency with which the laser hardening approach may be utilized to enhance the surface characteristics of titanium and its alloys is demonstrated by the effectiveness of microhardness measures to facilitate micro-structural research on the depth of particular locations in the laser-hardened surface.

4 Conclusions The investigation into the research on overlapping multitrack laser transformation hardening of Ti–6Al–4V, a two-phase (α + β) titanium alloy sheet of 2 mm thickness with CW spherical laser beam obtained the subsequent results listed below. 1. An as-received two-phase (α + β) Ti–6Al–4V titanium alloy exhibits a Vickers microhardness of 328HV. The Vickers microhardness is higher than the bulk material on T S , in the F z , at the F z -H az contact, and in the H az . The maximum values of VHN are on the T S , relatively constant throughout, and progressively diminish as one descends deeper. High hardness values of 450HV for high laser process parameters and 445HV for low laser process parameters are prevalent on the top surface. 2. As we descend from the hardened bead zone, the extent of martensitic Tiα, phase increases and martensitic α , drops so the micrhardness is decreased after 439HV to 409HV in F z is 408HV at the interface of F z -H az , 406HV to 395HV in H az , and as a final point to the bulk material 333HV to 327HV for LLPP, L p = 600

An Experimental Investigation on Overlapped Multipass Laser …

993

W, S p = 2000 mm/min, F p = −10 mm, and from 442 to 427HV in Fz, 422HV at interface of F z -H az , 419HV to 410HV in H az and lastly to the parent material 333HV to 327HV for HLPP, L p = 800 W, S s = 3000 mm/min, F p = −10 mm, respectively. 3. It was determined that a high laser process parameter combined with a low heat input value ensured a smallest normal values of hardened depth of 233 microns and an normal highest hardness value of 442HV to 427HV in the fusion or hardened zone and is desirable. Acknowledgements The researchers would like to express their gratitude to the management of the Welding Research Institute (WRI), BHEL, Tiruchirappalli-620014, Tamil Nadu, India, for granting them access to the Laser Materials Processing Laboratory. The authors are also pleased to thank the MANIT Bhopal faculty for their continuous encouragement throughout the research work.

References 1. Peng W, Zeng W, Wang Q, Yu H (2013) Comparative study on constitutive relationship of AS-Cast Ti60 titanium alloy during hot deformation based on Arrhenius-type and artificial neural network models. Mater Des 2013(51):95–104 2. Ning Y, Fu MW, Hou H, Yao Z, Guo H (2011) Hot deformation behavior of Ti-5.0 Al-2.40 Sn-2.02 Zr-3.86 Mo-3.91 Cr alloy with an initial lamellar microstructure in the (α + β) phase field. Mater Sci Eng A 528(3):1812–1818 3. Tan YB, Duan, JL, Yang LH, Liu WC, Zhang JW, Liu RP (2014) Hot deformation behavior of Ti-20Zr-6.5 Al-4V alloy in the α + β and single β phase field. Mater Sci Eng A 609:226–234 4. Yadroitsev I, Krakhmalev P, Yadroitsava I (2014) Selective laser melting of Ti6Al4V alloy for biomedical applications: temperature monitoring and microstructural evolution. J Alloy Compd 583:404–409 5. Anil P, Narendra P, Udaykar B, Srivastan TS (2011) On the use of gas metal arc welding for manufacturing beams of commercially pure titanium and a titanium alloy. J Mater Manuf Process 26(2):311–318 6. Ghosh SK, Chatterjee S (2010) On the direct diffusion bonding of titanium alloy to stainless steel. Mater Manuf Process 25(11):1317–1323 7. Dubey K, Yadava V (2008) Experimental study of Nd:YAG laser beam machining—an overview. J Mater Process Technol 195(1–3):15–26 8. Yao C, Xu B, Huang J, Zhang P, Wu Y (2010) Study on the softening in overlapping zone by laser-overlapping scanning surface hardening for carbon and alloyed steel. Optics Lasers Eng 48(1):20–26 9. Babic M, Balic J, Milfelner M, Beli I, Kokol P, Zorman M, Panjan P (2013) Robot laser hardening and the problem of overlapping laser beam. Adv Prod Eng Manage 8(1):25–32 10. Badkar DS, Pandey KS, Buvanashekaran G (2012) Application of the central composite design in optimization of laser transformation hardening parameters of commercially pure titanium using Nd:YAG laser. Int J Adv Manuf Technol 59(1):169–192 11. Ghorbani H, Heydarzadeh Sohi M, Torkamany MJ (2015) Liquid phase surface treatment of Ti–6Al–4V titanium alloy by pulsed Nd:YAG laser. J Mater Eng Perform 24(9):3634–3642 12. Li R, Jin Y, Li Z, Qi K (2014) Laser surface hardening of AISI 1045 steel. J Mater Eng Perform 23(9):3085–3091

994

D. S. Badkar

13. Isaac Adebiyi D, Fatoba O, Pityana S, Popoola P (2016) Parameters optimization, microstructure and microhardness of silicon carbide laser deposited on titanium alloy. In: XXX international conference on surface modification technologies (SMT30). 29th June–1st July, Milan, Italy 14. Badkar DS, Pandey KS, Buvanashekaran G (2010) Effects of laser phase transformation hardening parameters on heat input and hardened-bead profile quality of unalloyed titanium. Trans Nonferrous Metals Soc China 20(6):1078–1091 15. Hahn JD, Shin YC, Krane MJM (2013) Laser transformation hardening of Ti–6Al–4V in solid state with accompanying kinetic model. Surf Eng 23(2):78–82 16. Yan BG, Liu JC (2014) Calculation of laser transformation hardening with circular beam. Mater Technol 27(1):5–7 17. Qiu F, Uusitalo J, Kujanpää V (2013) Laser transformation hardening of carbon steel: microhardness analysis on microstructural phases. J Surf Eng 29(3):34–40 18. Ciurana J, Arias G, Ozel T (2009) Neural network modeling and particle swarm optimization (PSO) of process parameters in pulsed laser micromachining of hardened AISI H13 steel. J Mater Manuf Process 24(3):358–368 19. Yang LJ, Jana S, Tarn SC, Lim LEN (2007) The effects of process variables on the case depth of laser transformation hardened AISI 01 tool steel specimens. J Mater Manuf Process 9(3):475–492 20. Badkar DS, Pandey KS, Buvanashekaran G (2013) Development of RSM- and ANN-based models to predict and analyze the effects of process parameters of laser-hardened commercially pure titanium on heat input and tensile strength. Int J Adv Manuf Technol 65:1319–1338 21. Badkar DS, Pandey KS, Buvanashekaran G (2011) Parameter optimization of laser transformation hardening by using Taguchi method and utility concept. Int J Adv Manuf Technol 52:1067–1077 22. Badkar DS, Pandey KS, Buvanashekaran G (2012) Parameter optimization of laser transformation hardening by using Taguchi method and utility concept. Int J Adv Manuf Technol 59:169–192 23. Badkar DS, Pandey KS, Buvanashekaran G (2009) Laser transformation hardening of unalloyed titanium using Nd:YAG laser. Int J Mater Sci 4(3):239–250 24. De Formanoir C, Brulard A, Vivès S, Martin G, Prima F, Michotte S, Rivière E, Dolimont A, Godet S (2016) A strategy to improve the work-hardening behavior of Ti–6Al–4V parts produced by additive manufacturing. J Mater Res Lett 1–8 25. Laser materials processing lab, Welding Research Institute (WRI), Bharat Heavy Electricals Limited (BHEL), 2008–2009, Trichy-620014, Tamil Nadu, India

Clean and Green Electricity Generation by Zirconia-Based Hydroelectric Cell Device Without Greenhouse Gas Emission Rojaleena Das, Abha Shukla, Jyoti Shah, Sanjeev Sharma, Pritam Babu Sharma, and Ravinder Kumar Kotnala

Abstract Global warming has emerged as a very serious issue, and it demands immediate reduction of greenhouse gases. Energy generation sources are prime causes of greenhouse gases. Clean energy generation is of utmost requirement to cope up with greenhouse gases emission. In this direction, the Hydroelectric Cell (HEC), a device that produces clean and green energy from water splitting without use of electrolyte, acid/alkali, heat/light is a unique invention. The strategically synthesized oxygen-deficient nanoporous metal oxide used in HEC splits H2 O molecules to form OH− and H+ ions, which are collected by Zn anode and Ag inert cathode affixed onto metal oxide pellet. Hydroelectric cell generate current and voltage due to redox reaction, which sustains by continuous dissociation of physiosorbed water due to the electric field generated in the nanopores. In this work, we have reported the effect of Mg doping in ZrO2 for boosting the performance of ZrO2 based hydroelectric cell. The molar ratio of dopant and ZrO2 has been taken (0.1:0.9) and synthesized by a solid-state reaction route. The Zirconia and Zr(1−x) Mg(x) O2 (x = 0, 0.1)-based HEC of one inch 2 area generated maximum short circuit current of 10.4 mA, 14.7 mA and open cell-voltage 0.920 V, 0.90 V, respectively. Phase formation and strain developed in the lattice of Zr1−x Mgx O2 (x = 0, 0.1) has been confirmed by X-ray diffraction pattern. The porous microstructure of the samples has been confirmed by scanning electron microscope. The Nyquist plot of the cells confirmed the ionic diffusion by water dissociation. HEC polarization behavior with increase in current has been analyzed by V –I plot. It has been found that magnesium substitution in ZrO2 induces microstrains, dislocations in ZrO2 lattice thus defects are created to dissociate more water molecules. Increased current and voltage in Mgx Zr1−x O2 -based hydroelectric cells will play a huge role in diminishing greenhouse gas effect. R. Das (B) · S. Sharma · P. B. Sharma Amity University, Gurugram, Manesar, Haryana 122413, India e-mail: [email protected] A. Shukla · J. Shah · R. K. Kotnala CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, Delhi 110012, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_89

995

996

R. Das et al.

Keywords Hydroelectric cell · Water splitting · Green energy device · ZrO2 surface defects

1 Introduction The industrial growth across the world has led to environmental pollution and greenhouse gas emission, a threat to environment sustainability [1]. Use of fossil fuel to generate electricity contribute significantly toward air pollution and greenhouse gas emission. To reduce air pollution and greenhouse gas emission impact, the world has moved towards generating electricity from renewable energy sources. Major source of these are solar, wind, tidal to a larger extent [2]. However, these renewable sources have limitations such as weather dependent, unpredictable and intermittent generation. Recently invented hydroelectric cell (HEC) a green energy device is an exceptional paradigm [3]. On non-photocatalytic method, HEC exhibits potential to dissociate H2 O to form (H3 O+ and OH− ) ions without use of any extrinsic energy such as electrolyte/light/temperature and produce green electricity. This work aims at developing a unique, cost effective, adaptable and most importantly ecofriendly energy solution using ZrO2 as base material. Zirconia is an unique ceramic material possessing excellent physical properties such as exceptional mechanical strength, toughness, biocompatibility, high fatigue and wear resistance high melting temperature, chemical stability [4]. Zirconia (ZrO2 ) has already being explored for its water vapor sensing ability [5]. The effect of water adsorption and dissociation properties along with direct electricity generation by ZrO2 -based HEC has been investigated in this work. Oxygen vacancies are deliberately created by facile technique and optimizing pre-sintering, sintering temperature and time on ZrO2 . Doping of divalent Mg ion in ZrO2 causes substitution of Zr ion by Mg2+ ion leading to increased oxygen vacancy formation along with [Mg]0 center formation [6]. Oxygen vacancies and defects sites in metal oxides create dissociative and molecular water adsorption at these sites [7–18]. In this device, initial chemidissociation of adsorbed water molecules on oxygen deficient porous surface of Mg-doped ZrO2 HEC and subsequent physidissociation of H2 O molecule into H+ and OH− ions occurs due to developed electric potential by trapped hydronium ions inside the nanopores. The physidissociated ions are collected by silver (Ag) cathode and zinc (Zn) anode, and direct current is generated in an external circuit. Hydroelectric cell is a green energy device with biocompatible by-products (H2 ↑, Zn(OH)2 nanoparticles) [19, 20]. This device serves potential applications in prevalent green energy scenario towards a carbon-free earth and paves the way for new alternative energy solutions. It offers safe, clean, low cost, reliable power generation with portable size which uses few drops of water as fuel. Presently energized table lamp, fan, mobile charger, and torch. It can serve as a replacement for solar cell, fuel cell, batteries and other power backup; it can also be used in dye degradation in waste water and also used as a proton exchange membrane in fuel cell. But the biggest challenge is the device is now only available in laboratory scale with 1 inch 2 pellet. Industrial R&D unit will help in

Clean and Green Electricity Generation by Zirconia-Based …

997

bringing the technology one step closer to final usage for gamut of applications. In this article, water molecule interaction with oxygen vacancy defects for Mg-doped ZrO2 hydroelectric cell has also been explored. The increased lattice strain as a result distortion in lattice in Mg-doped ZrO2 has been confirmed by W –H plot. The results experimentally and scientifically analyzed for OH− ion migration through lattice defects and simultaneous current generation by a Zr1 −x Mgx O2 (x = 0, 0.1) based hydroelectric cell, an ecofriendly green energy device.

2 Material and Methods Pure ZrO2 and Mg substituted ZrO2 were synthesized by using analytical grade precursor powder of ZrO2 and MgCO3 to fabricate ZrO2-based HEC samples. These materials were processed by facile solid-state reaction technique. These powders (Mg: ZrO2 ) in molar ratio (0.1:0.9) individually pulverized for 40 min in a pestle mortar. The pulverized powders were pre-sintered at 800 °C for 6 h. Pre-sintered powders were again grinded for 30 min and pressed into 4.8 cm2 pellet. These pellets were finally sintered at 900 °C for 4 h. Electroding of the sintered pellets was executed by screen-printing inert silver (as cathode) having precision of 1 μm thickness on one side of the pellet, while the opposite side was pasted with a 0.3 mm thick zinc sheet (as anode). Conducting wires were soldered on both electrodes, and the current and voltage outputs were measured by using a current source meter. X-ray diffraction measurements were taken by Philips X’pert Pro diffractometer with CuKa (k = 0.154 nm) radiation. A scanning electron microscope (Zeiss: Evo MA-10) was utilized for analyzing surface morphology of samples. Impedance spectroscopy was performed by an LCR meter (Wayne Kerr 6500B). The current–voltage polarization was measured by varying load on Zr1−x Mgx O2 HECs using a Keithley source meter.

3 Results and Discussion 3.1 Composition Determination by EDX Energy Dispersive X-Ray Analysis (EDX) technique has been used to identify the elemental composition of sintered Zr1−x Mgx O2 (x = 0, 0.1) pellet. Tables 1 and 2 show the weight percentage and atomic percentage of Zr1−x Mgx O2 (x = 0, 0.1). Figure 1a shows the peaks of Zr and O peaks where as in Fig. 1b shows the presence of peaks of Zr, O, and Mg.

998 Table 1 Weight percentage and atomic percentage of Zr1−x Mgx O2 where x = 0

Table 2 Weight percentage and atomic percentage of Zr1−x Mgx O2 where x = 0.1

R. Das et al.

Element

Weight %

Atomic %

OK

25.34

65.93

Zr L

74.66

34.07

Total

100

100

Element

Weight %

Atomic %

OK

24.68

62.88

Mg K

2.81

4.72

Zr L

72.51

32.40

Total

100

100

Fig. 1 Diagram showing Zr1−x Mgx O2 (x = 0, 0.1) composition through EDX

3.2 Phase Determination by X-Ray Diffraction The XRD pattern of Mgx Zr1−x O2 has been analyzed for the determination of structure crystallinity as shown in Fig. 2i. Figure 2i exhibits X-ray diffraction (XRD) patterns of synthesized Zr1−x Mgx O2 HEC pellets at Mg (x = 0, 0.1) doping concentrations. The reported diffraction peaks for both sample are indexed being referred to ICDD Card No. 98-007-2002 and characterized for crystalline monoclinic P 121/c1 space group. The diffraction peaks for Zr1−x Mgx O2 (x = 0, 0.1) appeared at angle 17.40°, 24.03°, 24.47°, 28.20°, 31.45°, 34.14°, 34.48°, 35.33°, 35.89°, 38.67°, 40.80°, 41.45°, 44.91°, 45.56°, 49.25°, 50.10°, 50.58°, 51.28°, 54.11°, 55.40°, 56.15°, 57.22°, 58.18°, 60.00°, 61.45°, 62.04°, 62.95°, 64.33°, 65.83°, 69.07°, 71.19°, 72.36°, 74.74°, 75.33°, 76.50°, 77.40°, 79.04°. Figure 2i shows all Zr1−x Mgx O2 (x = 0) peaks matches with Zr1−x Mgx O2 (x = 0.1) peaks. Magnesium doping in ZrO2 resulted in reducing intensity peak of ZrO2 , and the XRD patterns revealed that maximum intensity peak at 31.45° was shifted toward lower angle values as evident from Fig. 2i. It represents a lattice distortion due to mismatch in interatomic spacing by substitution of Mg2+ ion (72 pm) at Zr4+ (80 pm) ion sites in monoclinic lattice of ZrO2 . The observed

Clean and Green Electricity Generation by Zirconia-Based …

999

(i)

(ii) Fig. 2 (i) X-ray diffraction pattern of sintered Zr1−x Mgx O2 (x = 0, 0.1) pellet. (ii) Williamson-Hall plot of Zr1−x Mgx O2 (x = 0, 0.1) HEC

XRD peak broadening is accounted to enhance structural disorder and reduced crystallinity of Zr1−x Mgx O2 pellets with Mg doping due to increased microstrain. Lattice constants of monoclinic structure unit cell calculated as, a = 5.1480 Å, b = 5.2060 Å, and c = 5.3210 Å. Average crystallite size of ZrO2 and Mg-ZrO2 has been computed by using Debye Scherrer’s formula as d = kλ/β cos θ

(1)

where d = crystallite size, β = full width at half maximum (FWHM) of the most intense peak, and λ = X-ray wave length, Cu X-ray λ = 1.54 Å The average crystallite size for Mgx Zr1−x O2 (x = 0, 0.1) has been calculated as 30.06 nm 29.01 nm. Williamson-Hall plots have been used for calculating the micro strain-induced in the

1000

R. Das et al.

(a)

(b) Fig. 3 a SEM micrograph of Zr1−x Mgx O2 (x = 0) HEC illustrating porous microstructure and grain size distribution using Image J software. b SEM micrograph of Zr1−x Mgx O2 (x = 0.1) HEC illustrating porous microstructure and grain size distribution using Image J software

particles. The strain may be due to crystal imperfections and distortion present in Zr1−x Mgx O2 (x = 0, 0.1) as shown in Fig. 2ii (a) and (b). The strains calculated are 3.82 × 10–4 and 4.47 × 10−4 for Zr1−x Mgx O2 (x = 0, 0.1) samples, respectively.

3.3 Surface Morphology and Pore Distribution by SEM The surface morphology of Zr1−x Mgx O2 hydroelectric cell pellets is investigated, and the effect of Mg doping is studied from SEM images, depicted in Fig. 3. The figure shows there is a decrease in grain size and more densified surface observed with Mg doping, Fig. 3a and b. Pure ZrO2 surface show large macropores with aggregates of non-uniform grains, with Mg doping there is a densification in the structure with

Clean and Green Electricity Generation by Zirconia-Based …

1001

uniformity in grain size as shown in the graph, because magnesia shows densification properties when reacted with ZrO2 [21]. The average grain size has been calculated by Image J software. The average grain size of ZrO2 and Mg-doped ZrO2 has been calculated as 500 nm and 470 nm.

3.4 Working Mechanism of Zr-Based Hydroelectric Cell The working mechanism of ZrO2 HEC is two fold (i) chemidisociation and followed by (ii) physisorption. The principle is basically based on the ambient temperature water molecule dissociation by the oxygen vacancies created due to the strain developed in the crystal lattice and unsaturated surface cations present on the surface. Zr4+ and oxygen vacancies form acid–base pair and water molecule as polar in nature attracts toward the surface defect by the presence of trapped charge and oxygen vacancies. These surface pairs drag the H2 O molecules nearer, and charge transfer occur proceeding into chemidissociation of water molecules to form OH− and H+ ion. Both Zr-OH2 and Zr-OH− are formed on a surface zirconium atom when the distance between the zirconium and the oxygen atoms in a H2 O molecule or an OH group is smaller than 2.5 A. The O–H bonds in Zr-OH2 and Zr-OH− are also defined as being formed when the distance is less than 1.2 A [22]. These chemisorbed hydroxyl groups gives high surface charge potential to physisorbed water through hydrogen bonding. Hopping of proton occurs on physisorbed water and get trapped in nanopores. Hydronium ions in huge concentration inside nanopores supplies high electrostatic potential which is high enough to further dissociate physisorbed water molecules [23]. Thus, water dissociation is continuously sustained on HEC surface. The proton hopping occurs by a chain reaction via hydrogen-bonded H2 O. The reaction on the pellet surface is as follows Zr − OH2 − Vo + H2 O → Zr − OH− + Vo H3 O+ [24] Subsequent fast proton transfer to an adjacent H2 O molecule occurs by the equation Vo + 2H2 O → OH− + H3 O+ This chain reaction of protonation via H2 O molecules is known one of the representative mechanisms of proton conduction named as the Grotthuss mechanism [24]. Through grain boundaries the OH− ion moves via oxygen vacancies toward Zn(−) anode and forms Zn(OH)2 with the release of 2e− and H3 O+ ion gets reduced at Ag electrode to give H2 gas as a result current flows in an external circuit [19, 20]. The equations are given below: At Zn electrode (Anode): Zn + OH− = Zn (OH)2 + 2e− At inert Ag electrode (cathode): H3 O+ + 2e− = H2 ↑ + H2 O.

1002

R. Das et al.

The byproducts of Zirconia-based hydroelectric cell are Zn(OH)2 and H2 gas. It is evident from the results that by Mg doping in ZrO2 increased the current to 14.7 mA compared to 10.4 mA in pure ZrO2 HEC, while the voltage dropped slightly from 0.92 V to 0.90 V. It may be due to divalent Mg ion (0.72 Å) interstially substituted in tetravalent zirconia ion (0.80 Å) creates deformation in ZrO2 lattice. Deformation in ZrO2 lattice created oxygen vacancies Vo in the lattice.

3.5 Voltage-Current (V–I) Characteristic of Mgx Zr1−x O2 Based HEC Hydroelectric cell working was confirmed by measuring the standard performance V –I curve showing the cell voltage performance with operating current [25]. A typical polarization plot of overvoltage versus cell current for Zr1−x Mgx O2 (x = 0, 0.1) based HEC is shown in Fig. 4a and b showing various types of losses in overvoltage curve. The open cell voltage at infinite load where no current flows through the cell. The over voltage for Zr1−x Mgx O2 (x = 0, 0.1) is obtained 0.92 V and 0.90 V, respectively. The open cell voltage has been found to be reduced with Mg doping concentration. Moreover, peak current is found to be in increasing trend for Zr1−x Mgx O2 (x = 0, 0.1) from 10.4 mA to 14.7 mA, respectively. Figure 5a and b shows the peak power curve of Zr1−x Mgx O2 (x = 0, 0.1) HECs. Table 3 shows the current density and power density values for Zr1−x Mgx O2 (x = 0, 0.1)-based HECs. Initial sharp decline (AB) in voltage known as activation polarization zone implies an energy barrier for charge transfer reaction predominant at Zn and Ag electrodes [34]. Large activation loss shown in pure Zr1−x Mgx O2 (x = 0) cells may be because of initial sluggish dissociation of water molecules at the cell pellet as compared to Zr1−x Mgx O2 (x = 0.1) cells due to less strain in the lattice, which in turn needs more energy for charge transfer reaction to continue. (BC) the ohmic zone where the voltage declines almost linearly with current depicts internal resistance of cell material to ion flow resistance offered by materials, electrodes and deionized water. (CD) the concentration loss region because of high concentration of ions on Ag and Zn surface. The loss was found to be limited with higher Mg doping due to increased micro strain of samples resulting into efficient diffusion of ions at Zn electrode surface promoting to reduced crowding of ions at anode surface, called mass transport [26].

3.6 Electrochemical Impedance Spectroscopy Analysis (Nyquist Plot) Figures 6 and 7a and b represents the ionic transport kinetics in Zr1−x Mgx O2 -based hydroelectric by electrochemical impedance analyzer both in dry and wet condition [27, 28]. It confirms from Fig. 6 that the HEC shows high reactance in the range

Clean and Green Electricity Generation by Zirconia-Based …

1003

Fig. 4 V –I polarization curve of Zr1−x Mgx O2 a x = 0 and b x = 0.1

Fig. 5 Power curve of Zr1−x Mgx O2 a x = 0 and b x = 0.1

Table 3 Current–voltage (V –I) response of the Zr1−x Mgx O2 based HEC Sample Zr1−x Mgx O2

Max. current output (mA)

Max voltage output (V)

Current density (mA/cm2 )

Power density (mW/cm2 )

x=0

10.4

0.920

2.16

1.99

x=1

14.7

0.900

3.06

2.75

of 106 Ω in dry condition for electronic charge conduction. It is due to the reason of excess ohmic resistance of Zr1−x Mgx O2 -based HEC which does not permit the electrons to pass through the cell. As soon as the ZrO2 based HEC cell sprinkled with few drops of high-resistance deionized water of the order of 16 MΩ, the reactance gradually decreased to 500 Ω. It could also be observed that at low frequency in wet cell shows a tail which can assigned to diffusion of hydronium ion and at high frequency, a capacitive tail appeared as a result of formation of Zn(OH)2 formed

1004

R. Das et al.

Fig. 6 Nyquist plot of Zr1−x Mgx O2 (x = 0, 0.1) in dry condition

Fig.7 Nyquist plot of Zr1−x Mgx O2 (x = 0, 0.1) in wet condition

by hydroxide ion reaction at anode. It is a clear evidence of ionic conduction in the samples by dissociation of water molecules as explained in Fig. 7.

4 Conclusion Zr1−x Mgx O2 has been processed by facile solid-state technique. Nanoporous structure of Mgx Zr1−x O2 HEC surface has been examined by scanning electron microscope (SEM). More micro strain as a result more defects in Zr1−x Mgx O2 (x = 0.1) HEC pellets provides greater rate of H2 O molecule dissociation compared to ZrO2 based HEC. A Nyquist curve of both Zr1−x Mgx O2 (x = 0, 0.1) HEC in the wet and dry state explains the ionic diffusion in the cells and affirms the impedance of Zr1−x Mgx O2 (x = 0.1) HEC reduces to a large extent with the addition of few drops of water. The fabricated Zr1−x Mgx O2 (x = 0.1) based HEC of 4.8 cm2 area of gives the power of 13.23 mW as compared to 9.56 mW for pure Zirconia-based

Clean and Green Electricity Generation by Zirconia-Based …

1005

HEC. Zr1−x Mgx O2 -based hydroelectric cell is an economical device which gives rise to green electricity generation with ecological derivatives (H2 gas and Zn(OH)2 nanoparticles) without adoption of any acid/alkali, electrolyte/light which results into a zero carbon emission greenhouse gas device.

References 1. Patnaik R (2018) Impact of industrialization on environment and sustainable solutions—reflections from a South Indian region. Proc IOP Conf Ser Earth Environ Sci 120:012016. https:// doi.org/10.1088/1755-1315/120/1/012016 2. Obula Reddy M, Srineetha U (2018) Review on sustainability of renewable energy in India. In: Proceedings of AIP conference 1992, p 040006. https://doi.org/10.1063/1.5047971 3. Kotnala RK, Shah J (2016) Green hydroelectrical energy source based on water dissociation by nanoporous ferrite. Int J Energy Res 40:1652–1661 4. Volpato CAM (2011) Application of Zirconia in dentistry: biological, mechanical and optical considerations. Ceram Environ. ISBN: 978-953-307-350-7 5. Stand der Technik Process Zirconia Oxygen Analyzer State of Art ZirkondioxideSauerstoffsensoren. tm-TechnischesMessen 77(1):1923. https://doi.org/10.1524/teme.2010. 0003 6. Chandra Dimri M, Khanduri H (2012) Room-temperature ferromagnetism in Ca and Mg stabilized cubic zirconia bulk samples and thin films prepared by pulsed laser deposition. J Phys D Appl Phys 45:475003. https://doi.org/10.1088/0022-3727/45/47/475003 7. Kotnala RK, Gupta R (2018) Metal oxide based hydroelectric cell for electricity generation by water molecule dissociation without electrolyte/acid. J Phys Chem C 122(33):18841–18849 8. Jain S, Shah J, Negi NS (2019) Significance of interface barrier at electrode of hematite hydroelectric cell for generating ecopower by water splitting. Int J Energy Res 43:4743–4755. https:/ /doi.org/10.1002/er.4613 9. Shah J, Jain S, Gahtori B, Sharma C, Kotnala RK (2021) Water splitting on the mesoporous surface and oxygen vacancies of iron oxide generates electricity by hydroelectric cell. Mater Chem Phys 258:123981. https://doi.org/10.1016/j.matchemphys.2020.123981 10. Bhargava R, Shah J, Khan S, Kotnala RK (2020) Hydroelectric cell based on a cerium oxidedecorated reduced graphene oxide (CeO2 -rG) nanocomposite generates green electricity by room-temperature water splitting. Energy Fuels 34:13067–13078. https://doi.org/10.1021/acs. energyfuels.0c02192 11. Das R, Shah J, Sharma S, Sharma PB, Kotnala RK (2020) Electricity generation by splitting of water from hydroelectric cell: an alternative to solar cell and fuel cell. Int J Energy Res 44:11111–11134. https://doi.org/10.1002/er.5698 12. Gaur A, Kumar A, Kumar P, Agrawal R, Shah J, Kotnala RK (2020) Fabrication of a SnO2 based hydroelectric cell for green energy production. ACS Omega 5:10240–10246. https://doi. org/10.1021/acsomega.9b03309 13. Gaur A, Kumar P, Kumar A, Shah J, Kotnala RK (2020) An efficient green energy production by Li-doped Fe3 O4 hydroelectric cell. Renew Energy 162:1952–1957. https://doi.org/10.1016/ j.renene.2020.10.016 14. Gupta R, Shah J, Das R, Saini S, Kotnala RK (2021) Defect-mediated ionic hopping and green electricity generation in Al2−x Mgx O3 -based hydroelectric cell. J Mater Sci 56:1600–1611. https://doi.org/10.1007/s10853-020-05280-4 15. Shah J, Verma KC, Agarwal A, Kotnala RK (2020) Novel application of multiferroic compound for green electricity generation fabricated as hydroelectric cell. Mater Chem Phys 239:122068. https://doi.org/10.1016/j.matchemphys.2019.122068

1006

R. Das et al.

16. Aarti, Gaur A, Shah J, Kotnala RK (2022) SnO2 decorated reduced graphene oxide (rGO)based hydroelectric cells to generate large current. ACS Omega 7:43647−43656. https://doi. org/10.1021/acsomega.2c04553 17. Saini S, Shah J, Kotnala RK (2022) Effect of Li+ , Mg2+ , and Al3+ substitution on the performance of nickel ferrite-based hydroelectric cells. Energy Fuels 36(13):7121–7129. https://doi. org/10.1021/acs.energyfuels.2c01244 18. Aarti, Gaur A, Shah J, Kotnala RK, Kumar D (2022) Development of Mg-doped hematite (α-Fe2 O3 )-based hydroelectric cell to generate green electricity. Roy Soc Chem 21158–21166. https://doi.org/10.1039/D2NJ03506E 19. Shah J, Jain S, Shukla A, Gupta R, Kotnala RK (2017) A facile non-photocatalytic technique for hydrogen gas production by hydroelectric cell. Int J Hydrogen Energy 42:30584–30590. https://doi.org/10.1016/j.ijhydene.2017.10.105 20. Shah J, Kumar Kotnala R (2017) Rapid green synthesis of ZnO nanoparticles using a hydroelectric cell without an electrolyte. J Phys Chem Solids 108:15–20. https://doi.org/10.1016/j. jpcs.2017.04.007 21. Relationship between effective ionic radii, structure and electro-mechanical properties of zirconia stabilized with rare earth oxides M2 O3 . J Mater Sci 44:234–243. https://doi.org/10. 1007/s10853-008-3085-0 22. Sato R, Ohkuma S, Shibuta Y, Shimojo F, Yamaguchi S (2015) Proton migration on hydrated surface of cubic ZrO2 , Ab initio molecular dynamics simulation. J Phys Chem C 119(52):28925–28933. https://doi.org/10.1021/acs.jpcc.5b09026 23. Structural and electrochemical properties of physisorbed and chemisorbed water layers on the ceramic oxides Y2 O3 , YSZ, and ZrO2 . https://doi.org/10.1021/acsami.6b03566 24. Grotthuss CJT (1806) Sur la decomposition de l’eau et des corps qu’elle tient en dissolution al’aide de l’electricite galvanique. Annalos de Chimie 58:54 25. Clark JH, Dell RM, Rand DAJ (2004) Clean energy. Royal Society of Chemistry, Camb 26. Sharaf ZO, Orhan MF (2014) An overview of fuel cell technology: fundamentals and applications. Renew Sustain Energy Rev 32:810–853. https://doi.org/10.1016/j.rser.2014. 01.012 27. Bockris O’MJ, Reddy KN (1970) Modern electrochemistry. Plenum Press, New York 28. Stroe D-I, Swierczynski M, Stroe A-I, Knap V, Teodorescu R, Andreasen SJ (2015) Ecological vehicles and renewable energies (EVER). In: Tenth international conference, pp 1–8

A Short Review on Tribological Behaviour of Epoxy Composites Containing Different Fibres and Nanoparticles Anurag Namdev, Rajesh Purohit, Amit Telang, Madhusudan Baghel, and Raghvendra Singh

Abstract The aim of this short review is to determine the tribological behaviour of epoxy composites containing different filler materials. The study gives the information about the effect of different fibres or nanoparticles on the wear and frictional properties of epoxy composites. It is determined that the reinforcement of different fibres improved the wear as well as mechanical behaviour but sometimes it may show some negative effect also. The nanoparticles also improve the tribological properties of epoxy nanocomposites. The amount of nanoparticles from a certain limit, diminish the wear properties of different nanocomposites also. The composites having hybrid reinforcement improves wear and mechanical behaviour more compare to individual ones. The bonding of matrix and filler gives the main impact on the wear properties of polymer composites. So that it is a great challenge for developing new epoxy composites for better wear characteristics for different tribological application. Keywords Epoxy · Polymer composites · Fibres · Nanoparticles · Tribological properties

A. Namdev (B) Department of Mechanical Engineering, Institute of Engineering and Technology, Lucknow, Uttar Pradesh, India e-mail: [email protected] R. Purohit · A. Telang · M. Baghel Department of Mechanical Engineering, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India R. Singh Department of Mechanical Engineering, Bundelkhand Institute of Engineering and Technology, Jhansi, Uttar Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Nayak et al. (eds.), Recent Advances in Materials and Manufacturing Technology, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-2921-4_90

1007

1008

A. Namdev et al.

1 Introduction The need for superior materials with great strength, stiffness, density, sustainability, and lower cost has arisen due to rapid growth in manufacturing industries. Composite materials come under this category with such improved properties that they may be used in various applications. Composites are the most prominent and promising material now a days [1]. The current technology is trending towards development of advanced materials with unique behaviour and characteristics for numerous applications. The most favourable materials used nowadays are reinforced polymer composites because of their various advantages over conventional materials. The advantageous properties include high strength, stiffness, corrosion resistance, impact resistance, and light weight. Different sectors like automobiles, aeronautics, transport, military, defence, sports, and biomedical are dependent on these composite materials for different applications [2–6]. Polymer composites have already marked their value in the growing market as lightweight materials. However, the top-most priority is to make it cost-effective and sustainable materials [7]. Epoxy is used as a matrix material due to its high rigidity, wear and thermal resistance, corrosion resistance, less volumetric shrinkage during curing, excellent electrical performance, and capable to be treated in different conditions [8, 9]. However, epoxy has tendency to crack initiation due to its brittle behaviour, which limits its use where high fracture toughness and strength is required [10]. The addition of different fibres and nanoparticles make suitable reinforced composites for different tribological applications. The composites having hybrid reinforcement improves wear and mechanical behaviour more compare to individual ones. Recent advances in the development of tribo-materials have been accomplished to the development of fibre reinforced polymer (FRP) composites. A balance of mechanical strength and tribological performance of PCM is required for their practical applications in various fields. As per the available literature, fibre reinforcement improves abrasive wear resistance and improves mechanical properties. Furthermore, for mechanical strength and wrinkle-free construction of complex components, bidirectional woven fibre is required for reinforcement in the polymer [11–13]. According to many literatures, the abrasive wear characteristics of FRP composites are controlled by several factors like load, velocity, distance, abrasive type, etc. [14]. Reinforcements are discovered to regulate polymer composites’ mechanical and wear behaviour among the major influencing parameters. Secondary fillers, such as inorganic nanofillers (CuO, Al2 O3 , TiO2, etc.) and carbon nanofillers such as carbon nanotube (CNT), GNP, graphite can improve the wear characteristics of FRP composites if utilized in the proper proportions. However, when it comes to the tribo-mechanical behaviour of nanocomposites (NCs), proper nanoparticle selection based on application is challenging [15–18]. According to most of the literature, the weight percent of nanoparticles is 0.1–1 wt.%, which enables increased mechanical and wear characteristics of PCM [17, 19, 20]. The impact of different natural or synthetic fibres, carbon nanoparticles and inorganic nanoparticles on tribological properties of epoxy composites have been showed in this review. The amount of nanoparticles shows improvement in wear behaviour

A Short Review on Tribological Behaviour of Epoxy Composites …

1009

of nanocomposites up to a certain weight percentage. The excess weight of nanoparticles degrades the tribological properties of epoxy composites. The hybridization of reinforcement improves wear characteristics more compare to individuals. The current study gives the detail information about wear characteristics of reinforced epoxy composites under different conditions.

2 Tribological Characteristics of Different Epoxy Composites This section gives the information of different fibres and nanoparticles used for developing different composites having epoxy. It also informed about the effect of fibres and particles on wear characteristics developed composites.

2.1 Effect of Carbon Fibres In this study epoxy composites were fabricated containing carbon fibre. Variations in density confirm the formation of a gradient at all different speeds. The highest wear resistance is obtained at the highest rpm centrifuged specimen compared to others up to the transition zone. Immediately following the transition zone, wear resistance dropped. After this zone, the sample at 900 RPM has maximum wear resistance due to the large no. of carbon fibres [21]. This research examined the abrasive wear performance of epoxy composites having carbon and glass both fibres. The results show carbon having epoxy composites shows better wear resistance shown in Fig. 1 [19]. It was determined the abrasive wear behaviour of CFRP composite filled with Al2 O3 and molybdenum disulphide (MoS2 ) filler. Taguchi L18 orthogonal array was used to perform the experiment. According to the study, 10% MoS2 , 15 N load, 30 m sliding distance, and 320 grit size are the optimal process parameters for better wear performance [22]. Experiment evaluated the wear behaviour of 30% short carbon fibre, 30% long carbon fibre reinforced epoxy, and neat epoxy for automobile applications. The fibre reinforced composites have high hardness compared to neat ones. LF composites have a less specific wear rate in adhesive mode compared to SF and UF composites. Under the abrasive mode, micro ploughing, fibre pull-out, and fibre breaking are the dominating failure morphology [23]. The abrasive wear behaviour of CFRP composites having silicon carbide (SiC) as filler using the design of experiments have been investigated. Three distinct compositions were made by varying the weight fraction of SiC (5 and 10 wt.%) while maintaining the fibre content at 60 wt.%. It was observed that adding 10% SiC particle increased the wear resistance of CFRP composite. Load is the main influencing parameter for abrasion resistance according to ANOVA analysis [24]. Carbon fibre,

1010

A. Namdev et al.

Fig. 1 a Wear volume loss. b Wear rate of different composites [19]

and silicon carbide whiskers with 2.5 and 5 wt.% reinforced hybrid epoxy composites were fabricated for the analysis. Results revealed that 5wt.% SiC shows the best mechanical and tribological properties compare to another one [25]. The tribological performance of CFRP composites determined under three environmental conditions. According to the findings, sliding in an environment with inert gas has the highest friction coefficient, followed by dry sliding and sliding with oil lubrication. Due to the formation of the layer, oil lubricated sliding shows the lowest value of COF shown in Fig. 2 [26]. This study evaluated the flexural and wear characteristics of glass and carbon fibre containing composites. Compare to GFRP composite, the flexural stress and modulus of the hybrid composite increased. The hybrid is estimated to have a minimum specific wear rate at 1100 m and 33.15 N [27]. The effect of graphite (0–30 wt.%) on the mechanical and tribological behaviour of CFRP composites were examined. According to experimental findings, adding large amounts of graphite causes materials to acquire high elastic, shear, and flexural modulus. For increases beyond 12.5 wt.%, it also causes an increase in brittle behaviour, which dramatically lowers failure strain. Due to the lubrication effect, the incorporation of graphite increases the wear resistance of developed composites. CFRP composites containing graphite show higher mechanical properties than CFRP composites [28]. The tensile strength of CF/phenolic resin composites filled GNP improved by 47.5% in comparison to unmodified composites. Addition of GNP to CF/epoxy composite enhanced the wear resistance. Theoretical study and experimental verification led researchers to conclude that the CF-uniform GNP’s distribution, efficient load transfer, and well-formed multiscale structures work in concert to reinforce composite materials [29]. The 0.45 wt.% MWCNT and 0.45 wt.% nanosilica filled epoxy composites show better tensile and tribological properties than single nanoparticles filled epoxy composites [30]. NanoSiO2 and discontinued carbon fibres were used to evaluate the wear of epoxy hybrid composites. The composite has substantially lower wear and friction than unfilled one. Epoxy is given superior tribological qualities together with well-balanced mechanical properties by grafting nanoSiO2 and short carbon fibres into it in modest amounts [31].

A Short Review on Tribological Behaviour of Epoxy Composites …

1011

Fig. 2 a Weight loss. b Coefficient of friction for CFRP under all three conditions [26]

2.2 Effect of Other Fibres The wear behaviour of GFRP and GFRP filled with 1 wt.% GNP composites under different conditions were evaluated in this research. Results of sliding wear tests under various loads showed that 1 wt.% GNP-filled GFRP composites perform best under oil lubricated circumstances. Under an applied load of 120 N, it was shown that the coefficient of friction (COF) is highest in dry air shown in Fig. 3 [32]. It was determined that homogeneous dispersion of 0.5 wt.% GNP-filled BFRP composites have the highest mechanical and wear characteristics. The strength of GNP-filled BFRP composites increased by approximately 43% compare to neat BFRP composites. When varying loads and surface pressures are considered, adding 0.5%, GNP reduce wear rate [33]. The impact of SiC and Al2 O3 on the wear characteristics of jute fibre having epoxy composite were examined. The lower coefficient of friction in the 15 wt.% nanoparticles filled jute-epoxy composites with both fillers shows more excellent wear resistance [34]. The effect of modified GNP on the wear characteristics of BFRP composites were determined under this analysis. The 0.3 wt.% GNPfilled nanocomposites have lowest wear rate [35]. The sliding test was conducted under high PV of the unidirectional conditions, BFRP composite outperforms GFRP composite in terms of wear resistance against erosive sand. Even though its specific wear rate was lower, the BFRP composite demonstrated better friction qualities than the GFRP composite [36].

2.3 Effect of Carbon Nanoparticles The wear behaviour of carbon nanocages (CNC) filled epoxy composites were investigated. For the improved CNC/EP nanocomposites, the friction coefficient and wear

1012

A. Namdev et al.

Fig. 3 Specific wear rate of a Neat GFRP b 1% GNP/GFRP composites and COF of c neat GFRP d 1% GNP/GFRP composites [32]

rate are lowered by 55.6 and 51.9%, respectively, compared to neat epoxy. The mechanical characteristics are considerably improved, while the wear rate decreases by up to 21.8% compared to conventional graphene/epoxy nanocomposites [3]. Carbon nanofillers and carbon nanocage were used to determine the dimensional effect in high temperatures. The friction coefficient and wear rate of the 0.1 wt.% one-dimensional carbon filler-filled composite can be reduced by 36% and 95%, respectively, compared to epoxy [37]. The tribological behaviour of MWCNT, and GO nanosheets reinforced epoxy composites were examined in this study. Testing reveals that the wear rate is higher than the neat matrix, but the friction coefficient is reduced at a high GO level of 0.5 phr shown in Fig. 4. The friction coefficient continues to rise while the specific wear rate declines at the ideal hybrid formulation, which contains 0.5 phr MWCNT and 0.1 phr GO [38]. Epoxy composite were developed by filling the coating of diamond, SiC, MoS2 , and graphite to evaluate friction and wear performance shown in Fig. 7. A film was seen on the worn surface of composites due to the lubricant fillers MoS2 and graphite, which can significantly reduce friction heating and additional matrix degradation [39]. Another study revealed that 0.2 wt.% functionalized GO filled epoxy composite show the best tribological behaviour. Due to improved dispersity and interface contact in the matrix, modified GO also improved mechanical properties [40]. The wear characteristics of kenaf-epoxy fibre composites were determined by

A Short Review on Tribological Behaviour of Epoxy Composites …

1013

Fig. 4 a Friction coefficient. b Specific wear rate of different composites having GO and MWCNT [38]

filling them with untreated, acid treated, and silane treated MWCNT having 0.5, 0.75, and 1 wt.%. At higher loadings, untreated MWCNT shows a reduction in wear rate, while acid-treated MWCNT composites have a higher wear rate at higher loadings. Unexpectedly, after the MWCNT underwent an acid and silane treatment, the wear rate increased [41]. The impact of hybrid nanoparticles on wear characteristics are more compare to single particle reinforcement. The composites with the highest wear resistance contain 5% Gr and 5% SiC [42]. It was investigated that incorporating functionalized MMT nanoclay and GNP drastically enhanced the wear resistance of the nanocomposites. The combination of 0.5 wt.% MMT and 0.15 wt.% GNP is best for all other composition regarding wear characteristics [43]. The tribological behaviour of alumina and GNP reinforced epoxy composites were examined in this research. According to the findings, the micro hardness and wear resistance value improved when reinforced with 2 wt.% of Al2 O3 and 0.5 wt.% of GNP [44].

2.4 Effect of Other Particles The wear rate of titanium carbide (Ti3 C2 ) filled epoxy nanocomposites minimum at a 1 wt.%, which was 72.1% less than neat epoxy. Additionally, the thermo-mechanical studies showed that adding Ti3 C2 nanosheets significantly raised the Tg and storage modulus of epoxy resin [45]. The wear characteristics of food waste fillers containing epoxy has been explored in this study. The citrus limetta peel (CLP) fillers are used in three different sizes 15% by weight. The fillers with sizes between 100 and 250 m showed the most excellent wear resistance. A non-biodegradable polymer (epoxy) with 15% by weight of food waste added will reduce the amount of plastic used overall and will undoubtedly be a green initiative in addition to improving wear behaviour [46]. This study used slag of the blast furnace as a filler in epoxy to evaluate

1014

A. Namdev et al.

Fig. 5 Wear rate of the nanocomposites [48]

tribological performance. Slag fillers often show more excellent wear resistance than Al2 O3 reinforced composites. The rate of wear accelerated when the applied force was increased. The addition of particles boosted thermal conductivity, which helped to improve wear performance [47]. Nano diamond (ND) used with different concentrations of 0.1, 0.2, and 0.3 wt.% in epoxy composites. According to findings, 0.1 wt.% ND-filled epoxy composites show better mechanical properties. With the addition of ND particles, the COF and wear rate decreased shown in Fig. 5. For the nanocomposites comprising more than 0.1 wt.% of ND particles, the existence of agglomerations prevented further advancements in wear properties [48]. The epoxy composite having oil displayed a reasonable specific wear rate and a low coefficient of friction. The composite’s thermal degradation temperature was raised by 22.8% by adding fillers [49]. Adding BN and MoS2 reduced the coefficient of friction and wear rate of epoxy composite [50]. Boron carbide (B4 C) nanofiller used to make epoxy nanocomposites. For the 2% sample under a 3 kg load, the specific wear rate and coefficient of friction is minimum. It is frequently noticed that when the load rises, the wear rate decreases while the COF rises proportionately [51]. The study revealed that adding nanoclay to epoxy improved the wear resistance. At 2 wt.%, nanoclay shows the highest tensile and flexural properties of nanocomposites. Further addition of nanoclay deteriorates the mechanical properties. ANOVA results reveal that the load and concentration of nanoclay affect mass loss more than time and speed for the wear test [52]. Polyvinylpyrrolidone was used to treat the nanoparticles before combining them with epoxy resin using a three-roll mill grinding process. Due to the pre-treatment of TiO2 particles, the wear rate of composite plates were also significantly decreased [53]. The physical, tribological, and viscoelastic behaviour

A Short Review on Tribological Behaviour of Epoxy Composites …

1015

of waste debris-filled epoxy composites were determined in this research. Result revealed that composites having debris shown better wear characteristics compare to neat epoxy [54].

2.5 Effect of Fibre and Nanoparticles Combination on Wear Behaviour of Epoxy Composites Neat glass fabric/epoxy composite (GEC) laminates with Al2 O3 filler with 3, 6, and 9 wt.% were fabricated to evaluate mechanical and tribological properties. In comparison to GEC, 6AGEC, and 9AGEC, 3AGEC demonstrates greater tensile and flexural strength in addition to better hardness. Comparing to other composites 3AGEC shows the lowest specific wear rate. It was discovered that filler agglomeration causes the composites’ mechanical and sliding wear characteristics to degrade above a filler level of 3%. Non-uniform filler dispersion, voids, and aggregation of the filler particles reduce the wear resistance and mechanical characteristics of composites loaded with a more significant percentage of Al2 O3 [55]. The mechanical and tribological characteristics (using 220 grit size SiC paper) of CFRP composites with and without GNP were analysed. Wear testing revealed that adding 1 wt.% GNP significantly reduced the wear rate compared to neat GFRP [56]. The other research by the same author used 400 grit size SiC paper to evaluate the mechanical and abrasive wear behaviour. The wear characteristics and hardness of the composite materials were also considerably improved by incorporating a 1 wt.% of GNP. The tensile strength slightly increased when 1 wt.% of GNP were added compared to 0.5 wt.% of GNP. Analysis of worn surface features revealed that adding GNP primarily enhanced the fibre-matrix interface, preventing facile fibre pull-out from the matrix and fillers and increasing wear resistance [57]. The response of adding tungsten carbide and tantalum niobium carbide powders on abrasive wear behaviour in GFRP composites were observed in this study. In comparison to GFRP composite systems without hard powder filling, these systems show lower wear volume loss and specific wear rates [58]. The experiment shows that the highest tensile and flexural strength value was achieved at 6 and 9 wt.% walnut filler-filled composites. It was discovered that the velocity and walnut content are the significance factors for wear using the design of experiment analysis [59]. GFRP composite having 3 wt.% of graphite show better mechanical and wear behaviour. More graphite content degrades mechanical behaviour and accelerates specific wear rates [60]. In this study it was observed the better mechanical and wear properties of epoxy composites date palm fibre shown in Fig. 6. The incorporation of graphite to fibre reinforced composite has better wear resistance and low COF. The high percentage of graphite up to a specific limit decreased the mechanical performance [61]. The effect of adding MWCNT fillers to glass and Kevlar reinforced epoxy composites on the mechanical behaviour were investigated under this study. 0.6% of MWCNT filled composite have better mechanical and wear characteristics than other composites [62]. Micro-size solid glass microsphere used to fill the epoxy

1016

A. Namdev et al.

to create composites. When compared to neat epoxy’s hardness, this increase is said to be roughly 450%. Flexural and impact strength increase with filler content, with the largest improvements reported at around 11% for flexural strength and about 50% for impact strength. According to the results, the specific wear rate increases with increasing sliding velocity and applied force while it reduces with increasing filler content [63]. 0.4 wt.% salinized GO gives the better wear properties of Basalt composites compare to other [64]. The wear behaviour of E-glass–epoxy/MWCNT (0, 0.5, 1, and 1.5 wt.%) composites were examined under this research. It is determined that the incorporation of MWCNT reduced the specific wear rate and friction coefficient [65]. Kenaf long fibre (10–40%) used as reinforcement to make the composite plate.

Fig. 6 Specific wear rate of different epoxy composites [61]

Fig. 7 a Weight loss for different fibre loading, b friction coefficient for different fibre loading [66]

A Short Review on Tribological Behaviour of Epoxy Composites …

1017

According to the results, the 30 wt.% fibre loading has the best wear resistance shown in Fig. 7 [66].

3 Conclusions This present review focused on the tribological properties of epoxy polymer composites with different reinforcement in terms of fibres and nanoparticles. The combination of epoxy matrix and different reinforcement showed better wear properties which helpful for the development of different materials for different applications like the automotive and aerospace industry. The main results of this review can be written as follow: 1. The carbon fibre shows better impact on tribological characteristics of composites when it is combined with other synthetic fibres. The natural fibre shows also improved wear properties but less improvement compares to synthetic fibre reinforcement. 2. The carbon nanoparticles have better impact on the wear characteristics of nanocomposites up to a certain weight percentage limit. The exceed amount shows reduction in wear behaviour of developed epoxy composites. 3. The fibre and nanoparticles combination shows great improvement in tribological behaviour of epoxy hybrid composites compare to individual reinforcement. The hybrid effect makes better composites for different tribological applications.

References 1. Namdev A, Telang A, Purohit R (2022) Experimental investigation on mechanical and wear properties of GNP/Carbon fiber/epoxy hybrid composites. Mater Res Express 9:025303 2. Takari A, Ghasemi AR, Hamadanian M et al (2021) Molecular dynamics simulation and thermo-mechanical characterization for optimization of three-phase epoxy/TiO2 /SiO2 nano-composites. Polym Test 93:106890 3. Bu Y, Xu M, Liang H et al (2021) Fabrication of low friction and wear carbon/epoxy nanocomposites using the confinement and self-lubricating function of carbon nanocage fillers. Appl Surf Sci 538:148109 4. Namdev A, Telang A, Purohit R (2021) Effect of graphene nano platelets on mechanical and physical properties of carbon fibre/epoxy hybrid composites. Adv Mater Process Technol 00:1–14 5. Kumar S, Singh KK (2020) Tribological behaviour of fibre-reinforced thermoset polymer composites: a review. Proc Inst Mech Eng Part L J Mater Des Appl 234:1439–1449 6. Arulvel S, Mallikarjuna Reddy D, Dsilva Winfred Rufuss D, Akinaga T (2021) A comprehensive review on mechanical and surface characteristics of composites reinforced with coated fibres. Surf Interfaces 27:101449 7. Parbin S, Waghmare NK, Singh SK, Khan S (2019) Mechanical properties of natural fiber reinforced epoxy composites: a review. Proc Comput Sci 152:375–437

1018

A. Namdev et al.

8. Kisiel M, Mossety-Leszczak B (2020) Development in liquid crystalline epoxy resins and composites—a review. Eur Polymer J 124:109507 9. Sankaramoorthy T, Balaji Krishna Bharathi A, Thirugnanasambantham KG, Karthikeyan R (2021) Study of graphene epoxy composites and its recent development in nano science. Mater Today Proc 45:2228–2231 10. Jeyapragash R, Srinivasan V, Sathiyamurthy S (2020) Mechanical properties of natural fiber/ particulate reinforced epoxy composites—a review of the literature. Mater Today Proc 22:1223– 1227 11. Bijwe J, Rattan R, Fahim M (2007) Abrasive wear performance of carbon fabric reinforced polyetherimide composites: influence of content and orientation of fabric. Tribol Int 40:844– 854 12. Kossentini Kallel T, Taktak R, Guermazi N, Mnif N (2018) Mechanical and structural properties of glass fiber-reinforced polypropylene (PPGF) composites. Polym Compos 39:3497–3508 13. Sharma M, Bijwe J, Mitschang P (2011) Abrasive wear studies on composites of PEEK and PES with modified surface of carbon fabric. Tribol Int 44:81–91 14. Kumar S, Singh KK, Ramkumar J (2020) Comparative study of the influence of graphene nanoplatelets filler on the mechanical and tribological behavior of glass fabric-reinforced epoxy composites. Polym Compos 41:5403–5417 15. Mohan VB, Lau K-t, Hui D, Bhattacharyya D (2018) Graphene-based materials and their composites: a review on production, applications and product limitations. Compos Part B Eng 142:200–220 16. Gantayat S, Rout D, Swain SK (2018) Carbon nanomaterial-reinforced epoxy composites: a review. Polym Plast Technol Eng 57:1–16 17. Friedrich K (2018) Polymer composites for tribological applications. Adv Ind Eng Polym Res 1:3–39 18. Ogbonna VE, Popoola API, Popoola OM, Adeosun SO (2021) A review on the recent advances on improving the properties of epoxy nanocomposites for thermal, mechanical, and tribological applications: challenges and recommendations. Polym-Plast Technol Mater 00:1–20 19. Suresha B, Chandramohan G, Siddaramaiah et al (2007) Three-body abrasive wear behaviour of carbon and glass fiber reinforced epoxy composites. Mater Sci Eng A 443:285–291 20. Baheti V, Militky J, Mishra R, Behera BK (2015) Thermomechanical properties of glass fabric/ epoxy composites filled with fly ash. Compos B Eng 85:268–276 21. Chand N, Sharma MK (2008) Development and sliding wear behaviour of milled carbon fibre reinforced epoxy gradient composites. Wear 264:69–74 22. Ramesh BN, Suresha B (2014) Optimization of tribological parameters in abrasive wear mode of carbon-epoxy hybrid composites. Mater Des 59:38–49 23. Sudhagar S, Kumar SS (2020) Determination of wear, friction behavior and characterization of carbon fiber reinforced epoxy composites for transport applications. Mater Res 23 24. Subbaya KM, Suresha B, Rajendra N, Varadarajan YS (2012) Taguchi approach for characterization of three-body abrasive wear of carbon-epoxy composite with and without SiC filler. Compos Interfaces 19:297–311 25. Suresha B, Adappa K, Subramani NK (2018) Mechanical and tribological behaviours of epoxy hybrid composites reinforced by carbon fibers and silicon carbide whiskers. Mater Today Proc 5:16658–16668 26. Agrawal S, Singh KK, Sarkar PK (2018) Comparative investigation on the wear and friction behaviors of carbon fiber reinforced polymer composites under dry sliding, oil lubrication and inert gas environment. Mater Today Proc 5:1250–1256 27. Kumar Jesthi D, Saroj S, Nayak S (2022) The influence of seawater on mechanical properties of hybrid carbon and glass fiber polymer composites. Mater Today Proc 49:507–509 28. Baptista R, Mendão A, Rodrigues F et al (2016) Effect of high graphite filler contents on the mechanical and tribological failure behavior of epoxy matrix composites. Theoret Appl Fract Mech 85:113–124 29. Wang B, Fu Q, Li H et al (2021) Mechanisms of simultaneously enhanced mechanical and tribological properties of carbon fabrics/phenolic resin composites reinforced with graphite nanoplatelets. J Alloy Compd 854:157176

A Short Review on Tribological Behaviour of Epoxy Composites …

1019

30. Moghimi Monfared R, Ayatollahi MR, Barbaz Isfahani R (2018) Synergistic effects of hybrid MWCNT/nanosilica on the tensile and tribological properties of woven carbon fabric epoxy composites. Theoret Appl Fract Mech 96:272–284 31. Guo QB, Lau KT, Rong MZ, Zhang MQ (2010) Optimization of tribological and mechanical properties of epoxy through hybrid filling. Wear 269:13–20 32. Singh KK, Kumar S (2021) Tribological performance of graphene nanoplatelets filled glass/ epoxy composites under dry, inert gas and oil-lubricated environmental conditions. Mater Lett 282:128881 33. Sukur EF, Onal G (2020) Graphene nanoplatelet modified basalt/epoxy multi-scale composites with improved tribological performance. Wear 460–461:203481 34. Sabeel Ahmed K, Khalid SS, Mallinatha V, Amith Kumar SJ (2012) Dry sliding wear behavior of SiC/Al2O3 filled jute/epoxy composites. Mater Des 36:306–315 35. Kazemi-Khasragh E, Bahari-Sambran F, Siadati SMH et al (2019) The effects of surfacemodified graphene nanoplatelets on the sliding wear properties of basalt fibers-reinforced epoxy composites. J Appl Polym Sci 136:1–9 36. Talib AAA, Jumahat A, Jawaid M et al (2021) Effect of wear conditions, parameters and sliding motions on tribological characteristics of basalt and glass fibre reinforced epoxy composites. Materials 14:1–18 37. Liang H, Xu M, Chen X et al (2021) Impressive high-temperature lubrication of carbon/ epoxy composites endowed by the microscopic dimensionality of carbon fillers. Wear 486– 487:204109 38. Shen XJ, Pei XQ, Liu Y, Fu SY (2014) Tribological performance of carbon nanotube-graphene oxide hybrid/epoxy composites. Compos B Eng 57:120–125 39. Hao Y, Zhou X, Shao J, Zhu Y (2019) The influence of multiple fillers on friction and wear behavior of epoxy composite coatings. Surf Coat Technol 362:213–219 40. Bao T, Wang Z, Zhao Y et al (2021) Improving tribological performance of epoxy composite by reinforcing with polyetheramine-functionalized graphene oxide. J Market Res 12:1516–1529 41. Sapiai N, Jumahat A, Jawaid M, Carlo S (2021) Abrasive wear behavior of CNT-filled unidirectional. Processes 9:1–16 42. Srinivas K, Bhagyashekar MS (2014) Wear behaviour of epoxy hybrid particulate composites. Proc Eng 97:488–494 43. Kazemi-Khasragh E, Bahari-Sambran F, Platzer C, Eslami-Farsani R (2020) The synergistic effect of graphene nanoplatelets–montmorillonite hybrid system on tribological behavior of epoxy-based nanocomposites. Tribol Int 151 44. Kesavulu A, Mohanty A (2019) Tribological investigation of alumina/graphene nanoplatelets reinforced epoxy nanocomposites. Mater Res Express 6 45. Xu Z, Shen X, Wang T et al (2021) Investigation on tribological and thermo-mechanical properties of Ti3 C2 nanosheets/epoxy nanocomposites 1–8 46. Sharma H, Misra JP, Singh I (2020) Friction and wear behaviour of epoxy composites reinforced with food waste fillers. Compos Commun 22:100436 47. Erdo˘gan A, Gök MS, Koç V, Günen A (2019) Friction and wear behavior of epoxy composite filled with industrial wastes. J Clean Prod 237 48. Ayatollahi MR, Alishahi E, Doagou-R S, Shadlou S (2012) Tribological and mechanical properties of low content nanodiamond/epoxy nanocomposites. Compos B Eng 43:3425–3430 49. Singh N, Sinha SK (2021) Tribological performances of hybrid composites of Epoxy, UHMWPE and MoS2 with in situ liquid lubrication against steel and itself. Wear 486– 487:204072 50. Chen B, Zhang M, Li X et al (2020) Tribological properties of epoxy-based self-lubricating composite coating enhanced by 2D/2D h-BN/MoS2 hybrid. Prog Org Coat 147:105767 51. Madhanagopal A, Gopalakannan S (2018) Dry slide wear behavior of boron carbide filled epoxy composites. Mater Today Proc 5:7289–7295 52. Shettar M, Kowshik CSS, Manjunath M, Hiremath P (2020) Experimental investigation on mechanical and wear properties of nanoclay-epoxy composites. J Market Res 9:9108–9116

1020

A. Namdev et al.

53. Tian J, Li C, Xian G (2021) Reciprocating friction and wear performances of nanometer sized-TiO2 filled epoxy composites. Polym Compos 42:2061–2072 54. Karthik Babu NB, Ramesh T, Muthukumaran S (2020) Physical, tribological and viscoelastic behavior of machining wear debris powder reinforced epoxy composites. J Clean Prod 272:122786 55. Shivamurthy B, Anandhan S, Udaya Bhat K (2015) Sliding wear and mechanical properties of alumina/glass fabric/epoxy composites. KGK, Kaut Gummi Kunstst 68:46–53 56. Kumar S, Singh KK, Ramkumar J (2021) The effects of graphene nanoplatelets on the tribological performance of glass fiber-reinforced epoxy composites. Proc Inst Mech Eng Part J: J Eng Tribol 235:1514–1525 57. Kumar S (2020) Comparative study of the influence of graphene nanoplatelets filler on the mechanical and tribological behavior of glass fabric-reinforced epoxy composites 1–15 58. Mohan N, Natarajan S, KumareshBabu SP (2011) Abrasive wear behaviour of hard powders filled glass fabric-epoxy hybrid composites. Mater Des 32:1704–1709 59. Gupta R, Kushwah K, Goyal S et al (2020) Ramie-glass fiber reinforced epoxy composites: impact of walnut content on mechanical and sliding wear properties. Mater Today Proc 44:3984–3989 60. Shivamurthy B, Udaya Bhat K, Anandhan S (2013) Mechanical and sliding wear properties of multi-layered laminates from glass fabric/graphite/epoxy composites. Mater Des 44:136–143 61. Shalwan A, Yousif BF (2014) Influence of date palm fibre and graphite filler on mechanical and wear characteristics of epoxy composites. Mater Des 59:264–273 62. Rangaswamy H, Harsha HM, Gowdru Chandrashekarappa MP et al (2021) Experimental investigation and optimization of compression moulding parameters for MWCNT/glass/Kevlar/ epoxy composites on mechanical and tribological properties. J Mater Res Technol 15:327–341 63. Agrawal A, Chandraker S, Sharma A (2018) Physical, mechanical and sliding wear behavior of solid glass microsphere filled epoxy composites. Mater Today Proc 29:420–426 64. Jamali N, Khosravi H, Rezvani A et al (2021) Viscoelastic and dry-sliding wear properties of basalt fiber-reinforced composites based on a surface-modified graphene oxide/epoxy matrix. J Ind Text 50:939–953 65. Bobbili R, Madhu V (2016) Sliding wear behavior of E-glass-epoxy/MWCNT composites: an experimental assessment. Eng Sci Technol Int J 19:8–14 66. Venkategowda T, Manjunatha LH, Anilkumar PR (2020) Adhesive and abrasive wear behavior of Kenaf long fiber reinforced epoxy composites. Mater Today Proc 45:150–155