Advances in Electromechanical Technologies: Select Proceedings of TEMT 2019 [1st ed.] 9789811554629, 9789811554636

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Lecture Notes in Mechanical Engineering

V. C. Pandey P. M. Pandey S. K. Garg   Editors

Advances in Electromechanical Technologies Select Proceedings of TEMT 2019

Lecture Notes in Mechanical Engineering Series Editors Francisco Cavas-Martínez, Departamento de Estructuras, Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain Fakher Chaari, National School of Engineers, University of Sfax, Sfax, Tunisia Francesco Gherardini, Dipartimento di Ingegneria, Università di Modena e Reggio Emilia, Modena, Italy Mohamed Haddar, National School of Engineers of Sfax (ENIS), Sfax, Tunisia Vitalii Ivanov, Department of Manufacturing Engineering Machine and Tools, Sumy State University, Sumy, Ukraine 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

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. 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

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More information about this series at http://www.springer.com/series/11236

V. C. Pandey P. M. Pandey S. K. Garg •

•

Editors

Advances in Electromechanical Technologies Select Proceedings of TEMT 2019

123

Editors V. C. Pandey Department of Mechanical and Automation Engineering HMR Institute of Technology and Management New Delhi, Delhi, India

P. M. Pandey Department of Mechanical Engineering Indian Institute of Technology Delhi New Delhi, Delhi, India

S. K. Garg Department of Mechanical Engineering Delhi Technical University New Delhi, Delhi, India

ISSN 2195-4356 ISSN 2195-4364 (electronic) Lecture Notes in Mechanical Engineering ISBN 978-981-15-5462-9 ISBN 978-981-15-5463-6 (eBook) https://doi.org/10.1007/978-981-15-5463-6 © Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are reserved 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

Contents

Optimization of Energy-Aware Flexible Job Shop Scheduling Problem Using VNS-Based GA Approach . . . . . . . . . . . . . . . . . . . . . . Rakesh Kumar Phanden, Rahul Sindhwani, and Lalit Sharma

1

Optimizing the Conveyor Belt Speed of a Bright Annealing Furnace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Akshay Naidu, R. Padmanaban, and R. Vaira Vignesh

13

FGM Plates with Circular Cut Out Analysis Resting on Elastic Foundations and in Thermomechanical Loading Environments . . . . . . Rajesh Kumar

21

Benchmarking the Integration of Industry 4.0 into the National Policies at Asia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sanjiv Narula, Surya Prakash, Maheshwar Dwivedy, Ajay Sood, and Vishal Talwar Exergy Analysis of Novel Combined Absorption Refrigeration System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vaibhav Jain, Ashu Singhal, and Harsh Joshi

31

47

Geothermal Energy: An Effective Resource Toward Sustainability . . . Suman Das and Arijit Kundu

61

Analysis of Double Square Loop FSS for Transmission Mechanism . . . Rahul Shukla and Garima Tiwari

73

Development of High-Temperature Shape Memory Alloys . . . . . . . . . . Shyam Singh Rawat, Raghvendra Sharma, Maneeram Singh Gurjar, and Manoj Sharma

79

FGM Plates with Elliptical and Rectangular Cutouts Analysis for Post-Buckling Resting on Elastic Foundations in Thermal Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rajesh Kumar

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Contents

Water Quality Examining Using Techniques of Data Mining . . . . . . . . Sanika Singh, Sudeshna Chakraborty, and Saurabh Mukherjee Application of Block Chain in EHR’s System for Maintaining the Privacy of Patients Record . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ifra Salaudin, Shri Kant, and Supriya Khaitan

103

113

Solar Power-Based Smart Greenhouse . . . . . . . . . . . . . . . . . . . . . . . . . Padma Wangmo, Vinay Kumar Jadoun, Anshul Agarwal, and Harish Kumar

127

Modelling of Slag Produced in Submerged Arc Welding . . . . . . . . . . . Brijpal Singh and Sachin Dhull

137

Automatic Land Defense System for Borders Using Radar, Laser Gun and Related Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rishab Kumar Bhardwaj, Sudhir Ranwa, Ranjan Kumar, Lokesh Meena, Anshul Agarwal, and Vinay Kumar Jadoun A Wideband Star-Shaped Rectenna for RF Energy Harvesting in GSM Band . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vishal Singh, Vinay Shankar Pandey, and Vivek Shrivastava Cylindrical Shell Panel of FGM Analysis Elastically Supported and Uncertain Material Parameters in Hygrothermomechanical Loading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rajesh Kumar Influence of Digital Technologies on Migration Flows and the Regional Labor Market of Russia . . . . . . . . . . . . . . . . . . . . . . Kruglov Dmitrii, Tsygankova Inga, Reznikova Olga, and Mikhailov Sergey Analysis of Actuators Prognostic Health Monitoring in Spacecraft Attitude Control Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kaustav Jyoti Borah Modified Mechanical Face Seal Geometry . . . . . . . . . . . . . . . . . . . . . . Ankita Kumari Design and Analysis of a CSP Plant Integrated with PCM Reservoirs in a Combined Storage System for Uninterrupted Power Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bikash Banerjee and Asim Mahapatra Application of Analytic Hierarchy Process for the Selection of Best Tablet Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shankha Shubhra Goswami and Soupayan Mitra

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Contents

vii

Postdeforming of FGM Laminates Analysis for Random Geometric Parameters in Thermal Environments . . . . . . . . . . . . . . . . . . . . . . . . . Rajesh Kumar and Vineet Shekher

237

Flow Simulation of Atmospheric Re-entry Vehicle at Varying Mach Number and Angle of Attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shivam Thakur, Harish Kumar, and Shrutidhara Sarma

245

Measuring the Relative Importance of Reconfigurable Manufacturing System (RMS) Using Best–Worst Method (BWM) . . . . Ashutosh Singh, Mohammad Asjad, Piyush Gupta, Zahid Akhtar Khan, and Arshad Noor Siddiquee E-Commerce Delivery Routing System Using Bellman–Held–Karp Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sugandh Agarwal, Naman Jain, Tanupriya Choudhury, Utkarsh Vikram Singh, and Ravi Tomar GAPER: Gender, Age, Pose and Emotion Recognition Using Deep Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deepali Virmani, Tanu Sharma, and Muskan Garg Enhanced Blockchain Application for Pub-Sub Model . . . . . . . . . . . . . Nomaun Rathore and Shri Kant A Study on Perception of Management Students Regarding Corporate Governance Practices of PSUs . . . . . . . . . . . . . . . . . . . . . . . Meenakshi Bisla, Pranav Mishra, Aparna Sharma, and Priyanka Tyagi Discrimination of Text and Non-text Images . . . . . . . . . . . . . . . . . . . . Pradipta Karmakar, Chowdhury MdMizan, Rani Astya, and Sudeshna Chakraborty Calibration of Temperature and Pressure Sensors for DAQ System in Air Conditioning Test Rig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vrinatri Velentina Boro, Vibha Burman, Amandeep Kaur, Manoj Soni, and Pooja Bhati On Condition Monitoring Aspects of in-Service Power Transformers Using Computational Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ujjawal Prakash Bhushan, R. K. Jarial, Vinay Kumar Jadoun, and Anshul Agarwal Communication Techniques in Smart Grid—A State of Art . . . . . . . . Aastha Khanna and Anuradha Tomar FGM Composite Cylindrical Shell Panel Analysis for Post Buckling Resting on Elastic Foundations and Thermomechanical Loading . . . . . Rajesh Kumar

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287 299

313 323

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Contents

PV-Based Water Pumping System—A Comprehensive Review . . . . . . Sahil Sharma, Anuradha Tomar, and Vishesh Bhagat

389

Economic Analysis of Battery Swap Station for Electric Three Wheeled Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Devanshu Grover, Ishan, Shubham Bansal, and R. C. Saini

399

Optimization of Inlet Swirl for Flow Separation in Annular Diffuser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hardial Singh and B. B. Arora

409

Innovative PMI-Based Inspection Planning for Planar and Cylindrical Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pratik Kalaskar, Surbhi Razdan, and Amol Jawalkar

421

Experimental Study of Sliding Wear Behavior of Journal Bearing Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vinayak Goel, Akshat Jain, Vibhor Heta, Sanchit Jain, and Vipin Kumar Sharma Deep Learning Architectures: A Hierarchy in Convolution Neural Network Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shruti Karkra, Priti Singh, Karamjit Kaur, and Rohan Sharma A Comprehensive Study of Different Converter Topologies for Photovoltaic System Under Variable Environmental Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preeti Gupta and S. L. Shimi A Novel Approach for Predicting the Compressive and Flexural Strength of Steel Slag Mixed Concrete Using Feed-Forward Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tanvi Gupta and S. N. Sachdeva

429

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459

475

Progress and Latest Developments in Hybrid Solar Drying with Thermal Energy Storage System . . . . . . . . . . . . . . . . . . . . . . . . . Narender Sinhmar and Pushpendra Singh

487

An Improved Maximum Power Point Tracking (MPPT) of a Partially Shaded Solar PV System Using PSO with Constriction Factor (PSO-CF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imran Pervez, Adil Sarwar, Arsalan Pervez, Mohd Tariq, and Mohammad Zaid

499

Maximum Power Point Tracking of a Partially Shaded Solar PV Generation System Using Coyote Optimization Algorithm (COA) . . . . Imran Pervez, Adil Sarwar, Arsalan Pervez, Mohd Tariq, and Mohammad Zaid

509

Contents

Design and Simulation of Front-End Converter Based Power Conditioning Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deepak Upadhyay, Shahbaz Ahmad Khan, and Mohd Tariq Enhancing Mechanical Properties via Semi-solid Metal Processing of A356 Alloy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nishant Bhasin, Harkrit Chhatwal, Aditya Bassi, Rahul Sarma, Sumit Sharma, and Vipin Kaushik Comparative Analysis of Different Materials for Cylinder and Justification by Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ujjwal Singh, Jatin Lingwal, Nirmal Chakraborty, Ankit Kumar, Sumit Sharma, and Vipin Kuashik Performance of an Outdoor Optical Wireless Communication Channel Through Gamma-Gamma Turbulence at Different Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nitin Garg and Anwar Ahmad Ergodic Capacity Analysis of Optical Wireless Communication Links Over M-Atmospheric Turbulence Channel with Pointing Losses Given by Beckmann Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nitin Garg and Anwar Ahmad

ix

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531

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561

Effect of Impurities on Charging Track in the Performance of Wireless Capacitive Charging Technique . . . . . . . . . . . . . . . . . . . . . Mohd Shahvez, Sameer Pervez Shamsi, and Mohd Tariq

573

“RESUME SELECTOR” Using Pyspark and Hadoop . . . . . . . . . . . . . Preeti Arora, Deepali Virmani, Aradhay Jain, and Akshay Vats

585

Implementation of Regenerative Braking System in Automobiles . . . . . Mohan Kumar and Md. Ehsan Asgar

595

Regression Approach to Power Transformer Health Assessment Using Health Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jagdish Prasad Sharma Functional Link Neural Network-Based Prediction of Compressive and Flexural Strengths of Jarosite Mixed Cement Concrete Pavements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tanvi Gupta and S. N. Sachdeva

603

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A Newer Universal Model for Attaining Thin Film of Varied Composition During Sputtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gaurav Gupta and R. K. Tyagi

629

A Novel Model for IoT-Based Meter Using ATmega328P Microcontroller and Google Cloud Store . . . . . . . . . . . . . . . . . . . . . . . Sufia Khalid, Mohammad Sarfraz, Vishal Singh, Aafreen, and Ali Allahloh

639

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Contents

Vibration Analysis of Curved Beam Using Higher Order Shear Deformation Theory with Different Boundary Conditions . . . . . . . . . . Md. Rashid Akhtar and Aas Mohammad

649

Performance Analysis of Alternate Purification System in Air Conditioning System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ashish Gangal and Vaibhav Jain

661

Weed Detection Approach Using Feature Extraction and KNN Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gurpreet Khurana and Navneet Kaur Bawa

671

Analysis of Transition Metal Dichalcogenide Materials Based Nanotube . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prateek Kumar, Maneesha Gupta, and Kunwar Singh

681

Automated CNC Programming by the Restricted Boltzmann Machine Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neelima and Vivek Chawla

691

Single Pass Wavy Channel Heat Exchanger for Thermal Management of Electric Vehicle Battery Pack—A Numerical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Babu Rao Ponangi, Pramath H. Srikanth, and Pratyush V. Heblikar

711

Comparative Microstructural Investigation of Aluminium Silicon Carbide–Mg and Aluminium Boron Carbide–Mg Particulate Metal Matrix Composite Fabricated by Stir Casting . . . . . . . . . . . . . . . . . . . Paridhi Malhotra, R. K. Tyagi, Nishant K. Singh, and Basant Singh Sikarwar

725

A Review on IoT-Based Hybrid Navigation System for Mid-sized Autonomous Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ajay K. S. Singholi, Mamta Mittal, and Ankur Bhargava

735

Development and Modelling of a Novel Wheelchair with Staircase Climbing Ability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gaurav Kesari

745

Design and Fabrication of Low-Cost Detachable Power Unit for a Wheelchair . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Issac Thomas, M. I. John, Robinson Lal, Jobi Lukose, and J. Sanjog

755

Computation of Rupture Strain from Macroscopic Criteria . . . . . . . . . Appurva Jain and Abhishek Mishra Biosignal Analysis Using Independent Components with Intelligent Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Suhani Pandey and Mohammad Sarfraz

765

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Contents

Modeling of Multiple Jointed Kinematic Chains Using the Polynomial Coefficients Derived from the Interactive Weighted Matrices of Kinematic Graphs . . . . . . . . . . . . . . . . . . . . . . . Vipin Kaushik and Aas Mohammad Application of Wavelet Analysis in Condition Monitoring of Induction Motors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amandeep Sharma, Pankaj Verma, Anurag Choudhary, Lini Mathew, and Shantanu Chatterji Optimization of Halon 1301 Discharge Through Fire Extinguisher Cylinder for IFSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reetik Kaushik, Yasham Raj Jaiswal, Vishal Dwivedi, and Ranganath M. Singari

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809

Barriers to the Use of Robots in Indian Industries . . . . . . . . . . . . . . . . Ravindra Kumar

821

Impacts of Regenerative Braking on Li-Ion Battery . . . . . . . . . . . . . . . Akshay Thakur, Kaleem Uz Zaman Khan, Jatin Gupta, Kunal Gupta, Mukund Vats, Chetan Mishra, and Aditya Roy

831

Development of the Latent Heat Storage System Using Phase Change Material with Insertion of Helical Fins to Improve Heat Transfer Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vishal Godase, Ashok Pise, and Avinash Waghmare

843

Experimental Investigation of Helical Coil Tube in Tube Heat Exchanger with Microfins Using Al2O3/Water Nano Fluid . . . . . . . . . . Nilesh K. Kadam and A. R. Acharya

855

Analysis of Vapour Compression Refrigeration System in Terms of Convective Heat Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pinaki Das, Dheeraj Chhabra, Mukul Krishnatrey, and Mayur

873

Microwave Welding of Mild Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sourav, Uma Gautam, Akshay Marwah, Ankit Sharma, and Lakshay Synthesis of Nanocellulose Fibrils/Particles from Cellulose Fibres Through Sporadic Homogenization . . . . . . . . . . . . . . . . . . . . . . . . . . . Nadendla Srinivasababu and Kopparthi Phaneendra Kumar A New Design of Li-Ion Battery for a Smart Suitcase . . . . . . . . . . . . . Anant Singhal, Karan Bhatia, Kaleem Uz Zaman Khan, Shivam Tyagi, Tarun Mittal, Chetan Mishra, and Aditya Roy Comparative Study on the Formability Behaviour of Different Grades of Aluminium Alloys Using Limiting Dome Height Test—An Analytical and Experimental Approach . . . . . . . . . . . . . . . . . . . . . . . . Praveen Kumar and Satpal Sharma

885

893 903

915

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Contents

A Novel Approach of Gearbox Fault Diagnosis by Using Time Synchronous Averaging and J48 Algorithm . . . . . . . . . . . . . . . . . . . . . Subrata Mukherjee, Rishubh Kaushal, Vikash Kumar, and Somnath Sarangi

927

Programmable Logic Controller Controlled 360 Degree Flexible Drilling Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Parth Patpatiya, Varun Bhatnagar, Harshita Tyagi, and Nupur Anand

937

“VISIO”: An IoT Device for Assistance of Visually Challenged . . . . . . Rashbir Singh, Prateek Singh, Deepak Chahal, and Latika Kharb

949

A Retrospective Investigation of Mechanical and Physical Properties of ABS Specimen Developed by Manual Injection Moulding and Fused Deposition Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Md Qamar Tanveer, Mohd Suhaib, and Abid Haleem Effect of Magnetic Pole Orientation on Viscoelastic Magnetic Abrasive Finishing Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Srinivas, Q. Murtaza, and A. K. Aggarwal MHO Shape Slot Microstrip Patch Antenna for X-Band . . . . . . . . . . . Palak Jain and Sunil Kumar Singh Optimization of Process Parameters in Electric Discharge Machining for SS420 Using Taguchi Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . Sudhir Kumar, Sanjoy Kumar Ghoshal, and Pawan Kumar Arora

965

979 989

997

Improvement in Starting Characteristics of a Hermetic Reciprocating Compressor by Offset Cylinder Arrangement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1005 Himanshu Kalbandhe, Anil Acharya, and Sumedh Nalavade Stair Shape Microstrip Patch Antenna for X Band . . . . . . . . . . . . . . . 1017 Nivedita Dash and Sunil Kumar Singh Power Consumption Estimation of SHA-3 for the Internet of Things Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1025 M. Tariq Banday and Issmat Shah Masoodi Joining of Metals Using Microwave Energy . . . . . . . . . . . . . . . . . . . . . 1035 Uma Gautam and Vipin

About the Editors

Dr. V. C. Pandey is Director and Professor in HMRITM, Delhi. He obtained his Ph.D. from Delhi College of Engineering (Delhi University) in 2011. He completed his M.E. in Industrial Engineering & Management in 1996 from SGSITS, DAVV Indore and B.E. in Mechanical Engineering from MMMEC Gorakhpur in 1994. He has more than 24 years rich experience of industry, research, teaching and administration in the organizations of repute in Bangalore and Delhi NCR. He worked as the head of the institution in three different engineering colleges in NCR. He has quality seven research publications in his credit. He guided more than 30 projects at undergraduate level. He attended more than twenty five workshops and conferences in best institutions of the country. His area of interest is Advanced Manufacturing Systems, Lean and Agile Manufacturing, Operation Management and Supply Chain Management. He is actively involved as the reviewer in the best journals of his research area. He is life member of various professional bodies like ISTE, SAE and IIIE. Dr. P. M. Pandey is currently working as Professor in IIT Delhi. He completed his B.Tech. from H.B.T.I. Kanpur in 1993 securing first position and got Master’s degree from IIT Kanpur in 1995 in Manufacturing Science specialization, he obtained his Ph.D. in the area of Additive Manufacturing/3D Printing from IIT Kanpur in 2003. Dr. Pandey diversified his research areas in the field of micro and nano finishing, micro-deposition and also continued working in the area of 3D Printing. He supervised 25 PhDs and more than 33 MTech theses in last 10 years and also filed 16 Indian patent applications. He has approximately 137 international journal papers and 44 international/national refereed conference papers to his credit. He received Highly Commended Paper Award by Rapid Prototyping Journal for the paper “Fabrication of three dimensional open porous regular structure of PA 2200 for enhanced strength of scaffold using selective laser sintering” published in 2017. He is recipient of Outstanding Young Faculty Fellowship (IIT Delhi) and J.M. Mahajan outstanding teacher award of IIT Delhi.

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About the Editors

Dr. S. K. Garg is Pro Vice Chancellor and Professor in Delhi Technological University (Formerly Delhi College of Engineering). He was appointed as Independent Director to a Navratna Public Sector Enterprise by GOI for a period of three years from 2012-2015. He is a recipient of Dewang Mehta National Education Award for Best Professor in Operations and manufacturing in the year 2015. Prof. Garg has more than 28 years of experience in industry, teaching and research. His teaching and research areas include supply chain management, manufacturing process automation and technology management, operations management, materials management, operations research, manufacturing strategy, production planning and control etc. He has guided 17 PhDs, 70 M.Tech thesis and 50 B.Tech projects. He has published 175 papers including 75 in international journals. Prof. Garg is member of the editorial boards of several international and national journals. He is reviewer of research papers of international journals, conferences and also examiner to Ph.D. and M.Tech thesis of different universities.

Optimization of Energy-Aware Flexible Job Shop Scheduling Problem Using VNS-Based GA Approach Rakesh Kumar Phanden, Rahul Sindhwani, and Lalit Sharma

Abstract In today’s world, the manufacturing systems are growing day by day and capable to produce the products on time as per the customers’ requirement. However, the energy consumption by these manufacturing systems has been ignored, and a higher amount of energy is consuming to increase the production rate. Therefore, it is must to consider the criteria of energy consumption along with other traditional objectives of performance measures. Thus, in the present work, energy consumption has been considered with other measures to solve the flexible job shop scheduling (JSS) problem. It is a non-polynomial (NP) hard problem, and this problem belongs to the class of combinatorial optimization, so it is difficult to solve with a simple and exact mathematical formulation. Thus, this article presents the modified genetic algorithm (GA)-based methodology to deal with flexible JSS problem. The GA has been modified in order to increase local search using variable neighbourhood search (VNS)-based mutation operator in order to avoid premature convergence of regular GA. The proposed approach considers multiple objectives in order to produce an optimized solution for flexible JSS problem such as makespan, processing cost as well as the energy consumption. In present work, an alternative (flexible) manufacturing process has been considered to extend the JSS problem. A suitable chromosome has been designed to code the schedule (solution) for JSS problem having additional processing flexibility. A case study (of 6 jobs and 15 machines) has been presented in order to assess the effectiveness of projected modified GA method. Results reveal that the proposed VNS-based approach in GA is effective enough to reduce makespan, processing cost as well as energy consumption performance measures. Keywords Flexible job shop scheduling · Variable neighbourhood search · Energy-aware optimization · Modified genetic algorithm

R. K. Phanden · R. Sindhwani · L. Sharma (B) Department of Mechanical Engineering, Amity School of Engineering and Technology, Amity University, Noida, Uttar Pradesh 201313, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 V. C. Pandey et al. (eds.), Advances in Electromechanical Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5463-6_1

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1 Introduction Since environmental awareness is increasing, the energy-efficient consumption factor among the other production functions has been identified as an important objective to be considered during the optimization of a manufacturing system. For the start of the new industrial revolution, the industrial sector comes with a higher amount of energy consumption to increase the production rate. Almost 33% of total energy has been consumed by production units, and they contribute 38% in greenhouse emission [1]. Furthermore, in order to satisfy the requirement of sustainable development, the production industries are facing new challenges, which help to achieve the ecological, social and economic aims. So, it is essential for industrial sectors that the manufacturing community has to access the systems which can increase the productivity of a production unit by decreasing its energy eating by incorporating latest technology capability as well as efficient planning and scheduling methods. Thus, in the present work, an approach has been presented to optimize the energy consumption with other performance-measuring criterion. The flexible job shop manufacturing environment has been considered to generate an optimized schedule. Multiple objectives have been taken into consideration, and a modified genetic algorithm (GA) approach has been applied to optimize the taken objective functions. Production scheduling concerns with allotment process of operations of all jobs to machines that depend on availability of machine tools as well as the time constraints of processes. Production scheduling becomes energy efficient when one undertakes the environmental impacts like energy consumption and carbon emissions during its optimization along with other traditional objective functions. Thus, the investigations to curtail consumption of energy in the production unit while production scheduling has been steadily booming [2]. Some of the acknowledged research studies considering the impact of energy consumption during the scheduling are discussed below. A model containing multiple objectives to reduce the consumption of energy as well as the total completion time while exploring the scheduling of jobs for a CNC machine has been proposed by Mouzon et al. [1]. Scheduling algorithms evolving integer programming models have been proposed for flow shop manufacturing environment in order to control the carbon footprints and power consumption with optimized makespan performance measure [2–4]. Also, the flow shop scheduling problems were solved to reduce time, makespan as well as energy consumption using branch and bound algorithms and NSGA-II [2, 5]. A mathematical model was presented to optimize energy consumption in flexible manufacturing system [6]. Also, the job shop scheduling (JSS) problem has been solved along with a mixed integer programming model for energy consumption criterion including makespan, robustness, noise reduction, processing cost and other performance measures using IBM ILOG CPLEX, simplex lattice design-based GA and whale algorithm of optimization [7–11]. The relationship between energy efficient, makespan and robustness has been identified, and disturbance has been studied during energy-aware production scheduling in a job shop [7]. It can be perceived from literature that, however,

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the GA has been comprehensively applied to solve the taken problem; it is unrealistic to state that the application of an individual algorithm is superior to other since they have heuristic way of working. It can easily be stated that the research on green production scheduling is in primeval phase. Most of research articles possessed very simple cases such as limited number of parts to be process on a machine and flow shops scheduling with limited number of connected machines. Another important concern is regarding the problem to evaluate the active status of resources (mainly machines) and its corresponding environmental effects with operations. Practically, only three statuses have been considered and explored which are “on”, “off” and “idle”. Theoretically, the “standby” status is also found in literature [8]. Moreover, the flexible JSS problem has been solved using a modified biogeography-based optimization algorithm integrated with the variable neighbourhood search algorithm, grey wolf optimization algorithm with double-searching mode, memetic algorithm, bi-population-based discrete cat swarm optimization algorithm and particle swarm optimization algorithm to optimize makespan, electricity consumption cost and tardiness performance measures [12–15]. Thus, the literature review clearly reveals that the genetic algorithm with neighbourhood search technique has not been applied to optimize the flexible JSS for multi-objectives of minimizing energy consumption, processing cost and makespan. Rest of the sections of this article presents a literature review on the energy-aware contributions for production scheduling problems, problem formulation and mathematical model. Also, the adopted methodology has been elaborated before results and discussion. In the end, conclusions have been drawn from the present study.

2 Problem Statement The flexible job shop problem can be stated as “a group of ‘n’ number of jobs (in which the job ‘J’ varies from 1 to ‘n’ numbers) are processed on a group of ‘m’ number of machines (in which the machine ‘M’ varies from 1 to ‘m’) and each job is characterized by a set of operations ‘O’ and set of alternative (flexible) process plan available to process”. The schedule is determined based on different constraints between operations. Operation of a job in various alternative process plans can be processed on diverse machines with varying level of energy consumption or on a similar machine having diverse processing parameters. Consequently, the operation of each job “j” on machine “m” has operation time and equivalent energy consumption. Thus, a problem is formalized to assigns the jobs to the machines as well to find the sequence of operations on each machine for optimal solution of processing cost, makespan and energy consumption with equal weighting. It follows the following assumptions and constraints: (a) all machines, as well as jobs, are always ready to start by zero-time unit, (b) more than one operation is not allowed on a machine, (c) part pre-emption is not allowed, (d) precedence is applied between operations of a job, and (e) each job must follow the sequence of operation on machines.

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3 Mathematical Model This section presents the energy-aware modelling for the production scheduling problem that occurs in a job shop floor considering flexible (alternative) process plans. The optimization of energy consumption, processing cost and makespan for flexible JSS problem is solved by a mathematical model (mixed integer programming) suggested by Dai et al. [16, 17]. In addition, the present work considers the minimization of processing cost as another function with the functions proposed by Dai et al. [16, 17]. Thus, three objectives of optimization, viz. energy consumption, makespan as well as processing cost have been taken to minimize for flexible JSS problem. The model is presented using the notations from Dai et al. [16, 17]. The model for consumption of energy has been established on basis of the existing research on energy-efficient manufacturing process [16, 17]. The total consumption of energy in the production process can be distributed into three categories—(a) basic consumption of energy: basic energy is the energy utilized to control the regular operation of a machine parts; it includes power consumption of the motor drives components, and servo feed drive parts, main spindle drive parts, supporting parts used for lubrication, cooling, hydraulic mechanisms and control devices as well as any other periphery parts; (b) consumption of energy for unloading activities: consumptions of energy for unloading like loading, unloading, positioning, fastening of workpiece and the exchanging of cutting tool and tool bit holders; (c) consumption of energy for cutting operation: consumption of energy for cutting is the actual cutting operation. This article considers the energy-efficient manufacturing processes; the primary contributors to the total energy consumption are the consumption of energy for unloading and the cutting. Thus, by considering the aforementioned assumption, the objective is to minimize the total consumption of energy (it is consisting of directenergy use up to remove material during the productive state and indirect-energy consumed in unfruitful states) [17]. Minimization of Energy Consumption        m  m 2 m m im α · (Pckl = j ) + β · Pckl j + Pu kl j · Tkl j · X l j · Ykl j k∈Ol j l∈G j j∈J i∈Pm m∈M





 

Pu m kl j ·

  im (i−1)m Ckl j − Tklmj · X i j · Yklimj − Cqr p

k,q∈Ol j,r p l,r ∈G j, p j, p∈J i∈Pm m∈M

  (i−1)m · X l j · X r p · Z klm jqr p ·X r p · Yqr p

Here, in the right-hand side, the first equation is representing the direct-energy consumed by removal materials in the productive state; the coefficients of the load consumption of energy are represented by α, β that can be found from the linear regression equations as per the idle consumption of energy with varying levels of spindle speed. In the right-hand side, the second equation is representing indirect consumption of energy, like energy for backup [17].

Optimization of Energy-Aware Flexible Job Shop Scheduling …

Minimization of Processing Cost =



Pm mjk Tklmj C Pm+

j∈J k∈Ol j

5



CRj

j∈J

  Minimization of Makespan = max Cklimj X l j Yklimj j∈J

4 Adopted Methodology There is a vast solution search space while optimizing the energy-aware model of scheduling problem of a job shop configuration having availability of different machines to process a given job mix. In the present work, a multi-objective function with constraints and mixed integer programming has opted from Dai et al. [16, 17]. It is required to find an optimal (or near-optimal) results on the basis of an intelligent algorithm to ease the optimization process as per the defined production scheduling criteria. Therefore, in this study, a modified GA that combines a GA with a VNS is adapted in order to find an optimal result of the objective’s functions given in previous section. Genetic algorithm is the nature inspired search method that works on the basis of the process of natural biological evolution [18]. GA has been extensively used to solve combinatorial optimization problems. The main advantage of GA is to get a good results quickly and efficiently for an objective function in a complex solution space. However, a major disadvantage of GA is that it possibly being stuck in a local optimum solution; this phenomenon is termed as premature convergence of GA. Therefore, the VNS has been planned to perform the mutation operation of GA. VNS is a local searching which emphasize on increasing the fitness of current solution through a procedure and continually changing the structures of the neighbourhood (solution) during the evolution of local search of solution space. VNS has been successfully applied to combinational complex problems [19]. The main advantage of VNS algorithm is that it can easily avoid the local convergence during optimization. Also, it can efficiently explore a difficult solution space in order to find the global optimum value of objective. Thus, in this paper, an attempt has been made to increase the strength of GA by incorporating the VNS. Figure 1 demonstrates the flowchart of VNS-based modified GA for the flexible JSS problem.

4.1 Chromosome Structure (Encoding) The present work utilizes multi-layered chromosome representation for the selection of alternative process plan and corresponding production schedule. Starting portion of the chromosome is reserved for process plan selection in which each gene represents a process plan number for each job. For example, if there are three jobs in the production

6 Fig. 1 VNS-based modified GA

R. K. Phanden et al.

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order, the first position (gene) represents the process plan number of job-1, the second position is for process plan number of the job-2, and third position is for process plan number of job-3. Hence, the length of the first layer is equal to the number of jobs in the production order. Next portion of the chromosome is utilizing operation-based representation of production schedule, in which a total number of operations of the production order is coded. Each gene represents the operation number of each job, i.e. the length of this portion is equal to the number of operations in production order.

4.2 Generation of Initial Population A feasible set of initial population is generated as follows; (i) set the length of an alternative process plan string  equal to the totalnumber of jobs in production order. (ii) The operations number of operations ol j l ∈ G j , j ∈ J of job j that contains maximum   between g j ( j ∈ J ) alternative process plans is precise as max ol j . Thus, the gene thread (of scheduling plan) have the total  equal  to the sumof the maximum    length in length of each job, i.e. expressed as nj=1 maxl ol j . Thus, n + nj=1 max ol j is l

the total length of the chromosome. In case, the length of chosen (of  gene  string  process plan) of job j does not match with the maximum number maxl ol j during decoding procedure, the components of operations of job j are detached from the last operation position to the first until unless the length is fulfilled through taken point. For example, if the size of the part mix is three and each part has four operations, then the chromosome embraces 15 numbers of total genes. In this study, three objectives have been considered to find a set of efficient results in a solution space, i.e. minimization of makespan and minimization of energy consumption and minimization of processing cost for the taken job shop environment.

4.3 GA Operations A regular GA works with three kinds of genetic operators, namely selection, crossover and mutation in a series. Each operator is equally important to achieve the optimized solution. Selection operator chooses the elite solutions from the population and transfers it for crossover and mutation. Crossover generates fresh individuals embracing parental information and mutation produce fresh offspring holding fresh information. (a) Selection. The present study considers linear-ranking selection along with stochastic universal sampling scheme. Here, chromosomes are arranged in decreasing order with respect to the absolute fitness values, and expected values are assigned to the ordered population as per their rank. The stochastic sampling scheme is used to choose the parents, and mating pool of selected chromosomes is created [20–26]. (b) Crossover. The method used for crossover operation is presented in Table 1.

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Table 1 Steps of crossover operation in modified GA Steps Description 1

Select two parent strings randomly from the population

2

Generate two blank chromosomes

3

Form the alternative process plan individuals in two blanks generated in Step 2

3.1

Pick a crossover position for a couple of alternative process plan individual, randomly. Also, the left portion and right portion from picked point termed as first and second sections of crossover, respectively

3.2

In the first section of the alternative process plan of the individual (from two parent strings), the bit should be transferred as per the crossover position into the same point as the two blank chromosomes

3.3

In the second section of alternative process plan of the individual (from two parent strings), the bit should be transferred as per the crossover position into the same point as the two blank chromosomes, reverse to the first section

4

Form the schedule individuals in two blanks generated in Step 2. Note: repeat the same procedure for schedule string as explained in Step 3.1–3.3 for alternative process plan individual’s crossover

5

Update the current population with the individuals undergoes crossover operation

(c) VNS-based Mutation. Mutation operator is used after the crossover operator in order to produce schedules containing fresh information. In mutation operator, there are numerous approaches for instance immunity-based mutation operators and uniform/non-uniform mutation which are utilized to resolve intricate (global) problems of optimization. In the present work, a VNS-based mutation procedure is considered to fit in the working of regular GA. In VNS, neighbourhood structure evaluation method acts with the important character to guide and control the performance of algorithm. Moreover, it has been concluded by various researchers that the local optimal solutions in diverse neighbourhoods are essentially undeviating. Also, a globally optimal solution is a locally optimal solution corresponding to the entire vicinity, and the local optimal solutions for various neighbourhood structures are quite near to each other. The idea behind the success of VNS optimization for local search is the changing capability of the neighbourhood structures methodically to improve the current optimized global solution. In the present work, TEN—“two-point exchange neighbourhood” and RIN—“random insert neighbourhood” structure have been adopted for evaluation. Both are simple neighbourhood structures. TEN exchanges two operations in the encoding sequence randomly. RIN method is randomly selecting one element from the particle and inserting it to another position of the encoding sequence. Hence, the VNS-based mutation operator can improve the searchability and the search efficiency of the algorithm by using two-point exchange neighbourhood structure [19]. In this study, two types of neighbourhood structures (TEN and RIN) are considered. N1 is selected as a principal neighbourhood (TEN), and N2 is set as a subordinate neighbourhood (RIN) in VNS-based

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Table 2 Steps of VNS-based mutation operation in modified GA Steps

Description

1

Retrieve initial population (x) from GA, after crossover

2

Create the sequence of neighbourhood structures (Sq), where, q = 1, 2, 3…, qmax

3

Start from q = 1 and follow the steps from 3.1 to 3.3 till q = qmax

3.1

Shaking: Create a neighbourhood solution (x 0 ) from qth neighbourhood (Sq) of (x), randomly.

3.2

Local Search: Find best neighbour (x00) of (x ) in the current neighbourhood (Sq)

3.3

Move or Not? If x 00 is better than x, let x = x 00 and q = 1, Restart local search of N1; otherwise, set q = q + 1

mutation operation. The steps of VNS-based mutation operation are presented in Table 2.

5 Results and Discussion The MATLAB® programming tool is used to implement the modified GA approach for flexible JSS problem. The personal computer is used to conduct the test with the configuration of Intel Pentium® 4 GB memory, 3.20 GHz processor speed and Windows 10 operating system. The proposed approach has been texted for a case study from Phanden and Jain [23]. The power (idle) consumptions for machines are taken from a job shop instance developed by Liu et al. [27]. It has been considered that all machines under examination are the mechanized and same value of cutting power has been set for each machine. A production order containing fifteen machines and six jobs has been taken from Phanden and Jain [23]. Figure 2 indicates Gantt chart of the optimized schedule. The value of optimized makespan was 183 units using the proposed approach. Table 3 presents the comparison of proposed VNS-based GA approach with the regular GA (without VNS-based mutation operator). Results show that the proposed approach is effective enough to optimize the multi-objectives. Results revealed that the makespan has been improved by 10.29%. Also, the value processing cost and energy consumption values have been optimized by 3.86% and 7.55%, respectively. The percentage improvement is computed as follows. % age improvement = (value of PM byRGA − value of PM by VGA)/ (value of PM by RGA) × 100

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M15 M14

J6 O2

J1 O1 J5 O2

M13

J1 O3 J1 O4

M12

J6 O4

J6 O1

M11

J6 O5

M10 J2 O2

M9

J1 O6

M8 J3 O3

M7 J4 O2

M6 M5

J2 O1

J6O3

J2 O4

J3 O1

M4

J4 O1

M3 M2 J5O1

J3 O2 J2 O3

M1 10

20

30

40

J1 O5 50

60

70

80

90

100

110

120

130

140

150

160

170

180

190

Fig. 2 Gantt chart of optimized schedule

Table 3 Comparison of the proposed approach with regular GA

Performance Regular GA measures (PM) (RGA) Makespan

Proposed approach (VGA)

%age improvement

204

183

10.29

Processing cost 518

498

3.86

Energy consumption

820

7.55

887

Thus, it can be safely concluded that formalized VNS-based GA approach is better than the conventional regular GA approach for makespan, processing cost and energy consumption.

6 Conclusion The present article addresses the issue of environmental awareness in terms of optimizing energy consumption along with other conventional performance measures during the scheduling for a flexible job shop manufacturing environment. Therefore, a modified GA-based approach to solve flexible JSS problem considering multiobjectives such as makespan, processing cost and energy consumption has been proposed. A mutation operator of regular GA has been modified with the application of VNS algorithm. TEN—“two-point exchange neighbourhood” and RIN—“random insert neighbourhood” structures have been successfully applied in place of mutation operation during regular GA. Results reveal that proposed approach outperformed regular GA. The proposed work can be extend to compare the proposed approach through another nature inspired algorithms like particle swam optimization, cuckoo search algorithm, ant colony algorithm, etc. Moreover, it can be extend to consider for the tardiness performance measure with the proposed modified GA approach.

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References 1. Mouzon G, Yildirim MB, Twomey J (2007) Operational methods for minimization of energy consumption of manufacturing equipment. Int J Prod Res 45:4247–4271 2. Liu G-S, Zhang B-X, Yang H-D et al (2013) A branch-and-bound algorithm for minimizing the energy consumption in the PFS problem. Math Probl Eng 2013:546810 (6 pp) 3. Fang K, Uhan N, Zhao F et al (2011) A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. J Manuf Syst 30:234–240 4. Bruzzone AAG, Anghinolfi D, Paolucci M et al (2012) Energy-aware scheduling for improving manufacturing process sustainability: a mathematical model for flexible flow shops. CIRP Ann Manuf Technol 61:459–462 5. Liu Y, Farnsworth M, Tiwari A (2018) Energy-efficient scheduling of flexible flow shop of composite recycling. Int J Adv Manuf Technol 1–11 6. Zhang L, Li X, Gao L et al (2012) Dynamic scheduling model in FMS by considering energy consumption and schedule efficiency. In: 2012 IEEE 16th international conference on computer supported cooperative work in design (CSCWD 2012), Wuhan, China, 23–25 May 2012. IEEE, New York 7. Salido MA et al (2013) Energy-aware parameters in job-shop scheduling problems. In: GREENCOPLAS 2013: IJCAI 2013 workshop on constraint reasoning, planning and scheduling problems for a sustainable future 8. May G et al (2015) Multi-objective genetic algorithm for energy-efficient job shop scheduling. Int J Prod Res 53(23):7071–7089 9. Yin L et al (2017) Energy-efficient job shop scheduling problem with variable spindle speed using a novel multi-objective algorithm. Adv Mech Eng 9(4):1687814017695959 10. Jiang T et al (2018) Energy-efficient scheduling for a job shop using an improved whale optimization algorithm. Mathematics 6(11):220 11. Jiang Tianhua, Deng Guanlong (2018) Optimizing the low-carbon flexible job shop scheduling problem considering energy consumption. IEEE Access 6:46346–46355 12. Zhang H et al (2017) A new energy-aware flexible job shop scheduling method using modified biogeography-based optimization. Math Probl Eng (2017) 13. Jiang T et al (2018) Energy-efficient scheduling for a job shop using grey wolf optimization algorithm with double-searching mode. Math Probl Eng (2018) 14. Böning C et al (2017) A memetic algorithm for an energy-costs-aware flexible jop-shop scheduling problem. Int J Soc Behav Educ Econ Bus Ind Eng 11(5):1223–1236 15. Nouiri M, Bekrar A, Trentesaux D (2018) Towards energy efficient scheduling and rescheduling for dynamic flexible job shop problem. IFAC-PapersOnLine 51(11):1275–1280 16. Dai M, Tang D, Giret A et al (2013) Energy-efficient scheduling for a flexible flow shop using an improved genetic simulated annealing algorithm. Robot Comput Int Manuf 29:418–429 17. Dai M, Tang D, Xu Y, Li WD (2014) Energy-aware integrated process planning and scheduling for job shops. Proc Inst Mech Eng Part B J Eng Manuf 229(1):13–26 18. Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. The MIT Press, Cambridge, MA 19. Gao L, Li X, Wen X, Lu C, Wen F (2015) A hybrid algorithm based on a new neighborhood structure evaluation method for job shop scheduling problem. Comput Ind Eng 88:417–429 20. Baker JE (1985) Adaptive selection methods for genetic algorithms. In: Grefenstette JJ (ed) Proceedings on the first international conference on genetic algorithms and their applications, pp 101–111. Lawrence Earlbaum, Hillsdale, NJ 21. Mitchell M (2002) An introduction to genetic algorithms. Prentice-Hall of India, New Delhi 22. Phanden RK, Jain A, Verma R (2012) A genetic algorithm-based approach for job shop scheduling. J Manuf Technol Manage 23(7):937–946 23. Phanden RK, Jain A (2015) Assessing the impact of changing available multiple process plans of a job type on mean tardiness in job shop scheduling. Int J Adv Manuf Technol 80(9– 12):1521–1545

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24. Phanden RK, Ferreira JCE (2019) Biogeographical and variable neighborhood search algorithm for optimization of flexible job shop scheduling. In: Advances in industrial and production engineering. Springer, Singapore, pp 489–503 25. Phanden RK, Palková Z, Sindhwani R (2019) A framework for flexible job shop scheduling problem using simulation-based cuckoo search optimization. In: Advances in industrial and production engineering. Springer, Singapore, pp 247–262 26. Phanden RK, Saharan LK, Erkoyuncu JA (2018) Simulation based cuckoo search optimization algorithm for flexible job shop scheduling problem. In: Proceedings of the international conference on intelligent science and technology. ACM, pp 50–55 27. Liu Y et al (2014) An investigation into minimising total energy consumption and total weighted tardiness in job shops. J Clean Prod 65:87–96

Optimizing the Conveyor Belt Speed of a Bright Annealing Furnace Akshay Naidu, R. Padmanaban, and R. Vaira Vignesh

Abstract A company’s growth is determined by an increase in its sales numbers and by a decrease in its expenditures. By doing this, the company attains a more significant profit (bottom line). In almost all manufacturing industries, annealing furnaces play a significant role. In this paper, an improvement in the operation of an annealing furnace used in a press shop is attempted. The press shop performs forging of watch cases, and annealing is performed in between every forging stage. This project focuses on increasing the productivity of the furnace by optimizing its conveyor belt speed. Experiments are conducted, and several annealed samples were collected for analysis. Furthermore, hardness and metallographic properties were also studied. With the help of regression plots of hardness and belt speed, and with the help of microstructures, an optimized belt speed was selected. The optimized belt speed is almost 42% more than the original speed used for production, hence increasing productivity. Keywords Annealing · Conveyor belt · Furnace · Hardness · Regression

1 Introduction The watch manufacturing process involves different stages like designing, prototyping, tool manufacturing, forging, annealing, machining, polishing, assembly and quality control. During this process, the majority of the problems like improper surface finish, inadequate reduction in hardness and sensitization while manufacturing the case centers (body of the watch) and back covers are observed during annealing. This heat treatment process is seen as a bottleneck in many industries. The annealing softens the watch-case centers and back covers in intermediate stages to facilitate forging processes. Some of the problems that are encountered are associated with inefficient usage of the furnace, high energy consumption and improper A. Naidu · R. Padmanaban (B) · R. Vaira Vignesh Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 V. C. Pandey et al. (eds.), Advances in Electromechanical Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5463-6_2

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material handling ways leading to safety concerns. In this work, a particular type of annealing furnace known as a bright annealing furnace is utilized to anneal stainless steel components. The old belt speed was set based on the previous experiments, and it was known that it could be improved further. If the belt speed is increased, it will enhance both the top line (sales) and bottom line (profit) of the company by increasing productivity. Research works have been carried out to explore the effect of heat treatment parameters on different materials. Raji et al. [1] report an increase in soaking time of the steel, a continual decrease in yield strength, tensile strength, hardness and impact strength. Thanakijkasem et al. [2] studied the effects of bright annealing on the formability of the SS304 in tube hydroforming (THF). The correct annealing parameters remarkably decreased the development of deformation-induced martensite. Sarkar et al. [3] found the optimum annealing cycle that would produce excellent mechanical as well as formability properties of extra deep drawing quality steel. Schino et al. [4] studied the effect of austenite–martensite transformation and development of microstructure after reversion of austenite at various annealing temperatures and times for an AISI 304 stainless steel. Annealing at low temperatures resulted in ultrafine-grain microstructure, and a Hall–Petch dependency was prevalent. Singh et al. [5] investigated the effects of a variety of cold rolling on the sensitization and intergranular corrosion (IGC) of SS304. The IGC of solution-annealed samples increased with an increase in sensitization temperature and time. The level of cold rolling was directly related to the increase in IGC resistance. De et al. [6] proposed a methodology that demonstrates that the martensite transformation may be effectively characterized in terms of volume fraction of phases formed during deformation through the analysis of a single XRD profile. Milad et al. [7] investigated the effect of plastic deformation introduced by cold rolling at ambient temperature on the tensile properties of AISI 304 stainless steel. The results after a 50% reduction in thickness indicate that the formation of strain-induced martensite led to a significant strengthening. Increase in cold rolling percentage up to 45% increased the tensile strength, yield strength and hardness. Statistical models based on the design of experiments provide a deep insight into the effect of process parameters on the behavior and properties of the materials processed. Some of the techniques successfully employed include regression using the design of experiments, Taguchi technique and response surface methodology [8]. When the relation between input parameters and response variable is nonlinear, soft computingbased models like artificial neural network, fuzzy logic and radial basis function have been utilized to explore the effect of processes on the properties of materials [9, 10]. The present work aims to optimize the conveyor belt speed of the bright annealing furnace taking into consideration the specified hardness and microstructure for case center of watch. In this study, the influence of belt speed on the microstructural evolution and microhardness of the specimen was investigated. An optimum belt speed was established based on the results of the developed model.

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2 Materials and Methods 2.1 Material SS304 is used for manufacturing case centers. Holes are made in the long strip of SS304 and are blanked into required dimensions. These blanked components are forged at several stages in the press shop to obtain the final shape. The workflow in the press shop is as follows: 1. 2. 3. 4. 5. 6.

Blanking Pre-cleaning and cleaning Annealing Oiling Forming Repeat from step 2 for consecutive forming process.

In this study, the case center is obtained after eight stages of forging process. A sample image of the case center that is formed after eight forging stages is shown in Fig. 1. Annealing Annealing is carried out after each forging stage to remove the internal and residual stresses. KOHNLE/BENCO bright annealing furnace was used for the annealing process. It is a conveyor belt-type continuous furnace with three heating zones and two cooling chambers. It has two sets of SS continuous belts that can be operated at different speeds. The current operating belt speed is 60 cm/min, and the peak temperature is 1120 °C. The inlet temperature of the cooling water in the cooling

Fig. 1 Front and back view of model 3072 CC

16 Table 1 Experiment trials

A. Naidu et al. Trial number

Belt speed (cm/min)

1

55

2

60

3

65

4

75

5

85

chamber is 18 °C with a flow rate of 55 m3 /s. In the current design, the total time from entry to exit is approximately 20 min. The entire annealing process is carried out in a cracked ammonia environment.

2.2 Experimental Design In this study, the belt speed was varied as given in Table 1. Three specimens were collected at the entry point and exit point of the annealing furnace for each annealing stage.

2.3 Microstructure Analysis The samples were taken from the last forging stage, as there is a drastic reduction in hardness after annealing. The microstructure of the specimens before annealing and after annealing was studied. The specimens were prepared by polishing the top face of the case center. After preparing the specimen, aqua regia (a mixture of nitric acid and hydrochloric acid in a molar ratio of 1:3) was used as an etchant to examine the grains and the grain boundaries using an optical microscope.

2.4 Microhardness Vicker’s microhardness tester method was used to measure the microhardness of the specimens, as per the standard ASTM E-384 [11, 12]. The 3 o’clock position on the back face of the case center was selected for measuring hardness so that measurement can be done on a flat surface. The microhardness was measured at a load of 10 kg f and for a dwell time of 10 s.

Optimizing the Conveyor Belt Speed of a Bright Annealing Furnace

17

2.5 Statistical Modeling A statistical model was developed using a quadratic function to study the effect of belt speed on the microhardness of the specimens. The models were developed at 95% confidence level.

3 Results and Discussion 3.1 Microstructure Results (After Eighth Forging Stage) The microstructure of the specimen processed at a belt speed of 55 cm/min before and after annealing is shown in Fig. 2a, b, respectively. Figure 2c, d shows the microstructure of the specimen processed at 60 cm/min before and after annealing, respectively. The microstructure of the specimen processed at a belt speed of 65 cm/min before and after annealing is shown in Fig. 2e, f, respectively. Figure 2g, h shows the microstructure of the specimen processed at 75 cm/min before and after annealing, respectively. The microstructure of the specimen processed at a belt speed of 85 cm/min before and after annealing is shown in Fig. 2i, j respectively. At the belt speed of 85 cm/min, no visual defects were observed. No effects of sensitization were observed in all the cases.

3.2 Microhardness and Statistical Regression Model The microhardness results indicate that the highest reduction in hardness happens in the first and last three annealing stages. It is also observed that the highest increase in hardness happens in the first and last three forging stages. The regression model was developed to interrelate the microhardness of the specimen and conveyor speed. The variation of hardness with conveyor belt speed is shown in Fig. 3. From the literature, it has been observed that decreasing the soaking time increases the hardness values [9, 10]. This is evident from our results as well. Figure 3a shows the influence of belt speed on the microhardness of the firststage annealing specimens. In the first-stage annealing, the microhardness of the specimens exhibits a crest parabolic trend with an increase in belt speed from 55 to 85 cm/min. The microhardness decreases linearly with an increase in belt speed in the second-stage annealing, as shown in Fig. 3b. In the third- and fourth-stage annealing, the microhardness of the specimen decreases with an increase in belt speed from 55 to 80 cm/min. The microhardness of the specimens increases with further increase in belt speed, as observed in Fig. 3c, d. However, in the fifth-stage annealing, the microhardness is found to increase with an increase in belt speed beyond 75 cm/min. Figure 3e shows the influence of belt speed on the microhardness

18

A. Naidu et al.

Fig. 2 Microstructures of the investigated case center after eight forging stages a trial 1 before annealing; b trial 1 after annealing; c trial 2 before annealing; d trial 2 after annealing; e trial 3 before annealing; f trial 3 after annealing; g trial 4 before annealing; h trial 4 after annealing; i trial 5 before annealing; j trial 5 after annealing

Optimizing the Conveyor Belt Speed of a Bright Annealing Furnace

19

Fig. 3 Regression plot for microhardness a first-stage annealing; b second-stage annealing; c thirdstage annealing; d fourth-stage annealing; e fifth-stage annealing; f sixth-stage annealing; g seventhstage annealing; h eighth-stage annealing

of the specimen in the fifth-stage annealing. Similar to the second-stage annealing, the microhardness of the specimens decreases with an increase in belt speed in the sixth stage of annealing, as shown in Fig. 3f. Figure 3g shows that the microhardness decreases with an increase in belt speed up to 80 cm/min. Beyond this, a slight increase in microhardness was observed. An increasing trend in hardness is observed from the belt speed of 75 cm/min in the eighth-stage annealing, as shown in Fig. 3h.

4 Conclusions The experimental results indicate that the obtained results are within desirable limits with no visual defects in all the five trials. At the present scenario and working conditions, the maximum optimum speed can be fixed at 85 cm/min. From this study,

20

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a 42% increase in belt speed was achieved which is advantageous to the company. A further increase in speed can lead to unfavorable and unforeseen defects unless and until verified by conducting additional experiments.

References 1. Raji N, Oluwole O (2013) Mechanical properties of cold-drawn low carbon steel for nail manufacture: experimental observation 2. Thanakijkasem P et al (2014) Effect of bright annealing on stainless steel 304 formability in tube hydroforming. Int J Adv Manuf Technol 73(9–12):1341–1349 3. Sarkar B, Jha B, Deva A (2004) Optimization of annealing parameters for improvement in formability of extra deep drawing quality steel. J Mater Eng Perform 13(3):361–365 4. Schino AD, Salvatori I, Kenny J (2002) Effects of martensite formation and austenite reversion on grain refining of AISI 304 stainless steel. J Mater Sci 37(21):4561–4565 5. Singh R et al (2001) Intergranular corrosion of deformed SS304 6. De AK et al (2004) Quantitative measurement of deformation-induced martensite in 304 stainless steel by X-ray diffraction. Scripta Mater 50(12):1445–1449 7. Milad M et al (2008) The effect of cold work on structure and properties of AISI 304 stainless steel. J Mater Process Technol 203(1–3):80–85 8. Padmanaban R, Balusamy V, Nouranga K (2015) Effect of process parameters on the tensile strength of friction stir welded dissimilar aluminum joints. J Eng Sci Technol 10(6):790–801 9. Ilangovan S et al (2018) Comparison of statistical and soft computing models for predicting hardness and wear rate of Cu-Ni-Sn alloy. In: Progress in computing, analytics and networking. Springer, pp 559–571 10. Vignesh RV, Padmanaban R, Chinnaraj K (2018) Soft computing model for analysing the effect of friction stir processing parameters on the intergranular corrosion susceptibility of aluminium alloy AA5083. Koroze a ochrana materiálu 62(3):97–107 11. A.I.H. Committee (1991) ASM handbook: heat treating, vol 4. ASM Intl 12. ASTM, E. 92-82. Standard test method for vickers hardness of metallic materials. 2001 Annual Book of ASTM, 2003

FGM Plates with Circular Cut Out Analysis Resting on Elastic Foundations and in Thermomechanical Loading Environments Rajesh Kumar

Abstract In the present study, FGM plates with circular cut outs resting on elastic foundations in thermomechanical loading environments have been studied. Basic formulation included nonlinear FEM and direct iterative-based first-order perturbation technique in MATLAB code. The plates are studied for uniaxial, biaxial thermomechanical loading, aspect ratios and different boundary support conditions. A study carried out is validated with available published literature and independent more robust method approach. Applicability of this study is in aerospace engineering. Keywords FGM composites · Collapse response · SFEM · Uncertain parameters · Foundation supports

Nomenclature Aij, Bij, etc. a, b h Ef , Em Gf , Gm Vf , Vm V m, V f af , am bi E 11 , E 22 G12 , G13 , G23 Kl Kg

Laminate stiffnesses Plate length and breadth Thickness of the plate Elastic moduli of fiber and matrix, respectively Shear moduli of fiber and matrix, respectively Poisson’s ratio of fiber and matrix, respectively Volume fraction of fiber and matrix, respectively Coefficient of thermal expansion of fiber and matrix, respectively Basic random material properties Longitudinal and transverse elastic moduli Shear moduli Linear bending stiffness matrix Thermal geometric stiffness matrix

R. Kumar (B) Department of Mechanical & Aerospace Engineering, NIET, NIMS University, Jaipur, Rajasthan, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 V. C. Pandey et al. (eds.), Advances in Electromechanical Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5463-6_3

21

22

D M αβ , mαβ ne, n Nx, Ny, Nxy nn Ni p C i jkl f , {f }(e) u, v, w u1, u2, u3 σ i j , εi j ψ y , ψx θx , θ y , θk x, y, z ρ, λ, Var(.) ω, ω RVs T, C α1, α2, β 1, β 2

R. Kumar

Elastic stiffness matrices Mass and inertia matrices Number of elements, number of layers in the laminated plate In-plane thermal buckling loads Number of nodes per element Shape function of ith node Reduced elastic material constants Vector of unknown displacements, displacement vector of eth element Displacements of a point on the mid-plane of plate Displacement of a point (x, y, z) Stress vector, strain vector Rotations of normal to mid-plane about the x- and y-axis, respectively Two slopes and angle of fiber orientation wrt x-axis for kth layer Cartesian coordinates Mass density, eigenvalue, variance Fundamental frequency and its dimensionless form Random variables Difference in temperatures and moistures Thermal expansion and hygroscopic coefficients along x- and y-direction, respectively

1 Introduction A lot of investigations using modeling of thermomechanically induced deformation of functionally graded laminates without considering uncertainty are carried out by earlier researchers [1, 3, 9, 10]. Comparatively fever research work is available on the application of postbuckling investigations functionally graded laminates with uncertain parameters. In earlier studies carried out by researchers, it is found that there are always uncertainties in material properties and in environmental conditions; therefore, analytical method is further extended for further studies by SFEM. It is evident from available literature that present investigation of superior graded structure resting on foundation support applying nonlinear SFEM is not applied earlier for investigations.

2 Mathematical Formulations Functionally graded laminate with cut out in center comprising hybrid material side (a), and side (b), thickness (h) is considered in present investigation. Elastic foundations parameters used in present analysis are given in [5, 8].

FGM Plates with Circular Cut Out Analysis Resting on Elastic …

23

P = K1w − K2∇ 2w ∇ 2 = ∂ 2 /∂x 2 + ∂ 2 /∂y2 and K 1 and K 2 are (normal), (shear layer) parameters, respectively, and w is the transverse displacement of the plate. For study round cut out size is studied as 2r/a where r is radius of round hole [14]. Constitutive relations, energy due to stress, foundations stiffness parameters, finite element models, solution approach, perturbation technique are given in [6].

3 Governing Equations Solution approach: Perturbation technique, variance of postbuckling load are given in [4, 6].

4 Numerical Results A computer oriented MATLAB [R2010a] code has been developed to investigate FGM plates with circular cut outs. [TD] material qualities of FG materials are used throughout the analysis, unless otherwise mentioned. The (COC) qualities are taken as 0.1. FGMs properties for temperature independent and temperature dependent are considered for investigation [11]. (pi ) presented with notations as: E c , E m , υc , υm , αc , αm , k1 , k2 , kc , km and n have different expansions, Winkler and Pasternak elastic foundations, λcr =

Ncr a 2 , π 2 D0

3

ch , and λT cr = α(T )cr × 103 where D0 = 12 E1−ν ( c2 ) For mathematical calculation, (a/h = 10, 15, 20, and 100), (n = 0.5, 1, 5 and 10).

4.1 Validation for Mean Values of Postbuckling Load Temperature considered is 300 K. Validation study parameters are as (N´ cr = N cr a2 /π 2 D0 ) Al/ZrO2 laminate having round cut out, a/h = 100 are taken for study as [2, 6] investigated in Table 1. The present results are in very consistent with [2, 6].

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R. Kumar

Table 1 Comparison of deforming force parameter N´ cr = N cr a2 /π 2 D0 of the Al/ZrO2 Plate, a/h = 100, Mode = 1, cut out size = 2r/a round cut outs Type of hole Hole size N´ cr = Ncr a2 /π2 D0 Circular

0.1

0.2

n=0

n = 0.5

n=1

n=2

n=5

Present HSDT

8.0675

6.4396

5.8303

5.3500

4.8598

Lal et al. [6]

8.0675

6.4396

5.8303

5.3500

4.8598

Zhao et al. [2]

8.0873

6.4749

5.5973

5.1270

4.6325

Present HSDT

6.7773

5.3994

4.8902

4.4963

4.0969

Lal et al. [6]

6.9279

5.5193

4.9989

4.5963

4.1880

Zhao et al. [2]

6.9711

5.4112

4.6858

4.0808

4.0609

SSSS Support

Table 2 Validation of deforming force where *D = Eh3 /12(1 − υ), E 1 = 130.0, GPa, E 2 = E3 = 10.0 GPa, G12 = G13 = 5.0 GPa, ν12 = ν13 = ν23 = ν32 = 0.35 Theoretical method

E (pa)

υ

b(m)

h(m)

(Theoretical) N cr b2 /Eh3

(ABAQUS) N cr b2 /Eh3

Husam et al. [7]

(FE Model) N cr b2 /Eh3 Present [HSDT]

Uniaxial case N cr = 4π 2 D/b2

2 × 1011

0.3

2.0

0.02

3.615

3.623

3.6794

Biaxial case N cr = 2π 2 D/b2

2 × 1011

0.3

2.0

0.02

1.807

1.811

1.8398

Laminate is (2000 mm × 2000 mm). The total thickness of laminates is 12 mm

Table 2 parameters used for present study are as *D = Eh3 /12(1 − υ), E 1 = 130.0 GPa, E 2 = E 3 = 10.0 GPa, G12 = G13 = 5.0 GPa, ν12 = ν13 = ν23 = ν32 = 0.35. Laminate size is (2000 mm × 2000 mm). Total thickness is 12 mm. It is clear from table present FEM [HSDT] results are in good agreement with those of theoretical and ABAQUS results of [7].

4.2 Validation for Random Postbuckling Load Figure 2 shows investigation of average deformed force of CCCC functionally graded laminates with circular cut out under thermomechanical loading, clamped support, biaxial compression. For validation purpose, 10,000 random are taken in MCS [12, 13]. Postbuckling loads in Uniaxial and biaxial compression for simply supported, FGM laminate having circular cutouts is investigated. The other dimensions for

FGM Plates with Circular Cut Out Analysis Resting on Elastic …

25

Fig. 1 a Geometric view of rectangular functionally graded laminate with cut outs. b One-fourth of functionally graded laminate with round cut out

present study are written in Table 3. The other dimensions for study are written in Table 3. The laminate is resting on Winkler and Pasternak foundations round hole shows more expected mean outcome and COV compared with laminate without hole due to concentration factors. Table 4 shows the combined effects of temperature and mechanical force, volume fraction index(n), laminate thickness ratios and Various Hole Sizes with uncertainties in laminate on the dimensionless Mean and COV of post buckling loads. When plates are resting on supports the average values deforming force increase on increasing volume fraction index. COV is significant for thin plates. Deformed laminate possess less stiffness with cut outs. Circular holed plates are preferred to other type of cut outs as studied. Table 5 shows the effects of clamped support, volume fraction index and Pasternak elastic foundations on FGM plates with circular cutouts. The other parameters taken for studies are mentioned in Table 5. The effects of amplitude ratios make the nonlinear stiffness matrices higher.

5 Conclusion Laminates with circular cut out is more sensitive for variations of environmental conditions studied. Therefore, FGM plates with rectangular cut outs with low n and Pasternak elastic foundations are more useful compared to other combinations. Clamped support is further desirable compared to simple support. Thin plate with circular cut out is unstable compared to thick laminate with cut outs would be desirable for various uses.

26

R. Kumar

Fig. 2 Investigation of average deformed force of CCCC functionally graded laminates with circular cut out under thermomechanical loading, clamped support, biaxial compression

(4.4605) 0.0664

3.3802

(5.0811) 0.0761

4.3757

(7.0145) 0.0886

6.1754

(k 1 = 00, k 2 = 0)

Linear

(k 1 = 100, k 2 = 0)

Linear

(k 1 = 100, k 2 = 10)

Linear

5.6282

(6.5583) 0.0776

4.0611

(5.0692) 0.0733

3.1392

(4.1746) 0.0664

5.2378

(6.0506) 0.0717

3.6057

(4.2441) 0.0661

2.6301

(3.3686) 0.0646

4.8000

(5.5000) 0.0677

3.3367

(4.0460) 0.0645

2.4439

(3.1500) 0.0651

3.3527

(3.9190) 0.0786

2.2823

(2.8515) 0.0714

1.7421

(2.3072) 0.0658

Cut out size = 0.1

n = 0.5

Cut out size = 0.1

Cut out size = 0.1

Cut out size = 0.2

n = 10

n = 0.5

Circular Cut out size = 0.2

Biaxial

Uniaxial

Hole type

Circular hole, a/h = 10, W max /h = 0.4. Average thermomechanical postbuckling force under thermal conditions

Thermomechanical

Loading

2.9780

(3.4730) 0.0789

2.0191

(2.5093) 0.0718

1.6622

(2.1958) 0.0660

Cut out size = 0.2

2.9637

(3.3492) 0.0678

1.8958

(2.2839) 0.0617

1.3560

(1.7415) 0.0637

Cut out size = 0.1

n = 10

2.6363

(2.9769) 0.0681

1.6811

(2.0160) 0.0621

1.2949

1.6592) 0.0641

Cut out size = 0.2

Table 3 Effect of thermomechanical deformed force, n, sizes of cut outs and {pi (i = 1, …, 7) = 0.1} for postbuckling force and coefficient of variations of temperature, SSSS functionally graded laminates (k 1 = 100, k 2 = 0), (k 1 = 100, k 2 = 10)

FGM Plates with Circular Cut Out Analysis Resting on Elastic … 27

(2.3069) 0.0661

1.7787

(2.783) 0.0738

2.2614

(3.6901) 0.3163

3.1848

(k 1 = 000, k 2 = 0)

Linear

(k 1 = 100, k 2 = 0)

Linear

(k 1 = 100, k 2 = 10)

Linear

3.0430

(3.5406) 0.3265

2.1675

(2.679) 0.0737

1.7006

(2.2086) 0.0669

2.7562

(3.1044) 0.1420

1.8693

(2.2242) 0.0666

1.3900

(1.750) 0.0651

(4.3036) 0.3196 3.7191

2.6268

2.6887

(3.2659) 0.0737

2.1705

(2.7428) 0.0677

(2.9766) 0.1420

1.7921

(2.1364) 0.0669

1.3297

(1.675) 0.0654

Cut out size = 0.2

n = 0.5

Cut out size = 0.2

Cut out size = 0.3

Cut out size = 0.2

n = 0.5

Circular Cut out size = 0.3

a/h = 100 n = 10

a/h = 20

Hole type

The dimensionless mean thermomechanical postbuckling force with thermal condition

Thermomechanical

Loading

3.5763

4.1572) 0.3210

2.5650

(3.1410) 0.0736

2.0182

(2.5956) 0.0666

Cut out size = 0.3

3.2404

(3.6396) 0.1577

2.2108

(2.6031) 0.0668

1.6918

(2.0798) 0.0670

Cut out size = 0.2

n = 10

3.1344

(3.5313) 0.1592

2.1241

(2.5163) 0.0665

1.5770

(1.9704) 0.0656

Cut out size = 0.3

Table 4 Effect of combined effect of temperature and mechanical force, n, laminate (a/h), cut out uncertainties in laminate {pi (i = 1, …, 7) = 0.1} on average and coefficient of variations of deformed force and thermal condition of SSSS supported biaxial compressed functionally graded circular laminate without and with Winkler (k 1 = 100, k 2 = 0) and Pasternak (k 1 = 100, k 2 = 10) circular hole having TD parameters, W max /h = 0.4

28 R. Kumar

(4.4618) 0.0666

3.3921

(5.4092) 0.0740

4.3624

(7.1836) 0.3218

6.0912

(k 1 = 000, k 2 = 0)

Linear

(k 1 = 100, k 2 = 0)

Linear

(k 1 = 100, k 2 = 10)

Linear

5.6484

6.7199) 0.2478

4.1583

(5.1904) 0.0738

3.2177

(4.2898) 0.0657

5.2177

(5.9430) 0.1348

3.6046

(4.3159) 0.0672

2.6499

(3.3769) 0.0651

4.8006

5.5364) 0.1055

3.4296

(4.1372) 0.0670

2.5146

(3.2469) 0.0644

7.2715

(7.5936) 0.1461

5.8723

(6.0077) 0.0960

5.4569

(5.6623) 0.0918

Cut out size = 0.2

n = 0.5

Cut out size = 0.2

Cut out size = 0.2

Cut out size = 0.3

n = 10

n = 0.5

Circular Cut out size = 0.3

CCCC

SSSS

Hole type

Average thermomechanical deformed force and thermal conditions

Thermomechanical

Loading

8.6568

8.7810) 0.1936

7.3188

(7.4467) 0.0980

4.2107

(4.3549) 0.0893

Cut out size = 0.3

5.9779

(6.2131) 0.1220

4.6013

(4.6900) 0.0886

4.2107

(4.3549) 0.0893

Cut out size = 0.2

n = 10

6.9745

(7.0642) 0.1362

5.6676

(5.7559) 0.0912

5.4259

(5.7563) 0.1323

Cut out size = 0.3

Table 5 Effect of thermomechanical force, n, cut out size with uncertainty {pi (i = 1, …, 7) = 0.1} for average and coefficients of variations of deformed force and thermal condition of functionally graded laminates (k 1 = 100, k 2 = 0), (k 1 = 100, k 2 = 10), circular hole, a/h = 15, W max /h = 0.4

FGM Plates with Circular Cut Out Analysis Resting on Elastic … 29

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R. Kumar

References 1. Wu TL, Shukla KK, Huang JH (2007) Postbuckling analysis of functionally graded rectangular plates. Compos Struct 81:1–10 2. Zhao X, Lee YY, Liew KM (2009) Mechanical and thermal buckling analysis of functionally graded plates. Compos Struct 90:161–171 3. Yang J, Kitipornchai S, Liew KM (2005) Second order statistic of the elastic buckling of functionally graded rectangular plates. Compos Sci Technol 65:1165–1175 4. Reddy JN (1984) A simple higher order theory for laminated composite plates. J Appl Mech Trans ASME 51:745–752 5. Shankara CA, Iyenger NGR (1996) A C0 element for the free vibration analysis of laminated composite plates. J Sound Vib 191(5):721–738 6. Lal A, Jagtap KR, Singh BN (2012) Postbuckling response of functionally graded plate subjected to mechanical and thermal loadings with random material properties. Appl Math Model 7. Al Qablan H, Katkhuda H, Dwairi H (2009) Assessment of the buckling behavior of square composite plates with circular cutout subjected to in-plane shear. Jordan J Civ Eng 3(2):184–195 8. Singh BN, Iyengar NGR, Yadav D (2002) A C0 finite element investigation for buckling of shear deformable laminated composite plates with random material properties. Int J Struct Eng Mech 113(1):53–74 9. Thai HT, Choi DH (2012) An efficient and simple refined theory for buckling analysis of functionally graded plates. Appl Math Model 36(3):1008–1022 10. Reddy BS et al (2013) Buckling analysis of functionally graded material plates using higher order shear deformation theory. J Compos 2013:12. https://doi.org/10.1155/2013/808764 11. Huang XL, Shen HS (2004) Nonlinear vibration and dynamic response of functionally graded plate in thermal environments. Int J Solids Struct 41:2403–2427 12. Kleiber M, Hien TD (1992) The stochastic finite element method. Wiley, New York 13. Zhang Y, Chen S, Lue Q, Liu T (1996) Stochastic perturbation finite elements. Comput Struct 23:1831 14. Lal A, Singh HN (2012) Stochastic mechanical and thermal postbuckling response of functionally graded material plates with circular and square holes having material randomness. Int J Mech Sci 62:18–33

Benchmarking the Integration of Industry 4.0 into the National Policies at Asia Sanjiv Narula, Surya Prakash, Maheshwar Dwivedy, Ajay Sood, and Vishal Talwar

Abstract The objective of this paper is to integrate Industry 4.0 (I4.0) into national policy framework of countries, and asses their readiness in adopting I40 through a literature review of their existing policy initiatives, while analyzing secondary data of some critical factors related to the future of production. I4.0 policy frameworks in both developing and developed countries are intended at enhancing modernization, endorsing the acceptance of up-to-date technology to fast-track financial development, boosting production output, and help in the holistic effectiveness of industries. Singapore, Japan, and Korea have been at the forefront of embracing I4.0 technologies. The findings of this study would help and support policymakers, researchers, and practitioners for the development of strategies for implementation of I4.0. Keywords Industry4.0 · Policy measures · Digitalization

S. Narula · V. Talwar School of Management, BML Munjal University, Gurgaon, Haryana 123413, India e-mail: [email protected] V. Talwar e-mail: [email protected] S. Prakash (B) · M. Dwivedy School of Engineering and Technology, BML Munjal University, Gurgaon, Haryana 123413, India e-mail: [email protected] M. Dwivedy e-mail: [email protected] A. Sood Automotive Industry Professional, Gurgaon, Haryana 123413, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 V. C. Pandey et al. (eds.), Advances in Electromechanical Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5463-6_4

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1 Introduction Industry 4.0 (I4.0) is on the verge of radically changing how organizations operate: [13, 11]. I4.0 technologies are futuristic, and are anticipated to boost grander industrial production, flexibility and productivity: [1]. The I4.0 terminology was incepted in Germany by the German administration to digitize the engineering industry. This terminology was made public at the ‘Hannover Messe Industrie (HMI Fair) in 2011’, in which it was proven how cyber-physical arrangements could be in control for developing innovative business models, conceiving thereby a transferral archetype in the manufacturing computerization sector: [10]. Countries and its industries would slowly but surely embrace I4.0 at different rates and in diverse ways [19]. For instance, nations with a high-cost skilled workforce would be able to make the most of an advanced level of automation [12]. Developing economies with a young, technology-savvy labor force might also jump at the prospect, and might even generate completely novel industrial models. I4.0 could offer incredible opportunities for innovative manufacturers, system suppliers, and entire regions. Nevertheless, as with preceding transformational advances, I4.0 also poses a specific risk to laggards [12]. As business models, economics, and talent necessities change, we might well see foremost alterations in top spots, at both the business, regional and countries level. To dynamically shape the makeover, countries, organizations including manufacturers and suppliers must take decisive action to embrace the technological advancement and address the need to become accustomed to the appropriate infrastructure and education [13]. Authors, earlier have carried out literature reviews of I4.0 policies mostly in developed economies, and have acknowledged the need for additional studies on the status of I4.0, especially in emerging economies [14, 15, 19]. This effort is an effort to bridge the above said research gap by examining and discussing the following themes: • Why it is essential for developing countries to adopt I4.0? • Evaluate the level of I4.0 integration into national industrial policies? • The benchmarking of readiness for Asian countries for the future of production.

2 Literature Review 2.1 What Is Industry 4.0 I4.0 is fundamentally label specified to the modernized phase of the industry, which is automation and information exchange between the industrial machinery and systems; primarily, it is based on the cyber-physical makeover of manufacturing [1, 7, 10, 12]. The preceding phases of the industrial revolution included the ‘Industry 1.0: mechanization, water, and steam power’; followed by ‘Industry 2.0: mass production and electricity into production’; ‘Industry 3.0: automation and computerization’; and finally ‘Industry 4.0: cyber-physical system’. In I4.0, the machinery is

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directly connected to other machineries and could accumulate statistics and analyze the configurations amid themselves; they would be intelligent systems to make judgments, which are difficult for humans [2, 10, 18, 20]. I4.0 emphasises on end-to-end digitization of every original data in a business process, right from input to the finish product, linking and assimilating digital settings with value chain partners up to the consumer [2, 5, 7, 10, 11].

2.2 Industry 4.0 Design Principles I4.0 is based on six design principles, explained in Fig. 1. These principles aid establishments in recognizing and executing I4.0 developments. Moreover, these principles help in creating smart factories by using cyber-enabled arrangements for real-time monitoring of physical procedures, fashioning computer-generated replicas of the physical setting and making business decisions [10, 13, 20]. These principles form the baseline for the machinery, devices, instruments, and individuals for realtime connection and communication with each other via the internet of things (IoT). Additionally, it is these systems that help in creating digital twins by combining sensor data to higher-value perspective information to support humans in taking decentralized decisions [12].

Interoperability

Industry 4.0 design principles

Transparency

Autonomous decisions

Modularity

The capability of digital systems or software for exchange and usage of the information between 'machines to machine' (M to M), 'machines to people' via IOT.

Big data and useful information all through interconnected system delivers huge volumes of valuable data and insights for faster decisions and bringing transparency in system. The capability of cyber physical arrangements for undertaking the self-decisions and to performing autonomously.

. Modular systems are intelligent to flexibly acclimatize to varying requirements in the overall value chain starting from design, development, suppliers, production, sales, service and end customers.

Technical assistance & Service oreintation

The capability of systems to aid human being by intelligent robotics, remote sensing devices with the capability of cognitive skills and self-learning devices.

Real time capability

The real-time accessibility of data and information in the entire value chain offers a massive prospective to improve effectiveness and efficiency in the business systems.

Fig. 1 Industry 4.0 design principles

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2.3 Why It Is Important for Developing Countries to Adopt I4.0 I4.0 facilitated machinery will steadily substitute more and more low skillful workforce conventionally connected with the industrial sector [13]. As labor expenses turn out to be a fewer in proportion to the industrial costs, the motivation to place plants in nations, with low cost advantages becomes low [13, 19]. As a substitute industrial unit will be positioned closer to the marketplaces for their precise products. Digital technologies are pushing innovative manufacturing practices, business models, and value- chains that will entirely take worldwide manufacturing systems to a different level [2, 5, 10, 11, 18]. The speediness and extensiveness of its progressions take along a stratum of intricacy for developing and realizing manufacturing policies that enhance productivity, quality, safety, delivery, and safety levels. I4.0 will influence all nations [13–15] and civilizations, administrations, and people by following questions to the policymakers. • How developing countries create and implement their master plan for I4.0? • Which role organizations will perform in the forthcoming worldwide valuecreation and value chains? • How do developing countries develop the skills of people for superior employability under shifting situations? Thus, developing countries may not be able to take the benefit of low-cost human resources in such scenarios; hence, it is essential for them to embrace the digital technologies of I4.0. We have taken Japan, China, Malaysia, Taiwan, Singapore, Indonesia, India, Korea, Pakistan, and Thailand in the scope of our study.

2.4 Evaluation of the National Policy Framework for I4.0 I4.0 lies at the center of Germany’s exertions to endure its worldwide headship in advanced manufacturing. Although numerous nations pursue in imitating Germany’s methodology to I4.0, limited hold an analogous underpinning of amassed technical and engineering proficiencies [10, 11, 13, 14]. The German government demonstrated an absolute commitment of assets over the past few years to turn out to be both a foremost provider, and the imperative marketplace for I4.0 associated products, machines, equipments and facilities [14]. Germany’s I4.0 policy can be characterized as a strategy and a learning procedure assimilating a multifaceted fusion of innovation, manufacturing, exploration, research, and other connected policies [10, 13, 14]. The German know-how underlines the relevance of multi-stakeholder synchronization and cooperation as the groundwork for its I4.0 policy and its deployment. Emergent technologies of I4.0 need to be leveraged to upkeep the administrations of

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Fig. 2 National policy measure framework for Industry 4.0

developing countries to strengthen their spot and transpire as factories of the future [12]. To empower this, administrations in other countries have embraced policy actions that are projected to offer a course to the implementation of I4.0: [13] and are given in Fig. 2. The national I4.0 policy framework mentioned in Fig. 2 functions as a strategic guide plan to empower the renovation of the industrial segment and fast-track the embracing of the I4.0 and are explained in Table 1 and its citation table is given in Table 2.

3 Discussion Singapore has a wide-ranging I4.0 policy, which focuses on building the capability of its people by re-skilling and transforming the industry, while creating smart cities for example. Japan, on the other hand, has been focusing on using I4.0 technologies for improving the lives of its people in the super smart society 5.0 program. Thailand 4.0 is intended at fashioning a value-centered economy. China and Korea are focusing on using digital technologies to improve the manufacturing capabilities and smart factory. Other countries are in the early stage of I4.0 policies to become global economies by retrieving net export benefit, increasing share of GDP from the industrial sector, and improving output, as an outcome from progression in technology and modernization. The authors were not able to find any I4.0 policy of Pakistan

Overview of policy

A human-centered society that equates economic progress with the resolution of social problems by integrating the physical space with cyberspace by using digitalization. Big data analytics, cloud computing, IoT, advanced robotics, artificial intelligence and regulatory reforms executed under one under fifth science and technology basic plan

Country, policy name and launch date

Japan Super Smart Society 2013

Digital society for the aging population Digital transformation of healthcare Promoting open innovation Collaborative research between industry and academia • Reduction of greenhouse gas emissions • Sustainable industrialization

• • • •

Key strategic priorities and initiatives

Table 1 Summary of the national policy framework of Industry 4.0

• • • • • • •

(continued)

Energy value chain Smart food chain Hospitality Automated driving and mobility Biotechnologies Manufacturing and robotics Safety management

Focus sectors

36 S. Narula et al.

Overview of policy

The objective is to make China world’s most advanced economy by 2049 based on its innovative manufacturing technologies The plan is to make China the leader for engineering high technology and quality products using indigenized R&D for developing advanced technology to replace foreign smart technologies

‘To aid in improved living, stronger societies and creating greater opportunities’ for everyone through information and computer technologies Three key pillars of this program include the digital economy, digital government, and digital society. This will be done by creating an enabling ecosystem supported by infrastructure, technologies, policies, culture, and capabilities Use advanced data and IT technologies for economic productivity and new business opportunities

Country, policy name and launch date

China Made in China 2025 2015

Singapore Smart nation Initiative 2014

Table 1 (continued)

• Facilitate smart solutions by test beds, collaboration and investment in R&D, with special focus on AI • Nurture sustained an innovation culture • Build computational capabilities and skills for the future • Increased cybersecurity • Digital inclusion, smart urban mobility, smart nation sensor platform, national digital identity • Regulatory reforms • Start-up accelerators

Focus on manufacturing innovation Integrate industry with technologies Strengthen the manufacturing base Promoting local brands Create 1000 green factories by 2020 Promote service-oriented manufacturing Advance reformation of the industrial sector • Internationalize manufacturing • Promote technology and manufacturing breakthroughs • Construct industrial innovation centers

• • • • • • •

Key strategic priorities and initiatives

• • • • •

• • • • • • • • •

Transport Environment Health Public sectors Services

(continued)

Automotive and Energy-saving vehicles Aviation Agriculture machinery NC tools and robotics High-tech maritime equipment Railway transport equipment Medical devices Information technology Power equipment

Focus sectors

Benchmarking the Integration of Industry 4.0 into the National … 37

Overview of policy

Building a creative economy by enhanced focus on core competency industries, manufacturing innovations, and developing smart factories by using CPS, IOT, additive manufacturing, big data analytics, cloud computing, and smart sensors

Taiwan is promoting productivity 4.0 to make smart machines a certainty and industrialization of smart machines industry. This includes the establishment of the smart industry ecosystem, innovative production processes to improve productivity and using ICT across the smart industry supply chain The key goal is to raise GDP per capita of the manufacturing industry by 60% (10 million NTD in 2024)

Country, policy name and launch date

South Korea Manufacturing Innovation 3.0 2014

Taiwan Productivity 4.0 2015

Table 1 (continued)

• Computerization of manufacturing • Development of technologies related to IOT, 3D printing, and big data • Embrace the whole business system, including small and medium level enterprises • Encouraging volunteer participation of small and medium level organizations • Motivating businesses to participate in innovations by benefit sharing • Create 10,000 smart factories by 2020 • Fostering innovation mindset among participating CEOs • Supply chain optimization of the leading industries adopting manufacturing 4.0, business 4.0 and agriculture 4.0 • Fostering new ventures and local contents • Development of the new technology (robotics, 3D printing, IOT, sensors, CPS and Big data) • Build talent • Supporting the industry with preferential policies

Key strategic priorities and initiatives

• • • • •

• • • • • • • • •

Electronics and information Metal transformation Machine tools Food Textiles

Smart cars 5G network Intelligent robots Customized wellness care Wearable smart devices Disaster safety management Deep sea offshore plant Renewable energy Virtual reality

Focus sectors

(continued)

38 S. Narula et al.

Thailand Thailand 4.0 2016

The strategy is to become a global manufacturing hub for sustainable growth, enable investments, fostering modernization, upgrading skills, and building the finest industrial sector under a national manufacturing policy The key goals are to upsurge the portion of the industrial sector to 25% of GDP, promote investment in industries, and enhance global competitiveness

India Make in India 2014

Key strategic priorities and initiatives

• Create 100 smart cities • Promote skill India, digital India, and start-up India • Make India the easiest place to do business • Facilitate investment, foster Innovation, and protection of intellectual property rights • Use technologies to leapfrog to I4.0 • Create best in class manufacturing infrastructure • Converge and integrate government departments to streamline the execution of plans Thailand 4.0 is a technology-centered initiative • Build digital network across the country to transform Thailand into a high-income • Create laws and regulations to support the country with a value-centric society and digital economy • Build an open data standard platform innovation-driven economy. Emphasis is on covering the whole of Thailand, including “security, wealth and sustainability” government-run e-service for businesses Focus areas of Thailand 4.0 are the growth of and citizens people to build an inclusive society by • Promote business competitiveness and people-centric and innovation-driven encourage SMEs to use I4.0 technologies governance • Develop workforce for smart factories and services

Overview of policy

Country, policy name and launch date

Table 1 (continued)

Energy Automotive Electronics Drugs and pharmaceuticals Petrochemicals Paper Defense equipment Aerospace Steel, cement, and fertilizers Textiles Leather Goods Food processing Gems and jewelry

(continued)

• Food, agricultural and biotechnology industries • Healthcare and medical technology • A parallel theme named ‘I4.0’ is being run to induce all manufacturing industries to adopt ICT and innovation to enhance productivity and quality

• • • • • • • • • • • • •

Focus sectors

Benchmarking the Integration of Industry 4.0 into the National … 39

Indonesia Making Indonesia 4.0 2018

The acronym ACT knows three key policy objectives of Malaysia’s initiative 1. Attract: Invite stakeholders to implement I4.0 technologies and make Malaysia a preferred manufacturing location 2. Create Setup the right ecosystem for adopting I4.0 and aligning it with industrial development 3. Transform: All-around accelerated the transformation of existing capabilities The outcome is a higher contribution to the economy by manufacturing, creation of high value-added products, and sustaining foreign direct investments

Malaysia Malaysia 4.0 2018

Key strategic priorities and initiatives

• Funding and outcome-based incentives to encourage companies to adopt new technologies • Create enabling ecosystem and efficient infrastructure to enable rapid digitization of industry • Establish a regulatory framework to ease the adoption of I4.0 • Upskilling the existing workforce and creating talent with new skills required for working in I4.0 • Giving all industries easy access to smart technologies and standards • Creating public-private partnerships to transfer knowledge about I4.0 technologies • Improve global innovation index ranging from 35 in 2016 to within the top 30 nations by 2025 Making Indonesia 4.0 is seen as a roadmap to • Reform the flow of goods create the prospect for Indonesia to become • Redesign industrial zones one of the topmost ten international economies • Embrace sustainability standards by 2030 with a net export rate of 10%, • Empower SMEs doubling labor productivity and allocating 2% • Build nationwide digital infrastructure of GPD to R&D and innovations • Attract foreign investments Five technology focus areas are IOT, Artificial • Upgrade human capital intelligence (AI), robotics, 3D printing, and • Establish an innovation ecosystem sensor technologies • Incentivize technology investment • Establish consistent regulations and policies

Overview of policy

Country, policy name and launch date

Table 1 (continued)

• • • • •

Food and beverage Textile and Apparel Automotive industry Electronics Chemical industry, including petrochemicals

Malaysian policy is related only to the manufacturing sector. It deals with • Transforming the whole of the manufacturing sector through restructuring and embracing I4.0 technologies • Focus is on SMEs in since they form 98.5% of all manufacturing industries and account for 42% of employment in the sector

Focus sectors

40 S. Narula et al.

Benchmarking the Integration of Industry 4.0 into the National … Table 2 Citation table of I4.0 national policy framework

41

Country

Citation

China

[10, 12–14]

Japan

[12, 13, 16, 17]

Singapore

[8, 13]

Taiwan

[4, 13]

Thailand

[5, 7]

South Korea

[6, 13, 19]

Malaysia

[3]

India

[9, 13, 18]

Indonesia

[11, 13]

Japan

Singapor e

Korea

China

Taiwan

Indonessia

Thailand

India

Mallyssia

Super smart society 5.0

Smart nation initiative

Manufacturing Innovation 3.0

Made in china 2025

Productivty 4.0

Indonessia 4.0

Thailand 4.0

Make in India

Mallyssia 4.0

2015

2018

2018

2014

2018

Early stage

Early stage

Early stage

Early stage

2013

20114

2014

2015

Advanced stage

Advanced stage

Advanced stage

Early stage

Early stage

Fig. 4 Timelines and stages of the national policy framework of I4.0

in extant literature. The timelines of the I4.0 framework of national policies and its stages has been given in Fig. 4.

4 Research Methodology This paper is centered on extensive literature review of I4.0 concepts, its principles, and national policy initiatives towards I4.0 adoption, along with secondary data analysis of some key factors related to the future of production. The research methodology has been summarized in Fig. 5.

5 Data Analysis I4.0 technologies have triggered the discovery of new manufacturing methods and business models, which are sustainable and futuristic. The authors have analyzed data from the ‘readiness for the future of production report 2018’ of world economic forum, which is a benchmark in itself, along with analytical instruments and statistics set to benefit nations to recognize their current level of ‘readiness for the future of

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Fig. 5 Research methodology

production’. The authors have developed the following framework (Fig. 6) from the ‘readiness for the future of production report 2018’. The framework to measure the I4.0 integration in national policies and benchmarking data for readiness for future of production is given in Figs. 7 and 8.

5.1 Discussion on Readiness for Future of Production (Figs. 7 and 8) Singapore, Japan and South Korea have been found to be the leading countries as regards the readiness for future of production, based on their high scores shown in Figs. 7 and 8. The ‘quality of education to build human resources’ has also had a high score for these countries, which tend to support and horne the skills required to adopt these technologies. Singapore specifically, seems to have better ranking in ICT enabled business models, embracing the disruptive ideas, sustainable development, and cybersecurity commitments; but here, they possibly need to improve their global ranking as regards the ‘quality’ of universities. Japan is a leading economy under most parameters, but may need to focus on embracing disruptive ideas in Japanese organizations. Despite being in the top ranking countries as regards economic valueadd in manufacturing, green field investments, quality of universities, China would do well to reflect upon policy measures for cyber security, skill building, manufacturing employment, scientific and technical research, FDI and technology transfer, ICT enabled business and sustainability initiatives. Regardless of being in top 10 countries in most of parameters, there is an opportunity for South Korea to reflect upon sustainable possessions, FDI and technology transfer, investment in futuristic technologies. Other Asian countries need to benchmark the best practices of Singapore, Japan, and Korea for expediting the transition for I4.0.

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Fig. 6 Framework to measure the Industry 4.0 integration in national policies

6 Conclusion The study started with the objective to understand the importance of I4.0 adoption in developing countries, while evaluating its integration into national industrial policies, and concluding with benchmarking the readiness for future of production in Asia. As seen in the earlier section, the results of benchmarking (i.e. Figs. 7 and 8) specify that Singapore, Japan, and Korea are the visible contenders for embracing I4.0 technologies in Asia. The authors have also focused on the intensification of

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Fig. 7 Benchmarking data –readiness for future of production part -1 (source Readiness for the future of production-2018 report of world economic forum)

technology espousal to fruitage great value-added products, bring into line research and development to speed up innovation compelled progression, while augmenting capacity building to raise the level of skilled labour. The existing ecosystem seem to be backing native establishments to advance into technology and solutions benefactors, enhancing the attractiveness of companies in the worldwide market. Organizations of developing countries need to flourish the ethos of innovation by intensifying the existing capacity and proficiency to profit from the I4.0 technology measures.

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Fig. 8 Benchmarking data –readiness for future of production part -2 (source Readiness for the future of production-2018 report of world economic forum)

Research limitations and implications: The work done in this research is qualitative in nature through extensive extant literature review. Going forward, validation of the findings using a quantitative method by primary data analysis can be a potential area of research. Our evaluation of the integration of I4.0 model into national and industrial policies, while benchmarking the readiness of countries for future of production, may support policymakers, researchers, academicians, and practitioners working on developing strategic plans to accelerate the implementation of I4.0 technologies. The following themes can be considered by future researchers for I4.0 implementation at the ground level; for instance, how can developing countries assimilate small and medium-sized enterprises, what skills would the labour force need to flourish in the forthcoming environment of digital technologies, how developing countries can manage the impacts of I4.0 technologies on employment, among others.

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References 1. Almada-Lobo F (2016) The Industry 4.0 revolution and the future of Manufacturing Execution Systems (MES). J Innov Manage 3(4):16–21 2. Brettel M, Friederichsen N, Keller M, Rosenberg M (2014) How virtualization, decentralization and network building change the manufacturing landscape: an Industry 4.0 perspective. Int J Mech Ind Sci Eng 8(1):37–44 3. Bahrin MAK, Othman MF, Azli NN, Talib MF (2016) Industry 4.0: a review on industrial automation and robotic. J Teknologi 78(6–13):137–143 4. Chen YC, Hsieh TC (2014) Big data for digital government: opportunities, challenges, and strategies. Int J Publ Adm Digit Age (IJPADA) 1(1):1–14 5. Daengdej J, Dowpiset K (2018) Using big data as a backbone for Thailand 4.0: a proposed framework in improving business performance through effective managerial training initiatives. IGI Global, pp 177–195 6. Hwang G, Park J, Lee J, Park J, Chang TW, Won J (2017) Analysis of IoT usage in Korean key manufacturing industries. J Soc e-Business Stud 21(4) 7. Jones C, Pimdee P (2017) Innovative ideas: Thailand 4.0 and the fourth industrial revolution. Asian Int J Soc Sci 17(1):4–35 8. Kong L, Woods O (2018) Smart eldercare in Singapore: negotiating agency and apathy at the margins. J Aging Stud 47:1–9 9. Kamble SS, Gunasekaran A, Sharma R (2018) Analysis of the driving and dependence power of barriers to adopting Industry 4.0 in the Indian manufacturing industry. Comput Ind 101:107–119 10. Kagermann H (2015) Change through digitization—value creation in the age of Industry 4.0. In: Management of permanent change. Springer Gabler, Wiesbaden, pp 23–45 11. Lasi H, Fettke P, Kemper HG, Feld T, Hoffmann M (2014) Industry 4.0. Bus Inf Syst Eng 6(4):239–242 12. Lu Y (2017) Industry 4.0: a survey on technologies, applications and open research issues. J Ind Inf Integr 6:1 13. Liao Y, Deschamps F, Loures EDFR, Ramos LFP (2017) Past, present, and future of Industry 4.0—a systematic literature review and research agenda proposal. Int J Prod Res 55(12):3609– 3629 14. Li L (2018) China’s manufacturing locus in 2025: with a comparison of “Made-in-China 2025” an “Industry 4.0”. Technol Forecast Soc Change 135:66–74 15. Lin K, Shyu J, Ding K (2017) A cross-strait comparison of innovation policy under Industry 4.0 and sustainability development transition. Sustainability 9(5):786 16. Nagasato Y, Yoshimura T, Shinozaki R (2018) Realizing Society 5.0: expectations from Japanese business. J Inf Manage 38(1):3–8 17. Obama S (2018) Editor’s message to special issue on intelligent transportation systems a mobile communication toward super smart society. J Inf Process 26:1 18. Sagar BS, Jadhav PD (2017) A study on the impact of Industry 4.0 in India. Int Adv Res J Sci Eng Technol 4(7):24–28 19. Sung TK (2018) Industry 4.0: a Korea perspective. Technol Forecast Soc Chang 132:40–45 20. Uhlemann THJ, Lehmann C, Steinhilper R (2017) The digital twin: realizing the cyber-physical production system for Industry 4.0. Procedia CIRP 61:335–340

Exergy Analysis of Novel Combined Absorption Refrigeration System Vaibhav Jain, Ashu Singhal, and Harsh Joshi

Abstract Nowadays, absorption systems are very much in demand due to its sole feature of utilizing waste heat energy of a system into other system providing cooling process. In here, a novel setup of combined absorption refrigeration (CAR) system is forthput and analyzed to produce −20 °C cold energy by utilizing the waste heat of low grade. The system formulated ahead consists of multiple (two) sub-systems: LiBr/H2 O absorption refrigeration (AR) cycle and NH3 /H2 O absorption refrigeration (AR) cycle. CAR system is utilizing the low grade not so useful heat using a cascade system method. Simulation has been done, analogous to a thermodynamic model built in Engineering Equation Solver (EES) software. Coefficient of performance (COP) and exergy efficiency are the few performance arguments. The overall COP of the system is 0.206 with exergetic efficiency of 24.1%. The present simulation results show that CAR has a tremendous adaptability. The work gives a new lead to produce low-temperature cold energy using waste heat of low grade. Keywords Low-grade waste heat · CAR · Ammonia–lithium bromide · Energy and exergy analysis

Nomenclature COP f h m˙ Q˙ T I˙

Coefficient of performance Flow ratio Specific enthalpy (kJ/kg) Mass flow rate (kg/s) Heat transfer rate (kW) Temperature (K) Irreversibility (W)

V. Jain (B) · A. Singhal · H. Joshi Department of Mechanical and Automation Engineering, Maharaja Agrasen Institute of Technology, Delhi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 V. C. Pandey et al. (eds.), Advances in Electromechanical Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5463-6_5

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Pump power (kW) W˙ P x Mass concentration of absorbent in the solution 1, 2, 3, … State points

Subscripts A C E G P Hex IN OUT

Absorber Condenser Evaporator Generator Pump Heat Exchanger Input Output

1 Introduction Today, reducing energy consumption and carbon footprint is one of the most considerable factors for the living worldwide, and this can be achieved by monitored and wise utilization of energy. Currently, it is a foremost concern in the field of refrigeration. Combined refrigeration cycles are required by cooling systems for energy saving. Absorption-absorption cascade refrigeration systems are taken into account as a great alternative to one-stage absorption/compression refrigeration systems as it is becoming a tedious task for the non-conventional one-stage absorption/compression refrigeration systems to achieve particular temperature of refrigeration below −20 °C with permissible operations and capital benefits [1]. A simple vapor absorption refrigeration (AR) system therefore comprises an expansion device, a condenser and an evaporator as analogous to the vapor compression system, and in addition, generator, an absorber, pump, heat exchanger and a pressure-reducing valve are also introduced in place of compressor. To obtain vapors of fed refrigerant from lean solution, heat is injected to the generator to carry out the process. This refrigerant then goes to the condenser, giving out a particular amount of heat as it is condensed. The refrigerant then leaves the condenser which is expanded to the evaporator where cold energy is produced as it is evaporated. After this, the vapors of refrigerant then move on to the next component which is absorber, where a solution coming from generator absorbs it, giving out some fixed amount of heat. Finally, the solution which consumed or absorbed the refrigerant goes back to the initial stage, i.e., generator, where preheating of the solution takes place from the generator through heat exchanger repeating the cycle. In ARs, two working fluids are commonly used as absorbent and refrigerant: ammonia–water (NH3 –H2 O) and

Exergy Analysis of Novel Combined Absorption …

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water–lithium bromide (H2 O–LiBr), which are richly accessible all around the globe. The AR can effectively recover the waste heat of about 50% of total and reuse it in industries and their process as and when required [2]. A way to enhance the working and efficiency of absorption system is its size. Improvising the method of heat and mass transfer can lead to reduction in the exchange area and size of absorber, which further results in reduction of weight, volume and cost of whole system. Also, method of exergy analysis done here is thoroughly best fit to achieve goal of using more efficient resources as it can determine weak spots, types and true magnitude of waste generated by the system. This can help in improving the designs of thermal systems to eliminate the sources of inefficiency and reducing the costs as much as achievable [3]. Theoretical as well as experimental studies have been done on topic of exergy/second-law efficiency analysis of absorption systems and also the heat pumps. With respect to single-effect LiBr–H2 O absorption systems, exergy analysis is forthput and written by Talbi and Agnew [4], where the study has been developed for an absorption refrigeration system and another model is forthput by Sencan ¸ et al. [5], where a model is made to carry out examination for cooling/heating purposes and for system’s separate components. Analogous outcomes were fetched, which conclude that exergy losses were relatively larger for absorber and generator. Morosuk and Tsatsaroni [6] gave a theory which consists of splitting of the exergy destruction further into endogenous/exogenous and unavoidable/avoidable parts with the aim of achievement of exergy analysis’ accuracy. Several studies on cascaded vapor compression–absorption systems have also been done. Chinnappa et al. [7] done the study of an absorption–compression cascade refrigeration system comprised in air conditioning and explored that COP of compression sub-system rose from 2.55 to 5 with the aid of absorption subsystem, while there was decrease of power usage of compression subsystem, lowering from 4.35 to 2.2 kW. A study on geothermal energy-powered absorption–compression cascade refrigeration cycle was done by Kairouani et al. [8]. It concluded that the coefficient of performance of cycle is 37–54% more than vapor compression cycle through calculations. Xu et al. [9] demonstrated a combined absorption–compression cascade refrigeration system that can achieve a temperature of evaporation below − 100 °C. But, so far, achievements in techniques for waste heat utilization are very limited. In research carried forward, novel combined configuration of absorption refrigeration system has been introduced which will utilize waste heat of low grade for producing −20 °C lower temperature energy. This newly introduced CAR overcomes the restrictions and limitation of refrigeration temperature, which is completely different from conventional vapor compression/vapor absorption refrigeration systems. CAR is inclusive of two sub-systems: LiBr/H2 O absorption refrigeration sub-system and NH3 /H2 O absorption refrigeration sub-system. The study here is done with objective to (1) demonstrate a new absorption–absorption combined refrigeration system to utilize the waste heat more efficiently and effectively; (2) carrying out the investigation on the thermodynamic performance of the combined

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refrigeration system; (3) determine the amount of work available from the system, i.e., exergy of the system; (4) determine the pros of the combined system. The work done here will prove to be a new approach of utilizing waste heat energy and obtaining output in form of cold energy.

2 Process Description New combined AR comprises two cycles: NH3 /H2 O AR cycle and LiBr/H2 O AR cycle. LiBr/H2 O AR cycle consists with an absorber, a generator and a heat exchanger, an evaporator and a condenser in cycle. High-pressure parts (HPP) consist of a generator and condenser; the low-pressure parts (LPP) consist of absorber and evaporator. The NH3 /H2 O AR cycle is analogous to LiBr/H2 O AR cycle. LiBr/H2 O system is suitable for moderate-temperature (5 °C and above) applications particularly air conditioning. Here, H2 O is refrigerant and LiBr is absorbent. NH3 /H2 O system is suitable for low-temperature (less than 5 °C) refrigeration/cooling applications with NH3 as refrigerant and H2 O being absorbent. The LiBr/H2 O pair comprises majority of the above-listed properties. For these reasons, LiBr and water systems are more in demand. The primary cycle of combined AR is the NH3 /H2 O AR cycle. As saturated refrigerant vapor enters into the absorber (1) from the evaporator, it is drawn by the absorbent reduction of pressure inside the absorber further leading to more flow of the refrigerant from the evaporator to the absorber. The strong solution from absorber is pumped into generator (4) which is at a higher pressure; heat from the heating sources is added to generator. This results as heating NH3 gas gets expelled from the strong solution in the generator and passes onto the condenser (8) where it is again condensed to the liquid and pass to the evaporator (9) via expansion valve (10). The weak solution in generator after NH3 is expelled passes through the reducing valve (6) to the absorber (7). Outlet consisting of water from the absorber of primary AR cycle (13) acts as an input for the absorber of the secondary cycle using a refrigerant as H2 O and the absorbent as LiBr. The strong solution is given into the generator (24); further heat is added to the generator. Resulting, saturated vapor gets expelled from the generator and moves to the condenser (28) where it is again condensed to the liquid and pass through the expansion valve (29) to the condenser of the primary NH3 /H2 O AR cycle. The waste heat of the condenser of NH3 /H2 O AR (12) is fed to input of the absorber. The waste heat of generator of NH3 /H2 O AR is fed to the input of the LiBr/H2 O generator, and waste heat of the condenser of LiBr/H2 O AR is fed to the absorber of LiBr/H2 O AR. The combined system is shown in Fig. 1.

Exergy Analysis of Novel Combined Absorption …

19 12

51

20

8

21

15

14

28

Generator

Generator

Condenser 2

Condenser 1 5

11

4

25

29

24

9

Expansion Valve 2

Expansion Valve 1

26

3

6

23

10 7

Pump 1

27

2

Pump 2

22

Absorber 2

Absorber 1 1 Evaporator

16

13 17

18

NH3/H2O AR

LiBr/H2O AR

Fig. 1 Schematic drawing of novel combined absorption refrigeration system

3 Theoretical Modeling of CAR 3.1 Energy Analysis Designed thermodynamic modeling consists of mass, material and energy equilibrium for all the used parts of CAR along with justified assumptions as shown below [10]. 1. The operation is carried out under steady state. 2. Heat loss and pressure in the parts and pipelines of the system are not taken into account. 3. Refrigerant will be saturated which occurs at exit of both components, i.e., evaporator and condenser. 4. Isenthalpic process is in expansion valves. 5. The strong and weak solutions which leave the generator and absorber, respectively, are taken to be saturated and at equilibrium at their respective pressures and temperatures. The group of dominant equations given below is applicable to all the parts used of CAR with its consideration as control volume [10]. Mass balance, Material balance, Energy balance,



Q˙ +



m˙ = 0

 

(1)

x m˙ = 0

W˙ +



(2) mh ˙ =0

(3)

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In this paper, the coefficient of performance, circulation ratio and concentration range are used as the execution arguments to examine CAR. The COP of absorption refrigeration is stated as heat load in the evaporator per unit heat load in the generator. The heat load of generator in this research is passed by waste heat of low grade, which relates to the system consumption. The expression of COP is given as:   COPNH3 /H2 O = Q˙ evap / Q˙ g1 + W˙ p1

(4)

    COPLiBr/H2 O = Q˙ c1 + Q˙ a1 / Q˙ g2 + W˙ p2

(5)

  COPCAR = Q˙ evap / Q˙ g1 + W˙ p1 + Q˙ g2 + W˙ p2

(6)

The circulation ratio is a necessary planning and modifying argument as it is straight in relation with the cost and size of the parts of the system. It is referred to quantitative relation of the solutions’ mass flow rate which is fed into generator to the refrigerant which is leaving the generator. f NH3 /H2 O = m˙ 2 /m˙ 8

(7)

f LiBr /H2O = m˙ 22 /m˙ 28

(8)

Concentration range refers to the input and output solutions’ concentration difference in generator. Concentration range of NH3 /H2 O AR is defined as: x2 = x4−x5

(9)

A larger measure of COP proves efficient performance and much higher energy utilization efficiency. The f is having an inverse relation with x. The larger measure of f belongs to a smaller value of x value and vice versa. The larger measure of f signals higher solution flow rate for unit mass of refrigerant, which can result into higher energy consumption, and therefore, it is a barrier to further improvise performance and efficiency of absorption refrigeration. The process data input to the system is given Table 1.

3.2 Exergy Analysis The exergy is referred to the maximum and achievable reversible work which can be derived from a stream or system in taking along the condition of an operation with a reference state. Exergy is never destroyed in an ideal process (except for those developing work) and terminated during real process. Neglecting few effects

Exergy Analysis of Novel Combined Absorption … Table 1 Process data to CAR under study

53

Parameter

NH3 /H2 O system LiBr/H2 O system

Cooling capacity (kW)

50

–

Condenser temperature (°C)

10

35

Evaporator temperature −20 (°C)

–

Absorber temperature (°C)

10

40

Generator temperature (°C)

110

85

Effectiveness of 0.80 solution heat exchanger

0.80

Efficiency of pump (%) 0.95

0.95

like magnetic, nuclear and electric effects, the exergy for any specific points with reference to environment can be put as: E˙ = m[(h ˙ − h 0 ) − T0 (s − s0 )]

(10)

In equation, h0 and s0 are taken at the reference environment temperature, T 0 = 293.15 K and atmospheric pressure. The operation done uses temperatures in Kelvin (K). In steady-state circumstance and overleap, the potential and kinetic part with the aid of an exergy balance in any open system from which the exergy destruction can be inferred using the irreversibility equation can be given as I˙ =

 n     T0 ˙ 1− . Qj + m˙ k E k T j k=1

IN

−

 n  k=1

 m˙ k E˙ k

−W

(11)

OUT

To achieve aim of determining the system performance and outcomes from laws of thermodynamics, i.e., first and second law, few assumptions were made in the further processing of the mathematical and calculative model for the adiabatic absorption cooling under study which are as follows. • At the entry and exit of the primary components, there is thermodynamic equilibrium. • The analysis has been done under steady-state conditions for the reference points of inlet and outlet. • The saturated solution is used at the absorber and generator exit and the same condition, i.e., saturated refrigerant at the condenser and the evaporator terminal. • There are heat and pressure drops in the tubing and various components which are taken infinitesimally small, except in components like pumps and valves.

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• Energy is fed in the generator and the evaporator using hot liquid usually water, whereas cold water is taken in for removal of heat from the condenser at the identical temperature. • The reference condition for the exergy analysis is atmospheric pressure and T 0 = 298.15 K. • Parameters like the heat load in the evaporator E Q, the efficacy of the solution heat exchanger (assumed 0.7) and temperatures at the terminal of the primary components are known. • Creating energy and exergy equilibrium for every primary component of the system. The equations given below can be obtained from Eqs. (10) and (11). 1. NH3 /H2 O AR Generator m˙ 4 = m˙ 5 + m˙ 8

(12)

Q˙ G1 = m˙ 5 h 5 + m˙ 8 h 8 − m˙ 4 h 4

(13)

  I˙G1 = E˙ 4 + E˙ 19 − E˙ 5 + E˙ 8 + E˙ 20

(14)

m˙ 8 = m˙ 9

(15)

Q˙ C1 = m˙ 9 h 9 − m˙ 8 h 8

(16)

  I˙C1 = E˙ 8 − E˙ 9 + E˙ 12 + E˙ 11

(17)

m˙ 10 = m˙ 1

(18)

Q˙ E1 = m˙ 1 h 1 − m˙ 10 h 10

(19)

  I˙E1 = E˙ 10 + E˙ 17 − E˙ 1 + E˙ 18

(20)

m˙ 12 + m˙ 1 + m˙ 7 = m˙ 2 + m˙ 13

(21)

Condenser

Evaporator

Absorber

Exergy Analysis of Novel Combined Absorption …

55

Q˙ A1 = m˙ 2 h 2 + m˙ 13 h 13 − m˙ 12 h 12 − m˙ 1 h 1 − m˙ 7 h 7

(22)

  I˙A1 = E˙ 12 + E˙ 1 + E˙ 7 − E˙ 2 + E˙ 13

(23)

m˙ 5 = m˙ 6

(24)

m˙ 3 = m˙ 4

(25)

m˙ 5 h 5 − m˙ 6 h 6 = m˙ 3 h 3 − m˙ 4 h 4

(26)

  I˙Hex1 = E˙ 3 + E˙ 5 − E˙ 4 + E˙ 6

(27)

W˙ P1 = vNH3 /H2 O (PA − PG )

(28)

I˙p1 = E˙ 2 − E˙ 3 + W˙ A

(29)

m˙ 24 = m˙ 25 + m˙ 28

(30)

Q˙ G2 = m˙ 25 h 25 + m˙ 28 h 28 − m˙ 24 h 24

(31)

  I˙G2 = E˙ 24 + E˙ 20 − E˙ 25 + E˙ 28 + E˙ 21

(32)

m˙ 15 = m˙ 28

(33)

Q˙ C2 = m˙ 15 h 15 − m˙ 28 h 28

(34)

I˙C2 = E˙ 28 + E˙ 29 + E˙ 14 − E˙ 15

(35)

Heat Exchanger

Pump

2. LiBr/H2 O AR Generator

Condenser

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Absorber m˙ 11 + m˙ 13 + m˙ 27 = m˙ 22

(36)

Q˙ A2 = m˙ 22 h 22 − m˙ 11 h 11 − m˙ 13 h 13 − m˙ 27 h 27

(37)

  I˙A2 = E˙ 15 + E˙ 13 + E˙ 27 − E˙ 22 + E˙ 16

(38)

m˙ 25 = m˙ 26

(39)

m˙ 23 = m˙ 24

(40)

m˙ 25 h 25 − m˙ 26 h 26 = m˙ 23 h 23 − m˙ 24 h 24

(41)

  I˙Hex2 = E˙ 23 + E˙ 25 − E˙ 24 + E˙ 26

(42)

W˙ P2 = vLiBr/H2 O (PA − PG )

(43)

I˙p2 = E˙ 22 − E˙ 23 + W˙ A

(44)

Heat Exchanger

Pump

Necessary arguments and parameters required for the analysis of the working of the system, with respect to first two laws of thermodynamics, are: the irreversibility of components and the whole system, the COP and the exergy efficiency. Now, since COP work has been worked out already, therefore efficiency of our system is calculated already. The irreversibility for whole cycle I CYCLE is framed as I˙CYCLE = I˙C(1+2) + I˙G(1+2) + I˙E(1+2) + I˙A(1+2) + I˙G(1+2) + I˙Hex(1+2) + I˙P(1+2) (45) Exergy efficiency can be estimated as ηsecond_ law = 1 −

I˙CYCLE ExergyIN

ExergyIN = Q˙ G1 ∗ Carnot1 + Q˙ G2 ∗ Carnot2 + W˙ P1 + W˙ P2

(46) (47)

Exergy Analysis of Novel Combined Absorption …

57

 Carnot1 = 1 −

T0 Tgh1

 (48)

where  Tgh1 =

   TING1 + 273.15 − TOUTG1 + 273.15

(T +273.15) ln T ING1 +273.15 ( OUTG1 )   T0 Carnot2 = 1 − Tgh2

(49)

(50)

where  Tgh2 =

   TING2 + 273.15 − TOUTG2 + 273.15

(T +273.15) ln T ING2 +273.15 ( OUTG2 )

(51)

4 Results and Further Discussion The thermodynamic CAR model depicted is applied to examine the performance of CAR as shown in Fig. 1. In the study done, the thermodynamic model contains very high nonlinear behavior equations and is solved with the help of software package Engineering Equation Solver (EES) (Klein, 2015). Many scholars and researchers who are working around the world in the area of refrigeration used EES for the thermodynamic modeling of their refrigeration systems. EES determine and combine equations that must be solved at same time on its own. This feature eases the operation for the operator and makes sure that the solver will always work at optimal efficiency. EES uses a technique of Newton’s method to solve systems of nonlinear algebraic equations. EES is made such so as to provide various built-in functions like mathematical and thermophysical property functions. After deriving the complete model, the process of simulation of the CAR is started under the variety of loading and design conditions. The refrigerant mass flow rate in NH3 /H2 O and LiBr/H2 O is 0.146 kg/s and 0.193 kg/s, respectively. Considering the data of low-grade and high-grade energies of various components of CAR, rate of heat transfer in generator of NH3 /H2 O and LiBr/H2O is 280.80 kW and 569.60 kW, respectively, whereas the circulation ratio of two systems is determined to be 2.48 and 13.19, respectively. The rate of heat transfer for condenser and absorber is 216.70 kW and 239.80 kW, respectively, for NH3/H2O sub-system, whereas for LiBr/H2 O sub-system, values come out to be 485.30 kW and 540.70 kW, respectively. The pump’s effect on total energy inputs is determined as negligible.

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The COP of NH3 /H2 O and LiBr/H2 O sub-systems is 0.6246 and 0.8015, respectively. The total COP of CAR system is determined as 0.206. After determining the COP of the components as a whole, exergy analysis has been done to identify the irreversible losses and exergy efficiency of the novel CAR system. Exergy analysis is a tool of great importance to locate the usage of useful energy (exergy) which is required to proceed the thermodynamic processes. The irreversibility rate of entire cycle comes out to be 135.90 kW; whereas the exergy efficiency is 24.1%. Exergy input given to the system is 814.22 kW. The maximum irreversible losses (29.32 kW) occur in the generator of NH3 /H2 O sub-system as the thermal gradient is high. Higher is the temperature difference, higher will be irreversible losses. Hence, generator of NH3 /H2 O sub-system is the most sensitive component of the proposed CAR. The most efficient component of CAR system is pumps and pressure-reducing valves, wherein the zero entropy generation has been observed. In reducing order of irreversible losses, various components of CAR could be aligned as generators, absorbers, condensers, heat exchangers, evaporators and expansion valves.

5 Conclusions CAR is made to produce −20 °C cold energy by consuming waste heat of low grade. To establish the above-depicted system, model is built in EES, according to the process data. A parametric study is performed to give the guidance for the system design which is based on the model. Regarding the features of CAR, two subsystems are analyzed separately from each other. In NH3 /H2 O AR cycle, the energy usage degree and other arguments are determined by concentration range which is similar to LiBr/H2 O AR cycle. This study provides efficient and an innovative lead to produce high-caliber cold energy using the redundant heat of low grade. CAR system is designed to attain a maximal coefficient of performance and exergy efficiency. The COP of the fashioned CAR is 0.206. The exergy efficiency of the system is found to be 24.1%. Generator of NH3 /H2 O sub-system is the most sensitive component of the proposed CAR, whereas the most efficient components are pumps and pressure-reducing valves.

References 1. Chandhok M, Sachdeva S, Jain V. Energy analysis of combined hybrid absorption refrigeration system. ISBN: 978-93-86256-88-1 2. Lian HK, Li Y, Shu GY, Gu CW (2011) An overview of domestic technologies for waste heat utilization. Energy Conserv Technol 166(29):123–133 3. Gutiérrez-Urueta G, Huicochea A, Rodríguez-Aumente P, Rivera W. Energy and exergy analysis of water-LiBr absorption systems with adiabatic absorbers for heating and cooling

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4. Talbi MM, Agnew B (2000) Exergy analysis: an absorption refrigerator using lithium bromide and water as the working fluids. Appl Therm Eng 20:619–630 5. Sencan ¸ A, Yakut KA, Kalogirou SA (2005) Exergy analysis of lithium bromide/water absorption systems. Renew Energy 30:645–657 6. Orosuk T, Tsatsaronis G (2000) A new approach to the exergy analysis of absorption refrigeration machines. Energy 33:890–907 7. Chinnappa JCV, Crees MR, Srinivasa Murthy S, Srinivasan K (1993) Solar-assisted vapor compression/absorption cascaded air-conditioning systems. Sol Energy 50(5):453–458 8. Kairouani L, Nehdi E (2006) Cooling performance and energy saving of a compression–absorption refrigeration system assisted by geothermal energy. Appl Therm Eng 26(2–3):288–294 9. Xu Y, Chen F, Wang Q, Han X, Li D, Chen G (2015) A combined low-temperature absorption– compression cascade refrigeration system. Appl Therm Eng 75:504–512

Geothermal Energy: An Effective Resource Toward Sustainability Suman Das and Arijit Kundu

Abstract Recently, the world’s biggest issue is climate change due to the greenhouse gas emissions of several conventional technological applications and exhaustion of fossil fuels. Thus, the importance has been given on the research of renewable energy. Geothermal energy is a such renewable energy source of the earth. Geothermal energy is the infinite source of energy, maintained to the nearly invariable level of temperature into the earth at a certain depth, almost throughout the year. This concept is being used as a sink for the geothermal heat pump coupled with a geothermal heat exchanger. This research paper assesses the recent projects and the advancement of the geothermal heat pump using for cooling and heating purposes or other business purposes in India. By using geothermal energy, power consumption can be reduced up to 50–60%. Keywords Geothermal · Renewable · Underground · Heat exchanger · Heat pump

1 Introduction Before, the air-conditioning devices were used as luxurious equipment, but in recent years, it becomes a necessity in daily life. Therefore, the demand of energy for cooling purposes has been increased. To meet this requirement, more amounts of fossil fuels have been burnt, and as a result, more amount of CO2 has been produced and as well as global warming has been increased. For reducing global warming problems, geothermal energy can be used to cool the desired space [1]. In India, 30% of total electricity has been consumed by the commercial sector-sand residential equipments. Out of this, 60% of electricity is used for cooling purposes [2]. Geothermal energy is an energy that is stored into the earth as a temperature. This geothermal energy is being generated by the absorption of solar energy on the earth’s surface and radioactive decompose of minerals. Geothermal heat is evaluated around 5500 °C at the earth’s core, similar to the temperature of sun’s surface [3]. S. Das (B) · A. Kundu Jalpaiguri Government Engineering College, Jalpaiguri 735102, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 V. C. Pandey et al. (eds.), Advances in Electromechanical Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5463-6_6

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1.1 Utilization of Geothermal Energy The geothermal energy can be utilized in two ways such as given below [4], • Drilled bore wells at geothermal fields and use the hot water or gas of these bore wells to generate electricity. • By using geothermal heat pump with a geothermal heat exchanger for space cooling and space heating. 1.1.1

Drilled Bore Well Technologies

Electricity can be produced in three ways by drilling bore wells as given below.

Dry Steam Power Plant This is one of the simplest types of geothermal power plant. Dry steam-type power plant was first established at Larderello, Italy, in 1904. This technology is currently used in Northern California. This type of power plant uses hydraulic fluids which are available primarily in the vapour state. The stream travels from the bore wells of the geothermal field directly to the turbine and operates that. Then, the turbine operates the generator and the generator produces electricity. This hot steam eliminates the necessity to burn the fossil fuels and also eliminates the cost to store the fossil fuels [5].

Flash Steam Power Plant Among three types of geothermal power plant, flash steam power plant is the most popular type of the geothermal power plan. Fluid (>182 °C) is pumped into a tank under high pressure where the pressure of fluid has been dropped certainly and some amount of fluid has been rapidly vaporized or flashed. Then, the vapor spins the turbine, and then, the turbine starts to operate the generator for producing electricity. If any fluid stays back into the tank, then it has been flashed in a secondary tank to extract more energy. The steam is usually compressed in a direct contact-type condenser, in which the cooled water of the cooling tower has been scattered into the tank and has been mixed with the steam. Then, the circuit of the cooling water has been formed by condensed steam, and a decent part is evaporated and is distributed into the outside environment through the cooling tower. Then, the surplus cooling water is released to the skin-deep injection wells. Alternatively, tube-type condenser and direct contact condenser’s shell are being used sometimes, as shown in Fig. 1b. In this plant, the compressed steam does not touch the cooled water and has been released in injection wells. The size of this type of power plant has been varied from 5 MW to over 100 MW. Depending on the specifications of steam, pressures,

Geothermal Energy: An Effective Resource Toward Sustainability

a. Dry steam Power Plant

63

b. Flash steam Power Plant

c. Binary Cycle Power Plant Fig. 1 Classification of geothermal power plant [6]

gas contents and design of power plant, the steam is required between 6100 kg and 9100 kg per hour for producing 1 MW electrical power. Small power plants (