Advances in Engineering Research and Application: Proceedings of the International Conference on Engineering Research and Applications, ICERA 2020 [178, 1 ed.] 9783030647186, 9783030647193

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Lecture Notes in Networks and Systems 178

Kai-Uwe Sattler · Duy Cuong Nguyen · Ngoc Pi Vu · Banh Tien Long · Horst Puta   Editors

Advances in Engineering Research and Application Proceedings of the International Conference on Engineering Research and Applications, ICERA 2020

Lecture Notes in Networks and Systems Volume 178

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA; Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada; Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.

More information about this series at http://www.springer.com/series/15179

Kai-Uwe Sattler Duy Cuong Nguyen Ngoc Pi Vu Banh Tien Long Horst Puta •

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•

•

Editors

Advances in Engineering Research and Application Proceedings of the International Conference on Engineering Research and Applications, ICERA 2020

123

Editors Kai-Uwe Sattler Department of Computer Science and Automation Ilmenau University of Technology (IUT) Ilmenau, Germany Ngoc Pi Vu Faculty of Mechanical Engineering Thai Nguyen University of Technology Thai Nguyen, Vietnam

Duy Cuong Nguyen Faculty of Electronic Engineering Thai Nguyen University of Technology Thai Nguyen, Vietnam Banh Tien Long Hanoi University of Science and Technology Hanoi, Vietnam

Horst Puta Institute for Automation and Systems Engineering Ilmenau University of Technology (IUT) Ilmenau, Germany

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-030-64718-6 ISBN 978-3-030-64719-3 (eBook) https://doi.org/10.1007/978-3-030-64719-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

Keynote Addresses Hardware Acceleration of Modern Data Management . . . . . . . . . . . . . . Kai-Uwe Sattler Electric Vehicle Development and Low-Carbon Transport in Vietnam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Le Anh Tuan

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ICERA 2020 Main Track A Common-Ground Single-Phase Boost Inverter with Suppressed Double-Frequency Ripple for Photovoltaic Applications . . . . . . . . . . . . . Minh-Duc Ngo, Quynh-Van Nong, Thuy-Ngan Ngo, Hong-Quang Nguyen, Tan-Tai Tran, and Seon-Ju Ahn A High Step-up DC-DC Converter with Semiconductor Voltage Stress Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hong-Quang Nguyen, Ngoc-Anh Tran, Van-Nghiep Dinh, Vinh-Thuy Nguyen, Minh-Duc Ngo, and Joon-Ho Choi A Method to Partition Accuracy in Workspace for Robot Arms . . . . . . Huu-Thang Nguyen, Long Pham Thanh, and Jen-Tzong Jeng A Novel Method for Shielding Problems with Taking Robust Correction Procedure into Account . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vuong Dang Quoc and Dinh Bui Minh A Review on Ultrasonic Stack Modelling . . . . . . . . . . . . . . . . . . . . . . . . Ngo Nhu Khoa, Nguyen Thi Bich Ngoc, and Tran Duc Tai A Solution to Power Load Distribution Based on Enhancing Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Truong-Giang Ngo, Thi-Thanh Tan Nguyen, Thi-Xuan Huong Nguyen, Trinh-Dong Nguyen, Van-Chieu Do, and Trong-The Nguyen

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A Study for Determination of the Pressure Ratio of the V12 Diesel Engine Based on the Heat Flow Density to Cooling Water . . . . . . . . . . Kien.Nguyen Trung

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A Study of Scissor Lifts Using Parameter Design . . . . . . . . . . . . . . . . . . Anh-Tuan Dang, Dinh-Ngoc Nguyen, and Dang-Hao Nguyen

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A Study on Prediction of Milling Forces . . . . . . . . . . . . . . . . . . . . . . . . Do Duc Trung, Tran Ngoc Giang, Tran Thi Hong, Bui Thanh Danh, Vu Van Khoa, Nguyen Dinh Ngoc, Nguyen Thanh Tu, and Vu Ngoc Pi

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A Study on Qualitative and Quantitative Characterization of Machining Quality of Aerospace Composite Structures . . . . . . . . . . . Nguyen Dinh Ngoc and Nguyen Thi Hue

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A Study on Prediction of Grinding Surface Roughness . . . . . . . . . . . . . 102 Do Duc Trung, Nhu Tung Nguyen, Hoang Tien Dung, Nguyen Van Thien, Tran Thi Hong, Tran Ngoc Giang, Nguyen Thanh Tu, and Le Xuan Hung A Vision-Based Measurement and Classification System for Robot Arm Under Controlled Lighting Condition . . . . . . . . . . . . . . . . . . . . . . 112 Quang-Cherng Hsu, Ngoc-Vu Ngo, Thanh-Long Pham, Quoc-Khanh Duong, and Duc-Vuong Vu About a Viewpoint of Calculating Spatial Dimensional Tolerance Chains According to Structure Group of a Parallel Robot . . . . . . . . . . . 120 Thuy Le Thi Thu, Trung Trang Thanh, Huu-Thang Nguyen, and Long Pham Thanh Adaptive Algorithm for Servo System Using Linear Electric Motor . . . 133 V. E. Kuznetsov, Phan Thanh Chung, Nguyen Thi Ha, and Nguyen Hoang Ha Adaptive Sliding Mode Control for a 2-DOF Robot Arm in Case of Actuator Faults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Le Ngoc Truc and Nguyen Phung Quang An Energy-Efficient Combination of Sleeping Schedule and Cognitive Radio in Wireless Sensor Networks Utilizing Compressed Sensing . . . . 154 Minh T. Nguyen, Thuong T. K. Nguyen, and Keith A. Teague An Enhancing Grasshopper Optimization for Efficient Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Trong-The Nguyen, Shi-Jie Jiang, Thi-Kien Dao, Truong-Giang Ngo, Thi-Thanh-Tan Nguyen, and The-Vinh Do An Evaluation of B-Spline for Synthesis of Cam Motion with a Large Number of Output Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Nguyen Thi Thanh Nga, Nguyen Van-Sy, Nguyen Thi Bich Ngoc, and Vu Thi Lien

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An Experimental Study on Vibration-Driven Locomotion Systems Under Different Levels of Isotropic Friction . . . . . . . . . . . . . . . . . . . . . . 181 Ngoc-Tuan La, Quoc-Huy Ngo, Ky-Thanh Ho, and Khac-Tuan Nguyen Analysis of Milling Chatter Vibration Based on Force Signal in Time Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 Minh-Quang Tran, Meng-Kun Liu, and Quoc-Viet Tran Analytical Study of the Power Parameters of Electric Traction Drive for Modern Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 Aleksey Kolbasov, Kirill Karpukhin, Dmitry Sheptunov, Povalyaev Andrey, Nguyen Khac Tuan, and Nguyen Khac Minh Automatic Extraction and Welding Feature Recognition from STEP Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210 Lan Phung Xuan and Linh Tao Ngoc Characterization of Gelatin and PVA Nanofibers Fabricated Using Electrospinning Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 Cuong Nguyen Nhu, Nhung Vu Thi, Nam Nguyen Hoang, Thao Pham Ngoc, Trinh Chu Duc, Van Thanh Dau, and Tung Bui Thanh Choice of Selection Methods in Genetic Algorithms for Power System State Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Thanh-Son Tran and Thi-Thanh-Hoa Kieu Collision-Free Path Following of an Autonomous Vehicle Using NMPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 Ngo-Quoc-Huy Tran, Ionela Prodan, and Nguyen-Duy-Minh Phan Compare the Efficiency of the Active Filter and Active Rectifier to Reduce Harmonics and Compensate the Reactive Power in Frequency Controlled Electric Drive Systems . . . . . . . . . . . . . . . . . . 242 Le Van Tung, Pham Thanh Long, Ngo Van An, and Bogdan Vasilev Comparing the Application of Gas Sensor Fabrication of Nanomaterials ZnO Fabricated by Hydrothermal and Chemical Vapor Deposition Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 Hoang Van Han, Dao Huy Du, and Do Anh Tuan Convergence Parameters for D-Type Learning Function . . . . . . . . . . . . 262 Cao Thanh Trung, Nguyen Thu Ha, Tran Kim Quyen, and Nguyen Doan Phuoc Current Harmonic Eliminations for Seven-Phase Non-sinusoidal PMSM Drives applying Artificial Neurons . . . . . . . . . . . . . . . . . . . . . . . 270 Duc Tan Vu, Ngac Ky Nguyen, Eric Semail, and Thi Thanh Nga Nguyen

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Design and Some Experimental Results of the Robust Current Controller of Doubly-Fed Induction Generator in Wind Power Plant with the Backstepping Technique Based Disturbance Observer . . . . . . . 280 C. X. Tuyen and N. T. Huong Design and Some Experimental Results of the U-Type Permanent Magnet Three-Phase Linear Motor Based Position Control System with the Backstepping Technique Based Disturbance Observer . . . . . . . 291 C. X. Tuyen and N. T. Huong Detail Design of IPM Motor for Electric Power Traction Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Bui Minh Dinh and Dang Quoc Vuong Detecting Common Web Attacks Based on Machine Learning Using Web Log . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 Xuan Dau Hoang Determination of Kinematic Control Parameters of Omnidirectional AGV Robot with Mecanum Wheels Track the Reference Trajectory and Velocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Trinh Thi Khanh Ly, Nguyen Hong Thai, Le Quoc Dzung, and Nguyen Thi Thanh Development of New Method for Choosing Standard Components Subject to Minimal Cycle Time and Minimal Sum of Purchasing Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 Tan Nguyen Dang and Manh Cuong Nguyen Dust Emission During Machining of CFRP Composite: A Calculation of the Number and Mass of the Thoracic Particles . . . . . 341 Dinh Nguyen Ngoc, Thi Nguyen Hue, Bui Van Hung, and Vu Duy Duc Dynamic Surface Control of the Axial-Flux Permanent Magnet Motor with Speed Sensorless Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350 Manh Tung Ngo, Quang Dang Pham, Huy Phuong Nguyen, and Tung Lam Nguyen Edge-Based Object Pose Estimation Using Differential Evolution Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 Ngoc Linh Tao and Lan Phung Xuan Effect of Changing Grounding Mode to Reduce Power Loss on Lightning Ground Wire by Induced Current - Northern Vietnam Overhead Power Transmission Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366 Nhat Tung Nguyen and Xuan Phuc Nguyen Electromagnetic Design of Synchronous Reluctances Motors for Electric Traction Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 Bui Minh Dinh, Do Trong Tan, and Dang Quoc Vuong

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Enhancing Accuracy of Surface Roughness Model Using Box-Cox Transformation in Surface Grinding AISI 5120 Alloy Steels . . . . . . . . . 379 Do Duc Trung, Nguyen Dinh Ngoc, Tran Thi Hong, Bui Thanh Danh, Nguyen Thanh Tu, Tran Ngoc Giang, Nguyen Thi Quoc Dung, and Vu Ngoc Pi Ensemble of Deep Learning Models for In-Hospital Mortality Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 Quang H. Nguyen and Quang V. Le Evaluating the Impact of Demand Response in Planning Micro-grids Considering Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 V. V. Thang and N. H. Trung Evolutionary Tuning of PID Controllers for a Spatial Cable-Driven Parallel Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411 Sy Nguyen-Van, Diem Thi Thu Thuy, Nga Nguyen Thi Thanh, and Ngoc Nguyen Dinh Experimental and Numerical Characterization of Mechanical Behavior for the Corrugated Cardboard . . . . . . . . . . . . . . . . . . . . . . . . 425 Duong Pham Tuong Minh, Dao Lien Tien, and Nguyen Quang Hung Experimental and Numerical Investigations into Evaporation Rates of Some Fuels Utilized in Aviation Gas Turbine Engines . . . . . . . . . . . . 434 Nam V. T. Pham, Kien T. Nguyen, Thin V. Pham, and Phuong X. Pham Experimental Evaluation of the Performance of Oil-Based Nanofluids in the Grinding of Ti-6Al-4V Alloy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 Trung Kien Nguyen, Hung Trong Phi, Got Van Hoang, Tam Ngoc Bui, and Son Hoanh Truong Fault Diagnosis for the Short-Circuit Fault of the Single-phase Five-Level VIENNA Active Rectifier . . . . . . . . . . . . . . . . . . . . . . . . . . . 452 Pham Thi Thuy Linh, Nguyen Ngoc Bach, and Doan Van Binh Feedforward Based Dual Loop PI Controller for 400 Hz Ground Power Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 Son Tran Que, Dich Nguyen Quang, Minh Y. Nguyen, Quy Do Ngoc, and Phu Do Ba Force-Velocity Relation of Dampers in Horizontal Washing Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 Nguyen Thi Hoa and Ngo Nhu Khoa Gear Fault Classification Using the Vibration Signal Decomposition and Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 478 Nguyen Trong Du and Nguyen Phong Dien

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Genetic Algorithm Based Optimization of Cutting Parameters in CO2 Laser Beam Cutting of Cow Leather . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 Thangaraj Muthuramalingam, Swaminathan Vasanth, Sanjeev Gupta, and Vu Ngoc Pi Influences of Cutting Parameters on Surface Roughness During Milling and Development of Roughness Model Using Johnson Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491 Do Duc Trung, Nguyen Dinh Ngoc, Tran Thi Hong, Vu Van Khoa, Nguyen Thanh Tu, Tran Ngoc Giang, Nguyen Thi Quoc Dung, and Vu Ngoc Pi Influence of Random Fiber Length on Macroscopic Properties of Short Fiber Reinforced Composites Due to Microscopic Physical Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501 Tien-Dat Hoang, Nhu-Khoa Ngo, Dinh Ngoc-Nguyen, Van-Truong Nguyen, Tuong Minh Duong Pham, Thi Thanh Nga Nguyen, and Viet Dung Luong Kinematic Analysis of the Class 2 Degree-of-Freedom Planar Parallel Mechanism via GRG2 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508 ThanhTrung Trang, Yueming Hu, Weiguang Li, ThanhLong Pham, TuanAnh Nguyen, and ThiThuThuy Le Material Removal Rate in Electric Discharge Machining with Aluminum Tool Electrode for Ti-6Al-4V Titanium Alloy . . . . . . . . . . . . 527 Nguyen Huu Phan, Vu Ngoc Pi, Shailesh Shirguppikar, M. S. Patil, Mohsen Asghari Ilani, Le Xuan Hung, T. Muthuramalingam, and Tran Quoc Hung Mathematical Modelling of Thermoacoustic Generator Systems and Simulation Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 534 Trong Tan Do, Duy Tung Le, Phuong Nam Dao, Minh Dinh Bui, and Huy Du Dao Measurement Setup for Temperature-Dependent Electrical Property of ZnO-Based Thermoelectric Thin Films . . . . . . . . . . . . . . . . . . . . . . . 541 Trinh Quang Thong, Nguyen Anh Minh, Nguyen Trong Tinh, Trieu Viet Phuong, and Dao Huy Du Modified Q-Learning Algorithm with Lifting Method for Discrete-Time Linear Periodic Systems . . . . . . . . . . . . . . . . . . . . . . . 548 Ngoc Trung Dang, Tien Hoang Nguyen, and Phuong Nam Dao Multi Response Optimization of Dressing Conditions for Surface Grinding SKD11 Steel by HaiDuong Grinding Wheel Using Grey Relational Analysis in Taguchi Method . . . . . . . . . . . . . . . . . . . . . . . . . 560 Tran Thi Hong, Ngo Ngoc Vu, Nguyen Huu Phan, Tran Ngoc Giang, Nguyen Thanh Tu, Le Xuan Hung, Bui Thanh Danh, and Luu Anh Tung

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Multi-objective Optimization of Process Parameters During Electrical Discharge Machining of Hardened 90CrSi Steel by Applying Taguchi Technique with Grey Relational Analysis . . . . . . . . . . . . . . . . . . . . . . . . 572 Tran Thi Hong, Nguyen Manh Cuong, Nguyen Dinh Ngoc, Luu Anh Tung, Tran Ngoc Giang, Le Thu Quy, Nguyen Thanh Tu, and Do Thi Tam Multi-objective Optimization of Surface Roughness and MRR in Surface Grinding of Hardened SKD11 Using Grey-Based Taguchi Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 584 Tran Thi Hong, Do The Vinh, Tran Vinh Hung, Tran Ngoc Giang, Nguyen Thanh Tu, Le Xuan Hung, Bui Thanh Danh, and Luu Anh Tung Multi-response Optimization in PMSEDM Process Using Taguchi-Grey Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 594 Tran Thi Hong, Nguyen Manh Cuong, Tran Ngoc Giang, Nguyen Anh Tuan, Le Thu Quy, Thangaraj Muthuramalingam, Nguyen Thanh Tu, and Do Thi Tam Numerical Identification of the Mechanical Behaviour of a Fluoroelastomer (FKM) Using Nanoindentation Test . . . . . . . . . . . 607 Florent Chalon, Julie Pepin, Nathan Le Pennec, Tien-Dung Do, Stéphane Meo, Clémence Fradet, Gaelle Berton, and Florian Lacroix On Room-Temperature Electrodeposition of Cobalt from a Deep Eutectic Solvent: A Study of Electronucleation and Growth Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613 Thao Dao Vu Phuong, Hoang Thi Thanh Thuy, Phuong Dinh Tam, and Tu Le Manh Optimal Design of Cab’s Isolation System for a Single-Drum Vibratory Roller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 619 Le Van Quynh, Nguyen Tien Duy, Nguyen Van Liem, Bui Van Cuong, and Le Xuan Long Optimization of Cutting Parameters and Nanoparticle Concentration in Hard Milling for Surface Roughness of JIS SKD61 Steel Using Linear Regression and Taguchi Method . . . . . . . . . . . . . . . . . . . . . . . . . 628 Thanh-Dat Phan, The-Vinh Do, Thanh-Long Pham, and Huong-Lam Duong Optimization of Dressing Parameters in Surface Grinding SKD11 Tool Steel by Using Taguchi Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 636 Tran Thi Hong, Nguyen Thanh Tu, Nguyen Anh Tuan, Tran Ngoc Giang, Nguyen Thi Quoc Dung, Le Xuan Hung, Bui Thanh Danh, and Luu Anh Tung

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Optimization of PMEDM Parameters for Improving MMR in Machining 90CrSi Steel - A Taguchi Approach . . . . . . . . . . . . . . . . . . . 648 Tran Thi Hong, Do Thi Tam, Do The Vinh, Luu Anh Tung, Le Thu Quy, Thangaraj Muthuramalingam, Vu Ngoc Pi, and Nguyen Manh Cuong Overshoot and Settling Time Assignment for Second-Order Systems with Time Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 658 Nam Hoai Nguyen and Phuoc Doan Nguyen Performance Ratio Analysis Using Experimental Combining Historical Weather Data for Grid-Connected PV Systems . . . . . . . . . . . 665 Ngo Xuan Cuong, Nguyen Thi Hong, Do Anh Tuan, and Do Nhu Y Power Control of Andronov-Hopf Oscillator Based Distributed Generation in Grid-Connected Microgrids . . . . . . . . . . . . . . . . . . . . . . . 675 Tobias Heins, Trung Tran, David Raisz, and Antonello Monti Prediction of Cutting Force When Surface Milling Using Face Milling Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 688 Nguyen Van Thien, Do Duc Trung, Vilaivanh Xaixavang, Tran Thi Hong, Nguyen Thanh Tu, Tran Ngoc Giang, and Le Xuan Hung Research Method for Calculating Additional Power Losses, Considering the Asymmetric Loads in the Low-Voltage Power Supply System Vietnam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695 Pham Trung Son Research to Improve the Quality Control for Drive System Tracking Electromechanical Takes into Account Nonlinear Undetermined Application in Industrial Production . . . . . . . . . . . . . . . . . . . . . . . . . . . 708 Tran Duc Chuyen, Do Quang Hiep, and Dao Huy Du Role of Electrolyte Media in the Exfoliation of MoS2 Nanosheets by Electrolysis Plasma-Induced Method . . . . . . . . . . . . . . . . . . . . . . . . . 724 Van-Truong Nguyen, Tien-Dat Hoang, Nguyen Thi Kim Ngan, Pham Minh Tan, and Dang Van Thanh Simulated Annealing Algorithm for Modeling Large Deflection of Flexible Links in Complaint Mechanisms . . . . . . . . . . . . . . . . . . . . . . 729 Nguyen Thi Thanh Nga, Nguyen Thi Bich Ngoc, Nguyen Van-Sy, Nguyen Dinh-Ngoc, Nguyen Quang-Hung, and Hoang Tien Dat Study on Thermal Convective Gas Gyroscope Based on Corona Discharge Ion Wind and Coriolis Effect . . . . . . . . . . . . . . . . . . . . . . . . . 741 Hang Nguyen Thu, Ngoc Tran Van, Cuong Nguyen Nhu, Van Thanh Dau, An Nguyen Ngoc, Trinh Chu Duc, and Tung Thanh Bui Studying Electron Transport Coefficients in C2H4-SiH4 Mixtures Using Bolsig+ Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 748 Pham Xuan Hien, Tran Thanh Son, and Do Anh Tuan

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Synthesis of Automatic Motion Control Systems of an AUV Based on Fuzzy Logic Methods with Neural Network Setting of Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755 Van Tuan Pham and Thi Ha Nguyen Taguchi-DEAR Based MCDM Approach on Machining Titanium Alloy in AWJM Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 764 T. Muthuramalingam, Vu Ngoc Pi, and Ammar H. Elsheikh The Characterization of Machined Damage of CFRP Composite: Comparison of 2D and 3D Surface Roughness Performance . . . . . . . . . 771 Nguyen Dinh Ngoc, Duong Pham Tuong Minh, Nguyen Van Sy, Luong Viet Dung, Nguyen Thi Thanh Nga, Nguyen Dang Hao, and Hoang Tien Dat The Dimensional Synthesis of the Four-Bar Mechanism with a Symbiotic Organisms Search Algorithm . . . . . . . . . . . . . . . . . . . 780 Sy Nguyen-Van, Ngoc Nguyen-Dinh, P. T. M. Duong, Nguyen Quang Hung, and Thi Thanh Nga Nguyen The Effect of Bonnet Skin and Bonnet Reinforcement Thickness on Pedestrian Head Injuries in Collisions . . . . . . . . . . . . . . . . . . . . . . . . 792 Van-Luc Ngo and Minh Khong The Effect of the Wheel Rotation Angle on the Braking Efficiency of the Tractor Semi-trailer on the Wet Roundabout Route . . . . . . . . . . 798 Nguyen Thanh Tung and Vo Van Huong The Effect of Welding Speed on the Mechanical Properties of the FSW Cu/Al . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 805 Tran Hung Tra, Quach Hoai Nam, Phi Cong Thuyen, Duong Dinh Hao, Truong Thanh Chung, Pham Trong Hop, Ho Huu Huy, Vu Lai Hoang, and Chu Hoang Duc Anh The New Method to Control Twin Rotor MIMO System (TRMS) . . . . . 810 Lai Khac Lai, Trinh Thuy Ha, Vu Nhu Lan, and Nguyen Tien Duy Theoretical and Experimental Study of Sound Transmission Loss Across Finite Clamped Composite Sandwich Plates . . . . . . . . . . . . . . . . 820 Tran Ich Thinh and Pham Ngoc Thanh Tool Wear Rate Analysis of Uncoated and AlCrNi Coated Aluminum Electrode in EDM for Ti-6Al-4 V Titanium Alloy . . . . . . . . . . . . . . . . . 832 Nguyen Huu Phan, Vu Ngoc Pi, Nguyen Quoc Tuan, Shailesh Shirguppikar, M. S. Patil, Mohsen Asghari Ilani, Le Xuan Hung, T. Muthuramalingam, and Tran Quoc Hung Tracking Control of Directed Acyclic Formation via Target Point Localization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 839 Dung Van Vu, Trung Thanh Cao, Minh Hoang Trinh, and Hyo-Sung Ahn

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Tracking Control of Rostock Delta Parallel Robot Based on the Dynamic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 846 Vu Le Huy, Le Thi Huyen Linh, and Nguyen Dinh Dzung Trajectory Tracking Control of a Caterpillar Vehicle . . . . . . . . . . . . . . 854 Do Trung Hai, Bui Thi Hai Linh, and Tran Ngoc Anh Truss Optimization Under Frequency Constraints by Using a Combined Differential Evolution and Jaya Algorithm . . . . . . . . . . . . . 861 Sy Nguyen-Van, Thi Thanh Nga Nguyen, Ngoc Nguyen-Dinh, and Qui X. Lieu Web Tension Observer Based Control for Single-Span Roll to Roll Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 874 Thi Ly Tong, Minh Duc Duong, Danh Huy Nguyen, and Tung Lam Nguyen Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 883

Keynote Addresses

Hardware Acceleration of Modern Data Management Kai-Uwe Sattler(&) TU Ilmenau, Ilmenau, Germany [email protected]

Abstract. Over the past thirty years, database management systems have been established as one of the most successful software concepts. In todays business environment they constitute the centerpiece of almost all critical IT systems. The reasons for this success are manyfold. On the one hand, such systems provide abstractions hiding the details of underlying hardware or operating systems layers. On the other hand, database management systems are ACID compliant, which enables them to represent an accurate picture of a real world scenario, and ensures correctness of the managed data. However, the currently used database concepts and systems are not well prepared to support emerging application domains such as eSciences, Industry 4.0, Internet of Things or Digital Humanities. Furthermore, volume, variety, veracity as well as velocity of data caused by ubiquitous sensors have to be mastered by massive scalability and online processing by providing traditional qualities of database systems like consistency, isolation and descriptive query languages. At the same time, current and future hardware trends provide new opportunities such as many-core CPUs, co-processors like GPU and FPGA, novel storage technologies like NVRAM and SSD as well as high-speed networks provide new opportunities. In this talk we present our research results for the use of modern hardware architectures for data management. We discuss the design of data structures for persistent memory and the use of accelerators like GPU and FPGA for database operations.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 3–3, 2021. https://doi.org/10.1007/978-3-030-64719-3_1

Electric Vehicle Development and Low-Carbon Transport in Vietnam Le Anh Tuan(&) Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]

Abstract. Vietnam’s social and economic development achievements are remarkable. However a steep rise in income and economic growth has led to rapid motorization and high energy demand. The transport sector is a major consumer of energy in Vietnam and thus it is one of the key sectors which produces most emissions including green house gas (GHG). As a result, according the emissions per GDP, Vietnam is ranked the 13th most carbon intensive economy in the world, and 4th among the low- and middle- income countries in East Asia. GHG emissions from the transport sector are expected to triple by 2030, to nearly 90 million tons carbon dioxide equivalent (CO2e). In road transport sector, there are about 40 million vehicles, including about 35 million motorbikes, over around 96 million population. Almost all of road vehicles use internal combustion engines which emit high GHG and high toxic emissions. A pathway for low-carbon transport is crucial, consisting the shift from conventional vehicles to electric ones. This talk addresses the global electric vehicle outlook, related technologies of electric vehicles, Vietnam current situation of automotive industry and contribution of low-carbon transport scenarios, in general, and of electric vehicles, in particular, to GHG reduction for Vietnam. Keywords: Vehicle outlook transport


Electric vehicle development
Low-carbon

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 4–4, 2021. https://doi.org/10.1007/978-3-030-64719-3_2

ICERA 2020 Main Track

A Common-Ground Single-Phase Boost Inverter with Suppressed Double-Frequency Ripple for Photovoltaic Applications Minh-Duc Ngo1(&), Quynh-Van Nong2, Thuy-Ngan Ngo3, Hong-Quang Nguyen1, Tan-Tai Tran4, and Seon-Ju Ahn4 1

Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] 2 Thai Nguyen University of Education, Thai Nguyen, Vietnam 3 Ha Noi Metropolitan University, Hanoi, Vietnam 4 Chonnam National University, Gwangju 61186, Korea

Abstract. A low-frequency current ripple is introduced at the DC side of the single-phase inverter topology decreasing efficiency in the photovoltaic (PV), and battery systems and degrading the lifetime of an energy storage device. In this paper, a common-ground single-phase single-stage boost inverter for PV applications is presented. The introduced topology consists of three capacitors, one inductor, five switches, and four diodes. The introduced topology has the main features as the common ground between the DC input voltage source and AC output voltage, and voltage boost capability. Furthermore, the lowfrequency input current ripple is significantly limited. Besides, the leakage current, which is one of the major problems in grid-connected PV applications, is limited in an introduced inverter. The operating principles, circuit analysis, Mathematical analysis, and PWM control strategy for the introduced inverter are discussed. The simulation results based on PSIM simulation are given at the end of the paper to confirm the feasibility, performance and viability of the introduced topology. Keywords: DC–AC inverter
Boost inverter
Photovoltaic systems
Doublefrequency ripple

1 Introduction In the recent period, because of global surface temperature and energy crisis, using solar energy has been receiving more and more attention from many researchers. The distributed generation systems combined with solar energy have been rapidly researched. The single-phase grid-tied transformerless dc–ac inverters in PV applications are becoming more common worldwide [1]. However, a grid-connected system without a transformer generates a leakage current. This current flows through the parasitic capacitance to the ground, causing EMI problems, insecurity, and the low reliability of the grid-tied transformerless PV inverters [2, 3]. To solve this problem, some research works have been presented recently to limit the leakage current [4–6]. By clamping the common-mode voltage during the freewheeling period, the leakage current is reduced © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 7–12, 2021. https://doi.org/10.1007/978-3-030-64719-3_3

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as discussed in [5, 6]. Recently, the transformerless PV inverters were introduced in [7– 9] by using common ground configurations. In these topologies, the ground of the PV array and the utility grid is directly connected, so there is no leakage current. However, these topologies are a buck power conversion where the peak AC output voltage is smaller than the dc voltage. In light of the above, a common-ground single-phase single-stage boost inverter is presented in this paper. The introduced topology has the main features as the common ground between DC input voltage source and AC output voltage, and voltage boost capability. In addition, the low-frequency input current ripple is significantly limited. The operating principles, circuit analysis, mathematical analysis, and PWM control strategy for the introduced inverter are given in Sect. 2. The simulation results based on PSIM simulation are given in Sect. 3.

i LB LB

CA

D1

S0

CB

Vg

S4

VCB

v dc D3

io

D2

Ll

a CC

S1

S2

S3

Rl

D4 b

Fig. 1. Proposed common-ground single-phase single-stage boost inverter.

2 Proposed Topology The proposed common-ground single-phase single-stage boost inverter, which is indicated as in Fig. 1, consists of five switches, four diodes, a boost inductor, three capacitors. The same ground between dc input and ac output is an interesting feature of the introduced topology. From Fig. 1, we can see that the negative polarity of the dc input is directly connected to the ac output. It is merit, especially for PV applications. Figure 2 shows operating states of the proposed common-ground single-phase singlestage boost inverter. Figure 3 indicates the proposed PWM control method for the introduced topology. As shown in Fig. 3, a control waveform, Vcontrol and a fixed voltage, VST are compared with a carrier waveform, vtri to generate control signals for switches from S0 to S4.

A Common-Ground Single-Phase Boost Inverter

9

Fig. 2. Equivalent circuit of the introduced inverter. (a) Circuit in boost-state, and (b)-(d) Circuits in non-boost-state.

VP VST

0

T

Vcontrol

vtri

DT

S0 S1 S2 S3 S4 vab : Shoot-through State

Fig. 3. Proposed PWM control method for the introduced topology.

In the boost state as shown in Fig. 2(a), four switches S0, S1, S3, and S4 are turned on while the switch S2 is turned off. As a result, the boost inductor is charged from the input dc source, Vg. The time interval in this state is D
T, where D and T represent the duty cycle and a switching period, respectively. 8 < L diLB ¼ V þ V B g CB dt : : VCA ¼ VCB

ð1Þ

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In the non-boost states as shown in Fig. 2(b), (c), and (d), the switch S0 is turned on. During the positive cycle as shown in Fig. 2(b), two switches S1, S4 are turned on while two switches S2, S3 are turned off. The output voltage, vab, in this case, equals VCC. During the negative cycle as shown in Fig. 2(c), two switches S2, S3 are turned on while two switches S1, S4 are turned off. The output voltage, vab, in this case, equalsVCC. Following the negative or positive cycle, two switches S2 and S4 are turned ON to generate a zero voltage. During the non-boost states, the boost inductor is discharged while the capacitor CB is discharged. The time interval in this state is (1–D)
T LB

diLB ¼ Vg þ VCB dt

and

VCC ¼ VCA þ VCB

ð2Þ

From (1) and (2), we get VCC ¼ VCA þ VCB ¼

2 Vdc : 1  2D

ð3Þ

The boost factor of the introduced topology is defined by B¼

Vbus VCC 2 1 ¼ ¼ 1  2D Vdc Vdc

ð4Þ

Table 1. Simulation parameters Parameters Input DC Voltage Inverter Output voltage Inductor (LB) Capacitors CA and CB CC Switching frequency Inductive Load Ll Rl

Values 96 V 220 Vrms/50 Hz 1 mH 1000 µF 2000 µF 30 kHz 30 mH 50 X

3 Simulation Results To prove the operating principle of the proposed common-ground single-phase singlestage boost inverter, simulation results based on PSIM simulation are given. The simulation parameters for the introduced topology are given as in Table 1. Figure 4 shows the simulation waveforms of the proposed common-ground single phase single stage boost inverter when the input voltage is 96 V. As shown in Fig. 4(a) and Fig. 4(b), we can see that the peak ac output voltage of the introduced topology is 310 V. The peak value of the load current is 6.2 A. As shown in Fig. 4(a), the lowfrequency current ripple is reduced significantly. From Fig. 4(d), the peak-to-peak inductor ripple current is 4 A. From Fig. 4(c) and Fig. 4(d), the stress voltage across

A Common-Ground Single-Phase Boost Inverter

11

Fig. 4. Simulation results of the introduced inverter when input dc voltage is 96 V. From top to bottom, (a) input voltage, input current, output voltage of the inverter, current load; (b) FFT of output voltage of the inverter, current load; (c)-(d) drain-source voltage of switches and zoom version of input current.

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four switches S1, S2, S3, and S4 are the same as dc bus voltage while the stress voltage across switch S0 is equal to half of dc bus voltage.

4 Conclusion This paper proposes the common-ground single-phase single-stage boost inverter for PV applications. The circuit analysis, and mathematical analysis, operating principles of the introduced inverter are provided. A simple PWM control method is introduced to modulate the proposed inverter. The introduced topology has the main features as the common ground between DC input voltage source and AC output voltage, and the voltage boost capability. Furthermore, the low-frequency input current ripple is significantly limited. Besides, the leakage current, which is one of the major problems in grid-connected PV applications, is limited in the introduced inverter. Since the proposed common-ground single phase single stage boost inverter has a high reliability, there is no leakage current and voltage boost capability, it is suitable for PV applications. The simulation results based on PSIM simulation are given at the end of the paper to confirm the feasibility, performance and viability of the proposed topology. Acknowledgment. This research was supported by Research Foundation funded by Thai Nguyen University of Technology.

References 1. Kjaer, S.B., et al.: A review of single-phase grid-connected inverters for photovoltaic modules. IEEE Trans. Ind. Appl. 41(5), 1292–1306 (2005) 2. Ribeiro, H., Borges, B., Pinto, A.: Single-stage DC–AC converter for photovoltaic systems. In: Proceedings IEEE Energy Conversion Congress and Exposition, Atlanta, USA, pp. 604– 610. IEEE (2010) 3. Gonzales, R., Lopez, J., Sanchis, P., Marroyo, L.: Transformerless inverter for single-phase photovoltaic systems. IEEE Trans. Power Electron. 22(2), 693–697 (2007) 4. Stalter, O., Wellnitz, P., Burger, B.: Flying capacitor topology for grounding of single-phase transformerless three-level photovoltaic inverters. In: 16th European Conference on Power Electronics and Applications, Lappeenranta, Finland, pp. 1–9, IEEE (2014) 5. Ji, B., Wang, J., Zhao, J.: High-efficiency single-phase transformerless PV H6 inverter with hybrid modulation method. IEEE Trans. Ind. Electron. 60(5), 2104–2115 (2013) 6. Xiao, H.F., Zhang, L., Li, Y.: An improved zero-current-switching single-phase transformerless PV H6 inverter with switching loss-free. IEEE Trans. Ind. Electron. 64(10), 7896–7905 (2017) 7. Vazquez, N., et al.: Integrating two stages as a common-mode transformerless photovoltaic converter. IEEE Trans. Ind. Electron. 64(9), 7498–7507 (2017) 8. Siwakoti, Y.P., Blaabjerg, F.: A novel flying capacitor transformerless inverter for singlephase grid-connected solar photovoltaic system. In: IEEE 7th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), Vancouver, Canada, pp. 1–6. IEEE (2016) 9. Siwakoti, Y.P., Blaabjerg, F.: Common-ground-type transformerless inverters for singlephase solar photovoltaic systems. IEEE Trans. Ind. Electron. 65(3), 2100–2111 (2018)

A High Step-up DC-DC Converter with Semiconductor Voltage Stress Reduction Hong-Quang Nguyen1, Ngoc-Anh Tran1, Van-Nghiep Dinh1, Vinh-Thuy Nguyen1, Minh-Duc Ngo1(&), and Joon-Ho Choi2 1

Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] 2 Chonnam National University, Gwangju 61186, Korea

Abstract. Nowadays, switched-inductor and switched-capacitor configurations are widely used for boost DC-DC converters to improve high boost ability. By applying the principle operating of charging and discharging of inductor and capacitor elements in parallel or series connection, it is considered that four diodes, one high voltage rating switch, two inductors are validated to show as a conventional switched-inductor boost converter. In this research, a novel highboost DC-DC converter based on the switched-inductor technique is introduced. The introduced converter is transformer-less topology and determined by changing the configuration of the conventional switched inductor structure and a semiconductor switch. As a result, the introduced converter can give low voltage rating active switches. Furthermore, the introduced converter is low in cost and achieve higher efficiency with simple topology. The operating analysis of the introduced converter is presented in detail. The simulation results with the output control are presented to verify the analysis. Keywords: Boost converter technique


Voltage stress reduction
Switched-inductor

1 Introduction In recent years, we are facing pressure from environmental protection, global surface temperature and the energy sources are depleting. Researchers are paying more attention to environmental protection, energy conservation and emission reduction. The distributed generation systems combined with available renewable energy sources have been rapidly researched. These distributed generation systems are generated by some sources such as fuel cell and photovoltaic (PV) arrays in the world [1, 2]. Generally, the available renewable energies are wind, solar, and fuel cells are dependent on the weather conditions, and their output voltages are low and instability. This problem leads to develop high boost DC-DC converters, which can generate high voltage DC-bus for the input terminal of grid-connected DC-AC inverter. Figure 1 presents a typical gridconnected renewable energy generation system. The high step-up DC-DC converters have been researched and obtained a high-voltage gain in both isolated and non-isolated topologies. However, the isolated topologies have to use the high-frequency transformer, which leads to the high cost and low efficiency for the converter [3, 4]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 13–19, 2021. https://doi.org/10.1007/978-3-030-64719-3_4

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Nowadays, the converters without transformer have more attention with high-voltage improvement, greater efficiency and low-cost design. The classical DC-DC converter with simple topology by using one inductor, one capacitor, one diode and one switch has been widely used for both research field and industrial applications. The research of classical DC-DC converter has developed in [4]. However, the voltage gain of the classical DC-DC converter is still low. Moreover, the cascaded [5], switched-capacitor, switched-inductor, and interleaved [6] techniques are also used for the classical DC-DC converter to achieve the high voltage boost ability. Most of them, the switched inductor technique is more effective, thanks to the improvement of the simple topology and the power density of the converter [7, 8]. The conventional switched-inductor converter with three-diode and two-inductor is shown in Fig. 2(a). This is modified by replacing the boost inductor in the classical boost converter. When a high-step-up voltage ability is required, a larger duty cycle should be used which leads to high voltage stress on the switches. To reduce the stress, the novel switched-inductor is introduced in this paper, which can reduce voltage stress on the semiconductor devices and improve the efficiency of the converter.

Renewable Energy Sources

DC/DC Converter

PV or Fuel Cell

Boost Converter

Applications Inverter

Load/ Utility

Fig. 1. The typical grid-connected renewable energy generation system.

2 Proposed Switched-Inductor DC-DC Converter 2.1

A Configuration of Proposed Switched-Inductor

Figure 2(b) depicts the introduced switched-inductor DC-DC converter. It consists of a modified switched-inductor cell, two power switches, and the load. When the operating principle of the proposed circuit is considered in the continuous conduction mode, the following assumptions were made: i) All components are ideal and no loss; ii) the capacitance of the capacitors is large enough to maintain the constant capacitor voltage; and iii) the current of the inductor is changed linearly. 2.2

Circuit Analysis

Figure 3 introduced the key waveforms of the introduced switched-inductor converter operating in the continuous conduction mode. Both switches S1 and S2 are ON-state simultaneously with the time interval of DT. The output switch S0 is ON-state with the time interval of (1-D)T. To analysis the operating principle, the following assumptions were made all semiconductor devices and passive components are set ideal; the

A High Step-up DC-DC Converter with Semiconductor Voltage Da L1 Vi

Da

L2

D0

Db Dc

L1 Co

S

Vo

R

Io

L2

Io Db

S1

S0 Co

Vi

15

Vo

S2

(a)

(b)

Fig. 2. Topology of (a) conventional switched-inductor DC-DC converter; (b) the proposed switched-inductor DC-DC converter. T

VGS1,VGS2

S1, S2 S0

VGS0

vLx, iLx

VS1,VS2 VS0

t

iL1=iL2

t

vL1=vL2

t

0.5(Vo-Vi) t

0.5(Vo+Vi) 0.5(Vo-Vi)

VDa,VDb

Vi

t

Vo

t

DT

t

VD0

Fig. 3. Typical waveform in continuous conduction mode of the proposed switched-inductor DC-DC converter.

capacitance is enough to generate the constant voltage; and the inductor current is linear. The operation of the introduced switched-inductor converter is divided into two modes, mode 1 is the circuit in ON-state when both switches S1 and S2 are turned on and S0 is turned off. Mode 2 is the circuit in OFF-state when both switches S1 and S2 are turned off and S0 is turned on. Mode 1: Both switches S1 and S2 are turned on and S0 is turned off. The diode Da is on and two inductors L1, L2 of the switched-inductor cell are connected in parallel.

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Two inductors are magnetized by an input voltage source, as shown in Fig. 4(a). The equations can be obtained Da L1 Vi

L2

Io

S1

L1 Co

S2

Io

L2

Vo

S0

Db

Co

Vi

(a)

Vo

(b)

Fig. 4. Equivalent circuit (b) Circuit in ON-state, and (c) Circuit in OFF-state.

8 < L diL1 ¼ L diL2 ¼ V 1 2 i dt dt : iin ¼ 2iL1 ¼ 2iL2 ;

ð1Þ

Mode 2: The converter operates in OFF-state. The switches S1 and S2 are turned off and S0 is turned on. The diode Db is on and two inductors L1, L2 of the switchedinductor cell are connected in series. The output capacitor is charged by two inductors and an input voltage source, as shown in Fig. 4(b). The equations can be given 8 diL1 diL2 > > ¼ L2 ¼ Vi L1 > > dt dt < diL1 diL2 þ L2 ¼ Vi  Vo L1 > > dt dt > > : iin ¼ iL1 ¼ iL2 ;

ð2Þ

From (1)–(2), the voltage gain and input current can be obtained as 8 < G ¼ Vo ¼ 1 þ D Vi 1  D : iin ¼ iL1 ð1 þ DÞ;

ð3Þ

A High Step-up DC-DC Converter with Semiconductor Voltage

17

3 Results The introduced switched-inductor DC-DC converter is verified by using PSIM software simulation when Vi = 50 − 100 V, Vo = 300 V is shown at Figs. 5–6. The frequency of switching is 20 kHz. The simulation parameters for the converter is presented in Table 1. The introduced switched-inductor DC-DC converter is verified by using PSIM software simulation when Vi = 50 − 100 V, Vo = 300 V is shown in Figs. 5–6.

Fig. 5. Simulation results of the introduced converter when Vi = 50 V.

Fig. 6. Simulation results of the introduced converter when Vi = 100 V.

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The simulation parameters for the converter is presented in Table 2. The obtained waveforms of output and input voltages and current, and voltage stress of semiconductor devices are shown in Fig. 5, when the input voltage is set 50 V and D = 0.72. The waveforms of output and input voltages, output and input currents are presented in Fig. 5. The peak values of voltage stress VS1, VS2, VS0, VDa, and VDb are 128 V, 178 V, 306 V, 128 V, and 50 V, respectively. Moreover, in the case of Vi = 100 V and D = 0.5, the observed peak values of voltage stress VS1, VS2, VS0, VDa, and VDb are also approximately 100 V, 200 V, 300 V, 100 V, and 100 V, respectively. It can be seen that the simulation results are matched with the principle analysis. The testing efficiencies at the input voltage of 50 V and 100 V are 94.9% and 96.7%, respectively. Table 1. Testing parameters Parameters Input DC range Output voltage operating Power rating Inductors Capacitor Switching frequency

Value 50–100 V 300 V 300 W 1 mH 110 µF 20 kHz

4 Conclusion The proposed switched-inductor boost converter is introduced with reduced voltage stress across the semiconductor devices. Compared to conventional switched-inductor converter, the proposed converter required less number of diodes and voltage stress across switches are low. Furthermore, the converter cost can be decreased due to the utilization of lower rating active switches. The detail continuous conduction mode operating principle and voltage gain is discussed. The waveform simulation results are presented which validated the theoretical analysis and functionality. Acknowledgement. This research was supported by Research Foundation funded by Thai Nguyen University of Technology.

References 1. Kjaer, S.B., Pedersen, J.K., Blaabjerg, F.: A review of single phase grid-connected inverters for photovoltaic modules. IEEE Trans. Ind. Appl. 41(5), 1292–1306 (2005) 2. Al-Greer, M., et al.: Advances on system identification techniques for DC–DC switch mode power converter applications. IEEE Trans. Power Electron. 34(7), 6973–6990 (2019)

A High Step-up DC-DC Converter with Semiconductor Voltage

19

3. Salvador, M.A., et al.: High step-up DC–DC converter with active switched-inductor and passive switched-capacitor networks. IEEE Trans. Ind. Electron. 65(7), 5644–5654 (2018) 4. Yang, L., et al.: Transformerless DC–DC converters with high step-up voltage gain. IEEE Trans. Ind. Electron. 56(8), 3144–3152 (2009) 5. Lakshmi, M., Hemamalini, S.: Nonisolated high gain DC–DC converter for DC microgrids. IEEE Trans. Ind. Electron. 65(2), 1205–1212 (2018) 6. Tang, Y., et al.: Multicell switched-inductor/switched-capacitor combined active-network converters. IEEE Trans. Power Electron. 30(4), 2063–2072 (2015) 7. Dwari, S., Parsa, L.: An efficient high-step-up interleaved DC–DC converter with a common active clamp. IEEE Trans. Power Electron. 26(1), 66–78 (2011) 8. Bhaskar, M.S., Meraj, M., Iqbal, A., Padmanaban, S., et al.: High gain transformer-less double-duty-triple-mode DC/DC converter for DC microgrid. IEEE Access 7, 36353–36370 (2019)

A Method to Partition Accuracy in Workspace for Robot Arms Huu-Thang Nguyen1,2(&), Long Pham Thanh2, and Jen-Tzong Jeng1 1

2

Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan [email protected] Faculty of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam

Abstract. This paper introduces a method to partition accuracy for robot arms. In this method, our technique divides workspace into small domains with different accuracy respectively and there are distinct boundaries between these domains. This will provide effective information for engineers to decide which tasks are able or unable to apply robot arms. The accuracy provided by manufactures is only the nominal value, but the real accuracy of robot arms fluctuated in the workspace and transformed according to times. Thus, the method in this paper is useful for determining the varied accuracy in the workspace and time of using industrial robot arms. Keywords: Accuracy Workspace


Robot arm
Interpolation
Shape functions


1 Introduction In machines, their catalogs usually provide information in terms of accuracy and repeated accuracy and they are only nominal values. These values are possible maximum distances between the tool center point (TCP) and the nominal position in all directions [1, 2]. For example, according to the catalogs of Robot ABB-IRB 1520 and Kuka KR 180 R2900-2, they have a repeated accuracy of ± 0.005 mm. However, this is not true because the accuracy is different in the workspace and is changed over time. This means that the accuracy is decreased according to using time and its value also fluctuates in the workspace [3–7]. For engineers, regarding determination of using machines, relying only on the nominal values of accuracy in catalogs is not enough. For each contour which TCP goes through in the workspace, the accuracy in each position is possibly different, but the required accuracy of this value in each task is constant. For effective determination of using machines, the partition of accuracy is necessary. In this paper, the authors introduce an interpolated method to determinate the region in the workspace of the robot arm that has only one certain accuracy. Interpolation is approximation that the accuracy is depended on some following requirements: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 20–28, 2021. https://doi.org/10.1007/978-3-030-64719-3_5

A Method to Partition Accuracy in Workspace for Robot Arms

21

• The shape function must be suitable • Number of data for key-point must be large enough • Type and number of boundary conditions for modeling must be suitable In the interpolation model, if the calculation error due to rounding is neglected, the most significant error is the error due to the incorrect assumption of shape function. Thus, some special techniques are needed to solve this problem. In research communities, some interpolated methods have been using, such as: Type-1 Fuzzy interpolation, trilinear and spline interpolation [8, 9], B-Spline interpolation, online fuzzy interpolation [10–12]. It clears that the accuracy of the interpolated method by using shape function is high, in this paper, the authors introduce a method to determinate the workspace of the robot arm with predefined accuracy.

2 Interpolated Model Using Shape Function The necessary condition for interpolation of one quality is its continuous property, the error of the end-effector is satisfied with this condition. To partition the accuracy of the robot arm, data of error in the workspace is first collected as shown in Fig. 1. As in Fig. 1, for determination of the position of the endeffector, there are two following independent monitors:

Fig. 1. A scheme for measuring error in the workspace of a robot arm by using cameras


 • Using encoders e1, e2,…,en to get the coordinate of p px ; py ; pz which is the desired position and this information is shown in the robot
interface.  • Using a camera to get the real coordinate of p0 px  dx ; py  dy ; pz  dz with the reference of O0 . The difference
between  the two coordinates is absolute error of the end-effector at point p and is dx ; dy ; dz . Assume that error at point pi has following 6 parts which are position and direction errors:

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  dpi ¼ dix ; diy ; diz ; dihx ; dihy ; dihz

ð1Þ

Let’s consider a sub-space of the triangular prism as shown in Fig. 3. In this space, the errors are measured at the tops of the prism and have 6 components as in Eq. (1) (Fig. 2).

E

F

D

NE ND

NF

Pi NC NB

NA

B C

A Fig. 2. Effect of sample points on the survey points by shape function

At each point pi in the triangular prism, we have some relationships as follows: 8 ðiÞ ðAÞ ðBÞ ðFÞ > < dx ¼ NA :dx þ NB :dx þ . . . þ NF :dx ... > : ðiÞ ðAÞ ðBÞ ðFÞ dhz ¼ NA :dhz þ NB :dhz þ . . . þ NF :dhz

ð2Þ

Where: ðiÞ ðiÞ ðiÞ ðiÞ ðiÞ dðiÞ x ; dy ; dz ; dhx ; dhy ; dhz are 6 errors at the point pi; NA ; NB ; NC ; ND ; NE ; NF are coefficient of stopping of shape functions which have effects on the error of point pi from 6 tops of the triangular prism. ðAÞ ðAÞ ðAÞ ðAÞ ðAÞ ðFÞ ðFÞ ðFÞ ðFÞ ðFÞ ðFÞ dðAÞ x ; dy ; dz ; dhx ; dhy ; dhz . . .dx ; dy ; dz ; dhx ; dhy ; dhz are real errors in terms of position and orientation of 6 tops of the triangular prism (A…F). From Eq. (2), we always have coefficients of stopping of shape function as follows: 2

3 2 ðAÞ NA dx 6 7 4...5 ¼ 4 ... ðAÞ dhz NF ðiÞ

... ... ...

3 dðiÞ x 6 7 ...7 . . . 5 :6 4 5 ðFÞ ðiÞ dhz d dðFÞ x

31

2

hz

ð3Þ

A Method to Partition Accuracy in Workspace for Robot Arms

23

h i ðiÞ T Where the matrix of dðiÞ is the error measured at the point pi in the x . . .dhz triangular prism and ½NA . . .NF TðiÞ are stopping values of 6 shape functions that have effects on the error of point pi. With data of n points, the results of n sets of stopping values of shape function are collected as follows: 2

NA

3

2

NA

3

2

NA

3

6 7 6 7 6 7 4 . . . 5 ; . . .; 4 . . . 5 ; . . .; 4 . . . 5 NF ð1Þ NF ðiÞ NF ðnÞ

ð4Þ

Thus, interpolation laws allow to determine shape function at the kth top as follows: ð1Þ

ðiÞ

ðnÞ

ðNk ; . . .; Nk ; . . .; Nk Þ ) f k ðx; y; zÞ ðk ¼ A; B; C; D; E; FÞ

ð5Þ

From Eq. (2), the error of arbitrary point (pi) inside the prism can be calculated. To have interpolated rectangular cuboid, we can assemble two triangular prisms and need to have two different models of Eq. (2) respectively.

3 Partition of Accuracy in Workspace for Robot Arms Using Shape Function Equation (2) allows us to determinate the error of arbitrary points in the workspace by using shape function. There are two problems based on this Eq. (2): Problem 1: Find out the error of position and orientation of point pi in the workspace when the coordinates of point pi (x,y,z) and shape function are known as follows: di ¼ f ðNA ; . . .; NF ; dA ; . . .; dF ; x; y; zÞ

ð6Þ

To find the error, we can use shape function in Eq. (5) and coordinates of survey points to find coefficients of shape function. Substitution these coefficients into Eq. (2), we can get the error of top pi. Problem 2: Partition of the accuracy of the robot arm in the workspace This is the opposite problem of Problem 1 with the shape functions in Eq. (5) are known, the coordinates that have the same errors di or are in the range of max di 2 ðdmin Þ, will form a domain inside the workspace and this domain is clearly i ; di distinguished with the rest. When this data is made for the entire workspace of robot arms, it can be the reference value for engineers. For example, these data help engineers decide whether or not a robot arm is suitable for a known task.

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Assume that the locus of points that have di ¼ a, from Eq. (6), we have: f ðNA ; . . .; NF ; dA ; . . .; dF ; x; y; zÞ ¼ a

ð7Þ

From [15], the previous problem can be transformed as follows: Min ½f ðNA ; . . .; NF ; dA ; . . .; dF ; x; y; zÞ  a xmin  x  xmax ymin  y  ymax zmin  z  zmax

ð8Þ

The solution of Eq. (8) is one or more sets of the same precisions which are needed to be found. It should be noted that the boundary of this space must contain the entire trajectory of the predefined precision a in Eq. (7), thus the robot will meet the required technical requirements, on the contrary, it needs other methods.

4 Examples The Denavit-Hartengerg (D-H) table of the robot arm is shown as in Fig. 3, data from cameras at survey points are provided in Table 1. The question is how to determine the error in the workspace of the robot arm? The dimension of arm robots are:

Fig. 3. The D-H table of robot arm

d1 = 335, a1 = 75, a2 = 270, a3 = 90, d4 = 295, d5 + d6 = 80 mm. Assume that the workspace of this robot arm is a quadrate as shown in Fig. 4. Pi is a point in the workspace of the robot arm. To evaluate the end effector error in this quadrate, the measurement and interpolation of shape function are performed for each triangular prism ABCDEF and AGCDHF (Table 2).

A Method to Partition Accuracy in Workspace for Robot Arms

25

Table 1. The accuracy of the robot arm by using Cognex 3D 5005 camera Point Desired position (mm) X Y Z A 300 275 200 B 300 175 200 C 200 275 200 G 200 175 200 D 300 275 300 E 300 175 300 F 200 275 300 H 200 175 300

Measured pose (mm) a12 −0.0996 −0.0996 −0.0996 −0.0996 −0.0996 −0.0996 −0.0996 −0.0996

a13 −0.2010 −0.2012 −0.2008 −0.2010 −0.2010 −0.2012 −0.2008 −0.2013

a23 −0.1000 −0.1000 −0.1000 −0.1000 −0.1000 −0.1000 −0.1000 −0.1002

a14 300.4288 300.4375 200.2540 200.2107 300.4797 300.5044 200.3056 200.1510

a24 275.5409 175.3727 275.5905 175.4230 275.5262 175.3645 275.5795 175.4189

a34 200.0781 200.0565 200.0506 199.9979 300.2378 300.2193 300.2131 300.0849

Fig. 4. The box for determination of error of the end-effector Table 2. The difference between the desired point and the measured point Point Desired position (mm) X Y Z A 300 275 200 B 300 175 200 C 200 275 200 G 200 175 200 D 300 275 300 E 300 175 300 F 200 275 300 H 200 175 300

Error measured pose (mm) Delta a12 0.0004 0.0004 0.0004 0.0004 0.0004 0.0004 0.0004 0.0004

Delta a13 −0.0010 −0.0012 −0.0008 −0.0010 −0.0010 −0.0012 −0.0008 −0.0010

Delta a23 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Delta a14 0.4288 0.4375 0.2540 0.2107 0.4797 0.5044 0.3056 0.4288

Delta a24 Delta a34 0.5409 0.0781 0.3727 0.0565 0.5905 0.0506 0.4230 −0.0021 0.5262 0.2378 0.3645 0.2193 0.5795 0.2131 0.5409 0.0781

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The camera resolution according to x, y and z directions are 44 µm, 44 µm and 8 µm and the repeated accuracy is 6 µm. In this experiment, the RSM is adjusted to 0.005 mm. Based on the results, the shape functions are given as follows: The shape functions in the 1st triangular prism are given as follows: NA = −2167.6 + 8.3793 X + 8.4567 Y − 0.04736 Z − 0.006500 X*X − 0.006258 Y*Y + 0.000110 Z*Z − 0.019734 X*Y + 0.000024 X*Z − 0.000082 Y*Z NB =2433.7 − 9.4041 X − 9.4933 Y + 0.04798 Z + 0.007305 X*X + 0.007021 Y*Y − 0.000128 Z*Z + 0.022173 X*Y − 0.000027 X*Z + 0.000079 Y*Z NC = −246.09 + 0.95888 X + 0.96963 Y − 0.008368 Z − 0.000756 X*X − 0.000714 Y*Y + 0.000013 Z*Z − 0.002282 X*Y + 0.000005 X*Z + 0.000002 Y*Z ND = 2583.4 − 10.0065 X − 10.0840 Y + 0.06412 Z + 0.007776 X*X + 0.007461 Y*Y − 0.000135 Z*Z + 0.023592 X*Y − 0.000034 X*Z + 0.000078 Y*Z NE = −2602.4 + 10.0723 X + 10.1508 Y − 0.05637 Z − 0.007825 X*X − 0.007510 Y*Y + 0.000140 Z*Z − 0.023748 X*Y + 0.000032 X*Z − 0.000077 Y*Z NF = 0. The shape functions in the 2nd triangular prism are given as follows: NB = − 450.21 + 3.8303 X − 0.0976 Y + 0.3499 Z − 0.007855 X*X − 0.000150 Z*Z - 0.000633 X*Z - 0.000584 Y*Z NC = 388.36 - 3.3109 X + 0.0959 Y - 0.3122 Z + 0.006804 X*X + 0.000129 Z*Z + 0.000554 X*Z + 0.000502 Y*Z NG = 48.534 − 0.39499 X + 0.00331 Y − 0.02586 Z + 0.000801 X*X + 0.000011 Z*Z + 0.000044 X*Z + 0.000043 Y*Z NE = 420.27 − 3.5691 X + 0.0872 Y − 0.3370 Z + 0.007329 X*X + 0.000144 Z*Z + 0.000612 X*Z + 0.000563 Y*Z NF = − 405.96 + 3.4447 X − 0.0888 Y + 0.3251 Z − 0.007080 X*X − 0.000135 Z*Z − 0.000576 X*Z − 0.000524 Y*Z NH = 0 From the previous shape function, the error in the sub-space will be determined. The calculation will be performed and the errors at 2604 key-points in the subworkspace are got. The errors of the end-effector in a range of 0.45-0.767 mm are shown as in Fig. 5. From the determination of error of each position in the sub-workspace, this work can provide data to make the error of the end-effector equal zeros or make error smaller than a defined error [13].

A Method to Partition Accuracy in Workspace for Robot Arms

27

Fig. 5. Error in the workspace of the robot arm

5 Conclusion The proposed accuracy partitioning method allows robot users to actively control the quality of equipment according to technical requirements. The accuracy itself as presented here cannot be represented by a nominal value, thus it is partitioned so that the description is most accurate, especially in tasks that require high precision. Over time, the precision changes and error models in terms of shape functions also need to be recalculated to match actual usage. The errors measured by the camera are also included the effects of robots and thermal deformation due to effects of the environment. Acknowledgments. This research was supported by the Thai Nguyen University of Technology (TNUT) of Vietnam.

References 1. Escande, C., Chettibi, T., Merzouki, R., Coelen, V., Pathak, P.M.: Kinematic calibration of a multisection bionic manipulator. IEEE/ASME Trans. Mechatron. 20, 663–674 (2015). https://doi.org/10.1109/TMECH.2014.2313741 2. Klimchik, A., Wu, Y., Abba, G., Garnier, S., Furet, B., Pashkevich, A.: Robust algorithm for calibration of robotic manipulator model. IFAC Proc. 46(9), 808–812 (2013). https://doi.org/ 10.3182/20130619-3-ru-3018.00449 3. Jing, W., Tao, P.Y., Yang, G., Shimada, K.: Calibration of industry robots with consideration of loading effects using Product-Of-Exponential (POE) and Gaussian Process (GP). In: IEEE International Conference on Robotics and Automation (ICRA), pp. 4380– 4385. IEEE (2016) 4. Klimchik, A., Caro, S., Pashkevich, A.: Optimal pose selection for calibration of planar anthropomorphic manipulators. Precis. Eng. 40, 214–229 (2015). https://doi.org/10.1016/j. precisioneng.2014.12.001

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5. Wu, Y., Klimchik, A., Caro, S., Furet, B., Pashkevich, A.: Geometric calibration of industrial robots using enhanced partial pose measurements and design of experiments. Robot. Comput. Integr. Manuf. 35, 151–168 (2015). https://doi.org/10.1016/j.rcim.2015.03. 007 6. Bai, Y., Wang, D.: On the comparison of an interval Type-2 Fuzzy interpolation system and other interpolation methods used in industrial modeless robotic calibrations. In: 2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp. 1–6. IEEE (2016) https://doi.org/ 10.1109/civemsa.2016.7524323 7. Borrmann, C., Wollnack, J.: Calibration of external linear robot axes using spline interpolation. In: Proceedings of 2014 International Conference on Modelling, Identification & Control, pp. 111–116. IEEE (2015) https://doi.org/10.1109/icmic.2014.7020737 8. Bai, Y., Smith, J.C., Zhuang, H., Wang, D.: Calibration of parallel machine tools using fuzzy interpolation method. In: IEEE International Conference on Technologies for Practical Robot Application, pp. 56–61. IEEE (2008) 9. Bai Y., Wang D.: Fuzzy logic for robots calibration — using fuzzy interpolation technique in modeless robot calibration. In: Bai Y., Zhuang H., Wang D. (eds.) Advanced Fuzzy Logic Technologies in Industrial Applications. Advances in Industrial Control. Springer, London (2006). https://doi.org/10.1007/978-1-84628-469-4_20 10. Bai, Y., Zhuang, H.: On the comparison of model-based and modeless robotic calibration based on the fuzzy interpolation technique. In: IEEE Conference on Robotics, Automation and Mechatronics, 2004, pp. 892–897. IEEE (2005) https://doi.org/10.1109/ramech.2004. 1438036 11. Bai, Y., Zhuang, H.: On the comparison of bilinear, cubic spline, and fuzzy interpolation techniques for robotic position measurements. IEEE Trans. Instrum. Meas. 54, 2281–2288 (2005). https://doi.org/10.1109/TIM.2005.858563 12. Ying Bai, Dali Wang, On the comparison of interpolation techniques for robotic position compensation. In: SMC’03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme-System Security and Assurance (Cat. No. 03CH37483) 4, pp. 3384–3389. IEEE (2004) https://doi.org/10.1109/icsmc.2003. 1244412 13. Huu, T.N., Quoc, K.D., Thu, T.L.T., Thanh, L.P.: A solution to adjust kinetic of industrial robots based on alternative trajectories. In: Sattler, K.-U., Nguyen, D.C., Vu, N.P., Tien Long, B., Puta, H. (eds.) ICERA 2019. LNNS, vol. 104, pp. 55–65. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37497-6_6

A Novel Method for Shielding Problems with Taking Robust Correction Procedure into Account Vuong Dang Quoc(&) and Dinh Bui Minh School of Electrical Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam [email protected]

Abstract. This paper presents a robust correction procedure based on the perturbation method to reduce errors around edges and corners related to thin structures via a sub-domain technique. The idea of the method is considered as several scenarios. A sub-domain involving with stranded or massive inductors alone is initially considered. A shielding approximation that neglects end and border effects is then added with an impedance-type condition across a surface. A volume correction is finally introduced to improve the inaccuracies from the shielding approximation. But, this volume correction usually faces with cancellation errors in the calculation of the local fields around corners and curvatures. Thus, in order to treat this inconvenience, a robust correction procedure is developed to take cancellation errors into account. Each sequence of the method is considered separately on its own mesh and domain without depending on other meshes and domains. Keywords: Magnetic field
Shielding approximation power loss
Sub-domain technique


Eddy current
Joule

1 Introduction Shielding structures developed by many authors [1, 2] are considered with a priori known 1-D analytical distributions to ignore meshing volume thin regions and are proposed by interface conditions (ICs). However, the ICs usually cancel edges and border effects, which lead to inaccuracies in the simulation of the fields in the vicinity of regional discontinuities near curvatures and edges, and it will be increased with higher thicknesses. To overcome this inconvenience, many authors have recently implemented a subproblem method for removing errors around edges and corners occurring from the shielding approximation [4, 5]. However, these implementations have neglected robust improvement procedures appearing in the volume corrections, this leads to errors in conducting regions. In this research, a robust improvement procedure is introduced in a novel method (i.e. sub-domain technique) to overcome the cancellation errors in magnetic materials that generally have not been treated in the volume correction [4, 5] yet, so far. The scenario of the method is based on the subproblem approach, allowing a full domain to divide into sub-meshes (SMs) with a sequence change of material properties [4, 5]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 29–37, 2021. https://doi.org/10.1007/978-3-030-64719-3_6

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V. D. Quoc and D. B. Minh

The method is solved with four steps: • In step 1: A sub-mesh (SM) or sub-domain (SD) involving with stranded inductors alone is first considered. • In step 2: A shielding approximation that neglects end and border effects is then inserted with an impedance-type condition across a surface without including any stranded inductors any more. • In step 3: A volume correction is finally given to overcome inaccuracies in step 2. Nevertheless, the volume correction is often sensitive to cancellation errors in conducting regions. • In step 4: A robust improvement procedure is presented to overcome cancellation errors in step 3. Each process of the method is solved with its own SM without depending on other SMs. The extended method is developed for the b-conformal formulation and is validated on the international TEAM work problem 28 [11].

2 Sub-domain Technique 2.1

Definition of Coupled Sub-domains

Each problem is solved on the separate SM, without solving again a new complete for each change. In this development, each sub-domain is constrained by sources (which can be either volume sources (VSs) or surface sources (SSs)), where VSs point out changes of material properties of volume thin regions and SSs present changes of ICs across the surfaces from previous SMs. 2.2

Magnetodynamic Problem

A canonical magnetodynamic problem i is performed in a domain Xi , with @Xi ¼ Ci ¼ Ch;i [ Ce;i . The studied domain is split into two parts, i.e. Xi ¼ Xc;i [ XCc;i , where stranded inductors belong to non-conducting region XCc;i . The eddy current is defined in conducting regions Xc;i : The set of Maxwell’s equations, associated relations and boundary conditions (BCs) of the SD i are [4–6] curl hi ¼ ji ;

ð1aÞ

div bi ¼ 0;

ð1bÞ

curl ei ¼ @t bi

ð1cÞ

hi ¼ l1 i bi þ hs;i ;

ð2aÞ

ji ¼ rp ei þ js;i ;

ð2bÞ

A Novel Method for Shielding Problems

31

½n  hi Ch;i ¼ jf ;i ;

ð3aÞ

½n
bi Cb;i ¼ bf ;i ;

ð3bÞ

n  hi jCh;i ¼ 0;

ð4aÞ

n
bi jCb;i ¼ 0;

ð4bÞ

where bi is the magnetic flux induction (T), hi is the magnetic field (A/m), ei is the electric field (V/m), js;i is the electric current density (A/mm2), ri is the electric conductivity, lq is the magnetic permeability and n is the unit normal exterior to Xi . The source fields (hs;i and js;i ) in (2a–2b) are VSs, where the field hs;i is expressed changes of the permeability, and the field js;q is presented changes of the conductivity. Hence, the changes from SD u (lu and ru ) to SD q (lq and rq ), the fields hs;q and js;q are defined [4–7] 1 hs;q ¼ ðl1 q  lu Þbu ;

ð5aÞ


 js;q ¼ rq  ru eu :

ð5bÞ

The updated fields of h and j are written as
 h ¼ hq þ hu ¼ l1 q bq þ bu ;

ð6Þ


 j ¼ jq þ ju ¼ rq eq þ eu :

ð7Þ

The surface fields jf ;q and kf ;q in (3a–3b) are SSs. These SSs can express as discontinuities across the negative and positive side of cq in Xq , with the notation ½
cq ¼ jcqþ  jcq [3–6]. 2.3

Shielding Structures

The constraints between SDs in the shielding model are considered as SSs. This has done for the magnetic vector potential formulation, i.e. [3]
 ½n  hq cq ¼ rb@t 2ac;q þ ad;q ; n  hq jcqþ


 1 ¼ rb@t 2ac;q þ ad;q þ 2 
 1 ¼ rb@t 2ac;q þ ad;q þ 2

 1 ad;q  n  hu jcqþ lb  1 ad;q ; jf ;q lb

ð8Þ

ð9Þ

where ac;q and ad;q are respectively the continuous and discontinuity components of aq . It should be note that is equal to zero on C ts;p of the shielding, that does not allow the

32

V. D. Quoc and D. B. Minh

magnetic flux to enter there. The discontinuity n  hu jcpþ in (9) is considered as a SS which will be appearing in the weak formulation.

3 Sequence of a Weak Formulations 3.1

Magnetic Vector Potential Formulations

Starting from the weak form of the Ampere’s law (1 a), the weak form of magnetic vector potential formulation for problem q (i  q) is written as i.e. [4–6] 0 0 0 0 ðl1 q curl aq ; curl a q ÞXq þ ðrq @t aq ; a q Þq þ ðrq grad mq ; a q ÞXc;q þ ðhs;q ; curl a q ÞXc;q 0 0 0 þ ðjs;q ; curl a q ÞXc;q þ n  hq ; a qCh;q cq þ n  hq ; a qCb;q
 þ  ½n  hq cq ; a0 qcq ¼ ðjs ; a0 q ÞXs;q ; 8a0 q 2 Fq1 Xq ;

ð10Þ
 where the function space Fq1 Xq defined in Xq is contained the basis functions for aq 0

and the test function aq as well; notation ð:; :ÞXi is a volume integral in Xq and h:; :iCq is a surface intergal on Cq of the product of their vector field arguments. The surface integral on Ch;q is given in (3a), usually zero. The shielding problem [3] indicated in the Eq. (12) is determined via term D

E 
  ½n  hcq ; a0 q ¼  rb@t 2ac;q þ ad;q ; a0 c;q c q cq Dh i E
 1 þ 12 rb@t 2ac;q þ ad;q þ lb ad;q ; a0 c;1 jf ;q

ð11Þ

c1

Robust Correction Procedure Replacing Shielding Representation The shielding solution obtained by combining Eqs. (9), (10) and (11), is now adjusted by the volume correction (e.g. problem k) via VSs ðjs;q ; curl a0 q ÞXc;q ðhs;q ; curl a0 q ÞXc;q are given by (6) and (7). These VSs have to be transferred from the mesh of the shielding problem q to the mesh of problem k by a projection method [9]. Therefore, the weak formulation for the problem k is 0



1 þ ð l1 k  lp



0 ðl1 þ ðrk @t ak ; ak ÞXc;k þ ðrk grad mk ; a0 k ÞXc;k k curl ak ; curl a k ÞX Dk E þ ðrk @t ap ; a0 k ÞXc;k ¼ 0; 8a0 k 2 Fk1 ðXk Þ: ðcurl ap ; curl a0 k ÞXk þ ½n  hk ct;k ; a0 k ct;k

ð12Þ At the discrete level, the source field ap solved in the mesh of shielding problem p is projected to the mesh of problem k in (12), where Xc;k is limited to the volume correction [9].

A Novel Method for Shielding Problems

33

As presented, the volume correction in (12) is often sensitive to cancellation errors in the computation of the field ap [5]. In order to avoid the cancellation error, it needs to be combined a problem k-n with 1 1 hkn ¼ l1 kn bkn þ ðlkn  lp Þðbq þ bu Þ;

ð13Þ

considering a perfect magnetic region in Xc;k (lkn ¼ 1; l1 kn ¼ 0Þ, and a problem k-m with
 1 1 hkm ¼ l1 km bkm þ ðlkm  lkn Þ bkn þ bq þ bu ;

ð14Þ

presenting a change to the actual permeability (lkm ¼ lvolume ¼ lk Þ. The problem k-n þ uses a SS only contributing on the positive side of Cc;k of Cc;k , that is ½n  hkn Cc;k ¼ n  hkn jC þ ¼ n  hq jC þ ¼ jf ;kn ; c;k

c;k

ð15Þ

with hs;kn = 0 and bkn 6¼ 0: The trace n  hq jC þ originally presents in (15) for the c;k

problem p limited to Cc;k ¼ Ct;q . It maybe also naturally presented via the volume in (15), that is D

½n  hkn Cc;k ; a0 k

E Cc;k

D E ¼  n  hq j C þ ; a 0 k

Cc;k

c;k

0 ¼ ðl1 p curl aq ; curl a k jCc;k ÞXTL ; ð16Þ

þ where XTL in (16) is only defined in one single layer of FEs touching Cc;k , because it 0

0

0

0

contributes only to the trace n  ak jC þ [5] (akn ¼ akm ¼ ak ). For the problem k-b, the c;k

VS has
 1 hs;km ¼ ðl1 km  lkn Þ bkn þ bq þ bu ;

ð17Þ

with lkm ¼ lvol ¼ lk and l1 kn = 0. Both the problem k-a and problem k-b gain at being solved at the same time, with the SS jf ;k ¼ jf ;kn and the resulting relation
 hk ¼ hkn þ hkm ¼ l1 bq þ l1 bkm þ l1 bkn þ bq þ bu p km km
 1 bkm þ bq þ ðl1 ¼ l1 k k  lq Þðbq þ bu Þ

ð18Þ

As the same way, in this procedure, a projection of the source field in the added magnetic region and in the layer of FEs surrounding this region need to be performed. 1 1 hkn ¼ l1 kn bkn þ ðlkn  lp Þðbq þ bu Þ;

ð19Þ

34

V. D. Quoc and D. B. Minh

considering a perfect magnetic region in Xc;k (lkn ¼ 1; l1 kn ¼ 0Þ, and a problem k-m with
 1 1 hkm ¼ l1 km bkm þ ðlkm  lkn Þ bkn þ bq þ bu ;

ð20Þ

presenting a change to the actual permeability (lkm ¼ lvol ¼ lk Þ. The problem k-n þ uses a SS only contributing on the positive side of Cc;k of Cc;k , that is ½n  hkn Cc;k ¼ n  hkn jC þ ¼ n  hq jC þ ¼ jf ;kn ; c;k

c;k

ð21Þ

with hs;kn = 0 and bkn 6¼ 0: The trace n  hq jC þ originally presents in (12) for the c;k

problem p limited to Cc;k ¼ Ct;q . It maybe also naturally presented via the volume in (15), that is D

½n  hkn Cc;k ; a0 k

E Cc;k

D E ¼  n  h q j C þ ; a0 k

Cc;k

c;k

0 ¼ ðl1 p curl aq ; curl a k jCc;k ÞXTL ð22Þ

þ where XTL in (22) is only defined in one single layer of FEs touching Cc;k , because it 0

0

0

0

constributes only to the trace n  ak jC þ [5] (akn ¼ akm ¼ ak ). For the problem k-b, c;k

the VS has
 1 hs;km ¼ ðl1 km  lkn Þ bkn þ bq þ bu ;

ð23Þ

with lkm ¼ lvolume ¼ lk and l1 kn = 0. Both the problem k-a and problem k_b gain at being solved at the same time, with the SS jf ;k ¼ n  hq jC þ ¼ jf ;kn and the resulting c;k

relation
 1 1 hk ¼ hkn þ hkm ¼ l1 p bq þ lkm bkm þ lkm bkn þ bq þ bu
 : 1 bkm þ bq þ ðl1 ¼ l1 k k  lq Þðbq þ bu Þ

ð24Þ

As the same way, in this procedure, a projection of the source field in the added magnetic region and in the layer of FEs surrounding this region need to be performed.

4 Application Test The application test is an international test problem (TEAM workshop problem 28) [10] (Fig. 1). It consists of two s inductors and an above shielding plate, with lr ¼ 200; rr ¼ 34 MS=m, f ¼ 50 Hz: The winding turns of the inner inductor is w1 = 960 turns, where the outer inductor is w2 = 576 turns. The excitation currents flowing in the inductors in opposite directions is a sinusoidal function, i.e. iðtÞ ¼ 20 sinð2pftÞð AÞ.

A Novel Method for Shielding Problems

35

Fig. 1. Geometry 2-D of TEAM problem 2 8 (dimensions in mm) [10].

The problem is performed with four scenarios presented Sect. 1. The magnetic vector potential on first problem is considered with stranded inductors alone shown in Fig. 2 (top left). The shielding solution that does not include inductors anymore is then added (Fig. 2, top right). A volume correction is introduced to replaces the shielding FEs with the actual volume covering the plates and their surrounding (Fig. 2, bottom left). The robust improvement procedure is finally presented to correct cancellation errors appearing from the volume correction (Fig. 2, bottom right). The relative errors

Fig. 2. Flux lines on the magnetic vector potential for the stranded inductor alones (top level), added a shielding model (second level), corrected volume (third level) and robust improvement procedures (bottom level) (d = 10 mm, f = 50 Hz, lr = 200 and r = 34 MS/m)

36

V. D. Quoc and D. B. Minh

16

Error of B on robust procedure (%)

Error of B on volume corretion (%)

on the volume correction and the robust improvement procedure of the magnetic induction along the shielding plate, with the different thicknesses, are pointed out in Fig. 3. The inaccuracy on the volume correction can reach 14.5% for the thickness d = 10 mm, or 7% for d = 5 mm, with lr = 200, r = 34 MS/m in both cases (Fig. 3, left). Significant errors on the robust improvement procedure are depicted in Fig. 3 (right). It is lower than 6% for d = 10 mm, or lower than 3% for d = 5 mm.

d = 3 mm d = 5 mm d = 10 mm

14 12 10 8 6 4 2 0

-0.06

-0.04

-0.02

0

0.02

0.04

Position along the shielding plate (m)

0.06

6

d = 3 mm d = 5 mm d = 10 mm

5 4 3 2 1 0

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

Position along the shielding plate (m)

Fig. 3. Relative inaccuracies on volume correction (top) and robust improvement procedure of the magnetic flux along the shielding plate, with the different thicknesses (f = 50 Hz, lr = 200 and r = 34 MS/m).

Joule power loss density 103(W/m)

The joule power loss density on the shielding solution, volume correction and robust improvement procedure along the shielding plate is presented in Fig. 4, for d = 3 mm, f = 50 Hz, lr = 200, r = 34 MS/m. The error between the shielding solution and the volume correction reaches 62% near edges and corners, and being lower than 20% between the volume correction and the robust improvement procedure as well. Finally, the robust improvement procedure is compare to be similar the reference solution in the computation of the traditional finite element method (FEM) [7, 8]. This is an agreement to demonstrate a very suitable validation of the robust improvement procedure presented in the SDM.

25

Reference Robust procedure Volume correction Shielding solution

20 15 10 5 0

-0.06

-0.04 -0.02 0 0.02 0.04 Position along the shielding plate (m)

0.06

Fig. 4. Joule power loss densities for the shielding solution, volume correction, robust procedure and full/complete solution along the shielding plate (d = 3 mm, f = 50 Hz, lr = 200 and r = 59 MS/m).

A Novel Method for Shielding Problems

37

5 Conclusions In this paper, the magnetic vector potential for the robust improvement procedure has been successfully proposed via a SDM in four scenarios. The development allows to correct inaccuracies around the shielding plate and cancellation errors in the magnetic region of the volume correction. In particular, the calculated result of the method is compared with the reference solution in the calculation of the FEM [7, 8]. This is a very good illustration between the studied method and the FEM. The development has been successfully solved with the linear problem in the frequency domain. The extension will be further performed in a two-way coupling [11]. The proposed method has been applied to the international TEAM workshop problem 28 [10].

References 1. Tsuboi, T., Asahara, F.K., Misaki, T.: Eddy current analysis on thin conducting plate by an integral equation method using edge elements. IEEE Trans. Magn. 33(2), 1346–1349 (1997) 2. Geuzaine, C., Dular, P., Legros, W.: Dual formulations for the modeling of thin electromagnetic shells using edge elements. IEEE Trans. Magn. 36(4), 799–802 (2000) 3. Dular, P., Vuong, Q.D., Sabariego, R.V., Krahenbuhl, L., Geuzaine, C.: Correction of thin shell finite element magnetic models via a subproblem method. IEEE Trans. Magn. 47(5), 1158–1161 (2011) 4. Vuong, Q.D., Dular, P., Sabariego, R.V., Krahenbuhl, L., Geuzaine, C.: Subproblem approach for “thin shell dual finite element formulations. IEEE Trans. Magn. 48(2), 407–410 (2012) 5. Dular, P., Sabariego, R.V.: A perturbation method for computing field distortions due to conductive regions with h-conform magnetodynamic finite element formulations. IEEE Trans. Magn. 43(4), 1293–1296 (2007) 6. Dular, P., Sabariego, R.V., Geuzaine, C., Ferreira da Luz, M.V., Kuo-Peng, P., Krahenbuhl, L.: Finite element magnetic models via a coupling of subproblem of lower dimensions. IEEE Trans. Magn. 46(8), 2827–2830 (2010) 7. Koruglu, S., Sergeant, P., Sabarieqo, R.V., Dang, V.Q., De Wulf, M.: Influence of contact resistance on shielding efficiency of shielding gutters for high-voltage cables. IET Electr. Pow. Appl. 5(9), 715–720 (2011) 8. Meunier, G.: The Finite Element Method for Electromagnetic Modeling. Wiley, Hoboken (2008) 9. Geuzaine, C., Meys, B., Hernotte, F., Dular, P., Legros, W.: A Galerkin projection method for mixed finite elements. IEEE Trans. Magn. 35(3), 1438–1441 (1999) 10. Karl, H., Fetzer, J., Kurz, S., Lehner, G., Rucker, W.M.: Description of TEAM Workshop Problem 28: An Electrodynamic Levitation Device 11. Quoc, V.D., Geuzaine, C.: Two-way coupling of thin shell finite element magnetic models via an iterative subproblem method. COMPEL Int. J. Comput. Math. Electr. Electron. Eng. ahead-of-print (2020). https://doi.org/10.1108/compel-01-2020-0035

A Review on Ultrasonic Stack Modelling Ngo Nhu Khoa1, Nguyen Thi Bich Ngoc1(&), and Tran Duc Tai2 1

2

Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] Thai Nguyen Vocational School, Vietnam General Confederation of Labor, Thai Nguyen, Vietnam

Abstract. In recent years, the main processing methods for difficult-to-machine materials have focused on the field of ultrasonic-assisted processing. Ultrasonic stack, the key part in ultrasonic equipment, is composed of transducer, booster, and horn. The present literature review aims to provide a broad overview of the recent achievement on the modal assembly of the ultrasonic vibration stack and guide the future development of ultrasonic vibration assisted technologies. With advancement of computer control in ultrasonic machining, this technology can be used for any material in future to achieve world class manufacturing. Keywords: Ultrasonic vibration
Ultrasonic horn/sonotrode
Ultrasonic transducer
Ultrasonic booster
Ultrasonic assisted machining

1 An Overview of Ultrasonic Stack and Applications The ultrasonic vibration energy used in machining can be divided into two different trends. Firstly, an ultrasonic machining (USM), is based on abrasive principle of material removal in which the tool is in the exact shape to be ground in workpiece and is attached to a horn. The second approach is based on the conventional machining technologies – ultrasonic assisted machining [1]. USM is a non-conventional mechanical machining process generally associated with low material removal rates (MRR), especially its applications are not limited by the electrical or chemical characteristics of the workpiece materials. It is used for machining both conductive and non-metallic materials; typically for those with low ductility [2–6] and hardness more than 40 HRC [7–11], e.g. inorganic glasses, silicon nitride, nickel/titanium alloys [12– 21]; machining holes with small diameter as 76 mm [22]; the depth to diameter ratio is about 3:1 [7–11]. Before investigating more details in ultrasonic stack modelling, let’s take a moment to overlook its components. Transducer, booster, horn and tool are sub-components of stack assembly. Transducers is basically to convert the electrical energy into ultrasonic vibration, with low amplitude and high frequency. The vibration amplitude is then increased or decreased by a booster. The horn is functioned as the amplifier of the ultrasonic vibration to the desired level. Figure 1 shows the schematic layout of the ultrasonic stack assembly. The present literature review aims to provide a broad overview of the recent achievement on the modal assembly of the ultrasonic vibration stack. This study starts © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 38–52, 2021. https://doi.org/10.1007/978-3-030-64719-3_7

A Review on Ultrasonic Stack Modelling

39

Fig. 1. Schematic layout of ultrasonic stack assembly [23, 24]

with basic configuration and design guidelines. The breadth of this report focuses on the background and practical modeling issues that affect simulation strategies.

2 Ultrasonic Horn The horn is variously referred to as an acoustic coupler, velocity/mechanical transformer, tool holder, concentrator, stub or sonotrode, the most critical and influential component in the ultrasonic stack that contacts the workpieces, is designed to provide the optimum contact area and amplitude of vibration to the workpieces [24]. In general, horns are located between ultrasonic transducers and workpieces. It works as a magnifier of the mechanical vibration produced by the ultrasonic transducer and transmits the vibration to the workpiece. This phenomenon is similar to the electric transformer in electric circuit. Therefore, apart from the role as mechanical displacement magnification, it also acts as mechanical impedance matching ultrasonic transducers and loads to achieve high energy transmission efficiency; and/or the isolator of the ultrasonic transducer from working environments of ultrasonic energy as high temperature, highly corrosive, and other extreme situations which are harmful to the ultrasonic transducers [25]. The two ends of the horn move in opposite directions to lengthen or shorten the horn at its resonant frequency. The critical point of horn design is that resonance frequency of horns must match the working frequency of transducers. Stress concentration is largest in the horn’s center or nodal area although no longitudinal motion occurs at this area. The horn design is based on the workpieces’ material, shape, size and profile. The amplitude of vibration in a horn depends on the horn material. Such materials which have good acoustic properties and resistance to fatigue cracking could have maximum amplitude. The most often used metals are monel, titanium, stainless steel, heat treated steel and aluminum [1]. Titanium has the best acoustical properties

40

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and high fatigue strength well-adapt for withstanding high cycle rates at high amplitudes [26]. On the other hand, the shape of a horn can be classified in two different groups: longitudinal and transverse cross section. Longitudinal cross section can be classified as stepped, exponential, conical, catenoidal shape [27–30], while the transverse cross section includes round and rectangular [31]. Normally, the area of cross sections at the larger end and the smaller end are in the ratio 2:1 ratio for steel and in the ratio 3:1 for titanium [32]. Among the commonly used horns, stepped horns have the largest displacement amplification [33]. Generally, the cross-section variation is given by different mathematical functions (exponential [34, 35], linear [36], catenoidal [36], tapered (or stepped) [37], Bezier [33], Gaussian [38], Fourier [39]). Compared with the commonly, horns with parametric curve profiles as Bezier, Gaussian, Fourier may offer higher displacement amplification. Many studies investigated in the wave propagation and horns design [40–43]. A problem that is associated with the horns design refers to the shape optimization. The optimization procedure is related to design parameters as working frequency [43], signal amplitude [44, 45], load variation in the manufacturing area [46], the objective function [47, 48], the impact of different design variables on the objective function [49], combined signals transmitted in horns [50], etc. Some authors [51, 52] have suggested that at constant amplitude, MRR is proportional to the second order of frequency, f, of 400 Hz. At higher frequencies, up to 5 kHz, this is a linear relationship instead. Above an upper threshold value, MRR decreases speedily and is proportional to square root of f [4, 52]. Researchers in [7, 20] showed that, in practice, when static load climbs up from zero value, with other parameter values remain constant, MRR and load are roughly linear. Above an optimum value, the fact that MRR decreases will lead to the abrasive grains size reduction and insufficient slurry circulation [7, 14, 51, 53–57]. It is indicated that when static load is smaller than optimal value, abrasive wear will be gradually decrease and tool life will increase. The performance of ultrasonic horn can be described by many parameters, which are resonance frequency, amplification factor, shape factor, input force impedance and bending stiffness. An important aspect of horn design is the calculation of the correct resonant length, in which resonant length should usually be in multiples of half the wavelength of the system [35]. The half wavelength is generally used for ultrasonic metal welding to for material saving and reducing manufacturing cost. The magnification factor is the ratio of the displacement or velocity at the output end and the input end; the shape factor is a critical value to measure the maximum vibration velocity that horns can achieve. The bending stiffness is an issue to present flexible ability. The longer the ultrasonic horn is, the greater the flexibility is. Bending stiffness is also related to the geometry of the horn [58]. There are studies on theoretical design of horn as presented in [6–8, 59, 60]. Traditional methods of acoustic horn design are based on a differential equation which considers the equilibrium of an infinitesimal element under the action of elastic and inertia forces, which is then integrated over the horn length to achieve resonance frequency [7, 8, 61]. Recently, numerical analysis based on FEM is more preferred for analysis of horn design than analytical and experimental methods because the solution of wave equation through analytical method is very difficult and experimental approach

A Review on Ultrasonic Stack Modelling

41

is not reproducible and time consuming. The modelling programs used are ANSYS [27, 29, 35, 58, 62–66], ABAQUS [32, 67], CAD [31, 68], SOLIDWORKS [69], Elmer FEM [48] … A systematic methodology as shown in Fig. 2 is carried out for designing a stepped horn using FEA using ABAQUS with the following objectives: 1) Extracting mode shapes and corresponding natural frequencies of the horn. CAD/CAE is applied to model the profile based on theory. 2) Determining the dynamic properties, design parameters as amplitude gain and von Mises stresses in the stepped horn. 3) The performance of horns by experiments and then compare them for optimum values. FEM has also been used to assess the working stresses to ensure safe stress limits [70]. The uniformity of the horn vibration limits the process to the cutting of small shapes typically less than 100 mm diameter. When the tool length is excessed to 2– 3 mm, the resonance normally reduces by approximately 0.5–1 kHz [71], but in case of drilling very deep holes, this loss can be solved by making the tool self-resonate [72]. A ratio of tool length to diameter less than 20:1 is practically preferred [26], but when the length is larger than 10 mm, the horn must be shortened by an amount equivalent to the weight of the tool [73]. Beside the fact that these researches were carried out theoretically, there are some assumptions applied to validate the modelling results as during the design, the width and thickness at the two ends of the horn are considered as a constant because of the dependence of the length horn and the amplification factor. because of the tool’s light weight, it is not considered during the design process, and so, it does not affect the condition of resonance. Furthermore, the holes for attaching the horn to the transducer are also neglected during designing of a horn. As for the measurement errors, the following factors should be considered. First, the real material parameters of the horn are not exactly as in theories. Second, in experiments, to measure horn parameters, the central pre-stressed metal bolt, which is not considered in textbooks, is used to clamp the metal cylinders and the piezoelectric material. Moreover, the experimental horns are not completely free from external forces, and this is not consistent with the assumption that the load mechanical impedance is zero and the boundary of the horns is free.

3 Ultrasonic Transducers Ultrasonic transducers/Converter/Probe are the most significant component of an ultrasonic system which mostly affect the precision and accuracy of the measurements. An ultrasonic transducer is to convert mechanical displacement to electrical voltage (direct piezoelectric effect) or vice versa (reverse piezoelectric effect). Therefore, it can be acted as either a transmitter or receiver for generating or detecting ultrasonic energy. Vibration transmit through a transducer in a longitudinal or compressive mode. A typical piezoceramic transducer is shown in Fig. 3 [74]. The piezoelectric material is placed between two electrodes and a backing material and functions to broaden the bandwidth and reduce ultrasound generation artifacts from the back side. The upper electrode is normally connected to ground to minimize noise from surroundings. An acoustic matching layer is located at the front of the transducer to compensate for the acoustic mismatch between the transducer and the medium.

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Selection of vibration frequency

Selection of type and materials

Calculation of wavelength

Calculation of theoretical dimensions

CAD Model of Sonotrodes Governing equation, boundary conditions, loads, material properties and element type FEM Model of sonotrodes

Modal and Harmonic analysis

Design requirements

Fabrication of sonotrodes

Experimental Trials

Conclusions

Fig. 2. Methodology for design of ultrasonic horn [32]

In mass production, a magnetostrictive [53, 65, 75] or a piezoelectric transducer is usually used. Magnetostrictive devices can be suitable for a wide frequency range (i.e. 17–23 kHz) and greater horn design flexibility, where the horn can be redesigned several times without critical amplitude errors [5, 76]. However, magnetostrictive transducers have high electrical losses and low energy efficiencies ( < Pi  Ui Uj Gij cosdij þ Bij sindij j2i   P ð5Þ Q  U Uj Gij sindij þ Bij cosdij > i i : j2i

Qi;min  Qi  Qi;max ; i 2 Reactive power supply set

ð6Þ

Vi;min  Vi  Vi;max ; i 2 1; 2; . . .; N

ð7Þ

Sl  Slmax ; i 2 1; 2; . . .; NL

ð8Þ

where, Qi;min , Qi;max are the upper and lower limits of reactive power output power; Vi;min , Vi;max are the upper and lower limits of voltage amplitude of load nodes; Sl is the power passing through branches; Gij and Bij are members of the matrix for nodal admissions; Slmax is the upper limit of line capacity; Vi is the voltage amplitude of the ith load node; N, NL are the number of system nodes and lines.

3 Enhancing PSO with Multi-objective Function 3.1

Basic Particle Swarm Optimization

There are N particles in the d-dimensional search space [12]. The position of the ith particle is expressed as Xi and the velocity is Vi . The best position it has experienced is Pid , and the best position the whole population has experienced is Pgd . Each particle updates its own velocity and position according to the following Eq. (14)     vtidþ 1 ¼ wvtid þ c1 randðÞ Ptid  xtid þ c2 randðÞ Ptgd  xtid tþ1 ¼ xtid þ vtid xid

ð9Þ ð10Þ

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where: t is the number of iterations; D is the d-dimensional variable in the ddimensional search space; c1 and c2 are the weight factors; rand () is the random number generated in the [0,1] interval; w is the inertia weight coefficient. The following formula is used for adaptive adjustment w ¼ wmax  iðwmax  wmin Þ=imax

ð11Þ

where imax is the maximum value of iteration, and i is the current number of iterations. PSO generally adopts real number coding [12], that its structure is relatively simple, and its running speed is very fast. However, it is easy to fall into local optimum, that is, premature convergence phenomenon. 3.2

Multi-objective Optimization

Due to the ease, randomness, and regularity of its variables, the premature phenomenon of the local optimal solution problem of chaos optimization algorithm may be dealt with by jumping out of the loop. Search space reduction process of optimization variables [15] is used to boost the performance of the search accuracy. The multiobjective optimization problem mathematical model is defined as follows. X ¼ ðx1 ; x2 ; . . .; xn Þ

ð12Þ

minF ð xÞ ¼ ff1 ð X Þ; f2 ð X Þ; . . .; fk ð X Þg

ð13Þ

s:t X 2 S ¼ fXjgi ðXÞ  0;

i ¼ 1; 2; . . .; mg

ð14Þ

where: X is the n-dimensional decision variable vector; F is the objective functions; gi ð X Þ is the ith constraint condition; S is the feasible region of the decision variable. Control variables u; v 2 S, if satisfied 8i 2 f1; 2; . . .; k g; fi ðuÞ  fi ðvÞ

ð15Þ

9i 2 f1; 2; . . .; k g; fi ðuÞ  fi ðvÞ

ð16Þ

Then u dominates v, u  v. Let X  2 S, if and only if there is no X in S so that X  x holds, then x is called the Pareto optimal solution of a multi-objective optimization problem [17]. All Pareto optimal solutions constitute the optimal solution set, which is also the global optimal solution set. 3.3

Enhancing PSO -Multi-objective

Selection of Optimal Solution of PSO: The main idea of multi-objective PSO based on Pareto optimal concept is to form a non-dominated set, and make the non-dominated solution set to approach the Pareto optimal solution set through iteration, and finally achieve the optimization purpose [17, 18]. When the optimal global solution is selected, one individual is randomly selected as the optimal global solution according

A Solution to Power Load Distribution

57

to the probability; otherwise, the minimum distance between each individual in the elite solution and other individuals is calculated, and the largest individual is selected as the optimal global solution. Chaos Optimization of the Optimal Solution: The chaotic search space is determined by the position of the optimal global solution of PSO. Through the inverse mapping back to the original solution space, the fitness value of each feasible solution experienced by the chaotic variable is calculated in the original solution space, and the best feasible solution is retained, and a particle in the current population is randomly replaced to improve the position of the particle so that the particle swarm can fly in a better direction and overcome premature convergence. Solving Non Dominated Sets: The fast non dominated sorting method is generally used to judge the dominating relationship of solutions [17]. However, when the proportion of non-dominated individuals in the population is high, a slow chain may appear in the late sorting stage. Experiments show that this method can effectively solve the slow chain problem [17, 19]. Using Elite Archiving Technology: The elitist set is used to save the non-dominated optimal solution found in the iteration, which is regarded as the optimal global candidate set of each particle in the particle swarm optimization. The final solution retained by the external elite set is the result of the algorithm. After each external iteration, only some particles that dominate the elite set in the non-dominated set or those that have no dominating relationship with all particles in the elite set are added to the elite set, and the dominated particles in the elite set are eliminated. Cluster Analysis is Used to Maintain the Capacity of Elite Set: The multi-objective optimization algorithm requires not only good convergence but also good distribution in the whole Pareto solution space. In this paper, the clustering algorithm is used to keep the distribution performance of the solution and ensure the capacity of the elite set. Since the clustering algorithm needs to calculate the distance between all particles, this paper adopts the hierarchical clustering algorithm based on the kernel like distance [17, 20]

4 A Solution to Optimal Load Distribution Treatment of Constraints: If the power flow calculated converges, the power balances condition Eq. (5) the equality constraint can be satisfied [3]; if the power flow does not converge, the fitness value of the individual to the objective function is designated as a larger value, which makes it unable to dominate in the optimization process. The control variables are limited to the allowable range during initialization and each update, so the inequality constraint (3) can also be satisfied [19]. The real constraint condition to be considered is Eqs. (6)–(8) that is transformed into a penalty function and added to the objective function as an objective minimization function.

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minf ¼ k1

   NG  ND  Nl  X X X Vi  Vilim Qi  Qilim Sl  Sllim þ k2 þ k3 ð17Þ Vimax  Vimin Qimax  Qimin SlmaX i¼1 i¼1 i¼1

where k1 , k2 and k3 are penalty factors. Vilim ; Qilim ; Sllim are defined as 8 < Vimax ; Vi [ Vimax Vilim ¼ Vimin ; Vi \Vimin : Vi ; others

Qilim

8 < Qimax , Qi [ Qimax ¼ Qimin , Qi \Qimin : Qi , others 

Sl ¼

Slmax ; Sl [ SlmaX Sl ; Sl \SlmaX

ð18Þ

ð19Þ

ð20Þ

Data Standardization: In the analysis of elitist disaggregation class, due to the different dimensions of coal consumption and network loss, using the original data directly may highlight the effect of those indexes with huge order of magnitude on clustering, which will completely change the clustering results once the indicators are changed. Therefore, it is necessary to standardize the original data to make each index value unified in common within the range of data characteristics. The original data are standardized twice to ensure the dispersion of the solution after clustering analysis. For the first time, the standard deviation is used to normalize the original data according to Eq. (21), and then the range standardization is used to normalize the results obtained for the first time according to Eq. (22). yij  yj rj

ð21Þ

xij  xjmin xjmax  xjmin

ð22Þ

xij ¼ 0

xij ¼

where yij is the data of the original matrix; yj and rj are the mean value and standard 0 deviation of each column of the matrix; xij and xij are the results after two standardization treatments. Determination of Optimal Compromise Solution: After getting Pareto solutions, a satisfactory solution is usually needed to choose as the final result, which is a decisionmaking mechanism. Moreover, the fuzzy membership function is used to represent the satisfaction of each objective function in each Pareto solution

A Solution to Power Load Distribution

ui ¼

8 < 1;

fi \fi;min fi;max fi fi;min \fi \fi;max ; : fi;max fi;min 0;

59

ð23Þ

fi;max \fi

where fi is the fitness value of the ith sub-objective, and the subscripts max and min are the upper and lower limits of the fitness function. ui ¼ 0 is completely dissatisfied with the value of an objective function; ui = 1 means that it is completely satisfied. For each solution in the Pareto solution set, the following Eq. is used to solve the standardized satisfaction value u ¼ k

Nobj X i¼1

uki =

Nobj M X X

! uki

ð24Þ

k¼1 i¼1

where: uk is the standardized satisfaction value of each solution; Nobj is the number of objective functions to be optimized; M is the number of elite solutions. The optimal compromise solution is the solution with the maximum standardized satisfaction value. Figure 1 shows the flowchart of the proposed EPSO for dispatch of the PLD problem.

Fig. 1. Flowchart of the proposed EPSO for dispatch the PLD problem

The specific calculation steps of applying the EPSO algorithm based on Pareto solution set to solve the multi-objective economic load distribution of PLD problem are as follows: Step 1 Initialization: set the data of the power system Step 2 The parameter, e.g., iterations, i ¼ 1, solutions n are randomly generated in the range of control variables, the individual, and global optimal of each particle is the initial positions, and the elite set is set to null. Step 3 According to the control variable value of the particle position, the power flow of the whole system is calculated, and the adaptive costs of the three

60

Step 4

Step 5 Step 6

Step 7

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objective functions, namely, the active power network loss, the coal consumption of the unit and the penalty term, under the optimal scheme represented by each particle, are calculated. According to the Pareto dominance, the advantages and disadvantages of particles are compared, and the non dominated solution set is constructed according to the improved fast non dominated sorting method, and it is added to the elite set to determine the individual optimal solution. The elite set is updated and maintained to determine the optimal global solution According to Eq. (9) (10), the velocity and position of particles are updated, and the current particle swarm optimization is carried out according to a certain probability. Judge whether the completion criteria are met, and the maximum-iteration or the objective function value corresponding to the optimal solution are reached. When the variable is less than the given value within the given iteration steps, the optimization is stopped and the result is output; otherwise, the iteration times i ¼ i þ 1, return to step 3

5 Experimental Results and Discussion The IEEE57 bus system is used to test the proposed scheme performance [21]. The detail of the system includes 57 nodes, 80 branches, 50 loads, seven generators, and the reference power is set to Pr (in which Pr =100, 150, 200 MVA). The obtained outcomes of the proposed scheme are compared with the other plans, e.g., Genetic algorithm (GA) [9] and Particle swarm optimization (PSO) [14] for the PLD problem. The algorithm parameters are set as follows. The population size is 50; the maximum iteration times is 2000; the elite set size is set to 100 for the algorithms. The constants c1 and c2 are set 1.45 for the PSO and EPSO respectively. The parameters of the crossover and mutation rate are set to 0.4 and 0.05, respectively, for the GA algorithm. In the system, unit 1 is the balance node, assuming that it is a hydropower unit, and the rest units are thermal power units. After energy-saving scheduling, it is determined that all units will be started and operated. Finally, a set of Pareto optimal solutions are obtained by repeating the experiment 50 times. Table 1 shows the results of the system in different optimization schemes, e.g. the proposed EPSO, PSO, and GA schemes for the PLD problems. It can be seen that the proposed EPSO provides the outstanding performance of the algorithms in comparison.

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Table 1. The optimal power obtained outcomes for systems of six generators

Figure 1 depicts the obtained outcomes of the proposed scheme compared with the other plans, e.g., Genetic algorithm (GA) and Particle swarm optimization (PSO) for the PLD problem.

3000 2000 1000 0

PSO (a) Comparison of the obtained result of the proposed EPSO scheme with the GA and PSO for the objecve funcon

EPSO

(b) Comparison of the power load distribuon of objecve values of two schemes of EPSO and PSO methods

Fig. 2. The obtained outcomes of the proposed EPSO scheme are compared with the GA and PSO for the PLD problem

Figure 2(a) displays the comparison of the obtained result of the proposed EPSO scheme with the GA and PSO for the objective function of the PLD problem in terms of the convergence speed. It is clearly seen that the proposed system offers faster than the GA and PSO algorithms. Figure 2(b) compares the outcomes of the power load distribution of objective values of two schemes of EPSO and PSO methods. It can be seen that the proposed system produces less loss of power load than the GA and PSO algorithms for the power load distribution.

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Table 2. The results comparing of different optimization methods for IEEE 57 bus system PSO-scheme MW MW MW Network loss/MW 18.4 34.0 26.4 Coal, fuel consumptions 2090 1503 1668

EPSO-scheme MW MW MW MW 18.8 33.2 26.4 23.5 2087 1506 1658 1774

Table 2 shows the results of the system in different optimization schemes, e.g., the proposed EPSO, PSO, and GA schemes for the PLD problem. The projects of the EPSO, GA, and PSO schemes adopt the objective network loss minimization and accurate fuel consumption minimization under considering constraint conditions of the upper and lower limits of reactive power outputs. It can be seen that the proposed EPSO provides the outstanding performance of the algorithms in comparison.

6 Conclusion In this paper, a solution to the multi-objective optimal load distribution problem was suggested based on hybridizing enhanced particle swarm optimization (EPSO) with Pareto. Multi-objective load dispatch model for minimal active network loss and minimum coal consumption of generating sets has been modeled based on the traditional load distribution dispatch and combining energy conservation dispatching goals. The built model of energy conservation systems has optimized based on the developing Pareto optimum-based multi-objective EPSO and extended optimum multi-objective load dispatch. In the simulation section, the benchmark of IEEE 57-bus was used to test the proposed scheme performance. The results compared with the other methods of the test power system show that the proposed method reduced the net loss of power system and coal consumption of generating sets and energy sources conserved while meeting the security constraints of the power system. The proposed method also made available to a community of Pareto optimal solution meaning more decision-makers efficient comparisons.

References 1. Tsai, C.F., Dao, T.K., Pan, T.S., Nguyen, T.T., Chang, J.F.: Parallel bat algorithm applied to the economic load dispatch problem. J. Internet Technol. 17, 761–769 (2016) 2. Chiang, C.L.: Improved genetic algorithm for power economic dispatch of units with valvepoint effects and multiple fuels. IEEE Trans. Power Syst. 20, 1690–1699 (2005) 3. Huneault, M., Galiana, F.D.: A survey of the optimal power flow literature. IEEE Trans. Power Syst. 6, 762–770 (1991) 4. Nguyen, T.-T., Wang, M.-J., Pan, J.-S., Dao, T., Ngo, T.-G.: A load economic dispatch based on ion motion optimization algorithm BT. In: Pan, J.-S., Li, J., Tsai, P.-W., Jain, L.C. (eds.) Advances in Intelligent Information Hiding and Multimedia Signal Processing, pp. 115–125. Springer, Singapore (2020)

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5. Nguyen, T.-T., Pan, J.-S., Dao, T.-K.: A novel improved bat algorithm based on hybrid parallel and compact for balancing an energy consumption problem. Information 10 (2019) 6. Dao, T.K., Pan, T.S., Nguyen, T.T., Chu, S.C.: Evolved bat algorithm for solving the economic load dispatch problem. In: Advances in Intelligent System Computing, pp. 109– 119 (2015) 7. Wang, C.-H., Nguyen, T.-T., Pan, J.-S., Dao, T.-K.: An Optimization Approach for Potential Power Generator Outputs Based on Parallelized Firefly Algorithm (2017) 8. Dao, T., Yu, J., Nguyen, T., Ngo, T.: A hybrid improved MVO and FNN for identifying collected data failure in cluster heads in WSN. IEEE Access 8, 124311–124322 (2020) 9. Devaraj, D., Roselyn, J.P.: Genetic algorithm based reactive power dispatch for voltage stability improvement. Int. J. Electr. Power Energy Syst. 32, 1151–1156 (2010) 10. Chu, S.C., Dao, T.K., Pan, J.S., Nguyen, T.T.: Identifying correctness data scheme for aggregating data in cluster heads of wireless sensor network based on naive Bayes classification. Eurasip. J. Wirel. Commun. Netw. 52(1–16) (2020) 11. Nguyen, T.-T., Dao, T.-K., Kao, H.-Y., Horng, M.-F., Shieh, C.-S.: Hybrid particle swarm optimization with artificial bee colony optimization for topology control scheme in wireless sensor networks. J. Internet Technol. 18, 743–752 (2017). https://doi.org/10.6138/jit.2017. 18.4.20150119 12. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: 1998 IEEE Int. Conf. Evol. Comput. Proceedings. IEEE World Congr. Comput. Intell. (Cat. No.98TH8360), pp. 69–73 (1998). https://doi.org/10.1109/icec.1998.699146 13. Zhang, Y., Wang, S., Ji, G.: A comprehensive survey on particle swarm optimization algorithm and its applications. Math. Probl. Eng. 2015 (2015) 14. Pandya, S., Roy, R.: Particle swarm optimization based optimal reactive power dispatch. In: 2015 IEEE International Conference on Electrical Computer Communication Technology, pp. 1–5. IEEE (2015) 15. Nguyen, T.T., Pan, J.S., Dao, T.K.: An improved flower pollination algorithm for optimizing layouts of nodes in wireless sensor network. IEEE Access 7, 75985–75998 (2019). https:// doi.org/10.1109/access.2019.2921721 16. Zakariazadeh, A., Jadid, S., Siano, P.: Economic-environmental energy and reserve scheduling of smart distribution systems: a multi-objective mathematical programming approach. Energy Convers. Manag. (2014). https://doi.org/10.1016/j.enconman.2013.10.051 17. Tripathi, P.K., Bandyopadhyay, S., Pal, S.K.: Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients. Inf. Sci. (Ny) 177, 5033–5049 (2007) 18. Dao, T.K., Pan, T.S., Nguyen, T.T., Chu, S.C.: A compact Articial bee colony optimization for topology control scheme in wireless sensor networks. J. Inf. Hiding Multimed. Signal Process. 6, 297–310 (2015) 19. Mostaghim, S., Teich, J.: Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). In: Proceedings of 2003 IEEE Swarm Intelligent Symposium SIS 2003 (Cat. No. 03EX706), pp. 26–33. IEEE (2003) 20. Nebro, A.J., Durillo, J.J., Garcia-Nieto, J., Coello, C.A.C., Luna, F., Alba, E.: SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: 2009 IEEE Symposium on Computing Intelligent Multi-Criteria Decision, pp. 66–73. IEEE (2009) 21. Sinha, A.K., Hazarika, D.: A comparative study of voltage stability indices in a power system. Int. J. Electr. Power Energy Syst. 22, 589–596 (2000)

A Study for Determination of the Pressure Ratio of the V12 Diesel Engine Based on the Heat Flow Density to Cooling Water Kien.Nguyen Trung1,2(&) 1

Faculty of Vehicle and Energy Engineering, PHENIKAA University, Hanoi 12116, Vietnam [email protected] 2 PHENIKAA Research and Technology Institute (PRATI), A&A Green Phoenix Group JSC, No.167 Hoang Ngan, Trung Hoa, Cau Giay Hanoi 11313, Vietnam

Abstract. Improving engine power density by using supercharging system with exhaust gas energy recovery (called an exhaust gas turbocharger) is a trend of improving non-turbocharged base engines in Vietnam. These engines are still capable of being used for a considerable time, but the power generating is not big enough to meet the requirements of improving vehicle maneuverability, especially special vehicles such us Russian or Vietnamese tanks. To determine the maximum level of pressure ratio of the engine according to the engine thermal load criteria, we can use direct and indirect indicators. The content of the paper is to determine the heat flow density for cooling water in the V12 engine according to the different pressure ratios. From that, the maximum level of pressure ratio for the engine is 2.1, but the safety limit allowing the heat to flow to the engine coolant to be guaranteed. Keywords: Special vehicles
V12 engine
Turbocharger
Heat flow density
Pressure ratio
Thermal load

1 Introduction The maximum power a given engine can deliver is limited by the amount of fuel that can be burned efficiently inside the engine cylinder. This is limited by the amount of air inducted into each cylinder each cycle. If this air is compressed to a higher density than ambient, prior to entry into the cylinder, the maximum power an engine of fixed dimensions can deliver will be increased. This is the primary purpose of supercharging. Today, one of the important development trends of internal combustion engines is the trend of “downsizing” (reducing combustion chamber capacity but still maintaining high power of the engine). In order to accomplish this, one of the technologies that the engine manufacturers in the world pay attention to and apply is the technology of supercharging, especially turbocharging method, where a turbocharger (a compressor and turbine on single shaft) is used to boost the inlet air density. Energy available in the engine’s exhaust stream is used to drive the turbocharger turbine which drives the turbocharger compressor and raises the inlet fluid density prior to entry to each engine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 64–74, 2021. https://doi.org/10.1007/978-3-030-64719-3_9

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cylinder [6, 7]. For old engines, increasing engine power by turbocharger is limited for two reasons: mechanical stress and thermal stress of the components surrounding the combustion chamber. Almost no work has mentioned the thermal load of the engine. In the theoretical documents there have been no issues of pressure ratio effects on the thermal load and quantification of the relationship between the pressure ratio and the engine’s thermal load. Therefore, to determine the turbocharging limit of the used engine, it is necessary to study the effect of the pressure ratio to the engine’s thermal load [1, 5]. Documents published just talk very little about this matter, and there have not been studies to determine the relationship between the pressure ratio and the engine’s the thermal load. Therefore, to determine the allowed pressure ratio of the used engine, it is necessary to study the thermal load. To assess the thermal load of the internal combustion engine, in theory, we often use direct and indirect indicators. The direct specifications are the thermal stress of the components and the maximum permissible temperature at the featured surfaces. Indirect specifications include: the heat flow density to cooling water, assumed heat stress parameters, exhaust gas temperature and the temperature of the piston-cylinder group where possibly measured [2]. From these thermal load parameters, the paper will focus on the impacts of the pressure ratio on the heat flow density to cooling water when the engine turbocharges so that the maximum pressure ratio could be defined. Besides, the oxygen necessary for the combustion is extracted from the air introduced into the working chamber. Therefore, with increasing fuel quantity added to the cylinder, naturally the amount of heat to be dissipated increases as well. The heat flows through the engine increase correspondingly. Additionally, at higher degrees of supercharging and without charge air cooling, the temperature of the charge air increases significantly, which results in further increased engine thermal loads. Therefore, to define the optimum pressure ratio of the old engine we have to research that effect on the heat flow density to cooling water. For engine manufacturers, when a new engine is designed and manufactured, the turbocharging issue has been calculated clearly by the manufacturer. The type of conversion from non-turbocharging engines to turbocharged engines is not the main research direction of developed countries; this problem is only suitable for developing countries like Vietnam [1, 5].

2 Numerical Study The heat flow density to cooling water is the ratio of the heat rejection rate to coolant in 1 s with the entire surface area inside the parts in contact with the in-cylinder gases [2], [kW/m2]. q¼

Qcool F:i

ð1Þ

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where: + F - area of the entire internal surface of the parts in contact with the in-cylinder gases; F ¼ 2:

p:D2 p:D2 e:p:D:S þ pD:ðS þ HC Þ ¼ þ e1 4 2

ð2Þ

+ i - the number of cylinders; + Qcool - heat rejection rate to coolant, [kJ/s] and determined from the heat balance equation. The object of study selected for simulation and experiment was a V12 engine, which is a high-speed diesel engine, a periodic cooling water. The engine consists of 12 cylinders, arranged in a V-shape, with a 60° angle between their axis and equipped on Russian and Vietnamese tanks. The main specifications of V-12 diesel engine are showed in Table 1.

Table 1. Specifications of V-12 diesel engine under study [8, 9] Parameters Number of cylinders Engine type

The working order of the cylinders Compression ratio Max. power Engine speed at the max. power Max. torque Engine speed at the max. torque Specific fuel consumption

Symbol i V-12

Value Unit 12 – Diesel, the vee (V) arrangement, the twelve cylinders are arranged in two banks of six, with a 60° angle between their axis. 1L-6R-5L-2R-3L-4R-6L-1R-2L-5R-4L-3R e 15±0.5 Ne.max 520 [HP] 387.4 [kW] nN 2000 [rev/min] Me.max 2256.3±10 [N.m] nM 1200 [rev/min] Ge.min 265±5 [g/kW.h]

Simulation of the V12 engine was made by GT-Power software as shown clearly in Ref [4], the heat flow density to the cooling water and the energy balance of the original V12 engine at speeds of 2000 [rev/min] and 1200 [rev/min] corresponding to with loads of 50%, 60%, 75% and 100% of full load are presented in Table 2 and Fig. 1.

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Table 2. Energy balance of the original V12 engine Load [%] n [rev/min] Energy water Qe [kW] 100 2000 388.00 1200 291.00 75 2000 290.00 1200 226.00 60 2000 238.00 1200 191.00 50 2000 191.00 1200 156.00

balance and the heat flow density to the cooling Qcool [kW] 244.74 158.54 190.66 115.42 152.95 87.45 142.56 68.99

Qeg [kW] 441.48 278.84 383.14 239.66 365.32 226.48 327.14 201.08

Qoil [kW] 82.72 56.03 66.38 44.82 58.22 38.83 50.82 33.13

Qmisc QT [kW] [kW] 23.61 1180.56 16.01 800.42 18.98 949.17 12.77 638.68 16.62 831.11 11.10 554.86 14.52 726.04 14.20 473.40

q [kW/m2] 161.66 104.72 125.94 76.24 101.03 57.76 94.17 45.57

Fig. 1. The heat flow density to cooling water of the original V12 engine at speeds of 2000 [rev/min] and 1200 [rev/min] corresponding to with engine load of 50%, 60%, 75% and 100%

According to [8, 9], the maximum allowed heat flow density to the cooling water is 211 [kW/m2]. With the engine energy balance shown in Table 2, the study of the ability to enhance the power density of the V12 engine can be completed by an exhaust gas turbocharger.

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3 Experimental Study 3.1

Flow Measurement of Cooling Water

To measure the circulating coolant flow in the engine, the water temperature range of 70  90 [°C] and 105 [°C] allows it to be maintained in a short time and requires no cut off or partially destroys the pipe. The author chooses the DYNAMETERS DMTFH ultrasonic flow sensor to do this experimental content. The installation diagram on the engine test platform is shown in Fig. 2.

Fig. 2. Flow measurement of cooling water

3.2

Test Facilities

The experiments were carried out in one of the engine test cells of the Factory Z153 the General Department of Techniques, Vietnam Ministry of National Defense. The computer systems were located in the control room of the test cells. When a stable working mode of the engine was established, measurement data on the display devices were collected.

4 Results and Discussion Results of the cooling water flow rate in the cooling system, inlet water temperature, outlet water temperature of the original V12 engine and the heat flow density (q) by experiment are shown in Table 3.

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Table 3. Results of the cooling water flow rate in the cooling system, inlet water temperature, outlet water temperature of the original V12 engine and the heat flow density according to the different loads and speeds by experiment Modes

100% 2000 1200 75% 2000 1200 60% 2000 1200 50% 2000 1200

[rev/min] [rev/min] [rev/min] [rev/min] [rev/min] [rev/min] [rev/min] [rev/min]

The cooling water flow rate Gn [kg/s] Gn [m3/h] 8.5 30.6 4.9 17.64 8.5 30.6 4.9 17.64 8.5 30.6 4.9 17.64 8.5 30.6 4.9 17.64

Cooling water temperature Tout [°C] Tin [°C] 95 88 92 85 85 80 83 78 78 74 75 71 73 69 70 67

q [kW/m2]

160.22 92.36 114.45 65.97 91.56 52.78 91.56 39.58

The objective of supercharging is to increase the charge air density of the working medium before it enters the work cylinder to precompress the charge. The density increase in the working medium leads to the increases in the power density. It can also be used to improve the combustion process with the aim to achieve lower exhaust gas and/or noise emissions. To describe the pressure ratio p2/p1, the ratio between start and end pressure of the compression, the symbol pk is frequently used: pk = p2/p1. To ensure the desired level of the pressure ratio, there are various measures used to adjust the end pressure of the compression, such as: removing air after compressor back to the suction line of the compressor, discharging exhaust gas before entering the turbine, etc. In this article, the author uses the method of changing the amount of fuel injected per cycle to obtain the desired level of the pressure ratio [4, 7]. The pressure ratio: " #
k1 T3 p4 k ðpk Þ ¼ 1 þ
:gTC : : 1  T1 p1 mC k1 k

:




mT

ð3Þ

:

with: mT - the mass flow through the turbine; mC - the mass air flow through the compressor; T3 - the temperature of exhaust gas into the turbine; T1 - the temperature of the air into the compressor; ηTC - the charger total efficiencies; p4/p3 - turbine pressure ratio. First of all, a turbocharged engine model was built with GT-Power software and verified the simulation model with experiment. Then, with this model, the author set up the optimum run {Run ! Optimizer (Direct)} in GT-Power software to determine the amount of fuel injected per cycle (Dgct) with a level of desired pressure ratio (pk) in each survey mode [4, 10–12]. The setup runs optimally as follows: – Input parameter: the desired pressure ratio value – Optimal parameter: excess air coefficient or the relative air/fuel ratio k – Parameter changed to obtain the optimal parameter: the amount of fuel injected per cycle (Dgct). – Specific results are presented in Fig. 3.

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Fig. 3. The amount of fuel injected per cycle according to the pressure ratios at the different engine loads and speeds

Results of the cooling water flow rate, the inlet water temperature, the outlet water temperature and the heat flow density to the cooling water of the turbocharged engine by experimental study are presented in Table 4. Table 4. The experimental results of the cooling water flow rate, the inlet water temperature, the outlet water temperature of the turbocharged V12 engine according to the pressure ratio pk = 1.8 and pk = 2.0 at the different loads and speeds Modes

pk = 1.8 60% 2000 1200 pk = 2.0 60% 2000 1200 pk = 1.8 50% 2000 1200 pk = 2.0 50% 2000 1200

[rev/min] [rev/min] [rev/min] [rev/min] [rev/min] [rev/min] [rev/min] [rev/min]

The cooling water flow rate Gn [kg/s] Gn [m3/h] 8.5 30.6 4.9 17.64 8.5 30.6 4.9 17.64 8.5 30.6 4.9 17.64 8.5 30.6 4.9 17.64

Cooling water temperature Tin [°C] Tout [°C] 92 86 90 84 96 90 93 86 90 85 87 82 94 89 91 85

q [kW/m2] 137.33 79.17 137.33 92.36 114.45 65.97 114.45 79.17

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For the original V12 engine, when comparing the heat flow density to cooling water between theoretical calculations and experimental results, we find that the largest relative error is 13.47% at 75% load and engine speed 1200 [rev/min], the smallest relative error is 0.89% at 100% load and engine speed 2000 [rev/min] as shown in Fig. 4.

Fig. 4. The heat flow density to the cooling water of the original V12 engine at the different loads and speeds according to simulation and experiment

For the turbocharging engine, when comparing the heat flow density to cooling water between theoretical calculations and experimental results, we find that the largest relative error is 9.70% at 50% load, engine speed 1200 [rev/min] and the pressure ratio pk = 2.0. The smallest relative error is 0.65% at 50% load, engine speed 1200 [rev/min] and the pressure ratio pk = 1.8 as shown in Fig. 5.

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Fig. 5. The heat flow density to the cooling water of the turbocharged V12 engine according to the pressure ratio pk = 1.8 and pk = 2.0 at the different loads and speeds

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The heat flow density to the cooling water, brake power and brake specific fuel consumption of the turbocharged V12 engine according to the different pressure ratio (pk) at different loads and speeds are shown in Fig. 6 and Fig. 7.

Fig. 6. The heat flow density to the cooling water of the turbocharged V12 engine according to the different pressure ratio (pk) at different loads and speeds

Fig. 7. The brake power, brake specific fuel consumption of the turbocharged V12 engine according to the different pressure ratio (pk) at different loads and speeds

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The above calculated results are completely consistent with the theoretical analysis presented in the literature on turbocharging engines and internal combustion engine. According to [8, 9], the maximum allowed heat flow density to the cooling water is 211 [kW/m2]. Therefore, in order to ensure that the V12 engine is stable according to the heat flow density to cooling water, the allowed maximum pressure ratio of the engine is 2.1.

5 Conclusion The following conclusions are drawn based on the simulation and experimental studies as given below: + It is observed from the simulated results that when the engine load increases and the pressure ratio is at the maximum brake power mode, the change of the heat flow density has a greater value than the others. + In order to ensure that the V12 engine is stable according to the heat flow density, the allowed maximum pressure ratio of the engine is 2.1. + With the maximum pressure ratio of 2.1, the maximum brake power increases nearly by 56.44% and 57,04%, while the specific fuel consumption decreases approximately by 6.6% and 4.3% at 2000 [rev/min] and 1200 [rev/min], respectively.

References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Hà Quang, M., Lê Đình, V.: Tăng áp cho động cơ. Học viện Kỹ thuật Quân sự (2014) Lê Viết, L.: Lý thuyết động cơ Diezel. NXB Giáo dục (2000) Pham Minh, T.: Lý thuyết động cơ đốt trong. NXB Khoa học và Kỹ thuật, Hà Nội (2012) Nguyễn Trung, K.: Nghiên cứu ảnh hưởng của mức độ tăng áp đến phụ tải nhiệt của động cơ diesel. Luận án tiến sĩ kỹ thuật, Học viện Kỹ thuật Quân sự, Việt Nam (2016) Lê Đình, V.: Nghiên cứu cường hóa động cơ B2 trên xe tăng họ T54/T55 bằng tăng áp tuabin khí thải. Đề tài Khoa học Công nghệ Cấp Nhà nước (2015) Heywood, J.B.: Internal Combustion Engine Fundamentals, 2nd edn. McGraw-Hill International Editions, London (2018) Hiereth, H., Prenninger, P.: Charging the Internal Combustion Engine, Powertrain Edited by Helmut List, SpringerWien NewYork (2003) Двигaтeли B-2 и B-6. Texничecкoe oпиcaниe. M.: Boeннoe издaтeльcтвo (1975) Baншeйдт B.A. Cyдoвыe двигaтeли внyтpeннeгo cгopaния: Cyдocтpoeниe (1977) Gamma technologies, GT-Power Tutorial, Version 7.3 (2012) Gamma technologies, Engine Performance, Version 7.3 (2012) Gamma technologies, Flow Theory Manual, Version 7.3 (2012)

A Study of Scissor Lifts Using Parameter Design Anh-Tuan Dang1, Dinh-Ngoc Nguyen2(&), and Dang-Hao Nguyen1 1

Faculty of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam 2 Faculty of International Training, Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected]

Abstract. Scissor lifts are applied for transporting or lifting various objects. Hydraulic cylinders are used to raise or lower platforms which have many ways to arrange. This study aims to determine appropriate dimensions in design 1X hydraulic scissor lifts. Using symbolic variables to control the dimensions, positions of the cylinder are calculated to ensure the effectiveness of working space and forces in the cylinders. Results obtained from the calculations indicate the practice of numerical methods and can be used to determine optimal dimensions for design 1X scissor lifts. Keywords: Cylinder, scissor lift


Kinetic analysis, parameter design

1 Introduction Scissor lifts are lifting devices, which employ scissors mechanism to raise or lower goods or people through relatively far distances. A choice of lift devices is made based on three main criteria: lifting height, lifting weight, and lifting equipment according to the drive devices. There have been many layouts of scissor lifts concerned with the arrangement of the cylinder. These dimensions involve the working space of lifts and the operation of cylinders; they play an important role in the platform movement. There have been studies dealing with studying scissor lifts, some of which focus on simulation software to evaluate the operation of the system or calculated strength of component lift to determine suitable design dimensions [1–8]. However, there have been few studies dealing with the arrangement of cylinders or construction of the specific equation to calculate thrust force for cylinders. This study analyzes a model of scissor lifts in the given figure (Fig. 1), from which the appropriate dimensions for the system can be calculated. Results from the study are shown in graphic charts covering the relationship between cylinder features and platform operations. Through the calculation, decisions can be made to select the appropriate dimension for design and manufacture similar devices.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 75–85, 2021. https://doi.org/10.1007/978-3-030-64719-3_10

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A

B

Q

2

4 C P

M 1

3 D

Fig. 1. Sketch of the mechanism: 1. Ground frame, 2. Inner frame, 3. Outer frame, 4. Cylinder, 5. Platform (hoisting terrace).

2 Kinematic Analysis Studying the movement of scissor lifts with different dimensions, it can be concluded that the designate dimensions in Fig. 2 are the elements with the most influence on the lift structure (in both movement and strength calculation). When the cylinder changes its length from Lmin to Lmax, the platform raises from the lowest position (Hmin) to the highest position (Hmax). However, this variable (H) also depends on the length of the frame (AD, BC) and the angle c between frames. Therefore, it is difficult for designers to choose the exact dimensions from the layout to simulate the system movement.

Fig. 2. Scissor lift with symbolic parameter

Using symbolic parameter, a = a*A, b = b*a, and lc = k*a (see Fig. 2) with 0.5 > a>0; 1  b > 0 and k > 0), the system containing the basic parameters is the length of frame A, and other parameters are related to the layout of the cylinder in the system (a, b and k). The kinetic analysis process will evaluate the position, velocity of joints, and links in the mechanism. During operation, the cylinder changes its length from kmin to kmax to raise the height of the platform from Hmin to Hmax, respectively. This height can be established by expressions:

A Study of Scissor Lifts Using Parameter Design

H ¼ A sin

c 2

77

ð1Þ

The angle between frames c is determined from the relation equation of triangular MPQ cos c ¼

a2 þ b2  l2xl a2 þ ðb:aÞ2 ðk:aÞ2 1 þ b2  k2 ¼ ¼ 2ab 2b:a2 2b

ð2Þ

Substituting to Eq. (1): sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi c 1  cos c k2  ð1  bÞ2 H ¼ A sin ¼ A ¼A 2 2 4b

ð3Þ

To maintain the balance of the structure, the maximum angle between frames of the scissor mechanism is chosen to range from cmin to cmax, i.e. cos cmin [ cos c ¼

1 þ b2  k 2 [ cos cmax 2b

ð4Þ

Substitute to Eq. (2): qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 þ b2  2b: cos cmax  k  1 þ b2  2b: cos cmin

ð5Þ

The raising ratio when lifting the object from the lowest to the highest position is determined by the following equation: Hmax Hmin kH ¼  ¼ A A

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi k2max  ð1  bÞ2 k2min  ð1  bÞ2  4b 4b

ð6Þ

Based on Eq. (6), it can be constructed in Fig. 3 describing the working area for the position of cylinder depending on the coefficients b and k: From Fig. 3, it is observed that the appropriate factor for the design system can be chosen. For instance, with the given factor b = 0.6, and the designate range of scissor angle c varies from 15° to 120°, the corresponding parameter for cylinder k ranges from 0.45 to 1.55. It is noticed that the stretch ratio of the cylinder can be found as follows. kc ¼ kmax =kmin ¼ 1:55=0:45 ¼ 3:44

ð7Þ

Nevertheless, it is impossible to choose a cylinder appropriate with this ratio since most cylinders have a stretch ratio smaller than 1.8.

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Fig. 3. Working range of the angle frame c with variables of b and k

With b = 0.15, and the operation angle ranges from 15° to 135°, the new parameter for the cylinder is kmin = 0.85 and kmax = 1.11, and the stretch ratio now is kc = 1.11/0.85 = 1.31. This ratio makes it easier to select the available cylinders. It can be seen that the ratio H/A and the angle c have the same curve, which depends on b and k (c.f. Figure 1), so that we can combine the ratio into Fig. 1 and calculate the raising coefficient for the system by using Eq. (6): kH ¼ 0:924  0:131 ¼ 0:793

ð8Þ

The meaning of this result is that if the lifting system uses scissor frame with the length of the frame A = 100 cm, the raising height of the system when it raises from the lowest position (c = 15°) to the highest position (c = 165°) is 0.793*100 = 7 9.3 cm. With the given structure of the system, if the value of b larger than 0.4, the initial value for c must be above 30° to maintain the value of kmin large enough and affect the dimension of the system. Some of the dimensions are selected to check the efficient operation of the system, shown in Table 1: Table 1. Raising efficiency and operation angle of the system with different values of b and k Number 1 2 3 4 5

b 0.2 0.3 0.3 0.35 0.35

kmin 0.82 0.85 0.75 0.85 0.85

kmax = kmin*1.35 1.11 1.15 1.01 1.15 1.15

Operation angle u Raising efficient kH 22°–119° 0.86−0.19 = 0.67 52°–113° 0.83−0.44 = 0.39 29°–83° 0.66−0.25 = 0.41 55°–104° 0.79−0.46 = 0.33 55°–107° 0.80−0.46 = 0.34 (continued)

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Table 1. (continued) Number 6 7 8 9 10

b 0.4 0.4 0.5 0.6 0.6

kmin 0.62 0.80 0.85 0.9 0.8

kmax = kmin*1.35 0.84 1.08 1.15 1.21 1.08

Operation angle u Raising efficient kH 15°–55° 0.46−0.13 = 0.33 49°–91° 0.71−0.41 = 0.30 58°–94° 0.73−0.48 = 0.25 63°–95° 0.74−0.52 = 0.22 53°–81° 0.65−0.45 = 0.20

For example, with b = 0.4, if kmin = 0.62 (corresponding to c = 15°), value of kmax should not larger than 0.621.35 = 0.837 (corresponding to c = 55°) and the raising efficiency kH = 0.46-0.13 = 0.33, too small for the idea design. Even when we are raising the initial angle (c = 49°) to get kmin = 0.80 and kmax = 0.801.35 = 1.08 (c = 91°), the raising efficiency still equals to kH = 0.71-0.41 = 0.30. ! ! ! ! RC ¼ l2 þ l31 þ l32

ð9Þ

! ! ! ! RE ¼ l2 þ l51 þ l52

ð10Þ

and

3 Kinetic Analysis Forces acting on the mechanism at joints with magnitude and direction change during the operation of the system. For the system of which the loading PG is located at point G is shown in Fig. 4. At the lowest position of the platform, G is placed between hinges A and B, but when the platform raises, point B moves near A while the distance between A and G is unchanged. This will impact the structure balancing and the force magnitude acting on the frames. However, most of the recent research of the scissor systems only bases on creating the concept model to run the simulation but did not propose appropriate methods to analyze the stability and optimization of the frame dimensions. lG

A

B

PG

G Hmax

PG G

B

Hmin

A

Fig. 4. Position of load PG during the operation of the system.

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Based on the analysis of the kinetic model, the authors continue to use the model in Fig. 4 to calculate the reaction forces on joints and frames. Because the system is in a 3D model, but the calculation is based on a 2D model, the results after the calculation will be divided in correspondence with the structure on the 3rd dimensions (the number of cylinders, the number of scissor frames). Assuming the movement of the platform is slow enough to neglect the effect of inertia, the weight of the frames in the system, which is much smaller than the weight of the object, can be ignored. This study mainly investigates the effect of the weight of loading PG on the frames and pin joints.

Fig. 5. The free-body diagram of the ground frame (CD) and platform (AB).

Release reaction forces at the platform and ground (c.f Fig. 5) and calculate the magnitude of forces in pins A, B, E, F. FB ¼ FD ¼

PG :lG L

FA ¼ FC ¼ PG  PB ¼ PG 

ð11Þ PG :lG L

Release the scissor joint between frame AD and BC (c.f Fig. 6)

ð12Þ

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Fig. 6. The free-body diagram of frame AD and BC.

Note: Fcyl is the reaction of cylinder acted along the axis of the cylinder, using equilibrium of moment for AD at center M:
FA þ M
FD þ M
Fcyl ¼ 0 MM ¼ M

ð13Þ

  M
Fcyl  ¼ jM
FA j
FD þ M

ð14Þ

A c A c A c A c Fcyl :b: sin / ¼ FA : cos þ FD : cos ¼ ðFA þ FD Þ cos ¼ PG cos 2 2 2 2 2 2 2 2 Fcyl ¼

PG A2 cos 2c b: sin /

ð15Þ ð16Þ

The value of sinu can be obtained by the relation equation between angles c and u from triangular MPQ: a lxl a:k ¼ ¼ sin / sin c sin c sin / ¼

ð17Þ

sin c k

ð18Þ

Substitute into Eq. (16): Fcyl ¼

PG A2 cos 2c b:a:A: sink c

¼

PG k ¼ 4:a:b: sin 2c

4ab:

PG :k qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ 2 2 k ð1bÞ 4b

PG :k rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi h i 2a: b k2  ð1  bÞ2

ð19Þ

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Use the vector addition method with forces on frame AD: ! ! ! ! ! FA þ FD þ Fcyl þ FMx þ FMy ¼ 0

ð20Þ

Project these forces on x and y directions:   c PG k c : cos / þ ¼ FMx ¼ Fcyl : cos / þ 2 4:a:b: sin 2c 2

ð21Þ

  c PG k c PG :lG ð22Þ FMy ¼ Fcyl : sin / þ  FA þ FD ¼  PG þ 2: c : sin / þ 2 4:a:b: sin 2 2 L Substitute Eq. (2) to Eq. (22), the angle using parameters can be rewritten as:    c c c c 2 c b1 c cos / þ cos /  sin2 : cos ¼ cos /: cos  sin /: sin ¼ cos ¼ 2 2 2 2 k 2 k 2 ð23Þ    c c c c 2 c bþ1 c cos / þ cos2 : sin sin / þ ¼ cos /: sin þ sin /: cos ¼ sin ¼ 2 2 2 2 k 2 k 2 ð24Þ Replace to the equation of FMx and FMy: FMx

P G ð b  1Þ c PG ðb  1Þ : cot ¼ : ¼ 4:a:b: 2 4:a:b: 2

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðb þ 1Þ2 k2 k 2  ð 1  bÞ 2

ð25Þ

3

4lG 6ðb þ 1Þ 7 þ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi FMy ¼ PG 4  15 4:a:b ðb þ 1Þ2 k2 A b

ð26Þ

Total reactions on joint M can be obtained by using the equation: FM ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 þ F2 FMx My

ð27Þ

To evaluate the accuracy of the proposed method, the Working Model to simulate the mechanism and measure the reactions at joints is utilized (see Fig. 7). Results from this process are compared with those obtained by the Eq. (15), (23), and summarized in Table 2. Results in Table 2 prove the accuracy of the numerical methods in determining force magnitudes in the system. The maximum difference takes up only 0.2% because the weight of frames caused a small change in the simulation model.

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Fig. 7. Results of the system constructed in Working Model. Table 2. Results of force between the two methods a = 0.3; b = 0.3; lG = 0.4 FA (N) Fcyl(N) FM (N) 3101.2 k = 0.8 F1 286.2 3142 F2 286.2 3143 3101.7 D% 0 0 0 k = 1.0 F1 236.3 2131 2153.8 F2 236.2 2131 2153.8 D% 0 0 0 k = 1.2 F1 61.9 1873 2237.7 F2 61.8 1873 2238.2 D% 0.2 0 0 With F1: Result from Working Model F2: Result using calculation equation   F 1 - F 2  D ¼  F :100%: Difference between 1 A = 10 m PG = 500 N

a = 0.4; b = 0.25; lG = 0.6 FA (N) Fcyl(N) FM (N) 187.4 3591.1 3647.4 187.6 3592.1 3648.1 0.1 0 0 100 1889.8 2144.5 100 1889.8 2144.5 0 0 0 356.5 1599.2 2795.7 357.1 1601.3 2798.8 0.2 0.1 0.1

a = 0.5; b = 0.4; lG = 0.5 FA (N) Fcyl(N) FM (N) 224.9 1196.2 1233.4 224.8 1195.2 1232.7 0 0.1 0.1 177.3 988.2 1119.1 177.3 988.2 1119.1 0 0 0 61.5 912.9 1278.8 61.5 912.9 1278.8 0 0 0

the two methods

It can be seen that in Eq. (15) the factor lG is omitted. This means the position of loading PG does not affect the magnitude of the force from the cylinder during the operation. Parameter a < 1 (position of the cylinder on the structure) changes the magnitude of thrust force in cylinders: smaller cylinder endures bigger forces.

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Fig. 8. Reaction on pin M and thrust force on cylinder with different variables of b and k (with constant values of A = 10 m, PG = 500 N, a = 0.5, lG = 0.5)

From the results in Table 2, it can be noticed that the lower the platform is, it requires bigger force from the cylinder Fcyl to operation, and so does the reaction force at pin M. Combining the results from Table 1 with those from Table 1, the relationship between Fcyl and FM according to the increase of height (H) is shown in Fig. 8: Results from the figure show that the smaller of b can improve the operation of the system (platform can raise to higher positions) but increase the force in the cylinder. Even with the small loading of PG = 500 N, the reaction in joint M and thrust force from cylinder Fcyl for the system to operate are still too big ( 6 times). These forces will affect the frames in the system, such as causing bending or even breaking the function of the structure. In some cases, the change of Fcyl (blue line) is so small that it nearly becomes a constant, making the selection of appropriate cylinders easier.

4 Conclusion The applicability of the numerical methods using a variable in design lifting devices using scissor-mechanisms is analyzed in this study. The following conclusions can be made: • The propitiate dimensions of the hydraulic cylinder and the whole system can be purposely selected. The suitable values of the system can enhance the working effectiveness. The maximum stroke can be up to 80 cm when the frame length and angle between two lifts are 100 cm and 135° respectively.

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• The selection process of a hydraulic cylinder can be executed thanks to the calculated effective loading. • The proposed method in this study can allow us to establish force equations at the limiting loaded places on the system. In this way, the design recommendations for dimension and structure can be made. Acknowledgments. The authors wish to thank Thai Nguyen University of Technology for supporting this work.

References 1. Hongyu, T., Ziy, Z.: Design and simulation based on Pro/E for a hydraulic lift platform in scissors type. Procedia Eng. 16, 772–781 (2011) 2. Dong, Ren G., et al.: An investigation on the dynamic stability of scissor lift. Open J. Saf. Sci. Technol. 2, 8–15 (2012) 3. Zhang, W., Zhang, C., Zhao, J., Du, C.: A study on the static stability of scissor lift. Open Mech. Eng. J. 9, 954–960 (2015) 4. Stawinski, L., Kosucki, A., Morawiec, A., Sikora, M.: A new approach for control the velocity of the hydrostatic system for scissor lift with fixed displacement pump. Arch. Civ. Mech. Eng. 19(4), 1104–1115 (2019) 5. Zhang, W., Zhang, C., Zhao, J., Du, C.: A study on the static stability of scissor lift. Open Mech. Eng. J. 9, 954–960 (2015) 6. Hongyu, T., Ziy, Z.: Design and simulation based on Pro/E for a hydraulic lift platform in scissors type. Procedia Eng. 16, 772–781 (2011) 7. Dong, Ren G., et al.: An investigation on the dynamic stability of scissor lift. Open J. Saf. Sci. Technol. 2, 8–15 (2012) 8. Manoharrao, S.A., Prof. Jamgekar R.S.: Analysis and Optimization of Hydraulic Scissor Lift. IJEDR, 4(4) (2016)

A Study on Prediction of Milling Forces Do Duc Trung1, Tran Ngoc Giang2, Tran Thi Hong3, Bui Thanh Danh4, Vu Van Khoa5, Nguyen Dinh Ngoc2, Nguyen Thanh Tu2, and Vu Ngoc Pi2(&)

3

5

1 Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi City, Vietnam 2 Thai Nguyen University of Technology, Thai Nguyen City, Vietnam [email protected] Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam 4 University of Transport and Communications, Hanoi, Vietnam National Research Institute of Mechanical Engineering, Ha Noi City, Vietnam

Abstract. The aim of this study is to develop a milling force predicting model which is based on the relation between surface roughness and cutting force resulting from surface milling. Based on the analysis of the available models, the roughness model which has more advantages than others is selected. The newly proposed milling force model is developed based on the roughness model. Eight influential parameters are included in the model such as cutting edge radius, feed rate, side cutting edge angle, end cutting edge angle, radial depth of cut, axial depth of cut, tool diameters and number of cutting edge. Experimental tests by milling C45 steels are conducted to validate the predicted results as well as the realiability of the model. The results show that the maximum percentage errors between prediction and experiments is an average of 16.7%. Keywords: Milling operation milling


Surface roughness
Milling force
Surface

1 Introduction Milling is one of the most important machining processes to cut redundant materials for getting the final shapes. This process is able to machine various geometries of workpiece. Cutting forces of milling process are typically selected as a representative reflecting the degree of the energy consume (or cutting capacity) [1–5]. Minimizing milling forces has been carried out by research community so far. Most of available studies have been dealing with exploring the influence of machining parameters on the milling forces. Hoang T.D et al. [6] when conducting milling of SKD61 steels revealed that cutting force values increase with increasing in depth of cut and feed rate. Jhy-Cherng T et al. [7] performed milling process of Inconel 718 using face mill cutter using the hard alloy cut piece coating TiAlN. It was said that spindle speed, federate per cutting, and edge depth of cut have crucial influence on the cutting forces in which the first two parameters are more important. However, the effect of depth of cut was minor. This © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 86–93, 2021. https://doi.org/10.1007/978-3-030-64719-3_11

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87

result is opposed to that documented by Wen-Hsiang L [1] where the influence of depth of cut on cutting forces was analyzed. The results showed that the depth of cut has the strongest effects on the cutting forces, i.e. an increase in depth of cut leads to an increase in cutting forces. Md. Anayet U Patwar et al. [8] when doing the end milling operation provided some conclusions that feed rate has more important impacts on the cutting forces than axial depth of cut and cutting speed which has the smallest influence. Furthermore, the cutting forces grow with a rise in feed rate and depth of cut, while the influence of cutting speed is unclear. Erol Kilickap et al. [4] studied milling of Ti-6242S alloy and detailed that feed rate, cutting speed, and depth of cut significantly impact on the cutting forces. Moreover, it was shown that the cutting forces rise when the depth of cut and feed rate expand or the cutting speed reduces. Gökkaya H [9] conducted the milling process of AA2014 (T4) alloy. It was exhibited that the tfeed rate has the most effect on the cutting forces, e.g. an increase in feed rate leads to an increase in cutting forces. Inversely, the cutting speed has little impact. For example, when cutting speed varies form 200 m/min to 500 m/min, the stable cutting force values are observed. Pathak et al. [10] carried out milling operation of Al-1Fe-1V-1Si and Al-2Fe-1V-1Si alloy. Their results showed that cutting parameters have important effect. The cutting forces grow when there is an increase in depth of cut and feed rate, and/or a decrease in cutting speed is executed. Based on the previously analyzed studies, it is known that milling forces are impacted by various parameters such as cutting parameters, hardness of workpiece materials, cutting tool materials, tool geometries, tool wear, and lubricant configuration. It is difficult to include the mentioned influential parameters at the same time within a specific study. On the other hand, experimental results cannot meet all requirements in practice. Hence, applying process of studied results for milling is less effective. In order to solve the existing problem, there have been cutting force models established by research community. In this study, a cutting force model based on the surface roughness model will be proposed.

2 Developing Cutting Force Model Based on Cutting Force – Surface Roughness Relation Cutting force – surface roughness relation has been documented in several studies in literature. In this study, a new model will be suggested based on those presented in [10] and [11]. It is noticed that when carrying out the milling process by face cutting cutters, the relation between cutting force and surface roughness can be described by Eq. (1).
Ra ¼ 0:0254 

593 293  Fc

2 ð1Þ

Regarding the surface roughness model, there have been studies considering the development of roughness model in both approaches, theoretical and experimental. Because of the weakness of models based on experiment, roughness model has been developed by researchers according to theoretical approach.

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Cieloszyk J et al. [13] proposed the equation as: Rt ¼

fz2 8re

ð2Þ

Boothroyd G et al. [14] suggested the roughness model: Ra ¼

0:0321fz2 re

ð3Þ

Junelia B.L et al. [15] reported the equation as: Ra ¼

fz2 32re

ð4Þ

Miko E et al. [16] introduced the roughness model: Ra ¼


0  fz tankr  tankr 4 tankr þ tankr0

ð5Þ

Palanisamy P et al. [17] developed the equation as: Ra ¼

318fz2 4Dc

ð6Þ

Montgomery D et al. [18] suggested the roughness model: 5 pffiffiffi 2 Ra ¼ 9

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi fz4 fz2 ¼ 0:60415  150  D2c Dc

ð7Þ

Stępiński L [19] recommended the roughness model: Ra ¼

f2 Dc z fz z 32 2  p

ð8Þ

In another study, Miko E et al. [16] presented the roughness model: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 


fz2 tankr  tankr0 2 3 2 tankr  tankr0 2p Ra ¼ þ e 1þ 1  cos 8 z 18 tankr þ tankr0 tankr þ tankr0 Lukasz Nowakowski [20] developed the equation as:

ð9Þ

A Study on Prediction of Milling Forces


 fz4 2hm r e  hm Ra ¼ pffiffiffi þ pffiffiffi 1 þ fz2 18 3re 9 3

89

ð10Þ

Where:Fc is denoted for cutting force; Ra is the arithmetical mean roughness value; Rt is the total height of the roughness profile; fz is the feed rate; re is the cutting edge radius; kr is the side cutting edge angle, kr0 is the end cutting edge angle; z is the number of cutting edge; Dc is the tool diameter. It can be said that each available roughness model is only true for limited testing conditions. Hence, it is necessary to carefully analyze the advantages of these models to continuously develop the new ones which can well predict roughness. From the previously mentioned models, some conclusions can be made as following; Model (1), (2), and (3) are simple because they contain only two influential parameters, e.g. feed rate and cutting edge radius. Model (5) takes into account three factors such as feed rate, side cutting edge angle, and end cutting edge angle. Model (6) and (7) have only two factors which are feed rate and tool diameter. Meanwhile, feed rate and the number of cutting edge, and tool diameter are considered in model (8). Consequently, model (1) to model (8) have weakness due to containing limited factors. Model (9) takes into account four influential parameters involving feed rate, number of cutting edge, side cutting edge angle, and end cutting edge angle. Model (10) has three parameters such as feed rate, cutting edge radius, and unreformed chip thickness parameter which is an important parameter affecting the surface roughness. It is realized that model (9) and (10) are better than those from (1) to (8) in terms of variable numbers. There is the fact that the influential parameters in model (9) and (10) are not similar, hence this study will propose a new model based on the mentioned models for predicting cutting forces. A newly proposed model is combined with model (1), (9), and (10) as follows: Fc ¼ 293  94:5 2




6 fz4 hm r e  hm 6 436pffiffi3ffir þ 9pffiffi3ffi 1 þ f 2 e z



vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi31=2 u  0 2 u fz2 tankr tankr u 7 72 tankr þ tankr0  7 þu 0  t 5 tank tank r 3 2 þ 32 e 1 þ tank þ tankr 0 1  cos 2p z r

r

ð11Þ The unreformed chip thickness parameter is defined by Eq. (12) hmin ¼

180  sinkr  ae  fz   e p  Dcap  arcsin Dacap

ð12Þ

Where: ae is the radial depth of cut, Dcap is the effective diameter [21]; ap is the axial depth of cut.

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Dcap ¼ Dc

2  ap tankr

ð13Þ

(11), (12), and (13) are combined to get the new Eq. (14) which can be used to predict cutting forces for various levels of eight parameters, i.e. cutting edge radius, feed rate, side cutting edge angle, and end cutting edge angle, radial depth of cut, axial depth of cut, tool diameters and number of cutting edge. 8 vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi312 2 u  > 0 2 > fz2 tankr tankr
 u > > 4 u 0 7 6 fz > h r  h 72 tank þ tank > m e m r r > Fc ¼ 293  94:5  6 þu >  7 t 0  > 5 436pffiffi3ffir þ 9pffiffi3ffi 1 þ f 2 > tank tank r 3 2 > e z > þ 32 e 1 þ tank þ tankr 0 1  cos 2p < z r r > > > > > > > > > > > > :

hm ¼

180sinkr ae fz pDcap arcsin



ae Dcap

2a

Dcap ¼ Dc tankpr

3 Validating the Accuracy of the Proposed Model for Predicting Cutting Forces Milling tests are conducted by using face mill cutters with hard piece. The specimens made of steels have the dimension of 50 mm  50 mm  50 mm corresponding to the length, the width, and the thickness. Cutting speed is fixed by 395 rpm (or 99.3 m/min). Other information of machining parameters can be seen in Table 1.

Table 1. Cutting parameters. Parameter Feed rate Cutting edge radius Side cutting edge angle End cutting edge angle Radial depth of cut Axial depth of cut Number of cutting edge Tool diameter

Symbol fz re kr kr0 ae ap z Dc

Value 0.3; 0.4; 0.5; 0.6 and 0.7 0.4 45 45 50 0.4; 0.5; 0.6; 0.7; and 0.8 1 80

Unit mm/tooth mm Degree Degree mm mm Tooth mm

The current can be measured before and during machining process. The cutting forces (Fc) and the cutting power (Pcut) can be determined by the following equation [22].

A Study on Prediction of Milling Forces

Pcut ¼ U  I Fc ¼

91

ð14Þ

Ncut v

ð15Þ

The values of cutting forces given by experiments and prediction can be seen in Tables 2 and 3. Table 2. Cutting forces for different feed rate.  mm  No. fz tooth PðKW Þ FcðmeasuredÞ Fcðcalculated Þ Deviation of Fc (%) 1 0.3 2 0.4 3 0.5 4 0.6 Mean

0.417 0.415 0.380 0.327

251.76 250.76 229.61 197.38

231.49 210.46 189.14 167.56

8.05% 16.07% 17.63% 15.11% 14.21%

It is observed that the predicted values of the cutting forces listed in Table 2 and Table 3 are close to those given by the experiments. When the feed rate varies, the maximum percentage error between prediction and experiment is 14.21%. The corresponding value in case of varying depth of cut is 19.6%.

Table 3. Cutting forces for different axial depth of cut.  mm  PðKW Þ FcðmeasuredÞ Fcðcalculated Þ Deviation of Fc (%) No. fz tooth 1 0.4 2 0.5 3 0.6 4 0.7 5 0.8 Mean

0.423 0.415 0.382 0.358 0.347

255.79 250.76 230.61 216.52 209.47

200.82 189.14 183.56 180.37 178.37

21.49% 24.57% 20.40% 16.70% 14.85% 19.60%

4 Conclusion Cutting force is an important parameter which have significant impacts on the machining quality, lifetime of cutting tool, and energy consume during milling. Studying the methods to predict milling forces has been attracted by research community. This study successfully develops a new milling force model which can predict the milling forces. The model takes into account eight influential parameters including cutting edge radius, feed rate, side cutting edge angle, and end cutting edge angle, radial depth of cut, axial depth of cut, tool diameters and number of cutting edge. The reliability of the proposed model is validated by comparing experiment and prediction. The mean percentage error between predicted and experimental values of milling forces

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is around 16.7%. Applying the results in this study in industry can contribute to enhance the effectiveness of milling operation. Acknowledgements. This work was supported by Thai Nguyen University of Technology.

References 1. Lai, W.-H.: Modeling of cutting forces in end milling operations. Tamkang J. Sci. Eng. 3(1), 15–22 (2000) 2. Fua, Z., Yanga, W., Wanga, X., Leopold, J.: Analytical modelling of milling forces for helical end milling based on a predictive machining theory. In: 15th CIRP Conference on Modelling of Machining Operations, Procedia CIRP, vol. 31, pp. 258–263 (2015) 3. Sekuli, M., Jurkovi, Z., Had Istevi, M., Gostimirovi, M.: The influence of mechanical properties of workpiece material on the main cutting force in face milling. METABK 49(4), 339–342 (2010) 4. Kilickap, E., Yardimeden, A., Çelik, Y.H.: Mathematical modelling and optimization of cutting force, tool wear and surface roughness by using artificial neural network and response surface methodology in milling of Ti-6242S. Appl. Sci. 7(1064) (2017). https://doi. org/10.3390/app7101064 5. Okokpujie, I.P., Okonkwo, U.C.: Effects of cutting parameters on surface roughness during end milling of aluminum under minimum quantity lubrication (MQL). Int. J. Sci. Res. 4(5), 2937–2943 (2013) 6. Hoang, T.D., Nguyen, N.-T., Tran, Đ.Q., Van Nguyen, T.: Cutting forces and surface roughness in face-milling of SKD61 hard steel, Strojniški vestnik. J. Mech. Eng. 65(6), 375– 385 (2019) 7. Tsai1, J.-C., Kuo1, C.-Y., Liu1, Z.-P., Hsiao, K.H.-H.: An investigation on the cutting force of milling Inconel 718. In: MATEC Web of Conferences, vol. 169 (2018) 8. Patwari, A.U., Nurul Amin, A.K.M., Faris, W.F.: Prediction of tangential cutting force in end milling of medium carbon steel by coupling design of experiment and response surface methodology. J. Mech. Eng. 40(2), 95–103 (2009) 9. Gökkaya, H.: The effects of machining parameters on cutting forces, surface roughness, built-up edge (BUE) and built-up layer (BUL) during machining AA2014 (T4) alloy. Strojniški vestnik J. Mech. Eng. 56, 584–593 (2010) 10. Pathak, B.N., Sahoo, K.L., Mishra, M.: Effect of machining parameters on cutting forces and surface roughness in Al-(1-2) Fe-1 V-1Si alloys. Mater. Manuf. Process. 28, 463–469 (2013) 11. Cus, F., Zuperl, U.: Model reference-based machining force and surface roughness control. J. Achieve. Mater. Manuf. Eng. 9(2), 115–122 (2008) 12. Hadi, Y.: Prediction of surface roughness for periodic end mill tool holder. Appl. Mech. Mater. 330, 262–268 (2013) 13. Cieloszyk, J., Olszak, W., Skrodzewicz, E., Sobkowiak, E.: Stan geometryczny powierzchni frezowanej czołowo z dużymi posuwami. Materiały Konferencji I Forum Prac Badawczych ‘‘Kształtowanie części maszyn przez usuwani materiału”. Koszalin, pp. 46–55 (1994) 14. Boothroyd, G., Knight, W.A.: Fundamentals of machining and machine tools. Marcel Dekker, New York (2006) 15. Junelia, B.L., Sekhon, G.S.: Fundameltals of Metal Cutting And Machine Tools, 1st edn. Wiley Eastern Limited, New Delhi (1987)

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16. Miko E.: Konstytuowanie mikronierówności powierzchni metalowych obrabionych narzędziem o zdefiniowanej stereometrii ostrzy. Monografie, studia, rozprawy 46. Kielce (2004) 17. Palanisamy, P., Rajendran, I., Shanmugasundaram, S.: Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations. Int. J. Adv. Manuf. Technol. (2006) 18. Montgomery, D., Altintas, Y.: Mechanism of cutting forces and surface generation in dynamic milling. ASME J. Eng. Ind. 160–168 (1991) 19. Stępiński L.: Wpływ przemieszczeń względnych w układzie: narzędzie przedmiot obrabiany, na chropowatość powierzchni frezowanej walcowo. Rozprawa doktorska, AGH. Kraków (1982) 20. Nowakowski, L.: Models for Prediction of Ra Roughness Parameters of Milled Surfaces (2015). https://doi.org/10.17814/mechanik.2015.8-9.414 21. Nowakowski, L., Skrzyniarz, M., Miko, E., Takosoglu, J., Blasiak, S., Laski, P., Bracha, G., Pietrala, D., Zwierzchowski, J., Blasiak, M.: Influence of the cutting parameters on the workpiece temperature during face milling. In: EPJ Web of Conferences, vol. 143, p. 02082 (2017). https://doi.org/10.1051/epjconf/201714302082 22. Son, N.H.: Effect of cutting parameters on cutting force and surface roughness of workpiece when milling 40Cr steel using PVD-coated cutter. Int. J. Sci. Eng. Investigat. 9(96), 13–18 (2020)

A Study on Qualitative and Quantitative Characterization of Machining Quality of Aerospace Composite Structures Nguyen Dinh Ngoc1(&) and Nguyen Thi Hue2 1

2

Faculty of International Training, Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] Faculty of Fundamental Science, Thai Nguyen University of Technology, Thai Nguyen, Vietnam

Abstract. Machining CFRP composites generally create defects in machined surfaces which are more irregular than those given by metal machining. Minimizing and controlling the occurrence of damage is a crucial task. To archive that, quantifying damage and correlating it to cutting parameters, as well as mechanical behavior is significant. This study investigates the influences of machining parameters such as spindle speed and feed speed on the ten-point max, Rz. This roughness parameter is recommended to adopt for quantifying the machining quality of CFRPs instead of Ra. Machining defects are identified by SEM observation. Rz values are determined in both longitudinal and perpendicular directions to the machined surfaces. The results show that the combination between high spindle speed and low feed speed can seriously create damage levels. Moreover, it is proved that Rz can well reflect damage evolution, qualitatively identified by SEM observation. This can be concluded that Rz can be a good indicator to quantify machining damage of composite materials instead of using expensive systems which are difficult to be afforded whenever. Keywords: SEM


Characterization of machining quality
Ten-point max Rz

1 Introduction Carbon fiber reinforced plastics (CFRP) composites is one of the advanced composite materials. It is frequently attracted by aerospace industry, automobiles, shipbuilding, etc. Typically, CFRPs are fabricated near-net shape to economize preparing time and enhancing productivity. However, machining operations are crucially required to meet required tolerances and surface integrity for assembly. The machined surface of CFRP structures are generally irregular because of induced defects such as delamination, matrix/fiber debonding, fiber pullout, thermal degradation of matrix [1–3]. The irregular surface can create stress concentrations at the positions of the defects induced. This can reduce the working ability of CFRP structure during services. Accordingly, the characterizing machining quality is crucially essential. The characterization normally involves both qualitative and quantitative techniques. For the qualitative technique, there have been effective methods applied so far. These are scanning electron © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 94–101, 2021. https://doi.org/10.1007/978-3-030-64719-3_12

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microscope (SEM), C-scan, optical [4], confocal 3D [5, 6], X-ray tomography [5, 7]. Among these methods, the last provides dimensional information in machined surface and subsurface. The remaining techniques aim to identify the degree of damage, but little provide a quantitative evaluation. The different standards used to describe surface texture fall into three main categories: average roughness heights (deterministic method), statistical method, and random process method [8]. The first has been widely utilized in community and industry research. Recently, Ghidossi. P et al. [9] suggested two new parameters, ‘‘percentage of damaged surface’’, and ‘‘depth of subsurface cracking’’ and correlated mechanical behaviors. It is showed that although these new parameters are promisingly adopted, they are not able to suit for all experimental results. Another new parameter has been proposed by several researchers [5, 6, 10]. For this method, the proposed parameters are based on a laser confocal microscopy through measurements of crater volumes. Nguyen-Dinh et al. [5] conducted the trimming process of CFRP composite and investigate the influences of machining parameters on the machining quality characterized using crater volumes. It was said that machining with high cutting speed and low feed speed can generate a high level of tool wear due to induced temperatures. Hence, machining damage severely occurrences for this combination of machining parameters. Wang F et al. [10] quantify machining quality by measuring the depths and volumes of craters along with the position of 135° fiber directions, and correlate machining damage to cutting parameters. Their results show that an increase in feed per tooth leads to an increase in the average depth of craters and the average volume of the cavity. Nevertheless, crater volumes can better quantify machining defects compared to the crater depth parameter. It can be said that although the new parameters as previously presented are promising, the price of the measurement system is difficult to equip. Hence, utilization of roughness parameters has been widely accepted by industry, as well as recommended by researchers [3, 8]. Ramulu M et al. [11] documented that the parameters of Ry and Rz are better descriptors of the machined surface compared to common roughness parameters of Ra and Rq. J. Sheikh-Ahmad et al. [3] when correlating the tensile strength of CFRP composite specimens to machining quality concluded that the roughness parameter of Rz is better-correlated defects to tensile strength than the roughness parameter of Ra. This paper attempts to provide more detailed investigations regarding the effects of cutting parameters (spindle speed and feed speed) on surface roughness (Rz) and the impact on the tool wear during machining CFRP materials. SEM observation will be selected to qualitatively evaluate the machining quality. Surface roughness determination is carried out in both longitudinal and perpendicular directions to machined surfaces.

2 Experimental Procedure The CFRPs are laminated by P2352 prepregs according to the stacking sequence of [90°/90°/−45°/0°/45°/90°/−45°/90°/45°/90°]s and have the dimensions of 300 mm  15 mm  5.2 mm. The fiber volumes are approximately 59%. The mechanical properties of machined specimens are listed in Table 1. A full factorial design of cutting

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conditions including three levels of feed speed, Vf, (500 mm/min, 1000 mm/min, 1500 mm/min), and two levels of spindle speeds, N, (8000 rpm and 13300 rpm) is utilized. A radial depth of cut of 2 mm is constantly kept for all cutting conditions. New polycrystalline diamond cutters (PCD) with two straight flutes and a diameter of 8 mm are selected for all cutting conditions (Ref. Figure 1). Three 300 mm long specimens corresponding with six faces were machined for each cutting condition. No coolant was utilized during the machining process. Trimming tests are carried out on a CNC center 3-axis milling machine (FANUC). The acquisition of cutting forces is recorded using a dynamometer (Kistler 9257BA). The experimental devices used trimming tests are schematically shown in Fig. 2. The machining quality will be characterized by SEM observations, 3D confocal laser technique (Alicona) which can also provide the 2D roughness information through extracting profile. The surface roughness is evaluated with a cut-off length of 0.8 mm and a transverse length is 4.0 mm. The surface roughness representative for each cutting condition is averagely calculated by three times.

Fig. 1. Milling cutter made PCD with two straight cutting edges. Table 1. Mechanical properties of P2352 prepreg. Density (g/cm3) 2.63

Longitudinal shear modulus (GPa) 6.21

Longitudinal Young’s modulus (GPa) 162

Compressive strength (MPa) 1552

Tensile strength (MPa) 2844

Poisson’s ratio 0.34

Fig. 2. Schematic view showing the machining configuration: axes, fiber orientation and the machining directions.

3 Results and Discussion 3.1

Evaluation of Machining Quality

After machining, the machined surfaces of specimens are identified by using SEM observations. The results reveal that damage induced has some forms such as burrs in

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free edge, failure fibers, fiber-matrix debonding. In the positions of 135° fiber directions, defects particularly mostly have the shape of craters/cavities, matrix smearing/cracking next to craters and failure fibers. However, the frequent occurrences of defects crucially depend on the cutting parameters and the cutting tool state, wear phenomenon. Figure 3 presents the SEM images of specimens resulting from cutting configuration of N = 13300 rpm and Vf = 500 mm/min at short cutting distance of 0.2 m (Fig. 3.a) and 1.2 m (Fig. 3.b). This cutting condition is the combination of high spindle speed and low feed speed. In this cutting condition, due to the high tool wear rate, the damage level becomes severe after shorting cutting distance. This means that the tool wear accelerates just short machining length. The results show that at first machining quality is good, little damage even in the positions of −45° fiber directions. Nevertheless, when cutting distance reaches approximately 400 mm, defects induced significantly appear in which defects in shapes of cavities/craters are dominantly visualized. Matrix smearing covers across machined surfaces. The machining temperature exhibits, in this case, is still inferior to the glass transition temperature (Tg). Hence, thermal degradations of the matrix are not observed. It is realized that SEM observation cannot provide a quantitative evaluation. In the next section, machining damage will be quantitatively estimated and correlated to machining parameters.

Craters

No damage

Matrix smearing

(a)

600 µm

(b)

600 µm

Fig. 3. SEM images of the machined surface with a feed speed of 500 mm/min and a spindle speed of 13300 rpm at a cutting distance of (a) 0.2 m and (b) 1.5 m.

3.2

Influence of Machining Parameters on the Surface Roughness

The studies in the literature [3, 11] have recommended that ten-point mean (Rz) can better describe machining damage than arithmetical mean surface roughness (Ra). In this study, the machined surface will be evaluated by using Rz instead of Ra. Values of

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Rz are identified in both longitudinal and transverse directions to machined surfaces in Fig. 4.a and 4.b respectively. From the results, it can be said that Rz values in the longitudinal direction are typically inferior to those measured in the perpendicular direction. For example, considering the combination of 8000 rpm and Vf = 500 mm/min, Rz of 4.2 µm is recorded in the longitudinal direction, while the corresponding Rz of 7.5 µm in transverse. This discrepancy is attributed to the fact that in the transverse direction the craters dominantly appearing in −45° fiber positions is generally taken into account the average values of Rz. On the contrary, these crates are whether included or not depending on the positions of measurements or extracted profile.

Fig. 4. Evolution of Rz in (a) longitudinal and (b) transverse direction as a function of cutting parameters.

It is noticed that in both directions the domination of Rz given by the combination of N = 13300 rpm and Vf = 500 mm/min, namely severe condition, is obviously seen. This well reflects the damage level as shown in Fig. 3, e.g. high wear rate can accelerate due to a higher level of friction between the workpiece and cutting tool.

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However, Rz recorded in the transverse direction (22 lm) is more important than that in the longitudinal direction (11 lm). Except for severe conditions, it is observed that an increase in feed speed leads to an increase in Rz. Nevertheless, spindle speed has an unclear impact on Rz. The recorded Rz shown in Fig. 4 and Fig. 5 are average values of 6 measurements of each cutting condition. Thus, the tool wear information is not profoundly described. In the next section, the influence of tool wear, presented in terms of cutting condition variation, on Rz will be analyzed. 3.3

Influence of Machining Distance on the Surface Roughness

Figure 5 shows the evolution of Rz versus tool wear when Rz is measured in the transverse direction. It is seen that except for severe conditions the influence of cutting distance or tool wear is not clear. This is due to the tool wear rate is small. It means that the Rz parameter can similarly describe the evolution of damage level forward tool wear as 3D roughness parameters (Sa), and newly proposed parameters (crater volume – C-v, and a maximum depth of defect – D) documented in [5]. The stability of the damage level can be confirmed by the SEM images at two different cutting conditions in Fig. 6 in which the sizes of craters in both cutting distances are almost similar. The figure exhibits the cutting-edge shape when N = 13300 rpm and Vf = 1500 mm/min is utilized. It is observed that after a cutting distance of 1.7 m, the radius increases, namely rounded cutting edge. However, it seems that the variation range of radius is not still reached the limiting value which can generate serious damage as that given by severe condition.

Fig. 5. Evolution of Rz vs cutting distance for (a) spindle speed of 8000 rpm and (b) spindle speed of 13300 rpm.

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Fig. 6. SEM images with N = 8000 rpm and feed speed of 1500 mm/min at a cutting distance of (a) 200 mm and (b) 1500 mm.

Fig. 7. Fig. 7. SEM images of cutting edge with N = 13300 rpm and feed speed of 1500 mm/min.

4 Conclusions In this study, machining defects are analyzed in both qualitative and quantitative evaluation. The ten-point max parameter, Rz, is selected due to better describing defects induced. It is determined in the longitudinal and perpendicular direction of machined surfaces. The following conclusions can be made: • Rz is evaluated along the longitudinal and perpendicular direction of machined surfaces. The results show that Rz values in the longitudinal direction are typically more important to those in the transverse direction. This is attributed to the fact that

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the existence of severe defects at −45° fiber position which is considered of Rz average values in longitudinal directions, but not in perpendicular direction. • Tool wear accelerates when machining is conducted with a severe condition (N = 13300 rpm and Vf = 500 mm/min). This leads to serious appearance defects after a short cutting distance of 400 mm. Temperatures induced are lower than Tg, hence thermal damage is not observed. • Rz can well describe the evolution of machining damage level when correlating it to machining parameters. Therefore, the analysis process aims to prove that Rz can be a good indicator to characterize machining quality of composite machining instead of using expensive technology, i.e., 3D confocal non-contacting laser, optical topography. Contacting Acknowledgments. The authors wish to thank Thai Nguyen University of Technology for supporting this work.

References 1. Hintze, W., Hartmann, D., Schütte, C.: Occurrence and propagation of delamination during the machining of carbon fibre reinforced plastics (CFRPs) – An experimental study. Compos. Sci. Technol. 71(15), 1719–1726 (2011) 2. Haddad, M., Zitoune, R., Eyma, F., Castanié, B.: Machinability and surface quality during high speed trimming of multi directional CFRP. Int. J. Mach. Mach. Mater. (2013) 3. Shahid, A.H., Sheikh-Ahmad, J.: Effect of edge trimming on failure stress of carbon fiber polymer composites_Jamal Sheikh-Ahmad_Hay dung bai nay (2013) 4. Duboust, N., et al.: An optical method for measuring surface roughness of machined carbon fibre-reinforced plastic composites. J. Compos. Mater. 51(3), 289–302 (2016) 5. Nguyen-Dinh, N., et al.: Surface integrity while trimming of composite structures: X-ray tomography analysis. Compos. Struct. 210, 735–746 (2019) 6. Hejjaji, A., et al.: Surface and machining induced damage characterization of abrasive water jet milled carbon/epoxy composite specimens and their impact on tensile behavior. Wear 376–377, 1356–1364 (2017) 7. Saoudi, J., et al.: Critical thrust force predictions during drilling: analytical modeling and Xray tomography quantification. Compos. Struct. 153, 886–894 (2016) 8. Ramulu, M., Wern, C.W., Garbini, J.L.: Effect of fibre direction on surface roughness measurements of machined praphite epoxy composite.pdf. Compos. Manufact., 4 (1993) 9. Ghidossi, P., Mansori, M., Pierron, F.: Influence of specimen preparation by machining on the failure of polymer matrix off-axis tensile coupons. Compos. Sci. Technol. 66(11–12), 1857–1872 (2006) 10. Wang, F., et al.: Influences of milling strategies and process parameters on the cavity defect generated during milling of carbon fiber reinforced polymer. Proc. Inst. Mech. Eng., Part C: J. Mech. Eng. Sci. (2020) 11. Ramulu, M.: Machining and surface integrity of fibre-reinforced plastic (1997)

A Study on Prediction of Grinding Surface Roughness Do Duc Trung1, Nhu Tung Nguyen1, Hoang Tien Dung1, Nguyen Van Thien1, Tran Thi Hong2, Tran Ngoc Giang3, Nguyen Thanh Tu3, and Le Xuan Hung3(&) 1

2

3

Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi City, Vietnam Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam Faculty of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen City, Vietnam [email protected]

Abstract. This paper presents a study on the prediction of surface roughness in surface grinding. Based on the results of several previous studies, the relation between the abrasive grain tip radius and the standard systems of grinding wheels and the surface roughness were predicted. Also, the surface roughness when surface grinding SKD11, SUJ2, and 3X13 steel by Al2O3 and CBN grinding wheels was anticipated. The predicted surface roughness values were found to be close to the experimental values. In addition, the average deviation between the predictive results and the experimental results was 15.11% for the use of Al2O3 grinding wheels and 24.29% for the case of using CBN grinding wheels. Keywords: Surface roughness
Surface grinding
Grinding
Standard system

1 Introduction Surface quality when grinding is assessed through many parameters: hardness, surface layer residual stress, surface roughness, etc. In particular, surface roughness is one of the parameters that greatly affect the workability and lifetime of machine details and it is often chosen as a parameter to evaluate the grinding process. To study the grinding roughness surface, experimental methods are often employed, however, they have some disadvantages that the results obtained in the experimental process can usually be applied only under similar conditions and the experimental cost is large [1]. To overcome the above limitations, studies of modeling simulating the grinding process to predict surface roughness have been carried out by various researchers. Typical studies include building models to predict surface roughness when grinding based on the analysis models of cutting thickness [2]; predicting the surface roughness when grinding with the assumption that the grits are uniformly distributed on the grinding wheel surface [3–5]; predicting the surface roughness when grinding through © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 102–111, 2021. https://doi.org/10.1007/978-3-030-64719-3_13

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determining the average value of the cutting depth into the machining surface of the abrasive grains [6]; applying probability theory when analyzing the cutting process of abrasive grains to predict surface roughness [7]; estimating surface roughness with the assumption that the grinding process is a mechanical-thermal equilibrium process [8]; constructing the relationship between surface roughness and unformed chip thickness with the assumption that the cross section of each cut is made by an abrasive on the surface of the workpiece with different geometric shapes (triangle, semicircular, curved, hyperbole) [1, 9–12]. However, applying the results of these studies is rather demanding because of the microstructure of the grinding wheel surface [1]. Therefore, it is necessary to build the correlation between the microstructure of the grinding wheel surface and its parameters, which will serve as the basis for predicting the achieved surface roughness by grinding.

2 The Model for Predicting Surface Roughness In surface grinding, the surface roughness can be found by the following formula [13–15]: Ra ¼

0; 25  VW0;4  tf0;6 0;2 0;2 KC0;4  ðVG þ VW Þ0;4 n0;4 g  De  qg

ð1Þ

In which, VW is the workpiece velocity; tf is the depth of cut; KC is the chip generation coefficient (the calculation of this parameter value is often difficult, in most cases it is possible to choose KC = 0,9) [13]; VG is the grinding wheel velocity; ng is the number of abrasive particles per unit area of the grinding wheel surface; De is the equivalent abrasive grains’ diameter; qg is the radius of the abrasive grain’s top. The determination of these parameters will be discussed in detail. The equivalent grinding wheel diameter is calculated as follows [16, 17]: De ¼

DG  DW DG  DW

ð2Þ

In which, DG and DW are the grinding wheel and the workpieces diameters, respectively. The plus sign (+) in formula (2) is used when external grinding, while the minus sign (−) is used for internal grinding. When grinding flat, DW ! 1, so that De = DG. The depth of cut is determined as follows [13–15]: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 13; 55  VW  t pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi tf ¼ 0; 739  t þ 0; 546  t2 þ KC  ðVG  VW Þ  nG  De  qg

ð3Þ

Where, t is the depth of the layer of metal removed after grinding; The plus sign (+) in formula (3) is used when external grinding, minus sign (−) is used for internal grinding.

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Regarding the abrasive grain tip radius qg, this parameter depends on the limited size of abrasive grain. However, the standard systems for grinding wheel do not indicate the abrasive grain tip radius or the limit size of abrasive grain. Therefore, it is needed to find a solution to determine the abrasive grain tip radius from the Standard marking systems of the grinding wheel. Currently, two commonly-used standard systems for grinding wheels are ISO 8486-1.2: 1996 (E) and GOST R 3647-80. According to ISO 8486-1.2: 1996 (E), the grain size is denoted by a number and by the letter F. Table 1 displays the limit size values of abrasive grain and the abrasive grain tip radius determined in some experimental studies. Table 1. The values of the limit size of abrasive grain Bg and the abrasive grain tip radius qgaccording to the experimental studies of the denoted grinding wheel according to ISO 84861.2: 1996 (E). Denoted Grain size, F 16 25 32 40 50 63 80 The limit size of abrasive grain, µm 160 240 315 400 500 630 800 The abrasive grain tip radius, µm – – 26 – – 45 – 13 19 – 28 – – – 11 17 25 – 41 – – – 19 – 30 – – 68 14 21 – 30 – – – – 18 26 – 43 – – 12 – – – – 48 – 13 19 27 28 38 – 60 Average 12,6 19 26 29 39,5 48 64

Ref. 100

125

160

200

1000 1250 1600 2000 – – 76 – – 80 – –

– – – 97 – 91 93 –

117 114 – 115 – – 119 –

– – – 130 – 138 149 –

76

95

115,3 139,5

[16] [18] [19] [20] [21] [22] [23] [24]

Based on the data in Table 1, the relationship between the abrasive grain tip radius qg and the limited size of abrasive grain is shown in Eq. (4). Alternatively, the relationship between the abrasive grain tip radius qg and denoted grain size is presented in Eq. (5). qg ¼ 0; 0535  B0;955 g

ð4Þ

qg ¼ 0; 8754  F 0;9659

ð5Þ

Or:

Table 2 presents the abrasive grain tip radius when calculated using (4), (5) and the values in experimental studies (data from Table 1).

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Table 2. Comparing the abrasive grain tip radius according to the predicted results and experimental results of the denoted grinding wheel according to ISO 8486-1.2: 1996 (E) standard. Denoted grain size, F 16 25 32 40 50 63 80 The limit size of abrasive grain, µm 160 240 315 400 500 630 800 The abrasive grain tip radius, µm 12.6 19 26 29 39.5 48 64 12.8 19.4 24.5 30.7 38.1 47.6

1.59 2.11 5.77 5.86 3.54 0.83

12.7 19.6 24.9 30.9 38.3 47.9

0.79 3.16 4.23 6.55 3.04 0.21

How to determine 100

125

160

200

1000

1250 1600 2000

76

95

115.3 139.5 Average experimental value 59.6 74.3 2.4 115.4 143 The calculated values by formula (4) 6.88 2.24 2.74 0.09 2.51 % Deviation from experimental values (4) 60.3 74.8 92.8 117.8 146.1 The calculated values by formula (5) 5.78% 1.58% 2.32 2.17 4.73 % Deviation from experimental values (5)

The data in Table 2 reveal that the values of the abrasive grain tip radius when calculated according to formulas (4) and (5) are greatly close to the experimental values. The values of deviation ranges 0.09–5.77% for formula 4, and 0.79–6.55% for formula 5. Therefore, it is drawn that one of the two formulas can be used to calculate the value of the abrasive grain tip radius for each grinding wheel according to Standard marking system ISO 8486-1.2: 1996 (E). Another standard system for grinding wheels is GOST R 3647-80. According to this standard, the size of abrasive grain is expressed through the number of sieve holes per square inch of sieve used for grading and it is denoted as M. Table 3 presents the abrasive grain tip radius of some grades. From the data in Table 3, the relationship between the abrasive grain tip radius and the M index is built in formula (6). qg ¼ 1245; 5  M 1;132

ð6Þ

Table 4 presents the calculated abrasive grain tip radius using Eq. (6) and experimental value (data from Table 3).

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Denoted grain The limit size of size, M abrasive grain, µm 10 2000–2500 12 1600–2000 16 1250–1600 20 1000–1250 24 800–1000 30 630–800 36 500–630 46 400–500 54 315–400 60 250–315 70 200–250 80 160–200 100 125–160 120 100–125 150 80–100 180 63–80 230 50–63 280 40–50 320 28–40

The average limit size of abrasive grain, µm 2250 1800 1425 1125 900 715 565 450 357.5 282.5 225 180 142.5 112.5 90 71.5 56.5 40 34

The abrasive grain tip radius, µm 85.1 68.7 55.0 43.9 35.5 28.5 22.7 18.3 14.7 11.7 9.4 7.6 6.2 5.4 4.2 3.7 2.4 2.0 1.7

Table 4. Comparing the abrasive grain tip radius according to the predicted results and experimental results of the denoted grinding wheel according to GOST R 3647-80 standard. Denoted grain size

Experimental value in Table 3

10 12 16 20 24 30 36 46 54 60 70 80 100 120

85.1 68.7 55.0 43.9 35.5 28.5 22.7 18.3 14.7 11.7 9.4 7.6 6.2 5.4

The abrasive grain tip radius The calculated values Deviation from by formula (6) experimental values (%) 91.9 7.99 74.8 8.88 54.0 1.82 41.9 4.56 34.1 3.94 26.5 7.02 21.6 4.85 16.3 10.93 13.6 7.48 12.1 3.42 10.2 8.51 8.7 14.47 6.8 9.68 5.5 1.85 (continued)

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Table 4. (continued) Denoted grain size 150 180 230 280 320

Experimental value in Table 3 4.2 3.7 2.4 2.0 1.7

The abrasive grain tip radius The calculated values Deviation from by formula (6) experimental values (%) 4.3 2.38 3.5 5.41 2.6 8.33 2.1 5.00 1.8 5.88

It is observed from the data in Table 4 that the calculated abrasive grain tip radius by formula (6) is very close to the experimental value. The deviation ranges from 1.82% to 14.7%. Hence, formula (6) can be used to estimate the calculated abrasive grain tip radius for each grinding wheel according to the GOST R 3647-80. The number of abrasive grains per unit area of grinding wheel surface is determined by the following formula [16]: ng ¼

6 2 p  B2g

ð7Þ

In formula (7) 2 is the percentage of the grain volume in the grinding wheel. For the conventional grinding wheel, it is determined by the formula (8) [16] with the index of the structure number of the grinding wheel. For the diamond and CBN grinding wheels, the values of the grinding wheels are investigated according to the index showing the concentration of abrasive particles, as shown in Table 5 [25]. 2 ð% Þ ¼ 2  ð32  SÞ

ð8Þ

Table 5. Relationship between 2 and index showing the abrasive concentration of diamond and CBN grinding wheels [25] Concentration, C 25 50 75 100 125 150 175 200 2 ð%Þ 6.25 12.50 18.75 25.00 31.25 37.50 43.75 50.00

The data in Table 5 disclose that if the index denotes the concentration of abrasive grains of diamond and CBN grinding wheels, their values are determined by formula (9). 2 ð% Þ ¼ 0; 25  C

ð9Þ

In the case of a medium structure grinding wheel, for simple denotation of grinding wheel, some grinding wheel manufacturers do not show the structure of the grinding wheel in their standard systems. In this case, for a conventional grinding wheel, it is possible to choose a structure S ¼ 8, but for diamond or CBN grinding wheel, choose C ¼ 100 [16].

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Formulas (1) to (9) show that it is possible to predict the grinding surface roughness according to each specific case of the parameters of the grinding wheel, the dimension of workpiece and the parameters of cutting mode. In particular, if the grinding wheel is denoted according to ISO 8486-1.2: 1996 (E), the abrasive grain tip radius is calculated by the formula (4) or (5), and if the grinding wheel is denoted according to with GOST R 3647-80, the abrasive grain tip radius is calculated by formula (6). The percentage of the abrasive grits in a grinding wheel is calculated by Eq. (8) with a conventional grinding wheel or by the formula (9) with the CBN or diamond grinding wheel.

3 Comparing Predicted Surface Roughness and Experimenting Results In this work, the experimental results in the study [26] in which Al2O3 and CBN grinding wheels were used for grinding SKD11, SUJ2 and X13M materials on the flat grinding machine are used to compare with the values obtained in this study. The grinding wheel and technological regime parameters used during the experiment of the study [26] are presented in Table 6 and 7 for cases of using Al2O3 grinding wheel and CBN grinding wheel, respectively. The experimental surface roughness values when grinding different materials and the calculated surface roughness values by formulas (1) to (9) are also included in these tables. From the data in Tables 6 and 7, the roughness comparison charts were built as shown in Figs. 1 and 2 for grinding by Al2O3 and CBN grinding wheels, respectively. Table 6. Surface roughness values in [26] and by calculating when using Al2O3 grinding wheel. Grinding wheel: 36A60LV, grain size M = 60, number of structure S = 8, diameter of grinding wheel DG ¼ 180, cutting speed: VG ¼ 26 m/s No. tðmmÞ VW Surface roughness, Ra ðm=minÞ The experimental surface roughness Ra(calculated) values [26] Ra(Exp-SKD11) Ra(Exp-SUJ2) Ra(Exp-3X13) 1 0.05 8 0.45 0.41 0.46 0.54 2 0.035 8 0.42 0.48 0.32 0.43 3 0.05 15 0.48 0.55 0.50 0.69 4 0.035 15 0.55 0.55 0.62 0.56

Tables 6, 7 and Figs. 1, 2 show that in the case of using Al2O3, the calculated surface roughness values are close to the experimental surface roughness values for all three steel types tested. The average deviation calculated for all three steel types tested is 15.11%. Regarding the case of using CBN grinding wheel, the average deviation between the calculated results and experimental results is approximately 24.29%. These results show that the method of surface roughness presented in this study can be used to predict surface roughness in specific cases.

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Table 7. Surface roughness values in [26] and by calculating when using CBN grinding wheel. Grinding wheel: 3 HY-100#, grain size M = 100, number of structure C = 100, diameter of grinding wheel DG ¼ 180, Cutting speed: VG ¼ 26 m/s No. t V Surface roughness, Ra ðmmÞ ðm=minÞ The experimental surface roughness Ra(calculated) values [26] Ra(Exp-SKD11) Ra(Exp-SUJ2) Ra(Exp-3X13) 1 0.01 5 0.50 0.32 0.23 0.39 2 0.02 5 0.63 0.38 0.32 0.59 3 0.01 15 0.82 0.65 0.6 0.60 4 0.02 15 1.06 0.55 0.64 0.92

Fig. 1. Comparison of surface roughness in [26] and by calculating when using Al2O3 grinding wheel.

Fig. 2. Comparison of surface roughness in [26] and by calculating when using CBN grinding wheel.

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4 Conclusion Based on the published results of surface roughness modeling when grinding, this study presents a method to determine the abrasive grain tip radius using the different standard marking systems of the grinding wheel. Also, formula for calculating the percentage of grinding grain volume for diamond and CBN grinding wheels was introduced. Additionally, the methods to predict the surface roughness when grinding with conventional, diamond or CBN grinding wheels were clarified. The accuracy of these methods has been verified by comparing the roughness values resulting from calculation and experiments. The results show that the obtained roughness values in both cases were very compatible with each other. Consequently, the results of this study can be used to predict the grinding surface roughness to reduce the machine time as well as to improve the efficiency of the grinding process. Acknowledgements. This work was supported by Thai Nguyen University of Technology.

References 1. Hecker, R.L., Liang, S.Y.: Predictive modeling of surface roughness in grinding. Int. J. Mach. Tools Manufact. 43, 755–761 (2003) 2. Lal, G.K., Shaw, M.C.: The role of grain tip radius in fine grinding. J. Eng. Ind., 1119–1125, August 1975 3. Nakayama, K., Shaw, M.C.: Study of finish produced in surface grinding, part 2. In: Proceeding of the Institution of Mechanical Engineers, vol 182 (1968) 4. Sato, K.: On the surface roughness in grinding. Technol. Rep., Tohoku Univ. 20, 59–70 (1955) 5. Yang, C., Shaw, M.C.: The grinding of titanium alloys. Trans. ASME 77, 645–660 (1955) 6. Zhou, X., Xi, F.: Modeling and predicting surface roughness of the grinding process. Int. J. Mach. Tools Manufact. 42, 969–977 (2002) 7. Basuray, P., Sahay, B., Lal, G.: A simple model for evaluating surface, roughness in fine grinding. Int. J. Mach. Tool Des. Res. 20, 265–273 (1980) 8. Steffens, K.: Closed loop simulation of grinding. Annals CIRP 32(1), 255–259 (1983) 9. Sanjay Agarwal, P., Rao, V.: A probabilistic approach to predict surface roughness in ceramic grinding. Int. J. Mach. Tools Manufact. 45, 609–616 (2005) 10. Sanjay Agawal, P., Rao, V.: Surface Roughness Prediction Model for Ceramic Grinding, pp. 1–9. International Mechanical Engineering Congress and Exposition, Orlando, Florida USA (2005) 11. Khare, S.K., Agarwal, S.: Predictive modeling of surface roughness in grinding. In: CIRP Conference on Modelling of Machining Operations, vol. 31, pp. 375–380 (2015) 12. Saxena, K.K., Agarwal, S., Das, R.: Surface roughness prediction in grinding: a probabilistic approach. In: MATEC Web of Conferences, vol. 82 (2016). https://doi.org/10.1051/ matecconf/20168201019 13. Novoselov, Y.: The Dynamics of Formation of Surfaces in Abrasive Machining. Publ. SevNTU, Sevastopol (2012)

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14. Bogutsky, V., Yu, N., Bratan, S.: Analysis of relation between grinding wheel wear and abrasive grains wear. In: 2nd International Conference on Industrial Engineering Procedia Engineering, vol. 150, pp. 809-814 (2016) 15. Bogutsky, V., Yu, N., Shron, L.: Forecasting the surface roughness of the work-piece in the round external grinding. In: International Conference on Modern Trends in Manufacturing Technologies and Equipment, Web of Conferences, vol. 129 (2017). https://doi.org/10.1051/ matecconf/201712901080 16. Malkin, S., Guo, C.: Grinding Technology - Theory and Application of Machining with Abrasives, 2nd edn. Industrial Press, New York (2008) 17. Marinescu Loan, D., Uhlmann, E., Brian Rowe, W.: Handbook of Machining with Grinding Wheels, CRC Press Taylor & Francis Group (2006) 18. Bajkalov, A.K.: Introduction to the Theory of Grinding Materials. Naukova dumka, Kiev (1978) 19. Maslov, E.N.: Theory of Grinding Materials. Mashinostroenie, Moscow (1974) 20. Murdasov, A.V., Wolff, A.M.: Peculiarities of Working of Grinding Wheels from Abrasive Grains of Different Shapes, (Abrasives and diamonds: scientific technical abstract collection vol 4) (Moscow: NIIMASH), pp. 65-69 (1967) 21. Vakser, D.B.: The Influence of the Geometry of Abrasive Grains on the Properties of the Grinding Wheel. Mashgiz, Moscow (1960) 22. Shaw, C.M.: Principles of Abrasive Processing. Oxford University Press, Oxford Series on Advanced Manufacturing New York (1996) 23. Korolev, A.V., Novoselov, Y.: Theoretical and Probabilistic Basis of Abrasive Treatment. Saratovsk. un-t, Saratov (1987) 24. El-Hofy: Fundamentals of Machining process: Conventional and Nonventional Processes, CRC Press, Boca Raton (2006) 25. https://www.noritake.co.jp/eng/catalog_type/download/ 8aa6080c86465c93cfebd53c07689c7f.pdf 26. Trung, D.D., Son, N.H.: An experimental study on prediction of surface roughness in grinding. Int. J. Mech. Prod. Eng. Res. Dev. 10(1), 47–58 (2020)

A Vision-Based Measurement and Classification System for Robot Arm Under Controlled Lighting Condition Quang-Cherng Hsu1, Ngoc-Vu Ngo2(&), Thanh-Long Pham2, Quoc-Khanh Duong2, and Duc-Vuong Vu2 1 National Kaohsiung University of Science and Technology, 415 Chien Kung Road, Sanmin District, Kaohsiung 80778, Taiwan, ROC 2 Thai Nguyen University of Technology, No. 666, 3/2 Street, Thai Nguyen City, Vietnam [email protected]

Abstract. This paper presents development of a measurement and classification system for robot arm using machine vision under controlled lighting environment. The proposed system uses a single camera as a sensor for measuring and classifying objects which are bolts and nuts. Using image processing and analysis, characteristics of objects was extracted and area of blob in binary image also was calculated for classification process. For coordinate calibration process, the quadratic transformation and regression analysis were used to determine relationship between image coordinate and the world coordinate. Experiment results showed that the proposed system can measure and classify the components exactly of 100% from all samples tested and measurement errors are suitable with the system which applied to a robot arm. Keywords: Machine vision
Classification
Robot arm
Calibration
Camera

1 Introduction One of the techniques for automating robots is the machine vision [1, 2]. With this technique, robots are equipped with a human-like perception that can distinguish the shapes and colors or locations of objects to perform operations according to human. Machine vision uses one or more cameras to collect images of objects, then image analysis and processing algorithms will be used to identify and locate objects. There have been many researches applying machine vision to develop automatic identification and sorting systems integrated with robots in production lines. In a study of Tsarouchi et al. [3], the authors used a machine vision system to identify and locate objects in the world coordinate. This system used a 2MP camera which is Basler A641FC. The camera is located and fixed on the top of conveyor belt. Their research used MATLAB software to implement image processing algorithms, identify and convert image coordinates to 2-D real coordinates. This system has been integrated with robots to perform operations in production lines. In another research of Phansak et al. [4], the authors have developed a flexible automated assembly system for © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 112–119, 2021. https://doi.org/10.1007/978-3-030-64719-3_14

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robotic arm. They created assembly parts to perform the experiments. These parts include triangles, squares and circles that can be assembled with the corresponding holes. Thus, to identify and classify the images above, they used a camera to capture images, then the image processing and analysis was carried out. After classifying the above objects, the positions of the shapes and the holes are determined to perform the assembly. An important work in machine vision system for industrial robots is coordinate calibration process. Coordinate calibration is to transform image coordinates to the world coordinates. There have been many studies related to coordinate calibration algorithms to enhance accuracy of this process. In the study of Ngo et al. [5], a 3-D coordinate calibration method has been proposed. This method used six defined points in a coordinate system in real space. With these points, the authors can identify all internal and external parameters of the double cameras used in the study and thereby determine the relationship between image coordinates and the world coordinates in three dimensions (3-D). In another research by Hsu et al. [6], a calibration 2-D coordinate method was investigated. This study used a quadratic transformation and regression analyze for the calibration process. This method has been compared with Matlab’s calibration tool and experimental results showed that it is accurate and suitable for their proposed visual system. Base on previous researches, in this study, the authors have developed a system that can classify and measure objects in two-dimensional space (2-D). In order to obtain good images for processing and analysis, the research used controlled lighting source. The quadratic transformation and regression analysis algorithms were used for coordinate calibration process and accuracy of this process also was investigated.

2 Experimental System The vision-based measurement and classification system in this study is shown in Fig. 1. This system used a camera CMOS which amount on the top of the workshop. Measured objects are metal components including bolts and nuts. They are random on the top of the lighting source. A computer was used to analyze images of desired objects and perform measuring and classification algorithms. To locate objects in the world coordinate system, calibration process was implemented using a calibration sample with the quadratic transformation algorithm and regression analysis. And this system was integrated with a robot arm via a controller. In fact, metallic components are very sensitive to ambient light. When light intensity changes, the degree of light reflection on the surface of the components also changes. This greatly affects the image processing. Therefore, to overcome this problem, this research used the controlled lighting source. With this light source, components are placed in the space between the camera and the light source [7]. Therefore, the boundary of the objects is exactly extracted, so that the resulting image can easily be processed by adjusting the binary threshold. The layout of this light source is also shown in Fig. 1. The objects in this study consist of 7 nuts and 5 bolts placed randomly above the light source, as shown in Fig. 2.

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Fig. 1. Experimental system

Fig. 2. Metallic components

3 Research Methodology 3.1

Image Processing

Extracting and processing objects in an image is an important work in a vision system. With this work, camera will capture objects and record into a computer. Here, threshold operation is performed to obtain the best binary image. In image processing, morphological operations are often used to recognize and enhance the quality of image and then extract characteristics of objects [8]. Morphological operations normally are performed onto the binary and gray images. With each image, the output values of each pixel are based on the input values of pixel, respectively, and their neighbor values. Here, the algorithm will give each pixel value with gray level in the range of 0–255. The threshold operation uses the highest and smallest threshold value in the range of

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0–255 to find the most suitable threshold for image, as shown in Eq. (1). In addition, the morphological method is also used to reduce noise in the image and smooth the boundaries of the objects of interest, thereby extracting the characteristics of the best object. 8 < 255 f ðx; yÞ  K1 f 0 ðx; yÞ ¼ f ðx; yÞ\K2 ð1Þ : 0 others Where f’(x,y) is the new gray level at point I(x,y). f(x,y) is a 2-D light intensity function. K1 is the low limit threshold value (0  K1  K2). K2 is the high limit threshold value (K1  K2  255). x, y are the image coordinates in the X and the Y directions, respectively. 3.2

Calibration Algorithm and Method

Calibration Algorithm. To determine relationship between image coordinate and the world coordinate, this study used quadratic transformation and regression analysis based on Eq. (2), as follows: 

X ¼ a1 x2 þ b1 xy þ c1 y2 þ e1 x þ f1 y þ g1 Y ¼ a2 x2 þ b2 xy þ c2 y2 þ e2 x þ f2 y þ g2

ð2Þ

Where a1, b1, c1, e1, f1, g1, and a2, b2, c2, e2, f2, g2 are transformation coefficients and x, y are image coordinates; X, Y are the world coordinates The regression analysis can be applied for Eq. (1) to determine transformation coefficients when image and the world coordinates of six points are known. Whereby, the summation of the least error square in X and Y direction is described in Eq. (3), as follows: 8 n  2 P > > Xi  ða1 x2i þ b1 xi yi þ c1 y2i þ e1 xi þ f1 yi þ g1 Þ < SX ¼ i¼1 n  2 P > > Yi  ða2 x2i þ b2 xi yi þ c2 y2i þ e2 xi þ f2 yi þ g2 Þ : SY ¼

ð3Þ

i¼1

Where SX and SY are the summation of the error square, n is number of calibration points. To determine the transformation coefficients in Eq. (3), partial derivatives of SX and SY with each the transformation coefficient were performed, as shown in Eq. (4): @SX @SX @SX @SX @SX @SX ¼ 0; ¼ 0; ¼ 0; ¼ 0; ¼ 0; ¼0 @a1 @b1 @c1 @e1 @f1 @g1 @SY @SY @SY @SY @SY @SY ¼ 0; ¼ 0; ¼ 0; ¼ 0; ¼ 0; ¼0 @a2 @b2 @c2 @e2 @f2 @g2

ð4Þ

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Calibration Method. This study used a coordinate calibration sample, as shown in Fig. 3 (a). This calibration sample consists of 156 black circles with a diameter of 11 mm. The distance between two successive circles is 28 and 22 mm in the X and Y directions, respectively. First, the vision system will collect the image of the calibration sample as an input data. And then, this image will be binary by adjusting the binary threshold.

(a)

(b)

Fig. 3. Coordinate calibration sample

To determine the center of the circles and their order, the Blob analysis was used. With this method, closing and opening functions are performed to eliminate noise and improve the quality of blobs in binary images. Then, the system will calculate the number of Blobs corresponding to the black circles in binary image to be determined and eventually give the image coordinates of the entire center of the calibration circles as well as their order, as shown in Fig. 3 (b). By using the calibration method, these coordinates will be interpolated to the world coordinates in 2-D space and the transformation coefficients in Eq. (2) will be determined and used for calculating coordinates of other objects in the workspace of the system.

4 Results and Discussion 4.1

Calibration Results

According to the calibration results, as shown in Fig. 4, the highest positive deviations of the calibration process are 0.63 mm and 0.72 mm in X and Y directions, respectively. The highest negative deviations are −0.67 mm and −0.80 mm in X and Y directions, respectively. Base on the calibration deviation graph, it can be seen that the number of calibration points with an outside deviation of 0.6 mm is small in both negative and positive ones. Most deviations are within the range of 0.4 mm to 0.5 mm in both negative and positive deviations. These deviations can be improved by enhancing the camera resolution and image quality. While the resolution of the proposed vision system was 0.56 mm/pixel and 0.6 mm/pixel for image size in X and Y directions, respectively. Besides, this vision system is applied to a robot arm, these

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deviations can be compensated by gripper of robot arm. Therefore, robot arm can still pick and place exactly all objects in workspace.

Calibration deviation 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1

1

12

23

34

45

56

67

78

OX axis

89 100 111 122 133 144 155 OY axis

Fig. 4. Calibration deviation chart

4.2

Measuring and Classification Results

Image processing for bolts and nuts is shown in Fig. 5. To achieve the best binary image quality, the intensity threshold values were controlled and selected. After obtaining the best binary images, Blob analysis was conducted to classify bolts and nuts. To distinguish these two types of components, the area of Blob (white color) in binary images was calculated and compared. The larger Blobs were identified as bolts and the nuts were the Blobs which have smaller area with black hole, and the system automatically assigned labels and names to the corresponding components. In classification process, nuts are non-directional distinction, so no need to identify their angle. However, the bolts need to be determined the direction and head. Direction of bolts is angle between center line of bolts and OX axis in counterclockwise. To identify the head of bolts, image of bolts was found both the left and right side of the center of bolts. Comparison of number of pixels between left side and right side of bolts was performed. The larger image region that number of pixels is bigger is head of bolt. After the classification process, the system showed labels and the center coordinates of the objects, as shown in Figs. 6 and 7. Through calibration process, the center coordinates of the objects were determined in real space 2-D. The classification results show that the nuts are more precisely defined than the bolts because the shape of the nut is quite simple while the shape of the bolts are more complicated, so their positions are skewed. This is a difficult problem in image processing and machine vision and it can be improved by improving the resolution of the camera and adjusting the lighting condition.

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a. Gray image

b. Binary image

Fig. 5. Gray and binary image

Fig. 6. Measuring and classification result

Fig. 7. Interface of proposed system

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5 Conclusions This research has successfully developed a system to automatically measure and classify metallic parts under controlled lighting conditions for robot arm. In addition, the coordinate calibration method using the quadratic transformation and regression analysis algorithm has been used to determine the world coordinates of 2-D objects with errors that match the resolution of the system. By using the controlled light source, the influence of light on the image quality for classified objects has been significantly reduced. The proposed system can automatically measure and classify the metal components exactly of 100% and measurement deviations are suitable with the system. In the next studies, the authors will continue to develop this system in a realistic light environment to better fit the systems and production lines in the factory to increase flexibility for machine vision system.

References 1. Peter, C.: Robotics, Vision and Control: Fundamental Algorithms in Matlab. Springer, New York (2013) 2. Fu, K.S., Gonzalez, R.C., Lee, C.: Robotics: Control, Sensing, Vision, and Intelligence. McGraw-Hill Book Company, New York (1987) 3. Tsarouchi, P., Matthaiakis, S.A., Michalos, G., Makris, S., Chryssolouris, G.: A method for detection of randomly placed objects for robotic handling. CIRP J. Manufact. Sci. Technol. 14, 20–27 (2016) 4. Phansak, N., Pichitra, U., Kontorn, C.: Using machine vision for flexible automatic assembly system. In: The 20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES2016, York, United Kingdom (2016) 5. Ngo, N.V., Hsu, Q.C., Hsiao, W.L., Yang, C.J.: Development of a simple three dimensional machine vision measurement system for in-process mechanical parts. Adv. Mech. Eng. 9, 1– 11 (2017) 6. Hsu, Q.C., Ngo, N.V., Ni, R.H.: Development of a faster classification system for metal parts using machine vision under different lighting environments. Int. J. Adv. Manufact. Technol. 100(9–12), 3219–3235 (2019) 7. Malamas, E.N., Petrakis, E.G., Zervakis, M., Petit, L., Legat, J.D.: A survey on industrial vision systems, applications and tools. Image Vis. Comput. 21(2), 171–188 (2003) 8. Frank, Y.: Shih: Image Processing and Mathematical Morphology: Fundamental and Applications. CRC Press, Taylor & Francis Group, Boca Raton (2009)

About a Viewpoint of Calculating Spatial Dimensional Tolerance Chains According to Structure Group of a Parallel Robot Thuy Le Thi Thu1(&), Trung Trang Thanh1,2, Huu-Thang Nguyen1,3(&), and Long Pham Thanh1 1

Faculty of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam {hanthuyngoc,nguyenhuuthang}@tnut.edu.vn 2 School of Mechanical and Automotive Engineering, South China University of Technology, Guangdong, China 3 Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan

Abstract. In robotics technology, tolerance design is a part of mechanical design and determines the final product quality. The spatial dimensional chain problem of the parallel structure with component links having an active assembly and the closed link having a given error is a complex one. Determining the relationship between the end effector tolerances and component link tolerances of each leg in general form is the basis for digitizing the problem. Calculating the initial approximation of tolerance for other structures in the group based on the sample solution has the meaning of deciding time and quality of calculation. This paper proposes a hypothesis that it is always possible to determine a reasonable initial approximation based on a sample solution. Therefore, the process of calculating tolerances of the component kinematic parameters of parallel industrial robots is significantly shortened. Keywords: Parallel robot End-effector accuracy


Tolerance
Kinematics
Structure similarity


1 Introduction After having the nominal dimensions, the structures should have tolerances to decide the processing technology measures, the cost and methods of assembly. Usually, the design process will give the accuracy and repeatability accuracy at the end of the actuator link, and the engineer needs to determine the component tolerances of the two objects are the tolerance of the generalized coordinates and the fabrication dimensions. Generalized coordinate tolerances are often used to select the resolution of electronics such as motors or sensors, while structural tolerances are used to make structural components. The problem is complicated when considering execution objects with the form of parallel structures (Fig. 1). A parallel robot is a machine with a special form. The links and joints connect together to form a closed chain. In these closed chains, the moving platform links with © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 120–132, 2021. https://doi.org/10.1007/978-3-030-64719-3_15

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Fig. 1. Several types of parallel robots.

the base platform via legs for end actuator working with very high load capacity, rigidity and precision. There have been studies on the accuracy and tolerance design of these parallel robots, but the number is limited. Some researches have tried to use different methods to find the relationship between the end-effector kinematic deviation with the link and joint tolerances, and then design, determine the suitable tolerance values. Each method of building a mathematical model is applied to certain types of parallel robots [1–6]. In this paper, the authors propose a procedure for calculating tolerances of kinematic parameters of parallel robots based on considering each branch acting as an independent serial robot. The calculation process goes through two clear steps: determining the preliminary tolerances as initial approximations and checking, adjusting these preliminary tolerances. In particular, the tolerance design process based on a parallel robot structure group relationship to shorten the calculation process is considered.

2 Tolerance Calculation for Parallel Robot Consider a general structure as shown in Fig. 2a, in which the E link is the working link. The accuracy of this link is given. At the design analysis step, it is necessary to indicate the errors of the active joints (the tolerances of the generalized coordinates) and the errors of its component links so that the given final link errors are guaranteed in the entire working area. The ith branch (ith leg) of the structure can form the following equation: fi ðO1 ; O0 ; qi ; di Þ ¼ pðjÞ

ð1Þ

With i ¼ 1; . . .; n n is the number of legs and is the degrees of freedom of the robot. The complete set of equation systems as Eq. (1) describes the kinematics of n legs and will fully describe the kinematics of the parallel robot.

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Fig. 2. Components of a parallel robot

p (j): pose of the end-effector O0: Basic coordinate system O1: local coordinate system mounted on the moving platform di: dimension parameter of the ith link on each leg ðaÞ ðbÞ ðaÞ qi: joint parameter, in which qi is divided into two types: qi ¼ ðqi ; qi Þqi are ðbÞ

active joint variables (generalized coordinates);qi are extra parameters. These parameters are not as active as the generalized coordinates, they act as cosine directions of the legs. Equation (1) will be rewritten in full form as follows: ðaÞ

ðbÞ

fi ðO1 ; O0 ; qi ; qi ; di Þ ¼ pðjÞ

ð2Þ

To characterize the accuracy or limit the final error in the workspace, a sphere with center pi and radius d is used. In which, Pi is the desired target position and radius d describes the permissible deviation limit at the position Pi. As shown in Fig. 3, with the center of the sphere as the desired position, the space in the sphere is the actual end position achieved and within the desired precision limits. Therefore, to determine the outside permissible region inside permissible region

permissible error radius r

End-effector

real position desired position

Fig. 3. The sphere limits the permissible error of the end-effector

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allowable deviations of the link and joint parameters of the robot, the following kinematic problems were established: ðaÞ

ðbÞ

ðjÞ

ð3Þ

ðaÞ

ðbÞ

ðjÞ

ð4Þ

ðaÞ

ðbÞ

ðjÞ

ð5Þ

ðaÞ

ðbÞ

ðjÞ

ð6Þ

ðaÞ

ðbÞ

ðjÞ

ð7Þ

ðaÞ

ðbÞ

ðjÞ

ð8Þ

fi ðoj ; o0 ; qi ; qi ; di Þ ¼ p1 ðxp þ d; yp ; zp Þ fi ðoj ; o0 ; qi ; qi ; di Þ ¼ p2 ðxp  d; yp ; zp Þ fi ðoj ; o0 ; qi ; qi ; di Þ ¼ p3 ðxp ; yp þ d; zp Þ fi ðoj ; o0 ; qi ; qi ; di Þ ¼ p4 ðxp ; yp  d; zp Þ fi ðoj ; o0 ; qi ; qi ; di Þ ¼ p5 ðxp ; yp ; zp þ dÞ fi ðoj ; o0 ; qi ; qi ; di Þ ¼ p6 ðxp ; yp ; zp  dÞ ðjÞ

ðjÞ

Where, p1


p6 is six representative points, surveying the allowed deviation around the position p (j) of the end-effector. J = 1… m; m is the number of survey points in the workspace. The inverse problem from (3) – (8) solved by [7] gives the allowed displacement of all parameters in Eq. (2) as follows: – With generalized coordinates: ðaÞ

2 ðqimin ; qimax Þ ¼¼ [ dqi

ðbÞ

2 ðqimin ; qimax Þ ¼¼ [ dqi

qi

ðaÞ

ðaÞ

ðaÞ

ð9Þ

ðbÞ

ðbÞ

ðbÞ

ð10Þ

– With extra parameters: qi – With link dimensions: di 2 ðdimin ; dimax Þ

¼¼ [ ddi

ð11Þ

This is a preliminary set of tolerances. To get the final tolerances, a retest is required to meet the accuracy required by the expression: ðaÞ

ðaÞ

ðbÞ

ðbÞ

fi ðO1 ; O0 ; qi  dqi ; qi  dqi ; di  ddi Þ  pðkÞ  d

ð12Þ

Because the forward kinematic problem is applied to parallel robots, it is always polynomial. For the forward kinematic problem to repeat at the exact point of the survey without giving new points due to its polynomial, it is necessary to provide the ðaÞ ðbÞ ðaÞ value of qi ; qi ; di in Eq. (12), not only the extrapolation coordinates qi as when regular solving. The results of calculation of tolerance after satisfactory inspection at Eq. (12) are used as follows:

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The deviation range of the generalized coordinates (in Eq. (9)) is used to select the sensors and the robot drive motors; The deviation range of the extra parameters (in Eq. (10)) is to determine the manufacturing and assembly quality for structures which produce the corresponding extra parameters; The deviation range of link dimensions (in Eq. (11)) is the tolerances written on the fabrication drawing to manufacture the links.

3 Tolerance Calculation for Parallel Robot Base on Structure Group Considering the problem, there are two parallel robots with the same structure, in which one robot already has tolerances or its tolerances calculated as Sect. 2. The remaining parallel robot needs to determine initial tolerance. The whole process of the calculation is divided into two clear steps: determining the first approximation tolerance according to the structure group and checking the endpoint accuracy response to adjust the first approximation to the final completed tolerance value (Fig. 4).

Fig. 4. Example of two parallel robots with the same structure type

Let A and B be two robots of the same structure type. A is a sample robot with links and joints tolerances already in place. B is a robot that needs to find tolerance. aAi ; diA and aBi ; diB are respectively the nominal dimensions of the ith link of robots A and B, daAi ; ddiA and daBi ; ddiB are their tolerances.r A ; r B are the radius of the sphere that limits the permissible error at the end-effector of robots A and B respectively. dA

rA

Call k ¼ diB the dimensional similarity ratio between two robots A and B, kr ¼ rB is i

the accuracy ratio between A and B.

About a Viewpoint of Calculating Spatial Dimensional

125

Fig. 5. The discrete branches of the parallel structure from the root structure are described as the open chain

Robot kinematic equations are built on each leg (Fig. 5) to form separate closed loops. Then, each branch will be equivalent to an open-chain robot and can apply the theory presented in [8]. Some situations occur as follows: 3.1

Two Branches of Dimensional Similarity

Suppose A and B are two similar branches of two parallel robots of the same structural form. Robot A has an end-effector accuracy and a kinematic tolerance given. At the time of the survey, branch A has a generalized coordinate set of A1 (q1, q2, q3)(1A) and the tolerance of the link parameters is ðdai ; ddi Þð1AÞ corresponding to that state. B is the same as branch A at that position, that is, has the same generalized coordinate set as A1 is B1 (q1, q2, q3)(1B). – Consider case 1: A and B are similar, k = kr. Having the following similarity relationship: ðai ; di ÞðAÞ ¼ kðai ; di ÞðBÞ

ð13Þ

Because the two branches satisfy the similar relationship, it is always possible to establish the derivative relation of eq. (14) for the ith survey point of the working area as: ðdai ; ddi ÞðiAÞ ¼ kðdai ; ddi ÞðiBÞ

ð14Þ

– - Consider case 2: A and B are similar, but k 6¼ kr. Considering the case of two arms that are similar but the similarity ratio differs from the accuracy ratio (k 6¼ kr), that is, the accuracy is not zoomed in the same proportion as the link dimension of the branch:

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k¼

di

rA or k 6¼ kr rB

6¼

ðBÞ di

ð15Þ

Call B’an intermediate branch: B’ has the configuration of B, that is k ¼

ðBÞ

di

ðB0 Þ di

¼ 1,

and the end-effector accuracy rB’ satisfies ðAÞ

k¼

di

ðB0 Þ di

¼

rA i:ek ¼ kr rB 0

ð16Þ

Thus, the tolerance of intermediate branch Bis found as in case 1. From here, we determine the tolerance of branch B according to B′. Suppose that the tolerance of tolerance B′ determined is ðB0 Þ ðda1 ; ::; dan ; dd1 ; ::; ddn Þ . The establishment of component relationships between the permissible error of the end-effector and the tolerances of the component links according to the rates is as follows: ð

rB 0 rB0 rB0 rB0 ðB0 Þ ; ::; ; ; ::; Þ da1 dan dd1 ddn

ð17Þ

Assume that this is also true for branch B. It is possible to apply the above ratio to distribute the permissible error of the end-effector rB for the tolerances of the component links ðda1 ; ::; dan ; dd1 ; ::; ddn ÞðBÞ to accelerate the computational speed. Let m be the common divisor of rB and r’B and j1 ¼

rB0 rB ; j2 ¼ m m

ð18Þ

Then, the relationship of the unit displacement between the joint space and the workspace of robot B′ is assumed to be ðB0 Þ

ðB0 Þ

ðB0 Þ

rB0 da dan dd1 ! ð 1 ; ::; ; j1 j1 j1 j1

ðB0 Þ

ddn ; ::; Þ j1

ð19Þ

If this ratio is applied to branch B based on similarity, the tolerances of the corresponding links will be ðB0 Þ

ð

ðB0 Þ

ðB0 Þ

da1 dan dd :j2 ; ::; :j2 ; 1 j1 j1 j1

ðB0 Þ

:j2 ; ::;

ddn j1

:j2 ÞðBÞ

ð20Þ

The reallocation of the tolerances of the joint variables of B also uses the above rule:

About a Viewpoint of Calculating Spatial Dimensional ðB0 Þ

ð

ðB0 Þ

127

ðB0 Þ

dq1 dq dqn :j2 ; ::; 2 :j2 ; ::; :j2 ÞðBÞ j1 j1 j1

ð21Þ

Thus, this method saves time because it does not have to recalculate the tolerance of B from the beginning. The ratio distributing the upper and lower deviations of the tolerance of B is taken from that of B’ as a sample. 3.2

Two Branches of the Same Structure but not Similar

Consider the case where the branches designed have the same structural form as a sample branch but not identical. Assume branch A has kinematic parameters: aAi ; diA i ¼ 1. . .n with the corresponding tolerances daAi ; ddiA i ¼ 1. . .n . Joint variable qi, i = 1… n, having dqi (rad). The sphere limiting permissible error has radius rA (mm); Let B be a branch of the same structural form but not similar as A, that is, the ratio between their respective links is not equal: diB diBþ 1 6¼ diA diAþ 1

ð22Þ

The positioning accuracies of the end-effectors rA and rD are different. The kinematic tolerance of robot B is found through robot A and intermediate robot C in two phases, as shown below: – Phase 1: Find rC for intermediate branch C – Phase 2: Find the tolerance of branch B based on branch C being similar to B Phase 1: Find rC for intermediate branch C Let C be a robot with a similar structure to robot B and in the same structure group but not similar to robot A, that is, diC diCþ 1 6¼ diA diAþ 1

ð23Þ

With the technique introduced in Sect. 2 and applied to branch A, the reasonable ratio between the tolerance and the corresponding link length of this type of robot structure was determined as

ddiA . diA

The natural kinematics of the robot according to this

ratio can be reasonable. Without loss of generality, suppose that robot C in the same group A uses the same ratio for link consideration, that is, it has the following relationship: ddiA ddiC dC ¼ C ! ddiC ¼ ddiA : iA A di di di

ð24Þ

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When dCi changes its nominal length compared with dA i , its tolerance is recalculated by this ratio. After the tolerance of links ddiC is defined as Eq. (24), rC is found by using the forward kinematic equation to sweep the combinations of C. The joint tolerances dqCi of C stay the same as those of robot A. The parameters are passed to the computational software to determine the radius of the spherically permissible region for all combinations and to find the maximum possible spherical radius rC = max(rCi). The parameter used for phase 2 is rC; therefore, after determining this value, phase 1 ends. Phase 2: Find the tolerance of branch B based on branch C being similar to B Branch B is in the same group as sample branch A and has an identical configuration as the intermediate branch C. We need to find the kinematic parameter tolerances of B according to the requirement from the end-effector accuracy rB. Since B is a branch with the same configuration as branch C rC 6¼ rB , the procedure is similar to problem 1 with case 2 presented above.

Fig. 6. The planar parallel robot 3RRR

4 Case Study Consider the planar parallel robot structure 3RRR as shown (Fig. 6) The planar parallel robot 3RRR has three legs structure forming three closed loops. The moving platform of the manipulator is a tripod ABC working in the 2D plane. Each leg is formed by two links ai and bi, linked together and with the base platform, the moving platform through three hinge joints. Here, the position and direction of the moving platform are determined by the center of the tripod O1 (xO1, yO1) and the direction angle u. Active joint variable ϴi, passive joint variable ai (i = 1, 2, 3 corresponds to 3 legs of the robot).

About a Viewpoint of Calculating Spatial Dimensional

4.1

129

Determining Tolerance for an Independent Robot

Planar parallel robot A has link dimensions a1 = a2 = a3 = 40 cm; b1 = b2 = b3 = 30 cm; limit of position deviation at the centre of the moving platform due to the robot dimension error is rA = 0.1 mm. The kinematic equations of the three legs of the robot are as follows:








xO1 yO1

xO1 yO1

xO1 yO1





# 
 " hpffiffi3 : cosðu þ 30Þ b1 : cosðh1 þ a1 Þ a1 : cosðh1 Þ 3 þ þ hpffiffi3 ¼ a1 : sinðh1 Þ b1 : sinðh1 þ a1 Þ : sinðu þ 30Þ


3




b : cosðh2 þ a2 Þ c a : cosðh2 Þ þ 2 þ ¼ þ 2 a2 : sinðh2 Þ b2 : sinðh2 þ a2 Þ 0




¼

"



 c  b3 : cosðh3 þ a3 Þ a3 : cosðh3 Þ 2 ffiffi p c 3 þ a : sinðh Þ þ b : sinðh þ a Þ þ 3 3 3 3 3 2

# pffiffi h 3 : cosðu þ 30Þ 3 ffiffi p h 3 3 : sinðu þ 30Þ

"

# pffiffi h 3 3pffiffi: sinðu þ 30Þ h 3 3 :cosðu þ 30Þ

Two separate problems are performed: determining link tolerances and determining joint tolerances. Based on solving the kinematic problem, the joint variables hi ; ai are certain values, calculating the allowed deviations ai, bi at the boundary positions of the permissible error region of the end-effector with r = 0.05 mm. Making statistics of errors, tolerances of the links are determined as dai ¼ 0:35 mm, dbi ¼ 0:32 mm.

Fig. 7. Simulation on the software of checking the design tolerances at a position in the working space of robot A

Similarly, with the radius controlling end-effector error r = 0.05 mm, the joint tolerances are determined as dqi ¼ 0:00034 rad.

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The calculated data are input into the specialized software with interchangeable plans the links and joints in the selected tolerance range. Test results show that the tolerances above are satisfied (Fig. 7). 4.2

Determination of Tolerance Based on Structural Group

a. Two parallel robots are similar and k = kr Robot B that needs to find tolerances has structural parameters: a1 = a2 = a3 = 20 cm; b1 = b2 = b3 = 15 cm; limit of position error at the center of the moving platform due to the kinematic dimension error of robot is rB = 0.05 mm. Thus, this is a case of determining the tolerance of a robot based on a similar relationship and the similarity ratio equal to the accuracy ratio. According to the theory presented in 3.1, the tolerance of this robot is determined as follows: dai ¼ 0:175 mm, dbi ¼ 0:16 mm, dqi ¼ 0:00017 rad (i = 1, 2, 3 corresponds to 3 branches (3 legs) of the robot). b. Two parallel robots are similar and k 6¼ kr Robot B has a1 = a2 = a3 = 20 cm; b1 = b2 = b3 = 15 cm. Assuming that in this case, the accuracy at the end-effector of robot B needs to be rB = 0.07 mm. Thus, the similarity of the structure and accuracy of the end-effector have different zoom ratios or k 6¼ kr. In this situation, using intermediate robot B’and found the tolerance for this intermediate 0 robot (at this time B’ is robot B in Sect. 4.1.a): r B ¼ 0:05 mm, r B ¼ 0:07 mm. The common divisor m = 0.01 j1 ¼

rB 0 rB ¼ 5; j2 ¼ ¼ 7 m m

So, the tolerance of robot B to find is: 0

0

0

daB dbB dqB dai ¼ i  j2 ¼ 0:245mm; dbi ¼ i  j2 ¼ 0:224dqi ¼ i  j2 j1 j1 j1 ¼ 0:000238rad c. Two parallel robots have the same type of structure but not the similarity Robot B, which needs to find tolerance, has kinematic parameters: a1 = a2 = a3 = 40 cm; b1 = b2 = b3 = 35 cm, the end-effector accuracy should be achieved rB = 0.1 mm. It is easy to see that sample robot A and robot B have the same structure as the planar parallel manipulator 3RRR but not similar in size. How to solve the problem is done according to theory Sect. 3.2 as follows: Call C an intermediate robot configured as the robot that needs to find tolerance B. With the internal ratio showing the reasonable relationship between the tolerance and aC

the link length of robot A, apply this ratio to robot C: daCi ¼ daAi : aiA ¼ 0: 175 mm, i

bC

dbCi ¼ dbAi : biA ¼ 0: 187 mm i

About a Viewpoint of Calculating Spatial Dimensional

131

Using the forward kinematic problem combined with statistics, the allowable error of the center of the moving platform caused by the link and joint error is determined as rC = 0.12 mm.

Fig. 8. Checking design tolerances for robot B

At the end of phase 1, the intermediate robot has the same configuration as the robot B that needs to find tolerance, has the link and joint tolerance and end-effector accuracy different from that of B. Therefore, the relationship between robot C and B is similar to the case of two robots of the same configuration but different from the end-effector accuracy resolved above (rB = 0.1 mm, rC = 0.12 mm). The final result, the tolerance of the kinematic parameters of robot B be found as follows:dai ¼ 0:145 mm, dbi ¼ 0:17 mm, dqi ¼ 0:00017 rad The verification process based on the interchange done on the software proved that tolerance values found by the above method are perfectly reasonable and satisfactory for the given end-effector accuracy (Fig. 8).

5 Conclusions Tolerance calculation for a parallel robot is a very complex problem. The splitting each branch (leg) of the robot and applying the hypothesis of the existence of the initial approximation tolerance in the structural group as we presented above shows that this is the correct direction to speed up the calculation process. Starting from any robot in the group that has a known tolerance, the initial approximation tolerances are determined quickly and they will be checked for a combination of factors on the specialized software for the final reasonable tolerance values. The tolerance technique for an independent robot is different from the tolerance technique for a serial robot that we mentioned earlier in [8] because the subject here is a parallel robot. The initial approximation tolerance is applied to each branch without counting once for all branches, i.e. all links such as chain robots. The above computational efficiency has shown that this technique deserves to be applied in parallel robot tolerance design process.

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Acknowledgment. The authors want to thank Thai Nguyen University of Technology (TNUT), Vietnam for sponsoring this research.

References 1. Takematsu, R., Satonaka, N., Thasana, W., Iwamura, K., Sugimura, N.: A study on tolerances design of parallel link robots based on mathematical models. J. Adv. Mech. Des. Syst. Manuf. 12(1), 2018. https://doi.org/10.1299/jamdsm.2018jamdsm0015 2. Sun, T., Wang, P., Lian, B., Liu, S., Zhai, Y.: Geometric accuracy design and error compensation of a one-translational and three-rotational parallel mechanism with articulated traveling plate. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 232(12), 2083–2097 (2018). https://doi.org/10.1177/0954405416683433 3. Huang, T., Chetwynd, D.G., Mei, J.P., Zhao, X.M.: Tolerance design of a 2-DOF overconstrained translational parallel robot. IEEE Trans. Robot. (2006). https://doi.org/10. 1109/TRO.2005.861456 4. Huang, T., Bai, P., Mei, J., Chetwynd, D.G.: Tolerance design and kinematic calibration of a four-degrees-of-freedom pick-and-place parallel robot. J. Mech. Robot. 8(6), 061018 (2016). https://doi.org/10.1115/1.4034788 5. Ni, Y., Shao, C., Zhang, B., Guo, W.: Error modeling and tolerance design of a parallel manipulator with full-circle rotation. Adv. Mech. Eng. 8(5), 1–16 (2016). https://doi.org/10. 1177/1687814016649300 6. Hassan, M., Notash, L.: Optimizing fault tolerance to joint jam in the design of parallel robot manipulators. Mech. Mach. Theor. 42(10), 1401–1417 (2007). https://doi.org/10.1016/j. mechmachtheory.2006.10.001 7. Trang, T.T., Li,. W.G., Pham, T.L.: A new method to solve the kinematic problem of parallel robots using general reduce gradient algorithm. J. Robot. Mech. 28-N03 (2016) 8. Thu, T.L.T., Thanh, L.P.: Tolerance calculation for robot kinematic parameters to ensure endeffector errors within a predetermined limit area. Int. J. Eng. Res. Technol. 12(9), 1567–1574 (2019)

Adaptive Algorithm for Servo System Using Linear Electric Motor V. E. Kuznetsov1, Phan Thanh Chung1, Nguyen Thi Ha2(&), and Nguyen Hoang Ha3(&) 1

Saint Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia 2 Thai Nguyen University of Technology, Thai Nguyen, Vietnam 3 Institute of Mechanical Engineering, Ha Noi, Vietnam [email protected]

Abstract. This article aims to build the construction of a mathematical model of a linear motor for moving the valve of an electro-hydraulic drive. First, an analytical description of its non-linear traction characteristic is given as well as the resulting reaction from the action of electromagnetic force and the elastic force of the center spring. Second, the Adaptive algorithm of servo control based on the Lyapunov theorem is built and an analysis of the effectiveness of the adaptive algorithm with the change of the servo system’s parameters. Simulation results on the model of the servo system with the controller in the MATLABSimulink software package are presented to demonstrate the effectiveness of the proposed approach in this paper. Keywords: Linear electric motor


Servo system
Adaptive control

1 Introduction The advancement of new types of aircrafts makes steering systems operate with low displacement amplitudes, which makes requirements for the dynamic properties of steering actuators and for the stability of their properties in the area of small signals even more demanding. In the area of small signals, there is a number of factors that decrease the dynamic and static properties of the system, namely essential nonlinearity in properties linear force motor (LM) and hydraulic valves [1, 2]. The peculiarity of these nonlinear properties is determined by steepness values of static parameters depending on the value of the input signal. Electrohydraulic system parameters changes depend on the changes in temperature and pressure of the service fluid, reduction in reserved control channels quantity, and the presence of external disturbances [3]. In this case, the dynamic properties of a servo system can vary in a wide range. Increasing the sensitivity and stability of an electrohydraulic steering system in the field of small signals under the given conditions can be efficiently achieved by means of adaptive control [2, 3].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 133–142, 2021. https://doi.org/10.1007/978-3-030-64719-3_16

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2 Model of Servo System Using Linear Electric Motor 2.1

Mathematical Model of Linear Electric Motor

Under the assumptions pertaining to the description of electromagnetic process [5], a complete mathematical model of a linear electric motor was obtained, which is described by the following system of differential equations:


di LðFi ;
xÞ dt ¼ u  Ri  Ce ðFi ;
xÞ dx Fi ¼ wi; dt ; 2  ðC  C ð
x ÞÞx  fL ; mlm ddt2x ¼ KFI ð
xÞi  blm dx M dt

ð1Þ

u ðUÞ; iðIÞ; R; w is correspondingly, voltage, current, resistance and number of turns of the LM’s winding; x is the absolute displacement of the armature of the LM; mlm is mass of the moving part; blm is coefficient of viscous friction; C stiffness of the centering mechanical spring, fL is the external force. LM is subject to a significant impact of internal nonlinear properties [2, 5]: – Static property of LFM power The nonlinear static mechanical characteristic of the force LM KFI ð
xÞ ¼


M þ 1 þ
x2 Þw 2kFM ðR ;
M þ 1 
x2 Þ2 ðR

ð2Þ

 1 k ¼ 2l0 Sf R2x0 , l0 are magnetic constant, Sf being square area of the pole tips;  Rx0 ¼ d l0 Sf ,
x ¼ x=d, d are air gap with motor in the mid position, the magneto
M ¼ RM =Rx0 ; the resistance of permanent motive force of permanent magnets FM ; R magnets RM ¼ RM1 ¼ RM2 . Сlm ( x ) x, kgf

100

С=128 kgf/mm С=100 kgf/mm С=70 kgf/mm С=45 kgf/mm

50 0 -50

-1

-0.5

0

0.5

x , mm

Fig. 1. 1. View of nonlinear resulting elastic force of a magnetic and mechanical spring at various stiffness values C

Adaptive Algorithm for Servo System Using Linear Electric Motor

135

– The resulting force and the electromagnetic force and of the elastic force The resulting force of the electromagnetic force and the elastic force fR ¼ Clm ð
xÞx !

2 4kFM

Clm ð
xÞx ¼ ½CM ð
xÞ  C x ¼


M þ 1 
x2 Þ d ðR 2

 C x;

ð3Þ

The most impact is done by the resulting elastic force Clm ð
xÞx, the property of which is given in Fig. 1. Its steepness changes depending on the stiffness of mechanical spring C and has two extreme points, which—when reached—make LM reduce its static stiffness. When LM’s operating gap is reduced down to 1/3—where the gradient of tilt angle change is insignificant—the property is linear. – The coefficient of linear force motor h i 2 2
2 2 R w ð þ 1 Þ 
x
M   wFM ðRM þ 1 þ
x Þ Ce ð
xÞ ¼
M þ 1 
x2 Þd ; L Fy ;
x ¼ 2R
M þ 1 
x2 Þ
M ðR Rx0 ðR

2.2

ð4Þ

Model of Servo System Using Linear Electric Motor

kui Uss -

kе

-

ki 1 Тi S

fL

kFi(x) ku Тu S + 1 - -

1 LS

fT

I

-

kvx 1 Vlm 1 xlm mlmS S

1 S

xss

blm

R UEM Се(х) kux

Сlm ( x)

Fig. 2. Mathematical model of a servo taking into account nonlinear properties of a LM.

Block diagram of a servo system with a completed nonlinear model of linear force motor that takes into account all the above-mentioned nonlinear factors is shown in Fig. 2. The diagram shows: ke is gain ratios, position error and error correspondingly; ki ; Ti are gain and time constant of current regulator; kvx being speed performance; ku ; Tu are gain and time constant of power amplifier; kui ; kux are coefficients of current sensor and position.

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3 Adaptive Regulator for Servosystem 3.1

Adaptive Algorithm

According to the above analysis, the system is a system of sufficiently high order, including nonlinear coefficients: Ce ; kFI ; Clm ; kvx . The system can be described as equations in state space: x_ ¼ Aðx; tÞx þ Bðx; tÞu; y ¼ Cx

ð5Þ

гдe x  n– dimensional state vector; y – dimensional output vector; u  m – dimensional control vector; Aðx; tÞ; Bðx; tÞ – non-stationary matrices (they depend on work zone and time), C– constant output matrix. The essence of applying adaptive control to a system is to design the regulator so that the system achieves the characteristics of the reference model (the desired mathematical model). Assuming that the reference model has the form (6), we have the goal of the control problem (7) x_ M ¼ AM xM þ BM u;

y ¼ CxM

ð6Þ

Where xM  n- dimensional state vector of the reference model AM ; BM ; C: constant matrices of the reference model we have the goal of the control problem (7) lim e ¼ lim ðx  xM Þ ¼ 0

t!1

ð7Þ

t!1

Equation (5) can be written by form x_ ¼ AM x þ BM u þ r;

y ¼ Cx

ð8Þ

Where r ¼ ½Aðx; tÞ  AM Þx þ ½Bðx; tÞ  BM u. When adding an adaptive signal z ¼ zðtÞ to the control object (8) satisfies condition (7), then Eq. (5) can be written: x_ ¼ AM x þ BM ðu þ zÞ þ r; y ¼ Cx. Consider a vector error: e ¼ x  xM . We have: e_ ¼ x_  x_ M ¼ AM x þ BM ðu þ zÞ þ r  ðAM xM þ BM uÞ ¼ AM ðx  xM Þ þ BM Z þ r ¼ AM e þ BM z þ r

;

ð9Þ

Choose Lyapunov function: VðeÞ ¼ eT Pe, where P is a symmetric positive definite matrix determined from the Lyapunov equation PAM þ ATM P ¼ Q, where Q is a diagonal positive definite matrix.
VðeÞ [ 0 ; 8e 6¼ 0 We can see: VðeÞ ¼ 0 ; e ¼ 0

Adaptive Algorithm for Servo System Using Linear Electric Motor

137

With the Eq. (9) we have:   _ VðeÞ ¼ x_  x_ M ¼ eT ATM P þ PAM e þ 2eT PBM z þ 2eT Pr     With condition zðtÞ ¼ h
sign BTM Pe ; h [ BM 
krk,
_ VðeÞ   eT Q1 e  2eT PBM jBMþ r  hj  0; 8e 6¼ 0 We have _ VðeÞ ¼ 0; e ¼ 0 thus, the system is asymptotic stability. According to the above base, in order to build adaptive algorithms for systems, the following steps must be performed: – building a reference model – building a system state observer – developing an adaptive mechanism for the system 3.2

Building a Reference Model

The reference model has the desired characteristics to meet the requirements of the system. The servo system is linearized and then added a modal controller to become a reference model. The linearized model is shown in Fig. 3, The model is linearized at the working points of the characteristics. The diagram in Fig. 3 contains a current loop with PI controller.

fL USS

fT

i=x4 ke E

1 kui

1

Di

kFi

Tems

1 ki k u

Xlm=x2

x3 1 mlm s

1 s

kvx

1 XSS=x1 s

blm Ce Clm kux

Fig. 3. Linear system

Where Di ¼ kui ki ku r 1 , Uss : task servo system x1 ¼ xss –output position of the servo system; x2– changing the armature of the LM; x3 – armature speed of LM; x4– current running in the winding of LM. State equations of the linearized system: x_ ¼ A0 x þ B0 u; y ¼ C0 x ;

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where 2 6 6 A0 ¼ 6 4 h B0 ¼ 0

0

0 0 0

kvx 0  mClmlm

0 1  mblmlm

 kkuxuikTeemDi

0

 rTCeme

0

ke D i kui Tem

iT

0 0  mkFIlm

þ 1Þ  ðDiTem

; C0 ¼ C ¼ ½ 1

0

3 7 7 7; 5

0

0 :

Matrices A0 ; B0 are the result of system linearization. Reference model: x_ M ¼ AM x þ BM u; y ¼ CxM ; where AM ¼ A0  BK ; BM ¼ B0 ; K ¼ ½ k1 k2 k3 k4 . K – matrix coefficient of modal regulator. To determine the modal regulator coefficients, the distribution of the roots of the characteristic equation using the standard Butterworth or Newton form is taken. H0 ðpÞ ¼ a0 p4 þ a1 x0 p3 þ a2 x20 p2 þ a3 x30 p þ a4 x40 where a0, a1, a2, a3, a4 are coefficients of Butterworth or Newton polynomials, x0 is desired cutoff frequency of system. 3.3

Building State Observer for Servo System

To obtain information on the state of the system in the absence of sensors, an observer is used. Equations of observer: ^x_ ¼ A0^x þ B0 u þ Gðy  ^yÞ; ^y ¼ C^x, where ^x is vector of state variable estimates; G – is vector coefficients feedback of observer. Matric G can be determined by the equation: det½pI  A þ GC ¼ Hg ðpÞ, where: Hg ðpÞ ¼ a0 p4 þ a1 xg p3 þ a2 x2g p2 þ a3 x3g p þ a4 x4g , xg ¼ ð3  5Þx0 3.4

Development of an Adaptive Mechanism for the System

For system (5), the signal adaptation algorithm is as follows 2.1: ua ðtÞ ¼ uðtÞ þ zðtÞ; zðtÞ ¼ hsgnðBTM PeÞ where e ¼ xM  ^x; uðtÞ – control input, zðtÞ – signal adaptation algorithm, h – coefficient gain, characterizing amplitude (restriction) relay control, h ¼ const > 0. P – symmetric ðP ¼ PT [ 0Þ positive definite ðn  nÞ, determined from the Lyapunov equation PAM þ ATM P ¼ Q. The specific values of the elements of the matrix Q are selected in accordance with the method, which is considered in [2]. A program of optimal choice of the values of the matrix Q is built [6], which determines the values of its diagonal elements based on the fulfillment of the condition of minimizing the ratio between the largest and the lowest eigenvalues of the matrix P.

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4 Simulation Results The system was simulated with the following parameters: Table 1. Parameters of the system simulation Parameters ke ki0 Ti0 r Tem kvx

Units Value – 1.5 – 3 – 0.1 Ohm 12 – 0.12 – 450000

Parameters kFi0 Ce0 blm mlm Clm0

Units Value – 0.12 – 162 – 1.99 kg 0.017 – 21820

During the research of the servo system, the following variables are compared: the position of the original servo system (ORG), the reference model (Ref), and the position of the servo system with an adaptive controller (AC), force load is added at the time t = 0.06 s.

1.2

x1, mm

1 0.8 0.6 ORG АC Ref

0.4 0.2 0

0.02

0.04

0.06

0.08

t, s 0.1

Fig. 4. Transient processes of the servo system when system at maximum speed (ki = 3, C = Clm)

The system in Fig. 4 has a setting of the Ref in speed equal to the speed of the speed part of the servo system (current loop ki = 3). The operation of algorithms AC leads to a satisfactory coincidence of the movement of the servo system to the Ref.

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1.2

x1, mm

1 0.8 0.6

ORG АC Ref

0.4 0.2 0

0.02

0.04

0.06

0.08

t, c 0.1

Fig. 5. Transient processes of the servo system at reduced speed (ki = 0.5, C = Clm)

With a decrease in the speed of the current loop (an increase in its constant time) (ki = 0.5), the transient processes are grouped around the motion of the Ref (Fig. 5). The work of AC is satisfactory.

1.2

x1, mm

1 0.8 0.6 ORG АC Ref

0.4 0.2 0

0.02

0.04

0.06

0.08

t, c 0.1

Fig. 6. Transient processes of the servo system when changing the parameters of the elastic part of the LM (ki= 2, C = 0.5Clm)

With the increasing speed of the mechanical part of the servo system (Fig. 6). LM resonance frequency is 2 times more than in the initial one. The adaptive controller still brings movement of the servo system close to the Ref.

Adaptive Algorithm for Servo System Using Linear Electric Motor

1.2

141

x1, mm

1 0.8 0.6

ORG АC Ref

0.4 0.2 0

0.02

0.04

0.06

0.08

t, c 0.1

Fig. 7. Transient processes of the servo system when changing the parameters of the elastic part of the LM (ki = 2, C = 2Clm)

By reducing the frequency of the resonance of LM in half (Fig. 7) adaptive controller brings the movement of the servo system closer to the reference. If the subsystem is correctly adjusted by the speed of the servo system (the current loop has a reduced gain factor ki = 2). The servo system has the necessary stability reserves in amplitude and phase, which allows the improvement its dynamic properties by means of an adaptive controller.

5 Conclusion From the mathematical proofs and simulation results, we see that with the optimal setting of the speed of the servo system and the dynamics of the reference model, adaptive regulators ensure a good convergence of the output variable of the system to the reference model when changing the parameters of the servo system: the speed of the LM current loop and the resonance of the mechanical part of the LM. Acknowledgement. The work described in this paper was supported by Thai Nguyen University of Technology for a scientific project.

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References 1. Kuznetsov, V., Konstantinov, S., Polyakhov, N., and others.: Electrohydraulic actuators of aircraft flight surface with adaptive control. St. Peterburg, Russia: Publishing House of the ETU “LETI”, p. 513 (2011) 2. Bortsov, YuA., Polyakhov, N.D., Putov, V.V.: Electromechanical systems with adaptive and modal control, p. 216. Energoatomizdat, Leningrad, USSR (1984) 3. Kuznetsov, V.E., Polyakhov, N.D.: Adaptive control of technical plants based on exo-model. In: (2015) Proceedings of International Conference on Soft Computing and Measurements, SCM 2015, art. no. 7190425, pp. 106–108, https://doi.org/10.1109/scm.2015.7190425 4. Kuznetsov, V.E.: Adaptive control of the steering servo system based on exo-model/Problems of improvement of robotic and intelligent aircraft systems: Collection of reports of the X AllRussian Scientific Technical Conference. – 26.6.2015, MAI – Moscow.2015. – C.204–208 5. Kuznetsov, V.E., Chung, P.T., Lukichev, A.N., Ha, N.H.: A Linear Electric Motor Servo System with the Adaptive Controller Based on Exo-model. (EIConRus), 28 Jan 2019, pp. 584–589. IEEE (2019) 6. Kuznetsov, V.E., Chung, P.T.: The program for calculating the parameters of the signal adaptive algorithm for the servo electro-hydraulic system/ Certificate of state registration of computer programs № 2018664091, Moscow 12.11 (2018)

Adaptive Sliding Mode Control for a 2-DOF Robot Arm in Case of Actuator Faults Le Ngoc Truc1(&)

and Nguyen Phung Quang2

1

2

Hung Yen University of Technology and Education, Hung Yen 17817, Vietnam [email protected] Hanoi University of Science and Technology, Hanoi 11615, Vietnam

Abstract. The paper presents an adaptive sliding mode controller for a 2-DOF robot arm suffering actuator faults. The type of actuator faults considered in this study is the proportional degradation of torque. The robot model is set up with unknown factors representing the degree of the actuator torque fault. Based on this model, an adaptive sliding mode methodology is designed to tolerate the faults. The system stability is guaranteed according to the Lyapunov approach. The adjustable controller coefficients can be adapted to greater values of unknown bounds, which satisfies the Lyapunov criterion. A performance comparison between the proposed control fashion and a popular robot controller is carried out via the MATLAB/Simulink simulation environment. The results show that the proposed controller can satisfactorily steer the joint responses following the desired trajectories in the faulty state with reasonable degrees of torque loss. Keywords: Adaptive control


Sliding mode control
Robot
Actuator fault

1 Introduction Nowadays, due to the sustained needs of using robots in production lines and/or in hazardous environments, the fault tolerance capability of a robot system has received significant interest. Usually, robot control systems respond to almost faults by halting the system or accommodating for the faults. The fault problem considered in this study is actuator torque degradation. The actuator faulty may be kept operating, or be put into a safe mode to avoid unnecessary damages. In general, topics related to fault investigation are detection, isolation, identification, diagnosis, and fault-tolerant control design. Many contributions regarding to the first four topics are presented. In [1], a nonlinear observer based on the robot dynamics to identify actuator faults is provided. The study in [2] proposes a fault identification scheme for robot actuators through a linear robust observer of fault time profiles. An approach to detect and isolate robot actuator faults through the adoption of Support-Vector-Machines is introduced in [3]. For actuator fault diagnosis of robot manipulators, a discrete-time framework processed by a decision-making system is devised in [4], and a performance analysis of a space manipulator in case of a single free-swinging joint is presented in [5]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 143–153, 2021. https://doi.org/10.1007/978-3-030-64719-3_17

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Several schemes have been proposed in the design of fault-tolerant controllers. In [6], an embedded adaptation scheme is synthesized to compensate the event of actuation degradation and to maintain the robot performance. The study in [7] provides a robust adaptive fault tolerant control for a class of Lipschitz nonlinear systems with actuator failure and bounded disturbances. Therein, for tuning adaptive parameters, two algorithms based on LMIs using the Lyapunov criterion are developed. An adaptive fault-tolerant control based on boundary estimation is designed for a space robot under partial loss of actuator effectiveness and uncertain parameters [8]. For a class of unknown single-input single-output nonlinear systems, a robust adaptive fault-tolerant control for partial loss of actuator effectiveness is developed in [9]. For uncertain multiple-input single-output nonlinear systems with actuator failures, several compensation control approaches are: two adaptive backstepping control schemes with unknown loss of actuator effectiveness [10], combination of a two-layer neural-network adaptive law and an automatic switching function mechanism [11], adaptive compensation with event-triggered input by estimating the bound of the actuator failure parameters [12]. Motivated by these results, this study introduces an adaptive sliding mode methodology for a non-redundant 2-DOF robot arm in case of actuator faults. The type of actuator faults put into investigation is the proportional degradation of torque. The switching gains of the proposed sliding-mode controller are adjustable by an adaptive law, and the unknown partial loss of actuator effectiveness can be tolerated with acceptable performance. The system stability is guaranteed based on the Lyapunov method. This paper is organized as follows: Sect. 2 gives a 2-DOF robot model and the description of actuator fault problem. The adaptive sliding-mode control design for the tolerance of actuator faults is presented in Sect. 3. The efficiency of the proposal method is expressed via simulation for a 2-DOF planar robot arm in Sect. 4. And in the final section, some important conclusions will be mentioned.

2 Problem Preliminaries Let us consider a 2-DOF robot dynamic model as follows M€ q þ Cq_ þ g ¼ sa

ð1Þ

where q 2 R2 is the joint variable, sa 2 R2 is the actual torque generated by actuators, M :¼ MðqÞ 2 R22 is the general inertia matrix, C :¼ Cðq; q_ Þ 2 R22 is the Coriolis/centrifugal matrix, g :¼ gðqÞ 2 R2 is the gravity term. Robot actuators are subjected to be the fault type: Proportional Degradation of Torque (PDT), where the actuator torque sa can be replaced by sa ¼ Usc

ð2Þ

where sc is the control torque, U ¼ diagðuÞ is the 2-by-2 diagonal matrix, u ¼ ½u1 ; u2 T is the torque degradation coefficient vector, ui 2 ½0; 1, i ¼ 1; 2. Actuator i has either no torque fault if ui ¼ 1 or a PDT fault if 0\ui \1. Especially, if ui ¼ 0,

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the torque of actuator i will be the total loss and joint i becomes free-swinging. Matrix M is invertible because of its symmetric and positive definite properties [13]. Hence, the dynamic Eq. (1) can be written as € ¼ M1 ðCq_ þ gÞ þ M1 Usc q

ð3Þ

€ ¼ f þ Bsc q

ð4Þ

f :¼ M1 ðCq_ þ gÞ; B ¼ M1 U

ð5Þ

or by the compact form

where

Due to the degradation coefficient U is unknown, matrix B is not also known ^ þB ~ where: B ^ is the nominal values of B, and B ~ is precisely. But we can have B ¼ B 2 the estimation errors. In the next section, with the desired joint trajectory qd 2 R and under the presence of actuator torque faults, an adaptive sliding mode controller is designed to steer q to qd and make the tracking error e ¼ qd  q convergent to zero.

3 Adaptive Sliding Mode Control Design The sliding manifold s ¼ ½s1 ; s2 T is chosen as s ¼ Ae þ e_

ð6Þ

where A ¼ diagðaÞ is the 2-by-2 diagonal matrix with a ¼ ½a1 ; a2 T , ai [ 0 (i ¼ 1; 2) is chosen such that sliding surface si ¼ ai ei þ e_ i has stable dynamics. Taking the time derivative of (6) yields €d  q € s_ ¼ A_e þ €e ¼ A_e þ q €d  ðf þ Bsc Þ ¼ A_e þ q ^ þ BÞs ~ cÞ €d  ðf þ ðB ¼ A_e þ q

ð7Þ

^ c Þ  Bs ~ c €d  ðf þ Bs ¼ A_e þ q The total loss of actuator torque is not considered in this study. Coefficient ui [ 0 ^ are invertible. The sliding mode control law is designed as therefore U, B as well as B sc ¼ sno þ sdis 1

^ ðA_e þ q €d  fÞ sno ¼ B 1

^ ^ wsgnðsÞ sdis ¼ B

ð8Þ

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where sno is the nominal control term, sdis is the adaptive discontinuous control term, h iT
 ^ ^ is the 2-by-2 diagonal matrix with w ^¼ w ^ ;w ^ ¼ diag w being the adjustable W 1 2 constant vector, sgnðsÞ :¼ ½sgnðs1 Þ; sgnðs2 ÞT is the vector of signum function sgnðsi Þ defined as 8 > < 1 if si [ 0 sgnðsi Þ ¼ 0 if si ¼ 0 > : 1 if si \0

ð9Þ

Substituting (8) into (7) gives ^ s_ ¼ WsgnðsÞ p

ð10Þ

e c ¼ ½p1 ; p2 T is the combined uncertain vector. It is an assumption that where p :¼ Bs there exists a constant vector w ¼ ½w1 ; w2 T satisfying wi [ jpi j (i ¼ 1; 2). Therefore, if ^ convergent to W ¼ diagðwÞ, every one-by-one element of an adaptive law can adjust W s_ will have opposite sign with that of s. We define the adaptive law as ^_ ¼ di jsi j ¼ di si sgnðsi Þ w i

ð11Þ

where di [ 0 (i ¼ 1; 2) is the chosen positive coefficient. The adaptation speed is determined by di . The larger di , the faster matching convergence. The matrix and vector e ¼ w  w, b respectively. Let us choose a e WW b and w of adaptation error are W¼ vector-type of Lyapunov function candidates as 1 1 ~ ~w V ¼ Ss þ D1 W 2 2

ð12Þ

where S ¼
diagðsÞ and D ¼ diagðdÞ are the 2-by-2 diagonal matrices, d ¼ ½d1 ; d2 T ,    1 T [ 0 is the inverse matrix of D. Thus, Lyapunov function D1 ¼ diag d1 1 ; d2 ~2 candidate Vi ¼ 12 s2i þ 12 d1 i wi [ 0 (i ¼ 1; 2). Taking the time derivative of V obtains ~_ ~w V_ ¼ S_s þ D1 W

ð13Þ

Substituting (10) into (13) gives ~_ ^ ~w V_ ¼ SðWsgnðsÞ  pÞ þ D1 W

ð14Þ

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~_ ¼ w_  w ^_ ¼ w ^_ and deduce Because W is a constant matrix, w ^_ ^ ^ w V_ ¼ SWsgnðsÞ  Sp  D1 ðW  WÞ

ð15Þ

From the adaptive law (11) we have ^_ ¼ DSsgnðsÞ w

ð16Þ

Substituting (16) into (15) yields ^ ^ V_ ¼ SWsgnðsÞ  Sp  D1 ðW  WÞDSsgnðsÞ ^ ^ ¼ SWsgnðsÞ  Sp  D1 WDSsgnðsÞ þ D1 WDSsgnðsÞ

ð17Þ

^ and D are diagonal, therefore they satisfy the commutative Matrices S, W, W, property of matrix multiplication. Thus, ^ ^ V_ ¼ SWsgnðsÞSpWSsgnðsÞ þ SWsgnðsÞ ¼ SpWSsgnðsÞ

ð18Þ

Considering the i th element of vector V_ is V_ i ¼ si pi  wi si sgnðsi Þ ¼ si pi  wi jsi j

ð19Þ

 jsi jjpi j  wi jsi j  ðjpi j  wi Þjsi j  0 The result in (19) shows that the stability of the proposed adaptive sliding mode ~ and control system is proven. The convergence to zero of both adaptation error w i sliding variable si is verified by the Lyapunov criterion. Hence, tracking error ei converges to zero within finite time.

4 Simulation Results for a 2-DOF Planar Robot Arm The efficiency of the proposed adaptive sliding mode control law for a 2-DOF robot under actuator torque degradation will be verified through an application example. Let us consider a 2-DOF planar robot arm depicted in Fig. 1 where li is the length of link i, ri is the length between joint i and the centroid of link i, mi is the mass of link i, O0 x0 y0 z0 is the base frame, Oi xi yi zi is the link frame attached at link i, y0 -axis is aligned with gravitational acceleration vector in the opposite direction.

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y2 x2 l2

y1

x1

l1

y0

r1 q1

r2

q2 x0

Fig. 1. A 2-DOF planar robot arm with attached frames.

The general inertia matrix, Coriolis/centrifugal matrix, and gravity term vector in the dynamic model for the 2-DOF robot arm (Fig. 1) are as follows: Mð1; 1Þ ¼ ðl21 þ 2l1 r2 cosðq2 Þ þ r22 Þm2 þ m1 r12 þ I1zz þ I2zz Mð2; 1Þ ¼ Mð1; 2Þ ¼ l1 m2 r2 cosðq2 Þ þ m2 r22 þ I2zz Mð2; 2Þ ¼  C¼  g¼

l1 m2 r2 q_ 2 sinðq2 Þ l1 m2 r2 q_ 1 sinðq2 Þ

m2 r22

ð20Þ

þ I2zz

l1 m2 r2 ðq_ 1 þ q_ 2 Þ sinðq2 Þ 0

gððl1 m2 þ m1 r1 Þ cosðq1 Þ þ m2 r2 cosðq1 þ q2 ÞÞ gm2 r2 cosðq1 þ q2 Þ

 ð21Þ  ð22Þ

where I1zz , I2zz are the principal moments of inertia of link 1, link 2 with respect to zaxis, respectively. For simulations, the robot’s parameters are m1 ¼ 4.0 kg, l1 ¼ 0.40 m, r1 ¼ 0.16 m, I1zz ¼ 110  103 kg m2 , m2 ¼ 2.0 kg, l2 ¼ 0.32 m, r2 ¼ 0.15 m, I2zz ¼ 56:5  103 kg m2 ; and the gravitational acceleration is 9.807 m=s2 . The robot’s initial configuration is set as qð0Þ ¼ q_ ð0Þ ¼ ½0; 0T . The desired joint trajectory qd ¼ ½q1d ; q2d T is given by q1d ¼ 0:5 þ 2 sinð2ptÞ ðradÞ; q2d ¼ 0:5 þ sinð2ptÞ ðradÞ

ð23Þ

The robot will be subject to PDT faults in two cases of torque degradation coefficient u ¼ ½u1 ; u2 T . Case 1: u1 ¼ u2 ¼ 0.9 corresponds to 10% loss of PDT, and case 2: u1 ¼ u2 ¼ 0.5 corresponds to 50% loss of PDT. In each case, with the same robot’s parameters, several simulations are implemented in turn with two schemes for performance comparison: a computed-torque controller with PD outer-loop (CTC-PD),

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and the proposed adaptive sliding mode controller (ASMC). To reduce the chattering phenomenon of sliding mode control system, saturation functions sat(si /ai ) described in (24) can be used instead of signum functions sgn(si ) in simulation. 8 if si   ai < 1 satðsi =ai Þ ¼ ðsi =ai Þ if  ai \si \ai ð24Þ : 1 if si  ai Firstly, by using CTC-PD without anticipating the PDT fault, the controller is given as _ q_ þ gðqÞ qd þ Kd e_ þ Kp eÞ þ Cðq; qÞ sc ¼ MðqÞð€

ð25Þ

where Kd ¼ diagð½20; 20Þ and Kp ¼ diagð½100; 100Þ. Secondly, by using the proposed ASMC with anticipating the PDT fault (Fig. 2), the controller parameters are chosen as: sliding manifold with a1 ¼ a2 ¼ 10, saturator functions with a1 ¼ a2 ¼ 0.5, adaptation speed coefficients d1 ¼ 200, d2 ¼ 1500. The simulations are conducted by MATLAB/Simulink using solver ode5 with sample time of 0.001 s. The system performances in case 1 and case 2 are depicted in (Fig. 3, Fig. 4) and (Fig. 5, Fig. 6), respectively.

Fig. 2. Simulation diagram of adaptive sliding mode control for the 2-DOF robot arm.

In case 1, under 10% loss of PDT faults in both joints (Fig. 4): Fig. 3 shows that the robot’s joint responses by using CTC-PD controller start to deviate from the desired trajectories with tracking errors e1 , e2 oscillating in [− 0.108, 0.081], [ 0.012, 0.123], respectively; whereas the ASMC controller gives better joint responses with tracking errors e1 , e2 varying in the much smaller bounds of [ 0.002, 0.001], [ 0.0002, 0.001], respectively. In case 2, when the percentage of actuator torque loss increases to 50% in both joints (Fig. 6), the performance of CTC-PD robot control system becomes significantly worse with large tracking errors (Fig. 5). The joint angles cannot follow the desired trajectories, especially at joint 2. Meanwhile, by using the ASMC controller, the joint responses still track the desired paths with acceptable tracking errors fluctuating in the ranges of [− 0.013, 0.010], [− 0.001, 0.011] for e1 , e2 , respectively.

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Fig. 3. Reference, response(left), and tracking error(right) of the 2-DOF robot using CTC-PD and ASMC under PDT faults of 10% actuator torque loss in both joints (case 1).

Fig. 4. Control torque and actuator torque of the 2-DOF robot using CTC-PD (left) and ASMC (right) under PDT faults of 10% actuator torque loss in both joints (case 1).

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Fig. 5. Reference, response(left), and tracking error(right) of the 2-DOF robot using CTC-PD and ASMC under PDT faults of 50% actuator torque loss in both joints (case 2).

Fig. 6. Control torque sc and actuator torque sa of the 2-DOF robot using CTC-PD (left) and ASMC (right) under PDT faults of 50% actuator torque loss in both joints (case 2).

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Through comparison with a CTC-PD controller, it is obvious that better robot’s joint responses can be achieved by using the ASMC controller despite the existence of actuator faults, i.e., PDT faults. In other words, the proposed ASMC methodology can tolerate the PDT fault event in a robot system.

5 Conclusions The paper aims at the topic of robot control design in case of actuator faults. The investigated actuator fault type is the proportional degradation of torque. An adaptive sliding mode control law is synthesized for 2-DOF robots to tolerate the mentioned fault. The system stability is guaranteed with the Lyapunov method in the control design procedure. The effectiveness of the proposed controller is verified by a comparison with a popular CTC-PD controller through simulation. The simulation results clearly express that the fault-tolerant capability of the proposed ASMC is considerably better than that of the CTC-PD controller. In case of using the proposed ASMC, the robot responses can track the reference trajectories with sufficiently small errors when the percentage of torque loss is up to about 10%. Although, the larger the torque loss is, the worse the tracking performance becomes, the robot responses are still acceptable even if the torque loss increases to 50%. However, robot parameter uncertainties and disturbances have not been considered in this study. These conditions will be addressed in our next work.

References 1. McIntyre, M.L., Dixon, W.E., Dawson, D.M., Walker, I.D.: Fault identification for robot manipulators. IEEE Trans. Robot. 21(5), 1028–1034 (2005) 2. De Luca, A., Mattone, R.: An identification scheme for robot actuator faults. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, Edmonton, Alta., Canada, pp. 2613–2617. IEEE (2005) 3. Caccavale, F., Cilibrizzi, P., Pierri, F., Villani, L.: Actuators fault diagnosis for robot manipulators with uncertain model. Control Eng. Pract. 17(1), 146–157 (2009) 4. Caccavale, F., Marino, A., Muscio, G., Pierri, F.: Discrete-time framework for fault diagnosis in robotic manipulators. IEEE Trans. Control Syst. Technol. 21(5), 1858–1873 (2013) 5. Domski, W., Mazur, A.: Emergency control of a space 3R manipulator in case of one joint failure. In: 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, Poland, pp. 384–389. IEEE (2017) 6. Liu, G.: Control of robot manipulators with consideration of actuator performance degradation and failures. In: 2001 IEEE International Conference on Robotics & Automation, Seoul, Korea, pp. 2566–2571. IEEE (2001) 7. Azmi, H., Khosrowjerdi, M.J.: Robust adaptive fault tolerant control for a class of lipschitz nonlinear systems with actuator failure and disturbances. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 230(1), 13–22 (2015) 8. Lei, R., Chen, L.: Adaptive fault-tolerant control based on boundary estimation for space robot under joint actuator faults and uncertain parameters. Def. Technol. 15(6), 964–971 (2019)

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9. Zhang, J.-X., Yang, G.-H.: Robust adaptive fault-tolerant control for a class of unknown nonlinear systems. IEEE Trans. Ind. Electron. 64(1), 585–594 (2017) 10. Wang, W., Wen, C.: Adaptive actuator failure compensation control of uncertain nonlinear systems with guaranteed transient performance. Automatica 46, 2082–2091 (2010) 11. Yang, Q., Ge, S.S., Sun, Y.: Adaptive actuator fault tolerant control for uncertain nonlinear systems with multiple actuators. Automatica 60, 92–99 (2015) 12. Xing, L., Wen, C., Liu, Z., Su, H., Cai, J.: Adaptive compensation for actuator failures with event-triggered input. Automatica 85, 129–136 (2017) 13. Tourassis, V.D., Neuman, C.P.: Properties and structure of dynamic robot models for control engineering applications. Mech. Mach. Theor. 20, 27–40 (1985)

An Energy-Efficient Combination of Sleeping Schedule and Cognitive Radio in Wireless Sensor Networks Utilizing Compressed Sensing Minh T. Nguyen1(&) , Thuong T. K. Nguyen1, and Keith A. Teague2 1

Thai Nguyen University of Technology (TNUT), Thai Nguyen, Viet Nam {nguyentuanminh,nguyenthikimthuong}@tnut.edu.vn 2 Oklahoma State University (OSU), Stillwater, USA [email protected]

Abstract. Conventional wireless sensor networks (WSNs) have been well exploited with many applications and also numerous techniques for improvements. Recently WSNs employ muti-media services that require more resources including frequency bandwidth and transmission rate. This encourages more exploration to support the networks to approach the increasing demand in quality of service. This paper shows an investigation to combine some techniques to meet some requirements. Cognitive radio (CR) have been known to use frequency band effectively. Compressed sensing (CS) applied in WSNs reduces the data transmission in the networks. Sleeping schedules for sensor nodes in such networks are also considered to save energy while still provide enough data needed. This work is a combination that provide analysis of network models, simulation results and shows promise for future WSNs. Keywords: Wireless sensor works
Cognitive radio Compressed sensing
Data reconstruction


Sleeping schedule


1 Introduction Wireless sensor networks (WSNs) have been explored for years with many leading applications in different fields [1]. Sensors/actuators are usually deployed randomly in sensing areas to collect data or to detect events [2]. The sensing data is transmitted between sensors or from sensors to the base-station (BS) for different purposes. Since the sensors often work on hash condition that people cannot access, pre-charged batteries may not be enough for the network to provide their services for a long time. Energy consumption for both sensing and communicating in such network is always a critical problem that researchers approach to solve. There are many existing research papers proposing novel methods to improve batteries, routing schemes to prolong the network lifetime [3, 4]. This paper investigates a combination between Cognitive radio (CR) [5] and sleeping schedule [6] in WSNs deploying Compressed sensing (CS) [7– 9]. As mentioned above, CS techniques support sleeping schedules for sensors to save energy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 154–160, 2021. https://doi.org/10.1007/978-3-030-64719-3_18

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Fig. 1. 1000 sensors are deployed in the sensing area; There are only 50 sensors are active at a time. The rest falls into sleeping mode to save energy.

The active sensors would be the ones creating a CS measurement. Another time for another group of sensors, one more CS measurement is created. With CR, each group of sensors takes turn to use the licensed bandwidth. CR based sensors can adapt to varying channels conditions and increase transmission efficiency that reduce energy consumption for communications. The remainder of this paper is organized as follows. Section 2 presents the network model and the background of technical problems that combine all the techniques mentioned above. Problem Formulation including the data collection algorithm combining with sleeping schedules and CS is addressed in Sect. 3. Energy efficiency is analyzed in Sect. 4. Simulation results are provided in Sect. 5. Conclusions and future work are finally mentioned in Sect. 6.

2 Network Model We assume that a WSN has N static sensor nodes randomly deployed in a sensing area as shown in Fig. 1, which are all equipped with single omni-directional antennas. There exists a base-station (BS) to collect the data from all the sensors. We also assume that all the sensor nodes have the same communication range Rc. In practise, in wireless networks, the packets transmitted by a sensor may be received by all the nodes within the communication range. Therefore, interference may occur among these nodes due to the broadcast nature of the wireless medium. For simplicity, noise is not considered for either analysis or simulation sections. All the sensors are programmed to have sleeping schedules frequently. They are also integrated with CR technology to be CR-WSNs. The sensors are able to detect unused spaces (white spaces) by the incumbents in the spectrum bands.

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Sleeping Schedule: The N sensors pick a probability, denoted as P, to take turn to be active for collecting data. The rest of sensors can fall in sleep status for energy saving purpose. If the number of sensors active a time is A, then the probability is P = A/N. There are (N-A) sensors fall to sleep as scheduled. In the sleeping mode, sensors spend much less energy than all others in the active mode. So, this mode should be combined with other methods to be able to achieve more energy efficient manners. Cognitive Radio (CR): The technique has the capability of sensing the spectrum and determining the vacant bands [10]. These CR techniques can operate in licensed bands as well as in unlicensed bands. Working in licensed bands with a specific license, while wireless sensing users are communicating over the allocated band, i.e., the primary user (PU), has the priority to access the channel. Secondary users (SU), in CR networks, can access the channel if they do not cause interference to the PU. Based on the problem mentioned in the sleeping schedule, k sensors at first can be PU and after that another group of k sensors will take turn to be PU to be able to use the frequency band. Compressed Sensing (CS): The techniques are well-known to offers many paradigms to sample and to reconstruct sparse signals with a certain number of CS measurements [11, 12]. The requirements to be able to apply CS the compression process is the sensing data should be sparse or compressible in proper domains.

3 The Proposed Algorithm The combination algorithm has three phases that can be addressed as follows. Phase 1. Network Setup: • All N sensors are given an active mode schedule: Probability P support A = PN active sensors on average at a time; • CR techniques are integrated in sensors to be able to use multiple channels for communications; • A greedy routing tree algorithm [8, 9] is given;
• Vector X 2 RN X ¼ ½x1 x2 . . .xN T represents all unknown data in the sensing area;
• Vector Y 2 RM Y ¼ ½y1 y2 . . .yM T represents all M CS measurements collected at the BS; • U: the measurement matrix in which each row has A non-zero elements representing A active sensors contributing to the corresponding CS measurement; • Cognitive Radio techniques are integrated into sensors. Phase 2. Network Data Collection: • Active sensors connect to others and forward their sensory readings to the BS based on CR; M measurements are collected as: Y ¼ UX; • A random seed is stored to be sent along with CS measurements. This is the hidden key for security and privacy purposes; • M periods of time defines M sampling times.

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Phase 3. Data Reconstruction: • The measurement matrix U is updated and created based on the CS measurements at the BS; • An appropriate W (scarifying matrix) is chosen as DCT or Wavelet;
^ is recovered as: • All sensing data X

^ ¼ Arg Min ^ ^ and X ^ ^ ¼ UWH; ^ ¼ UWH; H

H

subject to Y 1

4 Energy Efficiency Analysis As shown in Fig. 1, there are only 50 active sensors working at a time of sampling. The energy consumption for the whole network can be reduced significantly. Compared to existing work with all 1000 sensors being active all the time, this sleeping schedule can save up to 90% energy consumption among all sensors. And, the networks still satisfy their monitoring tasks. In general, energy consumed for transmitting and receiving data WSNs is considered as PTx and ERx which are respectively formulated as: ETx = ET0 + EA(d, N) and ERx = ER0 . Where ET0 and ER0 are the energy consumed at electrical elements for data processing, modulation or data coding. We do not considered these elements since they do not depend on transmitting distances (d) in our networks. Through out this work, only the consumed energy of the power amplifier EA(d, N) that includes d is considered. N is the number of sensor nodes representing all values of sensor readings, specified as scalar values. Hence, total energy consumption for data communication in the networks has two parts, sensor energy consumption transmitting over distance d and the data packets including N scalar values as: EA(d, N) = PA(d)N. In conventional WSNs, BS needs to collect all N sensor readings that cost a lot of energy. In our proposed WSNs, the number of CS measurements need to be able to reconstruct N scalar values is much smaller (M  N). So, this transmission method can also reduce significantly energy consumption.

5 Simulation Results In this section, we deploy 1000 sensors in a square sensing area with dimension of 100 100 unit square, as shown in Fig. 1. We schedule for only 50 sensors to be active at a sampling time that can save up to 95% energy. As shown in Fig. 2, temperatures are various but highly correlated between sensors. It means that the sensors which are close to each other measure quite similar values. These 1000 scalar values are dense in the data vector X 2 RN . However, this dense vector are sparse in some frequency domains as DCT or Wavelet. Figure 3 shows transformed coefficients achieved by both DCT and Wavelet. This can be applied CS techniques to sample and to reconstruct data. In Fig. 4, the numbers of CS measurements are chosen from 150 to 350.

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The normalized reconstruction errors reduce as the number of CS measurements increase. Both DCT and Wavelet bases work well with the CS recovery process.

Fig. 2. 1000 scalar values of temperatures collected from 1000 sensors in the sensing field

Fig. 4. Normalized reconstruction errors versus the number of CS measurements in the CS recovery process

Fig. 3. 1000 transformed coefficients transformed by both DCT and Wavelet basis

Fig. 5. All sensing data from 1000 nodes are reconstructed with 250 CS measurements vs. the original data

We fix the number of CS measurements as 250 to reconstruct 1000 sensing values from the area. Figure 5 depicts both the original data from the sensing area and the reconstructed data. They look quite close to each other. This also depends on the QoS required of specific applications. At this point the data transmission in such network utilizing our novel combination significantly is reduced.

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6 Conclusions and Future Work In this paper, an energy-efficient data collection method in wireless sensor networks that combines sleeping schedules, cognitive radio and compressed sensing is proposed. We analyze all the possibilities of exploiting the combination and choose some appropriate parameters for the system model. The system shows promising points that reduce data transmission in the networks, use spectrum effectively, and reduce the network energy consumption significantly based on the sleeping schedules. Simulation results prove that the system can work well to reconstruct sensory data based on a certain number of measurements. In the future work, we consider to optimize the total energy consumption in the networks. The trade-off between the number of active sensors and the communication range could be exploited for further energy saving. Acknowledgements. The authors would like to thank Thai Nguyen University of Technology (TNUT), Viet Nam for the support.

References 1. Nguyen, M.T., Tran, H.V., Nguyen, G.T., Do, K.H.: Wireless communication technologies and applications for wireless sensor networks: a survey. In: ICSES Transactions on Computer Networks and Communications (ITCNC), vol. 5, pp. 1–15, April 2019 2. Nguyen, M.T., Teague, K.A., Rahnavard, N.: CCS: Energy-efficient data collection in clustered wireless sensor networks utilizing block-wise compressive sensing. Comput. Netw. 106, 171–185 (2016) 3. Nguyen, M.T., Teague, K.A., Rahnavard, N.: Inter-cluster multi-hop routing in wireless sensor networks employing compressive sensing. In: 2014 IEEE Military Communications Conference, pp. 1133–1138, October 2014 4. Nguyen, M.T., Teague, K.A.: Random sampling in collaborative and distributed mobile sensor networks utilizing compressive sensing for scalar field mapping. In: 2015 10th System of Systems Engineering Conference (SoSE), pp. 1–6, May 2015 5. Akan, O.B., Karli, O.B., Ergul, O.: Cognitive radio sensor networks. IEEE Netw. 23, 34–40 (2009) 6. Lai, W., Paschalidis, I.C.: Routing through noise and sleeping nodes in sensor networks: latency vs. energy trade-offs. In: Proceedings of the 45th IEEE Conference on Decision and Control, pp. 2716–2721, December 2006 7. Nguyen, M.T., Rahnavard, N.: Cluster-based energy-efficient data collection in wireless sensor networks utilizing compressive sensing. In: MILCOM 2013 - 2013 IEEE Military Communications Conference, pp. 1708–1713, November 2013 8. Nguyen, M.T., Teague, K.A.: Tree-based energy-efficient data gathering in wireless sensor networks deploying compressive sensing. In: 2014 23rd Wireless and Optical Communication Conference (WOCC), pp. 1–6, May 2014

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9. Nguyen, M.T., Teague, K.A.: Neighborhood based data collection in wireless sensor networks employing compressive sensing. In: 2014 International Conference on Advanced Technologies for Communications (ATC 2014), pp. 198–203, October 2014 10. Yeoh, P.L., Elkashlan, M., Kim, K.J., Duong, T.Q., Karagiannidis, G.K.: Cognitive mimo relaying with multiple primary transceivers. In: 2013 IEEE Global Communications Conference (GLOBECOM), pp. 1956–1961, December 2013 11. Nguyen, M.T.: Data Collection Algorithms in Wireless Sensor Networks Employing Compressive Sensing. Ph.D thesis, Oklahoma State University (2015) 12. Candes, E., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theor. 52, 489–509 (2006)

An Enhancing Grasshopper Optimization for Efficient Feature Selection Trong-The Nguyen1, Shi-Jie Jiang1, Thi-Kien Dao1, Truong-Giang Ngo2, Thi-Thanh-Tan Nguyen3, and The-Vinh Do4(&) 1

3

Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China 2 Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam Information Technology Faculty, Electric Power University, Hanoi, Vietnam 4 Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected]

Abstract. Grasshopper optimization algorithm (GOA) is a new swarm intelligence algorithm that simulated a model of locust swarm behavior for optimization problems. Still, it has drawbacks, e.g., falling into a local optimum and slow convergence. This paper suggests a method enhancing GOA optimization (namely EGOA) based on an adaptive weight coefficient and nonlinear parameters to balance the global exploration and local development capabilities, to promote the convergence speed, and to avoid trapping of falling into a local optimum the algorithm. In the simulation experiment, four benchmark test functions and the feature selection problem are used to prove that the improved strategy used in the proposed method can effectively enhance the precision and convergence speed of the GOA. The experimental results on seven UCI datasets show that the proposed method can effectively provide the best selection features for increasing classification accuracy. Keywords: Enhancing grasshopper optimization
Swarm computation, feature selection
Optimization

1 Introduction Feature selection is considered as a critical link in the process of data preprocessing in machine learning [1]. It is not only to reduce the data dimension and improve the learning efficiency of the algorithm but also from the data set out on the performance of classifier to classify most useful characteristics, to promote the classification accuracy [2]. The conventional methods of feature selection can be roughly divided into categories such as filter type, package type, and embedded type [3]. The types of feature selection will learn performance as an evaluation standard of feature subset, so the way is also the most beneficial for learning of choosing the best feature subset [4]. However, with the data contains a large number of features, the package type would be used specific search feature subset that is difficult to achieve [5]. Therefore, how to make valid feature selection becomes a difficult problem. In recent years, many search methods [6, 7] of swarm intelligence optimization algorithms have been used as the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 161–172, 2021. https://doi.org/10.1007/978-3-030-64719-3_19

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search mechanism of wrapped feature selection, including the Particle Swarm Optimization (PSO) algorithm [8], Ant Lion Optimization (ALO) algorithm [9], and Whale Optimization (WOA) algorithm [10]. Grasshopper Optimization Algorithm (GOA) [11] is a new power swarm intelligence algorithm that puts forward a model of locust swarm behavior in nature to solve the optimization problems. GOA algorithm has been considered as a robust, proven effect algorithm in practical problem-solving for engineering fields [12]. However, similar to another approach of intelligent optimization algorithms, GOA still has some drawbacks. e.g., slow convergence speed, easy to fall into optimal local problems [13]. This paper suggests a solution to the problems of low accuracy of GOA optimization and slow convergence speed. In the proposed method, we introduce an adaptive weight coefficient to change the update method of the locust position to improve the optimization accuracy, and we use nonlinear parameters to balance the global exploration and local development capabilities, to promote the convergence speed, and to avoid trapping of falling into a local optimum the algorithm. In the simulation experiment, four benchmark test functions and the feature selection problem are used to prove that the improved strategy used in the proposed method can effectively enhance the precision and convergence speed of the GOA. The experimental results on seven UCI datasets show that the proposed method can effectively provide the best selection features for increasing classification accuracy.

2 Grasshopper Optimization Algorithm (GOA) The GOA algorithm is a new swarm intelligence optimization algorithm derived from simulating the swarm characteristics of grasshopper [11]. It means that the GOA simulates the behaviors of grasshoppers that often prey and migrate in large-scale gatherings. The grasshoppers move at a low speed when larval stages, but adult grasshoppers can move quickly in a large area. These behaviors’ actions are mathematical modeled as follows. Xi ¼ Si þ Gi þ Ai

ð1Þ

where, Xi represents the position of the ith grasshopper in the grasshopper population, Si is the interaction between the i th grasshopper and other individuals in the population, Gi is the gravity of the ith grasshopper, and Ai is the wind force of the ith grasshopper. Considering the influence of random factors, Eq. (1) can be rewritten as: Xi ¼ r1
Si þ r2
Gi þ r3
Ai

ð2Þ

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where r1 , r2 and r3 are random numbers between [0,1], and the expression of Si is as follows: Si ¼

N P

j¼1 j 6¼ 1

  s dij d^ij

ð3Þ

where, dij is the distance between the ith individual and the jth individual, d^ij is the unit x x vector from the ith individual to the jth individual, and d^ij ¼ jdij i , N is the number of individuals in the population, and s is the function defining the interaction between individuals: r

sðr Þ ¼ fe l  er

ð4Þ

where f indicates the strength of attraction, l is the attraction scale range. In this paper, f = 0.5, l = 1.5, through s can be the individual grasshopper space divided into attraction area, exclusion area, and comfort area. However, when the distance between individuals is greater than 10, the value of function s is close to 0, at this time no longer produces force to the individual, so this paper limits the position of individuals in the population within the range of [1, 4]. When the individual in the population is in the comfort area, the individual no longer has position updates, at which point the individual in the population is surrounded by the optimal solution, rather than all gathered in the location of the optimal solution. Therefore, the model of the formula (2) cannot be used directly to solve the optimization problem, which can be rewritten as: 0 Xid

1

B N C   B P  d xj xi C ubd lbd d B ^ c
2
s xj  xi 
dij C ¼c
B C þ Td @j ¼ 1 A j 6¼ i

ð5Þ

where ubd and lbd are upper and lower bounds of D-dimensional search space respectively, T^d is the optimal individual in the current population, regardless of the influence of gravity and assuming that the wind always points to the location of the optimal solution, c is the linear decline coefficient, and the expression is as follows: min c ¼ cmax  l
cmax c L

ð6Þ

where l represents the current iteration number of the algorithm, L represents the maximum iteration number; in this paper, cmax = 1, cmin = 0. 00001. Based on the above model, the main steps of the grasshopper optimization algorithm are as follows: (1) (2) (3) (4)

population and parameter initialization. select the optimal solution with the highest fitness in the current population. update parameter c according to Eq. (6). adjust the distance between individuals and update the position as Eq. (5).

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(5) check whether the individual has exceeded the search boundary after the update and if so, return to the position before the update. (6) update the optimal solution in the population. (7) determine whether the maximum number of iterations has been reached: no, then steps (3) to (6) of the cycle; yes, then the algorithm ends and returns the optimal solution.

3 Strategies for Improving GOA (IGOA) Several strategies of adjusting the mathematical model of the GOA to enhance the optimization accuracy and convergence speed of the algorithm (namely EGOA): e.g., the nonlinear parameters, adjusting position weight coefficient, and combining the limit threshold and proposing optimization. 3.1

Nonlinear Decline Coefficient

Some characteristics of grasshoppers, e.g., the attractiveness, repulsion, and agent’s search range of grasshoppers, are adjusted by decreasing the coefficient c. Refer to Eq. (5), the parameter c acting on the inside of the bracket can help reduce the repulsive force and attraction between individuals proportional to the number of iterations of the algorithm. This action outside of the bracket can lessen the search coverage range of individuals with an increase in the number of repetitions. Therefore, GOA uses parameter c to balance the global exploration and local exploitation capabilities of the algorithm during iteration. Refer to Eq. (6), c decreases linearly as the number of iterations increases, which will slow the convergence rate of the algorithm and make it easy to fall into the local optimization. Therefore, we use nonlinear parameters instead of the original linear decreasing coefficients and rewrites Eq. (6) as follows: qffiffii  h  c ¼ 1  sin 12
p
Ll
cmax  l
cmaxcmin L

ð7Þ

where, l represents the current number of iterations, and L is the maximum number of iterations. The nonlinear decreasing coefficient c can decrease at a faster rate in the early stage of algorithm iteration so that the individuals in the population can get closer to the objective quickly, and the convergence rate of the algorithm can be improved. However, in the later stage of algorithm iteration, the decreasing speed of c slows down, which enables individuals to search the surrounding space carefully and prevents the algorithm from falling into local optimization. Therefore, the use of a nonlinear decreasing coefficient can better balance the global exploration and local exploitation capability of the algorithm in different iteration periods. 3.2

Adaptive Weight Coefficient

When all the individuals in GOA [11] are falling in the comfort zone, e.g., optima local, they will no longer update their positions. At this time, the individuals are not clustered

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to reach the location of the optimal solution. This issue makes the algorithm prone to premature convergence. Refer to Eq. (5), the update of individual grasshopper position not only depends on other individuals in the population but also depends on the optimal solution in the current community. Therefore, the location of the optimal solution has an essential influence on the movement of other individuals. We should consider different periods of algorithm iteration, to find the place of the optimal global solution, individuals in the population have different dependence on the optimal solution in the current community. In this situation, nonlinear parameters are introduced as the weight coefficient of the optimal solution of the current population, which is defined as follows. w ¼ 1  sin



1 2


p


qffiffi

ð8Þ

l L

And we rewrite Eq. (5) as follows: 0 Xid

1

B N C  B P ub lb  d C d  xj xi C d d B c 2 s xj  xi  dij C þ w
T^d ¼ cB @j ¼ 1 A j 6¼ i

ð9Þ

where l represents the current number of iterations, and L is the maximum number of iterations. w is the weight coefficient defined in this paper, and the w value decreases nonlinearly with the number of iterations, that is, with the algorithm iteration, the influence of the optimal solution in the population on the location updates of other individuals also changes. At the beginning of the algorithm iteration w value is large, and the individual updates the position according to the optimal solution and the position information between the individuals. With the iteration of the algorithm, in order to avoid the individual in the population gradually in their own comfort area, the dependence of individuals in the community on the optimal solution position should be reduced with a lower w value, so that the individual in the population can move near the optimal solution, so as to avoid all the individuals stay around the optimal solution of the problem. In this way, the local exploitation ability of the algorithm can be enhanced, and the accuracy of the optimization can be improved. Moreover, the limits threshold setting is one of the ways used to judge whether the algorithm trapped in a local optimum. The number of stagnations caused the decreased quality of the optimal solution in the population. The limit threshold should be set up based on the specific issue if it needs to jump out of local optimal, the threshold would be set high level; however, it would be slow conveyance. If the limit is set too low, it would frequently be a random disturbance of individuals in the population, affecting the average fitness of the community. In order to ensure that the individuals in the population are close to the optimal solution with the algorithm iteration, the position update before the limit is set to 15 in the experiment section.

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Experimental Results and Analysis

In order to evaluate the potential performance of the proposed enhancing GOA (called EGOA), we use five selected benchmark test functions from the CEC 2013 [14] for experiments test. The obtained results of the proposed EGOA algorithm are compared with the variety of previous algorithms of GOA [11, 15]. The setting parameters for the algorithms: the population size Np is to 40, MaxIter is set to 1500, the boundaries and diminution are set to the same listed in Table 1. Table 1. Selected benchmark functions Function name Schwefel 2.22 Schwefel 1.2 Schwefel 2.21 Rastrigin Ackley

Test functions F1 ¼

D im P

jxi j þ

i¼1

F2 ¼

Dimensions Ranges Dim Q

jx i j

5/30

[−10,10]

Target function 0

5/30

[−100,100]

0

5/30

[−100,100]

0

5/30

[−5.12,5.12] 0

5/30

[−32,32]

i¼1

Dim P

i P

i¼1

j1

!2 xj

F3 ¼ maxi fjxi j; 1  i  Dg  F4 ¼ x2i  10 cosð2pxi Þ þ 10 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi! Dim P 2 1 xi F5 ¼ 20exp 0:2 Dim

0

i¼1

Dim  P 1 cosð2pxi Þ þ 20 þ e exp Dim i¼1

Table 1 shows the comparison of the proposed EGOA algorithm with the GOA and OBLGOA algorithms. Observed, the proposed approach produces the obtained optimization results is better accuracy and faster of convergence speed than the GOA and OBLGOA algorithms Table 2. Table 2. Comparison of algorithm optimization performance Function Dim 5 Mean F1 Std. Dev 30 Mean Std. Dev F2 5 Mean Std. Dev 30 Mean Std. Dev

GOA 2.36E 2.88E 1.68E 1.91E 8.27E 2.51E 2.60E 1.67E

+ 000 + 000 + 001 + 001 − 006 − 005 + 003 + 003

OBLGOA 1.49E + 000 2.06E +000 3.64E −010 3.63E − 010 7.17E − 008 2.38E − 007 6.78E − 016 9.99E − 016

EGOA 3.85E − 019 5.83E − 020 2.70E − 018 4.66E − 019 4.96E − 035 4.58E − 035 1.02E − 033 1.17E − 033 (continued)

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Table 2. (continued) Function Dim 5 Mean F3 Std. Dev 30 Mean Std. Dev F4 5 Mean Std. Dev 30 Mean Std. Dev F5 5 Mean Std. Dev 30 Mean Std. Dev

GOA 1.71E 2.71E 1.50E 4.05E 1.11E 7.57E 9.45E 3.30E 1.04E 2.52E 5.50E 1.76E

− 004 − 004 + 001 + 000 + 001 + 000 + 001 + 001 + 000 + 000 + 000 + 000

OBLGOA 1.17E − 006 3.92E − 006 1.93E − 010 2.15E − 010 7.85E + 000 5.19E + 000 0.00E + 000 0.00E + 000 7.42E −001 1.08E + 000 2.06E − 010 2.03E − 010

EGOA 2.82E − 018 8.14E − 019 3.89E − 018 4.29E − 019 0.00E + 000 0.00E + 000 0.00E + 000 0.00E + 000 8.88E − 016 0.00E + 000 8.88E − 016 0.00E + 000

Fig. 1. Comparison of the results of the EGOA algorithm with the GOA and OBLGOA algorithms for the first and second functions.

Figure 1 shows the comparison of the results of the EGOA algorithm with the GOA [11] and OBLGOA [15] algorithms for the first and second functions. It can be seen that in the search spaces of different dimensions, the convergence speed of the EGOA on the benchmark test functions is significantly better than the varieties of GOA algorithms.

4 A Solution to Feature Selection by Applied EGOA The problem of feature selection is considered as a multi-objective optimization problem, that is, selecting as few feature numbers as possible for the classifier to obtain the optimal classification accuracy. In this section, the proposed EGOA algorithm is used to solve this practical optimization problem. An EGOA-based feature selection method is recommended; the specific algorithm flow is shown in Fig. 2. In the feature

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selection problem, the EGOA population is represented as a set of feature combinations in the dataset that can be called the feature subset. The individual dimension is determined by the original number of features in the dataset, and the agent vector is composed of 0 and 1, 1 indicates that the corresponding feature attribute is selected, and 0 indicates that the feature attribute is not selected.

Begin Import dataset IGOA inializaon

Compute and Update feature subset by IGOA

Fitness evaluaon Stop criteria?

Provide the opmal soluon Output best feature subset and classificaon accuracy End

Fig. 2. Flow chart of applying EGOA for the feature selection

The EGOA population initialized values of individual dimensions are random numbers of [0,1], with its individual vectors in binary the population 0 and 1. Here if individuals are higher than 0.65, they are set as 1; otherwise, they are set to 0. In order to obtain the highest possible classification accuracy with as few feature numbers as possible, the fitness function for evaluating individual good or bad needs to consider both these factors, so the fitness function is defined as follows. jRj Fitness ¼ a
cD R ðDÞ þ b
jN j

ð10Þ

where, cR ðDÞ is the classifier error rate (this paper uses the KNN classification algorithm to evaluate the advantages and disadvantages of the feature subset (take K = 5)), jRj is the number of features contained in the current individual, jN j is the number of original features in the dataset, a and b are the coordination parameters that balance the classification accuracy and the length of feature subset, and b ¼ 1  a; a 2 ½0; 1, this paper takes a = 0.99. The accuracy rate of the classifier is used to evaluate the advantages and disadvantages of the proposed EGOA for feature selection, the number

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of feature selections, and the feature selection rate as the measurement indicators. A parameter of classification accuracy is used as a definition as follows. TN Accuracy ¼ TPP þ þN

ð11Þ

where, TP, TN, P, and N represent the real positive example, true negative model, positive and negative sample number, respectively. The definition of feature selection rate is given as follows. FsRatio ¼ M1

M P sizeð^gi Þ i¼1

D

ð12Þ

where M is the running times of feature selection algorithm, D is the number of original features in the dataset, ^gi is the optimal feature subset obtained by each run of the algorithm, and sizeð xÞ is the number of elements 1 in the vector x. In order to prove the effectiveness of the EGOA-based feature selection method proposed in this paper, the algorithm is tested on seven data sets [16] shown in Table 3. First, the EGOA-based feature selection method (EGOA FS) and traditional locust optimization are compared. The performance of the algorithm’s feature selection method (GOA⁃FS) [17] and the algorithm training with full features. Set the population size to 30, the maximum number of iterations of the algorithm to 100, and all algorithms to run independently ten times. The number of features is selected to evaluate the performance of the algorithm. The test results are shown in Table 4 (the bold type in the table is the optimal value in the compared algorithms). Table 5 depicts the comparison of the proposed EGOA-FS with the other methods, such as the Sine Cosine Algorithm (SCA) [19], Whale optimization algorithm (WOA) [20], and Ant Lion Approaches (ALO) [18] for feature selection on the medical dataset. It can be seen that the number of highlights in the table has belonged to the proposed EGOA-FS scheme. It means that the proposed method can be a robust altered competitor. Table 3. Description of the experimental dataset D1 D2 D3 D4 D5 D6 D7

Datasets Feature number Instances Number Breast Cancer EW 30 569 Zoo 16 101 Heart 12 270 Parkinson 22 197 Congress 16 435 Wine 13 178 Colon 2000 62

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Table 4. Comparison of feature selection performance of the algorithm on seven data sets Dataset D1 Accuracy Features D2 Accuracy Features D3 Accuracy Features D4 Accuracy Features D5 Accuracy Features D6 Accuracy Features D7 Accuracy Features

FULL 0. 951 30 0. 961 16 0. 763 12 0. 908 22 0. 940 16 0. 944 13 0. 677 2000

GOA-FS 0. 959 11.2 0. 931 6.6 0. 768 6. 6 0.949 8. 9 0. 945 5. 5 0. 951 6. 2 0. 745 675.2

Proposed EGOA-FS 0.976 13.5 0.963 7.1 0.801 6.4 0.949 8.4 0.970 3.3 0.960 5.8 0.833 691.9

Table 5. Comparison of performance of the proposed scheme EGOA with other algorithms Data set ALO [18] D1 0.930 D2 0.909 D3 0.826 D4 – D5 0.929 D6 0.911 D7 –

CSA [19] 0.903 0.937 0.788 0.908 – – –

WOA [20] Proposed EGOA 0.971 0.976 0.980 0.963 0.807 0.801 – 0.949 0.956 0.970 0.959 0.960 0.909 0.833

5 Conclusion In this paper, we proposed an approach to enhance the Grasshopper optimization algorithm (namely EGOA) based on an adaptive weight coefficient and nonlinear parameters for the optimization and feature selection problems. Although the Grasshopper optimization algorithm (GOA) is a robust swarm intelligence algorithm for optimization problems, it has drawbacks, e.g., falling into a local optimum and slow convergence. The global exploration and local development capabilities of the swarm intelligence algorithm could be improved by adjusting the parameters such as adaptive weight coefficient and nonlinear. We applied the customized strategies updating equations to promote the convergence speed, and to avoid trapping of falling into a local optimum the algorithm. In the simulation experiment, four benchmark test functions and the feature selection problem are used to prove that the improved strategy used in the proposed method can effectively enhance the precision and convergence

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speed of the algorithm. The compared results on selected test functions and datasets show that the proposed method can effectively provide the best selection features for increasing classification accuracy in comparison with those other approaches in the literature. Acknowledgment. The authors would like to thank Thai Nguyen University of Technology for the support of this research.

References 1. Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40, 16–28 (2014) 2. Molina, L.C., Belanche, L., Nebot, À.: Feature selection algorithms: a survey and experimental evaluation. In: 2002 IEEE International Conference on Data Mining, 2002. Proceedings, pp. 306–313. IEEE (2002) 3. Dao, T., Nguyen, T., Pan, J., Qiao, Y., Lai, Q.: Identification failure data for cluster heads aggregation in WSN based on improving classification of SVM. IEEE Access. 8, 61070– 61084 (2020). https://doi.org/10.1109/ACCESS.2020.2983219 4. Khalid, S., Khalil, T., Nasreen, S.: A survey of feature selection and feature extraction techniques in machine learning. In: 2014 Science and Information Conference, pp. 372–378. IEEE (2014) 5. Chu, S.C., Dao, T.K., Pan, J.S., Nguyen, T.T.: Identifying correctness data scheme for aggregating data in cluster heads of wireless sensor network based on naive Bayes classification. Eurasip J. Wirel. Commun. Netw. 1, 1–15 (2020) 6. Nguyen, T.T., Pan, J.S., Dao, T.K.: An improved flower pollination algorithm for optimizing layouts of nodes in wireless sensor network. IEEE Access. 7, 75985–75998 (2019). https:// doi.org/10.1109/ACCESS.2019.2921721 7. Nguyen, T.T., Qiao, Y., Pan, J.S., Chu, S.C., Chang, K.C., Xue, X., Dao, T.K.: A hybridized parallel bats algorithm for combinatorial problem of traveling salesman. J. Intell. Fuzzy Syst. 38, 5811–5820 (2020). https://doi.org/10.3233/jifs-179668 8. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 International Conference on Neural Networks, pp. 1942–1948 (1995) 9. Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015). https://doi.org/10. 1016/j.advengsoft.2015.01.010 10. Mirjalili, S., Lewis, A.: The whale Optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016). https://doi.org/10.1016/j.advengsoft.2016.01.008 11. Saremi, S., Mirjalili, S., Lewis, A.: Grasshopper optimisation algorithm: theory and application. Adv. Eng. Softw. 105, 30–47 (2017) 12. Simon, B., Gulyás, G.G., Imre, S.: Analysis of grasshopper, a novel social network deanonymization algorithm. Period. Polytech. Electr. Eng. Comput. Sci. 58, 161–173 (2014) 13. Abualigah, L., Diabat, A.: A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications. Neural Comput. Appl, 1–24 (2020) 14. Liang, J.J., Qu, B.Y., Suganthan, P.N., Hernández-Díaz, A.G.: Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Comput. Intell. Lab. Zhengzhou Univ. Zhengzhou, China Nanyang Technol. Univ. Singapore, Tech. Rep. 201212, (2013)

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15. Ewees, A.A., Abd Elaziz, M., Houssein, E.H.: Improved grasshopper optimization algorithm using opposition-based learning. Expert Syst. Appl. 112, 156–172 (2018). https://doi.org/10. 1016/j.eswa.2018.06.023 16. Bay, S.D., Kibler, D., Pazzani, M.J., Smyth, P.: The UCI KDD archive of large data sets for data mining research and experimentation. ACM SIGKDD Explor. Newsl. 2, 81–85 (2000) 17. Zakeri, A., Hokmabadi, A.: Efficient feature selection method using real-valued grasshopper optimization algorithm. Expert Syst. Appl. 119, 61–72 (2019) 18. Emary, E., Zawbaa, H.M., Hassanien, A.E.: Binary ant lion approaches for feature selection. Neurocomputing. 213, 54–65 (2016) 19. Taghian, S., Nadimi-Shahraki, M.H.: Binary Sine Cosine Algorithms for Feature Selection from Medical Data (2019). arXiv:1911.07805 20. Mafarja, M.M., Mirjalili, S.: Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260, 302–312 (2017)

An Evaluation of B-Spline for Synthesis of Cam Motion with a Large Number of Output Conditions Nguyen Thi Thanh Nga1(&), Nguyen Van-Sy1, Nguyen Thi Bich Ngoc1, and Vu Thi Lien2 1

Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] 2 Phenikaa University, Hanoi, Vietnam

Abstract. In the cam design process, cam motion functions play an important role due to affect not only kinematics but also the dynamics of the cam-follower systems. However, the selection of cam motion functions has been poor so far. This study aims to evaluate B-spline for the synthesis of cam motion with a large number of output conditions. The procedure for determining the cam motion was investigated. In addition, the comparison of the motion diagram between the cubic and quintic B-spline was also proposed. Moreover, Fourier analysis was used to examine the response of the kinematics in cam-follower systems. The results demonstrate that using quintic B-spline can be given good characteristics of the cam motion. Keywords: Cam-follower systems
B-spline function dynamics of cam-follower systems
Fourier analysis


Kinematics and

1 Introduction Kinematics and dynamics are importantly characterized by cam-follower systems. Many researches focus on the optimization of cam systems such as controlling the follower vibrations to reduce undesired dynamics [1], the optimization of cam profile for the cam-follower systems [2, 3], the dimension optimization of cam systems with translating roller follower [4], and cam optimization of high-speed cam for reducing the vibration [5]. Regarding the cam motion, the basic functions such as the harmonic, cycloidal, trapezoidal, polynomials used for designing cam mechanisms in kinematic diagrams were proposed in [6]. Kiran [7] presented the simulation and design of the cam profile using piecewise polynomials. Each piecewise polynomial is used by the 2-3 and 3-4-5 polynomials for designing motion programs. The synthesis of constant-breadth cams with flat-face double translating and oscillating was investigated by using Bezier functions [8]. Spline functions have been widely used in cam design. There have been many researches working on optimization problems for establishing the cam motion diagrams by using spline functions such as optimal synthesis for spring-actual cam systems [9], applying the genetic algorithm for optimizing the cam cross-section [10], © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 173–180, 2021. https://doi.org/10.1007/978-3-030-64719-3_20

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designing the spline for motion program [11, 12], optimizing motion curves with spline to reduce kinematics of the cam systems [13–16]. The above studies have been a lack of evaluating the spline function for the synthesis of motion programs. This paper proposes the evaluation of B-spline used for cam motion with a large number of output motions. A systematic method for describing the motion curve with B-spline was set up. The third and fifth degrees of B-spline were examined for motion programs. Finally, the Fourier analysis was utilized to evaluate the response of kinematic characteristics.

2 B-Spline Curve Description for Cam Motion The displacement motion of cam mechanisms described by using B-spline curve can be expressed in the interval [a, b] S¼

n P

Ni;p ðhÞPi ; for h 2 ½a; b

i¼1

i ¼ 1; 2; . . .; n

;

ð1Þ

Where n is the number of boundary conditions in displacements, velocities, accelerations, and jerks; p is representative for the degree of displacement; h presents the angle of camshaft; Pi is control points. The displacement curve is calculated based on B-spline basic functions that can be described as Ni;p ðhÞ ¼

hi þ p þ 1  u h  hi Ni;p1 ðhÞ þ Ni þ 1;p1 ðhÞ hi þ p þ 1  hi þ 1 hi þ v  hi þ p

ð2Þ

In Eq. (2), hi are representative of elements in the knot vector U. The knot vector can be written as U ¼ fh1 ; h2 . . .; hm g

ð3Þ

In which, m is the number of elements in the knot vector, m = n + p + 1. All elements are non-decreasing values, i.e., hi < hi+1, i = 1,2,…, n − 1. These elements are in the interval [a, b]; values of p + 1 the first elements are equal to and values of p + 1 the end elements are equal to b. The velocity, acceleration and jerk motions can be respectively expressed as VðhÞ ¼

AðhÞ ¼

n dSðuÞ X 1 ¼ Ni;p ðhÞPi ; dh i¼1

ð4Þ

n d 2 SðuÞ X 2 ¼ Ni;p ðhÞPi ; 2 dh i¼1

ð5Þ

An Evaluation of B-Spline for Synthesis of Cam Motion

JðhÞ ¼

3

d SðuÞ ¼ dh3

n X

3 Ni;p ðhÞPi :

175

ð6Þ

i¼1

1 2 3 In Eqs. (4–6), Ni;p ðhÞ; Ni;p ðhÞ; and Ni;p ðhÞ can be computed by the following equation

k Ni;p ¼

k p! X ak;j Ni þ j;pk ðhÞ; ðp  kÞ! j¼0

ð7Þ

The coefficients ak,j, can be calculated as a

; a0;0 ¼ 1; ak;0 ¼ ui þ pkk1;0 þ 1 ui ak1;j ak1;j1 a ak;j ¼ ui þ p þ jk þ 1 ui þ j ; ak;k ¼ ui þ p þk1;k1 1 ui þ k

ð8Þ

With j = 1, …, k − 1.

3 Control Point Determination In order to determine the motion curves as shown in Eqs. (1), (4), (5) and (6), control points Pi must be found. The procedure for calculating control points includes the steps as follows Step 1. Computing the knot vector U Based on the angle of the camshaft of boundary conditions, the knot vector can be computed as the uniform spaced method as found in [17]. Step 2. Calculating the basic functions as shown in Eq. (2) with degree p = 3 and p = 5. Step 3. Determining the values of the displacements, velocities, acceleration, and jerks at the boundary conditions. The values of displacements, velocities, accelerations, denoted as Vij (i = 1, 2, …, n and j = 1, 2, …, n), are respectively computed from the displacement, velocity, acceleration curves as shown in Eqs. (1), (4), and (5) at the points where the constraints are defined. The first and the second indexes in Vij imply the constraints and the Bspline basis functions. The constraint values are denoted as (i = 1, 2, …, n). The linear systems of equations can be written as P1 V11 .. . P1 Vn1

þ þ

P2 V12 .. . P2 Vn2

þ


þ þ


þ

Pn V1;n .. . Pn Vn;n

¼ C1 .. .

ð9Þ

¼ Cn

Step 4. Obtaining the control points by solving the linear systems (9). From these steps, the displacement, velocity, acceleration, and jerk curves are simply determined.

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4 Result and Discussion 4.1

Motion Program

We consider here 27 output conditions in displacements of the cam as found in [18]. Based on the fundamental law in cam design, the displacement, velocity, acceleration must be continuous to avoid the infinitive values at the discontinuous points. Therefore, 2 boundary conditions in velocity and 2 boundary conditions in acceleration at the first and the end points are added and they are equal to zero. The boundary conditions can see in star signs. In order to evaluate the B-spline for motion programs of a large number of output conditions, the cubic and quintic B-spline curves are selected. Based on the procedure of calculation as discussed in Sect. 3, the control points are obtained. The motion curves are thus established as shown in Eqs. (1), (4), (5) and (6). Figure 1 and Fig. 2 present the motion diagrams for a large number of output conditions using the cubic and quintic B-spline curves. It can be seen in Fig. 1a and Fig. 2a, the control points are adjusted in order to fit the B-spline curves with given output conditions. Referring the extreme value of the curves as shown in Fig. 1 and Fig. 2, the maximum values of the velocity, acceleration, and jerk curves in case of Bspline with p = 3 are 7.7621 (mm/rad), 58.015 (mm/rad2), and 846.1806 (mm/rad3), respectively. For the B-spline with p = 5, these values are orderly 7.2891 (mm/rad), 48.3205 (mm/rad2), and 1703.9 (mm/rad3).

Fig. 1. Motion diagram for large number of boundary conditions with degree p = 3: (a) Displacement, (b) Velocity, (c) Acceleration, and (d) Jerk

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Fig. 2. Motion diagram for large number of boundary conditions with degree p = 5: (a) Displacement, (b) Velocity, (c) Acceleration, and (d) Jerk

4.2

Fourier Analysis

The Fourier analysis indicates the harmonic content. Figure 3 shows the frequency of the acceleration using B-spline with degree p = 5 and p = 3. It can be observed from this figure that almost amplitudes of the acceleration at each number in the case of Bspline with p = 3 (see yellow color in Fig. 3) are much larger than that in the case of Bspline with p = 5. Due to this, the inertial force response of the cam-follower systems can be affected. Similarly, the Fourier analysis of jerk is depicted in Fig. 4. It can be clearly seen that the amplitudes of jerk at high-order harmonic components using Bspline with degree p = 3 are greatly enormous compared to the amplitudes of B-spline with degree p = 5. As known that the amplitudes of the harmonic content of jerk are affected by the vibration response of the cam-follower systems. Therefore, it should use the smaller amplitudes for high-speed cam-follower system.

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Fig. 3. Fourier analysis for acceleration

Fig. 4. Fourier analysis for jerk

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5 Conclusion In this work, the evaluation of the cubic and quintic B-spline for synthesizing the cam motion with a large number of output conditions was proposed. The methodology of computing cam motion using B-spline was established based on the boundary condition of the output motions. The conclusion of this paper can be drawn as follows – A general computation of cam motion with B-spline was exhibited. This computation can be applied for any output motions. – In comparison between two cases of B-spline, the motion diagrams in velocity and acceleration using the quintic B-spline obtain the good characteristics – The amplitudes of acceleration and jerk for almost numbers in Fourier analysis have the smaller values when using the quintic B-spline compared to those with the cubic B-spline. Acknowledgment. The work described in this paper was supported by Thai Nguyen University of Technology for a scientific project.

References 1. Gatti, G., Mundo, D.: On the direct control of follower vibrations in cam-follower mechanisms. Mech. Mach. Theory 45, 23–35 (2010) 2. Ouyang, T., Wang, P., Huang, H.: Cam profile optimization for the delivery system of an offset press. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 231, 4287–4297 (2017) 3. Xiao, H., Zu, J.W.: Cam profile optimization for a new cam drive. J. Mech. Sci. Technol. 23, 2592–2602 (2009) 4. DasGupta, A., Ghosh, A.: On the determination of basic dimensions of a cam with a translating roller-follower. J. Mech. Des. Trans. ASME 126, 143–147 (2004) 5. Liang, Z., Huang, J.: Design of high-speed cam profiles for vibration reduction using command smoothing technique. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 228, 3322– 3328 (2014) 6. Chen, H., Nguyen, T.T.N., et al.: Application of a cam workbench for education in mechanical engineering. In: New Advances in Mechanisms, Mechanical Transmissions and Robotics, pp. 177–186 (2017) 7. Kiran, T., Srivastava, S.K.: Analysis and Simulation of Cam Follower Mechanism Using Polynomial Cam Profile, pp. 211–215 (2013) 8. Cardona, S., Zayas, E.E., Jordi, L., Català, P.: Synthesis of displacement functions by Bézier curves in constant-breadth cams with parallel flat-faced double translating and oscillating followers. Mech. Mach. Theory 62, 51–62 (2013) 9. Kim, J.H., Ahn, K.Y., Kim, S.H.: Optimal synthesis of a spring-actuated cam mechanism using a cubic spline. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 216, 875–884 (2002) 10. Lampinen, J.: Cam shape optimisation by genetic algorithm. CAD Comput. Aided Des. 35, 727–737 (2003) 11. Mandal, M., Naskar, T.K.: Introduction of control points in splines for synthesis of optimized cam motion program. Mech. Mach. Theory 44, 255–271 (2009)

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12. Nguyen, T.T.N., Hüsing, M., Corves, B.: Motion Design of Cam Mechanisms by Using Non-Uniform Rational B-Spline Bewegungskurven von Kurvengetrieben unter Verwendung von Non-Uniform Rational B-Spline (2018) 13. Nguyen, T.T.N., Kurtenbach, S., Hüsing, M., Corves, B.: Improving the kinematics of motion curves for cam mechanisms using NURBS. Mech. Mach. Sci. 52, 79–88 (2018) 14. Nguyen, T.T.N., Kurtenbach, S., Hüsing, M., Corves, B.: A general framework for motion design of the follower in cam mechanisms by using non-uniform rational B-spline. Mech. Mach. Theory 137, 374–385 (2019) 15. Sateesh, N., Rao, C.S.P., Janardhan Reddy, T.A.: Optimisation of cam-follower motion using B-splines. Int. J. Comput. Integr. Manuf. 22, 515–523 (2009) 16. Sateesh, N.: Cam-follower systems using NURBS, pp. 15–21 (2014) 17. Nguyen, T.T.N., Kurtenbach, S., Hüsing, M., Corves, B.: Evaluating the knot vector to synthesize the cam motion using NURBS. Mech. Mach. Sci. 50, 209–216 (2018) 18. Kurtenbach, S., Wieja, F., Müller, I., Neidlin, M., Sonntag, S.J., Goetzenich, A., Hatam, N., Bruns, P., Chuembou Pekam, F., de la Fuente Klein, M., Radermacher, K., Hopmann, C., Autschbach, R., Steinseifer, U., Hüsing, M., Corves, B.: Motion analysis of the left ventricle of a human heart for realization in a cardiovascular mock-loop. Mech. Mach. Sci. 48, 17–29 (2018)

An Experimental Study on Vibration-Driven Locomotion Systems Under Different Levels of Isotropic Friction Ngoc-Tuan La1, Quoc-Huy Ngo2, Ky-Thanh Ho2, and Khac-Tuan Nguyen2(&) 2

1 Vinh University of Technology Education, Vinh, Vietnam Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected]

Abstract. This paper shows experimental analysis results of the study on vibration-driven locomotion system, which can be applied in capsule robots. The experimental apparatus provided a capacity of varying friction force when keeping the weight of the whole system unchanged. Twelve experimental sets with 16 runs for each set were implemented, providing a deep insight of the system behavior, both in progression rate and the relative motions of the masses. The experimental data revealed that, the force ratio between the excitation magnitude and friction level would not be totally correct to present the excitation effects in modeling the system. The level of friction force may have a significant effect on not only how fast the system move, but also which direction of the progression. The new findings would be useful for further studies on the design and operation of vibration driven locomotion systems. Keywords: Vibration-driven locomotion


Capsule robots
Isotropic friction

1 Introduction Conventional mobile robots usually consist of external propulsion mechanisms, such as wheels, legs or paddles. Hence, such systems would face with several boundaries in terms of mechanical complexity, controllability, physical size, failure of moving parts, and causing hazard to surrounding environment. Recently, the development of mobile devices employing vibration for automotive motion has become a very promising solution for encapsulate locomotion systems [1–3]. The principle of this solution, pioneered by Chernousko [4], is that the forward and backward progression of the system can be obtained in the presence of dry friction combined with a periodically driven internal mass interacting with the main body of the system. An earlier model using vibro-impact driven mechanism was proposed by Pavlovskaia et al. [5]. On the one hand, the friction force is usually considered as a resistance preventing the moving trend of the system, and thus should not be too large. On the other hand, the presence of friction plays an important role of external resistance to provide the locomotion of the system in desired direction [1, 6]. The system working under small friction would not be able to progress in the desired direction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 181–191, 2021. https://doi.org/10.1007/978-3-030-64719-3_21

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Generally, the internal mass has been designed to have one of the two motion principles: 1) the mass moves periodically without having impact with the body and 2) the mass moves periodically and has impact (vibro-impact) with the body. In the former case, the relative motion of the internal mass must follow a specially designed multi-phase acceleration-controlled (see [7–10] for example). When the inertial force, caused by acceleration changes, exceeds the friction threshold, the body locomotion is generated. In many previous studies [1–10], the friction force was usually assumed to be isotropic, i.e. the friction forces in both forward and backward motion of the frame body have the same value. The latter choice of self-propelled design is vibration-impact driven locomotion. In this system, the internal mass oscillates and periodically collides with an obstacle block. This results in a jump-up of the inertial force, making the system move. The friction force was usually assumed to be isotropic [5, 11–16]. The system working under anisotropic friction, i.e. the friction in forward direction is different from that in backward direction, was also examined. However, the examined system requires a special control of the internal mass motion (See [17] for example) or can only move in the direction with smaller friction (downward of an inclined chute [18]). The effectiveness of the robots has typically been considered by checking with the progression rate and dynamical response of the systems. In vibro-impact systems, the excitation force acting on the internal mass was usually in the sinusoidal form. In previous studies, the excitation force was treated as a dimensionless number, counted as the ratio between the real amplitude of the excitation force and the Coulomb friction value. The progression rate and/or moving direction of the system was checking as a function of such force ratio (See for example in [5, 12, 14–16]). In experimental studies, the effect of excitation force was taken into account with certain dry friction with given levels of frictional force (See [14, 19–22]). The effects of various friction levels on the system response have rarely been experimentally considered [3, 23] but not in interaction with the excitation force. Beside, some interesting observations have been reported [14, 24]. For example, when the elastic force acting on the capsule is larger than the threshold of the friction, backward motion of the capsule is observed [24]. At some situations, the average speed of forward progression of the capsule using small amplitude of excitation is much larger than the one with backward progression using large amplitude of excitation. This observation somehow reveals the fact that a large amplitude of excitation cannot improve the performance of the capsule system [14]. However, such interesting observations were found at certain conditions of experiments only and need more practical considerations and experimental validations. Consequently, this study was made to give deeper insights of the effect of different levels of friction force on the system response. Three levels of friction forces, representing for small, middle and large resistances were examined combined with four different values of the force ratios. The results revealed that with the same force ratio, the system have various behaviors with different levels of frictions. In addition, under the same friction force, varying the force ratio provides different trends of the moving direction of the vibration driven locomotion system. The paper is organized as below. Basic principle of the vibration driven locomotion system is briefly presented in Sect. 2. The experimental setup is then described in detail

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in Sect. 3. The results and discussions are given in Sect. 4. Some remarkable conclusions and recommendations are made in the last section.

2 Basic of the Study 2.1

Working Principle of the System

The physical model of a typical vibration-driven locomotion system with impacts is depicted in Fig. 1(a). The system is consisted of a body mass, m2 and an inertial mass, m1. An elastic spring with stiffness k couples the two masses. The two masses are connected using an elastic spring and a viscous damper, c. The system body m2 can move along a straight line on a resistive horizontal plane. The internal mass oscillates inside the body along the line parallel to the motion line of the body. A harmonic force Fm with amplitude A and frequency X exerts on both masses. The friction force, Fr, occurred at the contact surface between the body and the resistive plane is assumed to obey the Coulomb dry friction law, as shown in Fig. 1(b). In this study, the friction force is assumed to be isotropic, i.e. R+=|R−|=R.

Fig. 1. A vibration-driven locomotion model (a) and Coulomb isotropic friction model (b)

Impact characteristics between the two masses were modeled as stiffness k0. In Fig. 1, X1 and X2 represent the absolute displacements of the internal mass and the frame body, respectively. The motion Xi (i = 1, 2) is considered as forward motion if the value of Xi is positive and vice versa. The two masses are initially positioned with a gap G. When the relative displacement X1-X2 is greater than or equal to the gap G, impact occurs and the system can move forward. The friction force plays a very important role in the working principle of the vibration locomotion systems [4, 5]. The system may either not progress or move in unexpected direction if the friction force is too small or too large. Naturally, in order to increase the average velocity of motion, the coefficient of friction of the body mass should be increased and that between the internal mass and the body mass decreased [4]. The results of this study would be a practical recommendation not only on the

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upper limitation when increasing friction force but also the smallest friction, with respect to the excitation force, that can help the system move in desired direction. 2.2

Objectives of the Study

Generally, studies on the vibration-driven locomotion systems have usually been done by following steps: – Describe the physical and mathematical models of the system; – Implement experiments to validate the mathematical model; – Analysis the system using the validated mathematical model, usually focus on the progression rate and the dynamical response of the system. In order to expand the results to different scales of the prototypes, the mathematical model was usually transformed into dimensionless form. The equations describing the motion of the system shown in Fig. 1 can be written as: 8 d2 X
 dX2 1 < m1 dt21 ¼ Fm  Fspr  c dX dt  dt  H ½k0 ðX1  X2  GÞ

dX  d 2 X2 dX1 dX2 2 : m2 dt2 ¼ Fm þ Fspr þ c dt  dt þ H ½k0 ðX1  X2  GÞ  Rsgn dt Fm ¼ A cosðXtÞ

ð1Þ

where H is the Heaviside step function defined as: ( H¼

1 ; if ðX1  X2  GÞ [ 0 0 ; if ðX1  X2  GÞ  0

ð2Þ

Assuming the spring is linear with the stiffness k, the spring force can be described as: Fspr ¼ kðX1  X2 Þ

ð3Þ

Using the following non-dimensional variables and parameters: rffiffiffiffiffiffi k k k X s ¼ X0 t; x1 ¼ X1 ; x2 ¼ X2 ; X0 ¼ ;x¼ ; R R m1 X0 c A k0 k m2 ; a ¼ ; r ¼ ; c ¼ G; l ¼ f¼ 2m1 X0 R R k m1

ð4Þ

the dimensionless form of the model (1) can be expressed as: 


 x001 ¼ a cosðxsÞ  ðx1  x2 Þ  2f x01
 x02  hrðx1  x2  cÞ  x002 ¼ a cosðxsÞ þ ðx1  x2 Þ þ 2f x01  x02 þ hrðx1  x2  cÞ  sgnðx02 Þ l1

ð5Þ

where ()’ and ()’’ denotes the first and the second time derivative d()/ds and d2()/ds2, respectively; and h is a Heavisde function defined as:

An Experimental Study on Vibration-Driven Locomotion Systems

 h¼

1 ; if ðx1  x2  cÞ [ 0 0 ; if ðx1  x2  cÞ  0

185

ð6Þ

Generally, the effect of the excitation force (Fm) on the system behavior have been taken into account by varying the value of the force ratio, a (See [11, 13, 24, 25] for example). In the mentioned studies, a given value of a provided one value of the dimensionless x2. From Eq. (4), since both k and R are positive, x2 must have the same sign with X2. It means that, for a given value of a, different values of friction level R will result in different values of the progression X2, but not different in the moving direction. Our experimental results revealed that with the same force ratio a, the system can either move forward or backward, i.e. with different signs (either negative or positive) of the progression X2. In other words, the force ratio, a may not be totally correct to represent the effect of the excitation force, as usually suggested in many previous studies. Consequently, this study was made to experimentally examine of the effect of different levels of friction force on the system response. The results would provide a basic knowledge to give deeper insight into the locomotion systems working under different levels of the environmental resistance. Further work of modelling and analyzing the system can be made based on these findings. The objectives of this study thus are as following: – To develop an experimental apparatus which can vary the friction force when keeping the system weight; – To carry out how different levels of the friction magnitude can effect on the progression rate of the system body under the same force ratio; – To verify if the force ratio is not able to fully represent the excitation magnitude as previously suggested. In this experimental study, three levels of friction forces, representing for small, middle and large resistances were examined combined with four different values of the force ratios. The experimental setup and implementing method are presented in the next section.

3 Experimental Implementation The above model was realized as shown in Fig. 2, as developing from the apparatus built at TNUT’s laboratory by Nguyen et al. [3, 13, 20]. A mini electro-dynamical shaker (1) is placed on a slider of a commercial linear bearing guide (4), providing a tiny rolling friction force. An additional mass (3) was clamped on the shaker shaft with the support of sheet springs (2). Generally, applying a sinusoidal current to the shaker leads to relative linear oscillation of the shaker shaft with the mass added on. Hereafter, the moveable mass, combined by the addition mass and the shaker shaft, is assigned as inertial mass, m1, playing the role of the internal mass of the system. The relative motion of the inertial mass was measured by a noncontact position sensor (9). Motion of the shaker body was recognized by a linear variable displacement transformer (8). The body shaker, including the sensors and the

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Fig. 2. A photograph of the experimental apparatus

carbon tube, is referred as the mass m2. A force sensor (7) was used as the obstacle block to measure the impact force. In order to vary the friction force when remaining the body mass, a carbon tube (5) is connected with the shaker body by means of a flexible joint, avoiding any misalignment when moving. The tube can slide between two aluminium pieces in the form of a V-block (6). The two V-blocks are fixed on two electromagnets, as depicted in Fig. 3(a). Supplying a certain value of electrical current to the coupled electromagnets provides a desired clamping force on the tube and thus a corresponding value of sliding friction. The friction force was measured by pulling the body to move at a steady speed. By adjusting the voltage supplied to the coupled electromagnets, the corresponding friction force can be obtained (See [3] for more detail). The shaker is powered by a sinusoidal current generated by a laboratory function generator and then amplified by a commercial amplifier. The application of a sinusoidal current to the shaker leads to oscillation of the shaker shaft and the mass attached on it. As provided by the shaker supplier, the magnetic force, Fm is solely depended on the current supplied. Adjusting the sinusoidal supplying to the amplifier can provide a desired excitation force. A supplementary experiment was implemented to verify the relation of the magnetic force and the current supplied. A load cell was used as an obstacle resisting the shaker movement and thus to measure the magnetic force induced. A DC voltage was supplied to the shaker to generate the magnetic force. Varying the voltage, several pairs of the current passing the shaker and the force were collected. Experimental data confirmed that the excitation force is proportional to the current supplied to the shaker (see [13] for detailed information of how to determine this relation). For operational parameters, three levels of friction force were selected at first. With respect to the total weight of the two masses as 2.336 kg, the friction force levels were set as 2.4, 6.8 and 13.6 N, corresponding to three levels of friction coefficient as approximately 0.1, 0.3 and 0.6. These levels can be respectively considered as low, middle and high friction coefficients. The minimum magnitude of the excitation force chosen needs to be strong enough to make the mass oscillate and collide to the obstacle block. Consequently, three higher levels of the excitation force were selected as strong

An Experimental Study on Vibration-Driven Locomotion Systems

(a) α=0.59

(b) α=0.79

(c) α=0.99

(d) α=1.19

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Fig. 3. Progression velocity of the system for (a) a = 0.59; (b) a = 0.79; (c) a = 0.99 and (d) a = 1.19. For all sub-plots, Ff1 < Ff2 < Ff3.

enough to make significant differences between the system responses. The overall experimental parameters are given in Table 1. Table 1. Parameters of experiments. Parameter Internal mass Body mass Impact gap Friction force Force ratio Excitation frequency

Notation m1 m2 G Ff a = A/R fexc

Value Unit 0.518 Kg 1.818 Kg 0.5 mm 2.4; 6.8; and 13.6 N 0.59; 0.79; 0.99; and 1.19 – [5–20], 1 Hz step Hz

Twelve sets of experiments were implemented for four levels of the force ratio, a and three levels of the friction magnitude. Each experimental set, including 16 runs, was implemented at 16 values of the excitation frequency ranged from 5 Hz to 20 Hz with an incremental step of 1 Hz. Overall, excitation frequencies less than 5 Hz or higher than 20 Hz seemed as not efficient to operate the system. For each run, the data

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of supply current passing the shaker, the displacements of the two masses and the impact force were collected and then analyzed. The results are shown in the next section.

4 Results and Discussion Firstly, the progression rate of the whole system is considered. Figure 3 presents the average velocity of the system body at four levels of the force ratio, a. As can be seen, for a less than 1 (Fig. 3(a, b)), with the same a, higher friction forces provided higher forward velocities. Increasing a resulted in faster moving velocity of the forward motion of the system. However, forward velocities with a1 (Fig. 3c) provided a lower velocity, compared to that with a = 0.79. The backward motion of the system appeared at then higher range of excitation frequency, fexc= [12..18] Hz with the two highest levels of friction force (Ff2 and Ff3), as shown in Fig. 3d. From such observations, the following interesting issues can be remarked: – It seems to be agreed with previous studies that, increasing the relative magnitude of the excitation with respects to friction force (i.e. increasing the force ratio a) may increase the forward velocity of the system; – When the force ratio is higher than 1 (i.e. the excitation magnitude is larger than the friction force), backward motion would appear, even though the impact force is in forward direction; – A new remarkable finding is that, with the same force ratio, the motion of the system would be either forward or backward, depending on the friction level. This would not agree with several previous suggestions that with a certain set of parameters, a given value of the force ratio provides only one direction of the system progression (As mentioned in the end of Sect. 2). Another view of the effects of friction levels is depicted in Fig. 4 to support the above-mentioned ideas. At each of the two investigated levels of friction, the progression rate of the system with for levels of the force ratio are presented.

(a) Ff = 2.3 N

(b) Ff =13.6 N

Fig. 4. Progression velocity of the system for (a) Ff = 2.3 N; (b) Ff = 13.6 N. For all sub-plots, a1 < a2 < a3 < a4.

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As can be seen in Fig. 4, with the same friction level, higher force ratio would not provide higher progression rate. With low friction (Fig. 4a), the highest forward velocity was obtained at a3 = 0.99, whereas the highest force ratio (a4) mostly provided lower velocity. With high level of the friction (Fig. 4b), highest forward velocity appeared with a2, whereas highest backward velocity performed at the highest level of force ratio, a4. There has still been an open question about the reason of the backward motion of the system even though the impact force acting in the forward direction. Figure 5 illustrate the time history of the system motion. The two sub-plots have the same force ratio (a = 1.19) and other parameters, except that one for small friction (Fig. 5a, Ff = 2.3 N) and one for large friction (Fig. 5b, Ff = 13.6 N). Working with a small friction, the system appeared to move forward (Fig. 5a), whereas it moved backward with the larger friction (Fig. 5b). On each sub-plot, two red-thin vertical lines limit one oscillation period of the mass m1. Another black-dash line divided each period into two areas: Area 1 where the body m2 moved forward, and area 2 where the body m2 moved backward.

Fig. 5. Time histories of motions of the internal mass X1 (dash curve) and of the body X2 (blue solid curve): (a) Ff = 2.3 N, and (b) Ff = 13.6 N. A force ratio a = 1.19 was applied; fexc = 17 Hz.

At presented on the figure, the impacts occurred at the instant of the displacement peaks of m1. Under small friction (Fig. 5a), the body moved forward not by impact, but for reaction between the two masses. The forward motion of the body occurred at somewhere when the mass m1 was going backward. Under higher friction (Fig. 5b), forward motion of the body seemed to be the result of impact force – it happened just at the impact instant. Different from that of the former case, in each period, the backward displacement was larger than the forward motion. Consequently, the overall motion of the system in this case was backward, although the impact already acted as a source for the forward moving of the system. Noted that negative values of the displacements, X1 and X2, mean that the motions of the two masses were in backward direction, as relative to the origin coordinate (X1 = X2 = 0). From this practical observation, it can be concluded that forward motion of the system may not only depend on how large the impact force is, but also how the

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interaction of the relative motion of the two masses. It is worth noting that, the mechanism behind of moving forward or backward motion of the whole system is till an open dynamic problem. The data obtained would be a good resource for further studies in the field.

5 Conclusions This paper presented experimental observations and several initial analyses on a vibration driven locomotion apparatus. The experimental setup provided a capacity of varying friction force when keeping the weight of the whole system unchanged. Based on that, a series of experimental tests were implemented, providing a deep insight of the system behavior, both in progression rate and the relative motions of the masses. The following remarks would be useful for further studies: – The force ratio between the excitation totally correct to present the excitation – The level of friction force would be a system move, but also which direction

magnitude and friction level would not be effects in modeling the system; significant effect on not only how fast the of the progression.

Acknowledgement. This research was funded by Vietnam Ministry of Education and Training, under the grant number B2019-TNA-04. The authors would like to express their thank to Thai Nguyen University of Technology, Thai Nguyen University, and Vinh University of Technology Education for their support for the study.

References 1. Liu, P., Yu, H., Cang, S.: Modelling and analysis of dynamic frictional interactions of vibrodriven capsule systems with viscoelastic property. Eur. J. Mech. A. Solids 74, 16–25 (2019) 2. Yan, Y., et al.: Proof-of-concept prototype development of the self-propelled capsule system for pipeline inspection. Meccanica 53(8), 1997–2012 (2017) 3. Nguyen, V.-D., La, N.-T.: An improvement of vibration-driven locomotion module for capsule robots. Mech. Based Des. Struct. Mach. 1–15 (2020) 4. Chernous’ko, F.L.: The optimum rectilinear motion of a two-mass system. J. Appl. Math. Mech. 66(1), 1–7 (2002) 5. Pavlovskaia, E., Wiercigroch, M., Grebogi, C.: Modeling of an impact system with a drift. Phys. Rev. E: Stat. Nonlin. Soft Matter Phys. 64(5 Pt 2), 056224 (2001) 6. Chernous’ko, F.L.: Analysis and optimization of the motion of a body controlled by means of a movable internal mass. J. Appl. Math. Mech. 70(6), 819–842 (2006) 7. Nunuparov, A., et al.: Dynamics and motion control of a capsule robot with an opposing spring. Arch. Appl. Mech. 89(10), 2193–2208 (2019) 8. Huda, M.N., Yu, H.: Trajectory tracking control of an underactuated capsubot. Auton. Robots 39(2), 183–198 (2015) 9. Su, G., et al.: A design of the electromagnetic driver for the internal force-static friction capsubot. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (2009)

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10. Li, H., Furuta, K., Chernousko, F.L.: Motion generation of the capsubot using internal force and static friction. In: Proceedings of the 45th IEEE Conference on Decision and Control (2006) 11. Gu, X.D., Deng, Z.C.H.: Dynamical analysis of vibro-impact capsule system with Hertzian contact model and random perturbation excitations. Nonlinear Dyn. 92(4), 1781–1789 (2018). https://doi.org/10.1007/s11071-018-4161-x 12. Duong, T.-H., et al.: A new design for bidirectional autogenous mobile systems with twoside drifting impact oscillator. Int. J. Mech. Sci. 140, 325–338 (2018) 13. Nguyen, V.-D., et al.: The effect of inertial mass and excitation frequency on a Duffing vibro-impact drifting system. Int. J. Mech. Sci. 124–125, 9–21 (2017) 14. Liu, Y., Pavlovskaia, E., Wiercigroch, M.: Experimental verification of the vibro-impact capsule model. Nonlinear Dyn. 83(1–2), 1029–1041 (2015) 15. Liu, P., et al.: A self-propelled robotic system with a visco-elastic joint: dynamics and motion analysis. Eng. Comput. 36(2), 655–669 (2019) 16. Yan, Y., Liu, Y., Liao, M.: A comparative study of the vibro-impact capsule systems with one-sided and two-sided constraints. Nonlinear Dyn. 89(2), 1063–1087 (2017) 17. Liu, P., Yu, H., Cang, S.: Optimized adaptive tracking control for an underactuated vibrodriven capsule system. Nonlinear Dyn. 94(3), 1803–1817 (2018) 18. Xu, J., Fang, H.: Improving performance: recent progress on vibration-driven locomotion systems. Nonlinear Dyn. 98(4), 2651–2669 (2019) 19. Nguyen, V.-D., et al.: A new design of horizontal electro-vibro-impact devices. J. Comput. Nonlinear Dyn. 12(6), 061002 (2017) 20. Nguyen, V.-D., et al.: Identification of the effective control parameter to enhance the progression rate of vibro-impact devices with drift. J. Vib. Acoust. 140(1), 011001 (2017) 21. Ho, J.-H., Nguyen, V.-D., Woo, K.-C.: Nonlinear dynamics of a new electro-vibro-impact system. Nonlinear Dyn. 63(1–2), 35–49 (2010) 22. Su, G., et al.: A design of the electromagnetic driver for the “internal force-static friction” capsubot. In: The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE (2009) 23. Nguyen, V.-D., Woo, K.-C., Pavlovskaia, E.: Experimental study and mathematical modelling of a new of vibro-impact moling device. Int. J. Non-Linear Mech. 43(6), 542–550 (2008) 24. Liu, Y., et al.: Forward and backward motion control of a vibro-impact capsule system. Int. J. Non-Linear Mech. 70, 30–46 (2015) 25. Liu, Y., et al.: Modelling of a vibro-impact capsule system. Int. J. Mech. Sci. 66, 2–11 (2013)

Analysis of Milling Chatter Vibration Based on Force Signal in Time Domain Minh-Quang Tran1,2,3(&), Meng-Kun Liu3,4, and Quoc-Viet Tran5 1

2

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Industry Implementation 4.0 Center, National Taiwan University of Science and Technology, Taipei 10607, Taiwan [email protected] Center of Cyber-Physical System Innovation, National Taiwan University of Science and Technology, Taipei 10607, Taiwan 3 Department of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam 4 Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan

Abstract. Chatter is problematic during metal cutting process. It destroys the surface finish and reduce productivity and quality of productions. Therefore, prediction and identification of chatter vibration are needed to determine the range of stable cutting conditions. In this paper, the force signal in time domain is used to analyze the behaviors of the milling chatter vibration. The largest Lyapunov exponent index which can describe the different nonlinear characteristics of dynamical system behaviors is used as an effective indicator to distinguish the stable and unstable cutting conditions. The proposed chatter detection approach has been successfully validated by experiment. Keywords: Milling stability


Dynamic force model
Chatter analysis

1 Introduction The milling operation is one of the most common forms of machining by which the surface of workpiece is generated progressively as it is fed to a rotating cutter [1]. During the milling process, the engagement of each tooth with the workpiece is discontinuous. The cut of each tooth is less than half of the tooth revolution so the chip thickness varies periodically as the tooth enters and exits the cut [2]. As a result, an impact is produced when the edge touches the workpiece, a phenomenon also known as the forced vibration. Additionally, the tool could vibrate because of the variation of the chip thickness. This self-excited machining behavior is named chatter vibration [2–4]. The presence of chatter causes machining instability during the cutting process. Undoubtedly, the detection of machine-tool chatter is one of the most important topics in the milling process. Chatter has a more frequent occurrence when a fast machine tool is utilized along with a high cutting speed, large engagement angle, and high material removal rate. The combination of the aforementioned may cause © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 192–199, 2021. https://doi.org/10.1007/978-3-030-64719-3_22

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unexpected cutting outcomes such as noise, poor surface roughness, an increase of tool wear, reduction of tool life, and reduction in productivity rate [2, 4, 5]. Therefore, this phenomenon should be monitored and avoided during the cutting process to avoid machining instability and to improve the productivity. Regenerative chatter in milling is generated because the previous and current cuts are out of phase. The instantaneous chip thickness is variable, and it governs the cutting force which, in turn, affects subsequent tool vibrations [6, 7]. Therefore, the dynamic cutting force model plays an important role in analyzing and predicting chatter vibration. Furthermore, the accurate model could be used to analyze the machining process and optimize the cutting parameter. The cutting force was a function of instantaneous chip thickness, which is the engagement between cutter and workpiece. The uncut chip thickness was reportedly proportional to the cutting force [8]. Tsai [9] proposed a predictive force model in the end-milling process based on the geometrical analysis. A generalized geometric model of milling cutters was reported by Altintas [10]. The basics of chatter vibration in cutting operations described by an accurate model were proposed by Merritt [4]. An analysis of milling chatter vibration based on force signal in time domain is presented in the following sections.

2 Dynamic Cutting Force Model This section presents a dynamic cutting force model in which the cutting edges are decomposed into a set of elements by discretizing along the tool axis. The geometry of a general end mill with its schematic representation and a cutting force model with two orthogonal degrees of freedom in x and y directions is shown in Fig. 1 [11].

Fig. 1. The dynamic cutting force model.

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where the vibrations normal to the cutting surface are used to determine the instantaneous chip thickness. If the trajectory of the cutter is assumed to be a circular path, the instantaneous chip thickness for ith disk element, jth flute, and kth angular position is then represented by Eq. (1). hði; j; kÞ ¼ ft sinðhi;j;k Þ þ ½nðt  sÞ  nðtÞ þ q0

ð1Þ

where the first term represents the effect of feed per tooth. The second term represents the tool vibration in the normal direction, n, related to the dynamic displacement in x and y directions is determined by [2]. The last term represents the vector that contains the tooth-to-tooth radii error which was introduced in [3]. Assuming that the tooth radii error is not varied along the tool axis. The tool immersion angle and lag angle due to the helix angle of cutting edge was described in [12]. The different cutting forces in the normal and tangential directions are written as the following. The cutting force in each direction includes both the forces due to shearing (Kn, Kt) and the rubbing term (Kne, Kte) at the flank of the cutting edge [2]:


dFn ði; j; kÞ ¼ Kn hði; j; kÞdb þ Kne db dFt ði; j; kÞ ¼ Kt hði; j; kÞdb þ Kte db

ð2Þ

where h is the instantaneous chip thickness and db is the discretized axial depth of cut. The differential cutting force with respect to the workpiece coordinate frame is then represented by [2]: 

   cosð/j Þ dFx ði; j; kÞ ¼ sinð/j Þ dFy ði; j; kÞ

 sinð/j Þ  cosð/j Þ



dFt ði; j; kÞ dFn ði; j; kÞ

 ð3Þ

3 Experimental Setup A series of end milling experiments was conducted on a 3-axis CNC milling machine (with Heidenhain TNC620 controller). The workpiece was a block of Al6061-T6 which is commonly used in automobile and aerospace industries owing to its high strength to weight. An end mill cutter with a diameter of 12 mm, helix angle of 26°, and two flutes was utilized. The tooth-to-tooth radii error is 6 lm. The cutting force signals were measured by a Kistler dynamometer mounted between the workpiece and workbench, as shown in Fig. 2. The dynamics of tool tip was investigated using impact testing [13]. The cutting force coefficients yielded from experiments are shown in Table 1.

4 Simulated and Experimental Results The cutting force under certain cutting conditions is shown in Fig. 3 which illustrates a very good agreement between the simulated and experimental results.

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Fig. 2. Experimental setup. Table 1. Cutting force coefficients. Ks N/mm b (o) Kn N/mm Kt N/mm Kne N/mm Kte N/mm 813 65.9 331.8 742.2 12 23

Fig. 3. The cutting force at the spindle speed of 1500 rpm, depth of 1.6 mm, and feed rate of 150 mm/min.

The numerical simulations of cutting force under different cutting conditions are shown in Fig. 4. In which the displacement is determined by numerical integration [3]. Figure 4(a) and (c) represent the once-per-revolution sampled cutting force data of two cutting conditions (1.2 mm & 3750 rpm and 1.6 mm & 1500 rpm), respectively. Both of them show a repetitive behavior from one to the next revolution. It also represents that the single clutter of once-per-revolution sampled data shown in the plot of x and ydirection vibrations of two cutting conditions in Fig. 4(a) and (c). Evidently, this behavior indicates maintained stable operation. On the other hand, sampled force data in Fig. 4(b) and (d) show different behaviors with the quasi-periodic, the repetitive behavior from one to the next revolution does not occur in both cases. In addition, the sampled x and y vibrations show the elliptical distribution known as the Hopf instability [3]. This demonstrates unstable behaviors in both two cutting conditions (1.6 mm & 3750 rpm and 1.2 mm & 5250 rpm) in Fig. 4(b) and (d).

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Fig. 4. Numerical simulations of cutting force, historical displacements and vibratory plot in x and y directions (once-per-revolution sampled data ‘+’).

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5 Estimation of Lyapunov Exponent Lyapunov exponent from a single time series is known as a method for exploring the divergence or convergence of signal exponents in phase space, it could present nonlinear characteristics of dynamical system behavior [14, 15]. In this study, the average Lyapunov exponent is used to estimate the exponent of the phase plane from the experimental vibration signals. The algorithm used to calculate of largest Lyapunov exponents using phase space are follows. Assume N-point time series represented the vibration signal such that x = {x1, x2 … xN}. The time series is reconstructed into attractor dynamics based on delay coordinates. The reconstructed trajectory can be represented as: X ¼ ½X1 ; X2 ; . . .; XM T , where each row of X is phase space vector Xi ¼ ½xi ; xi þ s ; . . .; xi þ ðn1Þs Þ, s is reconstruction delay, n is embedding dimension, M = N − (n − 1)s. The reference point in the space is set by Xj, the nearest neighbor,  Xj’, could  be found by searching the point which has Euclidean norm as Lj ð0Þ ¼ minXj  Xj; . The distance between the jth pair of nearest neighbors steps (a period of time is equal to k.Δt) is  after k discrete-time  Lj ðkÞ ¼ Xj þ k  Xj; þ k . The largest Lyapunov exponent is determined in Eq. (4) [16]: k1 ¼

k X 1 1 M Lj ðkÞ ln kDt M  i j¼1 Lj ð0Þ

ð4Þ

According to the trend of the largest Lyapunov exponent, it can be used to distinguish whether the cutting is stable in the time domain. In this study, for each signal point index of the Lyapunov exponent of the signal, the average of thirty largest values are calculated in order to achieve a small amount of singularity of the values of the stable conditions. The results of Lyapunov exponents of different cutting conditions are shown in Fig. 5. It can see that the Lyapunov exponents are very sensitive to the chaos of the signal, the largest Lyapunov exponents proportional to the amount of chaos of the system are increasing with high depth of cut and high cutting speed. Particularly, when the system becomes unstable this value increases rapidly. Therefore, the milling stability can be predicted using Lyapunov exponent. In this system, the value of threshold plane is set with Lyapunov exponent of 0.96, which can successfully define the cutting stability, as shown in Fig. 5. In the stable cutting condition, the largest Lyapunov exponent is kept within small values. On the contrary, when increasing the spindle speed and depth of cut, the cutting became unstable cutting and the indicator grew rapidly. This implied that more nonlinear behaviors obviously emerged in the cutting process so that the measured cutting force deviated from the output of the cutting force model. While the original RMS of cutting force signal in time domain shown in Fig. 6 does not indicate these behaviors and it is not able to distinguish whether chatter occurs. The largest Lyapunov exponent index shows as an effective indicator to detect and predict the occurrence of chatter.

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Fig. 5. The largest Lyapunov exponent under different cutting conditions.

Fig. 6. RMS values of the cutting force in time domain.

6 Conclusions Machining stability monitoring has an important role in increasing productivity and tool life in modern manufacturing. In this study, a dynamic cutting force model based chatter stability analysis was presented. Once the chatter occurred, the nonlinear and chaotic behaviors were found from the cutting force signal. The stable and unstable behaviors were fully investigated within the force signal, followed by the experimental verification. Furthermore, it was found that the largest Lyapunov exponent which is proportional to the amount of chaos of the system was increased rapidly when the chatter happens. While the value of Lyapunov exponent will keep smaller if the stable cutting is maintained. Thus, it can be effectively used to distinguish the stable and unstable machining in the time domain.

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Acknowledgment. This work was financially supported by the “Center for Cyber-physical System Innovation” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.

References 1. Stephenson, D.A., Agapiou, J.S.: Metal Cutting Theory and Practice. Marcel Dekker, New York (1996) 2. Altintas, Y.: Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design, 2nd edn. Cambridge University Press, Cambridge (2012) 3. Schmitz, T., Smith, K.S.: Machining Dynamics: Frequency Response for Improved Productivity, pp. 1–303 (2009) 4. Merritt, H.E.: Theory of self-excited machine-tool chatter: contribution to machine-tool chatter research - 1. J. Manuf. Sci. Eng. 87(4), 447–454 (1965) 5. Quintana, G., Campa, F., Ciurana, J., Lacalle, L.: Productivity improvement through chatterfree milling in workshops. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 225, 1163–1174 (2011) 6. Cheng, K.: Machining Dynamics: Fundamentals, Applications and Practices (2009) 7. Tlusty, J., Polacek, M.: The stability of the machine tool against self - excited vibration in machining. International Research in Production Engineering. ASME (1963) 8. Kline, W.A., DeVor, R.E., Lindberg, J.R.: The prediction of cutting forces in end milling with application to cornering cuts. Int. J. Mach. Tool Des. Res. 22(1), 7–22 (1982) 9. Tssi, C.-L.: Analysis and prediction of cutting forces in end milling by means of a geometrical model. Int. J. Adv. Manuf. Technol. 31, 888–896 (2007) 10. Altintas, Y., Engin, S.: Generalized modeling of mechanics and dynamics of milling cutters. CIRP Ann. 50(1), 25–30 (2001) 11. Chung, C., Tran, M.-Q., Liu, M.-K.: Estimation of process damping coefficient using dynamic cutting force model. Int. J. Precision Eng. Manuf. 21, 623–632 (2020) 12. Liu, M.-K., Tran, M.-Q., Chung, C., Qui, Y.-W.: Hybrid model- and signal-based chatter detection in the milling process. J. Mech. Sci. Technol. 34(1), 1–10 (2020) 13. Liu, M.-K., Tran, M.-Q., Qui, Y.-W., Chung, C.: Chatter detection in milling process based on time-frequency analysis. In: ASME 2017 12th International Manufacturing Science and Engineering Conference, Los Angeles, U.S.A., vol. 1 (2017) 14. Skokos, C., Gottwald, G., Laskar, J.: Chaos Detection and Predictability. Lecture Notes in Physics, vol. 915, pp. 1–280 (2015) 15. López, A., Camacho, C., García, A.: Effect of parameter calculation in direct estimation of the lyapunov exponent in short time series. Discrete Dyn. Nat. Soc. 7(1), 41–52 (2002) 16. Yao, T., Liu, H., Xu, J., Li, W.: Estimating the largest Lyapunov exponent and noise level from chaotic time series. Chaos: Interdisc. J. Nonlinear Sci. 22(3), 033102 (2012)

Analytical Study of the Power Parameters of Electric Traction Drive for Modern Vehicles Aleksey Kolbasov1, Kirill Karpukhin1,2, Dmitry Sheptunov1, Povalyaev Andrey1, Nguyen Khac Tuan3, and Nguyen Khac Minh2,3(&) 1

2

NAMI Russian State Scientific Research Center, 2 Avtomotornaya Str., Moscow 125438, Russia Moscow Automobile and Road Construction State Technical University (MADI), 64 Leningradsky Ave., Moscow 125319, Russia [email protected] 3 Faculty of Automotive and Power Machinery Engineering, Thai Nguyen University of Technology, No. 666 Tich Luong Ward, Thai Nguyen 24131, Vietnam

Abstract. The development of electric freight transport is becoming increasingly important against the backdrop of growing environmental problems, especially in megacities. The selection of the actual parameters of the power plant for electric freight vehicle is important here. This article analyzes the implemented projects in the world. The information gains particular relevance against the background of the absence of industrial production of power plants in the Russian Federation. The paper presents data on freight vehicles with a traction electric drive and deduces patterns in the value of the power of the used electric motors relative to the mass of the car in order to clarify the most popular standard-size series. This study was carried out with the aim of a feasibility study of models of electric motors for commercial vehicles proposed for launch into serial production. Keywords: Electric vehicle
Hybrid vehicle
Ecology Electric drive
Electric machines
Electric truck


Energy efficiency


1 Introduction The prospect of electric-powered commercial vehicles in Russia has often been discussed, despite the slow growth, demand for each year. The level of demand for the growth curve in the world [1, 2]. Despite the fact that the prices for hydrocarbons in Russia are relatively low, the development of commercial vehicles on electric traction is developing rapidly [3, 4]. Currently, there are about 450 electric buses in use on public roads in Moscow, and trends are outlined for an increase in the fleet by 300 units annually. A further increase in the dynamics of development is impossible without the local production of electric motors. The electric vehicle market is constantly evolving. For example, sales of passenger electric vehicles increased from 450 thousand units in 2015 to 2.1 million in 2019. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 200–209, 2021. https://doi.org/10.1007/978-3-030-64719-3_23

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According to forecasts of the research service Bloomberg NEF, their sales will decrease to 1.7 million electric vehicles in 2020 due to the coronavirus but will increase to 8.5 million by 2025, 36 million by 2030, and 54 million by 2040 [5]. Currently, there are a large number of trucks with electric transmissions of various carrying capacities [6] and the electric drive manufacturer needs to determine which characteristics will be most in demand. This requires an analysis of existing vehicles and their characteristics. In Russia, despite the clearly insufficient number of electric stations, the lack of service for quickly replacing discharged batteries with charged ones by analogy with the Gogoro Network [7, 8], and other restrictions, the electric vehicle market is also gradually developing. If we assume that even an electric vehicle for commercial cargo transportation will not be so popular, then the operation of buses with hydrogen fuel cells looks quite promising since hydrogen can significantly increase the range, thus, the electric transmission remains relevant and does not depend on the scenario of generating electricity [9]. All of the above facts once again emphasize the relevance of the topic raised by the author of the article. The main purpose of the study was to determine the power parameters of the power plant of an electric freight vehicle, depending on the class. The data of the analysis of the parameters of the power plant can be used in the design of electric trucks, for the selection of primary power values for the purpose of further calculations on the mathematical model, as well as for the development of technical specifications for the creation of a power plant.

2 Methods During the research, the method of systematic analysis of the data on the parameters of the power plants of electric freight transport was used. Each selected component was analyzed as part of the problem, to identify positive and negative features. An empirical scientific approach is used, consisting of data collection, scientific analysis, hypothesis formulation, and theory development.

3 Research and Discussion 3.1

Data Collection

Collection of statistical data on the parameters of trucks of world manufacturers. The main parameters of electric trucks produced, as well as ready for production and denominated since 2017 [5–11] are summarized in Table 1.

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Name, features

Manufacturer

Full weight, tons

Characteristics of the electric motor, kW

Model 520, Transpower ElecTruck, 8 load class BYD 8R LDV EV80 ZRD BYD BYD5071XXYBEV BYD BYD5030XXYBEV1 BYD BYD5070XXYBEV BYD BYD5031XXYBEV BYD BYD5070XXYBEV1 BYD BYD5110XXYBEV BYD BYD1070A7BBEV BYD BYD5070XLCBEV BYD1180D8DBEVD (T8C) BYD BYD3250EEFBEVD BYD BYD5160GSSBEVD BYD BYD1030K77BEVD BYD1071A7BBEVD BYD1070A7BBEVD1 Jiefang J6F electric GINAF Dura Truck E2121, Renault Trucks D Emoss,

Peterbilt

36,00

280

BYD SAIC ZRD Auto BYD BYD BYD BYD BYD BYD BYD BYD BYD BYD BYD BYD BYD BYD FAW GINAF Renault Trucks Emoss Mobile Systems B.V VDL Bus & Coach E-FORCE ONE AG Cummins Thor Trucks Toyota Motor North America Inc RENAULT RENAULT RENAULT

26,00 3,50 2,00 7,49 2,78 7,32 3,50 7,32 10,70 7,32 7,32 18,00 25,00 16,00 3,50 7,50 7,32 4,50 12,00 13,00

2  148 92 18 125 80 150 160 125 150 110 150 270 180 270 80 125 90 50/100 280 103 350

37,00 44,00 34,00 36,00 36,00

240 350/550 276 224/522 2  250

3,10 16,00 26,00

57 130/185 260/370

27,00

400/330 4  560

VDL Iveco Eforce E44 Cummins AEOS, 4  2, Thor ET-One Toyota Fuel Cell Truck Semi, RENAULT MASTER ZE RENAULT TRUCKS D ZE RENAULT TRUCKS D WIDE ZE Volvo FL Electric, Futuricum 26E (Volvo FM), Freightliner eCascadia, Freightliner eM2 106 Tesla Semi. Nikola One Mercedes-Benz Sprinter, Orten ET35M

Volvo Designwerk Products AG Daimler Trucks Daimler Trucks Tesla Nikola ORTEN ElectricTrucks

36,00 5,5 4,2

545 358 750 746 81

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203

The Type of Power Plants Used

By the type of electric machines used, electric trucks are no exception and, like allelectric vehicles, they mainly use three-phase brushless DC motors with neodymium magnets, i.e. BLDC or PMSM motors (valve motor according to the Soviet abbreviation), which have significant advantages: The advantages of BLDC motors: 1) Lack of a brush assembly (as well as the lack of regular replacement of brushes, cleaning). 2) Simplicity of design and no excitation losses. 3) Higher specific power (kW/kg) in comparison with asynchronous motors (asynchronous motors of comparable power are noticeably heavier). 4) Low inertia with significant torque. 5) Maintaining the torque on the shaft, regardless of the rotor speed. 6) Higher starting torque than induction motors or brush motors of the same power. For electric vehicles, the moment of starting at the start is of great importance in order to move the vehicle from a state of rest. 7) High efficiency in the entire range of rotor speeds, including at reduced speeds. 8) Small dimensions. For example, an asynchronous machine of the same power and energy efficiency class is 2 times larger than an asynchronous motor. 9) Less cost of the controller in comparison with induction motors of comparable power. 10) More technological and cheaper in serial production. 3.3

The Choice of the Power Plant

The use of one or another electric vehicle on trucks is primarily associated with the basic requirements for a truck: A) B) C) D)

Gross vehicle mass. Maximum speed of movement. Dynamics of movement. The cost of a pair of motor + controller

When choosing a power plant, they are guided by the following criteria: A) B) C) D)

Engine power, nominal - maximum - peak Moment on the shaft, nominal - maximum - peak Motor weight Cooling type.

The type of transmission used and the gear ratio of the gearbox, which affect the torque and power characteristics of the selected power unit, are important when choosing. Trucks use various transmissions from the direct drive (through a gearbox) to a 6-speed automatic. The operation of the selected power plant is checked on a mathematical model, depending on the operating conditions.

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The power characteristics of the power plant are associated primarily with the total weight of the electric truck. Having analyzed this dependence, we build a power diagram versus the total mass of implemented electric truck projects (see Fig. 1), based on research data (see Table 1).

Fig. 1. Power of electric motor versus the total mass of implemented electric truck

It is not difficult to notice points with overestimated power characteristics, falling out of the general picture. Since products with overestimated characteristics are prototypes, we delete these points and build an approximating line showing us the average dependence of power on the total mass of an electric truck (see Fig. 2).

Fig. 2. The dependence of average power of electric motor on the total mass of electric truck

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This line can serve as a starting point for choosing the initial power of the power machine, with a known total mass, with further miscalculation and correction on a mathematical model. 3.4

The Range of Powers of the Applied Power Plants by Classes

The most effective classification of electric trucks should be considered the classification by gross weight, due to the fact that the weight of the supply battery assembly is quite significant and has a strong influence on the choice of the power plant [10]. The classification of trucks by gross weight is regulated by the standard UN 025 270-66. It is convenient to represent the designation system of automobile rolling stock in the form of a table (Table 2). Table 2. Vehicle types by purpose (operation). Weight, t to 1,2 1,2–2,0 2,0–8,0 8,0–14,0 14,0–20,0 20,0–40,0 up 40,0

Flatbed 13 23 33 43 53 63 73

Tractor 14 24 34 44 54 64 74

Dump Truck Tanker 15 16 25 26 35 36 45 46 55 56 65 66 75 76

Van 17 27 37 47 57 67 77

Special 19 29 39 49 59 69 79

Some classes from 18 to 78 are missing from indexing, this is a reserve. The designations are as follows: digit “1” - truck class (gross weight); digit “2” - type of automatic telephone exchange: 3 - A flatbed truck or pickup; 4 - Truck tractor; 5 - Dump truck; 6 - Tanks; 7 - Van; 8 - Reserve digit; 9 - Special vehicle. numbers “3” and “4” - serial number of the model; digit “5” - car modification; digit “6” - type of execution: 1 - Cold climate; 6 - Moderate; 7 - Tropical. We sort the data in Table 1 for compliance with the total mass and power, taking into account the points out of the sequence (clause 3) From the sample of the digital

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sequence, we obtain the range of powers of the power plants used in the corresponding weight class and enter it in Table 3. A similar classification of trucks is used in the United States [11]. In our case, it is necessary to obtain a range of applied capacities, therefore, the initial data is clearly insufficient for an assessment in this way. More objective data can be obtained from the graph of the correspondence of the total mass and power by means of mathematical operations. Create the upper and lower border of the averaged values (Fig. 3). According to the classification, we find the values of the power spreads in the corresponding range from the lower to the upper approximation line and the average value. Result in Table 4.

Table 3. Correspondence of total weight and power. Correspondence of total weight (tons) and power (kW) 2 18 2,78 80 3,5 80 4,5 50 7,32 150 7,32 125 7,32 110 7,32 150 7,32 90 7,5 125 7,5 125 10,7 150 13 103 16 150 18 150 25 180 34 276 36 280 36 300 37 240 44 350

Type

Power (kW)

1,2–2,0 2,0–8,0

18 50–150

8,0–14,0

103-150

14,0–20,0

150

20,0–40,0

180–300

up 40

350

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Fig. 3.

Table 4. Vehicle types by purpose (operation). 1 2 3 4 5 6 7

Type to 1,2 1,2–2,0 2,0–8,0 8,0–14,0 14,0–20,0 20,0–40,0 Up 40

Power range, kW Average value, kW to 50 25 10–70 40 18–145 82 65–195 130 110–235 173 150–340 245 Up 255 310

A significant spread in capacities is due to: 1. The use of transmissions with different gear ratios. 2. Different requirements for speed modes and dynamics. Example: If you set the task of determining the optimal characteristics of the power plant for electric trucks of category 5 (gross weight 14–20 tons), then 3 trucks can be safely included in this category based on Table 1: – RENAULT TRUCKS D ZE (van 16t) rated power 130 kW – BYD1180D8DBEVD (chassis 18t) rated power 150 kW – BYD BYD5160GSSBEVD (chassis 16t) rated power 150 kW

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Thus, for the 5th category of electric trucks (gross weight 14–20 tons), the initial choice of the power plant with the following parameters will be optimal: – type: permanent magnet synchronous motor. – rated power 150 kW – peak power 200 kW.

4 Conclusion After analyzing existing vehicles and their electric motors, it is possible to identify the most optimal power ranges for each specific type of truck. Knowing the market capacity of certain types of trucks, you can calculate the number of electric motors that will be in demand. This information can be used for technical and economic calculations when preparing an electric motor for production. The authors of the work are aware that for each vehicle it is necessary to do a power calculation, but most of this calculation depends on the transmission. Therefore, the initial choice of the engine can be carried out based on the classification carried out and the ratio of truck sizes with the power of the electric motor.

References 1. Tuan, N.K., Karpukhin, K.E., Terenchenko, A.S., Kolbasov, A.F.: World trends in the development of vehicles with alternative energy sources. ARPN J. Eng. Appl. Sci. 13(7), 2535–2542 (2018) 2. Kamenev, V.F., Terenchenko, A.S., Karpukhin, K.E., Kolbasov, A.F.: The strategy of creating urban electric vehicle: challenges and solutions. Int. J. Civil Eng. Technol. 8(12), 895–905 (2017) 3. Kozlov, V.N., Kolbasov, A.F., Karpukhin, K.E., Katanaev, N.T.: Mathematical model of an electric vehicle with a non-flat battery of photovoltaic converters. In: IOP Conference Series: Materials Science and Engineering, vol. 819, p. 012014 (2020). https://doi.org/10.1088/ 1757-899X/819/1/012014 4. Kolbasov, A.F., Kurmaev, RKh, Karpukhin, K.E.: Implementation of dual-circuit system for additional power supply based on photovoltaic converters for electric vehicles. Energies 12 (20), 4010 (2019) 5. Electric Vehicle Outlook 2020. Bloomberg Research Service. https://about.bnef.com/ electric-vehicle-outlook. Accessed 25 Aug 2020 6. The Gogoro Network Battery Swapping Platform. gogoro.com. https://www.gogoro.com/ gogoro-network 7. Lebkowski, A..: Electric vehicles trucks - overview of technology and research selected vehicle. Zeszyty Naukowe Akademii Morskiej w Gdyni, pp. 2451–2486 (2017)

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8. Truck News, Electric trucks to become more prominent: ACT Research 2018. https://www. trucknews.com/equipment/electric-trucksto-become-more-prominent-actresearch/ 1003087096/ 9. International Council on Clean Transportation, Transitioning to Zero-Emission Heavy-Duty Freight Vehicles (2017). https://www.theicct.org/sites/default/files/publications/Zeroemission-freighttrucks_ICCT-white-paper_26092017_vF.pdf. Accessed 25 Aug 2020 10. IEA 2016, Global EV Outlook 2016: Beyond One Million Electric Cars, IEA, Paris. https:// www.iea.org/publications/freepublications/publication/globalev-outlook-2016.html 11. Dygalo, V., Keller, A.: Real-time diagnostic complex of automated car active safety system unit. In: IOP Conference Series: Materials Science and Engineering, vol. 819, p. 012039 (2020). https://doi.org/10.1088/1757-899X/819/1/012039

Automatic Extraction and Welding Feature Recognition from STEP Data Lan Phung Xuan(&) and Linh Tao Ngoc Hanoi University of Science and Technology, No. 1 Dai Co Viet Street, Hanoi, Vietnam [email protected]

Abstract. Feature recognition has been considered as an important bridge between CAD and CAPP in process planning for welding. These recognition data can be used as input data not only for generating welding tasks in process planning but also for programming work of welding industrial robot without using teach pendant. This paper introduces an effective method to extract and recognize welding features from STEP file which can be provided by any 3D CAD system. The computer program quickly extracts data from various strings in the STEP file, saves them in the database and recognizes features for welding by the developed algorithm. The welding paths with detailed geometry characteristics are recognized in a short time. All recognition results are stored in the database for further processes. A case study is used to verify the validity of the recognition method. Keywords: STEP file


Welding feature
Feature recognition

1 Introduction Automatic feature recognition plays a crucial role in computer-aided process planning such as machining, welding, assembling, etc. A 3D model is composed of many design features, but they are not direct form features of the manufacturing process. Thus, it is necessary to develop a module for recognizing the form feature from design features. Form features include machining features, welding features, assembling features, etc. Due to increasing demands for automation, studies on feature recognition from 3D models have been developed strongly [1, 2]. Welding work is very common in many manufacturing areas including automobile or airplane production. However, most of the studies focus on machining features, there are very few studies focusing on welding [3]. Moreover, welding is done primarily by industrial robots in modern production lines. Programming for the industrial robots uses by teaching methods using teach pendant. These works are time-consuming with low accuracy. Thus, automation in welding process planning and robot programming is developed to improve quality, reduce time and labor costs. There are two popular input format types of recognition systems including the specific format of CAD/CAM commercial software and neutral file. The main limitation of the first type is mandatory for specific software. Thus, the computer program should be integrated with CAD/CAM software and it is necessary to install the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 210–215, 2021. https://doi.org/10.1007/978-3-030-64719-3_24

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commercial CAD/CAM software on the computer and only this software can use this module. However, it has the main advantage of using a huge library of geometric objects in the CAD/CAM software which is helpful for programming. Meanwhile, most of the neutral file formats can be imported or exported from any CAD/CAM software but the extraction algorithm will be more complex. In our module, the neutral file is chosen as an input data format that helps create an independent recognition system, regardless of any commercial CAD/CAM software. Among many neutral files (such as IGES, STEP, DXF, STL), STEP file is the most widely used neutral CAD format. It is a 3D model file formatted in Standard for the Exchange of Product Data, an ISO standard exchange format [2]. It includes boundary representation data that can be recognized by most CAD/CAM software such as SolidWorks, CATIA, Pro-E, NX, etc. Boundary representation is the principal solid modelling method in which the geometry of the model is described by its bounding surfaces. Moreover, it is interoperability between different design and manufacturing systems (e.g. STEP AP203 for the part and assembly design, STEP AP214 for automotive mechanical design processes, STEP AP242 for managed model-based 3D engineering, STEP AP224 for feature based process planning, STEP AP238 for manufacturing) [5].

2 Methodology The proposed methodology for the extraction and recognition process is shown in Fig. 1. After constructing a 3D model from any commercial software, it should be exported or saved as into STEP (AP203) file. The first step in the developed system is to extract geometry data from STEP file. The extraction information includes the entity’s information and dimension data. The specific database for each model is created for storing all extraction information. Based on extraction data, an algorithm for recognizing welding features is applied and the result is stored in the database. 3D CAD Model from any commercial software

Export into STEP file

Extract Entities such as Closed_Shell, Faces, Loops, Edges, Vertices v.v.

Extract dimension data of design feature

Create database and Save Geometry Information in SQL Server

Recognize welding feature by recognition algorithms

Computer aided process planning for welding Save feature recogntion result in database and file

Fig. 1. Flow chart of the proposed methodology

Dynamic kinematic calculations for robots

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Geometric Data Extraction from STEP File

STEP file is a text file that contains strings of characters that start with the hashtag (#). Its following number is entity identification. The hierarchical structure starts from CLOSED_SHELL which is a collection of one or more faces bounding a region. The following elements include ADVANCED_FACE, FACE_OUTER_BOUND, FACE_BOUND, Face Type, END_LOOP, ORIENTED_EDGE, EDGE_CURVE, Edge Type, AXIS_PLACEMENT_3D, CARTESIAN_POINT, DIRECTION and Dimension (Radius, Angle etc.) [4]. The extraction process is important to extract the object’s lines, circles, direction, vertex, Cartesian point and boundaries of the 3D model. The extraction data from STEP neutral file is stored in a structured database which is specifically designed for this purpose. The information of each entity is saved in one data table. Based on the hierarchical structure of the basic STEP file, the database for storing information is created and the relationship diagram among data tables is described in Fig. 2. A large amount of data is retrieved quickly and efficiently using the T-SQL command which is a standard language for accessing, manipulating, and communicating with the database. The developed program reads the STEP file line by line and the following steps are applied to extract information for each line. tbl_ClosedShell PK

PK

tbl_ClosedShelFace

ClosedShellID

ClosedShellID

ClosedShellName

AdvancedFaceID

tbl_Line

tbl_Circle

EdgeTypeID

PK

EdgeTypeName FK

PointID

FK

VectorID

EdgeTypeID

tbl_AdvancedFace AdvancedFaceID FK

FaceID

FK

FaceTypeID

FaceTypeID FaceTypeName

FK

AxisPlaceID MainDim1

tbl_FaceLoop PK

EdgeTypeName FK

tbl_FaceType PK

MainDim2

FaceID EdgeLoopID

AxisPlaceID

InOut

MainDim

tbl_EdgeLoop PK

EdgeLoopID

FK OrientedEdgeID tbl_Direction

tbl_Vector PK

VectorID

FK

DirectionID

PK

DirectionID DirX DirY DirZ

tbl_Point PK

DirectionType

PointID X Y Z PointType

tbl_AxisPlace PK

AxisPlaceID

FK

PointID

FK

DirectionID

FK

XDirectionID

tbl_OrientedEdge PK OrientedEdgeID FK

EdgeCurID

tbl_EdgeCur

AxisType PK

tbl_VertexPoint

EdgeCurID EdgeCurName

PK

VertexPointID

FK

SPointID

FK

PointID

FK

EPointID

FK

EdgeTypeID

Fig. 2. Entity-relationship diagram of extraction data from STEP files

Step 1. Split the line by semicolon and save in a list Step 2. Find the entity name in the first item of the list for example CLOSED_SHELL, PLANE, EDGE_CURVE, VERTEX_POINT, etc. Step 3. Get the first hashtag number and save as an entity identification like ClosedShellID, EdgeCurID, VertexPointID, etc.

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Step 4. For each entity, specific extraction process is applied depending on the components of each entity as shown in Fig. 3. For example, information in line “#20 = EDGE_CURVE (‘NONE’, #1268, #460, #3382,.T.)” is saved into tbl_EdgeCur where EdgeCurID = 20, EdgeCurName = ‘NONE’, SPointID = 1268 (Start Vertext Point), EPointID = 460 (End Vertex Point), EdgeTypeID = 3382. Step 5. Store all information in the database. After storing all extraction data in the database, it is easy to obtain specific information from many data tables based on many conditions using T-SQL language. The extraction data is used as input of the feature recognition process.

SP: (60, 0, 55) EP: (60, 0, -55)

#2907 (Shell) #1063(Edge) #1492 (Edge)

SP: (60, 0, -55) EP: (60, 0, 55)

SP: (0, -2.5, 140) EP: (0, -2.5, 20)

#1329 (Edge) #369 (Face)

SP: (20, -2.5, 0) EP: (140, -2.5, 0)

#290 (Edge)

#516 (Face) #326 (Shell)

#1472 (Face) Z Y X

#1568 (Face) #1382 (Shell)

Fig. 3. Assembly model for case study

2.2

Feature Recognition

Based on the extracted information in the previous step, the developed algorithm is applied to recognize the welding feature as follows: Step 1. Find all Closed_Shells represented for welding parts. Step 2. Consider each pair of different Closed_Shells. Step 3. Find each Face in each Closed_Shell and put it into a pair of Faces together. Step 4. Check and choose only two Faces that meet the contact condition on the domain together. If both Faces are Plane, the contact condition includes co-planner and direct contact. If both Faces are Cylindrical_Surface, the contact condition is co-axial and direct contact. Step 5. Determine base face, base part and weld face based on bounding condition according to the rule “If the Closed_Shells has bigger bounding box volume, it is a base part, the contact face is base face and weld face belongs to another Closed_Shell” Step 6. Determine common edges (co-edges) between two faces. The number of coedges is chosen depending on the length of co-edges and area of contact face. Step 7. Determine the basic characteristics of the weld edge from extraction information. For a line, the extraction information includes ID, Cartesian coordinates of start point, endpoint and direction which is defined as perpendicular to the

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co-edge and lies on the bisector plane of the two faces creating the co-edge. For an arc or circle, besides the above information, radius and direction are determined by the axis of the arc or circle. Repeat steps 3 to 7 until all faces of each pair of Closed_Shells are considered. Repeat steps 2 to 7 until all Closed_Shells of parts are checked. 2.3

Development of Module

The module has been designed and developed using Visual C# language with the STEP file as input. Microsoft SQL Server is used as a database management system. When starting the program, the main window opens as shown in Fig. 4. In this module, the input file is showed under a list box in the first region of the main window. Another region presents the extraction data including many view types. The 3D model is also viewed in the module and the last region is for showing feature recognition results.

Fig. 4. Result of extraction and recognition for the case study in developed module

2.4

Case Study

An assembly model shown in Fig. 3 is used to evaluate the efficiency of the method. This consists of three design parts corresponding to three CLOSED_SHELL (#326, #2907, #1382). Each shell composes of faces, edges and vertex points. The task of the module is to extract necessary data and recognize welding features along with the basic geometric parameters of welding features. There are two popular types of welding path in this case study including lines and circles.

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3 Result and Discussions The result of extraction and recognition is shown in Fig. 4. It takes 4 s for extracting and saving data in the database and about 5 s for recognizing welding features. In this case study, four weld edges in line form and two circle compound weld edges are recognized. Based on this preliminary result, welding experts may establish rules to select only a few or all these recognized welding paths. Each welding feature is automatically recognized with basic information such as weld edge type, position, radius, and direction in 3D space. For example, weld edge #290 is recognized as line with the start vertex point (20, −2.5, 0), end vertex point (140, −2.5, 0) and direction (0, −0.707, 0.707). It agrees with the characteristics of its design feature on the 3D model as shown in Fig. 3. The result showed that the proposed method for welding feature recognition can automatically identify the welding features in both line and circle type. This result can be used as input data for further process planning for welding or dynamic kinematic calculations for welding industrial robots.

4 Conclusions The main research contribution is the development of a module to automatically extract and recognize the welding feature directly from STEP neutral file. The module takes a short time to recognize and generate welding features with basic geometry characteristics. The process is an important step towards the development of an automatic welding system including computer-aided process planning for welding and weld path calculation and programming for industrial welding robots. Other welding feature recognition for B-curves or ellipse etc. and dynamic kinematic calculations for welding robot based on these recognition data will be focused on future work.

References 1. Bhandarkar, M.P., Nagi, R.: STEP-based feature extraction from STEP geometry for agile manufacturing. Comput. Ind. 41(1), 3–24 (2000) 2. Venu, B., Komma, V.R., Srivastava, D.: STEP-based feature recognition system for B-spline surface features. Int. J. Autom. Comput. 15(4), 500–512 (2018) 3. Kiani, M.A., Sa, H.A.: Automatic spot welding feature recognition from STEP data. In: 2019 International Symposium on Recent Advances in Electrical Engineering (RAEE), Pakistan, pp. 1–6. IEEE (2019) 4. Oussama, J., Abdelilah, E., Ahmed, R.: Manufacturing computer aided process planning for rotational parts. Part 1: automatic feature recognition from STEP AP203 Ed2. Int. J. Eng. Res. Appl. 1–13 (2014) 5. Rampur, V.V., Reur, S.: Compilation of step AP-214 data to generate computer-aided process planning for automotive parts. Int. J. Innov. Technol. Exploring Eng. (IJITEE) 9(1), 645–653 (2019)

Characterization of Gelatin and PVA Nanofibers Fabricated Using Electrospinning Process Cuong Nguyen Nhu1, Nhung Vu Thi2, Nam Nguyen Hoang3, Thao Pham Ngoc1, Trinh Chu Duc1, Van Thanh Dau4, and Tung Bui Thanh1(&) 1

University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam [email protected] 2 Vietnam Japan University, Vietnam National University, Hanoi, Vietnam 3 Hanoi University of Science, Vietnam National University, Hanoi, Vietnam 4 School of Engineering and Built Environment, Griffith University, Brisbane, QLD, Australia

Abstract. Nanofibers has been recently received tremendous attention from researchers due to its advantages. Among the fabrication methods for nanofibers, electrospinning appears to be an intriguing technique, allowing the fabrication of nanofibers in a simple and inexpensive way. In this paper, we prepared and fabricated nanofibers using the electrospinning process with two polymers: gelatin and PVA. The process and the fabricated fibers were examined under scanning electron microscope. The effect of the polymeric concentration was also studied. The experimental results indicate that nanofibers were successfully fabricated, and higher polymer concentration leads to the uniformity of the fibers. In addition, the nanofiber mat with silver nanoparticles was successfully fabricated, opening the potential for medical applications of the fabricated fibers. Keywords: Electrospinning


Nanofiber
Polymer fiber

1 Introduction Nanofibers have been recently received tremendous attention from researchers due to its advantages. Compared to conventional fibrous structures, nanofibers are lightweight and have smaller pores and high surface-to-volume ratio. Such fibers hold great potentials and have been successfully applied to various fields such as tissue engineering scaffolds [1], filtration [2], flexible electronics [3] or biomedical [4, 5]. Among the fabrication methods for nanofibers, electrospinning appears to be an intriguing technique. Electrospinning employs electrostatic force to produce long fibers of nano to micrometer diameter. This method offers the ability to consistently produce fibers in the submicron range and a versatility in spinning a wide variety of polymeric fibers such as collagen, cellulose, chitosan or polylactic acid. Electrospinning process allows the fabrication of nanofibers in a simple and inexpensive way. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 216–222, 2021. https://doi.org/10.1007/978-3-030-64719-3_25

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In this work, we prepared and fabricated nanofibers using the electrospinning process with two polymers, namely gelatin and PVA. The principle of electrospinning is to be discussed in-depth. The process and the fabricated fibers were also examined by simulation and scanning electron microscope images. The effect of the polymeric concentration is also studied. The experimental results lay the foundation for further application of the electrospinning in general and nanofibers in specific.

2 Nanospinning Process Electrospining is an intriguing approach that allows the creation of very thin fibers with the diameters ranging from a few to hundreds of nanometers. A typical electrospinning system, as shown in Fig. 1, is comprised of three major components: an extremely high voltage source, a syringe and a grounded collecting plate. The syringe is introduced with the polymer solution and controlled by a micro-pump. The collecting electrode can be in several shapes such as a rotating cylinder or a flat metal surface. The spinning process is started by injecting a liquid or melt polymer solution into the nozzle at a constant flow rate. A high voltage from the DC power source of several tens of kVs is placed at the tip of the syringe, creating a high electric field between the tip and the collecting plate. When an electric charge is induced on the liquid surface, there exists an extra electrostatic force pulling the polymer solution towards the collector of opposite polarity. As the supply voltage surpasses the threshold, the repulsive electric force overcomes the surface tension force, creating jet of polymer solution. As the jets travel to the grounded collector, they create a mat of fine and thin fibers.

Fig. 1. Setup of a typical electrospinning system

3 Material Preparation and Methods 3.1

Polymer Solution Preparation

Two polymers, namely PVA and gelatin, were chosen in this study. These polymers have a good biocompatibility and are non-toxic. The polymer powder is mixed with the appropriate solvent. PVA solution was dissolved in deionized water while gelatin was dissolved in acetic acid. Acetic acid solution was diluted in deionized water with the

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ratio of 1:1 in volume. The solution was stirred at the speed of 50 rpm/min while being baked on a hot plate at 80 degree Celsius for 45 min until the polymer was completely dissolved in the solvent, creating a homogeneous solution. The polymer solution then rested for another 30 min to cool down and rid of the undesired air bubbles suspended in the solution during the stirring process. 3.2

Nanospinning Process

The prepared polymer solution was added into a 60-ml BD syringe. A rotating drum was used as the collector and was located 10 cm apart from the spinnerets. The micropump was used to apply a pressure to the polymer solution through the syringe. As soon as the polymer solution reached the capillary tip, the flow rate was kept constant at 15 ml/min, and the high voltage supply, which can generate a high voltage up to 45 kV, was turned on. The high voltage was set to 35 kV. The tip of the syringe was wired to the anode of the voltage power supply while the cathode was connected to the collector. The collectors in the form of a rotating drum were covered with aluminum foil on which electrospun fibers are supposed to deposit on. The rotating drum was set to rotate at the angular velocity of 250 rpm/min. The spinning of the polymer solution was carried out for 45 min. The nanofiber mat rested for 30 min before being taken out in order for the solvent to evaporate completely even though most of the solvent had already evaporated during the spinning process.

4 Results and Discussion 4.1

Nanospun Fibers Fabrication

A numerical simulation was conducted to study the high voltage involved in electrospinning process. 3D simulation model was performed by COMSOL Multiphysics software to solve for the electric field with the meshing model shown in Fig. 2 (a). The electric potential contour is shown in Fig. 2 (b). It is can be observed from the results that the electric potential was particularly high at the region near the needle, the contour lines are dense. The electric potential profile as well as the electric field streamline is presented in Fig. 2 (c).

Fig. 2. (a) Meshing model (b) Electric potential contour (c) Electric potential of the system

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The formation of jets at the tip of the needle as the applied high voltage is captured and shown in Fig. 3 (a). It can be clearly seen that the electrospinning process occurred as there are jets of polymer solution developed at the region between the needle and the rotating drum. As comparing the simulation results of the electric potential line, and the jets, we could conclude that the direction of the jets follows the electric field line. This matches with the proposed theory of electrospinning process because the polymer solution induced charged as being place under high voltage thus force that is responsible for the repulsion obey Coulomb’s law. It is, however, should be noted that these jets are only stable in the vicinity of the spinnerets, as they are apart from the spinnerets, it becomes unstable and travels in whipping motion. In addition, instead of only one, there exists several jets developed at the tip of the needle.

Fig. 3. (a) Needles under electrospinning. Nanofibers on (b) aluminum foil (c) plastic food wrap

Figure 3(b) shows the nanofibers successfully deposited on the aluminum foil. In addition to the aluminum foil, we also tried to let the nanofibers deposit on other surfaces such as microscope glass slide, plastic food wrap as in Fig. 3 (c). This demonstrates the advantages of the electrospinning process as we can create nanofibers deposition on several surface, in other words, electrospinning process is not depositingsurface dependent. The fabricated samples were examined under scanning electron microscope (SEM). The SEM images of PVA 10% and gelatin 25% are shown in Fig. 4. The nanofibers for two polymer solutions were quite homogeneous. The diameter of PVA fibers was approximately 900 nm while that of gelatin was about 1 µm.

Fig. 4. SEM images of electrospinning (a) gelatin 25% (b) PVA 10% fibers with the scale bar of 500 nm and (c) gelatin 25% (d) PVA 10% fibers with the scale bar of 250 nm

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Dependence on Polymer Concentration

Motivated by the initial results with the fabrication of PVA and gelatin nanofibers, we continue to study the impact of polymer solution concentration to the formation of nanofibers. We conducted the experiments with four different concentrations of gelatin: 10%, 15%, 20% and 25% with the SEM images shown in Fig. 5. From the captured results, we can infer that there are significant differences among the nanofibers fabricated from various polymer concentrations. At the low concentration of polymer, i.e. 10% of gelatin, the mat of nanofibers is spares; the fibers are not uniform in diameter as in Fig. 5 (a). Interestingly, the nanofibers mat appears to have myriad beads. In contrast, there are almost no beads appear in nanofibers of higher gelatin concentration. For example, with the concentration of 15% as in Fig. 5 (b), no undesirable fibers are created. It is also noteworthy that at 15% gelatin, there are fibers of small and large size. The large fiber is 180 nm in diameters while small fiber has the diameter of 40 nm. It can be inferred that small fibers are not-fully developed fibers. Thus, they create the disconnection between the fibers. The nanofiber mats fabricated from 10% and 15% gelatin concentration do not possess good elasticity and durability. From the concentration of 20%, there fibers become more uniform as shown in Fig. 5 (c). At the concentration of 25% (Fig. 5 (d)), for example, there are almost no beads or undeveloped fibers. This could lead to the nanofibers with higher durability. In addition, the fibers created from higher-polymer-concentration solution also have larger average diameters. The diameter of nanofibers created from 15%, 20% and 25% are 120, 180 and 220 µm respectively. It can be concluded that highly concentrated polymer solutions create more uniform fibers. However, we should also consider the fact that highly concentrated polymer solution has significantly high viscosity, and thus, causing trouble working with the syringe and micropump.

Fig. 5. Scanning electron micrographs of electrospun fibers at different acid concentration (wt%): (a) 10% (b) 15% (c) 20% (d) 25%

4.3

Nanofibers with Silver Nanoparticles

We continue to fabricate with silver nanoparticles (AgNPs) 30 ppm mixed with the PVA solution of 5% concentration. The results are shown in Fig. 6. Some beads were undesirably formed, and the fibers are not quite uniform. However, with the knowledge from the previous study on the impact of concentration, we can infer that the issue is due to the low concentration of polymer. The heterogeneity of fibers and bead formation could have been mitigated by increasing the concentration of PVA. AgNPs have been demonstrated to possess effective antibacterial effect. Two widely accepted antibacterial mechanisms of AgNPs on bacteria are contact killing and ion-mediated

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killing [6, 7]. The corporation of AgNPs into the nanofibers open the potential for the application of the fabricated nanofibers in wound healing and medical tissue.

Fig. 6. SEM images of PVA + AgNPs fibers at the scale of (a) 2 lm (b) 300 nm

5 Conclusion The principle of electrospinning process has been discussed. The electric field distribution was analyzed by simulation. The nanofiber mats of two polymers, namely PVA and gelatin, were successfully fabricated using electrospinning and examined under SEM. Study on the impact of polymer concentration was also performed. From the SEM images, we concluded that lowly concentrated polymer solution leads to thinner fibers with unwanted beads, while those fabricated from solution with higher concentration tend to be more uniform and do not have beads or clumps. The same experiment setup was also employed to electrospin solution of PVA and AgNPs. The fabrication of nanofiber mat with AgNPs provides the foundation for the application of mats in medicals such as wound healing or medical tissue. Acknowledgements. This work has been supported by VNU University of Engineering and Technology under project number CN20.18.

References 1. O’Connor, R.A., McGuinness, G.B.: Electrospun nanofibre bundles and yarns for tissue engineering applications: a review. In: Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 230, no. 11, pp. 987–998. SAGE Publications Ltd, 1 November 2016. https://doi.org/10.1177/0954411916656664 2. Lv, D., et al.: Green Electrospun Nanofibers and Their Application in Air Filtration. Macromol. Mater. Eng. 303(12), 1800336 (2018). https://doi.org/10.1002/mame.201800336 3. Dinh, T., et al.: Polyacrylonitrile-carbon nanotube-polyacrylonitrile: a versatile robust platform for flexible multifunctional electronic devices in medical applications. Macromol. Mater. Eng. 304(6), 1900014 (2019). https://doi.org/10.1002/mame.201900014 4. Mohiti-Asli, M., Loboa, E.G.: Nanofibrous smart bandages for wound care. In: Wound Healing Biomaterials, vol. 2, pp. 483–499. Elsevier Inc. (2016) 5. Abbasi, N., Hamlet, S., Dau, V.T., Nguyen, N.-T.: Calcium phosphate stability on melt electrowritten PCL scaffolds. J. Sci. Adv. Mater. Devices (2020). https://doi.org/10.1016/j. jsamd.2020.01.001

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6. Le Ouay, B., Stellacci, F.: Antibacterial activity of silver nanoparticles: a surface science insight. Nano Today 10(3), 339–354 (2015). https://doi.org/10.1016/j.nantod.2015.04.002 7. B. Khalandi et al: A review on potential role of silver nanoparticles and possible mechanisms of their actions on bacteria. Drug Res. 67(2), 70–76 (2017). https://doi.org/10.1055/s-0042113383

Choice of Selection Methods in Genetic Algorithms for Power System State Estimation Thanh-Son Tran(&) and Thi-Thanh-Hoa Kieu Faculty of Electrical Engineering, Electric Power University, Hanoi, Vietnam {sontt,hoaktt}@epu.edu.vn

Abstract. The state estimator plays an important role in power system operation. It is used to monitor state parameters, thereby it helps the operators make control decisions when the parameters exceed the permissible limits to ensure the system operate in a normal and secure state. To solve this problem, we can use artificial intelligence methods such as genetic algorithms. The genetic algorithms consist of three main operators: selection, crossover, and mutation. Among them, the selection of individual parents plays an essential role as it affects the performance of the algorithm. This paper studies the effect of selection methods on results of power system state estimation. The results depend significantly on the choice of selection methods and show that the roulette wheel selection is the best choice. Keywords: Power System State Estimation
Genetic Algorithm
Roulette wheel selection
Boltzmann selection
Rank weighting selection
Cost weighting selection
Tournament selection
Pairing from top to bottom
Random pairing

1 Introduction Power System State Estimation (PSSE) problems estimate voltage magnitudes and angles at all buses of the system based on measurement data and mathematical formulations [1, 2]. Many methods have been developed to solve the problem, one of which is to apply weighted least square method (WLS). This method transforms the problem into an optimal one. To find the optimal solution, one can use traditional methods such as Normal Equations Method, Orthogonal Methods, linear programming,… [3–7] or artificial intelligence (AI) algorithms which including Artificial Neural Network (ANN), Particle Swarm Optimisation (PSO), Genetic Algorithm (GA),… [8, 9]. The application of genetic algorithms has been developed but not much and not yet specific as well. Actually, the GA is mainly studied for optimal placement of phasor measurement unit [10–12]. In [13], the GA is applied for PSSE. It is shown that GA can estimate the state of 6-bus system and cannot estimate the state of the IEEE 14-bus system. The selection operator is one of the main processes in the genetic algorithm. Choosing chromosomes to the matting pool plays an important role and has a significant impact on the result. There are different types of selection operators in GA, such as roulette wheel selection, Boltzmann selection, rank weighting selection, cost © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 223–231, 2021. https://doi.org/10.1007/978-3-030-64719-3_26

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weighting selection, tournament selection, pairing from top to bottom, and random pairing [14–18]. In this work, seven different types of selection operators of GA are implemented and then applied for power system state estimation. Through simulation of the 5-bus and the IEEE 14-bus systems, it is shown that the roulette wheel selection is the most suitable for PSSE by GA.

2 Power System State Estimation by GA 2.1

Formulation [1, 2, 19]

Consider a system which includes a set of measurements zi, i = 1 … m: 2

3 2 3 2 3 h1 ðx1 ; x2 ; . . .; xn Þ e1 z1 6 z2 7 6 h2 ðx1 ; x2 ; . . .; xn Þ 7 6 e2 7 6 7 6 7 6 7 z ¼ 6 .. 7 ¼ 6 7 þ 6 .. 7 ¼ hð xÞ þ e .. 4 . 5 4 5 4 . 5 . zm

hm ðx1 ; x2 ; . . .; xn Þ

ð1Þ

em

where: zi is the measurement value of the measurement i, hi(x) is the nonlinear function relating the measurement ith to the state vector x, ei is the measurement error of the measurement ith. Assume that the measurement errors are independent and each has the same Gaussian probability density function; ri is the standard deviation of measurement i. The weighted least square estimator is preferred to use for estimating the state of power system due to its good accuracy and its ability to process bad data [5]. This model minimizes the following objective function: J ð xÞ ¼ w2i ðzi  hi ð xÞÞ2 ¼

m X 1 2 e 2 i r i¼1 i

ð2Þ

In power system state estimation, the measurement values include active and reactive power injected into a bus (denoted by Pi, Qi), active and reactive power flows (denoted by Pij, Qij), voltage magnitudes and angles (denoted by Vi, hi), branch currents flowing through the transmission lines (denoted by Iij). The state vector x is the voltage magnitudes and angles of all the buses excluding the reference bus voltage angle. The formula of the function hi(x) depends on the measurement types. Measuring the active and reactive power injected into the bus i, the hi(x) is given by the following formula: Pi ¼ Vi

N X

  Vj Gij :coshij þ Bij :sinhij

j¼1

Qi ¼ Vi

N X j¼1



Vj Gij :sinhij  Bij :coshij



ð3Þ

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The formula of hi(x) for the active and reactive power flows from bus i to bus j is:     Pij ¼ Vi2 gsi þ gij  Vi Vj gij :coshij þ bij :sinhij     Qij ¼ Vi2 bsi þ bij  Vi Vj gij :sinhij  bij :coshij

ð4Þ

where: N is a set of bus numbers that are directly connected to bus i, hij = hi - hj, Gij + jBij is the ijth element in bus admittance matrix, gij + jbij is the admittance of the series branch connecting bus i and bus j, gsi + jbsi is the shunt admittance of the bus i. 2.2

Application of GA for Power System State Estimation

The Genetic Algorithm (GA) is an algorithm which is adapted to the evolutionary process of biological populations based on Darwin’s theory. GA was first introduced by Holland and was developed by Godlberg. In GA, the optimal individual search is initiated with a population. The chromosomes of the present populations are the source for the next generation population through three main operators: selection, crossover, and mutation. At each step, the individual is evaluated through its fitness value. Chromosomes which have good fitness value will survive and the others will be discarded. The classic GA works with the binary chromosomes. However, in many problems, to perform decimal variables which require high accuracy in binary form, the increase of chromosome length may happen. Besides, the binary algorithm requires encoding – decoding process. To prevent these disadvantages of binary GA, we can use GA algorithms with variables that are performed in floating-point number (The Continuous Genetic Algorithm - CGA). Compared to binary GA, the CGA algorithm requires less memory, allows the variable value to be performed according to the accuracy of the computer and has shorter calculation time because the encrypt and decrypt process are removed [14]. This paper uses the CGA for estimating the power system state. Initial Population. To begin the CGA, an initial population of N chromosomes is generated. Each chromosome composes of n continuous variables of which their values are generated in a random way between their boundaries. Fitness Function. The advantage of GA over the other classic algorithm is that the searching path is lead by the fitness value. The minimum objective function with constraining which illustrated in Sect. 2.1 can be transformed into a non-constrain problem maximize objective. Maximize the fitness function below: F ð xÞ ¼

1 J ð xÞ þ P ð xÞ

ð5Þ

where F(x) is the fitness function; J(x) is the objective function as in (2); P(x) is the penalty term which check the condition of state variable mentioned in (3)

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Pð xÞ ¼ k

n  X

n  X  2  2 max 0; xi  xmax þ k max 0; xmin  xi i i

i¼1

ð6Þ

i¼1

k is the penalty factor. Selection. To select a chromosome to the matting pool, the selection operator is applied. There are several selection methods such as roulette wheel selection, Boltzmann selection, rank weighting selection, cost weighting selection, tournament selection, pairing from top to bottom, random pairing [14–18]. Roulette Wheel Selection. According to this method, the probability of a selected individual i is proportional to its fitness value F(i): FðiÞ Pi ¼ Pn ; i ¼ 1; 2; . . .; N j¼1 F ð jÞ

ð7Þ

where Pi is the probability of chromosome i; F(i) is the fitness value of chromosome i; N is the number of chromosomes. The cumulative probability of chromosome i is evaluated as below CPi ¼

Xi j¼1

Pj

ð8Þ

where CPi is the cumulative probability of chromosome i. A random number r between zero and one is generated and compared with the cumulative probabilities. If r < CP1, chromosome number one is chosen to the matting pool. If r > CP1 and CPi-1 < r < CPi, chromosome i-th will be selected. Boltzmann Selection. Assume that the population follows the Boltzmann distribution. Firstly, transform the fitness of individuals through the following formulation F ðiÞ ¼ ef ðiÞ=T

ð9Þ

where f(i) is the fitness of individual i; F(i) is the Boltzmann fitness of individual i. Secondly, select the individuals using roulette wheel selection according to Boltzmann fitness of individuals. Rank Weighting Selection. The advantage of this method is the probability of chromosome i is calculated once from its rank. The rank depends on its fitness value. The best chromosome is ranked number one, and the worst is ranked number N. N  iþ1 Pi ¼ PN ; i ¼ 1; 2; . . .; N j¼1 j

ð10Þ

where Pi is the probability of chromosome rank i-th; N is the number of chromosomes. Cost Weighting Selection. The probability of selection calculates from the cost of the chromosome. In this process, the individuals are also sorted in a sequence depending

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on the objective function, the best one rank 1-th and the worst rank N-th. The parents will be selected from n best individuals in a set of N individuals. Individuals in the order of (n + 1) to N will be discarded. For each chromosome, to form a normalized fitness, we subtract the highest fitness of the discarded chromosomes from the fitness value of all the chromosomes in the mating pool. C'ðiÞ ¼ CðiÞ  Cðn þ 1Þ

ð11Þ

where C’(i) is the normalized fitness of chromosome i-th; C(i) and C(i + 1) are the fitness value of chromosome i-th and chromosome (n + 1)-th sequently. The probability of a selected individual i: C0 ðiÞ Pi ¼ Pn 0 j¼1 C ð jÞ

ð12Þ

Tournament Selection. In this process, a small subset of chromosomes (two or three) from the population is randomly picked and the chromosome with the best fitness value in this subset becomes a parent. The tournament repeats for every needed parent. Pairing from Top to Bottom. This method starts at the top of the list and pairs the chromosomes two at a time until the needed parent pairs are selected. More specifically, the algorithm pairs odd particles with even ones. Random Pairing. The selected chromosome can take from the whole population or from n best individuals in a set of N individuals. This operator generates an integer random number between one and N or n. The chromosome refers to that number becomes parent. Crossover. In crossover process, two parents will be selected to create offspring. In this paper, the CGA uses heuristic crossover [20]. Suppose that a parental pair is denoted as Da and Mo. Heuristic crossover uses values of the fitness function in determining the direction of the search. Consider that Fit(Da) and Fit(Mo) are the fitness values of chromosome Da and chromosomes Mo respectively. In this operator, only one offspring is generated.  C ði Þ ¼

bðiÞ:ðDaðiÞ  MoðiÞÞ þ DaðiÞ; if FitðDaÞ [ FitðMoÞ bðiÞ:ðMoðiÞ  DaðiÞÞ þ MoðiÞ; if FitðMoÞ [ FitðDaÞ

ð13Þ

where Da(i), Mo(i) and C(i) are the i-th gene of chromosome Da, chromosome Mo and offspring C respectively; b(i) is the coefficient regenerated for each gene in chromosome. Mutation. Amputation operation is performed to a state variable value to increase the search space and avoid the problem of premature convergence [14]. In this operator, the mutation position is randomly selected and its value is replaced with a random value within the limit of the state variable.

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The Population Regeneration. After a number of iterations, the individual set of the CGA algorithm tends to become homogeneous [21–23]. To overcome this situation, the new population will be created when 30 consecutive maximum error values between results of k-th and (k-1)-th iteration are smaller than the allowed values. The newly created population will retain several chromosomes from the old generation and the others are regenerated.

3 Results In this paper, the CGA is applied to estimate the state variables of two systems, which are the 5-bus system [24] and the IEEE 14-bus power system [25], as shown in Fig. 1. The measurement data are real and reactive power injected into all buses and the magnitude voltage at the slack bus as shown in Table 1.

(a)

(b)

Fig. 1. a) Diagram of 5 bus system; (b) Diagram of 14 bus system

The CGA parameters are set as: • • • • •

Maximum iteration: 15000 Population size: 40 Mutation rate: 0.05 Selection rate: 0.3 Reproduction rate: 0.2

The GAs is a randomized search algorithm. To eliminate the effect of random factors, and ensure the results of the algorithm, the program runs 70 times for each system. In each case study, the simulation accounts for ten times per one selection method and gets the best results for comparison. The results presented in Table 2 and Table 3 correspond to seven different cases of the selection method, as described in Sect. 2.

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Table 1. Supposed measured value for simulations (in per unit). Bus 5 buses P 1 0.8305 2 0.2 3 0.1 4 −0.5 5 −0.6 6 7 8 9 10 11 12 13 14

network Q V 0.0727 1.06 0.3181 0.0918 −0.3 −0.4

IEEE 14 bus P Q V 2.336 −0.0578 1.06 0.183 0.5951 −0.942 0.2046 −0.478 0.039 −0.076 −0.016 −0.112 −0.075 0 0 0 0 −0.295 −0.166 −0.09 −0.058 −0.035 −0.018 −0.061 −0.016 −0.138 −0.058 −0.149 −0.05

Table 2. Maximum errors of bus voltage magnitude estimation (%). Network Selection method Roulette Boltzmann wheel selection selection

Rank weighting selection

Cost weighting selection

Tournament selection

5 buses 0.536 14 buses 0.509

0.238 0.859

0.356 4.947

0.014 5.583

2.534 6.693

Pairing from top to bottom 0.165 4.158

Random pairing

0.495 5.591

Table 3. Maximum errors of bus voltage angle estimation (%). Network Selection method Roulette Boltzmann selection wheel selection

Rank weighting selection

Cost weighting selection

Tournament selection

5 buses 0.988 14 buses 1.843

2.149 8.199

0.657 14.300

0.027 13.177

20.529 39.606

Pairing Random from top pairing to bottom 0.255 8.475 30.185 42.343

In Table 2 and Table 3, the estimated results of voltage magnitude and voltage phase angle are shown, respectively. These tables show the differences of the estimated error percentage in seven categories of selection methods.

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For the 5-bus test system, the bus magnitude simulated results are all less than 0.6%, except for the case of Boltzmann selection, which has an error of about 2.5%. For voltage angle estimation, there are four selection processes which are roulette wheel, cost weighting, tournament, and paring from top to bottom, perform well with the errors less than 1%. The other functions (Boltzmann, rank weighting, and random pairing) have higher errors. The highest one is 20.529% when using Boltzmann selection. These tables illustrate that, in case of the 5-bus system, the CGA with tournament selection results in a better estimate than others. Moreover, within the error range, we can use CGA with roulette wheel selection, cost weighting selection, or pairing from top to bottom. The estimation result of the voltage magnitude of the IEEE 14-bus system has less than 0.9% error when using the roulette wheel selection and rank weighting selection. The remaining operators give the errors of greater than 4%. For the voltage angle estimation results, only the CGA with roulette wheel selection performs well with a result of about 1.84%. The CGA with other selection methods has higher errors with values up to 42.3% when using random pairing.

4 Conclusions In this paper, the effect of selection methods on the power system state estimation by the CGA is presented. The selection operators have a significant influence on the performance of the CGA. For the problem with a 5-bus grid, the estimated results of most case studies have acceptable errors. However, with the IEEE 14 bus system, it is noticeable that CGA with roulette wheel selection gives the best results. The comparison of the simulation results in both case studies shows that the roulette wheel selection in CGA is the most suitable for power system estimation.

References 1. Schweppe, F.C., Wildes, J.: Power system static-state estimation, Part I: exact model. IEEE Trans. Power Apparatus Syst. (PAS-89), 120–125 (1970) 2. Schweppe, F.C., Rom, D.B.: Power system static-state estimation, Part II: approximate model. IEEE Trans. Power Apparatus Syst. (PAS-89), 125–130 (1970) 3. Wu, F.F.: Power system state estimation: a survey. Int. J. Electric Power Energy Syst. 12, 80–87 (1990) 4. Karamta, M.R., Jamnani, J.G.: A review of power system state estimation: techniques, stateof-the-art and inclusion of FACTS controllers. In: Proceeding of the 2016 International Conference on Electrical Power and Energy Systems (ICEPES), Bhopal, India, pp. 533–538 (2016) 5. Holten, L., Gjelsvik, A., Aam, S., Wu, F.F., Liu, W.H.E.: Comparison of different methods for state estimation. IEEE Trans. Power Syst. 3, 1798–1806 (1988) 6. Jin, T., Shen, X.A.: Mixed WLS power system state estimation method integrating a widearea measurement system and SCADA technology. Energies 11, 408 (2018) 7. Brandalik, R., Wellssow, W.-H.: Power system state estimation with extended power formulations. Int. J. Electr. Power Energy Syst. 115, 105443 (2020)

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8. Rahman, Md.A., Venayagamoorthy, G.K.: A hybrid method for power system state estimation using cellular computational network. Eng. Appl. Artif. Intell. 64, 140–151 (2017) 9. Tungadio, D.H., Jordaan, J.A., Siti, M.W.: Power system state estimation solution using modified models of PSO algorithm: comparative study. Measurement 92, 508–523 (2016) 10. Kumar, A., Das, B., Sharma, J.: Genetic algorithm-based meter placement for static estimation of harmonic sources. IEEE Trans. Power Deliv. 20, 1088–1096 (2005) 11. Aminifar, F., Lucas, C., Khodaei, A., Fotuhi-Firuzabad, M.: Optimal placement of phasor measurement units using immunity genetic algorithm. IEEE Trans. Power Deliv. 24, 1014– 1020 (2009) 12. Müller, H.H., Castro, C.A.: Genetic algorithm-based phasor measurement unit placement method considering observability and security criteria. IET Gener. Transm. Distrib. 10, 270– 280 (2016) 13. Hossam-Eldin, A.A., Abdallah, E.N., ElNozahy, M.S.: A modified genetic based technique for solving the power system state estimation. Int. J. Electr. Comput. Eng. 3, 1438–1447 (2009) 14. Haupt, R.L., Haupt, S.E.: Pratical Genetic Algorithm, 2nd edn. Wiley, Hoboken (2004) 15. Pencheva, T., Atanassov, K., Shannon, A.: Modelling of a roulette wheel selection operator in genetic algorithms using generalized nets. Int. J. Bioautom. 13, 257–264 (2009) 16. Saini, N.: Review of selection methods in genetic algorithms. Int. J. Eng. Comput. Sci. 6 (12), 22261–22263 (2017) 17. Jebari, Khalid, Madiafi, Mohammed: Selection methods for genetic algorithms. Int. J. Emerg. Sci. 3(4), 333–344 (2013) 18. Jun, L.I., et al.: A real-coded genetic algorithm applied to optimum design of low solidity vaned diffuser for diffuser pump. J. Thermal Sci. 10, 301–308 (2001) 19. Abur, A., Expósito, A.G.: Power System State Estimation - Theory and Implementation. Marcel Dekker, New York (2004) 20. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd revised and extended edn. Springer, Heidelberg (1996) 21. Chelouah, R., Siarry, P.: A continuous genetic algorithm designed for the global optimization of multimodal functions. J. Heuristic 6, 191–213 (2000) 22. Bessaou, M., Siarry, P.: A genetic algorithm with real-value coding to optimize multimodal continuous functions. Struct. Multidiscip. Optim. 23, 63–74 (2001) 23. Arqub, O.A., Abo-Hammour, Z.: Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf. Sci. 279, 396–415 (2014) 24. Saadat, H.: Power System Analysis. McGraw-Hill, New York (1999) 25. http://labs.ece.uw.edu/pstca/pf14/ieee14cdf.txt. Accessed 28 Feb 2020

Collision-Free Path Following of an Autonomous Vehicle Using NMPC Ngo-Quoc-Huy Tran1(&), Ionela Prodan2, and Nguyen-Duy-Minh Phan1 1

The University of Danang-University of Technology and Education, 48 Cao Thang, Danang 550000, Vietnam [email protected] 2 Univ. Grenoble Alpes, Grenoble INP, LCIS, F-26000 Valence, France [email protected]

Abstract. This paper deals with the obstacle avoidance problem for an autonomous vehicle using NMPC (Nonlinear Model Predictive Control) while following an a priori given path. The repulsive potential of the operating space is constructed from the bounded convex regions describing the static obstacles for collision-free navigation. The contribution lies in using the Hausdorff distances among the obstacles and the agent in order to activate/inactivate the repulsive potential field. This potential field component is introduced in a NMPC framework to penalize collision. This proposal shows good results in simulations and comparisons with our previous work. Keywords: Obstacle avoidance


Path following
NMPC

1 Introduction Over the past two decades, the autonomy technologies for vehicles have had great advances and there have been so many types of autonomous vehicles (AVs) such as Unmanned ground vehicles (UGVs), Unmanned aerial vehicles (UAVs), Unmanned surface vehicles (USVs) and so on. Much of the literature has, in recent years, concentrated on the path following problem using various control techniques, for example, backstepping approach for 6-DOF quadrotor UAV [1]; sliding mode control was applied for a UGV [2]; or barrier Lyapunov function to handle state constraints for USV [3]. These methods are either difficult to deal with the constraints of the dynamical systems or not trivial to deal with non-convex constraints coming from the collision avoidance conditions. As also stated in the literature, we believe that, NMPC is an appropriate method which can handle constraints and references following. Usually, the non-convex constraints which arise from collision avoidance conditions are handled as explicit constraints, within a Mixed-Integer Programming

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 232–241, 2021. https://doi.org/10.1007/978-3-030-64719-3_27

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(MIP) framework [4]; or implicit constraints, within an Artificial Potential Field (APF) framework [5]. However, each approach has its own shortcomings: MIP is NPhard1 [6] and APF does not handle well the local minima issue [7]. This paper extends our previous work on NMPC using APF-based implicit constraints which was able to handle collision avoidance constraints [8]. The contributions reside in using Hausdorff distances instead of Chebyshev circles, which are considered in the inverse activation function in order to activate the repulsive potential fields of the bounded convex regions representing static obstacles. As a result, the exact distances between agents and obstacles can be obtained to guarantee a safe distance for obstacle avoidance. Moreover, this problem is also integrated in path following framework. For convenience in comparison with the previous work, the AV in this paper is specified as a USV. This paper is organized as follows: Sect. 2 presents the repulsive potential working space, inverse activation function and Hausdorff distance. Section 3 introduces an associated function which is integrated into a NMPC framework to activate the obstacle constraints. Section 4 shows the simulation results and comparison with another approach. Section 5 presents the conclusions and future work. The following notation will be used throughout the paper. For a vector x 2 Rn and pffiffiffiffiffiffiffiffiffiffiffi a positive (semi-)definite matrix P 2 Rnn , jjxjjP denotes the weighted norm xT Px. We denote by dH ðO‘ ; PÞ, the Hausdorff distance among the ‘th obstacle and the agent. A polytope is a bounded polyhedron and has a dual representation in terms of intersection of half-spaces or convex hull of extreme points: O ¼ fx 2 Rn : Sx  Kg ¼ fx 2 Rn : x ¼

P

ui vi ;

P

ui ¼ 1; ui  0g.

2 Preliminaries 2.1

Bounded Convex Regions

Let us first introduce the bounded convex regions representing the fixed obstacles and the autonomous agent. Indeed, from the geometrical point of view of the obstacles or agent, we can take them into account as the polytopic convex regions defined by [9]. Therefore, the workspace of an autonomous agent, W  Rn , which comprises a union of forbidden polytopic convex regions, characterizing multiple fixed obstacles as follows: W  O;

ð1Þ

where O is defined as follows: O¼

N obs [

O‘ ;

ð2Þ

‘¼1

1

I.e., the computational complexity increases exponentially with the number of binary variables used in the problem formulation.

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where O‘ is considered as the ‘th bounded polyhedral O‘ ¼ fx 2 Rn j a‘k x  b‘k ; k ¼ 1; . . .; n‘h g, with a‘k 2 R1n , b‘k 2 R, n‘h is the number of half-spaces describing O‘ , ‘ ¼ 1; . . .; Nobs , and Nobs is the number of forbidden polytopic regions accounting for fixed obstacles. Similar to the approach of the constructing polytopes for the fixed obstacles, a polytopic region standing for the safety region for an autonomous agent can be parameterized in relative coordinates: Pðxa Þ ¼ fx 2 Rn : am ðx  xa Þ  bm ; m ¼ 1; :::; nah g;

ð3Þ

where xa is the center of the bounded region defined by Pðxa Þ; am 2 R1n , bm 2 R, nah is the number of half-spaces describing Pðxa Þ. 2.2

Repulsive Potential Working Space

For dealing with the obstacle avoidance, the repulsive potential working space will be constructed from the set of bounded polyhedron of the static obstacles defined in (2). Let us consider first the sum function [10] defined by: ‘

c‘ ðxÞ ¼

nh X 

  a‘k x  b‘k þ a‘k x  b‘k  :

ð4Þ

k¼1

By analyzing Eq. (4), it is obvious that this piecewise affine function is zero whenever x 2 O‘ and strictly positive whenever x 62 O‘ . In other words, the value of the piecewise linear function will thus grow piecewise linearly with the distance from the polytopic region. Using (4), the union of repulsive potentials of the fixed obstacles are defined as: F¼

N obs [ ‘¼1

F‘ ðc‘ ðxÞÞ;

ð5Þ

with F‘ ðc‘ ðxÞÞ defined as: F‘ ðc‘ ðxÞÞ ¼

c1‘ ðc2‘ þ c‘ ðxÞÞ2

;

ð6Þ

where c1‘ and c2‘ are positive parameters representing the strength and effect ranges of repulsive potential. Figure 1b illustrates repulsive potential working space based on the bounded convex regions Fig. 1a representing two fixed obstacles.

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(a) Set of the bounded convex regions (2). (b) Repulsive potential fields using sum function (4).

Fig. 1. The operating space.

2.3

Hausdorff Distance

In order to activate the repulsive potential for obstacle avoidance, Hausdorff distance will be used and its main advantage is the fact that it gives the exact distance between the static obstacles and the agent in the bounded polyhedron framework.

Fig. 2. Hausdorff distance between the two polytopes (O1 ; O2 ) and agent (P).

Assuming that o‘ and p are points of sets O‘ and P respectively, and dH ðo‘ ; pÞ is any metric between these points; for simplicity, dH ðo‘ ; pÞ is considered as the Euclidian distance between o and p. As a result, the Hausdorff distance can be understood as the maximum distance of a set to the nearest point in the other [11] and described by: dH ðO‘ ; PÞ ¼ max min dH ðo‘ ; pÞ o2O‘ p2P

ð7Þ

Hence, from (7), the Hausdorff distance between the two polytopes (O1 ; O2 ) and agent (P) can be computed and illustrated as can be seen in Fig. 2.

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Inverse Activation Function

Activation function is used to determine the output in Artificial Neural Networks [12] or Deep Neural Networks [13], and a useful tool in the formulation of the forthcoming path following which activates the repulsive potential fields representing static obstacles based on the Hausdorff distances. We propose the so-called “inverse activation function”: I act ¼

1 ; 1 þ eaðxxo Þ

ð8Þ

where a [ 0, is considered as an activated coefficient. This function has the property shown in the Fig. 3, i.e., if x [ x0 then I act ¼ 0 (inactivation); or if x\x0 then I act ¼ 1 (activation). Figure 3 illustrates the inverse activation function for xo ¼ 0 and varying a.

Fig. 3. Inverse activation function with difference a.

3 Safe Navigation and Path Following in NMPC Framework All the elements presented above will be implemented in the NMPC framework for following a given path in the presence of fixed obstacles for autonomous surface vehicle. 3.1

Autonomous Surface Vessel Dynamic

Let me first describe a general form of continuous-time autonomous surface vessel model [14]:  x_ ¼ f ðxðtÞ; u(t)) ¼

g_ ¼ RðwÞv : M v_ ¼ CðvÞv  Dv þ u;

ð9Þ

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>

where the state vector, x ¼ ½ g m > 2 R6 includes the vector g ¼ ½ p w 2 R3 with p ¼ ½ x y > , the system position and w, the heading angle in the North-East Down frame. It also includes vector m ¼ ½ u v r > 2 R3 describing the surge, sway and yaw rates. The input vector, u 2 R3 , with u ¼ ½ T u T v T r > contains the surge, sway forces and yaw moment produced by the vessel actuators (propellers, thrusters, and rudders). Also, in (9), RðwÞ, M, CðmÞ and D 2 R33 are the rotation, mass, Coriolis and damping matrices, respectively. 3.2

Safe Navigation Through Repulsive Potential Activation

Safe navigation is guaranteed by activating the repulsive potential fields, the combination between repulsive potential and inverse activation function is necessary. This is trivial since both of them are scalar functions. Therefore, an associated function will be proposed through Hausdorff distance (7) which was defined between agent and static obstacle. To construct an associated function, we first redefine the inverse activation function as presented in (8) based on Hausdorff distance and associated safe distance w.r.t. ‘th fixed obstacle. It can be detailed as follows: I ‘act ðdH ðO‘ ; PÞÞ ¼

1 ; a [ 0; 1 þ eaðdH ðO‘ ;PÞDs Þ

ð10Þ

where dH ðO‘ ; PÞ and Ds ð [ 0Þ are Hausdorff and safe distance between agent and ‘th fixed obstacle respectively. As a consequence, an associated function, so-called repulsive potential activation which is used for collision-free is described as follows: X¼

Nobs X ‘¼1

I ‘act ðdH ðO‘ ; PÞÞF‘ ðc‘ ðxÞÞ;

ð11Þ

with F‘ ðc‘ ðxÞÞ was given in (6). 3.3

NMPC Implementation

Collision-free path following of autonomous vessel (9) is taken into account NMPC framework by solving a finite horizon open-loop OCP (optimal control problem) at time t, using the measured state xðtÞ over the prediction horizon Tpred : tþ ZTpred

½LðpðsÞ; uðsÞÞds þ Eðpðt þ Tpred ÞÞ;

min

uð:Þ;pð:Þ t

ð12Þ

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subject to: :

:

x ¼ f ðx; uÞ; xðtÞ ¼ xðtÞ; uðsÞ 2 U; uðsÞ 2 DU; xðsÞ 2 X; 8s 2 ½t; t þ Tp :

ð13Þ

The stage cost Lð
Þ has the following expression:  2   _ 2 þ XðsÞ; Lð
Þ ¼ pðsÞ  pref ðsÞQ þ kuðsÞk2R þ uðsÞ DR

ð14Þ

and the terminal cost is defined as:  2 Eðpi ðt þ Tpred ÞÞ ¼ pðt þ Tpred Þ  pref ðt þ Tpred ÞP :

ð15Þ

In (13) f ð
;
Þ is presented in (9), xðsÞ; uðsÞ are the predicted states and inputs while uð
Þ stands for the predicted input trajectory along the prediction horizon Tpred . :

In the cost per stage (14), uðsÞ denotes the predicted input variations, pref ð
Þ is the reference path, XðsÞ is the associated function for collision-free path given in (11). Q, R, and P are (semi)-positive definite weighting matrices of appropriate dimensions. It is worth noting that pðsÞ is prediction output vector with dimension is 1 0 0 0 0 0 pðsÞ ¼ xðsÞ, and pref ð
Þ in (14), (15) is a desired path. 0 1 0 0 0 0

4 Simulation Results The specifications of Cybership II2 [15] are used in the simulations and comparisons with another approach3. – Weighting matrices4: 2

3 2 cos w  sin w 0 25:8 RðwÞ ¼ 4 sin w cos w 0 5; M ¼ 4 0 0 0 1 0 2 3 0:72 0 0 D¼4 0 0:89 0:03 5 0 0:03 1:9

0 33:8 1:0115

3 0 1:0115 5; 2:76

– The surge, sway forces and yaw moment are T u 2 ½2; 2 [N], T v 2 ½2; 2 [N] and T r 2 ½1:5; 1:5 [N.m], respectively.

2

3 4

This vessel was identified and developed in the Marine Cybernetics Laboratory at Norwegian University of Science and Technology. I.e., our previous work which uses Chebyshev center instead of Hausdorff distance. For simplicity, the Coriolis matrix is neglected.

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The desired path of the vessel is a eight-shaped curve, pref ¼ ½ xref yref > , is described: xref ¼ 18 sinð0:01tÞ; yref ¼ 12 sinð0:02tÞ: The number of static obstacles, Nobs ¼ 2. The parameters of repulsive potential activation function (11) are given by a ¼ 2, c11 ¼ c12 ¼ 300; c21 ¼ c22 ¼ 0:5; Ds ¼ 0:65 [m]5. Other parameters of the NMPC optimization problem in (12): the prediction horizon, Tpred 6 is 8 and the number of control discretization, Nc is 4; the number of   10 0 1 0 , R¼ , simulation Nsim ¼ 320; the weighting matrices are Q ¼ 0 10 0 1   0:01 0 10 0 DR ¼ ,P¼ . 0 0:01 0 10 The simulations are done by using interior-point solver IPOPT [16] in the Casadi framework [17]. In here, the proposed method (i.e., using Hausdorff distance to activate the repulsive potential fields) and the previous method [8] are simulated and compared related to tracking error and obstacle avoidance. It is worth noting that the Integral of Absolute magnitude of the Error (IAE) over the position is used: TZ pred ¼8

IAE ¼

  p  pref dt

t¼0

The IAE values and the percentage errors over the trajectory are provided in Table 1. The percentage errors represent the average errors over a length unit, which are defined as Dl% ¼ IAE l 100% using the total length of the desired path l ¼ 131:3 [m]. Figure 4 illustrates the collision-free path following of USV using NMPC in two approaches, the solid blue line7 is actual motion of the proposed method, and the solid red line is actual motion of the previous method. Both trajectories are tracking the desired path (plotted in dashed blue line. The shapes of ship (of the proposed method) are shown at 8 different time instances (i.e., the eight light blue convex regions). At each time instance, the two Haudorff distances (i.e., dH ðO1 ; PÞ and dH ðO2 ; PÞ) are depicted by dotted red and black line respectively. As a consequence, the proposed approach gives us a better result than the method using Chebyshev circle [8] with an actual length of the trajectory is shorter. This is also detailed in Table 1 via tracking errors (IAE) and percentage errors (Dl%).

5 6

7

Safe distance, Ds is equal to the maximum length of a vessel’s hull. The prediction horizon is chosen enough large to guarantee obstacle avoidance but not too large because of the computational burden of the solver. It’s worth noting that the ship is very close the fixed obstacle 1 but does not collide due to the repulsive field.

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Fig. 4. Actual motion of the Cybership II under comparison with previous approach [8]. Table 1. Comparison of tracking errors. Tracking error Previous method [8] Proposed method IAE 5.92 5.70 Dl% 4.51% 4.34%

5 Conclusions This paper introduced an approach to activate the repulsive potential fields through Hausdorff distance for obstacle avoidance problem. In this approach, the real distances between agent and obstacles were computed exactly and directly instead of using an approximate method as Chebyshev circle. This improvement does not only provide a better percentage error for the path following but also guarantees obstacle avoidance. Future work is concentrated on the multi-autonomous vehicles and stability issues under uncertainties.

References 1. Wang, R., Liu, J.: Trajectory tracking control of a 6-DOF quadrotor UAV with input saturation via backstepping. J. Franklin Inst. 355(7), 3288–3309 (2018) 2. Matraji, I., Al-Durra, A., Haryono, A., Al-Wahedi, K., Abou-Khousa, M.: Trajectory tracking control of skid-steered mobile robot based on adaptive second order sliding mode control. Control Eng. Pract. 72, 167–176 (2018)

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3. Li, L., Dong, K., Guo, G.: Trajectory tracking control of underactuated surface vessel with full state constraints. Asian J. Control (2020) 4. Stoican, F., Prodan, I., Grøtli, E.I., Nguyen, N.T.: Inspection trajectory planning for 3D structures under a mixed-integer framework. In: 2019 IEEE 15th International Conference on Control and Automation (ICCA). pp. 1349–1354. IEEE (2019) 5. Wang, H., Huang, Y., Khajepour, A., Zhang, Y., Rasekhipour, Y., Cao, D.: Crash mitigation in motion planning for autonomous vehicles. IEEE Trans. Intell. Transp. Syst. 20(9), 3313– 3323 (2019) 6. Garey, M.R., Johnson, D.S.: Computers and Intractability, vol. 29. W.H Freeman, New York (2002) 7. Goerzen, C., Kong, Z., Mettler, B.: A survey of motion planning algorithms from the perspective of autonomous UAV guidance. J. Intell. Robot. Syst. 57(1–4), 65 (2010) 8. Tran, N., Prodan, I., Grøtli, E., Lefevre, L.: Potential-field constructions in an MPC framework: application for safe navigation in a variable coastal environment. IFACPapersOnLine 51(20), 307–312 (2018) 9. Boyd, S., Boyd, S.P., Vandenberghe, L.: Convex Optimization. Cambridge Univ. Press, Cambridge (2004) 10. Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, New York (2013) 11. Kraft, D.: Computing the Hausdorff distance of two sets from their signed distance functions. arXiv preprint arXiv:1812.06740 (2018) 12. Feng, J., Lu, S.: Performance analysis of various activation functions in artificial neural networks. J. Phys. Conf. Ser. vol. 1237, p. 022030. IOP Publishing (2019) 13. Srinivasan, P., Guastoni, L., Azizpour, H., Schlatter, P., Vinuesa, R.: Predictions of turbulent shear flows using deep neural networks. Phys. Rev. Fluids 4(5), 054603 (2019) 14. Fossen, T.I.: Marine control systems–guidance. navigation, and control of ships, rigs and underwater vehicles. Marine Cybernetics, Trondheim, Norway, Org. Number NO 985 195 005 MVA (2002). www.marinecybernetics.com, ISBN: 82 92356 00 2 15. Zheng, H., Negenborn, R.R., Lodewijks, G.: Trajectory tracking of autonomous vessels using model predictive control. IFAC Proceedings, vol. 47(3), 8812–8818 (2014) 16. Wachter, A., Biegler, L.T.: On the implementation of an interior-point filter line- search algorithm for large-scale nonlinear programming. Math. Program. 106(1), 25–57 (2006) 17. Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation (2018, in press)

Compare the Efficiency of the Active Filter and Active Rectifier to Reduce Harmonics and Compensate the Reactive Power in Frequency Controlled Electric Drive Systems Le Van Tung1,2(&), Pham Thanh Long3, Ngo Van An3(&), and Bogdan Vasilev1 1

St. Petersburg Mining University, St. Petersburg 199106, Russian Federation 2 Quang Ninh University of Industry, 18 street, Quang Ninh City 208830, Viet Nam 3 Thai Nguyen University of Technology, 666, 3/2 street, Tich Luong Ward, Thai Nguyen City 251750, Viet Nam [email protected]

Abstract. Currently, frequency converters using diode rectifiers generate a lot of harmonics, which adversely affect the quality of the grid and the durability of electrical equipment. On the other hand, asynchronous motors that consume a large amount of reactive power of the source cause losses on the grid and reduce the power factor at the input of the converter. Therefore, researching the method of using active filters at the input of frequency converters with diode rectifiers will reduce current harmonics and compensate reactive power for the grid. The active filter will produce harmonics with a phase angle inversely to the phase angle of the harmonics generated by the load, which will suppress most highorder harmonics. The frequency converters use active rectifiers with a direct power control method so that the reactive power q = 0 ensures the standard sine current at the input, the power factor equals 1, there is the stability of a DC voltage and the power is exchanged in two directions between the motor and the grid. The paper compares the effectiveness of the two methods when considering the load as three asynchronous motors that work in different modes such as motor mode and generator mode. The research results are verified by Matlab & Simulink software. Keywords: Active filter
Active rectifier
Direct power control
Three-phase inverter
Torque control
Uncontrolled rectifier

1 Introduction Currently, frequency converters using diode rectifiers are commonly used in industry and this is the cause of most harmonics on the grid. Current and voltage harmonics have a great influence on the quality of electricity, the transmission process, the working status, and the life expectancy of electrical equipment [1]. The diode rectifier © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 242–253, 2021. https://doi.org/10.1007/978-3-030-64719-3_28

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will generate current harmonics at the 5, 7, 11, 13, 17, 19, and so on. On the other hand, semiconductor devices in converters with nonlinear load characteristics will cause grid current distortions [2]. In electric drive systems, asynchronous motors are the devices that consume a lot of reactive power in the grid, about 60–65%. This results in losses on the line and reduces the power factor at the converter input [3]. A number of solutions to reduce harmonic and reactive power compensation in the motor frequency control drive system have been studied and applied widely. For instance, the use of reactors, capacitors, and passive filters [3]. However, the method of using an active filter (AF) has many technical advantages (Figs. 1 and 2).

Fig. 1. The waveform of voltage and current at the input of the frequency converter.

Fig. 2. The waveform of the voltage and current at the output of the converter.

Active filters use power electronic circuits, controlled according to the source voltage and the measured load current, to produce harmonics with an amplitude equal to but opposite the phase angle of the harmonics generated by the load. As a result, harmonic components are removed, and based on the measurement of load current and grid voltage, the reactive power consumed by the load is calculated, from which the filter producing reactive power needs to be compensated [4]. There is currently research and manufacturing of converters that require high quality of the power factor and harmonic reduction. Frequency converters with active rectifiers are gradually replacing diode rectifiers. The direct power control (DPC) method for an active rectifier will estimate the grid voltage and then control the active and reactive power. The advantage of this method is to compensate the reactive power, that is, to control the power q = 0 at the input. This ensures that the factor cosu = 1, reduces the grid current harmonics, controls the charging current for the capacitor, and causes energy to exchange in two different directions [5]. The working mode of the asynchronous motors also greatly affects the quality of supply at the frequency converter input. The paper uses the DTCSVM method to control the speed and torque of three motors with different working modes at different times. Direct torque control (DTC) is being widely used in industry with advantages such as very low torque response time, high reliability, and very good dynamic characteristics of electromagnetic torque [6, 7]. Through the working mode of the motors, the advantages and disadvantages of active filters and active rectifiers in the asynchronous motor frequency control system will be evaluated.

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2 Active Filter Design Method 2.1

Working Principle of Active Filter

The active filter consists of a capacitor combined with an active rectifier (IGBT) and sensors measuring voltage and current. By measuring the load current and the grid voltage, the control system for the rectifier will be designed to generate compensating currents with harmonics generated by the load to ensure that the grid current only has the basic wave component. The principle is described in Fig. 3.

Fig. 3. The working principle of active filter.

According to Akagi’s instantaneous power theory, in the electrical system, the instantaneous power p and instantaneous reactive power q of the load can be divided into two components: the component
p,
q corresponding to the basic wave component of the load current, and the oscillator component ~p, ~ p corresponding to the higher harmonic component. This theory is very flexible, allowing the selection of signals of the desired frequency, which is the basis for designing active filters [8].

Fig. 4. Active filter control structure by power method p-q.

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Where HPF – high pass filter; HCC – hysteresis current controller; PWM – threephase active rectifier which generates compensated current on the grid; PI – DC voltage regulator which compensates for power loss p0, FC-IM – frequency converter and induction motor. Assuming the current at the input of the frequency converter is distorted by harmonics, this is the load current iL. AF will measure iL current and calculate the compensation current iC so that the current of the source iS = iL + iC is always standard sine. As such, iC will compensate for most harmonics generated by the load. The AF control method consists of two loops: the outer circuit which calculates the compensating current icref based on the load current iL (the compensated current icref is the value set for the inner loop or the current that the active rectifier will generate) and the inner circuit which is responsible for generating a compensation current iC that follows the current to be compensated icref by the active rectifier control. When AF is working, the DC voltage change across the capacitor at the output of the active rectifier representing the active power loss p0. The auxiliary loop uses the PI regulator to maintain the DC voltage constant. The outer loop is responsible for measuring the current of the load and the source voltage is used to calculate the instantaneous power and the reactive power to be compensated, thereby calculating the currents to be compensated. 2.2

The Required Compensation Current Determination Method

The control structure of AF is shown in Fig. 4, with a three-phase electrical system without neutral wires, the current component i0 does not exist, satisfying ia + ib + ic = 0. Load current measured iL ¼ ½ iLa iLb iLc T and source voltage us ¼ ½ ua ub uc T , converted to a fixed coordinate system 0ab by Clarke transformation as follows [5, 8–10]:





ua ub ia ib





rffiffiffi" 2 1 ¼ 3 0

 12 pffiffi

#2 3 ua  12 pffiffi 4 u 5 b  23 uc

ð1Þ

rffiffiffi" 2 1 ¼ 3 0

 12 pffiffi

#2 3 iLa  12 pffiffi 4 i 5 Lb  23 iLc

ð2Þ

3 2

3 2

From Eqs. (1) and (2) calculate the total apparent power of the load on the coordinate system 0ab:    S ¼ u:i ¼ ua þ jub : ia  j:ib     S ¼ ua i a þ ub i b þ j ub i a  ua i b

ð3Þ

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Thus, the instantaneous active power and reactive power are determined: p ¼ ua ia þ ub ib ¼  þ  p p

ð4Þ

q ¼ ub i a  ua i b ¼  þ  q q

ð5Þ

Solve the system of equations to calculate ia, ib as follows: ia ¼

1 ðp:ua þ q:ub Þ u2a þ u2b

ð6Þ

ib ¼

1 ðp:ub  q:ua Þ u2a þ u2b

ð7Þ

The power will supply the DC power component to the load, the motors, and the power loss p0 of the active rectifier is seen at the filter circuit. The active filter is responsible for providing the AC power component ~p of p and the reactive power q including
q and ~q. Because asynchronous motors consume the reactive power of the grid, the active filter needs to compensate this reactive power including
q and ~ p. Also, the capacitor voltage is usually unstable. Hence so to ensure that the capacitor voltage is constant, the power supply needs to provide a power p0 to maintain the capacitor voltage constant. The AF filter will provide power: pAF ¼
p 
p  ~p þ p0

ð8Þ

qAF ¼ 
q  ~q

ð9Þ

From Eqs. (6), (7), (8) and (9), calculate the current to be compensated: ica ¼

1 ðp :ua þ qAF :ub Þ u2a þ u2b AF

ð10Þ

icb ¼

1 ðp :ub  qAF :ua Þ u2a þ u2b AF

ð11Þ

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From Eqs. (6) to (11), the grid current will be retested according to the coordinate system 0ab when compensation has been made and the result is only the basic harmonic wave component (iS = iL + iC): isa ¼ ica þ ia ¼

1 ð
p þ p0 Þ:ub u2a þ u2b

ð12Þ

isb ¼ icb þ ib ¼

1 ð
p þ p0 Þ:ub u2a þ u2b

ð13Þ

Equations (10) and (11) calculate the necessary compensation current on the 0ab coordinate system with two functions of harmonic filtering and reactive power compensation. From the compensation current in the system 0ab. We will calculate the current to compensate in the real coordinate system abc according to the formulas (1), (2), (10) and (11): 2

3 3 rffiffiffi2 1 0ffiffi
 ica p 2 3 1 4 icb 5 ¼ icref ¼ 42 5 ica 2pffiffi icb 3 icc  12  23

ð14Þ

Thus, the AF control algorithm according to the instantaneous power theory p-q is to find the required compensation current to be the value set for the inner loop. The inner loop will generate current following the preset compensation current.

3 Active Rectifier Control Method The DPC control structure (Fig. 5) is based on the active and reactive power control circuits. In the DPC structure, the switching states of the converter are chosen by the switch table based on the difference between the estimated value and the control value of active power p and reactive power q. Therefore, the DPC method requires a quick and accurate estimation of power p and q, [5, 8, 11]. In Fig. 5, the load is the inverter circuit and the asynchronous motor, PI - DC voltage regulator, PWM - active rectifier circuit (IGBT), L – reactor and cUL - phase shift angle between voltage vector and a axis. The PI regulator in the voltage loop works to keep the Udc voltage constant on capacitor C according to the set value. That is, control the flow of active power flowing to the capacitor C. Always ensure the output voltage of the rectifier is equal to the Udcref value set when the load changes. Estimating power is an important part of the system. The purpose is to determine the calculated power and then compare it with the set power to give a reasonable

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Fig. 5. The DPC control structure.

control signal. The power p and q of the active rectifier are calculated on the coordinate system 0ab as follows: p ¼ ua ia þ ub ib

ð15Þ

q ¼ ub i a  ua i b

ð16Þ

Set value of reactive power qref = 0. The corresponding power factor cosu = 1. The active power pref, at the output of the PI regulator, is compared with the active power at the output of the unit estimated power. The deviation between comparisons is the input of the power controller [12]. Two power regulators are designed with a hysteresis controller. The output signal of the active power regulator is determined as follows: ð17Þ ð18Þ The same, output signal of the reactive power regulator is as follows: ð19Þ ð20Þ Where Hp, Hq - delay range. The voltage vector is divided into 6 or 12 sectors on the coordinate system 0ab. However, through the research works, the switching table uses 12 sectors of higher quality [5, 10]. Based on the change of instantaneous power, it is possible to determine the control voltage vector on the plane [10] (Table 1).

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Table 1. The working mode of the motors corresponds to the load torque and the present speed. Motor Power (Kw) Speed x (rpm) and time t(s) Load torque Mc (N.m) and time t(s) 1 110 x = [1200 0] Mc = [0 550 −550] t = [ 0 4] t = [ 0 1.3 4.5] 2 110 x = [1300 0] Mc = [0 590 −590)] t = [ 0 4] t = [ 0 1.3 4.5] 3 150 x = [1400 0] Mc = [0 800 −800] t = [ 0 3] t = [ 0 1.5 4]

4 Results and Analysis Using Matlab & Simulink software to build functional blocks and simulate electric drive systems DTC-SVM (Fig. 6).

Fig. 6. DTC-SVM control diagram with an active rectifier.

Where: 1 – three-phase source; 2, 6, 12, 15 – current and voltage sensors; 3 – inductance; 4 – active rectifier (IGBT); 5, 11, 14 – voltage inverting circuit (IGBT); 7, 13, 16 – asynchronous motor; 8 – active rectifier control unit by DPC method; 9 – direct torque control (DTC); 10 – DC voltage sensor; Mc1, Mc2, Mc3 – torque load. The simulation results when using reactors at the input of the frequency converter with a diode rectifier are as follows (Figs. 7 and 8):

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Fig. 7. Three-phase current of the power supply (t < 1.3 s).

Fig. 8. Total harmonic distortion of the power supply current (THD = 17.7%).

The simulation results when using an active filter at the input of the frequency converter with a diode rectifier are as follows:

Fig. 9. Three-phase current of the supply source.

Fig. 10. The type of current to be compensated by the active filter.

Figure 9 shows that without an AF filter, the load current is equal to the supply current, iS = iL. When using an AF filter, the harmonic current generated by the load will be suppressed by the current of the AF filter (Fig. 10) and the iS current shape as shown in Fig. 9 and Fig. 11. Figure 12 shows that when the motors work in different modes, there are harmonics orders 3, 5, 7, 9 but there is a very small amplitude and the total harmonic distortion of

Fig. 11. The current iS of the source at the input of the frequency converter.

Fig. 12. Total harmonic distortion of the source current (THD = 4.25%)

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the grid current with THD is 4.25%. Compared with the IEEE STD 519 standard, THD value meets the above standard (THD < 5%).

Fig. 13. Active power and instantaneous reactive power of the source.

Fig. 14. Characteristics of the converter input power factor (cosu).

Figure 13 shows that the electric drive system consumes the reactive power of the grid to be compensated by the active filter (AF), which shows the instantaneous reactive power of the grid q  0 and the power factor cosu  1, as shown in Fig. 14. Simulation results when the frequency converter uses direct power control method for active rectifier:

Fig. 15. The phase current of the source is at the converter input.

Fig. 16. Total harmonic distortion of the source current (THD = 0,46%).

Figure 15 shows that at the time t  1.3s the motors work stably with load torque. The phase currents at the input of the converter are of standard sine form and the amplitude is smaller than the grid current amplitude in the active filtering method (Fig. 11). The results shown in Fig. 16 show that when the motors work in different modes, there are harmonics at the input source with very small amplitude (Imax 0.2A) and the total harmonic distortion of source THD is 0.46%. Compared with the IEEE STD 519 standard, the THD coefficient satisfies the standard (THD < 5%).

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Fig. 17. Active power and instantaneous reactive power of the source.

Fig. 18. Characteristics of the converter input power factor (cosu).

Figure 17 Shows that the reactive power of the grid consumed by the load will be compensated by the active rectifier, which shows the reactive power of the source q = 0. Figure 18 shows the increased power factor at the input of the converter (cosu = ± 1). Table 2. The comparison result of the total harmonic distortion of grid current. Filter method THDI (%) Not filtered 131.04 Reactor 17.7 Active filter 4.25 Active rectifier 0.46

Table 2 shows that using an active rectifier method will eliminate high-order harmonics and have advantages over the remaining methods.

Fig. 19. Torque characteristics of motors IM1, IM2, IM3 at different times using the DTC-SVM method.

Figure 19 shows that the electromagnetic torque characteristics of motors (IM1, IM2, IM3) are of good quality, always following load torque. At times of changing load torque, the motors do not have a torque overshoot and are stable.

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5 Conclusion Asynchronous motor frequency control electric drive system works in different modes which greatly affect the quality of the grid. Theoretical analysis and the results obtained from the simulation show that active filtering and active rectifier methods will reduce the distortion of the grid current and compensate for the reactive power. The frequency converter using an active rectifier has many advantages over the active filter, which not only reduces harmonic, compensates reactive power, higher power factor but also controls the charging current for the capacitor. Energy exchange in two directions between the grid and the motors should save energy for the system. The DTC-SVM control method in the inverter circuit with the active rectifier is an effective solution in a practical asynchronous motor frequency control system. Acknowledgments. The authors would like to express their thanks to the Thai Nguyen University of Technology for all support and encouragement.

References 1. Santosh, A., Tejulal, A.: Effects of harmonics on electrical equipment and their compensation by using shunt active power filter. Int. Refer. J. Eng. Sci. 3(11), 126–131 (2014) 2. Khlifi, K., Haddouk, A., Hlaili, M., Mechergui, H.: Harmonic pollution caused by non-linear load. Analysis and identification. Int. J. Energy Environ. Eng. 12(7), 510–517 (2018) 3. Zakis, J., Rankis, I., Zhiravetska, A.: Investigation of an active system of reactive power compensation for induction motors. Electron. Electr. Eng. 6(70), 10–15 (2006) 4. Narongrit, T., Areerak, K.L., Areerak, K.N.: The comparison study of current control techniques for active power filters. Int. J. Electr. Comput. Eng. 5(12), 1–6 (2011) 5. Malinowski, M.: Sensorless control strategies for three-phase PWM rectifiers. pp. 1–128. Ph. D, Thesis, Warsaw (2011) 6. Kozaruk, A.E.: Energy-efficient electromechanical complexes of mining and transport vehicles. Zapiski Mining Inst. J. Mining Inst. 218, 261–269 (2018) 7. Kozyaruk, A.E., Albert, M.K.: Improving the energy efficiency of the electromechanical transmission of an open-pit dump truck. J. Mining Inst. 239, 576–582 (2019) 8. Akagi, H.: Control strategy and site selection of a shunt active filter for damping of harmonic propagation in power distribution systems. IEEE Trans. Power Delivery 12(1), 25–32 (1997) 9. Pawar, D.S., Vadirajacharya, K.: Design of shunt active filter to improve power quality using pq theory. Int. J. Eng. Res. Technol. (IJERT) 5(3), 311–316 (2016) 10. Czarnecki, L.S.: On some misinterpretations of the instantaneous reactive power p-q theory. IEEE Trans. Power Electron. 19(3), 828–836 (2004) 11. Gui, Y., Kim, C.: Improved direct power control for grid-connected voltage source converters. IEEE Trans. Ind. Electron. 65(10), 804–8051 (2018) 12. Muangruka, N., Nungam, S.: Direct power control of three-phase voltage source converters using feedback linearization technique. Procedia Comput. Sci. 86(1), 365–368 (2016)

Comparing the Application of Gas Sensor Fabrication of Nanomaterials ZnO Fabricated by Hydrothermal and Chemical Vapor Deposition Method Hoang Van Han1(&), Dao Huy Du2(&), and Do Anh Tuan1(&) 1

Hung Yen University of Technology and Education, Hải Dương, Vietnam [email protected], [email protected] 2 Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected]

Abstract. In this report, we introduce a method of ZnO nanomaterials fabrication. Particularly, we study the structure of the material, compare the structure of the fabricated materials. The size of the created material is about 30–40 nm in diameter, the length is from 1 to 10 nm. The material responds well to NO2, the resistance of the material changes 25 times with 10 ppm of NO2 at 205 °C. Gassensing characterizations revealed that the ZnO sensors exhibited a relatively high response to sub-ppm NO2 with excellent stability of switching from NO2 to air without significant response reduction. Keywords: NO2
Nanosensors ZnO
Zinc oxide nanowire
High aspect ratio

1 Introduction ZnO is a potential sensing material because of its outstanding properties; it is n-type semiconductor that has a direct and wide band gap of 3.37 eV, interenergy large exciton at room temperature (*60 meV) [1, 2]. ZnO has high thermal and chemical stability, high electronic mobility, ease of synthesis, low cost, high sensitivity to target gases, and large surface-to-volume ratio. It has been demonstrated to have enormous applications in electronic [3], optoelectronic [4], electrochemical, and electromechanical devices [5, 6], field emission [7, 8], high performance nanosensors [9, 10], solar cells [11, 12], piezoelectric nanogenerators, and nanopiezotronics. The morphology, crystal quality and surface of the ZnO nanostructures can strongly affect the sensing properties. Therefore, it is expected that ZnO with one-dimensional nanostructures can be used to performance gas sensors. One-dimensional (1D) ZnO nanostructures have been synthesized by a wide range of techniques, such as wet chemical methods [12, 13], physical vapor deposition [14–16] metal–organic chemical vapor deposition (MOCVD) [17, 18], molecular beam epitaxy (MBE), pulsed laser deposition, sputtering, flux methods, eletrospinning and even top-down approaches by etching. In the last years, authors have studied vapour phase growth processes for different semiconducting oxide nanostructures [19–23]. Among them, they found that ZnO onedimensional nanostructures have a special feature, i.e. they can be obtained with a very © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 254–261, 2021. https://doi.org/10.1007/978-3-030-64719-3_29

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large yield even in a laboratory scale reactor. In this study, the application of gas sensor fabrication of nanomaterials ZnO fabricated by hydrothermal and chemical vapor deposition method is compared. Porous ZnO possesses a large specific surface area for gas adsorption.

2 Experimental 2.1

Hydrothermal Method

In a typical synthesis, we used 1,36 g zinc chloride powder (ZnCl2) and 1,36 g P123 were dissolved in 100 ml distilled water. The solution was stirred and adjusted pH by dropwise addition of ammonium (NH4OH). The final solution was then in a Teflon lined autoclave and hydrothermal treated at 150 °C for 20 h. Filtering solid precipitates are collected through centrifugation and washed with distilled water and ethanol. After drying, the resulting powder is oxidized at 500 °C for 8 h to remove surfactant polymers. 2.2

Chemical Vapor Deposition

In a typical synthesis, zinc oxide nanowires are synthesized through thermal evaporation and vapor transport of pure ZnO powder mixture (Merck, 99.99%) and graphite powder (Merck) in proportion. The weight ratio is 1: 1. The mixed material is put into a porcelain boat (size 1–8 cm) and put into the center of the horizontal quartz tube furnace (with a length of 100 cm and a road clear glass 4 cm). The furnace is heated at a rate of 10 °C/minute and the tube reaches a temperature of 1000 °C. One end of the pipe is connected to the flow control and air supply system. The gas mixture was introduced as high purity nitrogen (99.99%) and air at a volume ratio of 1.7: 1. with a constant flow rate of 2880 sccm into the furnace. The flow rate is modulated with digital block flow control sys (Aalborg, USA). The reactions begin within 2–3 min and continue for approximately 13–15 min, depending on the amount of initial material. A highlight here is the use of atmosphere as the source of oxygen for the reaction. The synthetic product observed was white in color and the diameter and length were very uniform. 2.3

Identification of Structural Morphology

The morphology, size distribution, crystallinity, and composition of synthesized products were characterized using electron microscope with field emission scanning (FE SEM 4800, Hitachi, Japan) operating at 10 kV. Transmission electron microscopy (TEM), selected area electron diffraction (SEAD), and high-resolution transmission electron microscopy (HRTEM) examinations were conducted using a JEOL JEM-3010 system at an accelerating voltage of 300 kV. The X-ray diffraction (XRD) patterns were obtained using a Siemens diffractometer with CuK radiation.

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Investigation of Gas Sensitivity Properties

To prepare for the investigation, the sensors with fabricated materials are used. First Pt microelectrodes were made using standard photolithography with a finger width of 10 µm and a distance size of 20 µm. After that, the dispersion containing ZnO NWs was dropped on Pt interdigitated microelectrodes. Then, after drying and removing ethanol, the manufactured gas sensors are placed at the sensor furnace and then heat treated at 600 °C for 2 h to remove any remaining organic contaminants. Furthermore, to recrystallize ZnO NWs to enhance the physical adhesion between the ZnO NWs layer and the Pt substrate. Sensor characteristics are characterized at operating temperatures about 150–350 °C. Before sensing measurements, all microelectrodes are digitally pre-conditioned at operating temperatures for 10 h. Different concentrations of NO2 are obtained through dilution of premixed concentrations of NO2 in air. The sensor response (S) is defined as the resistance of the device exposed to oxidizing gas compared to the resistance in the air.

3 Results and Discussion 3.1

Morphology and Structure of ZnO

To examine the structure of the materials, we use X-ray diffraction spectrum, ultraviolet absorption spectroscopy. Materials fabricated by the hydrothermal method Figure 2a shows that the powder XRD pattern of the synthesized material exhibits typical diffraction peaks at 31.801, 34.601, 36.201,47.501, 62.901, and 67.901 which belong to the (100), (002), (101), (102), (103), and (112) reflections of the hexagonal ZnO, respectively (JCPDS, No. 36-1451). The average crystal size calculated by using the Scherrer formula was found to be about 27 nm. Figure 2b, c indicate that the optical properties of the synthesized materials characterized by using UV adsorption and the UV adsorption spectrum exhibit strong adsorption at a wavelength of about 400 nm, which corresponds to the band ZnO adsorption (Fig. 2b). The plots be derived from the UV adsorption data are shown in the inset of Fig. 2c. The intercept of the tangent to the plot gives a good approximation of the band gap energy of about 3.2 eV. Surface morphology of fabricated ZnO nano samples with pH values of 8, 9, 10 and 11, respectively, is shown by scanning electron microscopy (SEM) as shown in Fig. 3. From the results of this SEM image it is shown that for each ZnO sample at a pH value, the ZnO particle size is relatively uniform and the shape is quite similar. However, when comparing the samples with different pH values, the morphology of ZnO materials created is significantly different. With a low pH concentration (pH = 8), granular material and the size about 100 nm, the particles tend to clump together to form clumps of particles (Fig. 3a). As the pH value increased, the particle size of the sample also increased but mainly developed into rice-like form. The ZnO nano tends to grow in length. With pH = 9, the length of the particles about 200 nm (Fig. 3.b), at pH = 10, the length of the particles can be up to more than 500 nm (Fig. 3.c). The size

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and shape of the particles are homogeneous when pH = 10. When pH = 11, we see that the sample size increases sharply (Fig. 3d) and has a hexagonal cylinder shape. Materials fabricated by the Chemical vapor deposition In the process of making ZnO nanowires by thermal evaporation method, we selected samples manufactured with N2 flow rate from 960 sccm to air flow of 80 sccm, giving the best number of products as indicated in Fig. 4e. The results on these figures are investigated for two sampling areas of the product; and region I is at the wall of the cup (corresponding to Fig. 4a, b) and zone II is at the center of the cup (corresponding to Fig. 4c, d). These FE-SEM image results show that the surface morphology of ZnO nanomaterials has uniform morphological structure. The morphological structure of synthetic materials can be divided into two quite distinct forms, the nanowire type and the form with three attached nanowire branches (tetrapod). The diameter of these nanowires is quite uniform with a diameter of about

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30 nm. However, we can observe quite clearly that the length of the nanowires formed tetrapod varies with the N2 flow. Similar to znO nanorods fabricated by hydrothermal method, we survey the crystal structure characteristics as well as evaluate the defects and lattice defects of ZnO nanowires fabricated through schematic analysis. X-ray diffraction (XRD). Figure 5 shows the X-ray diffraction diagram of ZnO nanowires, in which we can see a sample with a hexagonal (hexagonal) crystal structure typical of ZnO oxide. In which the diffraction peaks corresponding to the Miller index (hkl) are (100), (002), (101), (102),

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Based on the above results, it is realized that: ZnO nanomaterials fabricated by the two methods above for structural forms are rods and wires. The dimensions of the material and structure are quite similar. From here we can predict the gas-sensitive properties of similar materials. 3.2

Gas Sensitive Characteristics of ZnO Nanomaterials

After fabricating ZnO nanomaterials are covered by micro electrodes (Pt on SiO2/Si substrates) to form sensors, the sensor system is annealed to 600 °C. The heating process is slowly increased from room temperature to 600 °C for a period of 6 h then held for 6 h and naturally cooled to room temperature to obtain the sensor component. The gas-sensitive characteristics of ZnO nanorod sensors were studied with NO2 (concentration from 0,5 to 10 ppm) in the working temperature range 200–350 °C. Figure 6a, b shows the response of the sensor using ZnO nanorod and nanowires fabricated by hydrothermal and chemical vapor deposition method, at a temperature of 250 °C with the NO2 gas flow varying from 0.5 ppm to 10 ppm. As the adsorption of gas has an increased oxidizing resistance of the sensor, this proves that ZnO is a n-type semiconductor. We found that the response uses two materials with similar properties. The response to NO2 concentration of 10 ppm is about 35 times and the response increases with increasing gas concentration. Sensor used nanorod has faster response and recovery time when using nanowires. This can be explained by the fact that ZnO nanowires are made by porous evaporation methods rather than hydrothermal methods. Therefore, the adsorbed air into the more porous material will be harder to release and absorb with the nanorod material.

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From the above results, we realize that the ZnO nanomaterial fabricated by both methods respond well to NO2 gas.

4 Conclusions A simple hydrothermal method for fabricating single-crystal ZnO nanorods, effective NO2 gas nanosensor applications is demonstrated. The single-crystal ZnO nanorods has an average 900 nm length and 23 nm width. A simple chemical vapor deposition method is also used to fabricate single-crystal ZnO nanowires. The single-crystal ZnO nanorods has an average 10 µm length and 40 nm width. The nanorods and nanowires exhibited a high response to sub ppm NO2 gas concentration and excellent stability and fulfilled the practical application of a sensor for monitoring toxic gases.

References 1. Heo, Y.W., et al.: ZnO nanowire growth and devices. Mater. Sci. Eng. R Reports 47(1–2), 1–47 (2004) 2. Rodnyi, P., Khodyuk, I.: Optical and luminescence properties of zinc oxide (Review). Opt. Spectrosc. 111(5), 776–785 (2011) 3. Wang, C., Wang, Y., Zhang, G., Peng, C., Yang, G.: Theoretical investigation of the effects of doping on the electronic structure and thermoelectric properties of ZnO nanowires. Phys. Chem. Chem. Phys. 16(8), 3771–3776 (2014) 4. Samanta, P.K., Chaudhuri, P.R.: Substrate effect on morphology and photoluminescence from ZnO monopods and bipods. Front. Optoelectron. China 4(2), 130–136 (2011) 5. Wan, Q., et al.: Fabrication and ethanol sensing characteristics of ZnO nanowire gas sensors. Appl. Phys. Lett. 84(18), 3654 (2004) 6. Banerjee, D., et al.: Synthesis and photoluminescence studies on ZnO nanowires. Nanotechnology 15(3), 404–409 (2004) 7. Nguyen, T., et al.: Near-infrared emission from ZnO nanorods grown by thermal evaporation. J. Lumin. 156, 199–204 (2014)

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8. Lee, C.J., Lee, T.J., Lyu, S.C., Zhang, Y., Ruh, H., Lee, H.J.: Field emission from wellaligned zinc oxide nanowires grown at low temperature. Appl. Phys. Lett. 81(19), 3648 (2002) 9. Kumar, R., Al-Dossary, O., Kumar, G., Umar, A.: Zinc oxide nanostructures for no2 gas– sensor applications: a review. Nano-Micro Lett. 7(2), 1–24 (2014) 10. Wang, C., Yin, L., Zhang, L., Xiang, D., Gao, R.: Metal oxide gas sensors: sensitivity and influencing factors. Sensors 10(3), 2088–2106 (2010) 11. Choopun, S., Tubtimtae, A., Santhaveesuk, T., Nilphai, S., Wongrat, E., Hongsith, N.: Zinc oxide nanostructures for applications as ethanol sensors and dye-sensitized solar cells. Appl. Surf. Sci. 256(4), 998–1002 (2009) 12. Liu, H., Kameoka, J., Czaplewski, D.A., Craighead, H.G.: Polymeric nanowire chemical sensor. Nano Lett. 4(4), 671–675 (2004) 13. Hulanicki, A., Glab, S., Ingman, F.: Chemical sensors: definitions and classification. Pure Appl. Chem., 63(9), January 1991 14. Choi, M.-Y., Park, H.-K., Jin, M.-J., Ho Yoon, D., Kim, S.-W.: Mass production and characterization of free-standing ZnO nanotripods by thermal chemical vapor deposition. J. Cryst. Growth 311(3), 504–507 (2009) 15. Kim, J., Sohn, D., Sung, Y., Kim, E.R.: Fabrication and characterization of conductive polypyrrole thin film prepared by in situ vapor-phase polymerization. Synth. Met. 132(3), 309–313 (2003) 16. Tigli, O., Juhala, J.: ZnO nanowire growth by physical vapor deposition. In: 2011 11th IEEE International Conference on Nanotechnology, pp. 608–611 (2011) 17. Menzel, A., Subannajui, K., Bakhda, R., Wang, Y., Thomann, R., Zacharias, M.: Tuning the growth mechanism of ZnO nanowires by controlled carrier and reaction gas modulation in thermal CVD. J. Phys. Chem. Lett. 3(19), 2815–2821 (2012) 18. van Deelen, J., Illiberi, A., Kniknie, B., Steijvers, H., Lankhorst, A., Simons, P.: APCVD of ZnO:Al, insight and control by modeling. Surf. Coat. Technol. 230, 239–244 (2013) 19. Hsu, N.F., Chung, T.K.: A rapid synthesis/growth process producing massive ZnO nanowires for humidity and gas sensing. Appl. Phys. A Mater. Sci. Process. 116(3), 1261– 1269 (2014) 20. Kundu, S., Sain, S., Satpati, B., Bhattacharyya, S.R., Pradhan, S.K.: Structural interpretation, growth mechanism and optical properties of ZnO nanorods synthesized by a simple wet chemical route. RSC Adv. 5(29), 23101–23113 (2015) 21. Han, L., Wang, D., Cui, J., Chen, L., Jiang, T., Lin, Y.: Study on formaldehyde gas-sensing of In2O3-sensitized ZnO nanoflowers under visible light irradiation at room temperature. J. Mater. Chem. 22(25), 12915 (2012) 22. Mute, A., Peres, M., Peiris, T.C., Lourenço, A.C., Jensen, L.R., Monteiro, T.: Structural and optical characterization of ZnO nanowires grown on alumina by thermal evaporation method. J. Nanosci. Nanotechnol. 10(4), 2669–2673 (2010) 23. Bahruji, H., et al.: Pd/ZnO catalysts for direct CO2 hydrogenation to methanol. J. Catal, April 2016

Convergence Parameters for D-Type Learning Function Cao Thanh Trung1(&), Nguyen Thu Ha1, Tran Kim Quyen2, and Nguyen Doan Phuoc1 1

School of Electrical Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected] 2 College of Industry and Trade TuyHoa, PhuYen, Vietnam

Abstract. Two sufficient convergence conditions of D-type iterative learning function for practical applications are proposed in this paper. In these two convergence conditions, it is not required absolutely, that the relative degree of controlled repetitive plans has to be equal one, which is happened consistently in other convergence conditions. Hence, their opportunity for practical application range becomes wider. Some illustrative simulations afterward have confirmed this affirmation. Keywords: D-type learning functions
Iterative learning control
Model free control
Intelligent control

1 Introduction Iterative learning control (ILC) is a field of intelligent control concept, which can be used to overcome some of the inherent difficulties of conventional control methods related to system model inaccuracy. Based on ILC we can design an output tracking controller for repetitive processes, without using their mathematical model [1–4]. Within ILC conception the system inputs will be refined from previous trial to the next trial and so on, based on the measured system information of past executing performance, until the desired tracking error is reached. The essential of successful input refinements in ILC is an appropriate choice of learning function, also called update law algorithm, which will be used consistently from trial to trial to update the system inputs. Therefore, in the past decades the number of works of literature associated with choosing appropriately the learning functions is growing regularly [2–11]. Two overall surveys of them are founded in [9, 10]. The most used simple linear learning functions in ILC, under which also the D-, PD, and PID-Type, are introduced in [2–4, 7, 8]. The condition for the asymptotic convergence of learning processes by using the learning functions proposed in these references requires consistently that the relative degree of LTI repetitive plans has to be an equal one, i.e. CB of the plan ðA; B; CÞ has full range, or at least there exists an integer j so that CA j B has a full range. Additionally, the repetitive plans are assumed to be invertible. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 262–269, 2021. https://doi.org/10.1007/978-3-030-64719-3_30

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It has been shown furthermore in [4, 6, 8] that by applying these learning functions the ILC will bring the robust behavior to controlled systems. To applying these linear learning functions for nonlinear plans, these requirements about the relative degree of LTI systems will be replaced with the requisite that nonlinear plans have to be smooth and satisfy the Lipschitz condition [2, 7]. In the circumstance that no parameters of the chosen learning function satisfy available convergence conditions, an optimization outline will be used [2, 11]. However, in our opinion, the splendor of ILC concept, as already seen from its original point in [1], is that ILC is a very simple control technique and not difficult to implement, but provides an extremely effective improving the tracking performance for some repetitive production processes. It means consequentially, that any effort to complicate it academically will be not necessary, even worthless for praxis. Therefore, in this paper, we will focus mainly on the simplest learning functions, which is used commonly in ILC, the D-type learning function. More precisely, our aim here in this paper is to enlarge the application range of the linear D-type learning function in the sense that it could be applied also for repetitive processes of any relative degree, just only through determining appropriately its parameters.

2 Main Results Our object to study in this section is a periodically working plan, also often called the repetitive plan, which is described by the input-output mapping: yðsÞ ¼ f p ðuðsÞÞ for all 0  s  T:

ð1Þ

It is emphasized here that a model is always required for the convergence analysis of an applied learning algorithm, although for designing iterative learning controllers, the having of any mathematical model is not mandatory. It means that the mathematical model (1) may not be precise enough for designing any conventional controller, but it could be quite sufficient for a guideline to choose convergent parameters for a learning function. Next, we assume that the model (1) is linear, i.e. it satisfies: f p ðauðsÞ þ bvðsÞÞ ¼ af p ðuðsÞÞ þ bf p ðvðsÞÞ

ð2Þ

for any real numbers a; b. We consider here that the ILC controller will be carried out by using a general DType learning function: uk þ 1 ðsÞ ¼ uk ðsÞ þ f l ðek ðsÞÞ

ð3Þ

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where ek ðsÞ ¼ rðsÞ  yk ðsÞ

ð4Þ

is the output tracking error and the operator f l ð
Þ is linear, i.e. the equation:
   f l aek ðsÞ þ bzk ðsÞ ¼ af l ðek ðsÞÞ þ bf l zk ðsÞ

ð5Þ

is satisfied for all real number a; b. The index k in (3) above designates the iterative learning step, i.e. the trial number, and T in (1) indicates the periodically finite working time of repetitive processes (1). Such processes always execute their operation in a fixed working time interval and repeat it afterward over and over. During an operation we have: 0  s ¼ iTs \T or 0  i\N ¼ T=Ts ; with the sampling time Ts . As already shown by us in [12], the required convergence condition for learning process in the sense of:   e  k þ 1 ðsÞ \kek ðsÞk for all 0  s  T

ð6Þ

will be accomplished, if the following inequality holds:     1e  ðf p  f l Þ\1;

ð7Þ

where 1e denotes the identity operator. Note that besides the condition (7), an appropriate learning function is furthermore the one, which should fulfill additionally the asymptotic requirement [2, 3, 7]: limk!1 kek ðsÞk ¼ 0

ð8Þ

which will be leaved out momentary in this paper. However, even the condition (7) above of monotonous tracking error decreasing presented in [12] was still not realized there in detail for practical purposes. Hence, we will complete it in this paper. 2.1

Realization in Time Sequence

We consider here a repetitive plan (1) described approximately by the discrete time state model ðA; B; CÞ as follows: (

xðs þ 1Þ ¼ AxðsÞ þ BuðsÞ yðsÞ ¼ CxðsÞ

where x; u; y are the vectors of system states, inputs and outputs, respectively.

ð9Þ

Convergence Parameters for D-Type Learning Function

265

As mentioned before, the model (9) here may not be precise enough for designing any conventional controllers, but could be sufficient as a starting point for choosing convergence matrix K of D-type learning function: uk þ 1 ðsÞ ¼ uk ðsÞ þ Kek ðs þ 1Þ:

ð10Þ

Based on the approximated model (9) above we attain the plan output at a time instant 0  s  N, belonging to kth trial, as follows: yk ðsÞ ¼ CAs xk ð0Þ þ

s1 X

CAsi1 Buk ðiÞ;

ð11Þ

i¼0

from which we have also the output tracking error (4) at the same time instant s: ek þ 1 ðsÞ ¼ ðI  CBK Þek ðsÞ 

s2 X

CAsi1 BKek ði þ 1Þ

i¼0

1 ek ð1Þ C B .. C  B . s1 C
B ¼ CA BK ; . . . ; CABK ; 1  CBK B C @ ek ðs  1Þ A 0

ð12Þ

ek ðsÞ The index i in (11) denotes the updated time instant during periodically working time ½0; T of the repetitive plan (1). Hence, by rewriting (12) for all s ¼ 1; 2; . . . ; N we have: 0

1 0 ek þ 1 ð1Þ I  CBK B ek þ 1 ð2Þ C B CABK B C B .. B C¼@ .. @ A . . CAN1 BK ek þ 1 ðNÞ

0 I  CBK .. .

CAN2 BK







.. .






10 e ð1Þ 1 k ek ð2Þ C CB B CB . C A@ . C . A I  CBK ek ðNÞ 0 0 .. .

ð13Þ

which is equivalent to:   ek þ 1 ¼ ðI  UK Þek ) ek þ 1   kI  UK k
kek k;

ð14Þ

where: 0

1 0 ek ð1Þ CB B ek ð2Þ C B CAB B C e k ¼ B . C; U ¼ B .. @ @ .. A . CAN1 B ek ðNÞ

0 CB .. . CAN2 B







.. .






1 0 0 C : .. C . A

ð15Þ

CB

Therefore, the required convergence of the learning process in the sense of (7), i.e. of monotonous decreasing of Euclid norm of all tracking errors ek :

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  ek þ 1 \kek k

ð16Þ

will be satisfied, if the following inequality of induced matrix norm holds: kI  UK k\1:

ð17Þ

Finally, to summarize, we have: “In time sequence approach and under assumption that the repetitive plan (1) is described approximately by state model (9), the D-type learning function (10) will satisfy the convergence requirement (16), if its parameter K has been chosen accordingly to (17)”. 2.2

Optimal Realization

Again, we suppose here that the repetitive plan (1) could be approximated by the state model (9), which may not be precise enough for designing any conventional controllers, but could be quite sufficient for choosing the convergent parameter K of Dtype learning function (10). The previous subsection had accommodated that with the parameter K, which is determined accordingly to the requirement (17), will satisfy the required the convergence of learning process by using D-type learning function in the sense of (16). But a problem occurs here that whether such a parameter K exists and what we have to do in the insolvable situation. Since the requirement (17) is only a sufficient condition, a convergence parameter K may be existed, even it does not satisfy the requirement (17). Therefore, in this situation, in place of (16), we will try to find it accordingly to the following constrained optimization problem:   Kk ¼ arg mina  K  b ek þ 1  ¼ arg mina  K  b kðI  UK Þek k; ð18Þ with two bounded matrices a; b, where the comparison a  K  b is understood for each element of them. In this circumstance, the solution Kk is valid for D-type learning function only during the next trial, i.e. for the ðk þ 1Þth trial. Since the right side of (18) can be expressed as follows: kðI  UK Þek k ¼ ½ðI  UK Þek T ðI  UK Þek ¼ eTk ek  2eTk UKek þ eTk ðUK ÞT UKek we obtain:   Kk ¼ arg mina  K  b eTk ðUK ÞT UKek  2eTk UKek :

ð19Þ

It is emphasized again at this point that this obtained parameter matrix from the reduced optimization problem (19) will be used to refine the input only during ðk þ 1Þth trial. It means that:

Convergence Parameters for D-Type Learning Function

uk þ 1 ðsÞ ¼ uk ðsÞ þ Kk ek ðs þ 1Þ

267

ð20Þ

and so on. For the next trial, the learning parameter will be updated again based on measured system information from the previous trial, via solving the optimization problem (19). So, to summarize, we obtain: “Under the assumption that the learning process with the D-type learning function (10) is convergent, the optimal parameter Kk of it, using for the next trial k þ 1, could be determined by solving the constrained quadratic optimization problem (19)”.

3 Illustrative Simulations 3.1

Simulation 1

To illustrate the convergent condition in time sequence (17) proposed above, we consider the linear repetitive plan (9) with the following system parameters:  A¼

0:2 0:2

     0:3 0:3 0:2 ;B¼ and C ¼
0:3 0:5 0

ð21Þ

This plan will be operated periodically in a finite number of steps N ¼ 120 from initial states x0 ¼ 0. So in each trial afterward, the plan initial states have to be reset to the same x0 . By choosing the parameter K ¼ 0:1 for D-type learning function (10) the required convergence condition (17) will be satisfied with: kI  UK k ¼ 0:9976\1: Figure 1 demonstrates the convergence behavior of applied iterative learning process, while the reference is a trapeze signal: 8 < 2:5 for 25s  t  95s rðtÞ ¼ 0:1t for 0  t  25s : 0:1ðt  95Þ þ 2:5 otherwise As exhibited there, the learning process with the chosen parameter K ¼ 0:1, which is satisfied with the requirement (17), had met the expectative convergence. It means that this D-type learning function had produced the monotonous reduction of tracking error accordingly to the increasing of trials number k. Note that K ¼ 0:1 is only one of many parameters for D-type learning function (10) which satisfies the required convergence condition (17). However, since the fact that belonging to theses parameters, the bigger K is chosen, the faster the convergence will be, we should choose the biggest one of them to improve the learning process, what is related obviously to an optimization problem.

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2.5

2.5

a)

2

2

1.5

1.5

1

b)

1 reference output error

0.5 0 0

20

40

60

80

reference output error

0.5 0

100

120

0

20

40

60

80

100

120

Fig. 1. The simulation results about output tracking behavior of repetitive plan after a) 70 trials (left) and b) 700 trials (right) by using the convergence condition in time sequence.

3.2

Simulation 2

Now we consider the linear repetitive plan (9) with the following system parameters:  A¼

0:2 0:2

     0:3 1 1 ;B¼ and C ¼
0:3 1 1

ð22Þ

In comparison with the plan (21) in the previous simulation, the system model (22) here has CB ¼ 0, which deduces: kI  UK k  1 for all K: Hence, the condition in time sequence (17) is not applicable to this system. In this situation, we have to use the optimal realization (19). By choosing both bounded values a; b for determining the parameter Kk accordingly to solving constrained optimization problem (19), and the reference signal rðtÞ for system output y, as follows: a ¼ 0:01; b ¼ 10; rðtÞ ¼ sinð2p
s=N Þ where N ¼ 120 we obtain the simulation results exhibited in Fig. 2. 1

1 reference output error

a) 0.5

b) 0.5

0

0

-0.5

-0.5

-1

0

20

40

60

80

100

120

reference output error

-1

0

20

40

60

80

100

120

Fig. 2. The simulation results about output tracking behavior of repetitive plan after a) 20 trials (left) and b) 70 trials (right) by using the optimal convergence condition.

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As expected, these simulation results have confirmed the applicability of optimal realization (19) for situations, when the condition in time sequence (17) is insolvable. Moreover, through picking up from Fig. 2, it produces a convergence rate even faster (just after 70 trials in comparison with 700 trials in previous simulation already exhibited in Fig. 1).

4 Conclusions In this paper, we have presented two conditions for choosing convergence parameters for D-type learning function, which is often used in iterative learning control. The first one, called the “condition in time sequence”, does not require that the controlled plan, with an approximated state model (9), has to be satisfied CAi B ¼ 0 for all i ¼ 1; 2; . . . ; n. The second one, the “optimal condition”, does not require CB 6¼ 0. Two numerical simulations afterward have confirmed illustratively their applicability in the practice. After all, the remaining deficiency, that they satisfy additionally the asymptotic ability (8), will be our work in the future.

References 1. Uchiyama, M.: Formation of high-speed motion pattern of a mechanical arm by trial. Trans. Soc. Instrum. Control Eng. 14(6), 706–712 (1978) 2. Moore, K.L.: Iterative Learning Control for Deterministic Systems. Springer, Berlin (2012) 3. Arimoto, S.: Iterative learning control for robot systems, pp. 393–398. Proc. IECON, Tokyo (1984) 4. Cheng, Y., Wen, C.: Iterative Learning Control: Convergence Robustness and Application. Springer, Berlin (1999) 5. Barton, A.D., Lewin, P.L., Brown, D.J.: Practical implementation of a real-time iterative learning position controller. Int. J. Control 73(10), 992–999 (2000) 6. Shen, D., Li, X.: Iterative Learning Control for Systems with Iteration-Varying Trial Lengths: Synthesis and Analysis. Springer, Berlin (2019) 7. Xu, J.X., Tan, Y.: Linear and Nonlinear Iterative Learning Control. Springer, Berlin (2003) 8. Tian, S., et al.: A PD-type iterative learning control algorithm for singular discrete systems. Adv. Differ. Equ. 1, 321 (2016) 9. Owens, D.H., Amann, N., Rogers, E.: Iterative learning control-an overview of recent algorithms. Appl. Math. Comput. Sci. 5(3), 425–438 (1995) 10. Tharayil, D.A.B.M., Alleyne, A.G.: A survey of iterative learning control - a learning-based method for high performance tracking control. IEEE Control Syst. Mag. 26(3), 96–114 (2006) 11. Owen, D.H.: Iterative learning control. An optimization paradigm. Springer, Berlin (2016) 12. Nguyen, T.D., Pham, H.P., Nguyen, Q.D., Nguyen, D.P.: Iterative learning control for vshaped electro-thermal micro-actuator. Electronics 8(12), 1410 (2019)

Current Harmonic Eliminations for Seven-Phase Non-sinusoidal PMSM Drives applying Artificial Neurons Duc Tan Vu1,2(&) , Ngac Ky Nguyen1 , Eric Semail1 and Thi Thanh Nga Nguyen2

,

1

Univ. Lille, Arts et Metiers Institute of Technology, Centrale Lille, Yncrea Hauts-de-France, ULR 2697-L2EP, HESAM, F-59000 Lille, France [email protected] 2 Thai Nguyen University of Technology, Thai Nguyen, Vietnam

Abstract. This study is to deal with unwanted current harmonics in rotating (d-q) frames of a 7-phase non-sinusoidal permanent magnet synchronous machine (PMSM) in a wye-connected winding topology. The machine is supplied by a 7-leg voltage source inverter (VSI) fed by a DC-bus voltage. In control, current responses are expected to properly track their references. However, several unwanted harmonics of the non-sinusoidal back electromotive force (back-EMF) and the inverter nonlinearity generate unwanted harmonic components in d-q currents. These current harmonics cannot be nullified by controllers such as conventional proportional-integral (PI) controllers. Consequently, the current responses cannot track their references. In this study, a combination of conventional PI controllers and simple adaptive linear neurons (ADALINEs) is proposed to eliminate these current harmonics, improving current control quality of the drive. The effectiveness of the proposed control structure is verified by experimental results. Keywords: Multiphase machine
Seven-phase PMSM
Non-sinusoidal backEMF
Current harmonics
ADALINE
Artificial intelligence

1 Introduction Electric multiphase drives have become more interesting due to their advantages over the conventional three-phase drives such as higher reliability and lower power per phase rating [1]. In an electric drive, machine design and inverter nonlinearity can affect current control quality [2, 3]. Owing to the multi-reference frame theory [4], only one harmonic of back-EMF should be associated with each d-q frame. As a result, all currents and back-EMFs in d-q frames are constant, creating constant torques. It means that there are no current harmonics in d-q frames. In general, a n-phase machine should contain only (n − 1)/2 harmonics in its back-EMF. For example, a 7-phase machine has three d-q and one zero-sequence frames. Therefore, three is the maximum number of harmonics that should exist in the back-EMF to create constant torques with constant d-q currents. Nevertheless, because of machine design, more than one back-EMF © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 270–279, 2021. https://doi.org/10.1007/978-3-030-64719-3_31

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harmonic can be associated with each d-q frame. These unwanted back-EMF harmonics cause not only current harmonics in d-q frames but also torque ripples. In addition, extra current harmonics in d-q frames are generated by the inverter nonlinearity with dead-time voltages. These dead-time voltages are analyzed in [5] for inverters with different numbers of legs. To eliminate current harmonics, study [6] uses low-pass filters (LPFs) to obtain harmonics from winding currents. However, the computation is complicated. Hence, study [7] applies simple ADALINEs to compensate the unwanted back-EMFs and the dead-time voltages for 3-phase machines. For multiphase machines, there have been some studies [8, 9] applying either an improved model predictive control strategy or an inverse model-based disturbance observer to reduce the voltage and current harmonics for 5-phase or dual 3-phase machines. However, existing high-frequency components in [8] and calculating complications in [9] are several drawbacks. In this study, ADALINEs, with self-learning ability, fast convergence and simplicity as discussed in [7, 10, 11], are added to conventional PI controllers for current harmonic eliminations in d-q frames of a 7-phase non-sinusoidal PMSM drive. Current control improvements are verified with experimental results under the presence of the unwanted harmonics of back-EMF and dead-time voltages from the inverter nonlinearity. This paper is organized as follows. The modeling of a 7-phase drive is presented in Sect. 2. Origins of current harmonics are analyzed in Sect. 3. Solutions are discussed in Sect. 4. A verification with experimental results is presented in Sect. 5.

2 Modeling of a Seven-Phase PMSM A seven-phase drive with a 7-leg VSI fed by a DC-bus voltage VDC is shown in Fig. 1. Several assumptions of a 7-phase PMSM in the considered drive are described as follows: the 1st, 3rd, and 9th harmonics of its back-EMF account for highest proportions; unwanted harmonics such as 11th, 13th, and 19th have small but unneglected proportions in the back-EMF; 7 phase windings of the machine are equally shifted and wye-connected. The 7-dimensional phase voltage vector v of the machine can be expressed in (1).

VSI + VDC

D C B

E F

A

G

Fig. 1. Schematic diagram of a seven-phase drive in a wye-connected winding topology.

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v ¼ Rs i þ ½L

di þe dt

ð1Þ

where i and e are the 7-dimensional vectors of phase currents and back-EMFs, respectively; Rs is the resistance of one phase of stator; [L] is a 7 by 7 stator inductance matrix. To control the machine drive using the field-oriented control (FOC), Clarke and Park matrices are applied to convert the machine parameters from natural frame into d-q frames. For example, the transformation for currents is presented in (2). ½ id1

iq1

id9

2 6 6 6 rffiffiffi6 26 6 ½C  ¼ 6 76 6 6 6 4 2

iq9

id3

iz T ¼ ½P½C½ iA

iq3

1

cosðdÞ

cosð2dÞ

cosð3dÞ

0 1

sinðdÞ sinð2dÞ cosð2dÞ cosð4dÞ

sinð3dÞ cosð6dÞ

0 1

sinð2dÞ sinð4dÞ cosð3dÞ cosð6dÞ

sinð6dÞ cosð9dÞ

sinð3dÞ pffiffiffi 1 2

sinð9dÞ pffiffiffi 1 2

0 pffiffiffi 1 2

sinð6dÞ pffiffiffi 1 2

cosðhÞ sinðhÞ 6  sinðhÞ cosðhÞ 6 6 6 0 0 6 6 ½P ¼ 6 0 0 6 6 0 0 6 6 0 0 4

0

0

cosð9hÞ  sinð9hÞ

sinð9hÞ cosð9hÞ

0 0

0 0

0

0

0

0

0

0

iB

iC

cosð4dÞ

iD

iE

iG T

iF

cosð5dÞ

cosð6dÞ

ð2Þ 3

sinð5dÞ sinð6dÞ 7 7 7 cosð10dÞ cosð12dÞ 7 7 sinð8dÞ sinð10dÞ sinð12dÞ 7 7; 7 cosð12dÞ cosð15dÞ cosð18dÞ 7 7 7 sinð12dÞ sinð15dÞ sinð18dÞ 5 pffiffiffi pffiffiffi pffiffiffi 1 2 1 2 1 2 3 0 0 0 0 0 07 7 7 0 0 07 7 7 0 0 07 7 cosð3hÞ sinð3hÞ 0 7 7 7  sinð3hÞ cosð3hÞ 0 5 sinð4dÞ cosð8dÞ

0

0

1

where [C] is the Clarke matrix; d is the spatial phase shift angle 2p/7; [P] is the Park matrix associated with the 1st, 9th, and 3rd harmonics due to the assumptions of the considered back-EMF; h is the electrical position of the machine. In d-q frames, the 7-phase machine is decomposed into three fictitious 2-phase machines (FM1, FM2, FM3) with decoupled frames (d1-q1), (d9-q9), and (d3-q3), respectively, and one zero-sequence machine (ZM) with reference frame z [4]. Due to wye-connected stator windings, impacts of the zero-sequence component on current

Table 1. Fictitious machines and associated harmonics of a 7-phase machine (odd harmonics). Fictitious machine 1st machine (FM1) 2nd machine (FM2) 3rd machine (FM3) Zero-sequence machine (ZM)

Frame d1-q1 d9-q9 d3-q3 z

Associated harmonic ðm 2 N0 Þ 1, 13, 15, …7 m ± 1 5, 9, 19, …7 m ± 2 3, 11, 17, …7 m ± 3 7, 21, …7 m

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273

control and torque performances are eliminated. A fictitious machine with its corresponding d-q frame is associated with a group of harmonics as presented in Table 1.

3 Current Harmonic Analyses According to the multi-reference frame theory [4], the back-EMF of a 7-phase machine should have only 3 harmonics (1st, 9th, and 3rd, for example) associated with 3 d-q frames. However, in Table 1, two harmonics (1st, 13th) are associated with (d1-q1) while two harmonics (9th, 19th) harmonics are associated with (d9-q9). Then, two harmonics (3rd, 11th) are associated with (d3-q3). The back-EMF of a phase is described in (3). ( ej ¼

          2p 2p E 1 sin h  ðj  1Þ 2p 7 þ E 3 sin 3 h  ðj  1Þ 7 þ u3 þ E 9 sin 9 h  ðj  1Þ 7 þ u9 þ             2p 2p E 11 sin 11 h  ðj  1Þ 7 þ u11 þ E 13 sin 13 h  ðj  1Þ 7 þ u13 þ E19 sin 19 h  ðj  1Þ 2p 7 þ u19

ð3Þ where ej is the back-EMF of phase j (from 1 to 7, representing phases A to G); (E1, E3, E9, E11, E13, E19) and (u3, u9, u11, u13, u19) are the amplitudes and phase shift angles of the harmonic components of the back-EMF. By applying the classical Clarke and Park transformations, back-EMFs in d-q frames can be expressed as follows: 8 pffiffiffiffiffiffiffiffi > ed1 ¼ 7=2 E sinð14h þ u Þ > > pffiffiffiffiffiffiffiffi 13 pffiffiffiffiffiffiffiffi 13 > > E þ e ¼ 7=2 7=2 E13 cosð14h þ u13 Þ > q1 > pffiffiffiffiffiffiffiffi 1 > < ed9 ¼ 7=2 E 19 sinð28h þ u19 Þ pffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffi > eq9 ¼ 7=2 E 9 þ 7=2 E19 cosð28h þ u19 Þ > > p ffiffiffiffiffiffiffi ffi   > > > E e ¼ 7=2 sin ð 14h þu Þ d3 > pffiffiffiffiffiffiffiffi 11 pffiffiffiffiffiffiffiffi 11 > : eq3 ¼ 7=2 E 3 þ 7=2 E11 cosð14h þ u11 Þ

ð4Þ

All d-q back-EMFs in (4) are not constant because of the existing unwanted back-EMF harmonics. The back-EMFs have frequencies 14h in (d1-q1), 28h in (d9-q9), and 14h in (d3-q3). Especially, these back-EMF harmonics generate corresponding current harmonics in d-q frames. The current harmonic amplitudes in d-q frames depend on the harmonic distribution in the back-EMFs, and especially on the rotating speed. The 7-leg VSI supplying the 7-phase machine creates extra current harmonics in d-q frames due to its nonlinearity. Indeed, the dead time, a time interval in which both switches of one inverter leg are off, mainly causes the VSI nonlinearity. According to [5], the dead-time voltage in a phase of the VSI can be generally expressed in (5).

vj

dead

8   1    1    2p þ 5 sin 5 h  ðj  1Þ 2p > sin h  ðj  1Þ 2p 7 þ 3 sin 3 h  ðj  1Þ 7    1    1  7  4< 1 2p 2p ¼ Vdead þ 9 sin 9 h  ðj  1Þ 7 þ 11 sin 11 h  ðj  1Þ 7 þ 13 sin 13 h  ðj  1Þ 2p 7  p>           : 1 1 1 þ 17 þ 19 sin 15 h  ðj  1Þ 2p sin 17 h  ðj  1Þ 2p sin 19 h  ðj  1Þ 2p þ 15 7 7 7

ð5Þ

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dead with V dead ¼ TTPWM VDC where vj_dead is the dead-time voltage of phase j; Vdead is a constant voltage; Tdead is the dead time; TPWM is the switching period of the inverter; VDC is the DC-bus voltage. The harmonic amplitudes are inversely proportional to their orders so the considered harmonics can be up to 19h. The dead-time voltages in d-q frames are presented in (6).

8 > vd1 > > > > > vq1 > > < vd9 > v q9 > > > > > vd3 > > : vq3

dead dead dead dead dead dead

pffiffiffiffiffiffiffiffi ¼  7=2 ½4Vdead =p½1=13 þ 1=15 sinð14hÞ pffiffiffiffiffiffiffiffi ¼  7=2 ½4Vdead =pf1 þ ½1=13 þ 1=15 cosð14hÞg pffiffiffiffiffiffiffiffi ¼  7=2 ½4Vdead =p½1=5 sinð14hÞ þ 1=19 sinð28hÞ pffiffiffiffiffiffiffiffi ¼  7=2 ½4Vdead =pf1=9 þ ½1=5 cosð14hÞ þ 1=19 cosð28hÞg pffiffiffiffiffiffiffiffi ¼  7=2 ½4Vdead =p½1=11 þ 1=17 sinð14hÞ pffiffiffiffiffiffiffiffi ¼  7=2 ½4Vdead =pf1=3 þ ½1=11 þ 1=17 cosð14hÞg

ð6Þ

In (6), the dead-time voltages in (d1-q1) and (d3-q3) frames have a frequency of 14h while the voltages in (d9-q9) have two frequencies of 14h and 28h. Therefore, current harmonics in d-q frames are also caused by the inverter nonlinearity with the dead-time voltages. The amplitudes of these current harmonics do not depend on the rotating speed but Vdead. Voltage Vdead is related to Tdead, TPWM, and VDC as described in (5). Table 2. Current harmonics in d-q frames caused by the unwanted back-EMF harmonics and the inverter nonlinearity with dead-time voltages in a 7-phase PMSM drive. Frame d1-q1 d9-q9 d3-q3

Current harmonics caused by unwanted back-EMF harmonics 14h 28h 14h

Current harmonics caused by deadtime voltages 14h 14h, 28h 14h

Finally, unwanted current harmonics in d-q frames caused by the unwanted backEMF harmonics in (4) and the dead-time voltages in (6) are summarized in Table 2.

4 Eliminations of Current Harmonics with ADALINEs As described in Table 2, current harmonics in d-q frames have frequencies of 14h and 28h. Due to the complexity of the experimental drive, these values need to be automatically learned in real time to correctly eliminate these current harmonics. A current control structure is proposed in Fig. 2 in which current ix (can be id1, iq1, id9, iq9, id3, or iq3) is controlled by a PI controller with an adaptive compensating voltage (vx com ). A fictitious machine is represented by a transfer function with inductance Lx, resistance Rs and Laplace operator s. By using vx com , all current harmonics in d-q frames described in Table 2 can be eliminated. Voltage vx com for current ix is generated by an ADALINE, hence, there are 6 ADALINEs for 6 d-q currents (id1, iq1, id9, iq9, id3, or iq3)

Current Harmonic Eliminations for Seven-Phase Non-Sinusoidal PMSM Drives

w1_x

275

PI

w2_x

∑

w3_x

w 1_x w 2_x w 3_x w 4_x

w4_x

-+ Updating laws = w 1_x + η ierror cos(14θ ) = w 2_x + η ierror sin(14θ ) = w 3_x + η ierror cos(28θ ) = w 4_x + η ierror sin(28θ )

+

+ +

-+ Transfer funcƟon

Fig. 2. The proposed control structure for current ix with the ADALINE compensation.

in the considered 7-phase drive. The general structure of an ADALINE in Fig. 2 is based on current harmonics in Table 2. The ADALINE output (vx com ) is generally calculated from ADALINE inputs with harmonics 14h and 28h, as described in (7). vx

com

¼ ½w1

x

cosð14hÞ þ w2

x

sinð14hÞ þ ½w3

x

cosð28hÞ þ w4

x

sinð28hÞ

ð7Þ

where harmonic weights w1_x and w2_x are associated with harmonic 14h; w3_x and w4_x are used for harmonic 28h. In Fig. 2, weights of a harmonic are updated by Least Mean Square rule with learning rate η, current error ierror, and the corresponding harmonic. An increase in η can shorten the learning time but η should be between 0 and 1 to guarantee the weight convergence and the stability of the drive. According to Table 2, if x is d1, q1, d3 or q3, the ADALINE structure only has two weights for harmonic 14h. Meanwhile, if x is d9 or q9, the ADALINE uses four weights for both harmonics 14h and 28h. Therefore, the number of weights is optimized to avoid the calculation burden.

5 Experimental Results The proposed control structure in Fig. 2 is verified by an experimental drive as described in Fig. 3a and Table 3. The experimental 7-phase PMSM is mechanically connected to a load drive that is an industrial three-phase synchronous machine. The load drive is controlled to tune the rotating speed of the 7-phase PMSM. A 7-leg VSI with insulated gate bipolar transistors (IGBTs) supplies the 7-phase PMSM. Its switching frequency is set to 10 kHz. A dSPACE 1005 board with I/O interface is used to transfer PWM signals to the IGBT driver of the inverter and collect the measured data of speed and currents. The experimental 7-phase PMSM is introduced in [12]. This machine has an axial flux with double rotors. When the two rotors have different numbers of poles and spatially shifted an angle of 7°, the back-EMF waveform and its specific harmonics are presented in Fig. 3b. Proportions of the back-EMF harmonics over the 1st harmonic are 32.3% for the 3rd, 12.5% for the 9th, 10.3% for the 11th, 5.02% for the 13th, 1.98% for the 19th. The current harmonics in d-q frames of the considered experimental drive have been discussed in [3]. In this study, current harmonic eliminations of the proposed control structure with ADALINE compensation are

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100%

FM1

Percentage (%)

FM2

80

FM3 ZM

60 40

32.3% 12.5% 10.3% 9.4% 0.29% 1.26% 3.2% 5.02% 1.98% 1.7% 1.74% 1% 0.25%

20 0

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9

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13

15

17

19

21

23

25

27

Harmonic components

(a)

(b)

Fig. 3. The experimental drive (a), the speed-normalized back-EMF and its harmonic spectrum of the experimental 7-phase PMSM (b). Table 3. Electrical parameters of the experimental 7-phase PMSM drive. Parameter Unit X Stator resistance Rs Self-inductance L mH Inductance Ld1 = Lq1 mH mH Inductance Ld9 = Lq9 Inductance Ld3 = Lq3 mH Number of pole pairs p Rated RMS current A DC-bus voltage VDC V

No comp.

ADALINE compensaƟon

(a)

No comp.

ADALINE compensaƟon

(b)

Value 1.4 14.7 30.5 7.1 10 3 5.1 200

No comp.

ADALINE compensaƟon

(c)

Fig. 4. (Experimental result) Currents in (d1-q1) frame (a), currents in (d9-q9) frame (b), currents in (d3-q3) frame (c) without (No. comp.) and with the ADALINE compensation at 20 rad/s.

shown in Fig. 4. Current control quality is significantly improved, especially in (d9-q9) and (d3-q3) frames. Figures 5a and 6a show the weight updates with learning rate η = 0.0003 for (d9q9) and η = 0.0001 for (d3-q3). The convergence time is about 20 s for (d9-q9) and 25 s for (d3-q3). Meanwhile, Figs. 5c and 6c describe the improvements of d-q currents in one period at 20 rad/s with the ADALINE compensation compared to Figs. 5b and 6b, respectively.

Current Harmonic Eliminations for Seven-Phase Non-Sinusoidal PMSM Drives No comp.

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With ADALINE compensaƟon

w1_d9

w4_q9

w4_d9

w3_q9

w3_d9

w2_q9

w2_d9

w1_q9

(a)

(b)

(c)

Fig. 5. (Experimental result) Weight learning within 20 s of the ADALINE for currents in (d9-q9) (a), currents in (d9-q9) frame without (b) and with (c) the ADALINE compensation at 20 rad/s. No comp.

With ADALINE compensaƟon

w1_d3

w2_q3 w2_d3

w1_q3

(a)

(b)

(c)

Fig. 6. (Experimental result) Weight learning within 25 s of the ADALINE for currents in (d3-q3) (a), currents in (d3-q3) without (b), and with (c) the ADALINE compensation at 20 rad/s.

8

8 100%

7

6

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5 4 3

32.4%

5 4 3

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2 1

5%

0 1

(a)

100%

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13.4% 5.8%

1.2%

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9 11 13 15 17 19

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0 1

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0.9% 0.6% 0.8% 0.9% 1.5%

9 11 13 15 17 19

Harmonic components

(c)

Fig. 7. (Experimental result) Phase-A current without (iA_no_com) and with (iA_com) the ADALINE compensation (a), harmonic spectrums of iA_no_com (b) and iA_com (c) at 20 rad/s.

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10 rad/s

(a)

30 rad/s

(b)

Fig. 8. (Experimental result) Current control with variable speeds (a), and variable current references (b) in (d9-q9) frame using the ADALINE compensation.

The current of phase A without and with the ADALINE compensation is shown in Fig. 7a. Current waveforms in the two cases are slightly different but the root mean square current is almost unchanged at 5.1 A. The current harmonics in natural frame generated by the unwanted back-EMFs and dead-time voltages are mostly eliminated as shown in Fig. 7c compared to Fig. 7b, especially the 11th harmonic from 5.8% to 0.9%. Finally, almost only the 1st, 3rd, and 9th harmonics remain in the phase current. Variations of the rotating speed (from 20 to 10 and 30 rad/s) in Fig. 8a and changes in current references in Fig. 8b have modest effects on current control quality in (d9-q9) frame. The current control performance in (d1-q1) and (d3-q3) frames is similar. The performance validates the dynamic responses of the proposed control structure.

6 Conclusion This paper has introduced an option to improve control quality of a non-sinusoidal multiphase machine drive. Simple adaptive linear neurons combined with PI controllers have eliminated most current harmonics in d-q frames caused by the unwanted backEMF harmonics and the inverter nonlinearity. Dynamic performances have been validated with sudden variations in the rotating speed or current references. The proposed control structure can be further applied to an industrial drive thanks to its adaptivity and easy implementation. Acknowledgment. We would like to thank Thai Nguyen University of Technology, Thai Nguyen city, Vietnam, and the CE2I project of European Union for the financial support.

References 1. Barrero, F., Duran, M.J.: Recent advances in the design, modeling, and control of multiphase machines part I. IEEE Trans. Industr. Electron. 63(1), 449–458 (2016) 2. Hwang, J., Wei, H.: The current harmonics elimination control strategy for six-leg threephase permanent magnet synchronous motor drives. IEEE Trans. Power Electron. 29(6), 3032–3040 (2014)

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3. Vu, D.T., Nguyen, N.K., Semail, E., Moraes, T.J.D.S.: Control strategies for non-sinusoidal multiphase PMSM drives in faulty modes under constraints on copper losses and peak phase voltage. IET Electr. Power Appl. 13(11), 743–1752 (2019) 4. Semail, E., Kestelyn, X., Bouscayrol, A.: Right harmonic spectrum for the backelectromotive force of an n-phase synchronous motor. In: 39th IEEE Industry Applications Conference, Seattle, WA, USA, vol. 1, pp. 71–78 (2004) 5. Grandi, G., Loncarski, J.: Analysis of dead-time effects in multi-phase voltage source inverters. In: 6th IET International Conference on Power Electronics, Machines and Drives (PEMD 2012), pp. 1–6 (2012) 6. Liu, G., Chen, B., Wang, K., Song, X.: Selective current harmonic suppression for highspeed PMSM based on high-precision harmonic detection method. IEEE Trans. Industr. Inf. 15(6), 3457–3468 (2019) 7. Wang, L., Zhu, Z.Q., Bin, H., Gong, L.M.: Current harmonics suppression strategy for PMSM with non-sinusoidal back-EMF Based on adaptive linear neuron method. IEEE Trans. Ind. Electron, p. 1 (2019) 8. Li, G., Hu, J., Li, Y., Zhu, J.: An improved model predictive direct torque control strategy for reducing harmonic currents and torque ripples of five-phase permanent magnet synchronous motors. IEEE Trans. Industr. Electron. 66(8), 5820–5829 (2019) 9. Karttunen, J., Kallio, S., Peltoniemi, P., Silventoinen, P.: Current harmonic compensation in dual three-Phase PMSMs using a disturbance observer. IEEE Trans. Industr. Electron. 63(1), 583–594 (2016) 10. Sediki, H., Bechouche, A., Abdeslam, D.O., Haddad, S.: ADALINE approach for induction motor mechanical parameters identification. Math. Comput. Simul. 90, 86–97 (2013) 11. Nguyen, N.K., Semail, E., Belie, F.D., Kestelyn, X.: Adaline neural networks-based sensorless control of five-phase PMSM drives. In: IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, pp. 5741–5746 (2017) 12. Locment, F., Semail, E., Piriou, F.: Design and study of a multiphase axial-flux machine. IEEE Trans. Magn. 42(4), 1427–1430 (2006)

Design and Some Experimental Results of the Robust Current Controller of DoublyFed Induction Generator in Wind Power Plant with the Backstepping Technique Based Disturbance Observer C. X. Tuyen1(&) and N. T. Huong2 1

Faculty of Electrical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] 2 Electronics Faculty, Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected]

Abstract. With the increasing requirements of quality of electrical network, now grid codes determine that wind turbine connected to the network, especially for wind farm, must be able to connect to the grid during three phase voltage dips, and must be able to support the voltage of the grid during and immediately following the grid fault by supplying reactive power to the network, that guarantees the stability of the network. The robust current controller of doubly fed induction generator in wind power plant with the Backstepping technique based disturbance observer has solved the above problem when the voltage sags to the 15% of its normal value. The results have been verified on experiment system based on the dSpace Multi Processors ALPHA-COMBO system. Keywords: Doubly fed induction generator
Backstepping technique
Disturbance observer
Robust control
Wind power plant
dSpace Multi Processors ALPHA-COMBO system

1 Introduction Nowadays, the demand for electricity in the world is increasing and the trend of the use of clean energy sources in the world, especially the wind electricity power source, is developing strongly [1]. Among the wind electricity power systems used today, the wind electricity power system using doubly-fed induction generator (DFIG), which is called with the name as the wound – rotor induction machine, is the most commonly used in large-capacity wind electricity turbine systems, because of its flexibility and ability to control active and reactive power [2]. In this system, the power of the inverter is reduced and thus costs are reduced, because the inverter is connected to the rotor circuit of the generator, its capacity is usually only one third of the total system power, and the attached devices such as filters are also cheaper [1, 3, 4]. So, in this paper, we consider the wind electricity power system using doubly-fed induction generator. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 280–290, 2021. https://doi.org/10.1007/978-3-030-64719-3_32

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Figure 1 gives an overview of the structure of a wind power generation system using the doubly fed induction generator (DFIG).

Fig. 1. Overview of the structure of a wind power generation system using the doubly fed induction generator

In Fig. 1, NIVT is grid inverter, GIVT is generator inverter, GB is gear box. The system in Fig. 1 consists of a DFIG with a stator coil directly connected to the threephase grid. The rotor side coil is connected to an inverter system (using semiconductor valves) which can control the flow of energy in two directions. The semiconductor valves are controlled to open and close according to the principle of spatial vector modulation [15] by the digital signal processor (DSP). The main drawback of wind electricity power turbines using the DFIG is sensitive to any grid disturbance, especially in faulty grid [1]. Faulty grid in the power system, even far from the turbine placement, will cause a drop in the grid voltage, result in an oscillating transient flux, which induces a high electromotive force in the rotor circuit and if it is greater than the maximum power that the inverter can produce, it will cause loss of rotor current control and cause a large rotor current [1], which may damage the inverter. In order to meet the high requirements of the present grid codes [5], now there are three applied fault ride-through (FRT) strategies for DFIG based wind turbines. The first is the protective devices/circuits only during transient state, the second is the reactive power injecting-devices during transient state and the third is the proper control structures during both steady state and transient state. In this paper, the third strategy is emphasized combining with the remaining two strategies. To now, some of the advanced FRT control strategies include: (a) robust control [6], (b) sliding-mode control [7], (c) adaptive control [8], (d) model predictive control [9], (e) Fuzzy-Logic control [10], and (f) Input-Output Feedback Control [11]. However, the above advanced FRT control strategies do not consider all the nonlinear disturbances that happen when faulty grid occurs. In order to meet the high requirements of the present grid codes [5], we design the robust current controller of doubly fed generator in wind power plant with the Backstepping technique based disturbance

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observer that considers all disturbances in detail when faulty grid occurs, that are nonlinear disturbances of the rotor circuit angle frequency, the stator flux, the stator voltage and the generator speed [12, 13]. The results have been verified on experiment system based on the dSpace Multi Processors ALPHA-COMBO system [14], that the control system has the ability to connect to the grid during three phase voltage dips and support the voltage of the grid during and immediately following the grid fault by supplying reactive power to the network, that guarantees the stability of the network.

2 Design the Robust Controller of Doubly Fed Generator in Wind Power Plant with the Backstepping Technique Based Disturbance Observer According to [1, 12, 13], when the grid is collapsed, the rotor current and the stator flux fluctuate, the electric torque of the generator also fluctuates, the rotor speed and thus the rotor circuit angular frequency also fluctuate. This leads to the fluctuation of the slip factor and the induced voltage in the rotor circuit. If the amplitude of the rotor voltage is too large beyond the capacity of the inverter, the rotor current will be out of control for a short time and a large overcurrent occurs. From the above analyses and comments, in order to improve the control quality, the controller needs to consider the fluctuating factors as mentioned above. Where, the variables of the system are expressed in the synchronous d-q coordinate fixed to the space stator voltage vector [15]. The oscillation of the rotor circuit angle frequency, the stator flux, the stator voltage and the generator speed are taken into account by the corresponding nonlinear oscile0 ; w e0 ; e e r; x e; w lating noise components x u sq , which are added to the their corsd sq u sd ; e responding values in the normal mode as follow: b 0 ¼ w0 þ w e0 ; w b 0 ¼ w0 þ w e0 ; b er ; x b ¼ xþx e ;w b r ¼ xr þ x u sd ; b u sq x sd sq sd sd sq sq u sd ¼ usd þ e ¼ usq þ e u sq ð1Þ e0 ; w e0 ; e e r; x e; w Where: x u sq are attenuation nonlinear fluctuation disturbances of sd sq u sd ; e the rotor circuit angle frequency, rotor speed, direct and quadrature stator flux, direct and quadrature stator voltage; x; xr ; w0sd ; w0sq ; usd ; usq are their normal values. In order to meet the increasing requirements of quality of electrical network that the wind electrical power system must be able to connect to the grid during three phase voltage dips, and must be able to support the voltage of the grid during and immediately following the grid fault by supplying reactive power to the network, that remains the stability of the network, the control system of doubly fed induction generator in wind power plant needs to consider the fluctuating factors as mentioned above. To achieve that goal, firstly we must identify the above nonlinear fluctuation disturbances by using the disturbance observer, then we choose the appropriate control technique to design the controller, which has abilities to cancel the above nonlinear fluctuation

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disturbances and is suitable for nonlinearity of DFIG. In this paper, we use the Lyapunov stability theory based Nonlinear Backstepping technique to design both controller and disturbance observer. There are two control loops in the control system of DFIG in wind power plant. The outer control loop is the active and reactive power control loop, the inner control loop is the current control loop. Among them, the current control loop is the most important, which determines the quality of the control system. Therefore, in this paper, we focus on the design of the current control loop. According to [15], the rotor current model of DFIG written in the synchronous d-q coordinate fixed to the space stator voltage vector is given by following equations: (

dird dt dirq dt

¼ aird þ xr irq þ ew0sd  bxw0sq þ curd  dusd ¼ airq  xr ird þ ew0sq þ bxw0sd þ curq  dusq

ð2Þ

Where: ird ; irq ; urd ; urq are the direct, quadrature rotor current and voltage components in d-q coordinate; usd ; usq are the stator direct and quadrature votage components in =

d-q coordinate; wsd ¼ wsd =Lm ; wsd is the direct stator flux; Lm – mutual inductance of   DFIG; w=sq ¼ wsd =Lm ; wsq is the quadrature stator flux; r ¼ 1  L2m =Lr Ls ; Lr, Ls are rotor and stator inductances of DFIG; x is the rotor speed of DFIG; xr is the rotor angle frequency of DFIG; Tr = Lr/Rr; Ts = Ls/Rs; Rr, Rs are the rotor and stator resistances per phase of DFIG; a ¼ 1=rTr  ð1  rÞ=rTs ; b ¼ ð1  rÞ=r,c ¼ 1=rLr , d ¼ ð1  rÞ=rLm ,e ¼ ð1  rÞ=rTs : From (1) and (2), we will design the proposed control system in the next parts by using the Lyapunov stability theory based Nonlinear Backstepping technique [15]. According to Backstepping technique, to make a close control loop with a feedback state variable, the error of the desired and the real values of that state variable is defined, then to make a Lyapunov control function corresponding with the state variable error. From condition of negative Lyapunov control function, we choose an appropriate value for the next state variable and equations for disturbances if they are present in the model of the object. The process will stop if the final state variable is the real control variable. Where, the real control variable is the rotor voltage. 2.1

Design the Robust Direct Current Controller of Doubly Fed Generator in Wind Power Plant with the Backstepping Technique Based Disturbance Observer

Select ird as the control variable, its desired value ird* is taken from the active power controller. Define the difference between ird and its desired value ird* as: z1 ¼ ird  ird

ð3Þ

  z_ 1 ¼ ðdird =dtÞ  dird =dt

ð4Þ

Derivate z1, we have:

Choose the Lyapunov control function as:

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V1 ¼ ð1=2Þz21

ð5Þ

V_ 1 ¼ z1 z_ 1

ð6Þ

Derivate V1, we have:

From (2), (4) and (6), we have:    V_ 1 ¼ z1 aird þ xr irq þ ew0sd  bxw0sq þ curd  dusd  dird =dt

ð7Þ

We choose curd as the real control variable, from (7), to make the derivation of V1 negative, we choose the real control variable urd as curd ¼ aird  xr irq  ew0sd þ bxw0sq þ dusd þ dird =dt  k1 z1

ð8Þ

Where k1 is the positive constant. When grid fault is occurred, there are nonlinear fluctuation disturbances, Eq. (8) becomes ^ r irq  ew^0 sd þ bx ^ w^0 sq þ d^ c^urd ¼ aird  x usd þ dird =dt  k1 z1

ð9Þ

From (1) and (9), we have: 0 0 e0 c^urd ¼ aird  xr irq  ewsd þ bxwsq þ dusd þ bx w sq 0 e0  x e 0 þ de e wsq þ b x ew e r irq  e w þ bx u sd  k1 z1 þ dird =dt sq sd

ð10Þ

Replace curd in (7) with c^urd in (10), we have   0 e 0 þ bx e0  x e 0 þ de e e e w þ b x w i  e w u V_ 1 ¼ z1 k1 z1 þ bx w r rq sd sq sq sq sd

ð11Þ

From (11), we see that, the derivation of V1 is not negative, so the controller is not stable. To have the stable controller, we choose the Lyapunove control function, which considers the nonlinear fluctuation disturbances, as follows:  ^1 ¼ V1 þ V

e xw sq 0

2

2c01

 þ

e w0sq x

2

2c11

 þ

e ew x sq 0

2c21

2



e r irq x þ 2c31



2 þ

e0 w sd



2c41

þ

ðe u sd Þ2 ð12Þ 2c51

^1 in (12), we have Where, c01 ; c11 ; c21 ; c31 ; c41 ; c51 are positive constants. Derivate V ^_ 1 ¼ V_ 1 þ V

 0   0  e e xw sq d x w sq c01



   e w0sq d x e w0sq x



   e0 d x e0 ew ew x sq sq

þ þ dt c11 dt c dt  0   0  21     e e w sd d w sd e r irq e r irq d x x ðe u sd Þ u sd Þ d ðe þ þ þ c51 dt c41 dt c31 dt

ð13Þ

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From (11) and (13), we have     0  d  0  1  0  d 0  e e ^_ 1 ¼ k1 z21 þ 1 x w e e x w V bz bz x w þ c þ c þ x w 01 1 11 1 sq sq sq sq c01 dt c11 dt      d  1  e0  d  e0  1  e w sq þ c21 bz1 þ e r irq  c31 z1 e w sq e r irq þ x x x x c21 dt c dt     31     1 e0 d e0 1 d w sd  c41 ez1 þ u sd Þ þ c51 dz1 þ w sd ðe u sd Þ ð e c41 dt c51 dt ð14Þ ^1 negative, we choose From (14), to make the derivation of V    8  0 ~ 0 þ c bz1 ¼ xw ~ 0 ; d xw < d xw ~ 0sq ; dtd ð~ ~ usd usd Þ þ c51 dz1 ¼ ~ 01 sq dt sq þ c11 bz1 ¼ xw dt  sq      : d x ~ 0  c ez1 ¼ w ~0 ; d x ~0 ~ 0 þ c bz1 ¼ x ~w ~ r irq ; dtd w ~w 21 41 sq sq dt ~ r irq  c31 z1 ¼ x sd sd dt ð15Þ The Eqs. (15) describe the model of the Backstepping technique based disturbance observer of the direct current controller (10). In (15), the nonlinear disturbances of 0 ~ 0 ; xw ~0 ; x ~0 ~w xw usd ; x sq ~ sq ; ~ sq ~ r irq ; wsd are observed for their corresponding values in the direct current controller (10). The Eqs. (15) and (10) are combined to make the robust direct current controller of doubly fed generator in wind power plant with the Backstepping technique based disturbance observer. We call this controller with that name, because we have designed the controller by Backstepping technique as mentioned above. 2.2

Design the Robust Quadrature Current Controller of Doubly Fed Generator in Wind Power Plant with the Backstepping Technique Based Disturbance Observer

Do the same as the design of the robust direct current controller of doubly fed generator in wind power plant with the Backstepping technique based disturbance observer we have the robust quadrature current controller described in the Eq. (16) with the Backstepping technique based disturbance observer described in the Eq. (17) as below: 0 0 e0 c^urq ¼ airq þ xr ird  ewsq  bxwsd þ dusq  bx w sd

e þx e þ de e wsd  b x ew e r ird  e w b x u sq  k2 z2 þ dirq =dt sd sq 0

0

0

ð16Þ

 8   0    ~ 0  c bz2 ¼ xw ~ 0 ; d xw < d xw ~ 0sd ; dtd ~ usq usq þ c52 dz2 ¼ ~ 02 sd sd dt ~ sd  c12 bz2 ¼ xw dt     0 0 0 0 : d x ~ ~ ; d ðx ~ ~ ~w ~ r ird ; dtd w ~w sd  c22 bz2 ¼ x sd dt ~ r ird Þ  c42 z2 ¼ x sq  c32 ez2 ¼ wsd dt

ð17Þ

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Where, k2 ; c02 ; c12 ; c22 ; c32 ; c42 ; c52 are the positive constants. 0 ~0 ; x ~0 ~ 0 ; xw ~w usq ; x In (17), the nonlinear disturbances of xw sd ~ sd ; ~ sd ~ r ird ; wsd are observed for their corresponding values in the quadrature current controller (16). The construction of the proposed generator side controller is described in Fig. 2. There are main blocks in Fig. 2 as follow: RVC is the block calculating the values of current references [15], PCTL is the bock controlling the active power of the system [15], QCTL is the block controlling the reactive power of the system [15], REALVC is the block calculating the real values [15], PA&FM is the block measuring phase angle and angle frequency of grid voltage [15], CCTL is the block controlling currents expressed by Eqs. (10) and (16). DOB is the disturbance observer expressed by Eqs. (15) and (17).

Fig. 2. The construction of the proposed generator side controller

3 Experimental Results 3.1

Experimental System

The experimental system [14] is shown in Fig. 3, it consists of the following main blocks: Power electronics block (Power module) is the Electronic power block BUS623 manufactured by Baumueller (Germany); a doubly fed induction generator; three-phase asynchronous machines (acting as a source of wind); Control algorithm is installed on multi-microprocessor system of dSpace, ALPHA COMBO.

Design and Some Experimental Results of the Robust

287

DFIG

Power module

Induction motor acts as wind source

Alpha Combo

Fig. 3. The experimental system

ALPHA COMBO multi-microprocessor system includes: a DS1003 master DSP board, which performs digital input/output tasks; an extra processor (SLAVE) DS1004, which is primarily used for computing tasks; Peripheral cards: I/O card, ADC, DAC, Encoder interface. The values of the experimental parameters as follow: - The parameters of DFIG: number of pole pair zp = 2; rated power: 4 kW; the normal voltage of stator: 230/400 V(D/Y); the normal voltage of rotor: 366 V; the normal stator frequency: 50 Hz; the normal stator current: 15.2/8.8A(D/Y); power factor: 0.78; the normal rotor speed: 1950 rpm; Rs = 1.07Ώ; Rr = 1.32Ώ; Lm = 0.1601H; Ls = 0.2261 Ώ; Lr = 0.1699 Ώ; the inertial constant: J = 0.032 kg.m2. - The parameters of grid side: the inductance of filter Ld = 0.0002H; the resistance of filter Rd = 0.01 Ώ; the capacitance of RC filter Cf = 400 lF; the resistance of RC filter Rf = 0.2 Ώ; the capacitance of intermediate DC circuit capacitor: Cdc = 1470lF. 3.2

Experiment with the Robust Current Controller When Grid Voltage Is Collapsed with Remaining Voltage of 15% of Its Normal Value and the Time Lasts 8 Cycles, Ird = 1.5A, Irq = −2.7A

The results of the experiment in this case are shown in Fig. 4.

Current (A)

ird

6

ird*

4

2

irq

0

-2

irq*

-4

-6

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

time (s) Fig. 4. Responses of the rotor current when grid fault occurs with the remaining grid voltage of 15% of its normal value

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Through the results of the experiment in Fig. 4, it is clear that when the grid voltage is 15% of its normal values, with the disturbance observer based robust current controller, the real values of the rotor current components remain well according to their set values. That means, the goal of control of active and reactive power and connection of DFIG to grid in the case of grid voltage collapse is achieved. 3.3

Experiment with the Current Controller Without Disturbance Observer When Grid Voltage Is Collapsed with Remaining Voltage of 15% of Its Normal Value and the Time Lasts 8 Cycles, Ird = 0A, Irq = − 2.7A

In this case, the experimental results are shown in Fig. 5.

Current (A)

Current (A)

10

20

ird

irq

5

10

0

0

-5

-10

-10 -20

-15 -30

-20

-40

-25 -30 -0.5

0

0.5

a)

time (s)

1

1.5

-50 -0.5

0

0.5

b)

1

time (s)

1.5

Fig. 5. a) Response of the direct rotor current and b) response of the quadrature rotor current when grid fault occurs with the remaining grid voltage of 15% of its normal value with the current controller without disturbance observer

In Fig. 5, when we do experiments with the backstepping current controller without disturbance observer in the case of the grid voltage collapsed with remaining grid voltage of 15% of its normal value and the grid collapse lasts 8 cycles, the value of direct rotor current component is set to zero, the value of the quadrature rotor current component is set to −2.7A. That means, the active power of DFIG is set to zero and the reactive power is supplied to the network to hope that, the stability of the network is still achieved and the DFIG is still controlled to connect to the grid during grid fault. However, the experimental results show that, the control system is not stable due to the large current amplitude (the rotor component of ird has a maximum amplitude of −25A, it is much more different from its desired value of 0A, the rotor component of irq has a maximum amplitude of −40A, it is approximately 15 times greater than its rated value of −2.7A), so the protector of the rotor inverter has disconnected the system from the grid and the goal of control and connection of the system to the grid is failed. The cause of this phenomenon is that the current controller do not consider the strong nonlinear fluctuation disturbances when the grid voltage collapse occurs.

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4 Conclusion The experimental results have shown that, with the nonlinear backstepping disturbance observer based robust current controller, the control system of the doubly-fed induction generator in wind power plant has been able to connect to the grid during three phase voltage dips, and supports the voltage of the grid during and immediately following the grid fault with remaining grid voltage of 15% of its normal value by supplying reactive power to the network, that remains the stability of the system. In addition, the experimental results of the control system based on the backstepping current controller without disturbance observer in the same situation have shown that, the control system without disturbance observer cannot meet the high requirements of quality of electrical network. Acknowledgment. This research was supported by a grant for the university research from Thai Nguyen University of Technology (TNUT). We thank our colleagues from TNUT who provided insight and expertise that greatly assisted the research.

References 1. Jackson, J.J., Francis, M., Jin, W.J.: Doubly-fed induction generator based wind turbines: a comprehensive review of fault ride-through strategies. Renew. Sustain. Energ. Rev. 2(6), 255–265 (2015) 2. Mahdi, J.H., Sahand, G.L., Mohammad, T.: Compensating stator transient flux during symmetric and asymmetric faults using virtual flux based on demagnetizing current in DFIG wind turbines. Int. Power Syst. Conf. (PSC) 3(8), 357–364 (2019) 3. Tazil, M., Kumar, V., Bansal, R.C., Kong, S., Dong, Z.Y., Freitas, W.: Three-phase doubly fed induction generators: an overview. IET Electric Power Appl. 4(2), 75–89 (2010) 4. Mwasilu, F., Justo, J.J., Ro, K.S., Jung, J.W.: Improvement of dynamic performance of doubly fed induction generator-based wind turbine power system under an unbalanced grid voltage condition. IET Renew Power Gen. 6(6), 424–434 (2012) 5. Naderi, S.B., Negnevitsky, M., Jalilian, A., Hagh, M.T., Muttaqi, K.M.: Low voltage ridethrough enhancement of DFIG-based wind turbine using DC link switchable resistive type fault current limiter. Int. J. Electr. Power Energ. Syst. 6(86), 104–119 (2017) 6. Costa, D., Patric, J., Pinheiro, H., Degner, T., Arnold, G.: Robust controller for DFIGs of grid-connected wind turbines. IEEE Trans. Ind. Electron. 58(9), 4023–4038 (2011) 7. Benbouzid, M., Beltran, B., Amirat, Y., Yao, G., Han, J., Mangel, H.: High-order sliding mode control for DFIG-based wind turbine fault ride-through. In: Industrial Electronics Society, IECON 39th Annual Conference of the IEEE, pp. 7670–7674 (2013) 8. Beheshtaein, S.: Optimal hysteresis based DPC strategy for STATCOM to augment LVRT capability of a DFIG using a new dynamic references method. In: Industrial Electronics (ISIE), IEEE 23rd International Symposium on, pp. 612–619 (2014) 9. Hou, G., Wang, Z., Jiang, P., Zhang, J.: Multivariable predictive functional control applied to doubly fed induction generator under unbalanced grid voltage conditions. In: Industrial Electronics and Applications, vol. 8(9), pp. 2644–2650 (2009) 10. Riouch, T., Rachid, E.B.: Improvement low-voltage ride-through control of dfig during grid faults. In: Multimedia Computing and Systems (ICMCS), International Conference, pp. 1596–1601 (2014)

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11. Liu, J.H., Chu, C.C., Lin, Y.Z.: Applications of nonlinear control for fault ride-through enhancement of doubly fed induction generators. IEEE J. Emerg. Select. Top. Power Electron. 2(4), 749–763 (2014) 12. Lopez, J., Sanchis, P., Roboam, X., Marroyo, L.: Dynamic behavior of the doubly fed induction generator during three-phase voltage dips. IEEE Trans. Energ. Conver. 200–209 (2006) 13. Lima, F.K.A., Luna, A., Watanabe, E.H.: Rotor voltage dynamics in the doubly fed induction generator during grid faults. IEEE Trans. Power Electron. 25(1), 255–262 (2010) 14. Lan, P.N.: Linear and nonlinear control approach of doubly – fed induction generator in wind power generation. Doctoral thesis, TU-Dresden (2006) 15. Tuyen, C.X.: Synthesis of nonlinear algorithms on the basis of Backstepping method to control doubly fed induction machines in wind power generation system. Doctoral thesis, HaNoi University of science and technology (2008)

Design and Some Experimental Results of the U-Type Permanent Magnet Three-Phase Linear Motor Based Position Control System with the Backstepping Technique Based Disturbance Observer C. X. Tuyen1(&) and N. T. Huong2 1

Faculty of Electrical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] 2 Electronics Faculty, Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected]

Abstract. Nowadays, permanent magnet linear motors are widely used in high accuracy CNCs, because they make the linear moving without the mechanical system, that transfers rotating moving to linear moving, therefore the accuracy in position control of the system is improved. Among them, U-type permanent magnet three-phase linear motors are more widely used because there are not attractive forces in these motors and the thrust force is high. However, the nonlinearity of the motor and the force disturbances that are caused by the end effect, load, slot tooth, high order harmonic waves still reduce the accuracy in position control of the system. In order to solve this problem, this paper presents the design of the U-Type permanent magnet three-phase linear motor based position control system with the Backstepping technique based disturbance observer. The proposed position control system is experimented on the DSP based experimental system, which uses DSP TMS 320F2812. The experimental results show that the performance of the proposed position control system has high accuracy, that meets the commands of the high precision CNC machines in the industry. Keywords: Position control system
U-Type permanent magnet three-phase linear motor
Backstepping technique based disturbance observer
DSP TMS320F2812
High precision CNC machines

1 Introduction Nowadays, the trend of using the permanent magnet three-phase linear motors in the industry becomes more and more popular in developed countries, the main advantage of this type of motors is that they make the linear moving without the mechanical system, that transfers rotating moving to linear moving, therefore the accuracy in position control of the system is improved [1]. Among them, U-type permanent magnet © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 291–302, 2021. https://doi.org/10.1007/978-3-030-64719-3_33

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three-phase linear motors (UPMLM) are more widely used because there are not attractive forces in these motors and the thrust force of the U-type permanent magnet linear motor is high [2]. However, the U-type permanent magnet three-phase linear motor is defined as a high nonlinear dynamic, MIMO electromechanical actuator including adverse impacts of the end effect and the adverse effect of the force of highorder harmonic waves [3, 4] and the adverse effect of variation of the viscous friction coefficient, these features of UPMLM have reduced the accuracy of the position control system of UPMLM [2, 3]. To deal with the end effect, there are some ways in literature, that include: measure the impact of the end effect by experiment and compensate it [4], including the end effect in the model of the motor [5]. The solution in [4] requires additional experiments that are expensive. The solution in [5] requires a big calculation and it is too complicated to implement in reality. In this paper, the end effect is considered as a force disturbance for simplicity, also it is necessary to select the method of designing the position control system in accordance with the nonlinear properties of the motor including the load change, the variation of the viscous friction coefficient, the adverse impacts of the end effect and the adverse effect of the force of high-order harmonic waves in the system. In this paper, the Backstepping technique [6] is used to design the disturbance observer-based position control system of UPMLM based on field-oriented control [3]. The proposed control system is experimented on the DSP based experimental system, which uses DSP TMS320F2812. The experimental results show that the error of position is quite small, about 1.5  10−8 m, these results prove that the performance of the proposed position control system has high accuracy and meets the commands of the high precision CNC machines in the industry

2 Construction, the Working Principle and Mathematical Model of UPMLM 2.1

Construction of UPMLM

According to [2], the structure of the U-type linear motor is described as shown in Fig. 1, the U-shaped static part is attached with permanent magnets on the sides, in the middle of UPMLM there is a double steel core, on which two identical three-phase windings are placed each one on each side and independently of each other.

Fig. 1. The structure of UPMLM

Design and Some Experimental Results of the U-Type

2.2

293

Working Principle of UPMLM

The working principle of UPMLM is based on the principle of every single permanent magnet three-phase linear motor (SPMLM). In terms of structure, SPMLM is a flat shape of a permanent magnet synchronous three-phase rotary motor, in other words, SPMLM is a permanent magnet synchronous three-phase rotary motor when the rotor radius tends to infinity, with the permanent magnets are fixed to the stator part and the moving magnetic circuit carries the three-phase windings, so the working principle of each SPMLM is similar to the permanent magnet synchronous three-phase rotary motor, except that the torque making the rotor to rotate in permanent magnet synchronous three-phase rotary motor is replaced by electromagnetism thrust force to make the mover part of SPMLM to move. From the construction of UPMLM that is presented above, the electromagnetism thrust force of UPMLM is the sum of electromagnetism thrust forces of two SPMLMs. 2.3

Mathematical Model of UPMLM

From the structural characteristics of UPMLM, we see that the motor consists of two identical single permanent magnet three-phase linear motors with a rigidly connected moving part, so UPMLM’s mathematical model consists of two mathematical models of an SPMLM combined with a common load of 2FL, that contains load force and multi-noises of force. According to [3], we have the system of mathematical equations which describes for each single permanent magnet three-phase linear motor in d-q coordinate [3] as follows: 2 dird 3

2

2pvLrq 1 sLrd irq þ Lrd urd 2pvwp 1 1 Trq irq þ Lrq urq  sLrq

 T1rd ird þ

dt 6 6 dirq 7 6  2pvLrd ird  6 dt 7 ¼ 6 sLrq 4 dv 5 6 3ppwp Bv FL 4 dt sm irq  m v  m ds dt v

3 7 7 7 7 5

ð1Þ

Where, ird and irq are the direct current (A) and the quadrature current (A) respectively; urd is the direct voltage (V); urq is the quadrature voltage (V); R is the resistance in each phase of the motor (X); Lrd is the direct self-inductance in each phase of a motor (H); Lrq is the quadrature self-inductance in each phase of the motor (H); p is the number of pole pairs of motor. Wp is flux linkage amplitude of permanent magnet (Vs); v is the velocity of the motor (m/s); s is mover position (m); FL is the load force (N); Bv is the viscous friction coefficient (Ns/m); Trd = R/Lrd; Trq = R/Lrq; m is the mass of moving part (kg); s is the pole pitch (m).

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3 Design the U-Type Permanent Magnet Three-Phase Linear Motor Based Position Control System with the Backstepping Technique Based Disturbance Observer 3.1

The Overall Structure of the UPMLM’s Position Control System

The overall control system structure for UPMLM is depicted in Fig. 2, where each SPMLM is controlled by a separate position control system. In Fig. 2, the PCTR1 block controls the first single linear motor SPMLM1, the PCTR2 block controls the second SPMLM2, LD block is the load for UPMLM.

S (feedback position)

PCTR1

SPMLM1 LD

S*(Reference position)

PCTR2

SPMLM2

Fig. 2. The overall structure of the UPMLM’s position control system

3.2

The Overall Structure of the SPMLM’s Position Control System

The overall structure of the SPMLM’s position control system is depicted in Fig. 3. In the Fig. 3, there are following basic blocks: (1) - the direct current controller; (2) - the position controller; (3) - the inverse Park transformation block, which transforms motor’s voltages (urd, urq) in d-q coordinate to motor’s voltages (ura, urb) in a-b coordinate; (4) - the Park transformation block, which transforms motor’s currents (ira, irb) in a-b coordinate to motor’s currents (ird, irq) in d-q coordinate; (5) - the Clark transformation block, which transforms motor’s phase currents (ia, ib, ic) to motor’s currents (ira, irb) in a-b coordinate; (6)- the space vector pulse width modulation; (7) the voltage source inverter; (8) - the current sensor block, which is used to measures phase currents ia, ib, ic; (9) – the single permanent magnet three phase linear motor (SPMLM); (10) - the velocity sensor, which measures the velocity of the mover; (11) – the block, which calculates the electrical position of the SPMLM; (12) – the block, which calculates the position of the SPMLM; (13) - the velocity control block; (14) the quadrature current control block; (15) - the Backstepping technique based disturbance observer block.

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295

Fig. 3. Structure of the single PMLM’s position control system

3.3

Design the Disturbance Observer-Based Position Control System for SPMLM

The disturbance observer-based position control system of SPMLM is designed based on the Backstepping technique. According to the Backstepping technique [6], firstly, the outer closed control loop, which is the closed position control loop, is designed. Secondly, the middle closed control loop, which is the closed velocity control loop, is designed. Finally, the interest closed control loop, which is the closed current control loop, is designed. In order to design the position control system, the errors of position, velocity, and currents are defined as the following equations: e1 ¼ s  s 

ð2Þ

e2 ¼ v  vdes

ð3Þ

e3 ¼ irq  irqdes

ð4Þ

e4 ¼ ird  ird

ð5Þ

Where: s* is the position reference; ird is the direct current reference; vdes is the desired velocity, which is the output of the closed position control loop; irqdes is the desired quadrature current, which is the output of the closed velocity control loop. Step1: Design the closed position control loop From (2), we derivate the position tracking error and have e_ 1 ¼ s_  s_  ¼ v  s_  Where v is the velocity of the mover. Choose v as a control variable, choose the Lyapunov Function [6] as

ð6Þ

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V1 ¼ ð1=2Þe21

ð7Þ

Derivate V1 and combine with (6), we have V_ 1 ¼ e1 e_ 1 ¼ e1 ðv  s_  Þ

ð8Þ

According to Lyapunov stability theory-based Backstepping technique [6], to make the position tracking error converge to zero, the derivative of V1 has to be negative, to achieve that, we choose the desired control variable v as below: v ¼ k1 e1 þ s_  ¼ vdes

ð9Þ

Where k1 is a positive constant. From (9), we have the position controller, which is depicted in Fig. 4.

+ s

e1 -

-k1

+ +

s*

vdes

d/dt Fig. 4. The structure of the position controller

However, v is just a state variable and not the real control variable, we implement step two. Step two: Design the velocity control loop Do the same as step 1 with an extended Lyapunov Function [6] as V2 ¼ ð1=2Þe21 þ ð1=2Þe22

ð10Þ

To combine with (1) and to make the derivation of V2 negative, we choose the desired value of irq as ! bv B 2 k1  k1 e1 m ! bv b v s_  bL B sB sF sm€s k1  þ þ e2 þ m 3pwp 3pwp 3pwp

bi rqref ¼  sme1  sm 3pwp 3pwp 

sm 3pwp

ð11Þ

The parameters Bv, FL are changed in the working of SPMLM because of end effect, load disturbance, and the force of high-order harmonic waves, so we do not have their b L and (11) becomes b v; F exact values. Their estimated values are B

Design and Some Experimental Results of the U-Type

bi rqref

! bv B 2 k1  k1 e1 m ! bv b v s_  bL B sB sF sm€s k1  þ þ e2 þ m 3pwp 3pwp 3pwp

297

sme1 sm ¼  3pwp 3pwp 

sm 3pwp

ð12Þ

However bi eqref is just a state variable and not the real control variable, we continue to implement step three. Step three: Design the quadrature current control loop with disturbance observer based on the Backstepping technique With the estimated values of Bv and FL, the Eq. (4) becomes be 3 ¼ irq  bi rqref

ð13Þ

b t and the real values Bv, Ft as b v; F Define the difference between the observed values B the disturbances, which are expressed as below: b v  Bv ; F eL ¼ F b L  Ft ¼ DFL þ DFend ev ¼ B B

effect

þ DFhigh

hm

ð14Þ

Where: DL is the load disturbance, Dend effect is the disturbance caused by end-effect, Dhigh hm is the disturbance caused by high order harmonic waves. The Eq. (14) means that we consider adverse impacts of the load disturbance, of the end effect, of the force of high-order harmonic waves, and of variation of the viscous friction coefficient in the proposed control system. We also do the same as step 1 with an extended Lyapunov Function that considers the above disturbances in (14) as below:   1 2 1 ~2 1 ~ 2 2 Ve ¼ e þ e2 þ ^e3 þ Bv þ FL 2 1 c1 c2

ð15Þ

Where c1 ; c2 are positive constants? To combine with (1) and to make the derivation of Ve negative, we choose the disturbance observer that is expressed in Eqs. (16), (17), and the desired value of the control variable urq as (18). ^_ v ¼ c1  B

sk1 s s sk12 sk1 s€s e2 s_  e3 þ e1 e3 þ e2 e3 þ e1 e3  e2 e3 þ e3 þ 3 3pwp Trq 3pwp Trq 3pwp Trq 3pwp 3pwp 3pwp m

!

ð16Þ ^_ t ¼ c2 F

s e3 3pwp Trq

ð17Þ

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urq ¼

 3pwp Lrq 2pLrd Ird 2pLrd Ird 2pLrd Ird s_  1 2 sm smk12 e2 e3 þ e1 e2  e3 e2  k1 e1 e3 þ e3 þ e  e1 e3 þ e1 e3 Trq 3 3pwp Trq e3 sm sLrq sLrq sLrq 3pwp Trq 

2pwp 2pwp k1 2pwp s_  smk1 smk1  sm smk1 smk13 €s e3 þ e2 e3 þ e2 e3  e1 e3 þ e3  e2 e3 þ þ e1 e3  e1 e3 3pwp 3pwp Trq 3pwp Trq sLrq sLrq sLrq 3pwp 3pwp ^_ v k1 ^_ v ^_ v s_  ^_ L smk12 sB sB sB sF smsv sBv k1 sBv þ e2 e3  e1 e3 þ e2 e3 þ e3 þ e3 þ e3  k1 e23  e1 e3 þ e2 e3 3pwp 3pwp 3pwp 3pwp 3pwp 3pwp 3pwp Trq 3pwp Trq # sBv s_  sk 2 Bv sBv k1 sBv€s Bv sFL þ e3 þ 1 e1 e3  e2 e3 þ e3 þ e23 þ e3 3pwp Trq 3pwp 3pwp 3pwp m 3pwp Trq

ð18Þ

Step four: Design the direct current control loop We do the same as step1 with a Lyapunov Function as V4 ¼ ð1=2Þe24

ð19Þ

To combine (19) with (1) and (5), in order to make the derivation of V4 negative, we choose the desired value of urd as     urd ¼ k4 Lrd e4 þ ðLrd =Trd Þird  2pvLrq =s irq þ dird =dt

ð20Þ

Where k4 is a positive constant.

4 Experiments and Discussion 4.1

DSP Based Experimental System for UPMLM

In order to evaluate the performances of the proposed position control system, we do experiments for UPMLM. The principle diagram of DSP based UPMLM driving system is described in Fig. 5 and the DSP based experimental system for UPMLM driver is shown in Fig. 6. The TMS320F2812 DSP kit, which is connected to the computer, also is used to measure the position and the position error of the UPMLM. In this experimental system for UPMLM, we use one DSPTMS320F2812 to control two SPMLMs of UPMLM. The UPMLM has following values of parameters: p = 4, s = 20 mm, m = 2 kg, R = 3. Ώ, Lrd = 2.182 mH, Lrq = 2.012 mH, wp = 9.35 Wb, FN = 90 N, Fmax = 380 N, normal velocity vN = 1.5 m/s, normal current IN = 1.1 A, Bv = 0.002 Ns/m. In Fig. 5, the one-phase AC voltage source with a frequency of 50 Hz is connected to the rectifier consisting of four diodes. The output DC voltage of the rectifier is filtrated by a filter capacitor. The two three-phase inverter modules are supplied with the DC voltage source, each three-phase inverter module has six MOSFETs connected to three-phase windings of each SPMLM of UPMLM. The six PWM outputs (from

Design and Some Experimental Results of the U-Type

299

Fig. 5. The DSP based UPMLM driving system

Current sensors DSPTMS320 kit

Rectifier and Inverter modules

Spring load

Cable connected to Computer

encoder

UPMLM

Fig. 6. DSP based experimental system for UPMLM driver

PWM1 to PWM6) of the DSP supply six MOSFETs of the first three-phase inverter module of the first SPMLM of UPMLM with pulses through the driver circuit. The other six PWM outputs (from PWM7 to PWM12) of the DSP supply six MOSFETs of the second three-phase inverter module of the second SPMLM of UPMLM with pulses through the driver circuit. The Encoder is used to measure the mover’s velocity of the motor. Two outputs of the encoder are connected to specifically applied inputs of QEP0

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and QEP1 of DSP TMS320F2812. Four hall effect current sensors are used to measure the line currents of UPMLM, their outputs are connected to analog inputs of ADCA0, ADCA1, ADCA2, ADCA3 of the DSP through the low pass filters (LPFs). The load of UPMLM is the spring load that contributes to making disturbances. The proposed control system is experimented by using TMS320F2812 DSP kit of Texas Instruments. We use Matlab/Simulink to build the control system, then connect with Code Composer Studio software of Texas instruments to make and load the code of controllers to the TMS320F2812 DSP kit. 4.2

Results of Experiments

The desired reference trajectory of the motor mover is depicted in Fig. 7. In Fig. 7, the position trajectory of the motor mover is commanded to track the linear line in the forward direction from 0 m at the time of 0 s to 0.4 m at the time of 0.75 s, then the motor stops at this position. After that, at the time of 0.25 s, the motor changes the direction and rotates according to the linear line in the reverse direction to the original position at 0.35 s and stops at this position. The real trajectory of the motor’s mover is shown in Fig. 8, and the position error is shown in Fig. 9.

Fig. 7. The desired reference trajectory of the motor’s mover

The results of experiments show that the U-Type permanent magnet three-phase linear motor based position control system with the Backstepping technique based disturbance observer makes the real trajectory of the motor’s mover to reach the desired reference trajectory with the quite small error of a position, it is about 1.5  10−8 m. These results prove that the performance of the proposed position control system has high accuracy and meets the commands of the high precision CNC machines in the industry.

Design and Some Experimental Results of the U-Type

Fig. 8. The real trajectory of the motor’s mover

Fig. 9. The position error

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5 Conclusions This paper presents the design of the U-Type permanent magnet three-phase linear motor based position control system with the Backstepping technique based disturbance observer and experiments of the proposed position control system, which is based on DSP TMS320F2812. The experiments are implemented with different velocities and different directions. The experimental results show that the performance of the proposed position control system is quite good, the position error of the proposed position control system is quite small and meets the increasing requirements of high accuracy of CNC machines in the industry. The experiments also show the feasibility of the proposed position control system in the industry, especially, in high accuracy CNC machines. Acknowledgment. This research was supported by a grant for the university research from Thai Nguyen University of Technology (TNUT). We thank our colleagues from TNUT who provided insight and expertise that greatly assisted the research.

References 1. Dailin Zhang, D., Youping Chen, Y., Ai, W., Zude Zhou, Z.: Precision motion control of permanent magnet linear motors. Int. J. Adv. Manuf. Technol. 6(35), 301–308 (2007) 2. Team, A.: U Channel Linear Motors Hardware Manual. Aerotech, Inc, EC Declaration of Conformity (2012) 3. Nam, D.P.: Improve the quality of linear motion systems by using linear motor drives. The thesis of engineering doctor, Hanoi university of technology (2012) 4. Cupertino, F., Giangrande, P., Pellegrino, G., Salvatore, L.: End effects in linear tubular motors and compensated position sensorless control based on pulsating voltage injection. IEEE Trans. Ind. Electron. 58(2), 494–502 (2011) 5. Quang, N.H.: Research and control design for Electric drive systems using a Polysolenoid linear motor when the end effects are considered. The thesis of engineering doctor, Thai Nguyen university of technology (2019) 6. Tuyen, C.X.: Synthesis of nonlinear algorithms on the basis of Backstepping method to control doubly-fed induction machines in wind power generation system. The thesis of engineering doctor, Hanoi university of technology (2008)

Detail Design of IPM Motor for Electric Power Traction Application Bui Minh Dinh(&) and Dang Quoc Vuong Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]

Abstract. This paper deals with the electromagnetic and thermal analysis of an interior permanent-magnet (IPM) synchronous motor with V shape used as traction drive in a medium commercial electric vehicle (EV) according to the traction requirements of the electric vehicle under normal operating conditions and overload conditions or peak power. The key dimensions were calculated on an analytical program by Matlab. The finite element method (FEM) simulation model of the IPM motor was built by using SPEED software. The influenced geometric structures of the IPM motor including the PM dimensions and skewed PMs on electromagnetic torque were investigated, and the temperature distribution of the motor under rated operating condition and the condition of maximum speed were calculated. Finally, the thermal simulation results of the IPM motor running in various operating modes were investigated and the medium commercial electric vehicle driving applications were required. The contributions of this paper are optimal V angle of magnetic and skew angle slot to minimize torque ripples for a 60 kW IPM 12P/72 slots. Keywords: Electric vehicle Traction motor


IPM motor
Electromagnetic design
FEM


1 Introduction Electric vehicles (EV) have been identified as the most viable solution to reduce emissions in the field of transportation and have received increasing attention in automobile industry and transportation applications such as passenger car, commercial bus and trolley bus, etc. [1–3]. There are some key issues for a typical EV motor: torque density in terms of weight and volume, torque-speed capability, costs in manufacturing and maintenance [4, 5]. Permanent-magnet (PM) motors are good candidates for EV traction system because they have higher torque density and efficiency compared with IMs. There are several types of IPM motors, i.e. surface-mounted PM (SPM), interior PM (IPM), flux switching PM (FSPM) motor and so on for EV drive [5–7]. The rotor of an IPM motor with the V-shaped PMs has a robust structure, highspeed operational capabilities, and magnetic saliency effect [8, 9]. Magnetic saliency effect can lead to the reluctant torque generation; thereby high-speed operation of a motor through flux weakening control is enabled [10, 11–13]. Therefore, the IPM motor with the V-shaped PMs has a much larger overload torque over the entire speed © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 303–310, 2021. https://doi.org/10.1007/978-3-030-64719-3_34

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range [14], a safer back electromotive force (EMF) in uncontrolled generator operation [15], and little sensitivity to PM temperature. The IPM motor has higher joule loss at low speed, but a properly stator slots number and rotor segments can keep the harmonic loss under control. This paper focused on an IPM motor for medium commercial EV traction application. A 2D FEM simulation model was built according to the theory of PM motor. In order to improve the electromagnetic torque, the geometric structure of the V-shaped PM and the influence of PMs skewed along the axial direction on air-gap flux density were investigated. In addition, the temperature distribution of the motor under two typical operating conditions was calculated. Finally, the experimental results and simulation results of the prototype motor running in various driving modes were compared. This paper focused on an IPM motor for medium commercial EV traction application. A FEM simulation model was built according to the theory of PM motor. In order to improve the electromagnetic torque, the geometric structure of the V-shaped PM and the influence of PMs skewed along the axial direction on air-gap flux density were investigated. In addition, the temperature distribution of the motor under two typical operating conditions was calculated. Finally, the experimental results and simulation results of the prototype motor running in various driving modes were compared.

2 Specification of Electric Power Traction Specific motor designed for EV application should consider torque-speed curves under peak (dashed line) and continuous (solid line) conditions. Figure 1 shows the standard characteristics of a motor used in the medium commercial EV. The EV requires constant torque at low speed and constant power at high speed in both rated (continuous) and overload conditions.

Fig. 1. Specification of the EV power train

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Basic parameters design of IPM V shape is requested for medium commercial vehicle as in Table 1. In order to ensure thermal safety when the designed motor operates continuously at rated load and at least for a couple of minutes at overload, the stator liquid cooling was adopted. During the calculation, the temperature of stator winding and PM was set to be 120 °C. Table 1. Target parameter design No 1 2 3 4 5 6 7 8 9 10 11 12

Parameters AC voltage (V) 380 Power rating P1 (kW) Torque rating T1 (Nm) Rating efficiency (%) Current rating (A) Rating speed (rpm) Max speed (rpm) Max Current@1000 rpm (A) Max Torque T0@1000 rpm (Nm) Max Power@1000 rpm (kW) Max Power@4500 rpm (kW) Effective motor size (mm)

Value 380 60 385 95.5 150 1500 4500 380 1150 120 120 U340  L200

3 Electromagnetic Performance Evaluation In order to determine operation points of permanent magnet circuit, some basic parameters of magnetic circuit have calculated in an analytical model. This point depends on remanent flux density and silicon steel material. In this paper, the magnet of LSPMSM has been carried out by the analytical model in Fig. 2 and NdFeB35 is magnet material used due to its good thermal stability and remanent flux density (*1.3T) allowing its use in applications exposed to high temperature about 180 °C. The flux density is estimated about 0.88 T. Based on the analytical method, some geometry parameters of stator and rotor can be calculated as the following chart in Fig. 3. An analytical model was undergone many calculating steps to define basic parameters. Based on torque volume density TVR from 20 to 30 kNm/m3 [5], if we assume rotor diameter equal to rotor length, the rotor diameter D and length L sizes of LSPMSM is determined as follows: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi T 47:75 3 3 D¼L¼ p ¼ 3:14  TVR 4 4  25

ð1Þ

The main parameters (such as outer diameter, rotor diameter, motor length, stator slot and air gap length) are defined by taking some practical factors into account with desired input requirements [3]. The main part of the process is to design the rotor

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Fig. 2. Magnetic properties of NdFeB35

Fig. 3. Calculation process

configuration which is embedded permanent magnet. The PM configuration needs to create sufficiently magnetic voltage for magnetic circuit. After investigation I, U and V permanent magnetic shape, the V shape PM configuration has a great influence on motor efficiency and material cost. After sizing the main dimension and choosing the winding parameters of the proposed IPM motor in Fig. 4, the next stage for the design should be based on the 2D FEM which models saturation, PM material characteristics and nonlinearity of core materials to provide the rated power at base speed and to simulate all performance of the motor running at full speed range.

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Table 2. Motor parameters Parameter Air gap length Stator inner diameter Stator outer diameter Rotor outer diameter Rotor shaft diameter Rotor/stator length PM poles Number of PM piece Stator slot

Value 0.5 240 340 239.5 180 180 12 24 72

Unit mm mm mm mm mm mm mm – –

Fig. 4. Stator and rotor layout (a) and skew angle (b) of PM.

The rotor PM skewed distance is one teeth width, 11 mm, namely, the skew mechanical angle is 5°. Figure 5 shows the air-gap flux density waveform and its harmonics amplitude considering the PM skew and no skew when the IPM motor running at no-load and rated load respectively. Where the air-gap flux density waveform of PM skewed is the average value of the superposition of flux density on the axial direction. It can be seen that the total harmonics TDH of Back EMF is weakened obviously when the slot skew distance is equal to one teeth pitch. In order to evaluate overload 1150 Nm and maximum speed 4500 rpm. The parameters such as total losses and efficiency are also listed in Table 2 calculated by FEM (Fig. 6).

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Fig. 5. Back EMF waveform no skew TDH 11% (a) with skew 1 slot THD 4.8%

Fig. 6. Torque waveform under overload (a) and efficiency at maximum speed 4500 rpm (b)

The thermal simulation of the IPM motor was calculated in the whole speed range and overload condition. It is noted that this IPM motor can run safely at peak torque, 1150 Nm, the efficiencies of the motor in most of the operation areas are more than 90%, and the maximal efficiency can achieve 95.7% which has met the requirements of the manufacturer. The temperature distribution of IPM was shown in Fig. 7. This paper simply presents the temperature distribution under special operating conditions by simulation, and the thermal analysis and the temperature rise test of the IPM motor will be discussed in detail in another paper.

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Fig. 7. Temperature distribution of IPM

4 Conclusion An IPM motor with a V-shaped PM rotor was designed on the basis of the traction requirement characteristics of a medium commercial electric vehicle, and its performance was analyzed and tested. Firstly, the key dimensions of the designed motor were computed by empirical formulas of the permanent magnet motor theory. Then, the FEM simulation model, which considered the model saturation, skin effect, slot skew and nonlinearity of core materials for the IPM motor, was built; and the performance of the rated operating point were calculated and checked with the traction characteristics of the EV. The influence geometric structures of the IPM motor including the PM dimensions and skewed PMs on electromagnetic torque were investigated, and the temperature distribution of the motor under rated working condition and maximum speed mode were calculated. The performance of three typical operating modes, i.e. rated speed, overload torque and max speed were investigated respectively. The performance of the IPM control system, such as the flux-weakening rate, overload ability, peak torque, speed range and so on, which are influenced by these parameters determined by the V-shaped PM, will be researched. Acknowledgment. This paper has received supports from the Viettel High Technology-VHT, Viettel Group for simulating and calculating Software and PCs.

References 1. Kim, K., Kim, S.J., Kim, W.H., Im, J.B., Cho, S., Lee, J.: The optimal design of the rotor bar for LSPMSM considering the starting torque and magnetic saturation. In: 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation (CEFC) (2010) 2. Sorgdrager, A.J., Wang, R-J., Grobler, A.J.: Transient performance investigation and Taguchi optimization of a line-start PMSM. In: 2015 IEEE International on Electric Machines & Drives Conference (IEMDC), pp. 590–595 (2015)

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3. Isfahan, A.H., Vaez-Zadeh, S.: Effects of magnetizing inductance on start-up and synchronization of line-start permanent-magnet synchronous motors. IEEE Trans. Magn. 47(4), 823–829 (2011) 4. Hendershot, J.R., Miller, T.J.E.: Design of Brushless Permanentmagnet Motors. Magna Physics publishing and Clarendon press, Oxford (1994) 5. Zeraoulia, M., Benbouzid, M., Diallo, D.: Electric motor drive selection issues for HEV propulsion systems: a comparative study. IEEE Trans. Veh. Technol. 55(6), 1756–1764 (2006) 6. Zhu, Z.Q., Chan, Q.Q.: Electrical machine topologies and technologies for electric, hybrid, and fuel cell vehicles. In: Proceedings of the IEEE VPPC, Harbin, China, pp. 1–6, September 2008 7. Boldea, I., Tutelea, L.N., Parsa, L., Dorrell, D.: Automotive electric propulsion systems with reduced or no permanent magnets: an overview. IEEE Trans. Ind. Electron. 61(10), 5696– 5711 (2014) 8. Zhu, Z.Q., Howe, D.: Electrical machines and drives for electric, hybrid, and fuel cell vehicles. Proc. IEEE 95(4), 746–765 (2007) 9. Pellegrino, G., Vagati, A., Guglielmi, P., Boazzo, B.: Performance comparison between surface-mounted and interior PM motor drives for electric vehicle application. IEEE Trans. Ind. Electron. 59(2), 803–811 (2012) 10. Cao, R.W., Mi, C., Cheng, M.: Quantitative comparison of flux- switching permanentmagnet motors with interior permanent magnet motor for EV, HEV, and PHEV applications. IEEE Trans. Magn. 48(8), 2374–2384 (2012)

Detecting Common Web Attacks Based on Machine Learning Using Web Log Xuan Dau Hoang(&) Posts and Telecommunications Institute of Technology, Hanoi 10000, Vietnam [email protected]

Abstract. SQL injection (SQLi), Cross-site Scripting (XSS) attacks have long been considered major threats to web-based applications and their users. These types of web attacks can cause serious damage to web applications and web users, ranging from bypassing authentication systems, stealing information from databases and users, to even taking control of server systems. To cope with web attacks, many measures have been researched and applied to protect web applications and users. Among them, the detection of web attacks is a promising approach in the defensive layers for web applications. However, some measures can only detect a single type of web attacks, while others require frequent updates to the detection rule sets, or require extensive computational power because of using complex detection methods. This paper proposes a web attack detection model based on machine learning using web log. The detection model is built using the inexpensive decision tree algorithm and it does not require frequent update. Our experiments on a labelled dataset and real web logs show that the proposed model is capable of detecting several types of web attacks effectively with the overall detection accuracy rate of 98.56%. Keywords: Web attack detection
SQLi detection
XSS detection traversal detection
Machine learning-based attack detection


Path

1 Introduction Web attacks, such as SQLi, XSS, CMDi (operating system Command injection) and Path traversal have been considered constant and dangerous threats to websites, web applications and web users [1, 2]. These types of attacks are very common because of the popularity of websites and web applications and the availability of the web attack tools on the Internet [3]. We name the web attack group of SQLi, XSS, CMDi and Path traversal as “common web attacks”. Common web attacks can cause serious consequences to websites and web applications and their users. They can help the attackers to bypass the web systems’ authentication mechanisms, to carry out unauthorized modifications to web content, databases, to extract data from web application databases, to steal sensitive information of web servers and web users, and even to take control of the web servers and/or database servers [1, 2]. Figure 1 shows an example of SQLi attack to a website, in which the Attacker inserts the malicious code into the search keyword to delete a table of the website’s database.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 311–318, 2021. https://doi.org/10.1007/978-3-030-64719-3_35

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Fig. 1. An example of SQLi attack to a website

Due to the danger of common web attacks, a number of measures have been researched and applied into practice to detect and prevent these attacks to protect websites, web applications and web users. Generally, there are 3 defensive approaches for these attacks, including (1) validate all data inputs, (2) reduce the attacking surfaces and (3) use “defense in depth” strategy [1, 2]. Specifically, approach (1) requires all input data to web applications must be checked thoroughly and only legitimate inputs are passed to next steps for processing. On the other hand, approach (2) requires dividing a web application into several parts and applies suitable access controls to limit user accesses. For approach (3), several defensive measures are applied in layers to protect the web applications and web users. This paper proposes a model to detect common web attacks based on machine learning using web log, which belongs to approach (3). We attempt to use a machine learning technique to construct the detection model in order to eliminate the manual construction and update of input data filters and to increase the detection rate. On the other hand, web logs generated by the web server for each hosted website by default are used as the input to the detection model. The rest of this paper is organized as following: Sect. 2 presents some related works; Sect. 3 is our proposed method, experiments and results, and Sect. 4 is the conclusion of the paper.

2 Related Works As mentioned in Sect. 1, a number of measures of the three approaches have been proposed and applied to defend again common web attacks. Because of the paper length limit, we only review some closely related proposals to our paper, including OWASP Core Rule Set (CRS) [4], SQL-ID [5], XSS-GUARD [6], Liang et al. [7] and Pan et al. [8]. CRS is a set of rules developed by OWASP for the detection of various types of web attacks in top 10 OWASP [1] with low false alarm rate. It can be used in ModSecurity that is a web application firewall (WAF) module attached to Apache web server. CRS is well-supported by OWASP and the web security community. However, it may be a difficulty to use CRS in some other WAFs or to integrate with other web servers, such as Microsoft Internet Information Services. SQL-ID [5] is SQLi attack detection system based on specifications. SQL-ID first built a set of specification rules described structures of legitimate SQL queries generated by the web application to be sent to database server for execution. Then, it monitors, pre-processes and classifies incoming SQL queries based on the built rule set. Only SQL queries classified as ‘legitimate’ are sent to the database server for

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execution. Otherwise, illegitimate queries will be blocked and logged. SQL-ID is reported to have 0% false alarm rate and it can be used to protect multiple websites because it is implemented as a proxy server between the web server and the database server. However, the manual construction of the specification rule set is site-specific and time consuming. XSS-GUARD [6] is a framework that monitors and prevents XSS attacks by generating a ‘shadow page’ and comparing it with the real page before sending the real page to client. The shadow web page is created in parallel with the real web page from the response of the web server, but with the clean input (without scripts) generated automatically, which has the same length as the real input. Experiments show that XSSGUARD is able to prevent various types of XSS attacks listed by OWASP. In addition, it does not require bulky and frequent-updated rule sets. However, XSS-GUARD adds considerable loads to web servers because of the generation and comparison of shadow pages with real pages. Using other approach, Liang et al. [7] proposes to use deep learning of RNNs to build the web attack detection models. Experiments on the CSIC 2010 dataset [9] show that the proposed method achieves the overall detection accuracy of over 98%. In addition, the proposed method can eliminate the manual and time-consuming task of the feature selection and extraction. On the other hand, Pan et al. [8] proposes the Robust Software Modelling Tool (RSMT) to monitor and extract run-time information of an application and then use collected information to train the stacked de-noising auto-encoder to build the detection model. Experiments show that the proposed approach can detect various types of web attacks and achieves the F1-score of over 91% on average. From the above reviews, we can draw some comments as the following: OWASP Core Rule Set [4] can detect web attacks effectively, however it requires frequent update and it faces the compatible problem in the practical deployment; SQL-ID [5] or XSS-GUARD [6] can only detect one type of web attacks. Moreover, they either face the problem of the manual construction of rule sets, or the performance degradation of servers; Liang et al. [7] and Pan et al. [8] both use deep learning for web attack detection. This is a new approach in the field, however deep learning is generally expensive and it may not be suitable for real-time web attack detection. In addition, their detection performance is lower (Pan et al. [8]), or slightly higher than that of the traditional machine learning (Liang et al. [7]). Furthermore, using RSMT to monitor server execution may cause serious performance degradation to the server. In our approach, we propose a model to detect common web attacks based on machine learning using web log with the following advantages over the previous works: (1) our model can detect 4 major types of web attacks, including SQLi, XSS, CMDi and Path traversal; (2) the decision tree - an inexpensive machine learning technique is used to achieve good detection performance; (3) the process of feature selection, extraction and model construction can be done automatically; (4) our detection model does not require frequent update; and (5) the web logs are used as the input of the detection model, which are available by default on most web servers. This means the data collection is fairly simple and this makes it easier for practical deployment.

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3 Proposed Detection Model for Common Web Attacks 3.1

Proposed Web Attack Detection Model

The proposed detection model consists of two stages: (1) the training stage and (2) the detection stage. The training stage as shown in Fig. 2 includes the following steps: 1. Collection of the training data set, including normal URIs (Uniform Resource Identifier) and attacked URIs; 2. Training data set is pre-processed to extract features. After the pre-processing, each URI is converted into a vector; 3. Training data set in the form of vectors is put into Training process to construct the Classifier or the Model that is used for the detection stage.

Fig. 2. Proposed web attack detection model: the training stage

Fig. 3. Proposed attack detection model: the detection stage

The detection stage as described in Fig. 3 consists of the following steps: 1. URIs are extracted from web logs and each URI is the detection process’s input; 2. The URI is pre-processed using the same method done for training data. The output is a vector for the URI, which is used in the next step; 3. The URI’s vector is classified using the Classifier built in the training stage. The result of this step is either Normal or Attacked.

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3.2

315

Experiments on HTTP Param Dataset

The HTTP Param Dataset [10]. This data set consists of 31,067 URI payloads of web requests, including the payload length and labels of payloads. There are 2 payload labels: Norm (no attack) and Anom (attack). Anom payloads in turn have 4 types: SQLi, XSS, CMDi and Path-traversal. The data set is divided into 2 parts: • Training set consists of 20,000 payloads for training to construct the detection model; • Testing set consists of 11,067 payloads for checking the model’s performance. Pre-processing. This step is responsible for extracting URI features and vectorizing these features. The pre-processing includes two tasks as following: • Extracting URI features using n-gram method. The n-gram method is selected because it is simple and fast. We select 3-gram to extract URI features. • Vectorizing URI features using the TF-IDF (Term Frequency-Inverse Document Frequency) method. For each 3-gram, a tf-idf value is calculated as following:

tf ðt; d Þ ¼

f ðt; d Þ maxff ðw; d Þ : w 2 d g

ð1Þ

N jfd 2 D : t 2 d gj

ð2Þ

idf ðt; DÞ ¼ log

tf-idf ðt; d; DÞ ¼ tf ðt, dÞ  idf ðt; DÞ

ð3Þ

where tf(t, d) is term frequency of 3-gram t in URI d; f(t, d) is the number of occurrences of 3-gram t in URI d; max{f(w, d):w 2 d} is the maximum number of occurrences of any 3-gram in URI d; D is the set of all URIs and N is the total number of URIs. Because the number of URI features (3-gram) is large, the PCA (Principle Component Analysis) method is used to reduce the number of features to 256, which is selected by based on empirical. Training. The training step uses CART decision tree algorithm supported by sklearn library in Python. The reason that the decision tree algorithm is selected is it is fast and therefore suitable for online/real-time detection systems. The result of this step is the Classifier or the Model for use in the Detection stage. Experimental Measurement. The experimental measurement is ACC (Accuracy) that is calculated as the following: ACC ¼ ðNumber of records detected correctlyÞ=ðTotal number of recordsÞ  100% ð4Þ

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The ACC is calculated for each type of web attacks, for the classification of normal URIs and all URIs in the testing set. Detection and Results. In this stage the Testing set is used to validate the detection performance of the Classifier built in the training stage. Each URI of the Testing set is pre-processed using the same procedure done for URIs of the Training set. Then, each URI vector is classified using the Classifier and the result is either Normal or Attacked (together with type of attacks). Table 1 shows the detection accuracy on Testing set. Table 1. Detection accuracy on Testing set Detection label Normal payload SQLi attack XSS attack CMDi attack Path traversal attack Overall detection rate

Accuracy (%) 98.60 99.34 85.88 73.33 97.94 98.56

Discussion. The experimental results shown on Table 1 confirm that: • The proposed web attack detection model achieves high overall detection rate of 98.56% on the testing data set. This is slightly better than the highest accuracy of 98.42% of Liang et al. [7] and much better than that of 91.40% of Pan et al. [8], which use expensive deep learning techniques for web attack detection. • The detection rate for SQLi attacks is very high at 99.34%, followed by the rates for Normal payloads and Path traversal attacks. The detection rates of XSS and CMDi attacks are not high because the amount of training data for these types of attacks is not sufficient to build a good detection model. 3.3

Experiments on Real Web Logs

In this section, we provide experimental results on real web logs collected from our web servers. Generally, common web servers, such as Mozilla Apache, Microsoft IIS and Ng ngix can create logs for each website they host on the web requests of users. Most web logs are in the form of plain text and a text line is a log record. Although there have been many log formats, the W3C Extended log file format [11] is most widely used and supported by most modern web servers. Figure 4 shows a part of web log file created by Microsoft IIS using the W3C Extended log file format. From the web log file, we extract each access URI that is a combination of cs-uristem and cs-uri-query fields of a log record. Then the URI is put into the detection stage as described on Fig. 3. Experiments on real web logs confirm that our proposed model is able to detect common web attacks correctly and efficiently. Table 2 shows typical attack payloads detected using real web logs.

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Fig. 4. A part of web log file using W3C Extended log file format Table 2. Typical attack payloads detected using real web logs Detection label SQLi attack XSS attack CMDi attack Path traversal attack

Attack payload used fpw = (select%20convert(int%2cCHAR(65))) type = vh01i’>ooq5g fpw = WEB-INF/web.xml%3f type = ../../../../../../../../../../../etc/passwd%00

4 Conclusion This paper proposes a web attack detection model that is based on supervised machine learning using web logs. The proposed model is capable of detecting 4 types of most dangerous web attacks, including SQLi, XSS, CMDi and path traversal. Experiments on the labelled data set and real web logs confirm that our model achieves the overall high detection rate of 98.56% and it is able to detect common web attacks effectively. The proposed model is trained using the inexpensive decision tree algorithm to gain good detection performance. The training process to construct the model can be done automatically. For future work, we will extend the proposed model so that it can detect more types of web attacks as well as have higher detection rates for some special types of web attacks, including XSS and CMDi.

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References 1. OWASP Project. https://owasp.org. Accessed 20 June 2020 2. Baranwal, A.K.: Approaches to detect SQL injection and XSS in web applications, EECE 571B, Term Survey Paper. University of British Columbia, Canada, April 2012 3. Website Attack Tools. https://sourcedefense.com/glossary/website-attack-tools/. Accessed 20 June 2020 4. OWASP ModSecurity Core Rule Set. https://www.owasp.org/index.php/Category:OWASP_ ModSecurity_Core_Rule_Set_Project. Accessed 20 June 2020 5. Kemalis, K., Tzouramanis, T.: SQL-IDS: a specification-based approach for SQL injection detection. In: SAC 2008, Fortaleza, Ceará, Brazil, 16–20 March 2008, pp. 2153–2158. ACM (2008) 6. Bisht, P., Venkatakrishnan, V.N.: XSS-GUARD: precise dynamic prevention of cross-site scripting attacks. In: Proceeding of 5th Conference on Detection of Intrusions and Malware & Vulnerability Assessment. LNCS, vol. 5137, pp. 23–43 (2008) 7. Liang, J., Zhao, W., Ye, W.: Anomaly-based web attack detection: a deep learning approach. In: ICNCC 2017, Kunming, China, 8–10 December 2017, pp. 80–85 (2017) 8. Pan, Y., Sun, F., Teng, Z., White, J., Schmidt, D.C., Staples, J., Krause, L.: Detecting web attacks with end-to-end deep learning. J. Internet Serv. Appl. 10(16) (2019) 9. HTTP Dataset CSIC 2010. https://www.isi.csic.es/dataset/. Accessed 20 June 2020 10. HTTP Param Dataset. https://github.com/Morzeux/HttpParamsDataset. Accessed 20 June 2020 11. Extended Log File Format. https://www.w3.org/TR/WD-logfile.html. Accessed 20 June 2020

Determination of Kinematic Control Parameters of Omnidirectional AGV Robot with Mecanum Wheels Track the Reference Trajectory and Velocity Trinh Thi Khanh Ly1(&), Nguyen Hong Thai2, Le Quoc Dzung1, and Nguyen Thi Thanh3 1

Faculty of Automation Technology, Electric Power University, Hanoi, Vietnam [email protected] 2 Department of Mechanical Design and Robotics, School of Mechanical Engineering, Hanoi-University of Science and Technology, Hanoi, Vietnam 3 Department of Control and Automation, Faculty Electrical Engineering, Economic and Technical University of Industry, Hanoi, Vietnam

Abstract. Nowadays, in order to save the floor space of the production workshops, the mecanum wheels have been applied to the design of Automated Guided Vehicles (AGV) robot to create highly flexible and omnidirectional AGV robot. Omni-directional robotic platforms have vast advantages over a conventional design in terms of mobility in congested environments. These environments are commonly found in factory workshops offices, warehouses, hospitals and elderly care facilities. To be able to control these robots in any given trajectory, it is necessary to set up the kinematic control parameters. The paper presents the method of establishing the kinematic equation of omnidirectional robots by the centre of instantaneous velocity, thereby determining the control parameters of AGV robot following a reference trajectory. In addition, the authors also designed the robot’s motion trajectories using the NURBS curve to solve the robot’s trajectory planning problem according to different application scenarios of modern industrial production. Keywords: AGV robot
Kinematic of AGV wheel
Trajectory design
NURBS curve


Omnidirectional mecanum

1 Introduction Mecanum omnidirectional wheels described in Fig. 1 were invented by a Swedish engineer Ilon in 1973 at Mecanum Company [1] and applied to the design of the first AGV in 1997. When torque is transmitted to the wheels, the rollers on the wheel that come into contact with the floor form two velocity components: in the direction of the wheel’s movement and is perpendicular to the roller axis and depends on the direction of applied torque. Therefore, when controlling the whole motor, the wheels will form a resultant pushing AGV robot in different directions, increasing the flexibility of the robot. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 319–328, 2021. https://doi.org/10.1007/978-3-030-64719-3_36

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Therefore, in recent years, this wheel has also been used in the design of AGV robots for logistics, transportation in tight spaces, which is not enough to design the turning path for AGV. such as the omnidirectional AGV model of L. Schulze et al. [2] using mecanum wheels with the conveying and towing functions described in Fig. 2, or that of Michael Göller et al. [3, 4] with mecanum wheels for supermarket customers. In addition, there are a number of other studies [5, 6] that show different AGV robots for different industrial production application scenarios.

ω

ωL

Fig. 1. Mecanum wheels

Fig. 2. Omnidirection AGV robots using mecanum wheels [2]

To kinematically control the omnidirection AGV robot in the desired orbit, the first thing is to set up the kinetic model of the AGV robot. In terms of this, Gfrerrer [7] and Tatsuro [8] determined the effect of roller size, wheels and number of rollers on the speed of robots, or another research of Hamid [9] and Lin [10] using the matrix method and the non-slip condition of the wheel on the floor to establish forward and inverse kinematic model, thereby designing adaptive controller. That has led to too many parameters in the kinematic equation and such a complex problem, to overcome the above disadvantage, in this article the authors set up the inverse kinematic equations directly with instantaneous center of velocities. On that basis, the parameters of dynamically controlling ominidirection AGV robots by the reference trajectories and still achieve the desired speed of the robot. In addition, the article also presents the direction design method for robots with Non-uniform rational B-spline (NURBS) in the general case.

2 Determination of Kinematic Control Parameters of Omnidirection AGV Robot with Mecanum Wheels 2.1

Establishment of Kinematic Equations of Omnidirectional AGV Robot with Mecanum Wheels

In order to establish the kinematic equation of AGV robot, we let #f fOf xf yf g as a fixed reference frame attached to the floor of the robot; #R fGR xR yR g is a reference system mounted on AGV robot (see Fig. 3);

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321

Fig. 3. Diagram of AGV robots with mecanum wheels

qf ¼ ½ xf ðtÞ yf ðtÞ /ðtÞ T is the robot positioning parameters in the frame #f , and qR ¼ ½ xR ðtÞ

/ðtÞ T

yR ðtÞ

is

the

parameter

in

the

frame

#R ,

if

qf ¼

T

½ xf ðtÞ yf ðtÞ /ðtÞ  is in the frame of #f fOf ; xf ; yf g, we can determine the control parameter q_ R of Robot with: q_ R ¼ Qð/ÞT q_ f Where: 2

cos /  sin / Qð/Þ ¼ 4 sin / cos / 0 0 #R to the reference frame #f . For the reference frame #R 

ð1Þ

3 0 0 5 is a directing cosine matrix of the reference frame 1 of robot: To the wheel No. 1 (Fig. 3), we can see:

V1 ðtÞ þ VL1 cos c ¼ VGyR ðtÞ  L2 XðtÞ VL1 sin c ¼ VGxR ðtÞ  d2 XðtÞ

ð2Þ

Where: L, d is the distance between the two wheels, the distance between the front and rear wheels; X (t) is the angular velocity of AGV robot; V1 ðtÞ is the speed of wheel 1 such as V1 ðtÞ ¼ rx1 ðtÞ where x1 ðtÞ is the angular velocity of wheel No.1 and r is the radius of the wheels (see Fig. 4)

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Fig. 4. The velocity components on the mecanum wheels

De-velocity of roller VL1 in Eq. (2), we get the velocity of wheel No. 1 as follows:   L d 1 V1 ðtÞ ¼ VGyR ðtÞ  XðtÞ þ VGxR ðtÞ  XðtÞ 2 2 tgc

ð3Þ

We apply the same method to wheels No. 2, 3, 4, after all we have:   1 8 L d V ðtÞ ¼ V ðtÞ  XðtÞ þ V ðtÞ  XðtÞ 1 Gy Gx > R R 2 2 tgc > > < V ðtÞ ¼ V ðtÞ þ L XðtÞ  V ðtÞ  d XðtÞ 1 2 GyR Gx R 2 2   tgc 1 > V3 ðtÞ ¼ VGyR ðtÞ þ L2 XðtÞ þ VGxR ðtÞ þ d2 XðtÞ tgc > >   1 : L d V4 ðtÞ ¼ VGyR ðtÞ  2 XðtÞ  VGxR ðtÞ þ 2 XðtÞ tgc

ð4Þ

To change the direction of the omnidirection AGV in any direction. Set c ¼ 45 in (4) and change to the algebraic form, we have: 2

3 2 1 x1 ðtÞ r 6 x2 ðtÞ 7 6  1 6 7 ¼ 6 1r 4 x3 ðtÞ 5 4 r  1r x4 ðtÞ

1 r 1 r 1 r 1 r

3 3  2r1 ðL þ dÞ 2 1 7 VGxR ðtÞ ðL þ dÞ 2r 74 VGyR ðtÞ 5 1 5 2r ðL þ dÞ XðtÞ  2r1 ðL þ dÞ 2

Let: x ¼ ½ x1 ðtÞ x2 ðtÞ x3 ðtÞ

1 r

61 r x4 ðtÞ T and J ¼ 6 4 1 r

 1r

1 r 1 r 1 r 1 r

ð5Þ

3  2r1 ðL þ dÞ 1 7 2r ðL þ dÞ 7 1 5 ðL þ dÞ 2r  2r1 ðL þ dÞ

Equation 5 is rewritten as follows: x ¼ J q_ R

ð6Þ

Determination of Kinematic Control Parameters

323

2.2

Determination of Kinematic Control Parameters by the Given Trajectories and Velocity  T _ VG , we determine q_ f ¼ x_ f ðtÞ y_ f ðtÞ /ðtÞ in the With orbit nðxf ðtÞ; yf ðtÞÞ and ~ reference frame #f , then substitute into Eq. 1 and combine with Eq. 6 we have: x ¼ JQT ð/Þq_ f

ð7Þ

From (7) we determine the control parameters of the matching variables on the mecanum wheels:

8 _ > x1 ðtÞ ¼ 1r aðtÞ_xf ðtÞ þ bðtÞ_yf ðtÞ  12 ðL þ dÞ/ðtÞ > >

> > > _ < x2 ðtÞ ¼ 1 aðtÞ_yf ðtÞ  bðtÞ_xf ðtÞ þ 1 ðL þ dÞ/ðtÞ r 2 

 1 1 > _ > _ x ðtÞ ¼ ðtÞaðtÞ þ bðtÞ_ y ðtÞ þ ðL þ dÞ /ðtÞ x 3 f f > r 2 >

> > : x ðtÞ ¼ 1 bðtÞ_x ðtÞ þ aðtÞ_y ðtÞ  1 ðL þ dÞ/ðtÞ _ 4 f f r 2

ð8Þ



aðtÞ ¼ cos /ðtÞ  sin /ðtÞ bðtÞ ¼ cos /ðtÞ þ sin /ðtÞ Equation 8 determines the control parameters of the angular velocity of the driven motors on the Mecanum wheel axle so that the omnidirection AGV robot follows the trajectory and achieves the given velocity. Where:

3 Design of Moving Trajectory of AGV Robots The trajectory of AGV robots is usually designed as straight lines along the corridors used as the paths of the production line [11, 12] and at the intersections softened by curves by robot’s breakdown velocity and safety corridor. This will modelize the trajectory into a fixed map in the memory of the robot. To make it easier to design and encode the robot’s route map, we use the coordinate matrix as a database, and the interpolation will be done by the NURBS to guide the robot well. According to the work [13] the non-uniform B - Spline rational curve (NURBS) is defined by: PðtÞ ¼

nX þ1

Bi Ri;k ðtÞ

ð9Þ

i¼1

Where Bi is the vertices of the interpolated polygons in 2D space, and Ri;k ðtÞ is the basis function of the B-Spline rational curve and is given by: hi Ni;k ðtÞ Ri;k ðtÞ ¼ n þ 1 P hi Ni;k ðtÞ i¼1

ð10Þ

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As: hi  0 for all values of i, Ni;k ðtÞ given by the recursive formula: 8  > xi  t  xi þ 1 < Ni;1 ðtÞ ¼ 1 0 > : Ni;k ðtÞ ¼ ðtxi ÞNi;k1 ðtÞ þ ðxi þ k tÞNi þ 1;k1 ðtÞ xi þ k1 xi xi þ k xi þ 1

ð11Þ

xi is the value of the node vector and satisfies the condition xi  xi þ 1 . The tangent to the curve is given by: 2

3 _ hi Ni;k ðtÞ hi Ni;k ðtÞ7 nX þ1 6 h N_ ðtÞ 6 i i;k 7 i¼1 _   PðtÞ ¼ Bi 6n þ 1 2 7 4 5 nP þ1 P i¼1 hi Ni;k ðtÞ hi Ni;k ðtÞ nP þ1

i¼1

ð12Þ

i¼1

Thus, at each coordinate point K(xf, yf) on the interpolation curve {n} can we completely determine the tangent vector ~ s.

4 Simulation Examples Applied to omnidirectional AGV robots with dimension parameters: radius of mecanum r = 5 cm; The distance between the two wheels L = 50 cm; Distance between front and rear wheel d = 80 cm; With the assumption: (1) Considering at the time the omnidirection AGV robot moves at a steady velocity VG = 0.3 m/s; (2) there is no horizontal and vertical sliding of robots on the road surface. The coordinates of the grid node on the trajectory are given in Table 1 below.

Table 1. Coordinates of polygon nodes designing trajectory of the vehicle. B1  0 0

B2 B3 B4    6:5 6:5 3 0 5 5

B9 B8   6:5 6:5 13 8

B10  3 8

B11  3 5

B5 B6 B7    3 6:5 6:5 8 8 13 B12 B13   6:5 6:5 5 0

Figure 5 is the roadmap of the AGV robot and Fig. 6 is the trajectory of the robot after interpolation with NURBS curve.

Determination of Kinematic Control Parameters yf

B7

325

y [m]

B8 13

3

12 10

B6

B3

B10

B5

B9

B12

B4

B11

8 4

2

6

{ξ }

4 2

B2

B1 Of

1

xf B13

Fig. 5. Roadmap of nodes on the trajectory of AGV robot

-8

-6

-4

-2

x 0

2

4

6

8

Fig. 6. Robot’s trajectory is interpolated with NURBS curves

Corresponding to each point K(xf, yf) on the trajectory {n} in the reference frame #f we determine the tangent vector ~ s through Eq. 12. Attach the center coordinate GR(xG, yG) of the AGV robot to the point K(xf, yf) and the yR axis coincide with ~ s then the angle /ðtÞ is determined: /ðtÞ ¼ \ð~ sðtÞ; yf Þ

ð13Þ

VG _ /ðtÞ ¼ XðtÞ ¼ qðtÞ

ð14Þ

_ And /ðtÞ is found by

Where q(t) is the curvature radius of {n} corresponding to the point K(xf, yf) at time t and depicted in Fig. 7, and the graph depicting the angular velocity X(t) of the robot (The speed changes by the direction in the trajectory) is shown in Fig. 8.

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0.3 35

1

3

1

0.2

Ω (rad/s)

ρ (t) (m)

30 25 20

0.1 0

1

2

4

3

1

-0.1

15 10

-0.2

4

2

t(s)

5 0 0

50

100

150

0 -0.3

200

Fig. 7. Turning center radius on the moving path of AGV robot

t (s)

150

200

Fig. 8. Velocity changing robot change direction

4

3

2

100

50

ω1 [rad/s]

10

5

t (s) 0

20

40

60

80

100

120

140

160

180

0

20

40

60

80

100

120

140

160

180

0

20

40

60

80

100

120

140

160

180

0

20

40

60

80

100

120

140

160

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ω2 [rad/s]

10

5

t (s) 200

ω3 [rad/s]

10

5

t (s) 200

ω4 [rad/s]

10

5

t (s) 200

Fig. 9. The angular velocity of the wheels as the AGV robot moves along the trajectory

Insert Eqs. 13, 14 and 15 into Eq. 8 to determine the angular velocity x1 ðtÞ; x2 ðtÞ; x4 ðtÞ; x4 ðtÞ, so that the AGV robot can follow the given trajectory and achieve the velocity VG = 0.3 m/s. Figure 9 is a graph of angular velocity of four wheels. On the other hand, from the previous VG velocity we have:

Determination of Kinematic Control Parameters



x_ f ðtÞ ¼ VG sin /ðtÞ y_ f ðtÞ ¼ VG cos /ðtÞ

327

ð15Þ

From Figs. 5, 6, 7, 8 and 9, we find that: i) When making the trajectory map of AGV robot with NURBS interpolation method through grid nodes. As a result, the database that stores the robot’s path in memory is greatly reduced. Instead of having to remember all the data for the path, the route designer just needs to enter the grid nodes. ii) When the AGV Robot travels from its initial position “1” to “2”, the robot’s rotation in the trajectory changes from clockwise to counter-clockwise (see Fig. 8) and this cycle repeats 4 times according to the symmetry of the moving trajectory. Especially when passing through position “2” and “3”, it suddenly changes direction, the angular velocity of the robot has a greater variation at the position of “2” and “4”and much greater than the positions “1” and “3”. The reason is that the instantaneous radial radius at positions “2” and “4” is the smallest (see Fig. 7). Therefore, in order not to have a sudden change of direction when designing the trajectory, it is necessary to avoid small turning radius and at these points the wheels get sliding phenomenon on the road surface causing errors of position and direction.

5 Conclusion This study has established four inverse kinematic control parameters for omnidirection AGV robots with mecanum wheel to ensure that the robot follows the given trajectory and achieves the desired velocity. Using the grid of coordinates as a database to store the robot’s road map will reduce the memory capacity of the robot and the interpolation with the NURBS curve will be softer and more flexible than the studies previously finding equivalent arcs through the problem of breakdown velocity and the safety corridor of the workshop. In addition, the study also showed that at the locations where the robot changes direction, it is necessary to determine the minimum radius of curvature so that sliding does not occur on the road surface, causing the position errors of robots. This is an issue that authors will continue to publish in the near future. Acknowledgement. This research was funded by the Ministry of Industry and Trade in a ministerial-level scientific and technological research project, conducted in 2020, code: DTKHCN.076/20.

References 1. Doroftei, I., Grosu, V., Spinu, V.: Omni Directional Mobile Robot – Design and Implementation; Bioinspiration and Robotics Walking and Climbing Robots, pp. 511–528 (2017)

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2. Schulze, L., Behling, S., Buhrs, S.: Development of a micro drive - under tractor research and application. In: Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. II, Hong-Kong, 16–18 March 2011 (2011) 3. Göller, M., Kerscher, T., Zöllner, J.M., Dillmann, R., Devy, M., Germa, T., Lerasle, F.: Setup and control architecture for an interactive shopping cart in human all day environments. In: Proceeding of the International Conference on Advanced Robotics, pp. 1–6 (2009) 4. Göller, M., Kerscher, T., Ziegenmeyer, M., Ronnau, A., Zöllner, J.M., Dillmann, R.: Haptic control for the interactive behavior operated shopping trolley InBOT. In: International Conference on Advanced Robotics (2009) 5. Puppim de Oliveira, D., Pereira Neves dos Reis, W., Morandin Junior, O.: A qualitative analysis of a USB camera for AGV control. Sensors (2019).https://doi.org/10.3390/ s19194111 6. Gfrerrer, A.: Geometry and kinematics of the mecanum wheel. Comput. Aided Geom. Des. 25, 784–791 (2008) 7. Peng, T., Qian, J., Zi, B., Liu, J., Wang, X.: Mechanical design and control system of an omni-directional mobile robot for material conveying. Procedia CIRP 56, 412–415 (2016) 8. Terakawa, T., Komori, M., Yamaguchi, Y., Nishida, Y.: Active omni wheel possessing seamless periphery and omnidirectional vehicle using it. Precis. Eng. 56, 466–475 (2019) 9. Taheri, H., Qiao, B., Ghaeminezhad, N.: Kinematic model of a four mecanum wheeled mobile robot. Int. J. Comput. Appl. 113(3) (2015). 0975-8887 10. Lin, L.-C., Shih, H.-Y.: Modeling and adaptive control of an omni-mecanum-wheeled robot. Intell. Control Autom. 4, 166–179 (2013) 11. Lee, J.H., Lee, B.H., Choi, M.H.: A real-time traffic control scheme of multiple AGV systems for collision free minimum time motion: a routing table approach. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 28(3), 347–358 (1998) 12. Cook, G.: Mobile Robots Navigation Control and Remote Sensing. Wiley (2011) 13. David, F.R.: An Introduction to NURBS. Morgan Kaufmann Publishers (2001)

Development of New Method for Choosing Standard Components Subject to Minimal Cycle Time and Minimal Sum of Purchasing Cost Tan Nguyen Dang1 and Manh Cuong Nguyen2(&) 1

2

Hanoi University of Mining and Geology, Hanoi, Vietnam Thai Nguyen University of Technology, Thái Nguyên, Vietnam [email protected]

Abstract. An assembly line is made from different machines and a machine can contain different stations and function carriers. Each function carrier even is performed by different standard function carrier variants. The designer can calculate and select the available function carrier variants to construct into a new machine. The advantages of using the available function carriers are the low cost, reduction of design and manufacture time and improvement of machine working life. If a machine has n function carriers, each function carrier contains m variants. Hence, there are nm combinations to make the machine. The number of these combinations increase rapidly according to a large number of function carriers. To select the suitable function carrier variants subject to optimal cycle time and total purchasing cost, the designer cannot manually select the best solution from so many options. Nowadays, there is no effective method to solve this problem. To solve this problem, this paper will set up the operating cycles for an assembly machine and establish linear optimization in standard form for choosing the best function carrier variants from the given database. To minimize both the cycle time and total purchasing cost of an assembly machine, the linear programming must contain two stages and run in sequence. The large linear optimization is programed and solved by using the IBM ILOG CPLEX Optimizer. The optimal results will be exported to tables in a database. This makes the designer easy evaluate and select the best solution. In addition, designer can expand the scope of study to design other machines. Keywords: Assembly machine
Cycle time
Function carrier optimization
Standard component
Total purchasing cost


Linear

1 Introduction 1.1

Compensation of Cycle Time

The product portfolio of numerous companies, such as Schunk, Zimmer Group, Festo or Bosch etc., includes standardized components with technical data, which are stored in catalogues or databases. These data mainly include dimensions, load capacities, repeat accuracy, speeds and accelerations as well as possible cycle times [1, 2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 329–340, 2021. https://doi.org/10.1007/978-3-030-64719-3_37

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To perform the assembly and handling, function carriers are often used that implement assembly and handling task. A strong trend in designing machine is the use of standardized component units to reduce design time of complexity assembly lines [3, 4]. With a focus on selecting suitable components, criteria of function carriers must be defined, such as types, capacity, price, effectiveness and service [5]. The priority for a desired system is not only the shortest cycle times, but also an optimum price/performance ratio [6]. Minimizing the cycle time T is an important step in theplanning and design of an assembly system. By minimizing the cycle time, different target values can be achieved [7]. The assembly and handling operations are assigned to the various stations. Due to the different assembled products with the different assembly and handling operations, there are different cycle times between the stations [8]. To compare the cycle times of individual stations, the cycle time diagram should be created. A cycle time diagram shows the station with the maximum cycle time Tmax , which is the so-called bottleneck station and reduces the efficient utilization in the assembly line. This assembly and handling processes in the stations with shorter cycle times than Tmax must wait for the bottleneck station [9]. The following methods can be used to reduce the different cycle times and eliminate the bottleneck station. To balance the cycle times between the stations in assembly line, the assembly and handling operations are renewed to allocate to the different stations [7]. 1.2

Removal of the Bottleneck Station

To select elements from sets of function carrier variants according to the objective function of the cycle time and purchasing cost, nowadays there is no efficient method. Many companies in the Germany, designing assembly machines such as SIM Automation GmbH, USK GmbH etc., manually select these elements from the supplier, and then calculate the raw cycle time and total purchasing cost. If a machine has n function carriers, each function carriers contains m variants, there are nm different options for selecting the components of the assembly machine. Therefore, if the selecting process is manual, the number of tests will be extremely large, and it will not be able to find an optimal solution. The goal of this research is to build an algorithm to find an optimal solution according to the given conditions in term of minimal cycle time and total purchasing cost [10]. An assembly system comprises different stations, a station consists of the different function carriers.

2 Methods of Study 2.1

Describe the Operations of Assembly and Handling Function Carriers

The operations of all function carriers are determined based on the assembly and handling task. These individual operations of a function carrier in the station decide a cycle time of a station. To determine the cycle time of the station, all operations must be indicated in path-step diagrams and divided into different steps. A change of

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331

operation, e.g. switching a rotary indexing table, starting or stopping a slide unit, is referred to a step. A cycle begins with step 1 and ends with step n (see Fig. 1). After the last step n has completed, a new cycle starts. With a focus on minimizing cycle time, a diagram should be set up so that carrier functions have the least waiting time as possible.

Fig. 1. Path-step-diagram of a station

As a means to define the time variables of the function carriers and the cycle time of a station, the change of operation is labelled in the path-step diagrams. For example, eight changes from 1 to 8 are defined for the oscillating movement. Thus, the events of this function carrier differ from other function carriers. The optimization program selects the function carrier variants (FCV) from the function carrier list (FCL) according to the defined restrictions. Therefore, the FCL serve as the input parameters of the optimization problem. The execution time and purchasing cost of an FCV are the criteria for selection. Thus, the times, e.g. test or measurement time, movement time or response time of FCV as well as the purchasing cost, must be given in the databases (see Fig. 2).

Fig. 2. Structure of database of an assembly system in MS access

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Establish the Optimization Problem by Mathematical Formulas

The optimization problem will be described by following standard form: Stage 1: minimization of cycle time In this section, the optimal cycle time T of the entire assembly system is assigned by the biggest cycle time Tmax of all stations, or in other word, the cycle time of the assembly system T is determined by the slowest station. ð1Þ

T ¼ maxr2S Tr where: S - Set of all stations of the entire assembly system Tr - Cycle time of a station r 2 S

The cycle time of the station r is defined by all points of time of the function carriers of the path-step diagram: r Tr ¼ max j 2 F tj;d ; 8r 2 S

d2

ð2Þ

r Drk

where: Fr - Set of all function carries of the station r 2 S Drj - Set of points of time in the path-step diagram of the function carrier j 2 Fr in the station r r - Points of time according to a change of operation d 2 Drj of a function carrier tj;d j in the station r Therefore, the minimization of the cycle time T applies to a min_max optimization problem. That means, the objective function is to minimize the maximum cycle times of all stations: 1

0

C B C B B r C min T , minðmaxr2S Tr Þ , minBmax r 2 S tj;d C C B A @ j2F

ð3Þ

r

d 2 Drj

Currently, the objective function () has non-linearity, but can easily be reformulated and linearized by a series of constraints:

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333

objective function min T under the constraints 8r 2 S T  Tr ; r Tr  tj;d ; 8r 2 S; j 2 Fr ; d 2 Drj

ð4Þ

To shrink the many constraints above, the end times of the cycles of each station must be determined. In addition, the times of all function carriers in all stations must also be defined. These times are determined by the following conditions: – Relationships between the times of the different function carriers inside a station and outside other stations: In the path-step-diagram, there is one or more changes of operations in a step. To calculate the points of time, the relationships between these changes must be considered. For example, in a step i (path-step-diagram) of a station r, a function r ~ begins a carrier j ends a movement at the point of time tj;d , and a function carrier j movement at the point of time tj~r;~d . Thus, such relationships must be represented by the following constraint. To determine a start-time movement of a function carrier in step 1 (cycle start), its time is assigned 0. r tj~r;~d  tj;d

ð5Þ

– Relationships between the start and end times of a function carrier: For an operation p (for example, opening or closing of a gripper) of a function r r carrier j of a station r, there are a start time tj;p as well as an end time tj;p , its START END difference represents the actual operating time of the operation p. Therefore, the following constraint must be defined: r r r tj;p  tj;p  tj;p END START

ð6Þ

r on the right side represents the minimum actual where: the positive variable tj;p operating time of the execution p and depends on the function carrier variants (FCVs), which is selected for the function carrier j.

In most cases, the right side of the constraint (8) must be considered not only for a single FCV, but also for the FCL. Therefore, a binary variable krj;v is introduced for an FCV v of the FCL. The value of the binary variable krj;v shows, whether an FCV v is selected in the list of a function carrier j.  krj;v ¼

1; chosen 0; otherwise

ð7Þ

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For a function carrier j, only one FCV v is selected. Therefore, the sum of the values of binary variables is written: X kr ¼ 1; 8r 2 S; j 2 Fr ð8Þ v2C j;v j

where: Cj - Set of all FCV of the function carrier j 2 [ r2S Fr r Thus, the variable tj;p is redefined: r ¼ tj;p

X v2Cj

spv
krj;v ; 8r 2 S; j 2 Fr ; p 2 Prj

ð9Þ

where: Prj - Set of all executions/processes of the function carrier j of the station r. It should be noted that 8p 2 Prj : fpSTART ; pEND g  Drj . spv - Catalogue parameter represents the nominal execution time of the execution p of the FCV v of the function carrier j 2 Fr in the station r. From the Eq. (11), the secondary condition (8) is rewritten: X r r tj;p  t  sp
krj;v ; 8r 2 S; j 2 Fr ; p 2 Prj j;p END START v2C v j

ð10Þ

Stage 2: minimum total purchasing cost according to the condition of cycle time Stage 1 minimizes the cycle times of all stations. This means, that only the FCV with the smallest execution times from all FCL are selected. Therefore, all stations contain the FCV with the shortest execution times. There are usually different cycle times between these stations. The stations, which have cycle times less than the cycle time of the system T ¼ Tmax , can choose another FCV, can select FCV to have a longer execution times and cheaper purchasing costs. For this reason, the optimization problem will be run in stage 2. For the second stage, the cheaper FCV than FCV in stage 1 are searched, so that the new cycle times of the stations do not exceed the cycle time T. The different results between the first and second step are shown in Fig. 3.

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Fig. 3. Difference between stage 1 and stage 2

It is essential in stage 2 to integrate total purchasing cost and the cycle time T into one optimization model. To optimize the total purchasing cost, the total cost of all selected FCV must be minimized. The purchasing cost of an FCV v of the function carrier j is called cv . Thus, the objective function of stage 2 is defined: X X X min CSUM , min c
krj;v ð11Þ r2S j2F v2C v r

j

Cycle Time and Total Cost of Diagram After solving the optimization problem in stage 1 and 2, only one solution is found. The FCV according to minimal cycle time and total purchasing cost are issued. The minimum cycle time is often not a primary goal to select an assembly machine. A designer wants to know, how much a machine costs with a given cycle time or how long the cycle time takes with a given total purchasing cost. By manually changing the FCV in a database, the different values of the cycle time T and total cost CSUM can be determined. However, it requires different tests and is not suitable for such an assembly system with numerous FCV. To solve this problem, the  given total cost CSUM will be defined in stage 1. X

X r2S

j2Fr

X v2Cj

 cv
krj;v  CSUM

ð12Þ

3 Application Example An example for assembly product consists of three individual parts: a socket, a chip and a chip holder. Since both the chip and the socket are inserted into the chip holder, the chip holder is a base part. The shape and dimensions of the individual parts are shown from Fig. 4.

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Fig. 4. Shapes and dimensions of socket, chip and chip holder

To assemble a chip, socket in chip holder, a handling process will be arranged in five stations: • • • • •

Station Station Station Station Station

1: 2: 3: 4: 5:

Handover a chip holder Insert a chip into chip holder Insert a socket into chip holder Attendant test a chip in chip holder Ejection a chip holder

The new method for choosing standard components subject to minimal cycle time and minimal sum of purchasing cost can be applied in the different assembly and handling machines. In this paper, the new method is used in finding the best function carrier variants (handling module) of an automatic rotary indexing machine. This machine contains five stations (see Fig. 5). To optimize the cycle and purchase cost of components, it is necessary to point out the structure of each station, component as well as the work cycle. This example will present the configuration of station 1 as well as the path-step-diagrams of station 1 to station 5. The remaining stations are conducted similarly. To optimize the cycle and purchase cost of components, it is necessary to point out the structure of each station, component as well as the work cycle. This example will present the configuration of station 1 as well as the path-step-diagrams of station 1 to station 5. The remaining stations are conducted similarly.

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Fig. 5. Automatic rotary indexing machine

1- divider; 2- gripper; 3- vertical slide; 4- horizontal slide; 5- rotary indexing table Fig. 6. Construction of station 1

Figure 6 shows the function carriers of station 1 and their working principles. A Chip holder is separated and handed over to the chip holder feeder. A transfer of the chip holder to the workpiece carriers on the rotary indexing table is generated by a pick & place module, consisting of a horizontal and a vertical axis. In this example, the automatic rotary indexing machine contains five stations, so it requires to create five path-step diagrams. The cycle begins with the transfer of a chip holder in the gripper to the workpiece holder on the rotary indexing table. The path-step diagram of station 1 consists of nine steps and is described in Fig. 7.

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Fig. 7. Path-step diagram of station 1

where: ZTCT1 to ZTCT4—change points of the divider No. 1 GFCT1 to GFCT4—change points of the gripper No. 2 VSCT1 to VSCT8—change points of the vertical slide No. 3 HSCT1 to HSCT4—change points of the horizonal slide No. 4 RST1, RST2—change points of the rotary indexing table No. 5 The results after stage 1 and 2 in Fig. 8 represent only the list of selected FCV. To control the points of time of each function carrier in the path-step diagram, the selected FCV, points of time, and costs are written to the other tables in database 2. Based on the points of time of the selected FCV, the time-step diagrams will be created. The differences between the selected variants in stage 1 and 2 are the moving times and purchasing costs. These differences are shown by the path-time diagram in Fig. 16, example for station 1. The cycle time of station 1 was determined by the end times of the rotary indexing table No. 5, horizontal slide No. 4 and divider No. 1. In stage 1, these times are: 1 1 tRST;2 ¼ 0;76 s; tHSCT;4 ¼ 0;89 s;

and

1 tZTCT;4 ¼ 0;89 s

In stage 2, the other variants of the rotary indexing table with the bigger step times and lower costs is selected. The step time of the rotary indexing table has a value of 0.44 s instead of a value of 0.32 s. The end time of the rotary indexing table is 1 tRST;2 ¼ 0;89 s and does not exceed the cycle time T  ¼ 0;89 s.

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Fig. 8. Path-time diagram of station 1

4 Conclusions The most important parameters of an assembly line are capacity and assembly cost. To reduce the assembly cost and increase the quality of machine, the designer should use the standard components. A large amount of function carrier variants can be chosen. It makes the designer difficult decide, which variants are better. For each solution, he must calculate the cycle time, total purchasing cost. This manual method spends a lot of time and cannot find the best solution. This study has shown the new method to optimize the cycle time and total purchasing cost by selecting the suitable variants from a given database. The main contents of this new method can be described as follows: • The path-step diagrams of all station of a machine and assembly line must be established. These diagrams will determine the constraints of linear optimization. • Based on the function carriers, the function carrier variants are listed and added to the tables of Microsoft Access. • The standard form of linear optimization contains two stages. The first stage minimizes the cycle time of an assembly line. The second stage minimizes the total purchasing cost. The constraints of linear optimization are execution times of function carrier variants and path-step diagrams. To find out the other minimal cycle time and total purchasing cost, the constraint of total cost is defined in the stage 1.

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• This study used the computer software IBM ILOG CPLEX to solve the linear optimization. All of cycle time, total purchasing cost and selected function carrier variants are exported in the tables of Microsoft Access. Based on these results, the designer can easily choose the suitable solution. Acknowledgement. The work described in this paper is funded by Thai Nguyen University of Technology.

References 1. Krahn, H., Nörthemann, K.H.: Konstruktionselement 3 – Beispielsammlung für die Montage- und Zuführtechnik, pp. 57–60. Vogel Buchverlag und Druck GmbH & Co. KG, Würzburg (1999) 2. Loferer, M.: Rechnergestützte Gestaltung von Montagesystem. Dissertation Technische Universität München, München, pp. 98–121 (2001) 3. Roth, K.: Konstruieren mit Konstruktionskatalogen - Band 2 Kataloge, pp. 11–16. Springer, Heidelberg (2001) 4. VDI 2221: Gesellschaft Entwicklung Konstruktion Vertrieb – Methodik zum Entwickeln und Konstruieren technischer Systeme und Produkte. Verein Deutscher Ingenieure, Beuth Verlag GmbH, pp. 5–12 (1993) 5. Römisch, P., Weiß, M.: Projektierungspraxis Verarbeitungsanlagen, pp. 33–41. Springer, Wiesbaden (2014) 6. Sauer, H.P.: Auswahlkriterien von Siebmaschinen in der Aufbereitungstechnik, pp. 367– 372. Aufbereitungs-techn, Berlin (2015) 7. Pröpster, M.H.: Methodik zur kurzfristigen Austaktung variantenreicher Montagelinien am Beispiel des Nutzungsfahrzeugbaus. Dissertation Technische Universität München, München, pp. 81–92 (2015) 8. Halubek, P.: Simulationsbasierte Planungsunterstützung für Variantenfließfertigung, pp. 16– 32. Vulkan-Verlag, Essen (2012) 9. Grundig, C.G.: Fabrikplanung Planungssystematik - Methoden – Anwendungen, pp. 47–62. Carl Hanser Verlag München, München (2013) 10. Berger, M., Nguyen Dang, T.: Funktionsbasierte Methode zur Optimierung der Taktzeit von Montageanlage, pp. 81–101. Fachtagung FüMoTeC, Germany (2017)

Dust Emission During Machining of CFRP Composite: A Calculation of the Number and Mass of the Thoracic Particles Dinh Nguyen Ngoc1(&), Thi Nguyen Hue2, Bui Van Hung3, and Vu Duy Duc3 1

2

Faculty of International Training, Thai Nguyen University of Technology, Thái Nguyên, Vietnam [email protected] Faculty of Fundamental Science, Thai Nguyen University of Technology, Thái Nguyên, Vietnam 3 Faculty of Mechanical Engineering, University of Communications and Transport, Hanoi, Vietnam

Abstract. Machining of CFRP composite creates many kinds of induced damage like delamination, fiber pullout, etc. these kinds of damage strongly are associated with appearance of dust generated during machining. The small dust particles can be inhaled by operators in machining areas, which can make them get some diseases. However, there have been few studies dealing with this problem. This study aims to investigate the influences of cutting parameters (cutting speed, feed speed and depth of cut) on the number and mass of thoracic which possess aerodynamic diameter particles lower than 32 µm. Dust particles were collected using a dust monitor. The results show that when cutting speed increases the number of thoracic particles increases. Moreover, an increase of feed speed or depth of cutting leads to reduce the number and mass of thoracic particles. Keywords: Thoracic


Trimming
CFRPs
Dust analysis
Composite

1 Introduction Machining of composite materials is accompanied by a series of brittle fractures because of shearing and cracking of matrix materials under applying cutting forces due to the interaction between cutting tool and workpiece [1]. As a result, several kinds of damage such as delamination, uncut fibers, thermal and/or mechanical degradation of the matrix, inter-laminar cracks are created in the machined surfaces [2–4]. In industry, dry machining (without using lubricant) is absolutely recommended by companies. Hence, the generation of above mentioned defects is also strongly accompanied with the emission of fine dust particles with extremely small diameter and sharp edges [1]. These particles are dispersed/suspended in the air and can be inhaled as well as exposed by operators. Therefore, the potential abilities of damaging breathing system and toxic irritations because of exposing can possibly happen. The increasing usage of composite materials in industry leads to more frequently expose with fine dust particles. For this © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 341–349, 2021. https://doi.org/10.1007/978-3-030-64719-3_38

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reason, further studies of particles generated during machining composite materials are imperative and should be carefully considered by research communities. Surprisingly, there have been few studies of these issues so far [5–7]. Haddad M et al. [7] analyze the influence of tool geometries, cutting parameters (cutting speed and feed speed) on dust particles generated during trimming laminated composite materials (CFRP) at two ranges of cutting speed, e.g. standard and high cutting speed. For standard cutting speed, three kinds of cutting tools including burr tools coated and uncoated and four flute end mills were utilized, while only burr tool uncoated was used at high cutting speed. The results showed that at standard cutting speed, tool geometries influence on a number of harmful particles when dust particles created by four flutes end mills were superior to those generated by using both coated and uncoated burr tools. Moreover, coating has a little impact on the generation of dust particles. Moreover, the number of harmful particles increases with decreasing feed speed and increasing cutting speed in both case of cutting speed ranges. Nguyen-Dinh et al. [6] investigate the influence of cutting parameters such as cutting speed, feed speed, radial depth of cut and tool geometries on the number of harmful particles. The results showed that machining with high cutting speed, low feed speed and low depth of cut strongly reduces the emission of dust. This result is similar to that documented by Haddad [7]. Furthermore, four serrated straight flute tools exhibit the ability of minimizing harmful particles compared to other (2 straight flute tools, 2 helix flute tools).

Fig. 1. The inhalable, thoracic and respirable conventions as percentages of total airborne particles [8].

According to [9], the emitted dusts in the air can be divided in some categories depending on the aerodynamics diameters. Figure 1 presents the evolution between total particles in suspension and aerodynamics diameters. It is observed that the particles having size lower than 10 lm denoted by “1” -harmful particles reach the deepest to the respiratory system. The previous studies have dealt with this category. However, it is observed that dust particles which is larger than 10 lm assigned by “2” – thoracic particles are also dangerous when they reach inside the operator’s body. However, there have been few studies dealing with this category of emitted dust particles.

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The main objective of this work is to analyze the impact of cutting parameters (cutting speed, feed rate, and radial depth of cut) on the number and mass of thoracic fraction of dust particles which have the aerodynamic diameters lower than 30 lm. This means that the investigated fraction includes the harmful fraction which has been analyzed in [6]. CFRP specimens are trimmed using PCD tool with a full experimental design including three levels of feed rate, and two levels of cutting speed. The number of particles generated during trimming is counted using a GRIMM dust monitor.

2 Experimental Procedure The CFRP laminates (T700-M21) used in this study were twenty layers of prepregs corresponding to the thickness of 5.2 mm and have stacking sequence of [90°/90°/ −45°/0°/45°/90°/−45°/90°/45°/90°]s. The mechanical properties of machined specimens are listed in Table 1. A full factorial design of cutting condition including three levels of feed rates (500 mm/min, 1000 mm/min, 1500 mm/min) and two levels of cutting speeds (150 m/min and 250 m/min) was utilized. A radial depth of cut of 2 mm was constantly kept for all cutting conditions. New polycrystalline diamond cutters (PCD) with two straight flutes and diameter of 6 mm were used for all cutting conditions (Ref. Fig. 2). Three 280 mm long specimens corresponding with six faces were trimmed for each cutting condition. No coolant was utilized during machining process. Trimming process is carried out on a CNC center 5-axis milling machine. The acquisition of cutting forces is recorded using a dynamometer (Kistler 9272). Machining temperatures are registered by an infrared camera ‘Thermo Vision’ A40. In order to measure the number and the average size of the dust particles generated during trimming, a GRIMM dust monitor (model 1.109) is utilized. This device can count the number of particles which have sizes ranging from 0.25 µm to 32 µm present one liter of air. The interval for each measurement was set up for 6 s. The average number of particles is calculated to get the representative for each machining condition. The details of experimental devices used trimming test are schematically shown in Fig. 3.

Fig. 2. PCD tool used with two straight flutes.

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Composite materials (T700/M21) Ply thickness: 0.26 mm Fiber content: Vfiber = 59% Stacking sequence with respect to the feed direction: [90°/90°/−45°/0°/45°/90°/−45°/90°/45°/90°]s Young modulus: E1 = 142 Gpa, Et = 8.4 Gpa Shear modulus: G12 = 3.8 Gpa Glass transition temperature: Tg = 187 °C Energy release rate: GIC = 0.35 N.mm, GIIC = 1.21 N.mm

Fig. 3. Schematic view showing the machining configuration: axes, fiber orientation and the machining directions.

3 Results and Discussion 3.1

Influence of Cutting Parameter on the Number of Thoracic Particles

The thoracic fraction of particles emitted during machining is measured using dust monitor. The authors [8] documented that the thoracic particles can reach deeper breathing system at the position of trachea, bronchus, pharynx, larynx. Hence, dust particles of this fraction can destroy the component of the respiratory system. Determination for this fraction is crucially necessary for both industry and research community. According to [9] this fraction can be determined from the fraction of total particles. The protocol to calculate the thoracic particles can be found by the Eq. (1). EI ¼ 50ð1 þ exp½0:06DÞ ET ¼ 0:01EI ER ¼ 0:01ET

ð1Þ

Where: D is the aerodynamic diameter (µm), EI, ET, ER are the Inhalable fraction, Thoracic fraction, and respirable fraction respectively.

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Figure 4 shows the evolution between the number of thoracic particles and the size of measured particles (aerodynamics diameter). It is observed that an increase in feed speed leads to reduce the number of thoracic particles. Moreover, when cutting speed reduces, the number of thoracic particles seems to reduce, too. For example, when feed speed varies from 500 mm/min to 1500 mm/min the thoracic particles number reduces by 32% and 7% corresponding to cutting speed of 150 m/min and 250 m/min. Regarding the influence of cutting speed, the thoracic particle number increases by 33%, 36%, 48% when cutting speed augments from 150 m/min to 250 m/min at feed speed of 500 mm/min, 1000 mm/min, and 1500 mm/min respectively.

Fig. 4. Evolution of the thoracic particle number and cutting parameters.

The influences of cutting speed and feed speed can be explained by the chip formation process of machining composite materials. Indeed, Janardhan et al. [10] proposed a concept, theoretical chip thickness which is described by the Eq. (2) and graphically presented in Fig. 5. It is visualized that theoretical chip thickness is strongly dependent on cutting parameters. hq ¼ fz

rffiffiffiffiffi ae D

ð2Þ

Vf fz ¼ 1000:Vc  Z pD Where: fz feed per tooth; Vf feed speed; Vc cutting speed; D – diameter of cutting tool, and ae – radial depth of cut.

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Fig. 5. The relations between theoretical chip thickness and cutting condition.

Trimming is carried out with high theoretical chip thickness corresponding to high feed speed or low cutting speed, the surface integrity is typically rougher. For this reason, majority of broken chips or dust particles are therefore larger and quickly drop because of their heavier weight. In opposite way, just remaining particles having smaller sizes can emit in the air and measured by dust sensor. As a result, the number particles received by trimming with high theoretical chip thickness (increase of feed speed or decrease of cutting speed) are smaller than those gotten when trimming with low theoretical chip thickness (decrease of feed speed or increase of cutting speed). Based on the previous analysis, it can be concluded that the variation of thoracic particles vs cutting parameter for thoracic fraction is identical to those for inhalable fraction which were detailed in literature [6, 7]. 3.2

Influence of Cutting Parameter on the Mass of Thoracic Particles

Figure 6 presents the evolution of total number of particles presenting in 1 L of the air with the particle size. We can see that the number of particles and percentage of dangerous particles are high, but their sizes are too small. Haddad [7] showed that the number of harmful particles is about 98% on the total particles. However, in term of mass, the weight of this particle category is minor. Hence, the information of the particle number is not enough to conduct quantitatively the effect of particles on the human health.

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Fig. 6. The total number of particles present in 1 L of air vs. particle sizes for different cutting conditions.

Fig. 7. The mass of thoracic particles in supposed cubic form presents in 1 L of air vs. particle sizes for different cutting conditions.

The cubic form which was proposed by [7] is utilized to calculate the mass of thoracic fraction particles in this study. The protocol of mass calculation can be consulted in [7]. Figure 7 shows the influences of cutting parameters (cutting speed and feed speed) on the mass of thoracic fraction. It can be seen that an increase in cutting

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speed and/or a decrease in feed speed leads to augment the mass of thoracic particles. For example, when cutting speed of 150 m/min is utilized, the mass increases by 19% if feed speed reduces from 1500 mm/min to 500 mm/min. Corresponding variation with cutting speed of 250 m/min is 41%. This influential tendency of cutting parameters on the thoracic mass is identical to that on the number of thoracic particles. The explanation can be followed by that for the influence of cutting parameters on the thoracic number as previously mentioned in which the theoretical chip thickness has a strong impact.

4 Conclusions This work presents an experimental study of dust emission during trimming CFRP laminates by using PCD tools. The thoracic fraction of dust particles is measured by a dust monitor. The influences of cutting parameter on the number and mass concentration of thoracic particles are investigated. Based on an experimental analysis, the following conclusions are made. The number of particles generated during trimming present in one liter of the air is influenced by cutting conditions (cutting speed and feed speed). Trimming process carried by higher theoretical chip thickness (high feed speed and low cutting speed) creates less particles in the air than that carried by low theoretical chip thickness (low feed speed and high cutting speed). The effect of cutting parameters on thoracic fraction particles exhibits the same tendency to the total number of particles presenting in the air. Cutting conditions clearly influence the mass of thoracic particles estimated by cubic forms. When feed speed varies from 500 mm/min to 1500 mm/min, the thoracic particle mass reduces by 19% and 41% with cutting speed of 150 m/min and 250 m/min respecitvely. Acknowledgments. The authors wish to thank Thai Nguyen University of Technology for supporting this work.

References 1. Ramulu, M., Kramlich, J.: Machining of fiber reinforced composite-review of enviromental and health effects (2004) 2. Hintze, W., Hartmann, D., Schütte, C.: Occurrence and propagation of delamination during the machining of carbon fibre reinforced plastics (CFRPs) – an experimental study. Composit. Sci. Technol. 71(15), 1719–1726 (2011) 3. Azmi, A.I.: Chip formation studies in machining fibre reinforced polymer composites (2013) 4. Sheikh-Ahmad, J., Urban, N., Cheraghi, H.: Machining damage in edge trimming of CFRP. Mater. Manuf. Process. 27(7), 802–808 (2012) 5. Klocke, F.: Environmental effects and safety in machining fibrous composites (1996) 6. Nguyen-Dinh, N., et al.: New tool for reduction of harmful particulate dispersion and to improve machining quality when trimming carbon/epoxy composites. Composit. Part A Appl. Sci. Manuf. 131 (2020)

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7. Haddad, M., et al.: Study of the surface defects and dust generated during trimming of CFRP: Influence of tool geometry, machining parameters and cutting speed range. Composit. Part A Appl. Sci. Manuf. 66, 142–154 (2014) 8. Environnement, E.-S. Élimination des particules (2006) 9. STANDARD, B. European Standard Norm EN 481. Workplace atmospheres – size fraction definitions for measurement of airborne particles (1993) 10. Janardhan, P., Sheikh-Ahmad, J., Cheraghi, H.: Edge trimming of CFRP with diamond interlocking tools (2006)

Dynamic Surface Control of the Axial-Flux Permanent Magnet Motor with Speed Sensorless Algorithm Manh Tung Ngo1,2, Quang Dang Pham1, Huy Phuong Nguyen1, and Tung Lam Nguyen1(&) 1

2

Hanoi University of Science and Technology, 1 Dai Co Viet Road, Hanoi, Viet Nam {lam.nguyentung,lam.nguyentung}@hust.edu.vn Hanoi University of Industry, No.298, Cau Dien Street, Bac Tu Liem District, Hanoi, Viet Nam

Abstract. The permanent synchronous motor drive system incorporates magnetic bearings to perform speed control and balance rotor control between the two stators. The paper designed a system to perform adjusting motor speed sensorless based on measured current and voltage components. The electromotive force (back-EMF) generated in the stator is estimated by a High-Gain observer. The angular position and velocity rotor are calculated through the a-b components of the back-EMF. The motor drive is built in a vector control structure based on the rotor flux. In which based on the Lyapunov stabilization function, the dynamic surface control method is used to calculate the axial position and speed controller of the motor. Simulation results show that the proposed controller and observer are fully capable of meeting the system control requirements. Keywords: Magnetic self-bearing motor Dynamic surface control


Back-EMF
High-gain observer


1 Introduction In recent years, the motor with integrated magnetic bearing has been increasingly studied and applied due to its advantages compared to traditional ball bearing [1]. The motor which has the magnitude of the air gap between the stator and the rotor is constantly varying, affecting the motor parameters and increasing the nonlinearity of the target. To deal with this problem, the control process needs to separate the channel between the torque generating the rotation and the force acting the axial position. Hence, based on the principle of control based on the rotor flux, the current id controls the axial force, while the radial current iq controls the torque [2]. Dynamic surface control method is used to design the axial position controller and the motor speed control. This is a technique applied to nonlinear system objects, applied to grip control and stability for the system [3–5]. Dynamic Surface Control (DSC) technique features

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 350–358, 2021. https://doi.org/10.1007/978-3-030-64719-3_39

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backstepping and sliding multi-surface control (MSS) [6]. However, it has added a first order filter to overcome the problem of explosion of term. During the control process, the control loops need to perform coordinate transformation [7]. This leads to a need for accurate angular position information of the rotor by sensors that measure the rotational angle. This increases costs, size, and affects the mechanical strength and requires extensive maintenance. Due to this, methods of replacing speed sensors with calculation/observer techniques are studied extensively. In the published works on non-measuring rotation speed control for synchronous motors, two common approaches for estimating the angular position and rotor speed can be given in [8, 9]. This article presents a High-Gain observation based on the back-EMF electrodynamic reactivity estimation, it has the advantage that the rule of observation is simple, easy to design, and there is no project using HG observers for magnetic motors. Although [10] uses a similar observational structure, the hydraulic system applies. The paper proposes a drive system using a vector control structure, in which the HG observer estimates the back-EMF value from the stator current and the reference voltage value, thereby calculating the Rotor angle position and rotation speed. The feasibility of the proposed methods is demonstrated through simulation of the control system without speed sensor on the software. Simulation results show that both position response and speed response follow the set trajectory in fast time. The system is also stable to the coupling effects of the two control loops.

2 Mathematical Model The permanent magnet synchronous motor incorporating the axial magnetic bearing has the structure shown in Fig. 1. The motor structure consists of a rotor and two stators [1]. The torque T that rotates the rotor is the sum of the two component moments and the F axial force that stops the rotor from moving in the z direction is calculated by the difference of the two axial forces. To design the control system, the mathematical model of the motor is shown on the dq rotating coordinate or ab static coordinate system. For L’sd0 and L’sq0: the d- axis and q- axis inductance per gap unit; Lsl: leakage inductance; g = g0 ± z: clearance between stator and rotor; g0: clearance at equilibrium position; z: displacement. Voltage vector equation of a stator on the ab coordinate system is given as: Us ¼ is Rs þ Ls dis =dt þ Es

ð1Þ

The inductance is presented as [1, 11]: Lsd ¼ 3L0sd0 =2g þ Lsl ; Lsq ¼ 3L0sq0 =2g þ Lsl

ð2Þ

Where is the stator current vector, Us is the stator voltage vector. Es is a sine function and is expressed as components on the ab coordinate system as follows:

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Fig. 1. The structure of the motor

Esa ¼ jkm jxe sin he ; Esb ¼ jkm jxe cos he

ð3Þ

With xe is the rotor speed, he is the position of the rotor flux vector. From (1) we have: (

disa =dt ¼ Rs isa =Lsd þ usa =Lsd  Esa =Lsd disb =dt ¼ Rs isb =Lsq þ usb =Lsq  Esb =Lsq

ð4Þ

We have the equation for calculating the total torque and total axial force as follows [11, 12]: F ¼ 4KFd if id ; T ¼ 2KT iq

ð5Þ

3 Dynamic Surface Controls The motor’s rotation equation can be written as follows: T  TL ¼ J
dx=dt or can be written as x_ ¼ ðT  TL Þ=J

ð6Þ

x_ ¼ iq
2KT =J  TL =J ¼ M
iq  N

ð7Þ

According to (6) we have:

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The definition of a sliding surface is: z1 ¼ x  x d

ð8Þ

z_ 1 ¼ x_  x_ d ¼ Miq  N  x_ d

ð9Þ

The derivative of z1 is:

According to DSC, virtual control signal: iq ¼ cx z1 =M þ ðN þ xd Þ=M

ð10Þ

Choose cx > 0, the signal controls iq through the filter which is the first-order filter to obtain the reference signal iqref: _iqref ¼ ðiq  iqref Þ=sx

ð11Þ

Choose time constant sx [ 0. Set: ~iq ¼ iqref  iq

ð12Þ

Define a Lyapunov candidate function as follows: V ¼ z21 =2 þ ~i2q =2

ð13Þ

In order for the speed loop to be controlled asymptotically, then the derivative of V: V_ ¼ z1 z_ 1 þ ~iq~_iq  0

ð14Þ

According to (14), the time constant satisfies the following inequality: sx  ex =B2   where B >0 is a maximum constant such that _iq   B. According to (5): €z ¼ ðF  FL Þ=m ¼ 4KFd if id =m  F=m ¼ P:id  Q

ð15Þ

ð16Þ

Definition of sliding surface: S ¼ z_ þ kzðwith k [ 0Þ

ð17Þ

Using similar approach to the speed controller design, virtual control signal are defined as [10]:

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id ¼ Q=P  k_z=P  cz :satðSÞ; _idref ¼ ðid  idref Þ=sz

ð18Þ

where cz > 0, sz > 0 and the time constant should be satisfied: sz  ez =A2

ð19Þ

  Where A is a maximum constant that _id   A. The position control loop will stabilize after a finite time with z approaching sliding surface S.

4 High-Gain Observer From (3) and (4) we have: disa :  Rs isa þ usa ¼ha dt disb :  Rs isb þ usb ¼hb esb ¼ jkm jxe cos he ¼ Lsb dt

esa ¼ jkm jxe sin he ¼ Lsa

ð20Þ

The update law for ^esa and ^esb according to the HGO set is written as follows [10, 13], with 1=ea and 1=eb are the coefficients of HGO: ^e_ sa ¼ ðLsa _isa  Rs isa þ usa  ^esa Þ=ea ¼ ^esa =ea þ ha =ea ^e_ sb ¼ ðLsb _isb  Rs isb þ usb  ^esb Þ=eb ¼ ^esb =eb þ hb =eb

ð21Þ

The definition of the error between the actual and the estimated value is as follows: ~esa ¼ esa  ^esa ; ~esb ¼ esb  ^esb

ð22Þ

According to [13], we have the following values satisfying the inequality:     j~esa j  eð1=ea Þt j~esa ð0Þj þ ea :qa ðtÞ; ~esb   eð1=eb Þt ~esb ð0Þ þ eb :qb ðtÞ

ð23Þ

From (20), the values esa and esb are harmonic functions, so the coefficients h_ amax and h_ bmax exist such that:   h_ a   h_ a

max

  and h_ b   h_ bmax

ð24Þ

  Thus, the smaller the coefficients ɛa and ɛb, the smaller j~esa j and ~esb  are bounded   by the smaller bounding regions j~esa ð1Þj and ~esb ð1Þ. Then, the smaller the value ɛa and ɛb, the faster the rate of convergence of the estimated value follows the real values esa and esb of the back-EMF. From the output signal of High-gain observer, the values of the angular position and rotor speed are calculated as follows:

Dynamic Surface Control of the Axial-Flux Permanent Magnet Motor

^he ¼ arctanðE ^ sa =E ^ sb Þ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 þE ^ sa ^ 2 =km ðaÞ or: x ^ e ¼ d^ ^e ¼ E h=dt ðbÞ x sb

355

ð25Þ ð26Þ

Note that if the speed is calculated according to (26a), the value of the speed depends on the linkage flux km, which is influenced by the ambient temperature. According to (26b), the speed is more reliable, especially in the medium and highspeed range, but is affected by process noise.

5 Simulation and Result The parameters of the motor include: phase resistance is 2.6 X; the air gap between the stator and the rotor is 1 mm; the rotor mass is 0.28 kg; the moment of inertia is 10.6  10−6 kgm2; the amplitude of the flux generated by the permanent magnet is km = 0.022 Wb. When the set speed is 4500 rpm, at the moment of 0.65 s the load torque affects the system. Figure 2 shows that the estimation speed is very fast with real speed value and the error is negligible.

Fig. 2. Speed response when set value is 4500 rpm under the influence of load torque

Figure 3 shows the response of the two current id and iq, where the iq current at the beginning has a large value to accelerate the motor to quickly reach the set value, then the value drops very small at the end of the transition mode. Figure 4 shows that when

Fig. 3. Current response id and iq

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Fig. 4. esa and esb component of the motor and esa and esb set of the observation

Fig. 5. Speed response

the load is applied, the back-EMF component on of the observer adheres to the backEMF component of the motor very fast and few errors. Thus, in this case the HG observer estimates that the components esa and esb have a sinusoidal form, with the same amplitude and following the observed value. When changing the speed setting value from 2500 rpm–3500 rpm–1500 rpm in Fig. 5 and Fig. 6. Estimated speed is still capable of adhering to the real speed. The components es at the output of the HGO continue to be the same sinusoidal form and follow the motor Back-EMF. Changing the speed setting value does not change the estimation time of the monitor.

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Fig. 6. Back-EMF components on a-b trục axis when speed setting value is changed

6 Conclusion The paper presented the control system without measuring the rotation speed for synchronous motor with integrated magnetic bearing, calculating the dynamic surface controllers and designing the HGO unit in the system. This proposed system is simpler and easier to design than the ones in [8] and [9]. By taking the input signal to the observer, the reference voltage and stator current perform an estimate of the back-EMF electromotive force as the basis for calculating the position and rotor speed. The system works stably at medium speed range upwards, in which the interaction between axial position control and speed control has also been limited. However, the several spikes caused by the disturbances affects the quality of the output speed.

References 1. Nguyen, Q., Ueno, S.: Salient pole permanent magnet axial-gap self-bearing motor no. Im (2009). https://doi.org/10.5772/intechopen.83966 2. He, R., Han, Q.: Dynamics and stability of permanent-magnet synchronous motor. Math. Probl. Eng. vol. 2017 (2017). https://doi.org/10.1155/2017/4923987 3. Hedrick, B.S.J.K.: Dynamic Surface Control of Uncertain Nonlinear Systems. Springer (2011). https://doi.org/10.1007/978-0-85729-632-0 4. Lan, Y.H., Lei-Zhou.: Backstepping control with disturbance observer for permanent magnet synchronous motor. J. Control Sci. Eng. 2018 (2018). https://doi.org/10.1155/2018/4938389 5. Yang, Z.J., Nagai, T., Kanae, S., Wada, K.: DYnamic surface control approach to adaptive robust control of nonlinear systems in semi-strict feedback form, vol. 16, no. 1. IFAC (2005)

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6. Nguyen, T.D., Tseng, K.J., Zhang, S., Nguyen, H.T.: A novel axial flux permanent-magnet machine for flywheel energy storage system: design and analysis. IEEE Trans. Ind. Electron. 58(9), 3784–3794 (2011). https://doi.org/10.1109/TIE.2010.2089939 7. Quang, N.P., Dittrich, J.-A.: Power Systems Vector Control of Three-phase AC Machines. Springer (2015). https://doi.org/10.1007/978-3-662-46915-6 8. Nguyen, Q.D., Ueno, S.: Sensorless speed control of inset type axial gap self-bearing motor using extended EMF. In: 2010 International Power Electronics Conference - ECCE Asia -, IPEC 2010, pp. 2260–2264 (2010). https://doi.org/10.1109/ipec.2010.5542012 9. Nguyen, T.D., Foo, G.: Sensorless control of a dual-airgap axial flux permanent magnet machine for flywheel energy storage system. IET Electr. Power Appl. 7(2), 140–149 (2013). https://doi.org/10.1049/iet-epa.2012.0048 10. Won, D., Kim, W., Shin, D., Chung, C.C.: High-gain disturbance observer-based backstepping control with output tracking error constraint for electro-hydraulic systems, no. m, pp. 1–9 (2014). https://doi.org/10.1109/TCST.2014.2325895 11. Nguyen, Q.D., Ueno, S.: Axial position and speed vector control of the inset permanent magnet axial gap type self bearing motor. In: IEEE/ASME International Conference Advance Intelligence Mechatronics, AIM, pp. 130–135 (2009). https://doi.org/10.1109/aim. 2009.5230025 12. Nguyen, Q.D., Ueno, S.: Analysis and control of nonsalient permanent magnet axial gap self-bearing motor. IEEE Trans. Ind. Electron. 58(7), 2644–2652 (2011). https://doi.org/10. 1109/TIE.2010.2076309 13. Khalil, H., Praly, L.: High-gain observers in nonlinear feedback control. Int. J. Robust Nonlinear Control 24(6), 993–1015 (2014). https://doi.org/10.1002/rnc.v24.6

Edge-Based Object Pose Estimation Using Differential Evolution Algorithm Ngoc Linh Tao(&) and Lan Phung Xuan School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]

Abstract. The paper presents an object-tracking method to estimate threedimensional position of objects based on an edge image and the object’s 3D model. The system uses differences between a chamfer matching map and 2D edge gained from a pose hypothesis as the fitness function. Differential Evolution algorithm uses the fitness function in order to find the most suitable position of objects. The experiments with the initialization task were carried out. The results of initialization problem proved the effectiveness of our method in solving the most difficult problem of object tracking. The correct poses were found in reasonable runtime. After correct initialization, the searching space is significantly reduced, as the more effective of the method could track the object in continuous frames. Keywords: Edge-based tracking


3D pose estimation
Differential evolution

1 Introduction Until recently, researchers have archived significant improvement in solving object detection and tracking problem by using keypoint features [1]. As coordinate transformations and lighting condition changing has little effect on keypoints searching methods, they seem to be a perfect choice for solving image matching problems from slightly different viewpoints [2]. However, keypoint-based approaches work well in textured objects but insufficient textured objects. Textured objects have various keypoints, those have high potential appearing on both images. After finding keypoints, sample consensus such as RANSAC [3] calculates the most suitable transformation of the object from reference position to current position. The more matched keypoints, the more accurate the transformation is. On objects with insufficient texture, lacking of keypoint repeatability and stability neither reduces the accuracy of sample consensus method nor leads to wrong tracking results. Like keypoints, edges are also invariant to general geometric transformations and illumination changes [4]. Using edges is more suitable as a general approach even with texture-less objects in object pose estimation problem. In early computer vision research, to find the best alignment between two edge maps, a given prior set of edge templates compare their suitability to the current edge maps to draw the most suitable transformation. The currently proposed method of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 359–365, 2021. https://doi.org/10.1007/978-3-030-64719-3_40

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chamfer distance matching [5] enhances the cost functions enable for applying global searching algorithm into object tracking problems. Harris [6] and various proposed edge-based tracking system such as [7] used edges and contours for visual tracking task. One drawback of using edges is that they are not distinctive enough to provide effective discrimination in complex background or occlusions, there have been efforts to enhance the previous one by unifying interest points or considering multiple but limited hypotheses on edge correspondences. For consideration of multiple hypotheses in a more general sense global searching algorithm should come into consideration. We propose an approach of using Differential Evolution (DE) [8] as the global searching method to calculate initial position and continuously track 3D positions of an object in camera coordinate.

2 Methodology Initialization is the most important step of the tracking algorithm and is an attractive topic of the paper. The following steps presents the implementation pipeline of the initialization: – Edge image is employed to archive edge images from query images. – Distance maps or chamfer matching maps are calculated from edge images. – Differential Evolution is employed to calculate the best fit pose of the object which create a 2D edge images fitted into the chamfer matching maps for initialization. After initialization, a narrower searching boundary is used to get the accurate results at on-line speed. If the cost function goes large, initialization is required. 2.1

Chamfer Matching Maps from Query Images

Edge Detection The white lines in Fig. 1 are output of Canny [9] edge detection method. Canny method includes five different steps: Step 1: Noise removal step by using Gaussian filter. Step 2: Calculate the intensity gradients of the image Step 3: Apply non-maximum suppression to get rid of spurious response to edge detection Step 4: Apply double threshold to detect potential edges. Step 5: Track edge by hysteresis: finalizing the detection of edges by suppressing all the other edges that are weak and not connected to strong edges. Chamfer Matching Maps In the comparison step, points get higher score when they come closer to the edge point. By indexing the value of pixel with its distance to the closest edge point, we get the distance map as shown in Fig. 2.

Edge-Based Object Pose

2.2

361

Camera Model and Edges from CAD Model

From a calibrated RGB camera and object CAD model, we are able to archive ideal visible edges of objects by using a camera model matrix.

Fig. 1. Edge detection result

Fig. 2. Chamfer matching map

This matrix converts a point with coordinate of (x, y, z) in real coordinate to an image point (u, v) as Eq. 1. 2 3 2 u fx 4v5 ¼ 4 0 1 0

0 fy 0

32 x 3 cx =z cy 54 y=z 5 1 1

ð1Þ

To determine visibility of object edges we use edge features-based method from [12]. Figure 3 shows an edge between adjacent faces A ¼ hv0 ; v1 ; v2 i and B ¼ hv0 ; v1 ; v3 i with unit face normal nA and nB calculated as in Eq. 2, 3. nA ¼ normalizeð½v1  v0   ½v2  v0 Þ

ð2Þ

nB ¼ normalizeð½v3  v0   ½v1  v0 Þ

ð3Þ

Fig. 3. Edge between two surfaces

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To determine the visibility of edge E ¼ hv0 ; v1 i, we use additional vector ve with direction from v0 to the camera center. E is a visible edge if cross manipulation value of ve with nA or ve with nB positive. 2.3

Initial Pose Searching

Cost Function Calculation The cost function for global searching algorithm is a comparison result between edge maps from image and edge maps from CAD model. To gain an equivalance between ideal edge maps, a re-sampling step is applied, so the number of edge points in different ideal maps is set equally at N = 200 points. The error cost function is calculated as in Eq. 2, FðR; tÞ ¼ f ðnÞ

n 1X ðE i  M i Þ2 n2 1

ð4Þ

where f ðnÞ is function depended on the number of inlier (n) as in Eq. 3. Ei is value of real edge images at inlier i, Mi is value of CAD model edge images at inlier number i. f ð nÞ ¼ 1 

n N

ð5Þ

3 Differential Evolution Algorithm Differential evolution (DE), proposed by Storn and Price, is a very popular EA. Like other EAs, DE is a population-based stochastic search technique. It uses mutation, crossover and selection operators at each generation to move its population toward the global optimum minimum. a) Initialization in DE The initial population was generated uniformly at random in the range lower boundary (LB) and upper boundary (UB).   XG¼0 ¼ lbj þ randj ð0; 1Þ ubj  lbj i;j

ð6Þ

where randj ð0; 1Þ a random number in [0,1]. b) Mutation operation

  G G G ¼ X ; X ; . . .; X In this process, DE creates a mutant vector, XG i i;1 i;2 i;n . For each individual at each generation XG i is a target vector in the current population. There are several variants of DE based on mutation schemes, which are: DE/rand/1, DE/best/1, DE/current to best/1, DE/rand/2, DE/best/2, DE/rand to best/1.

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c) Crossover operation G After mutation process, DE performs a binomial crossover operation on XG i and Vi to   G G G generate a trial vector UG i ¼ Ui;1 ; Ui;2 ; . . .; Ui;n for each particle i as shown as Eq. 5. ( UG i

¼

VG i;j XG i;j

if randj  CR or j ¼ jrand otherwise

ð7Þ

where i = 1, …, NP. j = 1,…, D is randomly chosen integer in ½1; D, CR 2 ½0; 1 is the crossover control parameter. d) Selection The selection operator is to select the better vector between the target vector XG i;j and the trial vector UG to enter the next generation. i;j þ1 XG ¼ i



UG i XG i

   G if f UG i  f Xi otherwise

ð8Þ

þ1 is target vector in the next generation where i = 1, …, NP, XG i

4 Experiment and Results 4.1

Experiment Setup

To implement the algorithm, the system hardware included an iBuffalo BSW20KKM11BK camera. All code is implemented on C ++ code on a Macbook Air laptop computer empowered with 1.3 GHz Intel Core i5. In the experiment, we used the box object with size of 145  95  40(mm) in white colour as the tracking object. The object “*.ply” extension use the input model. The searching boundary for the object translation is in [-100, 100] and [p=2; p=2] for rotation angles of roll-pitch-raw. 4.2

Results

Figure 4 shows the results of boundary search of the box with the position as Fig. 1. The box boundary is in blue colour in the left image. The result images showed that, the method was able to find the position of the object so the boundary could fit into the edge maps. Depend on the objects, symmetric or non-symmetric, we need to set different searching boundaries for the searching algorithm (DE). Figure 5 shows the convergence trend of DE. Table 1,2 show consuming time and success rate depending on population number of DE respectively. The success rate stops ỉmpove from population of 250.

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5 Discussion and Conclusions Object tracking has been a challenging task in computer vision. Recently, evolution based global searching methods have proved its potential in solving the tracking problem, finding robust and accurate global optima solutions. We proposed a novel approach of using differential evolution as a global searching method to find the best 3D position of objects. The experimental results showed promising results. In the future, we would like to improve cost function with the method to narrow the searching area for more accuracy.

Table 1. Time consuming on population size Pop size 400 300 200 100 20 Runtime (ms) 1145 801 519 298 60

Table 2. Convergence rate on pupulation size Pop size 300 250 200 150 100 Success rate % 85 88 80 75 70

Fig. 4. Tracking results

Fig. 5. Error convergence trend

References 1. Lowe, D.G.: Distinctive image features from scale-invariant key- points. IJCV 60(2), 91–110 (2004) 2. Grauman, K., Darrell, T.: The pyramid match kernel: Discrimina- tive classification with sets of image features. In: ICCV, vol. 2, pp. 1458–1465 (2005) 3. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM 24, 381–395 (1981)

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4. Luc, V., Caifeng, S., Tommaso, G.: Real-time robust background subtraction under rapidly changing illumination conditions. Image Vis. Comput. 30(12), 1004–1015 (2012) ISSN 0262–8856, http://dx.doi.org/10.1016/j.imavis.2012.08.017 5. Barrow, H., Tenenbaum, J., Bolles, R., Wolf, H.: Parametric correspondence and chamfer matching: Two new techniques for image matching. In: IJCAI, pp. 659–663 (1977) 6. Harris, C.: Tracking with rigid objects. MIT Press (1992) 7. Comport, A.I., Marchand, E., Chaumette, F.: Robust model-based tracking for robot vision. In: IROS, vol. 1, (2004) 8. Storn, R.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Global Opt. 11(4), 341—359 (2007) https://doi.org/10.1023/a: 1008202821328 9. Canny method, http://docs.opencv.org/master/da/d22/tutorial_py_canny.html#gsc.tab=0 10. Bradski, G.: Opencv_library. Dr. Dobb’s Journal of Software Tools (2000) 11. OpenCV Edge Detection Implementation. http://docs.opencv.org/2.4/doc/tutorials/imgproc/ imgtrans/canny_detector/canny_detector.html 12. Morgan, M., John, F.H.: Hardware-determined feature edges. In: Proceedings of the 3rd International Symposium on Non-Photorealistic Animation and Rendering (2004) http://doi. acm.org/10.1145/987657.987663

Effect of Changing Grounding Mode to Reduce Power Loss on Lightning Ground Wire by Induced Current - Northern Vietnam Overhead Power Transmission Line Nhat Tung Nguyen1 and Xuan Phuc Nguyen2(&) 1

2

Faculty of Electrical and Electronics Engineering, ThuyLoi University, Hanoi, Vietnam [email protected] Power Network Planning Deptartment, Institute of Energy, Hanoi, Vietnam [email protected]

Abstract. In Vietnam, the overhead power transmission lines are usually equipped with lightning ground wires, include optical fiber composite ground wire (OPGW) and common ground wire (CGW). Both lightning wires are grounded at each tower to ensure safety for transmission line but directly affect the power loss, caused by electromagnetic induction. Therefore, the calculation of quantitative power loss shows the urgent necessity to reduce the loss, by changing the grounding mode of lightning ground wire. Both field measurement and ATP-EMTP simulation of induced current and voltage of lightning ground wires in transmission lines are carried out in this paper. The results allow proposing a solution to reduce the loss on the lightning ground wire of power transmission grid. Keywords: Induced voltage and current


Ground wire
Transmission grid

1 Introduction In Vietnam, the lightning ground wires (gw) grounded at each tower [1], make induced voltage and induced current through the electromagnetic and electrostatic coupling by transmission circuit [2–4]. Overall, the induced voltage and current are related to grounding way of gw. Overhead lightning wires that have continuous grounding points at electrical tower produce the large induced currents and large power loss consequently. Another way, insulated overhead gws has one end grounded and another insulated, produce a great induced voltage in it [5]. With the objective of reducing power loss on grid, the study of grounding lightning protection wires on 220 kV and 500 kV lines is necessary. Combined with the parameters of the Northern Vietnam overhead transmission lines, this paper simulates and measures on grid the induced voltage and induced current in lightning gw’s under different grounding mode of those wires. The analyzes of their impact factors are used to propose solutions to reduce losses on these lightning ground wires. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 366–372, 2021. https://doi.org/10.1007/978-3-030-64719-3_41

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This study is also demanded by the Vietnam Electricity (EVN) to consider separating the grounding of the lightning protection wire on the 220 kV and 500 kV transmission grid.

2 Induced Current and Power Loss in Northern Vietnam Overhead Transmission Lines 2.1

Simulation the Induced Current and Power Loss in Lightning Ground Wires

The study includes 4 different transmission lines of the Northern Vietnam transmission grid, Table 1. The electric wires are mainly ACSR-330/43 for 220 kV level and ACSR-330/42 for 500 kV level; the lightning gw used type of OPGW 70, 80 and Phlox 116 for CGW. Tower type of 220 kV and 500 kV overhead transmission lines in Northern Vietnam and their parameters are shown in Fig. 1. Especially, line 220 kV T500Pho Noi-Pho Noi has an area without CGW wire. Table 1. Parameters of overhead transmission lines studied Transmission line

220 kV T500 Pho Noi- Pho Noi

Thanh Hoa -Nghi Son 2

220 kV West of Hanoi

T500 Pho Noi - Thuong Tin

500 kV

Case study Length (km) Number of circuits

1 15,33 2

2 65,81 2

3 12,7 4

4 34,26 2

Fig. 1. Line tower in Vietnam overhead transmission line 220 kV and 500 kV

Simulation software used in this analysis is the electromagnetic transient program ATP-EMTP. The induced voltage and induced current depend on the position of wires on the transmission grid; therefore, the paper uses data line model [6] to simulate the overhead lines. The results in normal operation with different power transmission (%) are showed in Fig. 2 and Fig. 3.

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Fig. 2. Induced current in lightning gw’s of 550 kV T500PhoNoi-Thuong Tin

With the lightning gws are grounded at each tower, the induced current is stable in the middle of the line, Fig. 2, little affected by earthing resistances. The simulation indicates that, the induced current on lightning gw is up to 150 A at 500 kV transmission line; 50 A (double lines) and 90 A (quadruple lines) at 200 kV transmission line, Fig. 3. This current increases with the increasing of transmission power, number of circuit and voltage lever of the transmission grid. Large induced current in overhead ground wires will bring adverse effects, such as energy loss and operational life reduction. The power loss due to induction on the lightning gws is calculated by the total losses on the lighting wires at each span and the loss on the earth wire of each tower, specifically according to the following equations: DP = DP1 þ DP2 DP1 ¼

N X

2 ICWG :RCWG þ

1

N X

2 IOPGW :ROPGW

ð1Þ ð2Þ

1

DP2 ¼

N X

2 Ign :Rgn

ð3Þ

1

With RCGW, ICGW; ROPGW, IOPGW; Rgn, Ign are respectively the resistance and current value of CGW, OPGW of each span n, and earth wire of tower n. Results calculation, Fig. 4, indicate that the average values of power loss on the two gw’s is 1,36 kW/mile and 19,3 kW/mile at 220 kV and 500 kV respectively (calculated at 70% transmission power), similarity with value 1,2 kW/mile at transmission grid 345 kV in [7]. However, at 500 kV level, due to the greatly reduced ground resistance at electrical tower, the current induced increases and makes the bigger value of power loss.

Effect of Changing Grounding Mode to Reduce Power Loss on Lightning Ground Wire

0

1000 800 600 400

Transmission Power (%) 20

40

60

80

100

CGW (A) _500 kV_2 circuits

OPGW (A) _ 500 kV_2 circuit

CGW (A)_ 220 kV_2 circuits

OPGW (A)_220 kV_2 circuits

CGW (A)_220 kV_4 circuits

OPGW (A)_220 kV_4 circuits

Power Los (lW)

1200

Induced Current (A)

161 141 121 101 81 61 41 21 1

369

ΔP_220 kV_Phố Nối ΔP_500 kV_2 circuits ΔP_220 kV_2 circuits ΔP_220 kV_4 circuits

200 0 0

20

40

60

80

Transmission Power (%)

100

Fig. 3. Induced current in lightning ground Fig. 4. Power loss with different transmission wires with different transmission power power (%)

The similar calculations are performed for Northern Vietnam transmission lines within 2018 data, in the condition of 50% transmission power, the results indicate that the values of power loss are 0,81 kW/mile and 10 kW/mile for 220 kV and 500 kV respectively. 2.2

Measurement of Induced Current on Lightning Gw’s

The measurements were made with 220 kV and 500 kV Pho Noi - Thuong Tin transmission lines, under 35% transmission power condition, Fig. 5. For 500 kV lines only measurement on the CGW wire are made causes of dangerous for the measuring personnel. The measured and simulated values are showed in Table 1; those values are very close to each other (about 5–7% difference) and specify the exact simulations.

CGW wire 220 kV

OPGW wire 220 kV

CGW wire 500 kV

Fig. 5. Measurement the induced current on grid 220 kV and 500 kV Table 2. Comparation of measurement and simulated current in lightning ground wires Lightning GW Span number CGW of line I 16–17 OPGW of line I 16–17 CGW of line IV 340–341

Measured Current (A) Simulated Current (A) 19,9 21,03 23 25,63 55,8 53

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3 Impact Factors to Reduce the Power Loss on Lightning GW The reduction technique of power loss on lightning ground wires are the transposition, sectionalization and open loop of lightning gw’s, Fig. 6 [7–13]. The open loop of lightning gw’s is difficult and expensive; for this reason, it isn’t considered in this paper. 3.1

Impact of Transposition and Changing the Number of Grounded Points

Table 3. Effect of sparce grounded point on GW loss Distance 0,32 km 1 km 2,5 km 5 km 10 km DP (W) 1624 1622 1624 1592 1589

Table 4. Effect of number of transpositions on GW loss Number of gw transpositions 0

Fig. 6. GW loss reduction Schemes. a) Transposition, b) Sectionalization; c) Open loop

DP (W)

1

2

3

4

1624 1680 1664 1675 1667

Taken transmission line 220 kV double circuit Thanh Hoa - Nghi Son 2, under different segment length from 1 km to 10 km between two grounded point on the lightning gw, Table 3 shows impact of the sparse grounding to the power loss. The reduction of power loss isn’t significant effective. The same results happened to the transposition technique, by increasing the number of transpositions from 1 to 4, Table 4. The reason for those results is that, with the double and quadruple circuits of transmission line, the phases wires and the lightning gws are completely symmetrical along the line length, so the transposition makes no significant inhibitory effect on the electromagnetic induction. The changing number of grounded points causes the small decrease of power loss, corresponding with a small change in the induction parameters. 3.2

Impact of Sectionalization

This scheme is implemented by dividing the lightning gw’s into several sections [8, 9]. Each section length is between a two navigations tower and grounded at one point, opened at the end, and insulated elsewhere. The point grounded serves to reduce the capacitively induced voltage. According to regulation [1], the CGW and OPGW gw’s should be grounded at each electrical tower within 2 km around the station.

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Fig. 7. Power loss on gw’s when changing grounded mode

The calculations, Fig. 7, show that the power loss is significantly reduced when implementing the lightning gw sectionalization solution, reduces 85% at 220 kV level and 91% at 500 kV level. The calculations are performed for Northern Vietnam transmission lines within 2018 data, the results indicate that the values of power loss are reduced at value 0,12 kW/mile and 1,23 kW/mile for 220 kV and 500 kV grids respectively.

4 Conclusion In this study, the simulation of the normal operation mode of double-circuit and quadruple-circuit of transmission grid is performed. The simulation results are verified by experiments, show the results with high accuracy. The power loss is not significantly reduced when changing the number of points grounded of lightning gw’s or make the transposition. The proposed solution when separating the gw’s and grounding one end has shown a significant reduction in loss on lightning gw’s. These initial studies show the feasibility of reducing loss on the transmission grid by reducing loss on the lightning gw’s system. The calculation of the impact factors of the proposed solution on technical specifications of the grid is a matter of research and supplementation.

References 1. QCVN: 2015/BCT, Quy chuẩn kỹ thuật quốc gia về KTĐ – Phần 1: Hệ thống lưới điện 2. Baba, Y., Rakov, V.A.: Voltages induced on an overhead wire by lightning strikes to a nearby tall grounded object. IEEE Trans. Electromagn. Compat. 48(1), 212–224 (2006) 3. Xuefeng, W., Yanping, L.: Research on reducing the energy loss in lightning shield line. High Voltage Eng. Chin. 31(9), 28–30 (2005) 4. Kai, L., Yi, H.: Analysis and research of grounding modes of optical fiber ground composite wire. In: Proceedings IEEE Power Energy Engineering Conference Asia Pacific, pp. 1–4 (2010)

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5. Xiande, H., Hao, Z.: Simulation and analysis of induced voltage and induced current on overhead ground wire of Jindongnan-Nanyang 1000 kV UHV AC transmission line. In: Proceedings 4th International IEEE Electric Utility Deregulation and Restructuring Power Technology Conference, pp. 622–625 (2011) 6. Simões, M.: Transmission line modeling for real-time simulations, Instituto Superior Técnico (2012) 7. Keri, A.J.F., Nourai, A., Schneider, J.M.: The open loop scheme: an effective method of ground wires loss- reduction, IEEE Trans. Power App. Syst. PAS-103(12), 3615–3624 (1984) 8. Rojas, P.E.M.: The effect of discontinuities in a multi-conductor line on lightning-induced voltages. IEEE Trans. Electromagn. Compat. 51(1) 53–66 (2009) 9. Foehmer, B., Thomas, P.: Sectionalizing static wire prevents 500 kV outages. Trans. Distrib. 30, 56–57 (1978) 10. Zhenqiang, L., et al.: Effect of UHV ground wire disposition on its electric energy loss and second arc current. Power Syst. Technol. Chin. 34(2), 24–28 (2010) 11. Militaru, C.: Sectionalizationed OPGW on extra high voltage transmission lines. In: Proceedings of 57th IWCSInternational Wire & Cable Symposium (2008) 12. Benliang, L., et al.: Operation mode of ground wire to reduce ground wire loss of HVAC transmission Lines. PowerSyst. Technol. 35(3), 98–102 (2011) 13. Wang, J., Wang, Y., Peng, X., Li, X., Xu, X., Mao, X.: Induced voltage of overhead ground wires in 500 kV single-circuit transmission lines. IEEE Trans. Power Del. 29(3), 1054–1062 (2014)

Electromagnetic Design of Synchronous Reluctances Motors for Electric Traction Vehicle Bui Minh Dinh(&), Do Trong Tan, and Dang Quoc Vuong Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]

Abstract. The paper presents comparative performances of different rotor structure synchronous reluctance motors used for electric traction in automotive application. The electrical machine under study in this paper is a synchronous reluctance machine (SynRM) with 8 poles and 48 slots. The study design has implemented two V and U shape flux barriers with 4 layers. Pure Synchronous Reluctance motors potentially operate at high speed due to a cost-effective rotor compared to PM and induction motors. In this paper, thermal simulation and mechanical stress was also investigated to evaluated flux bar or sizing of the radial ribs. The approach leads to an original positioning of the radial ribs able to preserve the performances of the motor at high operating speed enhancing the mechanical integrity of the rotor. Some significant contributions of this study are new 4U layer barrier rotor and mechanical stress of ribs and bridges at maximum speed. Keywords: Synchronous Reluctance Motor (SynRM)
Traction motors
Electric vehicle
Torque ripple
Permanent Magnetic Assistant Synchronous Motor- PMASynM

1 Introduction Electric machines have become the primary candidate for mobility [1, 2], improving motor solutions mainly based on high performance permanent magnets (PM) manufactured with rare-earth materials [3–6]. The synchronous reluctance machine (SynRM) has similar structure to an induction machine and permanent magnetic assistant synchronous reluctance machine PMA-SynRM. There is a growing attention in alternative solutions to reduce permanent magnet for electric machines [7]. Many electromagnetic designers also aim to enhance the specific power of the motor-drives often by increasing their maximum operating speed. Many rotor bars or slot layer topologies can be designed to increase the saliency ratio. From the point of view of the lamination orientation, the common two topologies employed are shown in Fig. 1 (a) and (b), axial laminated rotor V and U shape laminated rotor. For electric vehicle application, the most important parameters of this machine is increasing electromagnetic torque in high speed, which is dependent on the inductance difference between d- and q- axis and saliency ratio, respectively. Those parameters can © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 373–378, 2021. https://doi.org/10.1007/978-3-030-64719-3_42

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Fig. 1. Rotor V (a) and U (b) shape laminated rotor.

be improved by the geometrical parameters of the rotor and stator of SynRM. One more problem of the ribs and bridges areas of the rotor would fail because of the high centrifugal forces. In order to increase the maximum speed of the SynRM, some small rotor slots can change ribs and bridges or fill the barriers with nonmagnetic materials with high Young’s Module value [8]. This paper will use software SPEED and finite elements method in order to study the influence of different parameters of the rotor on the machine performances. In order to obtain the best performances (torque ripple, efficiency), rotor slot in U and V shapes have been investigated to obtain those performances.

2 Electromagnetic Performance Evaluation The analytical support used to obtain the main parameters of SynRM is dedicated software SPEED developed by CDAdapco’s. Here, using a graphical interface, the user can decide any pole shapes of the machine (Fig. 2).

Fig. 2. Electromagnetic design program of SynRM 3 layers

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375

For all electrical machines used in automotive applications, the combination between the number of poles and slots is the most important method to getting low torque ripple value. As outlined in the literature the electrical machines used in electrical propulsion in combination with gearbox, are working in the large range of speed (usually between 0 and 20.000 rpm) [5, 6]. In order to obtain the best performances, it is necessary to reduce the iron loss by using a low number of poles to obtain the low value of frequency with direct influences in iron loss. For the SynRM the number of poles recommended in literature is 4, iron losses above this value are very high at considered nominal speed. The design procedure was started according with the performance requirements presented in Fig. 3, and the geometrical constraints: stack length (L = 200 mm), stator outer diameter (Dout= 350 mm).

Fig. 3. SynRM performance requirements

In order to improve the electrical machine performances, several rotor barrier topologies in combination with 8 poles will be analyzed with purpose to reduce the torque ripple. However, the machine was designed initially to develop higher torque and to obtain the shape of electromagnetic torque. The structure obtained in SPEED will be introduced in electromagnetic analysis performed in FEM. The analysis was performed considering several number of barrier layers of U and V rotor shape with number of poles (p = 8) (Fig. 4). Having designed the machine, the following step is to validate torque, efficiency at base speed of 4500 rpm by SPEED software in Fig. 5. For the simulation performed SPEED, the type of steel lamination is M270-35A. The efficiency of the machine over the entire current and speed range of 12.000 rpm is presented. Efficiency values lower than 90% are obtained for high currents and low speed because of the high current density required, producing the desired torque, at high speeds. The efficiency is higher than 95% because of the low iron losses.

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Fig. 4. Torque ripple of 4 V (a) and 4U (b) barrier layers of SynRM

Fig. 5. Torque, power and efficiency vs base speed 4500 rpm and maximum speed 12000 rpm.

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3 Temperature and Mechanical Stress Verification Thermal simulations conducted for the analysis of the temperature of the rotor, stator and winding were conducted based on copper and iron losses. The Fig. 6 shows the temperature distribution for a motor RMS current of 300 A.

Fig. 6. Temperature distribution of SynRM

The thermal simulations have been performed using the commercial software package Motor-CAD that is a code devoted to electrical motor thermal analysis. The implemented model is based on an analytical lumped circuit. Maximum temperatures of rotor, winding or shaft is 104 °C lower than 160 °C. The mechanical equivalent stress map at max speed (12000 rpm) is reported in Fig. 7, the maximum values is 127 MPa.

Fig. 7. Mechanical stress and displacement of rotor

Figure 7 shows the deformation of the rotor at the air-gap at maximum speed operation. In each case, the deformation is less than 10% of the air-gap to avoid any risk contact between rotor and stator considering also possible fluctuation of machine due to vibration modes, tolerances and the bearings selection.

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4 Conclusion The paper presents the analytical a numerical results SynRM 48 Slot/8poles concerning torque ripple, temperature and mechanical stress and displacement in electric vehicle application. The influence of rotor designs of 4U and 4 V barrier shape in has been investigated in this paper. The configuration with 48 slots and 4U layers flux-barrier has the lowest torque ripple. Also this configuration has been numerically evaluated, and the obtained performances has presented in this paper. In continuous power the efficiency is 96% and in peak power condition is 89% at rated speed. Acknowledgment. This paper has received supports from the Viettel High Technology-VHT, Viettel Group for simulating and calculating Software and PCs.

References 1. Credo, A., Fabri, G., Villani, M., Popescu, M.: High speed synchronous reluctance motors for electric vehicles: a focus on rotor mechanical design. In: IEEE International Electric Machines & Drives Conference (IEMDC) (2019) 2. Chan, C.C.: The state of the art of electric and hybrid vehicles. Proc. IEEE 90(2), 247–275 (2002) 3. Zhu, Z.Q., Howe, D.: Electrical machines and drives for electric, hybrid, and fuel cell vehicles. Proc. IEEE 95(4), 746–765 (2007) 4. Hendershot, J.R., Miller, T.J.E.: Design of Brushless PermanentMagnet Machines. Motor Design Books LLC, Tokyo (2010) 5. Williamson, S., Emadi, A., Rajashekara, K.: Comprehensive efficiency modeling of electric traction motor drives for hybrid electric vehicle propulsion applications. IEEE Trans. Veh. Technol. 56(4), 1561–1572 (2007). ISSN 0018-9545 6. Boulanger, A.G., Chu, A.C., Maxx, S., Waltz, D.L.: Vehicle electrification status and issues. Proc. IEEE 99(6), 1116–1138 (2011) 7. Boldea, I., Tutelea, L., Parsa, L., Dorrell, D.: Automotive electric propulsion systems with reduced or no permanent magnets: an overview. IEEE Trans. Industr. Electron. 61(10), 5696–5711 (2014) 8. Widmer, J.D., Martin, R., Kimiabeigi, M.: Electric vehicle traction motors without rare earth magnets. Sustain. Mater. Technol. 3, 7–13 (2015). Elsevier 9. Online database. www.evspecifications.com 10. Pellegrino, G., Jahns, T., Bianchi, N., Soong, W., Cupertino, F.: The Rediscovery of Synchronous Reluctance and Ferrite Permanent Magnet Motors. Springer, Cham (2016) 11. Babetto, C., Bacco, G., Bianchi, N.: Synchronous reluctance machine optimization for highspeed applications. IEEE Trans. Energy Convers. 33(3), 1266–1273 (2018) 12. Ferrari, M., Bianchi, N., Doria, A., Fornasiero, E.: Design of synchronous reluctance motor for hybrid electric vehicles. IEEE Trans. Ind. Appl. 51(4), 3030–3040 (2015)

Enhancing Accuracy of Surface Roughness Model Using Box-Cox Transformation in Surface Grinding AISI 5120 Alloy Steels Do Duc Trung1, Nguyen Dinh Ngoc2, Tran Thi Hong3, Bui Thanh Danh4, Nguyen Thanh Tu2, Tran Ngoc Giang2, Nguyen Thi Quoc Dung2, and Vu Ngoc Pi2(&) 1

3

Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi City, Vietnam 2 Thai Nguyen University of Technology, Thai Nguyen City, Vietnam [email protected] Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam 4 University of Transport and Communications, Hanoi, Vietnam

Abstract. The aim of this paper is to present a research study on enhancing the model accuracy of surface roughness when surface grinding. The used material is this study is AISI 5120 alloy steel and the grinding wheels are made of CBN. The experimental matrix is prepared according to response surface method in terms of Central composite design. Four cutting parameters, i.e. workpiece speed, feed rate, depth of cut, and dressing depth, are selected to investigate their influences on the surface roughness. Moreover, the interactions between the cutting parameters are also considered. Regression models for predicting surface roughness are suggested. Box – Cox transformation is applied to transform the non-normal distribution data to the new ones exhibiting normal distribution shapes. The results show that the new model (using Box – Cox transformation) predicts surface roughness better than the old model (without using Box – Cox transformation) does. Keywords: Surface grinding
Steel AISI 5120
CBN grinding wheel Surface roughness model
Transformation Box-cox




1 Introduction Surface roughness which has been widely utilized to characterize the machining quality of metallic machining has strong influence on the working ability and the lifetime of mechanical components. Hence, improving machining quality corresponding to reduce surface roughness is an important topic of various machining operations such as milling, trimming, turning, grinding. Among these operations, surface grinding process is more frequently adopted to get the finishing machining quality than others. Studying the influence of machining parameters on the surface roughness and establishing surface roughness predicting model for specific machining conditions has been attracted by researchers in literature. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 379–390, 2021. https://doi.org/10.1007/978-3-030-64719-3_43

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Pauwan. K et al. [1] utilized response surface method to analyze surface roughness when grinding process for EN24 steel is carried out using grinding wheel made of Aluminiun Oxide (A60L5V). The machining parameters investigated are grinding speed, workpiece speed, and depth of cut. It is revealed that grinding speed has the strongest influence on the surface roughness. Inversely, workpiece speed, depth of cut, as well as the interaction between these parameters have insignificant influences on the surface roughness. Moreover, a predicting surface roughness model was proposed. Response surface method is also used in a study by Periyasamy S et al. [2], in which surface grinding for AISI 1080 steel was conducted by using grinding wheel made of Aluminiun Oxide (A60V5V). Depth of cut, feed rate, and dressing depth are the considered machining parameters. Their results showed that surface roughness increases with increasing in feed rate, while depth of cut has a complicated impact on the surface roughness. Dressing depth has little effect on the surface roughness. Moreover, a regression model with polynomial degree 2, expressing the relation of the machining parameters, was suggested. Binu. T et al. [3] performed the grinding of ceramic materials using diamond grinding wheels. Design of Experiment was carried out according to response surface method with 27 tests to investigate the influences of machining parameters (workpiece speed, cutting speed, and depth of cut) on the surface roughness. It was reported that three parameters have important impacts on the surface roughness, and the workpiece speed has greater effects than two remaining parameters. Grinding operations of EN31steel were executed by Waikar. O et al. [4]. In this study, machining quality characterized by surface roughness was analyzed in terms of investigating influences of cutting parameters such as workpiece speed, depth of cut, and coolant flowrate. A plan of experiments based on Taguchi method (L9) was designed. The influencing order of machining parameters on the surface roughness is workpiece speed, depth of cut, and coolant flowrate. Surface roughness increases with increasing in depth of cut, and decreasing in workpiece speed and coolant flowrate. Nurul. A.Y et al. [6] used the response surface method to design testing plan for grinding of Inconel 718 steel. The dependence of surface roughness on workpiece speed, depth of cut, and number of passes. The results showed that both workpiece and depth of cut has bigger influence on surface roughness than number of passes. Nguyen H.S et al. [7] studied the impacts of workpiece speed, feed rate, and depth of cut on surface roughness resulting from grinding process of SUJ2 steel using CBN grinding wheel. Fifteen tests based on Box-Behnken design were performed. Their results detailed that the mentioned parameters have important effect on the surface roughness where the feed rate has the strongest impact, and the depth of cut has the weakest effect. The interactions between the investigated parameters also have significant effects on the surface roughness. Other studies of Nguyen H.S et al. [8, 9] conducted the grinding process of 3X13 and SKD11 steels. Workpiece speed, feed rate, and depth of cut were selected to check their influences on the surface roughness. Cutting speed and workpiece speed are found to have strongest influences on the surface roughness for 3X13 and SKD11 steels, respectively. The lowest impacts belong to depth of cut and workpiece speed corresponding to 3X13 and SKD11 steels. The optimum input process parameters for minimum surface roughness when surface grinding 90CrSi tool steel were also reported in [10, 11]. In addition, the optimum

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dressing factors have been proposed to get the minimum surface roughness when grinding 90CrSi steel [12, 13]. Based on the previously mentioned studies, it is realized that the influencing degrees of cutting parameters on the surface roughness are different due to dissimilar machining conditions. Moreover, response surface methods have been utilized in most mentioned studies. This means that this method is highly suitable to design the testing plans. AISI 5120 steels include low carbon percent but possess high strength. This kind of steel has been widely used to fabricate mechanical parts in automobile such as cam shaft, transmission shafts, tube, rods, etc. The mentioned parts are necessary to grind with the good surface quality before using in services. Regarding the materials of grinding wheels, CBN is one of the most effective grinding wheels strongly recommended in industry due to its properties and machinability. For example, this kind of grinding wheel materials can be used for dry grinding which is not able to conduct by other materials. The grinding process of AISI 5120 steels using CBN grinding wheel has not attracted research community so far. In this study, the influences of workpiece speed, feed rate, depth of cut, and dressing depth on the surface roughness (Ra) will be conducted when grinding operation of AISI 5120 steels. The materials of grinding wheels are CBN. Design of experiments will be carried out to develop a predicting surface roughness model according to response surface method. The Box–Cox transformation will be used to improve the accuracy of the proposed model. The predicted values of surface roughness will be compared with experiments to validate the ratability of the proposed models.

2 Grinding Test 2.1

Material Preparation

The tested specimens have a rectangular shape with dimension of 60 mm, 40 mm, and 10 mm corresponding to the length, the width, and the height. Specimens made AISI 5120 steels exhibit the hardness of 56–58 HRC after Carburizing process. The chemical components of specimens are presented in Table 1 (Table 2). Table 1. Denotation of AISI 5120 steels in various standards USA Germany China Japan France England ISO AISI DIN GB JIS AFNOR BS ISO 5120 SCr4204 20Cr Scr420 18C3 527A20 SCr4204

2.2

Machine Tool and Grinding Wheel

Grinding machine used in this study is a surface type, APSG-820/2A (Taiwan origin). The CBN grinding wheel, HY-100# (South Korea origin) is selected herein. The dimensions of external diameter, thickness, and hole diameter of grinding wheel are

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D. D. Trung et al. Table 2. Chemical components C (%) Si (%) Mn (%) P (%) S (%) Cr (%) Ni (%) Cu (%) 1.02 0.212 0.51 0.018 0.017 0.78 0.017 0.021

orderly 180 mm, 13 mm, and 31.75 mm. The grinding tests will be conducted by fixing following parameters: cutting speed of 26 m/s, feed rate of 100 mm/min, and coolant of emulsion with concentration of 6% - flowrate of 10 (l/min) (Fig. 1).

Fig. 1. Grinding machine

2.3

Design of Experiment

The testing plan is designed according to response surface method. The input parameters are workpiece speed, feed rate, depth of cut, and dressing depth. The testing matrix, therefore, are 2 k = 16 (k = 4), 2 k = 8. The values of parameters at different levels are listed in Table 3. The testing plan is shown in Table 4.

Table 3. Testing levels of input parameters Parameters

Denotation Level −2 −1 0 1 2 Workpiece speed, m/min v 5 10 15 20 25 Feed rate, mm/stroke f 2 4 6 8 10 Depth of grinding d 0.005 0.01 0.015 0.02 0.025 Dressing depth t 0.005 0.01 0.015 0.02 0.025

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Table 4. Experimental matrix and results No Code v f 1 0 0 2 0 2 3 −2 0 4 0 0 5 0 0 6 0 −2 … 29 1 1 30 1 1

2.4

d

t

0 0 0 −2 0 0

0 0 0 0 2 0

Real value v (m/min) 15 15 5 15 15 15

−1 1 20 −1 −1 20

Ra (µm) f (mm/stroke) 6 10 6 6 6 2 8 8

d (mm) 0.015 0.015 0.015 0.005 0.015 0.015

t (mm) 0.015 0.015 0.015 0.015 0.025 0.015

0.920 1.260 0.530 0.847 1.886 0.760

0.01 0.01

0.02 0.01

0.670 1.112

Roughness Tester

A Roughness tester, SJ-201 (Mitutoyo – Japan), is adopted to measure the surface roughness of machined specimens (c.f. Fig. 2). The measuring direction is perpendicular to that of the cutting speed direction.

Fig. 2. Roughness tester SJ-201

3 Results and Discussion The surface roughness values listed in Table 4 are averagely calculated by three continuous measurements. The results are analyzed based on Minitab@19, and shown in Table 5, Fig. 3, and Fig. 4. It is observed from Table 5 that the dressing depth has the strongest impact, i.e. the surface roughness increases with increasing in dressing depth. The similar tendencies are also visualized for the cutting speed and the feed rate. However, totally, the workpiece speed, the feed rate and the depth of cut has little effects on the surface

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Fig. 3. Influences of cutting parameters on the surface roughness.

Table 5. Analysis of variance Coefficients Intercept 0.90367 v 0.08138 f 0.04538 d −0.00354 t 0.32121 v2 −0.03911 f2 0.03589 d2 −0.02824 t2 0.05501 v*f 0.02831 v*d −0.01919 v*t 0.03019 f*d 0.13106 f*t −0.07831 d*t 0.07844 Multiple R R Square

Standard error t Stat P-value Lower 95% 0.10294 8.77834 0.00000 0.68425 0.05147 1.58097 0.13474 −0.02833 0.05147 0.88156 0.39192 −0.06433 0.05147 −0.06881 0.94605 −0.11325 0.05147 6.24052 0.00002 0.21150 0.04815 −0.81240 0.42927 −0.14174 0.04815 0.74533 0.46759 −0.06674 0.04815 −0.58653 0.56624 −0.13086 0.04815 1.14255 0.27114 −0.04761 0.06304 0.44912 0.65976 −0.10605 0.06304 −0.30437 0.76503 −0.15355 0.06304 0.47887 0.63894 −0.10418 0.06304 2.07906 0.05518 −0.00330 0.06304 −1.24228 0.23321 −0.21268 0.06304 1.24426 0.23250 −0.05593 0.88371 Adjusted R Square 0.78095 Standard Error

Upper 95% 1.12308 0.19108 0.15508 0.10617 0.43092 0.06351 0.13851 0.07438 0.15763 0.16268 0.11518 0.16455 0.26543 0.05605 0.21280

roughness. The surface roughness progressively increases when these parameters increase. The details can be graphically presented in Fig. 3. The interaction between the input parameters are found to be unimportant. The influencing degrees on surface roughness reduce in the following order: feed rate and depth of cut; depth of cut and dressing depth; workpiece speed and feed rate; workpiece speed and depth of cut. The

Enhancing Accuracy of Surface Roughness Model

Workpiece speed – feed rate interaction

Feed speed – depth of cut interaction

Workpiece speed – depth of cut interaction

Workpiece speed – dressing depth interaction

Feed rate – dressing depth interaction

Depth of cut – dressing depth interaction

385

Fig. 4. Interaction impact between input parameters on surface roughness.

previous statements can be seen in Fig. 4. Based on the results listed in Table 5, a regression model is proposed and described by Eq. (1) to predict the surface roughness. The determination coefficient in this case is 0.7809. Ra ¼ 0:90367 þ 0:08138  v þ 0:04538  f  0:00354  d þ 0:32121  t  0:03911  v2 þ 0:03589  f 2  0:02824  d2 þ 0:05501  t2 þ 0:02831  v  f  0:01919  v  d þ 0:03019  v  t þ 0:13106  f  d  0:07831  f  t þ 0:07844  d  t

ð1Þ

4 Developing Surface Roughness Model Using Box-Cox Transformation In order to improve the accuracy of predicting surface roughness model, Box-Cox transformation is used to transform non-normal dependent data into a normal shape (normal distribution) [14]. Figure 5 shows the shape of data before using Box-Cox transformation. It is seen that the data are not located inside the reference lines (blue lines). On the other hand, P-value of 0.01 is smaller than significance of 0.05. Hence, it can be said that the data are non-normal distribution. Hence, they can be transformed by Box-Cox technique to get the normal distribution ones.

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Fig. 5. Distribution shape of data before using Box-Cox transformation.

The Box - Cox transformation can be executed by formula as Eq. (2):


X 0 ¼ X k when k 6¼ 0 X 0 ¼ lnð X Þ when k ¼ 0

ð2Þ

Where: X and X′ are the data before and after using transformation respectively; k is the power parameter. The values of k can be found based on the standard that standard deviation of X′ are minimized. Typically, in the process of Box-Cox transformation, the values of k can lie in the range between “−5” and 5. After that, the final values can be rounding to the popular values which are listed in Table 6. The values of k are determined to be zero as shown in Fig. 6. The proposed model using Box-Cox transformation is described by Eq. (3) which exhibits the determination coefficient of 0.7930. The surface roughness given by model (1) and model (4) are presented in Table 7. Table 6. The popular value of power parameters in Box – Cox transformation process k k¼2 k ¼ 0:5

Transformation formula

X0 ¼ X2 pffiffiffiffi X0 ¼ X k¼0 X 0 ¼ ln X pffiffiffiffi k ¼ 0:5 X 0 ¼ 1= X k ¼ 1 X 0 ¼ 1=X

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Fig. 6. Diagram of Box–Cox transformation of surface roughness.

Figure 7 presents the distribution shape of data before using Box-Cox transformation. It is observed that the data are placed inside the reference lines. The P-value of 0.463 is hugely larger than significance of 0.05. Hence, it can be concluded that the data using Box-Cox transformation are normal distribution. Ln ðRaÞ ¼  0:1014 þ 0:0795  v þ 0:0506  f  0:0122  d þ 0:3549  t  0:0507  v2 þ 0:0379  f 2  0:0294  d2  0:0335  t2 þ 0:0327  v  f  0:0393  v  d þ 0:0076  v  t þ 0:1065  f  d  0:1010  f  t þ 0:0873  d  t

ð3Þ Or: Ra ¼ EXPð 0:1014 þ 0:0795  v þ 0:0506  f  0:0122  d þ 0:3549  t  0:0507  v2 þ 0:0379  f 2  0:0294  d2  0:0335  t2 þ 0:0327  v  f  0:0393  v  d þ 0:0076  v  t þ 0:1065  f  d  0:1010  f  t þ 0:0873  d  tÞ

ð4Þ In order to investigate the accuracy of two proposed models, the mean absolute errors – MBA and mean square errors – MSE given by prediction will be compared with the experiments in terms of determination coefficient and listed in Table 7. %MBE ¼

%MSE ¼

! n   1X e  p i i  100% n i  ei  n 1X j e i  pi j 2 n i

ð5Þ

!  100%

ð6Þ

Where: e is denoted for the experimental data, p is representative for the predicted data, n is the number of tests.

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Order

Experimental surface Roughness Ra (µm)

Predicted surface roughness Ra (µm)

Absolute error

Without transformation (Eq. 1)

With Transformation (Eq. 2)

Without transformation

Square error

1

0.920

0.904

0.904

1.78%

1.79%

0.03%

2

1.260

1.138

1.163

9.68%

7.66%

0.94%

0.59%

3

0.530

0.584

0.629

10.28%

18.73%

1.06%

3.51%

4

0.847

0.798

0.823

5.81%

2.81%

0.34%

0.08%

5

1.886

1.766

1.607

6.36%

14.79%

0.40%

2.19%

6

0.760

0.956

0.950

25.85%

25.04%

6.68%

6.27%

7

0.660

0.784

0.784

18.73%

18.78%

3.51%

3.53%

29

0.670

1.169

1.111

74.42%

65.76%

55.38%

43.25%

30

1.112

0.779

0.784

29.92%

29.51%

8.95%

8.71%

With transformation

Without transformation

With transformation 0.03%

…

Fig. 7. Distribution shape of data after using Box-Cox transformation.

Table 8 shows the comparison between the surface given by model (1) and model (4) compared with experiments. It is seen that the model using Box - Cox transformation and the other display the errors of 13.05% and 17.2% respectively when compared with experiments. Regarding the mean square errors, the corresponding values are in order 6.88% and 3.77%. Based on the above analysis, it can be concluded that the surface roughness model using Johnson transformation can predict more exactly than the other model. Moreover, we can see that the determination coefficient of model (1) is larger than that given by model (4).

Table 8. Comparison between two proposed model of surface roughness Models % Mean absolute error % Mean square error R2 With Box - Cox transformation 13.05% 3.77% 0.7930 Without Box - Cox transformation 17.21% 6.88% 0.7809

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5 Conclusions In this study, surface grinding process of AISI 5120 steel by grinding wheel of CBN. The following conclusions can be drawn: The dressing depth has the strongest influence on the surface roughness when compared to two others. The surface roughness increases with increasing in dressing depth. The workpiece speed, the feed rate and the depth of cut have little influences. The interaction between the workpiece speed, the feed rate, the depth of cut, and the dressing depth have no influence on surface roughness. The accuracy of the proposed model has improved when using Box-Cox transformation. The model with and without using Box-Cox transformation give differential errors when compared with experiments. Acknowledgements. This work was supported by Thai Nguyen University of Technology.

References 1. Kumar, P., Kumar, A., Singh, B.: Optimization of process parameters in surface grinding using response surface. Methodology 3(2), 245–252 (2013) 2. Periyasamy, S., Aravind, M., Vivek, D., Amirthagadeswaran, K.S.: Optimization of surface grinding process parameters for minimum surface roughness in AISI 1080 using response surface methodology. Adv. Mater. Res. 984–985, 118–123 (2014) 3. Thomas, B., David, E., Manu, R.: Modeling and optimization of surface roughness in surface grinding OFSiC advanced ceramic material. In: 5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014), Assam, India, pp. 333.1–311.7 (2014) 4. Sarika, S., Manikantesh, K., Pavan Kumar, D.: Experimental investigation of surface roughness and temperature on surface grinding of AISI1040 steel using MQL technique. Int. J. Sci. Eng. Technol. Res. 6(2), 2364–2369 (2017) 5. Waikar, O., Nikam, S., More, R., Shinde, S., Chakule, R.: Optimization of machining parameter for surface roughness. IOSR J. Mech. Civ. Eng. 25–30 (2017) 6. Yaakob, N.A., Ganesan, H.N., Harun, N.H., Abdullah, R.I.R., Kasim, M.S.: Influence of grinding parameters on surface finish of inconel 718. J. Mech. Eng. 3(2), 199–209 (2014) 7. Son, N.H., Trung, D.D.: Analysis on the effects of cutting parameters on surface roughness of workpiece in surface grinding. Int. J. Sci. Res. Sci. Eng. Technol. 6(5), 277–282 (2019) 8. Son, N.H., Trung, D.D.: Investigation of The effects of cutting parameters on surface roughness when grinding 3X13 steel using CBN grinding wheel. J. Multidisc. Eng. Sci. Technol. 6(10), 10919–10921 (2019) 9. Hong Son, N., Duc Trung, D., Nguyen, N.-T.: Surface roughness prediction in grinding process of the SKD11 steel by using response surface method. IOP Conf. Ser. Mater. Sci. Eng. 758 (2020). https://doi.org/10.1088/1757-899x/758/1/012029 10. Tung, L.A., Pi, V.N., Hung, L.X., Banh, T.L.: A study on optimization of surface roughness in surface grinding 9CrSi tool steel by using Taguchi method. In: International Conference on Engineering Research and Applications, pp. 100–108. Springer, December 2018 11. Hong, T.T., Cuong, N.V., Ky, L.H., Tung, L.A., Nguyen, T.T., Vu, N.P.: Effect of process parameters on surface roughness in surface grinding of 90CrSi tool steel. In: Solid State Phenomena, vol. 305, pp. 191–197. Trans Tech Publications Ltd. (2020)

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12. Tu, H.X., Hong, T.T., Nga, N.T.T., Gong, J., Pi, V.N.: Influence of dressing parameters on surface roughness of workpiece for grinding hardened 9XC tool steel. In: IOP Conference Series: Materials Science and Engineering, vol. 542, no. 1, p. 012008). IOP Publishing, June 2019 13. Trung D.D., Son N.H., Hong T.T., Van Cuong, N., Vu, N.P.: Calculating effects of dressing parameters on surface roughness in surface grinding. In: Sattler, K.U., Nguyen, D., Vu, N., Tien Long, B., Puta, H. (eds.) Advances in Engineering Research and Application, ICERA 2019. Lecture Notes in Networks and Systems, vol 104. Springer, Cham (2019). https://doi. org/10.1007/978-3-030-37497-6_19 14. Van Du, N., Binh, N.D.: Design of experiment techniques, Science and technics publishing House, Ha Noi, Viet Nam (2011)

Ensemble of Deep Learning Models for In-Hospital Mortality Prediction Quang H. Nguyen1(&) and Quang V. Le1,2 1

School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected] 2 ARS Viet Nam Co., Ltd., Ho Chi Minh City, Vietnam [email protected]

Abstract. Using machine learning in health care for supporting doctors to diagnose diseases is receiving a lot of research interest. Currently, many hospitals use Electronic Health Records (EHR) to store medical records, which is an extremely valuable data source for machine learning. This paper uses the Medical Information Mart for Intensive Care (MIMIC-III) dataset to solve the problem of predicting mortality in hospitals. We have proposed standardizing numerical attributes in two ways: normalizing using mean and variance on Training set and standardizing using mean and variance according to 48 h of each sample. Neural network architectures based on CNN models have been proposed and tested. Ensemble technique has been applied to each parameter representation type and each model type. Test results show that the ensemble method has improved the performance of the system. Test results on the Test set achieve AUROC is 0.851, AUPRC is 0.452 and min (Se, + P) is 0.459. Keywords: Information Mart for Intensive Care (MIMIC-III) dataset
Electronic Health Records (EHRs)
Deep learning
In-hospital mortality prediction
Convolution Neural Network
Long-short term memory
Multi-head attention

1 Introduction Nowadays, in Vietnam, electronic medical record technology has been used in some large hospitals and the Ministry of Health is expected to deploy to all hospitals by 2020 [13]. This is an opportunity for researchers to exploit medical data for helping diagnose diseases in Vietnam. In the world, the use of Machine Learning to help doctors diagnose the disease has been proven to be very effective than traditional evaluation methods. There are many medical problems that can be solved by machine learning methods. This paper proposes Deep Learning models to handle the In-hospital mortality prediction problem. In fact, many patients in the intensive care unit want to spend the final time with their loved ones instead of being in the hospital. At the same time, the patient holding in the ICU (Intensive care unit) for a long time can be very difficult for the patient’s family members, while also making it difficult for the local hospital to arrange the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 391–398, 2021. https://doi.org/10.1007/978-3-030-64719-3_44

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manpower and equipment for those patients. It is, therefore, necessary to predict whether a patient will survive a hospital stay. To be able to use Machine Learning models, especially Deep Learning, we need enough data. Many large electronic health data sets (EHRs) have been published recently by the community, typically Medical Information Mart for Intensive Care (MIMIC-III) [12]. In this study, we use the standard data processing method proposed by Hrayr Harutyunyan et al. [1]. Data were extracted from the MIMIC-III data set with 17 attribute fields such as pH, Heart Rate, Oxygen saturation, Weight, Glucose … over time series. After pre-processing the data, we obtained 17 attributes per hour of hospitalization. This data is collected during the first 48 h when the patient enters the ICU. From this data, we need to build a model that predicts the patient’s mortality. The use of Recurrent Neural Network (RNN) models for time series data processing issues often yields the best results [1, 8–10] because this model helps to retain information from previous time points. But training these models is often quite complex and time-consuming. Another idea is to treat data as a 2-dimensional matrix with one dimension being time and one dimension being attributes. Then deep learning Convolutional Neural Network (CNN) model can be applied to this problem. The remaining of the report will be organized in the following order: Section 2 will describe pros and cons of previous research on the problem of predicting mortality in hospitals. Section 3 describes how to extract information from the MIMIC-III dataset, standardization and parameter enhancement methods and CNN architecture for this problem. Section 4 will be a summary of the empirical and evaluation results. And finally, it will be conclusions and further researches on this topic.

2 Related Works In recent years, there have been many studies related to the problem of predicting mortality in hospitals. These studies suggest different directions from the proposed new model [5–10], suggesting new data processing [1–4], methods for training with fewer data problems and faster ways for models to converge [2]. Here are some previous studies on this issue. First of all, the research of Hrayr Harutyunyan and colleagues [1] simultaneously solved all 4 tasks: risk of mortality, forecasting length of stay, detecting physiologic reverse, and phenotype classification. The model proposed by this paper is the Multitask LSTM network, which means that instead of using LSTM for individual tasks, they proposed implementing a network architecture that can do all these problems. An important contribution of this study was that they proposed a standard data processing for 4 tasks. With the same MIMIC-III data set, Brandon Malone and his colleagues proposed a new data processing method aimed at solving the problem of “missing data” using representative communication-based learning diagrams [2]. This method is particularly effective in processing missing data and reducing noise but does not increase the effectiveness of the prediction. Along with that, Yanbo Xu and his colleagues also proposed a different way of processing data on the MIMIC-III data set [3]. While this data processing is intended to be a non-fatal problem, the paper proposes a data

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processing method that relates to data processing on the MIMIC-III large data set. The data processing model mentioned is the Recurrent Attentive and Intensive Model (RAIM), which integrates continuously monitored data with separate clinical event sequences and develops predictive clinical models in ICU. Sanjay Purushothama and colleagues also proposed a new method of data processing [4], by taking all the raw attributes of the patient, through many steps of data cleansing and trying to retain the majority of the attributes. The Deep Neural Network is used to find automatically the hidden properties of the data instead of manually selecting [1, 2, 7]. This study proposes a variety of data processing methods using various Deep Neural Network models, all of which show that using the Deep Neural Network is more effective than the conventional attribute selection. But the problem with this method is that using all the patient attributes on all tables of the MIMIC-III data set makes it extremely difficult to clean the data. Especially studied by Jeeheh Oh et al. [11] using the CNN-1D model and the data processing method proposed by Hrayr Harutyunyan [1] and there was a slight change in the reprocessing of data for quite satisfactory results. The same problem of predicting mortality in hospital but not on MIMIC-III but on other electronic medical data sets, Bryan Lim and Mihaela van der Schaar [5] proposed the time-to-event model to address constraints due to computational difficulties when applying to height data sets. But because the authors use unpublished datasets for the community, evaluating and comparing the results in this report is difficult. In a paper published in January 2018, Alvin Rajkomar et al. [8] demonstrated the use of machine learning models in predicting mortality. They used an extremely large dataset of data provided by the US academic medical center with 216,221 patients and up to 46,864,534,945 data items. In another study, Fengyi Tang et al. [6] made comparisons on the use of traditional models such as SAPS and SOFA used to assess mortality risk and the most popular Machine Learning models such as LSTM, CNN, etc. Studies show that SAPS-II predicts in a wide range while Machine Learning models provide better efficiency in a smaller and more accurate range. This confirms that the use of machine learning models is better than traditional assessments. A number of other papers, such as those of Manahil Sadiq BSc [9], propose new models for processing time series data, by Priyanka Gupta [10] using the Transfer Learning technique for RNN models.

3 Proposed Approach 3.1

MIMIC-III Dataset

MIMIC-III (Medical Information Mart for Intensive Care III) is a large database of data related to the health of more than 40,000 patients cared for in the Health Care Center’s intensive care units of Beth Israel Deaconess from 2001 to 2012. This database includes information such as demographics, key marker measurements taken at the bed (*1 data point per hour), review results, laboratory tests, procedures, medications, caregiver notes, visual reports and mortality (both inside and outside the hospital). MIMIC-III can be used to support studies including epidemiological analysis,

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improving clinical decision rules, and developing electronic tools. MIMIC-III has great advantages, which are free for researchers around the world, including a large number of patients, containing high time resolution data [12]. In this study, we only used a single pre-treatment dataset provided by Hrayr Harutyunyan for the prediction problem [1]. This data included 20,139 patients of whom 2,797 died. In order to compare with the results of other studies, we keep the same Test set as in [1]. The details of the training set, validation set, and test set are described in Table 1. Looking at this table, we see a data imbalance between two classes: mortality (*10%) and non-mortality (*90%). Table 1. Data set distribution for In-hospital mortality detection problem Dataset Mortality Non-mortality Total Training set 1919 12403 14322 Validation set 504 3077 3581 Test set 374 2862 3236

3.2

Feature Engineering

The extraction of features for the mortality prediction problem was used based on the benchmarks published by Hrayr Harutyunyan et al. [1]. This data set contains information of patients located in the ICU department of more than 40,000 patients. Patients under 18 were then removed from the dataset. It will also eliminate all patients hospitalized for multiple stays with ICU stays or transitions between different ICU units. The determination of death in hospital or not is done by comparing the date of death of the patient with the time of admission and discharge. These extracted properties are based on suggestions from Ikaro Silva et al. [7]. The standard data will include 17 time-varying data including a number of attributes such as pH, Heart Rate, Oxygen saturation, Weight, Glucose, etc. These attributes are quite closely related to the patient’s ability to survive. The total number of survey hours is the first 48 h when patients enter the ICU. A preprocessing operation is performed to ensure each data is valid for each hour of the 48 h. The method is as follows: in the case of multiple measurements in an hour, the last value will be used, and if the value is missing during that hour, the value of the last measurement was used, if there is still no measured value, the default value (the value of a normal person) will be used. In these 17 attributes, there are 5 categorical attributes, meaning each of them has only a certain number of options. These properties will be encrypted in one-hot form. Therefore, the total number of attributes will be 59. In addition, each attribute is also checked to see if it is measured in each hour. The test results will be coded with 1 if this attribute is measured in that hour, and 0 if not. From there, we propose the standardization and data enhancement methods for each of the 48 h as follows: • PARAM-TYPE-1: calculates the mean and variance of each of the 12 noncategorical attributes with Training set data, using them for normalization to both Training set, Validation set and Test set. The total number of features per hour is 59 and each sample (per patient) will be represented by the 48  59 matrix (48 h).

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• PARAM-TYPE-2: added 17 properties reflecting the presence/absence of each feature in each hour of measurement in addition to PARAM-TYPE-1. The total number of features per hour is 76 and each sample (per patient) will be represented by the 48  76 matrix. • PARAM-TYPE-3: add 12 non-categorical attributes normalized to 48 h of each sample to PARAM-TYPE-2. The total number of features per hour is 88 and each sample (per patient) will be represented by the 48  88 matrix. 3.3

CNN Architecture

The first approach, when treating time data as an image, we use the Convolution Neural Network (CNN) to extract hidden properties about the space inside the data that normal data processing cannot be extracted (our model was built based on the VGG 16 architecture). The CNN architecture is depicted in Fig. 1.

Fig. 1. Architecture of the system

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In the network architecture described in Fig. 1, Convolution 1D and Max Pooling layers use parameters like kenel_size = 3, pool_size = 2, strides = 2. The Convolution 1D and Dense layers use the nonlinear activation functions ReLU. To speed up the convergence of the models, alternating between Convolution 1D layers is Batch Normalization layers. Also, to avoid over-fiting for the model, we added Dropout layers between blocks of Convolution 1D and Dense layers. 3.4

Training Deep Learning Models

During training, we use the following techniques: • When designing deep neural networks, the number of parameters is chosen to ensure the models have roughly the same number of parameters. • Use the Loss function as a binary cross entropy function. • The Adam optimal technique is used with a learning rate starting at 0.001. • In addition, the Reduce Learning Reate On Plateau technique is also used to gradually reduce the learning rate when the Loss function of the Validation set starts to increase. • The Early Stopping method is used to find the optimal model during training: the model with the smallest Loss function value on the Validation set. • To solve the problem of data imbalance between mortality (about 10% in the data set) and non-mortality (about 90% in the data set), we use class-weight method during the training phase with weight of 0.9 for mortality data and weight of 0.1 for non-mortality data. • The above neural network models are implemented on Keras which is a toolkit that provides a lot of APIs that make it easy to build and deploy deep neural networks. The test was performed on computers with Intel Core i7 processor, GPU 2080TI, 32 GB RAM. 3.5

Model Evaluation

Because the proportion of data samples between deaths and non-deaths is very large imbalanced, so we cannot use accuracy to evaluate that need some other measurement. Measures used to evaluate the performance of models include: AUROC (Area Under Receiver Operating Characteristic), AUPRC (Area Under Precision-Recall Curve and min (Se, P+) [1]. 3.6

Ensemble Models

We have proposed a method to ensemble the deep learning models: • Each model we designed to return P (In-hospital mortality | X). • Suppose there are N models, then Pensemble (In-hospital mortality | X is the average of the probability distributions of the component models. • Tests are performed on each type of parameter representation as well as each type of deep learning model.

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4 Experiments and Discussions Experimental results are summarized in Table 2. From the results in Table 2, we have the following comments: Table 2. Experimental results of different parameter types Param type PARAM-TYPE-1 PARAM-TYPE-2 PARAM-TYPE-3 Ensemble CNN models

AUC-ROC 0.836 0.819 0.826 0.851

AUC-PR 0.431 0.398 0.433 0.452

Min (Se, P+) 0.445 0.413 0.464 0.459

• The results have shown the effectiveness of the ensemble deep learning models method. • The results are equivalent to Hrayr Harutyunyan [1] when tested with multi-task channel-wise LSTM (In this test, they used all data from all 4 tasks: risk of mortality, forecasting length of stay, detecting physiologic reverse, and phenotype classification to training model whereas we only use data set of only one task of risk of mortality).

5 Conclusion and Future Works In this paper, we have presented the method of predicting risk of mortality of patients in ICU. The data were extracted from MIMIC-III dataset. We have proposed standardizing numerical attributes in two ways: normalizing on the Training set and standardizing 48 h of each sample. We have experimented with deep neural network based CNN model. The ensemble method on the full parameter set gave results equivalent to state-of-the-art results when they used data from four different tasks whereas we only use data set of only one task of risk of mortality in the training process. In the future, we will apply the multi-head attention model by adding parameters describing the chronological order of the attributes. We will then try to build an integrated model of the above models as end-to-end models. Acknowledgment. Quang H. Nguyen gratefully acknowledges the support of BKAV Corporation, Viet Nam with the donation of the Server and GPU used for this research. The research was partially supported by ARS Viet Nam, Co. Ltd. from Hanoi, Viet Nam. We thank our colleagues from ARS Viet Nam Company, who provided insight and expertise that greatly assisted the research.

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References 1. Harutyunyan, H., Khachatrian, H., Kale, D.C., et al.: Multitask learning and benchmarking with clinical time series data. Sci. Data 6, 96 (2019). https://doi.org/10.1038/s41597-0190103-9 2. Malone, B., Garcia-Duran, A., Niepert, M.: Learning representations of missing data for predicting patient outcomes. arXiv preprint arXiv:1811.04752 (2018) 3. Xu, Y., et al.: Raim: Recurrent attentive and intensive model of multimodal patient monitoring data. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2565–257. ACM (2018) 4. Purushothama, S., Mengb, C., Che, Z., Liu, Y.: Benchmark of deep learning models on large healthcare mimic datasets. J. Biomed. Inf. 83, 112–134 (2018) 5. van der Schaar, B.L.M.: Disease-atlas: navigating disease trajectories using deep learning. In: Proceedings of the 3rd Machine Learning for Healthcare Conference, PMLR, vol. 85, pp. 137–160 (2018) 6. Tang, F., Xiao, C., Wang, F., Zhou, J.: Predictive modeling in urgent care: a comparative study of machine learning approaches. JAMIA Open 1(1), 87–98 (2018). https://doi.org/10. 1093/jamiaopen/ooy011 7. Silva, I., et al.: Predicting in-hospital mortality of ICU patients: the physionet/computing in cardiology challenge 2012. In: 2012 Computing in Cardiology, pp 245–248. IEEE (2012) 8. Rajkomar, A., Oren, E., Chen, K., Dai, A.M., Hajaj, N., Hardt, M., Liu, P.J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G.E., Irvine, J., Le, Q., Litsch, K., Mossin, A., Tansuwan, J., Wang, S., Wexler, J., Wilson, J., Ludwig, D., Volchenboum, S.L., Chou, K., Pearson, M., Madabushi, S., Shah, N.H., Butte, A.J., Howell, M.D., Cui, C., Corrado, G.S., Dean, J.: Scalable and accurate deep learning with electronic health records. NPJ Digit. Med. 1(1) (2018) 9. Sadiq, M., Field, P., Pentyala, S.K.: 2017 interpretable deep learning framework for predicting all-cause 30-day ICU readmissions. Technical report (2018) 10. Gupta, P., Malhotra, P., Vig, L., Shroff, G.: Transfer learning for clinical time series analysis using recurrent neural networks. J. Healthc. Inf. Res. 4(2), 112–137 (2020) 11. Oh, J., Wang, J., Wiens, J.: Learning to exploit invariances in clinical time-series data using sequence transformer networks. In: Proceedings of the 3rd Machine Learning for Healthcare Conference, PMLR, vol. 85, pp. 332–347 (2018) 12. Johnson, A.E.W., Pollard, T.J., Shen, L., Lehman, L., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L.A., Mark, R.G.: MIMIC-III, a freely accessible critical care database. Sci. Data (2016). https://doi.org/10.1038/sdata.2016.35 13. List of hospitals implemented Electronic Health Record Management. https://ehealth.gov.vn/ Index.aspx?action=Detail&MenuChildID=391&Id=4369, Electronic Health Administration, Ministry of Health, Vietnam 14. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Adv. Neural. Inf. Process. Syst. 2017, 5998–6008 (2017) 15. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

Evaluating the Impact of Demand Response in Planning Micro-grids Considering Uncertainties V. V. Thang(&) and N. H. Trung Faculty of Electrical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam {thangvvhtd,nguyenhientrung}@tnut.edu.vn

Abstract. This study proposes a planning framework to optimize the configuration of grid-connected micro-grids under the impact of the demand response program. The uncertain parameters are integrated into the optimal model by different discrete states that are divided by the clustering technique from the probability distribution functions. A life cycle cost objective function including investment cost, operation cost, energy cost, and emission cost of the system together with constraints are presented in a mixed-integer programming model. The simulation result by GAMS/CPLEX for the test micro-grid shows the effect of the proposed model for micro-grids planning problems which include uncertain parameters. Moreover, the proposed model allows for analyzing the performance of the demand response program during the planning period. By performing the demand response program, both life cycle cost and invested power of RS together with emission decreased. Keywords: Demand response Uncertainty


Life cycle cost
Micro-grid
Planning


1 Introduction Demand response (DR) is defined as the shifting of electricity demand by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time that can increase flexibility on the demand side by reducing peak demand or temporarily shifting to avoid the equipment investments and high electricity purchase price [1]. Additionally, the advantages of DR in improving system reliability and security, reducing electricity price, reduction in transmission line bottlenecks are also introduced in [2]. DR programs positively impact on the users that participate in a DR program as well as the distribution companies and this is a developing trend for the future of energy systems planning and operating [3, 4]. In recent years, more numbers of researchers have an interest in modeling and integrating the DR programs in energy systems [5, 6]. In [7], a sizing optimization model for considering the demand response of household appliances on an island micro-grid is proposed with micro pumped storage used as an energy storage system. DR can improve the utilization of PV and WT and reduce storage costs. Similarly, a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 399–410, 2021. https://doi.org/10.1007/978-3-030-64719-3_45

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novel model for designing and operation of flexible micro-grids is proposed to optimize the sizing and siting of distributed generation as well as performing the DR programs [8]. The power of renewable sources, energy demand and price are uncertain parameters and thus it is considered in energy system planning and operation problems and is reflected by probabilistic distribution functions (pdf). In [9], a stochastic framework is proposed to expansively plan of energy storage in micro-grids, which contain renewable sources (RS) and DR program. Similarly, the demand response program is also integrated into a multi-objective optimization framework for optimal planning of the micro-grid. Moreover, DR programs are utilized to determine the size and place of different components in micro-grid which helps to consider uncertainties, improve reliability and power quality, and minimize power loss as shown in [10] while [11] introduces a novel approach of DR program in the optimal planning of multi-carrier micro-grids. The results show that considering DR programs changes the size of components together with the configuration of micro-grids significantly and enhances the effects of these systems. However, the different lifetime and uptime of equipment in micro-grids are ignored lead to analysis results incorrect because of the nonconformity of computed parameters with reality. To consider the different lifetime and uptime of equipment, the objective function of the life cycle cost (LCC) has been introduced in the recent year to find the optimal size of battery energy storage systems (BESS) [12], the capacity configuration optimization for island micro-grid [13], and optimal designing of hybrid renewable energy systems [14]. Besides, the energy resources optimization to enhance the efficiency, reduce the pollutant emissions, and impact on climate changes of energy systems should be considered in micro-grid planning problems. Therefore, a planning framework is proposed in this study to optimize the configuration of grid-connected micro-grids under the impact of the DR program. Not only the lifetime and uptime of RS considered by the objective function LCC but also the rated power of them with discrete values are integrated into the model. The uncertainty of parameters is analyzed by pdf and thus increases in the accuracy of planning results. A mixed-integer problem model is programmed by GAMS and is solved by CPLEX solver [15]. The rest of the paper is organized as follows: Sect. 2 presents the structure of the micro-grid and mathematics model of the planning framework. Section 3 discusses the simulation results, and Sect. 4 presents the conclusive remarks and a few insights for future work.

2 Planning Framework of Micro-grids Integrated Demand Response 2.1

Structure of Micro-grids

Two structures of the micro-grids introduced are the grid-connected and the islanded types [16]. In the grid-connected type, the loads are simultaneously supplied from both distributed generators and utility grid through the point of common coupling (PCC). The RS such as the PV and WT as well as BESS have been successfully developed and

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are applied in this structure because they improve the efficiency and reliability and reduce the investment cost of micro-grid shown in Fig. 1 [17]. In this structure, the uncertainty of RS is remedied by BESS and utility grid through PCC. Hence, gridconnected micro-grids can optimize the electric energy purchased from utility gird and RS is invested to improve the effectiveness of the micro-grids. Bus

PCC Utility Grid

WT

PV

DC AC

DC AC BESS Electrical Load

Fig. 1. The structure of grid-connected micro-grids

2.2

Demand Responses Modeling

The DR programs can be classified into two types, consisting of price-based DR and incentive-based DR [18], of which the price-based mechanism is the most important for the successful implementation of DR. Hence, in this study, the price-based DR is chosen to evaluate the effects of DR on the optimal planning of micro-grids. The change in demand under the impact of DR is presented in Eq. (1) [19, 20]. Plh ¼ Pl0 ð1 þ kDR Þ :

qh  q0 q0

ð1Þ

where Pl0 ; q0 are the initial electricity demand and price. qh is the spot electricity price which can be calculated with real-time prices. kDR is the demand-price elasticity coefficient which depends on the customer types and historical load demand data. 2.3

Uncertain Parameters Modeling

The uncertain parameters consist of the output power of RS, electricity prices, and electrical demand. These parameters are often modeled by PDFs and are divided into different states by k-mean clustering technique. There is a specific value with the related probability in each state [21]. The randomness of solar radiation is expressed by beta PDF as Eq. (2) with the Iir (the intensity of solar radiation), l (mean) and r (the standard deviation) [22, 23]. The value and probability of intensity of solar radiation in each state are determined, and hence the output power of each module PV is calculated as expression (3). In the

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equation, kF.s is the fill factor, and n is the number of modules [24, 25]. Is and Us are the generated current and voltage. ISC and UOC are short circuit current and open circuit voltage. UMPP and IMPP are current and voltage at the maximum power point, respectively. kU and kI are coefficients while hA, hN are the ambient and nominal operating temperature. Similarly, the wind speed v is represented by Rayleigh PDF which is a special case of Weibull PDF as Eq. (4) with the scale index c. The output power of WT in each state wt is calculated as Eq. (5) [26] with output power Pwt s , rated power Pr together with cutin speed vci, rated speed vcr, and cut-off speed vco.


ða1Þ

½Cða þ bÞ=CðaÞCðbÞ : Iir 0 else b ¼ ð1  lÞ : ½l : ð1 þ lÞ=r2  1;

fb ðIir Þ ¼

: ð1  Iir Þðb1Þ if 0  Iir  1; a; b

ð2Þ

a ¼ l : b=ð1  lÞ

Ppv s ðIir Þ ¼ n : kF : s : Us ðIir Þ : Is ðIir Þ; kF : s ¼ UMPP : s : IMPP : s =UOC : ISC Us ðIir Þ ¼ UOC  kU : ½hA þ Iir : s : ðhN  20Þ=0:8 Is ðIir Þ ¼ Iir : s : ½ISC þ kI : ðhA þ Iir : s : ðhN  20Þ=0:8  25Þ h i fr ðvÞ ¼ ð2v=c2 Þ exp ðv=cÞ2 8 vs  vci or vco  vs (pm + n)  ( pm + n+1) data samples in order to obtain the result of this linear equation. Now, we proposed the modified Q-leaning algorithm for online implementation. Algorithm 1: Modified Q-learning algorithm 1.

Initialization: The stabilizing policy uK0

2.

Policy Evaluation: Use Least-Squares to solve the equation j vecv( Kj )T vecv( Kj 1 )T vecs( H j 1 ) K

3.

0 L102 xK is chosen (21)

Policy Update: Update control policy using

L12j 1

uKj

1

H 22j 1

L j 1 xK

1

H 21j 1

0 L12j 1 xK

(22) (23)

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In this policy evaluation stage, the Bellman Eq. (20) is implemented for vecsðH j þ 1 Þ under the data collection along the system trajectories to establish the data matrices. The solution of (21) can be achieved by using the Least-Squares:  1 vecsðH j þ 1 Þ ¼ ðZ j ÞT Z j ðZ j ÞT Y j In the policy update stage, an improved policy  ujKþ 1 is computed through minimizing the Q-function of the jth policy. It should be noted that according to (21) we j12þ 1 . Therefore, we have to form complete feedonly obtain non-zero elements, i.e. L back matrix (27). The PE condition [1, 4, 15] need to be satisfied to guarantee the convergence of Algorithm 1 in the optimal policy. Therefore, we need to add more a PE probing noise so that the optimal policy can be precisely learned. The works in [8, 9] shown that the additional term of PE probing noise does not cause bias in Q-function estimation. The convergence of the proposed algorithm is expressed in the following theorem. 2; B  2 Þ be controllable and u0K be an initially stabilizing control. Theorem 2. Let ðA 1 Then, the sequence fH j gj¼0 convergences to the optimal matrix kernel H  as j ! 1 j ! L   as j ! 1. and the feedback gain L Proof. In the previous works [1, 4, 9, 13], the convergence of Q-learning algorithm is  B  Þ is controllable, i.e. all of poles are conshown due to the couple of matrices ðA; trollable and we can change all of them. For linear periodic discrete-time system (1),  B  Þ is stabilizable under Assumption 1. But both the optimal feedback matrices (11) ðA; and the jth iterative feedback gain (22) only change controllable poles of the modified system according to Remark 4. Therefore, Algorithm 1 satisfies conditions of proofs in [1, 4, 9, 13]. Additionally, the convergence of the Algorithm 1 is guaranteed. This completes the proof of the convergence. ∎ Remark 5. Algorithm 1 is implemented online in real time using only the state variables data, which collected along the state trajectories without requiring any knowledge of system matrices.

4 Simulation Results In this section, a simulation example is developed to illustrate the effectiveness and convergence of the modified Q-learning algorithm. Consider a linear periodic discrete-time system (1) with several terms:  x ð k þ 1Þ ¼

1 0:1 cosð0:2pkÞ

   0:2 sinð0:2pkÞ 0 xð k Þ þ uðk Þ 0:9 0:1 cosð0:2pkÞ

The initial state is given as x0 ¼ ½3; 2 T . The periodic of system is p ¼ 10. The infinite performance index is defined as (3) with Qk ¼ Q0 ¼ I2 ; Rk ¼ R0 ¼ 1.

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To verify the effectiveness of the proposed algorithm, we use the Matlab function “DARE” to solve the Riccati Eq. (9). Then, the optimal feedback gain is obtained as  12 ¼ L



0:7035

0:4002

0:0322

0:0962

0:4853

0:6575

0:3806

0:7127

0:4866

0:1329

0:0786

0:1056

0:0858

0:0921

0:0382

0:0574  0:0757 0:4053 T

0:0822 0:2594

We add a random noise to control input to ensure PE condition until the learning process is successful. Figure 1 shows norm of the difference between optimal feedback  and the obtained feedback matrix L  j . Figure 1 also shows state trajectory matrix L curves and the control signal. We see that the feedback gain sequence converges to the optimal gain and the system is stable in learning process. After 6 iterations, the feedback gain converges to 12 ¼ L



0:7035 0:7127

0:4002 0:4866

0:0322 0:1329

0:0962 0:0786

0:4853 0:1056

0:6575 0:0858

0:3806 0:0921

0:0382 0:0574  0:0757 0:4053 T

0:0822 0:2594

 to its optimal values, state variables, and control signal Fig. 1 Convergence of matrix L

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which is the same as the optimal gain which is determined by system matrices and Matlab function. The method introduced in [18] has to use the system matrices to determine the optimal controller, while proposed method only use state variable data without any knowledge of system matrices. According to the simulation results, we can conclude that the proposed algorithm for linear periodic discrete-time systems is appropriate.

5 Conclusion In this paper, we presented a modified Q-learning strategy using a lifting method to deal with the periodic LQR problem for linear periodic discrete-time system. The proposed algorithm overcame the controllability condition of the existing algorithms as well as ensured the convergence. Moreover, the algorithm is implemented online without depending any knowledge of the system matrices. The simulation results have determined that the proposed algorithm guarantees convergence in the optimal solution. As no prior knowledge of system dynamics is required during the learning process, the proposed strategy provides a potentially feasible solution to various control applications. Acknowledgements. This research was supported by Research Foundation funded by Thai Nguyen University of Technology.

References 1. Al-Tamimi, A., Lewis, F.L., Abu-Khalaf, M.: Model-free Q-learning designs for linear discrete-time zero-sum games with application to H-infinity control. Automatica (2007) 2. Allen, M.S., Sracic, M.W., Chauhan, S., Hansen, M.H.: Output-only modal analysis of linear time-periodic systems with application to wind turbine simulation data. Mech. Syst. Signal Process. 25(4), 1174–1191 (2011) 3. Bittanti, S., Laub, A.J., Willems, J.C.: The Riccati Equation. Springer, Heidelberg (1991) 4. Bradtke, S.J., Ydstie, B.E.: Adaptive linear quadratic control using policy iteration. In: Proceedings of 1994 American Control Conference, June, vol. 3, pp. 3475–3479 (1994) 5. Chauvin, J., Corde, G., Petit, N., Rouchon, P.: Periodic input estimation for linear periodic systems: automotive engine applications. Automatica 43(6), 971–980 (2007) 6. Grasselli, O.M., Longhi, S.: Pole placement for nonreachable periodic discrete-time systems. Math. Control. Signals Syst. 4, 439–455 (1991) 7. Hench, J.J., Laub, A.J.: Numerical solution of the discrete-time periodic Riccati equation. IEEE Trans. Autom. Control 39(6), 1197–1210 (1994) 8. Jiang, Y., Kiumarsi, B., Fan, J., Chai, T., Li, J., Lewis, F.L.: Optimal output regulation of linear discrete-time systems with unknown dynamics using reinforcement learning. IEEE Trans. Cybern. 50, 1–10 (2019) 9. Landelius, T.: Reinforcement learning and distributed local model synthesis. Ph.D thesis, Linkoping University, Sweden (1997) 10. Lewis, F.L., Vrabie, D.: Reinforcement learning and adaptive dynamic programming for feedback control. IEEE Circuits Syst. Mag. 9(3), 32–50 (2009)

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11. Li, J., Chai, T., Lewis, F.L., Ding, Z., Jiang, Y.: Off-policy interleaved Q-learning: optimal control for affine nonlinear discrete-time systems. IEEE Trans. Neural Netw. Learn. Syst. 30(5), 1308–1320 (2019) 12. Meyer, R.A., Burrus, C.S.: A unified analysis of multirate and periodically timevarying digital filters. IEEE Trans. Circuits Syst. 22, 162–168 (1975) 13. Rizvi, S.A.A., Lin, Z.: Output feedback Q-learning control for the discrete-time linear quadratic regulator problem. IEEE Trans. Neural Netw. Learn. Syst. 30(5), 1523–1536 (2019) 14. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Iintroduction. MIT Press, Cambridge (2011) 15. Vrabie, D., Pastravanu, O., Abu-Khalaf, M., Lewis, F.L.: Adaptive optimal control for continuous-time linear systems based on policy iteration. Automatica 45(2), 477–484 (2009) 16. Wei, Q., Liu, D., Lin, Q., Song, R.: Adaptive dynamic programming for discretetime zerosum games. IEEE Trans. Neural Netw. Learn. Syst. 29(4), 957–969 (2017) 17. Yang, Y.: An efficient algorithm for periodic Riccati equation with periodically time-varying input matrix. Automatica 78, 103–109 (2017) 18. Yang, Y.: An efficient LQR design for discrete-time linear periodic system based on a novel lifting method. Automatica 87(301), 383–388 (2018)

Multi Response Optimization of Dressing Conditions for Surface Grinding SKD11 Steel by HaiDuong Grinding Wheel Using Grey Relational Analysis in Taguchi Method Tran Thi Hong1, Ngo Ngoc Vu2, Nguyen Huu Phan3, Tran Ngoc Giang2, Nguyen Thanh Tu2, Le Xuan Hung2, Bui Thanh Danh4, and Luu Anh Tung2(&) 1

Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam 2 Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] 3 Faculty of Mechanical Engineering, Hanoi University of Industry, Ha Noi, Vietnam 4 University of Transport and Communications, Ha Noi, Vietnam

Abstract. This paper presents a multi response optimization for dressing parameters for surface grinding SKD11 steel using HaiDuong grinding wheel by grey relational analysis (GRA) in the Taguchi method. The input parameters including feed rate (S), depth of rough dressing (ar), rough dressing times (nr), depth of fine dressing (af), fine dressing times (nf) and non-feeding dressing (nnon) to flatness tolerance (Fl) were investigated and optimized to obtain the smallest flatness tolerance and the highest material removal rate (MRR). The experimental results showed that optimum caculation model with dressing process for surface grinding SKD11 steel using Haiduong grinding wheel is suitable. Keywords: Surface grinding ANOVA
Optimization


Orthogonal array
Grey relational analysis


1 Introduction Dressing is an important work in grinding operation. Up to now, there have been a variety of researches relating dressing. This work includes two steps which are dressing and cleaning operations. Dressing is to generate a defined work-piece profile and micro-topography for grinding wheel, as shown in Fig. 1. In grinding process, grinding wheel is affected by cutting forces, cutting temperature and complex chemical interactions, so grinding wheel is worn. Here, perimeter and edge wear are the macro grinding wheel wear and wear of abrasive grains is micro wear. It includes 4 types, as shown in Figs. 2 and 3. Therefore, the minimum depth of dressing must be double wear types [1, 2].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 560–571, 2021. https://doi.org/10.1007/978-3-030-64719-3_62

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There are some methods to measure topography of grinding wheel. One of the methods uses sensors [3–6]. Accordingly, the times of dressing is based on the relationship between the force of the grinding process and the machined surface roughness. The depth of dressing bases on topography analysis of grinding wheel. The wear of grinding wheel and optimum dressing conditions were also presented [7]. In another study [8], optimum dressing conditions were investigated to reduce cutting forces and enhance surface roughness for Al2O3 grinding wheel with SPK12080 hardening workpiece material. The influence of grinding ratio and cutting forces on grinding life was presented by [9]. In a study [10], authors compared effective of dressing using diamond dressing tool with dressing using laser method for grinding 100Cr6 hardening material using SiC grinding wheel.

Fig. 1. Dressing process [1].

Fig. 2. Macro grinding wheel wear [2].

Fig. 3. Micro grinding wheel wear [2].

The dressing conditions for external grinding were also presented in many researches. Milton C. Shaw et al. [11] proposed the dressing conditions for external grinding as follows: the single-point diamond dressing tool with a = 100  200; rough dressing with depth of dressing dd  25 µm and feed rate S  500 µm/rev; fine dressing with depth of dressing dd  12.5 µm and feed rate S  125 µm/rev. In another study, L.M. Kozuro et al. [12] also proposed the dressing conditions for surface

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grinding to obtain surface roughness Ra = 0.32−1.25 lm including the multi-point diamond dressing tool, feed rate S = 1.5 (m/min); four times with depth of dressing dd = 0.03 mm/single stroke and four times of the non-feeding dressing; with the single-point diamond dressing tool, feed rate S = 1.0 (m/min); six times with depth of dressing dd = 0.02 mm/single stroke and four times of the non-feeding dressing. Besides, the dressing conditions also were presented in other researches [13, 14]. Based on the literature review, it can be said that until now, there has not been research on the optimum dressing conditions for surface grinding SKD11 steel quenching using HaiDuong grinding wheel. Therefore, this study focuses on the optimum dressing conditions for surface grinding SKD11 steel using HaiDuong grinding wheel through 3 steps: rough dressing, fine dressing and non-feeding dressing. Besides, to examine the impact of process factors on the responses, the Taguchi method is very common. It has been used successfully in several studies [15–24]. Therefore, the method has been selected for designing and analyzing the experiment in this work.

2 Experimental Setup and Research Methodology 2.1

Experimental System

In this study, the work-piece material was SKD11 (C: 1.4  1.6%, Si  0.4%, Mn 0.6%, Cr: 11.0  13.0%, P, S  0.03%, Mo: 0.8  1.2%, W: 0.2  0.5%, Cu  0.25%, V  0.25%) with the dimensions of 70  40  25 mm and the hardness of 58–62 HRC. The experimental equipment specifications are shown in Table 1 and the grinding conditions are shown in Table 2. Table 1. Experimental equipment. Equipment type Equipment name Grinding machine MOTO –YOKOHAMA Grinding wheel Cn46TB2GV1 Dressing tool (single-point type) 3908-0088C type 2 Measuring equipment for flatness tolerance Mitutoyo CMM544 Lubricant Cantext Aquatext 381 Concentration of lubricant 3% Flow rate of lubricant 10 l/min

Table 2. Grinding conditions. Dressing conditions Depth of cut (mm) Table velocity (m/min) Radial feed (mm/stroke) Cutting speed (m/s)

Values 0.01 8 8 26.7

Country Japan Vietnam Russian Japan

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The flatness tolerance is determined by the difference between the highest and lowest values of 27 points which were measured on the surface of the workpiece after grinding. The material removal rate (MRR) is determined by the material removal volume in a unit of grinding life. 2.2

Experimental Design

The factors and their levels considered in this study are shown in Table 3. Minitab 19 software was used to design experiment based on Taguchi method. A designed experiment plan with 6 factors including 4 factors at 4 levels and 2 factors at 2 levels (44x22) was established, as shown in Table 3. Each experiment was repeated three times with the following dressing process: rough dressing depth trd with rough dressing times nrd; fine dressing depth tfd with fine dressing times nfd; and non-feeding dressing nnon. The results of MRR and Fl also are shown in Table 4. Table 3. Factors and levels. Parameters

Unit

Levels 1 2 Dressing feed rate (S) m/min 1.6 1.8 0.015 0.02 Rough dressing depth (trd) mm Rough dressing times (nrd) times 1 2 Fine dressing depth (tfd) mm 0.005 0.01 Fine dressing times (nfd) times 0 1 Non-feeding dressing (nnon) times 0 1

3 – 0.025 3 – 2 2

4 – 0.03 4 – 3 3

Table 4. L16 orthogonal array with factors and responses. TT trd

nrd nnon nfd tfd

S

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

1.6 1.8 1.6 1.8 1.8 1.6 1.8 1.6 1.8 1.6 1.8 1.6 1.6 1.8 1.6 1.8

0.015 0.015 0.015 0.015 0.02 0.02 0.02 0.02 0.025 0.025 0.025 0.025 0.03 0.03 0.03 0.03

0 1 2 3 1 0 3 2 2 3 0 1 3 2 1 0

0 1 2 3 2 3 0 1 3 2 1 0 1 0 3 2

0.005 0.005 0.01 0.01 0.01 0.01 0.005 0.005 0.005 0.005 0.01 0.01 0.01 0.01 0.005 0.005

Fl (µm) MRR (mm3/s) Trial 1 Trial 2 Trial 3 Trial 1 Trial 2 11.8 12.7 13.5 2.44 2.42 24.6 25 24.8 2.05 2.00 14.5 14.8 14.2 1.92 1.89 20.8 21.2 21.8 1.83 1.82 13.3 13.9 14.2 1.84 1.79 10.9 10.3 11.2 2.40 2.36 12.9 13.9 13.5 2.06 2.05 11.3 11.5 12 1.93 1.90 10.5 10.9 11 1.87 1.91 8.8 9 9.5 2.30 2.25 9.5 10.1 10.5 1.75 1.93 17.3 17.6 17.8 1.92 1.88 11.8 11.5 12.1 1.98 1.92 13.5 13.9 13.3 1.89 1.86 19.6 19 19.5 1.91 1.87 11.8 12 12.5 1.83 1.81

Trial 3 2.38 1.94 1.88 1.85 1.80 2.37 2.04 1.64 1.92 2.27 1.92 1.89 1.95 1.87 1.86 1.79

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3 Optimization Steps Using Grey Relational Analysis for S/N Ratio Step 1.

Determine the S/N ratios

In this study, the desired flatness tolerance is ``smaller is better’’, thus the ratio can be determined as follows [25]: SN ¼ 10log10 ð

n 1X y2 Þ n i¼1 i

ð1Þ

The desired material removal rate is “larger is better”, the S/N ratio can be determined as follows: SN ¼ 10log10 ð

n 1X 1 Þ n i¼1 y2i

ð2Þ

Where n is the repeated times at each experiment and yi is the measured value at measuring times i = 1, 2, …n (n = 3). Table 5. Values of SN ratio, normalized values of S/N ratio and the absolute values. TT S/N Fl

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

−22.07 −27.89 −23.23 −26.56 −22.8 −20.67 −22.57 −21.29 −20.67 −19.19 −20.04 −24.89 −21.44 −22.65 −25.74 −21.66

Zij MRR Fl MRR Reference value 1.000 1.000 7.66 0.67 1.00 6.00 0.00 0.34 5.56 0.54 0.16 5.27 0.15 0.05 5.16 0.59 0.00 7.51 0.83 0.94 6.23 0.61 0.43 5.15 0.76 0.00 5.57 0.83 0.16 7.14 1.00 0.79 5.39 0.90 0.09 5.56 0.34 0.16 5.79 0.74 0.26 5.46 0.60 0.12 5.47 0.25 0.13 5.15 0.72 0.00

Dj(k) Fl MRR

0.33 1.00 0.46 0.85 0.41 0.17 0.39 0.24 0.17 0.00 0.10 0.66 0.26 0.40 0.75 0.28

0.00 0.66 0.84 0.95 1.00 0.06 0.57 1.00 0.84 0.21 0.91 0.84 0.74 0.88 0.87 1.00

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The most reliable results are at the larger S/N ratio and they are insignificantly affected by noise. This ratio is normalized by Zij (0  Zij  1). It can be calculated as follows:   SNij  min SNij ; j ¼ 1; 2; ::k     Zij ¼ max SNij ; j ¼ 1; 2; ::n  min SNij ; j ¼ 1; 2; ::n

ð3Þ

Where j is the number of the experimental data items (j = 16). The S/N ratio and the normalized value Z corresponding each output objective are shown in Table 5.

Table 6. Grey relational coefficient and grade. TT Grey relational coefficient ci Fl MRR 1 0.602 1.000 2 0.333 0.430 3 0.518 0.374 4 0.371 0.344 5 0.546 0.334 6 0.746 0.898 7 0.563 0.468 8 0.674 0.333 9 0.746 0.375 10 1.000 0.707 11 0.837 0.356 12 0.433 0.374 13 0.659 0.402 14 0.557 0.363 15 0.399 0.364 16 0.638 0.333

Step 3.

c

0.801 0.382 0.446 0.357 0.440 0.822 0.515 0.504 0.560 0.853 0.596 0.403 0.531 0.460 0.382 0.486

Calculate the interaction coefficient in fuzzy relational co-efficient for the normalized S/N ratio cðkÞ ¼

Dmin þ fDmax Df ðkÞ þ fDmax

ð4Þ

Where: þ Þ j ¼ 1; 2; . . .n; k ¼ 1; 2; . . .:m; n is the number of experimental data items, m is the number of responses.

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þ Þ D0j ¼ Z0 ðkÞ  Zj ðkÞ is the absolute value of the difference between Z0(k) (reference sequence) and Zj(k) (the specific comparison sequence). þ Þ Dmin ¼ min min Z0 ðk Þ  Zj ðkÞ is the smallest value of D0j 8j2i

8k

8j2i

8k

þ ÞDmax ¼ max max Z0 ðkÞ  Zj ðkÞ is the largest value of D0j +) f is the distinguish coefficient, which can be determined in range 0  f  1. This value can be adjusted based on the requirement of system. In this study, f = 0.5. Step 4. Determine the grey relational grade cj ¼

m 1X c k i¼1 ij

ð5Þ

This is the average value of interaction in the grey relational grade determined at step 3. k is number of performance characteristics. Table 6 shows the grey relational grade corresponding with objective and average value of grey relational grade. Table 7. Influence degree of parameters on fuzzy relational coefficient. Response Table for Means nrd nnon Level trd 1 0.4965 0.5830 0.6763 2 0.5702 0.6292 0.4016 3 0.6035 0.4849 0.4925 4 0.4644 0.4375 0.5642 Delta 0.1391 0.1917 0.2746 Rank 3 2 1 c ¼ 0:534

nfd 0.5450 0.5030 0.5563 0.5304 0.0533 6

tfd S 0.5603 0.5927 0.5070 0.4746

0.0533 0.1181 5 4

Fig. 4. Main effects plot for means data means.

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Step 5.

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Determine the optimal factor and its level combination

The higher grey relational grade implies the better product quality. Table 6 shows the grey relational grade for each experiment and interaction of the grey relational grade. In Table 6, the 10th experiment including two times of the rough dressing with dressing depth of 0.005 mm, three times of the non-feeding dressing with feed rate of 1.6 m/min has the highest interaction grey relational grade of 0.853. This shows that the 10th experiment has a S/N ratio similar to the normalized S/N ratio and it has various good characteristics among sixteen experiments. However, this level is not the optimum one of all factors. Therefore, the average grey relational grade of each factor at different levels needs to be determined and they are shown in Table 7 and Fig. 3. A factor is optimum if its grey relational grade at any level is the highest. Therefore, based on Table 7 and Fig. 3, the optimum parameters of the dressing process for surface grinding are suitable with both MRR with “larger is better’’ and Fl with “smaller is better”, as follows: trd3/nrd3/nnon1/nfd3/tfd1/S1 corresponding with two times of the rough dressing and depth of cut trd = 0.025 mm; two times of the fine dressing and depth of cut trd = 0.005 mm with feed rate S = 1.6 m/min; the non-feeding dressing is not used. Step 6.

Perform ANOVA for identifying the significant factors

The main purpose of the analysis of variance (ANOVA) is the application of a statistical method to identify the effect of individual factors. The regression analysis results of variance are shown in Table 8 and Fig. 4.

Table 8. Results of ANOVA on grey relational grade. Analysis of Variance for Means Source DF Seq SS Adj SS 3 0.049591 0.049591 trd nrd 3 0.092794 0.092794 nnon 3 0.161580 0.161580 nfd 3 0.006359 0.006359 tfd 1 0.011369 0.011369 S 1 0.055755 0.055755 Residual Error 1 0.003860 0.003860 Total 15 0.381308 Model Summary S R-Sq 0.0621 98.99%

Adj MS F 0.016530 4.28 0.030931 8.01 0.053860 13.96 0.002120 0.55 0.011369 2.95 0.055755 14.45 0.003860

P C% 0.338 13.01 0.253 24.34 0.194 42.38 0.730 1.67 0.336 2.98 0.164 14.62 1.01 100.00

R-Sq(adj) 84.82%

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Table 8 shows that the output parameters are significantly affected by the nonfeeding dressing times nnon (42.38%), followed by the rough dressing times (24.34%), the feed rate S (14.62%), the depth of the rough dressing trd (13.01%), the depth of the fine dressing tfd (2.98%) and the fine dressing times nfd (1.67%). Step 7.

Calculate the predicted optimum values

The fuzzy relationship value is determined as follows: cop ¼ gm þ R5i¼1 ðg  gm Þ ¼ trd3 þ nrd2 þ nnon1 þ nfd3 þ tfd1 þ S1  5  T

ð6Þ

Where T is the average grey relational grade T = 0.534; trd3, nrd2, nnon1, nfd3, tfd1, S1 are the grey relational grade of factors corresponding with the optimum levels in Table 6. Where by, cop ¼ 0:950. Confidence interval CI can be calculated as follows: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  ffi 1 1 þ CI ¼ Fa ð1:fe Þ:Ve : ¼ 0:315 Ne R

ð7Þ

Where F/ ð1; fe Þ ¼ 39:864 is the coefficient with significance level a% = 90%, fe = 1 is the degree of freedom of error, Ve = 0.00386 is the average deviation of error, Ne is the number of effective iterations, R = 3 is the number of iterations of an experiment. Total experiments 1 þ number of degree of freedom of input parameters 48 ¼ 3:2 ¼ 1þ3þ3þ3þ3þ1þ1

Ne ¼

Therefore, when a = 90%, the grey relational grade can be predicted with the suitable levels of input parameters trd3/nrd2/nnon1/nfd3/tfd1/S1 as follows: 0:6  cop  1:0 Based on the optimum levels of input parameters, the optimum values of output parameters (MRR and Fl) are determined as follows: ðFl; MRRÞop ¼ trd3 þ nrd2 þ nnon1 þ nfd3 þ tfd1 þ S1  5  T

ð8Þ

Where: + ðFl; MRRÞop is the flatness tolerance or the optimum material removal rate. þ trd3 is the average flatness tolerance or the material removal rate when depth of rough dressing is at level 3. þ nrd2 is the average flatness tolerance or the material removal rate when the rough dressing times is at level 2.

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þ nnon1 is the average flatness tolerance or the material removal rate when the nonfeeding dressing is at level 1. þ nfd3 is the average flatness tolerance or the material removal rate when the fine dressing times is at level 3. þ tfd1 is the average flatness tolerance or the material removal rate when depth of fine dressing is at level 1. þ S1 is the average flatness tolerance or the material removal rate when feed rate is at level 1. þ T is the average flatness tolerance or the material removal rate of all experiments. Thus: ðFlÞop ¼ 6:87 lm ðMRRÞop ¼ 2:37 mm3 =s To investigate the accuracy of calculation process, the optimum dressing parameters are as follows: two times of the rough dressing with trd = 0.025 mm; two times of the fine dressing with tfd = 0.005 mm and feed rate S = 1.6 m/min; the non-feeding dressing was not used. The experimental and predicted values are shown in Table 9.

Table 9. Compared values between experimental and predicted ones. Characteristics

Fl (µm) MRR (mm3/s) Fuzzy relational grade

Optimum parameters Predicted values trd3, nrd2, nnon1, nfd3, tfd1, S1 6.87 2.37 0.950

Experimental values trd3, nrd2, nnon1, nfd3, tfd1, S1 6.15 2.12

% deviation 10.48 10.54

The experimental results show that the maximum deviation compared with the predicted values is 10.54% corresponding with the flatness tolerance, therefore, this calculation method can be used to accurately predict two characteristics, MRR and Fl, at the same time.

4 Conclusions In this study, the material removal rate and flatness tolerance have been predicted by analyzing the grey relational grade using Taguchi method. This study showed that the output parameters is significantly affected by the non-feeding times nnon (42.38%),

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followed by the rough dressing times nrd (24.34%), feed rate S (14.62%), depth of rough dressing trd (13.01%), depth of the fine dressing tfd (2.98%), and the fine dressing times nfd (1.67%). The optimum parameters of the dressing process for surface grinding suitable for both MRR with “larger is better” and Fl with “smaller is better” are trd3/ nrd3/nnon1/nfd3/tfd1/S1 corresponding with two times of the rough dressing with trd = 0.025 mm, two times of the fine dressing with tfd = 0.005 mm, feed rate S = 1.6 m/min, and the non-feeding dressing was not used. Acknowledgements. This work was supported by Thai Nguyen University of Technology.

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17. Tran, T.H., Hoang, T.D., Le, H.K., Do, T.T., Bui, T.H., Nguyen, M.C., Luu, A.T., Vu, V.P.: Analysis of effects of machining parameters on surface roughness in electrical discharge machining tablet shape punches using taguchi method. Mater. Sci. Forum 977, 12–17 (2020) 18. Hung, L.X., Pi, V.N., Hong, T.T., Ky, L.H., Lien, V.T., Tung, L.A., Long, B.T.: Multiobjective optimization of dressing parameters of internal cylindrical grinding for 9CrSi aloy steel using taguchi method and grey relational analysis. Mater. Today Proc. 18, 2257–2264 (2019) 19. Hong, T.T., Cuong, N.V., Ky, L.H., Nguyen, Q.T., Long, B.T., Tung, L.A., Nguyen, T.T., Pi, V.N.: Multi-criteria optimization of dressing parameters for surface grinding 90CrSi tool steel using taguchi method and grey relational analysis. Mater. Sci. Forum, 998, 61–68 (2020). https://doi.org/10.4028/www.scientific.net/msf.998.61 20. Tung, LA et al.: Optimization of dressing parameters of grinding wheel for 9CrSi tool steel using the taguchi method with grey relational analysis. In: 10th International Conference on Mechatronics and Manufacturing (ICMM 2019), IOP Conference Series: Materials Science and Engineering, Vol. 635, Bangkok, Thailand 21–23 January 2019 21. Ky, L.H., Hong, T.T., Dung, H.T., Tuan, N.A., van Tung, N., Tung, L.A., Pi, V.N.: Optimization of dressing parameters for grinding table shape punches by CBN wheel on CNC milling machine. Int. J. Mech. Eng. Technol. 10, 960–967 (2019) 22. Vu, N.-P., Nguyen, Q.-T., Tran, T.-H., Le, H.-K., Nguyen, A.-T., Luu, A.-T., Nguyen, V.T., Le, X.-H.: Optimization of grinding parameters for minimum grinding time when grinding tablet punches by CBN wheel on CNC milling machine. Appl. Sci. 9, 957 (2019) 23. Son, N.H., Hong, T.T., Van Cuong, N., Vu, N.P.: Calculating effects of dressing parameters on surface roughness in surface grinding. In: International Conference on Engineering Research and Applications, pp. 164–169 (December 2019) 24. Hong, T.T., Cuong, N.V., Ky, L.H., Tung, L.A., Nguyen, T.T., Vu, N.P.: Effect of process parameters on surface roughness in surface grinding of 90CrSi tool steel. Solid State Phenom. 305, 191–197 (2020). Trans Tech Publications Ltd 25. Jeyapaul, R., Shahabudeen, P., Krishnaiah, K.: Quality management research by considering multi-response problems in the Taguchi method - a review. Int. J. Adv. Manuf. Technol. 26, 1331–1337 (2005)

Multi-objective Optimization of Process Parameters During Electrical Discharge Machining of Hardened 90CrSi Steel by Applying Taguchi Technique with Grey Relational Analysis Tran Thi Hong1, Nguyen Manh Cuong2, Nguyen Dinh Ngoc2, Luu Anh Tung2, Tran Ngoc Giang2, Le Thu Quy3, Nguyen Thanh Tu2, and Do Thi Tam2(&) 1

3

Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam 2 Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] National Research Institute of Mechanical Engineering, Ha Noi City, Vietnam

Abstract. The aim of this study is to optimize output responses as minimum surface roughness and maximum material removal rate (MRR) of Electrical Discharge Machining (EDM) process when machining hardened 90CrSi steel. Thank to this process the combination of machining parameters obtained by minimizing problems is found. The machining parameters for EDM process under consideration include concentration of powder, pulse – on - time, pulse off - time, current, and voltage. The experiments are set up based on the L18 orthogonal array of Taguchi method, and analysis has been carried out using Grey Relational Analysis (GRA) to find the optimal set of machining parameters. ANOVA is also used to determine which parameters have the significant effects on the output responses. Finally, the experiments are performed to validate the optimal set of machining parameters. The results show that Current has the strongest influence (32,63%), and Pulse off time has the smallest effects (1,04%) on the grey grade value. The confirmation experiment and ANOVA show the reliability of the proposed model which can be considered as an effective method to predict the surface roughness and material removal rate. Keywords: EDM
Hardened 90CrSi steel Taguchi method
Optimization


Grey Relational Analysis


1 Introduction EDM (Electrical Discharge Machining) is one of the non-conventional operations for machining materials. The principles of this process are removing electrically conductive materials by using precisely controlled sparks appearing between an electrode and a work-piece in a dielectric fluid. This process is specially needed for cutting © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 572–583, 2021. https://doi.org/10.1007/978-3-030-64719-3_63

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difficult-to-machining materials due to their high strengths, high hardness and great brittleness in the high-speed machining (see Fig. 1) [1].

Fig. 1. Schematic illustration of electrical discharge machining [1]

In EDM, the input process parameters are normally the current, the pulse on time, the pulse off time and the voltage [2–7] which have important impacts on various output responses, i.e. surface roughness, tool wear rate, material removal rate [3, 6, 8, 9]. These responses also strongly impact on the cost of EDM process. Hence, in order to reduce the cost as well as to improve the quality product, there have been many studies dealing with finding optimum input parameters which can minimize the cost of EDM operation. Optimizing the output parameters leads to the fact that multiple objectives are naturally derived based on investigating various input parameters with differential levels. To meet this requirement, Grey relation analysis (GRA) coupled with Taguchi technique shows a distinct selection [1, 8, 10–14]. The Taguchi method has been used effectively to find the effects of input factors on the responses in many experimental works [15–17]. The effectiveness of this method is higher when it is combined with GRA such as when finding optimum dressing regimes in internal grinding [18] and surface grinding [19, 20] or determining optimum input factors in powder-mixed electrical discharge machining [21]. The obtained results presented in [10] show that the Taguchi Grey Relational Analysis is an effective technique to optimize the machining parameters for EDM process. In the study, some process parameters like current, pulse on time, and pulse off time are used to find the evolution of three responses such as material removal rate (MRR), wear rate of cutting tool, and surface roughness with machined materials of mild steel IS 2026 using copper electrode. The combination between GRA and Taguchi method (using L9 orthogonal array) is utilized to analyze the experimental results to find the optimal levels of machining parameters which can generate higher material removal rate, lower wear rate of cutting tool, and lower surface roughness. The predicted results are checked with the experimental ones and a good agreement is visualized. Subrata et al. [14] investigated the influences of some process parameters such as laser power, scan speed, and powder speed rate on laser machining operation. L9 orthogonal array is adopted to perform the experiments. The GRA is utilized to find out the optimum parameters which are power of 1.25 kW, scan speed of

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0.8 m/min, and a powder feed rate of 11 gm/min. The responses of optimization are selected in study of [22] are surface roughness and MRR. In this study, an attempt has been carried out to optimize the multiple objective responses, e.g. surface roughness and material removal rate resulting from EDM process of hardened SKD11 steel. The investigated parameters are the powder concentration, the pulse on time, the pulse off time, the servo current, and the servo voltage. Among those, the first parameter will be considered at six levels, and the others at three levels. Taguchi method coupled with GRA has been applied to find the combination of machining parameters generating the optimum responses. ANOVA will be performed by Minitab@18 to determine which parameters have significant effects on the multiple responses. Furthermore, experiments will be conducted to validate the combination of machining parameters which are predicted by Taguchi method coupled with GRA.

2 Experimental Preparation 2.1

Design of Experiments

In order to evaluate the performance of output responses, surface roughness and MRR, five machining parameters are selected such as Concentration of powder, Pulse on time, Pulse off time, Current, and Voltage. The first parameter will be considered at six levels, and the others at three levels. The information of machining parameters is listed in Table 1. A Taguchi L18 (6^1 3^4) orthogonal array is designed with 18 runs to perform experiments.

Table 1. Investigated factors and their levels Variable

Level 1 2 Concentration of powder Cp [g/l] 0 2.0 Pulse on time Ton [µs] 6 10 14 21 Pulse off time Toff [µs] Current IP [A] 4 8 Voltage SV [V] 3 4

2.2

3 2.5 14 30 12 5

4 3.5 – – – –

5 4.0 – – – –

6 4.5 – – – –

Materials and Specimens

The specimens are in shapes and dimension as shown in Fig. 2. The materials used are hardened 90CrSi steel with a hardness of 58-62 HRC. The chemical composition of specimens can be seen in Table 2. The EDM process is performed using electrode of copper, and dielectric solution mixed with nanopowder of SiC 500 nm. The principles of EDM process can be schematically described in Fig. 3. In order to keep nanopowder of SiC consistent when contained in the dielectric solution, the speed of stirring reaches 90 r/min. The EDM

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process can be kept stable by using a nozzle and a pump. Chip generated during EDM can be collected by the magnetic plated. The details of EDM process are further consulted in Table 3 and in [3].

Fig. 2. Shape and dimension of machined workpiece.

Table 2. Chemical composition of 90CrSi steel (%) C Si 0.85* 1.2 0.95 * 1.6

Mn 0.3 * 0.6

P  S  Cr  Mo  Ni  V  W  Others 0.03 0.03 0.95 * 1.25 0.2 0.35 0.15 0.2 Cu  0.3; Ti  0.03

Fig. 3. Shema of EDM process [3]

The surface roughness Ra is measured by using the roughness tester of Mitutoyo 178-923-2A, SJ-201. The surface roughness values are averagely calculated by three times of measurements. MRR is determined by removed material volume per time unit of pulse. Each experimental run was repeated three times to lessen the experimental error. The EDM process is carried out according to the experiment design as previously mentioned and the results of Ra and MRR are listed in Table 4.

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T. T. Hong et al. Table 3. EDM parameters Items EDM machine Electrode Workpiece Work-piece dimensions Dielectric fluid Surf – test instrument Nanopowder

Description Sodick A30 Copper 90CrSi alloy steel; 58-62 HRC hardness 60 mm  35 mm  25 mm Diel MS 7000 oil SV 3100 SiC 500 nm

3 Grey Relational Analysis and Discussion on Experiment Results The signal to noise (S/N) ratio for each test can be determined based on the expectation for surface roughness that is “the smaller is the better’’, S=N ¼ 10log10 ð

1 Xn 2 yÞ i¼1 i n

ð3:1Þ

And conversely, that is “the larger is better’’ for MRR[16]: SN ¼ 10log10 ð

1 Xn 1 Þ i¼1 y2 n i

ð3:2Þ

In which, n is the total of tests, yi the observed data. The normalized values of grey relation for both surface roughness and MRR can be determined by Zij (0  Zij  1):   SNij  min SNij ; j ¼ 1; 2; ::k    Zij ¼ max SNij ; j ¼ 1; 2; ::n  min SNij ; j ¼ 1; 2; ::n 

ð3:3Þ

The values of S/N ratio and normalized values of grey relation are shown in Table 5. The grey relation coefficient, cðkÞ, is calculated to express the relationship between reference and experimental data normalized data as follows: cð k Þ ¼

Dmin þ fDmax Dj ðkÞ þ fDmax

ð3:4Þ

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Where: Δoj is the deviation of reference data. Dj ðkÞ ¼ Z0 ðkÞ  Zj ðk Þ and Z0(k) is the reference data or best data. þ Þ Dmin ¼ min min Z0 ðkÞ  Zj ðk Þ minimum value of Dj ðkÞ. 8j2i

8k

þ ÞDmax ¼ max max Z0 ðkÞ  Zj ðkÞ maximum value of Dj ðkÞ. 8j2i

8k

+) f is distinguishing or identification coefficient and its value varies from 0 to 1. We select its value of 0.5 herein.

Table 4. Orthogonal array with factors and responses TT Cp Ton Toff IP SV Ra (µm) Trial 1 Trial 2 1 0 6 14 4 3 2.960 2.930 2 0 10 21 8 4 2.239 2.161 3 0 14 30 12 5 5.066 5.117 4 2 6 14 8 4 2.411 2.434 5 2 10 21 12 5 2.749 2.839 6 2 14 30 4 3 4.942 5.200 7 2.5 6 21 4 5 2.158 2.232 8 2.5 10 30 8 3 3.895 3.882 9 2.5 14 14 12 4 3.840 3.733 10 3.5 6 30 12 4 2.791 2.620 11 3.5 10 14 4 5 3.421 3.559 12 3.5 14 21 8 3 2.685 3.068 13 4 10 30 4 4 2.959 2.795 14 4 14 14 8 5 2.646 2.670 15 4 6 30 8 5 1.614 1.655 16 4.5 10 14 12 3 3.752 3.613 17 4.5 14 21 4 4 4.404 4.298 18 4.5 14 30 8 3 2.864 2.732

Trial 3 2.928 2.383 5.125 2.482 2.601 5.174 2.196 3.868 3.790 2.528 3.490 2.906 2.763 2.785 1.741 3.926 4.491 2.795

MRR (g/h) Trial 1 Trial 2 0.0192 0.0193 0.0101 0.0101 0.2475 0.2465 0.0034 0.0034 0.3304 0.3302 0.0321 0.0321 0.0018 0.0018 0.0392 0.0392 0.3384 0.3384 0.4537 0.4523 0.0505 0.0506 0.0025 0.0025 0.3252 0.3249 0.0078 0.0078 0.0089 0.0088 0.3102 0.3099 0.0070 0.0070 0.0080 0.0080

Trial 3 0.0193 0.0101 0.2470 0.0034 0.3302 0.0322 0.0018 0.0391 0.3381 0.4528 0.0505 0.0025 0.3255 0.0077 0.0089 0.3102 0.0070 0.0080

Determining the gray relational grade: cj ¼

m 1X c k i¼1 ij

ð3:5Þ

Based on this equation, the average value of interactions of grey relation is found at third step; k is the number of objectives optimized. Table 6 shows the values of grey relational coefficient of both surface roughness and MRR.

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T. T. Hong et al. Table 5. S/N ratio values, normalized S/N ratio values and the absolute value. TT S/N Ra

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

−9.365 −7.093 −14.156 −7.757 −8.728 −14.163 −6.831 −11.780 −11.568 −8.460 −10.858 −9.220 −9.067 −8.631 −4.459 −11.517 −12.866 −8.936

MRR

−34.316 −39.896 −12.147 −49.361 −9.623 −29.859 −54.958 −28.143 −9.415 −6.879 −25.928 −51.916 −9.758 −42.212 −41.064 −10.170 −43.082 −41.951

Zij Ra MRR Reference data 1.000 1.000 0.494 0.429 0.729 0.313 0.001 0.890 0.660 0.116 0.560 0.943 0.000 0.522 0.756 0.000 0.246 0.558 0.267 0.947 0.588 1.000 0.341 0.604 0.509 0.063 0.525 0.940 0.570 0.265 1.000 0.289 0.273 0.932 0.134 0.247 0.539 0.271

Dj(k) Ra MRR

0.506 0.271 0.999 0.340 0.440 1.000 0.244 0.754 0.733 0.412 0.659 0.491 0.475 0.430 0.000 0.727 0.866 0.461

0.571 0.687 0.110 0.884 0.057 0.478 1.000 0.442 0.053 0.000 0.396 0.937 0.060 0.735 0.711 0.068 0.753 0.729

The higher value of grey relation refers to the better quality of product. Hence, it can be estimated the influence of factors and the optimum degree for each controlled factor. Table 6 presents the values of grey relation for each specific test and predicted grey relation values. It is observed that the tenth test (Cp4, Ton1, Toff3, IP3, SV2) corresponding to machining configuration of Cp = 3.5 (g/l), T = 6 (µs), Toff = 30 (µs), IP = 12 (A), SV = 4 (V) exhibits the highest value of predicted grey relation, 0.774. This refers that at the tenth test the value of S/N ratio reaches approximately normalized ones and better than other tests shown in Table 6. However, it is noticed that this test is not the best one. For this reason, it is needed to calculate the average values of each factor at different levels. The main effects of each factor with different levels on grey grade are presented in tabular form and graphical form corresponding to Table 7 and Fig. 4 respectively. It is noticed that the highest grade value of each factor refers to the optimum value for corresponding factor. Hence, it is observed that the optimum set combining all machining parameters which makes the smaller is better for the surface roughness and the larger is the better for MRR is Cp5/Ton1/Toff3/IP3/SV3 or for specific value: Cp = 4.0 (g/l), Ton = 6 (µs), Toff = 30 (µs), IP = 12 (A), SV = 5 (V). In order to

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determine which factor have significant effects on the grey grade, Analysis of Variance (ANOVA) is performed (c.f. Table 8).

Table 6. Grey relational co-efficient and grey grade values TT Grey relational co-efficient ci Ra MRR 1 0.497 0.467 2 0.648 0.421 3 0.333 0.820 4 0.595 0.361 5 0.532 0.898 6 0.333 0.511 7 0.672 0.333 8 0.399 0.531 9 0.406 0.905 10 0.548 1.000 11 0.431 0.558 12 0.505 0.348 13 0.513 0.893 14 0.538 0.405 15 1.000 0.413 16 0.407 0.880 17 0.366 0.399 18 0.520 0.407

c

0.482 0.535 0.577 0.478 0.715 0.422 0.503 0.465 0.655 0.774 0.495 0.426 0.703 0.471 0.706 0.643 0.382 0.463

Table 7. Main effects on grey grades. Level Cp 1 0.5313 2 0.5383 3 0.5410 4 0.5650 5 0.6267 6 0.4960 Delta 0.1307 Rank 2 c ¼ 0:55

Ton 0.5972 0.5105 0.5415

Toff 0.5330 0.5575 0.5587

IP 0.4727 0.5422 0.6343

SV 0.4800 0.5627 0.6065

0.0867 0.0257 0.1617 0.1265 4 5 1 3

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Fig. 4. Factor effects on grade values. Table 8. Results of ANOVA on grey grade Source DF 5 Cp Ton 2 2 Toff IP 2 SV 2 Residual Error 4 Total 17

Seq SS 0.028752 0.023142 0.002521 0.078922 0.049515 0.058983 0.241834

Adj SS 0.028752 0.023142 0.002521 0.078922 0.049515 0.058983

Adj MS 0.005750 0.011571 0.001260 0.039461 0.024757 0.014746

F 0.39 0.78 0.09 2.68 1.68

P C% 0.835 11.89 0.516 9.57 0.920 1.04 0.183 32.63 0.296 20.47 24.39 100.00

According to the presented results, it is visualized that Current (IP) has the strongest influence on the grey grade corresponding to 32.63%. This percentage contribution is followed by Voltage (SV), Concentration of Powder (Cp), Pulse on time (Ton), and Pulse off time (Toff) corresponding to 20.47%, 11.89%, 9.71%, and 1.04%, respectively. The optimum grey grade value can be confirmed by following process: cop ¼ gm þ

4 X

ðg  gm Þ ¼ Cp5 þ Ton1 þ Toff 3 þ IP3 þ SV3  4  T

ð3:5Þ

i¼1

Where: T is the average grey grade, T = 0.55; Cp5, Ton1, Toff3, IP3, SV3 present the optimum values listed in Table 5. Hence, cop ¼ 0:824. Confidence interval can be determined as: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  ffi 1 1 ¼ 0:218 CI ¼  Fa ð1:fe Þ:Ve : þ Ne R

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Where: F/ ð1; fe Þ ¼ 5:448 is the tabular constant with the significant level a% = 90%, fe = 4 is denoted for the degrees of freedom of errors, Ve = 0.014746 is the average deviation of errors, Ne is the effective repetition; R = 3 represents for the repetition of each test. Total number of test 54 ¼ 1 þ total number of degrees of fredom of factors 1 þ 5 þ 2 þ 2 þ 2 þ 2 ¼ 3:857

Ne ¼

Based on the above results, the values of grey grade can vary in the following range: 0:606  cop  1:00 The optimum values of surface roughness and MRR can be found by following equation: ðRa; MRRÞop ¼ Cp5 þ Ton1 þ Toff 3 þ IP3 þ SV3  4  T In which: Cp5 is the average value of concentration of powder at level 5 Ton1 is the average value of Pulse on time at level 1 Toff 3 is the average value of Pulse off time at level 3 IP2 is the average value of Current at level 3 SV3 is the average value of Voltage at level 3 T is the average value of surface roughness and/or MRR. As a result, we can get: ðRaÞop ¼ 3:009lm ðMRRÞop ¼ 0:4348g=h In order to validate the reliability of the proposed regression model, the optimal set of parameters as previously analyzed is selected to perform the confirmation experiment. The chosen parameters are Cp = 4.0 g/lit, Ton = 6 µs, Toff = 30 µs, IP = 12 A, SV = 5 V. The experimental results and predicted ones are compared and listed in Table 9. Table 9. Comparison of prediction and experiment Prediction Cp5/Ton1/Toff3/IP3/SV3 Surface roughness, Ra (µm) 3.009 Material removal rate (g/h) 0.4348 Grey grade value 0.824

Experiment Error (%) Cp5/Ton1/Toff3/IP3/SV3 2.783 7.51 0.4016 7.64

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Based on the results of comparison, it is seen that the maximum error between the experiment and the prediction is 7.64% belonging to MRR. This means that the proposed model and calculating process are significantly reliable and it could be adopted for simultaneously and accurately predicting the optimum values of surface roughness and MRR.

4 Conclusions In the current research work, the EDM process of hardened 90CrSi steel is performed. The electrode is made of copper, while the dielectric solution is mixed with the powder of nano SiC 500. The Taguchi technique is combined with Grey relational analysis to minimize the surfaces roughness and maximize the material removal rate. Based on this aim, the optimum set of machining parameters such as Concentration of powder, Pulse on time, Pulse off time, Current, and Voltage is found. The following conclusions are made: – Current has the strongest influence on the grey grade value (32.63%), and it is sequentially followed by Voltage (20.47%), Concentration of powder (11.89), Pulse on time (9.57%), and Pulse off time (1.04%). – The optimum set of machining parameters for EDM process of hardened 90CrSi steel is Cp = 4.0 (g/l), Ton = 6 (µs), Toff = 30 (µs), IP = 12 (A), SV = 5 (V). – The confirmation experiment and ANOVA show the reability of the proposed model which can be considered as an effective method to predict the surface roughness and material removal rate when conducting EDM process of hardended 90CrSi steel using nano SiC 500 nm powder. Acknowledgements. This work was financially supported by the scientific project No. B2019TNA-03.

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Multi-objective Optimization of Surface Roughness and MRR in Surface Grinding of Hardened SKD11 Using Grey-Based Taguchi Method Tran Thi Hong1, Do The Vinh2, Tran Vinh Hung3, Tran Ngoc Giang2, Nguyen Thanh Tu2, Le Xuan Hung2, Bui Thanh Danh4, and Luu Anh Tung2(&) 1

Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam 2 Thai Nguyen University of Technology, Thai Nguyen 23000, Vietnam [email protected] 3 Faculty of Mechanical Engineering and Mechatronics, PHENIKAA University, Hanoi 100000, Vietnam 4 University of Transport and Communications, Hanoi, Vietnam

Abstract. The dressing plays an important role in wheel preparation in the grinding process. In this study, Grey Relational Analysis (GRA) based Taguchi method is applied to optimize the dressing parameters for minimizing the surface roughness and maximizing the material removal rate (MRR) in surface grinding of hardened SKD 11 steel. The Taguchi technique L16 is used to organize experiments that include six input parameters of the dressing process. There are two two-level parameters and four 4-level parameters including dressing feed rate, rough dressing depth, rough dressing times, fine dressing depth, fine dressing times, and non-feeding dressing. As shown in the result, the optimal dressing process for the minimum surface roughness and maximum MRR consists of 2 times of the rough dressing with a depth of 0.015 mm/stroke, a feed rate of 1.6 mm/min, 3 times non-feeding dressing, and no fine dressing was performed. Also, the dressing feed rate has the strongest impact on multiple performance characteristics (with 43.24% contribution), followed by the rough dressing depth (with 26.20% contribution). A verification experiment has demonstrated the appropriateness of the predictive model to the measurement data. Keywords: SKD11


Surface grinding
Taguchi
GRA

1 Introduction Grinding is a metalworking process that is commonly used in roughing as well as finishing. During the grinding process, the dressing functions to sharpen the abrasive particles and remove metal debris adhering to the surface of the grinding wheel [1, 2]. During the working process, the grinding wheel is constantly worn. Hence, the dressing process is required to maintain the initial specifications of the grinding wheel. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 584–593, 2021. https://doi.org/10.1007/978-3-030-64719-3_64

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The performance of the grinding wheel, that is reflected in profile, topography, and wear behavior, is highly dependent on the dressing process [3–5]. In small scale production, dressing is often done empirically. Therefore, the efficiency of the process is not high. Optimizing the dressing process or finding out the effect of dressing parameters on quality and productivity as well as wheel life, wear of wheel have attracted many researchers [6–9]. However, there are many parameters of dressing process such as the coarse dressing, the fine dressing and non-feeding dressing, which have not been adequately studied. In metal cutting, surface roughness is an important indicator that reflects the quality of processed products. However, that reducing roughness and increasing machining productivity are carried out at the same time is a difficult request. The process of simultaneously optimizing two or more goals is called multi-objective optimization. Grey-based Taguchi method has been used to solve multi-objective optimization in many researches including grinding [4, 10–16], turning [17, 18], milling [19, 20], etc. In the grinding process, surface grinding is the most common process. Therefore, the improvement of the efficiency of the surface grinding process such as improving the roughness, increasing grinding performance, increasing the wheel life is the topic that attracts many researchers and manufacturers [21–28]. In this work, Grey-based Taguchi method was used for multi-objective optimization to minimizing the surface roughness and maximizing MRR in surface grinding of hardened SKD 11 steel. The effect of dressing parameters on multiple performance characteristics was also analyzed by using ANOVA. Besides, a verification experiment was performed to evaluate the results of the predictive model.

2 Experimental Procedures In this study, all experiments were carried out by using a surface grinding machine MOTO – YOKOHAMA (Japan). The workpiece was SKD 11 steel with chemical composition shown in Table 1. The workpieces have a dimension of 70  40  25 mm and hardness of 5862 HRC. Cantext Aquatex 3810 was used as cooling fluid with a concentration of 3% and a flow of 10 l/min. Besides, the grinding wheel was Cn46TB2GV1.300.32.127.30 m/s of Hai Duong company (Vietnam) and the dressing tool was 3908-0088C type 2 (Russia). Table 1. Chemical composition of SKD 11 steel. C 1.4 1.6%

Si Mn Cr P S Mo W Cu V   1113%   0.81.2% 0.20.5%  0.25%  0.25% 0.4% 0.6% 0.03% 0.03%

The grinding conditions fixed for all experiments were cutting velocity of 26.7 m/s, depth of cut of 0.01 mm, longitudinal feed rate of 8 mm/stroke, and table speed of 8 m/min. The dressing parameters including rough dressing depth (trd), rough dressing times (nrd), non-feeding dressing (nnon), fine dressing times (nfd), fine dressing depth

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(tfd), and dressing feed rate (S) were selected for multi-objective optimization to minimize the surface roughness and maximize MRR as shown in Table 2. The design of experiment is conducted by applying the L16 orthogonal array of Taguchi technique. Table 2. Factors and levels Parameters

Unit

Dressing feed rate S Rough dressing depth trd Rough dressing times nrd Fine dressing depth tfd Fine dressing times nfd Non-feeding dressing nnon

m/min mm Times mm Times Times

Levels 1 2 1.6 1.8 0.015 0.02 1 2 0.005 0.01 0 1 0 1

3 – 0.025 3 – 2 2

4 – 0.03 4 – 3 3

3 Results and Discussion Table 3 indicates the experimental results. The surface roughness was measured by using an instrument of Mitutoyo company, model 178-923-2A, SJ-201. MRR was determined by measuring the weight before and after each experiment using a highprecision scale (precision of 0.001 g). Table 3. L16 Orthogonal array with factors and responses No. trd

nrd nnon nfd tfd

S

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

1.6 1.8 1.6 1.8 1.8 1.6 1.8 1.6 1.8 1.6 1.8 1.6 1.6 1.8 1.6 1.8

0.015 0.015 0.015 0.015 0.02 0.02 0.02 0.02 0.025 0.025 0.025 0.025 0.03 0.03 0.03 0.03

0 1 2 3 1 0 3 2 2 3 0 1 3 2 1 0

0 1 2 3 2 3 0 1 3 2 1 0 1 0 3 2

0.005 0.005 0.01 0.01 0.01 0.01 0.005 0.005 0.005 0.005 0.01 0.01 0.01 0.01 0.005 0.005

Surface roughness Ra (µm)

T1 0.661 0.471 0.433 0.452 1.225 1.267 0.984 0.524 1.286 0.73 0.757 0.51 0.363 0.591 0.841 0.998

T2 0.685 0.485 0.45 0.517 1.042 1.322 1.104 0.453 1.362 0.74 0.731 0.465 0.358 0.613 0.848 1.027

T3 0.647 0.516 0.413 0.516 1.252 1.35 1.06 0.528 1.332 0.767 0.756 0.464 0.462 0.609 0.833 1.105

Material Remove Rate MRR (mm3/s) T1 T2 2.44 2.42 2.05 2.00 1.92 1.89 1.83 1.82 1.84 1.79 2.40 2.36 2.06 2.05 1.93 1.90 1.87 1.91 2.30 2.25 1.75 1.93 1.92 1.88 1.98 1.92 1.89 1.86 1.91 1.87 1.83 1.81

T3 2.38 1.94 1.88 1.85 1.80 2.37 2.04 1.64 1.92 2.27 1.92 1.89 1.95 1.87 1.86 1.79

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Table 4. SN ratio values, normalized SN ratio values and the absolute value. TT S/N Ra

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

3.55 6.18 7.29 6.09 −1.41 −2.37 −0.43 5.97 −2.46 2.55 2.52 6.37 8.02 4.37 1.51 -0.38

Zij MRR Ra Z0(k) 1.000 7.66 0.57 6.00 0.82 5.56 0.93 5.27 0.82 5.16 0.10 7.51 0.01 6.23 0.19 5.15 0.80 5.57 0.00 7.14 0.48 5.39 0.48 5.56 0.84 5.79 1.00 5.46 0.65 5.47 0.38 5.15 0.20

Dj(k) MRR Ra MRR 1.000 1.00 0.34 0.16 0.05 0.00 0.94 0.43 0.00 0.16 0.79 0.09 0.16 0.26 0.12 0.13 0.00

0.43 0.18 0.07 0.18 0.90 0.99 0.81 0.20 1.00 0.52 0.52 0.16 0.00 0.35 0.62 0.80

0.00 0.66 0.84 0.95 1.00 0.06 0.57 1.00 0.84 0.21 0.91 0.84 0.74 0.88 0.87 1.00

After the data collection process, Grey-based Taguchi technique was used for multi-objective optimization. This technique has been used very successfully in many studies [29–32]. The basic steps of the optimization process according to Grey-based Taguchi method are given as follows: In the first step of Grey-based Taguchi method, S/N is calculated for corresponding responses. The goal of this study is to reduce the roughness and increase MRR. Therefore, the smaller is the better type of S/N ratio is selected for surface roughness and the larger is the better type is selected for MRR as calculated in following: The smaller is the better S/N: SN ¼ 10log10

 X  1 n 2 y i¼1 i n

ð1Þ

 X  1 1 n i¼1 y2 n i

ð2Þ

The larger is the better S/N: SN ¼ 10log10

Where: yi is the data received by experiment, n is the number of experiments.

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c

0.770 0.585 0.626 0.537 0.346 0.617 0.425 0.526 0.354 0.598 0.422 0.567 0.701 0.476 0.405 0.359

In the second step, a data preprocessing is conducted to normalize the raw data (S/N data). The linear normalization of the S/N ratio is carried out by the grey relational generating (a range between zero and unity). The normalized S/N ratio Zij for the ith performance characteristic in the jth experiment can be determined by (3). Table 4 shows the calculated values of S/N, normalized Zij and Δj(k):   SNij  min SNij ; j ¼ 1; 2; ::k     Zij ¼ max SNij ; j ¼ 1; 2; ::n  min SNij ; j ¼ 1; 2; ::n

ð3Þ

The grey relation coefficient is calculated in a third step as expressed in Eq. (4) cð k Þ ¼

Dmin þ fDmax Dj ðkÞ þ fDmax

ð4Þ

Where j = 1, 2…n; k = 1, 2…m, n is the number of experiments, k is the number of objectives. Dj(k) is the deviation sequence and calculated by DjðkÞ ¼ Z0 ðkÞ  Zj ðk Þ. And: Dmin ¼ min8j2i min8k Z0 ðkÞ  Zj ðkÞ; Dmax ¼ max8j2i max8k kZ0 ðkÞ Zj ðkÞk. f is the distinguishing coefficient 0  f  1. In this case, f = 0.5.

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After averaging the grey relation coefficients, the grey relational grade ci is calculated as Eq. (5): cj ¼

1 Xm c i¼1 ij k

ð5Þ

Where cj is the grey relational grade for the jth experiment; k is the number of objectives (in this study, k = 2) The values of the grey relational coefficient and grey relational grade are shown in Table 5. In the next step, Taguchi method is used to determine the effects of the input factors on the grey relational grade. Table 6 shows the main effect of the input factors on the grey relational grade. Figure 1 indicates main effects plot for the grey relational grade. As shown in Table 6 and Fig. 1, the optimal dressing process for the minimum surface roughness and maximum MRR consists of 2 times of the rough dressing with a depth of 0.015 mm/stroke, a feed rate of 1.6 mm/min, 3 times non-feeding dressing, and no fine dressing performed. Table 6. Main effects on the grey relational grade Level trd 1 0.6294 2 0.4784 3 0.4852 4 0.4852 Delta 0.1511 Rank 2 c ¼ 0; 520

nrd 0.5426 0.5689 0.4694 0.4973 0.0995 3

nnon 0.5418 0.4757 0.4954 0.5653 0.0896 4

nfd 0.5596 0.5584 0.4819 0.4783 0.0813 5

tfd S 0.5027 0.6011 0.5364 0.4380

0.0337 0.1631 6 1

Fig. 1. Main effects plot for the grey relational grade

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The result of variance analysis for the grey relational grade is shown in Table 7. Based on the ANOVA, the dressing feed rate has the strongest impact on multiple performance characteristics with 43.24% percent contribution, followed by the rough dressing depth with 26.20% percent contribution. Fine dressing times contributes 10.13%, rough dressing times contributes 9.71%, and non-feeding dressing contributes 8.27%. The effect of fine dressing depth is negligible with 1.84%. Table 7. Analysis of variance for the grey relational grade Source trd nrd nnon nfd tfd S Re. Error Total

DF 3 3 3 3 1 1 1 15

Seq SS 0.064517 0.023906 0.020368 0.024940 0.004540 0.106470 0.001477 0.246217

Adj SS 0.064517 0.023906 0.020368 0.024940 0.004540 0.106470 0.001477

Adj MS F 0.021506 14.56 0.007969 5.40 0.006789 4.60 0.008313 5.63 0.004540 3.07 0.106470 72.10 0.001477

P C% 0.190 26.20 0.304 9.71 0.327 8.27 0.298 10.13 0.330 1.84 0.075 43.24 0.60 100.00

The grey relation grade is determined by the following equation: cop ¼ gm þ

X5 i¼1

ðg  gm Þ ¼ trd1 þ nrd2 þ nnon4 þ nfd1 þ tfd2 þ S1  5  T

According to Table 6, trd1 ¼ 0:6294 nrd2 ¼ 0:5689 nnon4 ¼ 0:5653 nfd1 ¼ 0:5596 tfd2 ¼ 0:5364 S1 ¼ 0:6011 T ¼ 0:520 By (6): cop ¼ 0:863

ð6Þ

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Based on the result of the optimization process, the minimal value of output factor is determined by Eq. (7): ðRa; MRRÞop ¼ trd1 þ nrd2 þ nnon4 þ nfd1 þ tfd2 þ S1  5  T

ð7Þ

Where trd1 is the average of output response for trd at level 1, nrd2 is the average of output response for nrd at level 2, nnon4 is the average of output response for nnon at level 4, nfd1 is the average of output response for nfd at level 1, tfd2 is the average of output response for tfd at level 2, S1 is the average of output response for S at level 1, and T is the average of output response in all experiments. Table 8. The results from model and experiment Properties

Optimal parameters predicted result measured result Error (%) trd1, nrd2, nnon4, nfd1, tfd2, S1 trd1, nrd2, nnon4, nfd1, tfd2, S1 Ra (µm) 0.211 0.231 9.48 2.17 8.13 MRR (mm3/s) 2.362 GRA value 0.863

So: ðRaÞop ¼ 0:211 lm ðMRRÞop ¼ 2:362 mm3 =s A verification experiment was conducted with the optimal dressing parameters consisting of 2 times of the rough dressing with a depth of 0.015 mm/stroke, a feed rate of 1.6 mm/min, 3 times non-feeding dressing, and no fine dressing performed. A comparison between the predicted result and measured results is shown in Table 8. The error between the predicted result and measured result is small. It means that the research result is reliable.

4 Conclusions In this study, Grey-based Taguchi method was applied for multi-objective optimization to minimize the surface roughness and maximize MRR in surface grinding of hardened SKD 11 steel. Some main conclusions can be given as following: – The optimal dressing process for the minimum surface roughness and maximum MRR consists of 2 times of the rough dressing with a depth of 0.015 mm/stroke, a feed rate of 1.6 mm/min, 3 times non-feeding dressing, and no fine dressing performed. – The dressing feed rate has the strongest impact on multiple performance characteristics with 43.24% percent contribution, followed by the rough dressing depth with 26.20% percent contribution.

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– A verification experiment has demonstrated the appropriateness of the predictive model to the measurement data. Acknowledgements. This work was supported by Thai Nguyen University of Technology.

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Multi-response Optimization in PMSEDM Process Using Taguchi-Grey Method Tran Thi Hong1, Nguyen Manh Cuong2, Tran Ngoc Giang2, Nguyen Anh Tuan3, Le Thu Quy4, Thangaraj Muthuramalingam5, Nguyen Thanh Tu2, and Do Thi Tam2(&) 1

Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam 2 Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] 3 University of Economic and Technical Industries, Ha Noi, Vietnam 4 National Research Institute of Mechanical Engineering, Ha Noi, Vietnam 5 Department of Mechatronics Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India

Abstract. The optimization of input parameters on Powder Mixed Sinking Electrical Discharge Machining (PMSEDM) is critical for manufacturing industries for achieving higher productivity and product quality. In this work, a multi-target optimization of input factors for minimizing the surface roughness and maximizing the material removal rate (MRR) in PMSEDM process using Taguchi-Grey approach is presented. The five input parameters including the pulse-on-time (Ton), the pulse-off-time (Toff), the pulse current (IP), the server voltage (SV), and the powder concentration (Cp) are chosen for optimizing two responses i.e. the surface roughness and MRR. The results show that the optimal input parameters are Ton of 6 ls, Toff of 30 ls, IP of 4 A, SV of 5 V, and Cp of 6%. The optimized surface roughness obtained is 3.527 µm and the optimized MRR is 0.0724 g/min. The discovered technology mode has been applied to the real machining process and the outcome shows a considerable improvement in comparison with the default setting modes. Keywords: PMSEDM Taguchi-grey method


Silicon carbide powder
Multi-objective


1 Introduction Electrical Discharge Machining (EDM) plays an important role in modern industry due to the continuous improvement of part accuracy [1, 2]. In the EDM process, it is very important to enhance the input parameters to improve the process efficiency [2]. Therefore, the analysis of their impact on machining characteristics is essential. The general process parameters to control the machining operation in EDM are Ton, Toff, IP, SV, and Cp. Subsequently, surface roughness (Ra) and MRR are the vital responses to decide the productivity and product quality [3]. Thus, the optimization of these parameters is especially important in EDM process because of its direct impact on the efficiency of the machining process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 594–606, 2021. https://doi.org/10.1007/978-3-030-64719-3_65

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Currently, numerous researches have been carried out to optimize input parameters which contribute considerably to enhance the efficiency of the EDM process [4–10]. In 2017, Luboslav Straka [4] performed a study on the impact of the input parameters on the surface quality after die-sinking EDM with a SF-Cu electrode. Another remarkable research was conducted by Alexia Torres Salcedo [5], where a model of energy density and optimum input factors of Inconel 600 was carried out using Cu-C electrodes. Based on that, the optimal input parameters were the current intensity of 8 A, the pulse time of 100 µs, and the duty cycle of 0.6 to gain a maximum MRR of 30.49 (mm3/min) [5]. Furthermore, Mustufa Haider Abidi [6] analyzed the impact of three important input parameters on various output factors, such as overcut, taper angle and surface roughness in micro-EDM process for a nickel-titanium shape memory alloy. In this work, a methodology of multi-response optimization has been developed by using Grey PCA. In addition, based on response surface methodology, Tran Thanh Hoang and Vu Ngoc Pi investigated the surface roughness of part in EDM tablet shape punches [7, 8]. In the study, the optimum input parameter values were determined to minimize surface roughness [7, 8]. Similarly, Phan H. Nguyen [9] analyzed the impacts of input parameters on surface quality after EDM process. By using Taguchi-TOPSIS technique, the optimal process factors were also determined to minimize surface hardness and surface roughness [9]. Recently, the effects of input parameters in EDM process have been explored to achieve better surface quality [10–13]. Based on TOPSIS method, the optimal set of input parameters has been determined as a duty factor of 0.6, voltage of 80 V, current of 15 A, and tool of tungsten carbide [13]. However, the study on multi-objective optimization in PMSEDM process for 90CrSi steel to maximize the surface quality and MRR has not before been published. Therefore, this work proposes an experimental study on the influences of the input factors including Ton, Toff, IP, SV and Cp on MRR and surface roughness in PMSEDM process. In addition, the optimal values of these input parameters were determined to maximize the surface quality and MRR by using Taguchi-Grey method.

2 Methodology 2.1

Experimental Setup

An experiment was designed to maximize the surface quality and MRR in PMSEDM process. The description of experimental machine and equipment is presented in Table 1. In addition, the diagram of the experimental set-up is illustrated in Fig. 1. In this setup, an AG40L CNC sinker EDM machine was used to perform experiments. 90CrSi steel with a hardness of 55–60 HRC with the dimensions of 13  13  30 (mm) was used as the work-piece material. The electrode material was copper and the dielectric fluid was HD-1 oil. Furthermore, 45–55 (nm) silicon carbide powders were applied to add to the dielectric fluid.

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Equipment and materials Machine for sinking EDM Electrode material Dielectric fluid Work-piece material Work-piece dimensions Roughness measurement machine

Specifications AG40L CNC sinker EDM (Sodick Europe Ltd. UK) Copper EDM oil HD-1 90CrSi 13  13  30 mm SURFTEST SV-3100 (Japan)

Fig. 1. The diagram of the experimental setup: 1) machining tank; 2) work-piece; 3) electrode; 4) stirring; 5) magnets

For the experiment, five input parameters (Table 2) were selected to assess their effects on the surface roughness and the MRR in the PMSEDM process. The selected three levels for each of the five input parameters are shown in Table 2. Table 2. Input factors and their levels No. Input factors Unit Experimental levels Low level (1) Base level (2) High level (3) 1 Ton ls 6 14 – 2 Cp g/l 0 0.03 0.06 3 Toff ls 14 21 30 4 IP A 4 8 12 5 SV V 3 4 5

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597

Design of Experiment

In this work, the Taguchi method and an orthogonal array L18 (21 and 34) are applied to design the experiments [13–16]. The experimental plan with eighteen experiments is demonstrated in Table 3. Based on Minitab_18 statistical software, an analysis is implemented. Table 3. Standard L18 (21 and 34) orthogonal matrix Experiment number Factors Ton Cp 1 6 0 2 6 0 3 6 0 4 6 0.03 5 6 0.03 6 6 0.03 7 6 0.06 8 6 0.06 9 6 0.06 10 14 0 11 14 0 12 14 0 13 14 0.03 14 14 0.03 15 14 0.03 16 14 0.06 17 14 0.06 18 14 0.06

Toff 14 21 30 14 21 30 14 21 30 14 21 30 14 21 30 14 21 30

IP 4 8 12 4 8 12 8 12 4 12 4 8 8 12 4 12 4 8

SV 3 4 5 4 5 3 3 4 5 5 3 4 5 3 4 4 5 3

The Taguchi method is employed to analyze the obtained results by using signal-tonoise ratio. In PMSEDM process of 9CrSi steel, the key target is to decrease the surface roughness and increase the MRR. With the target function of the MRR, the Signal to Noise ratio is determined by the following equation [13–16]:
X 1 n S=N ¼ 10log10 1=y2i Þ i¼1 n

ð1Þ

With the target function of surface roughness, the Signal to Noise ratio is determined by the following equation [10–13]: S=N ¼ 10log10 ð

1 Xn 2 y Þ i¼1 i n

ð2Þ

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Where n is the number of tests in trials and yi is the result of a particular experiment. In this work, the number of tests in trials has been chosen as ‘3’. 2.3

Multi-criteria Optimization

For multi-criteria optimization, Taguchi-Grey method has been exploited. Based on the experimental results, the grey relational analysis is utilized to convert the multi-criteria optimization into the one-criterion optimization of the grey relational grade. The purpose of this work is to solve the multi-criteria optimization problem in which the surface roughness is minimized and the MRR is maximized instantaneously. The optimization procedure, using Taguchi-Grey method, is explained below: Step 1: Normalizing signal to noise ratio of experimental results for all output responses; Step 2: Determining grey relational coefficient; Step 3: Determining grey relational grade (GRG); Step 4: Evaluating experimental results by using GRG and ANOVA; Step 5: Selecting optimal value of input parameters;

3 Results and Discussion After selecting input parameters and their ranges, the experimental results were obtained. To increase the accuracy of the experiment, each trial was repeated 3 times. All the gathered data from experiments are illustrated in Table 4. Based on that, the S/N ratio for various response parameters was determined by using Eqs. (1) and (2) as illustrated in Table 4. Table 4. Experimental results of L18 (21 and 34) and their S/N ratios No Factors and their levels Mean and S/N ratio of experimental results MRR (g/min) Ton Cp Toff IP SV Ra (lm) Mean S/N Mean S/N 1 6 0 14 4 3 3.075 −9.758 0.0111 −39.125 2 6 0 21 8 4 2.330 −7.348 0.0026 −51.629 3 6 0 30 12 5 3.124 −9.894 0.0701 −23.082 4 6 0.03 14 4 4 3.177 −10.040 0.0113 −38.954 5 6 0.03 21 8 5 2.434 −7.725 0.0033 −49.511 6 6 0.03 30 12 3 3.211 −10.132 0.0712 −22.949 7 6 0.06 14 8 3 2.512 −8.000 0.0065 −43.728 8 6 0.06 21 12 4 3.925 −11.876 0.0114 −38.876 9 6 0.06 30 4 5 2.273 −7.131 0.0477 −26.423 10 14 0 14 12 5 7.408 −17.394 0.0768 −22.298 (continued)

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Table 4. (continued) No Factors and their levels Mean and S/N ratio of experimental results MRR (g/min) Ton Cp Toff IP SV Ra (lm) Mean S/N Mean S/N 11 14 0 21 4 3 5.882 −15.391 0.0207 −33.685 12 14 0 30 8 4 3.869 −11.752 0.0071 −42.938 13 14 0.03 14 8 5 4.341 −12.751 0.0063 −43.966 14 14 0.03 21 12 3 6.862 −16.729 0.0720 −22.855 15 14 0.03 30 4 4 5.476 −14.769 0.0160 −35.910 16 14 0.06 14 12 4 7.027 −16.936 0.0827 −21.647 17 14 0.06 21 4 5 5.273 −14.441 0.0194 −34.260 18 14 0.06 30 8 3 7.027 −16.936 0.0071 −43.031

3.1

Normalization of S/N Ratio and Computation of Grey Relational Grade

3.1.1 Normalization of S/N Ratio of Experimental Results for All Output Responses The first step of the optimization procedure using Taguchi-Grey method is the normalization of S/N ratio. The formula for normalizing the output characteristic with “larger the better” as below [17–20]:   SNij  min SNij     Zij ¼ max SNij  min SNij

ð3Þ

The formula for the normalization of the output characteristic with “lower the better” as below [17–19]:   max SNij  SNij     Zij ¼ max SNij  min SNij

ð4Þ

Where: Zij is the normalized S/N ratio; SNij is the S/N ratio; min(SNij) and max(SNij) are respectively minimum and maximum values of S/N ratio; j is the number of experiments (j = 18). The normalization values for each output target are shown in Table 5.

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MRR −39.125 −51.629 −23.082 −38.954 −49.511 −22.949 −43.728 −38.876 −26.423 −22.298 −33.685 −42.938 −43.966 −22.855 −35.910 −21.647 −34.260 −43.031

Zij Ra 0.744 0.979 0.731 0.717 0.942 0.708 0.915 0.538 1.000 0.000 0.195 0.550 0.452 0.065 0.256 0.045 0.288 0.045

MRR 0.417 0.000 0.952 0.423 0.071 0.957 0.264 0.425 0.841 0.978 0.599 0.290 0.256 0.960 0.524 1.000 0.579 0.287

Di(j) Ra 0.256 0.021 0.269 0.283 0.058 0.292 0.085 0.462 0.000 1.000 0.805 0.450 0.548 0.935 0.744 0.955 0.712 0.955

MRR 0.583 1.000 0.048 0.577 0.929 0.043 0.736 0.575 0.159 0.022 0.401 0.710 0.744 0.040 0.476 0.000 0.421 0.713

3.1.2 Calculation of Grey Relational Co-efficient Grey relational co-efficient illustrates the relationship between ideal conditions and the actual conditions of the normalized characteristic. The following formula is utilized to determine the value of the grey relational coefficient [17–20]: ci ð k Þ ¼

Dmin þ fDmax Di ðkÞ þ fDmax

ð5Þ

Where: ci ðkÞ is the grey relational coefficient; n is the number of experiments (n = 18); m is the number of output targets (m = 2); f is the distinguishing coefficients ð0  f  1Þ. Because multi-responsive features include both larger-the-better and smaller-the-better, f is chosen as 0.5 in this case [17–20]; Di ðkÞ is the deviation sequence of reference sequence Z0 ðkÞ and comparability sequence Zj ðk Þ, i.e.D0j ¼ Z0 ðk Þ  Zj ðkÞ is absolute value of deviation between Z0 ðkÞ and Zj ðkÞ.Dmin and Dmax are the minimum and maximum absolute values of D0j . Based on Eq. (5), the values of the grey relational coefficient are determined as illustrated in Table 6.

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Table 6. Grey relational coefficient and grey grade values No Grey relational coefficient (ci ðkÞ) Ra MRR 1 0.661 0.462 2 0.959 0.333 3 0.650 0.913 4 0.638 0.464 5 0.896 0.350 6 0.631 0.920 7 0.855 0.404 8 0.520 0.465 9 1.000 0.758 10 0.333 0.958 11 0.383 0.555 12 0.526 0.413 13 0.477 0.402 14 0.348 0.925 15 0.402 0.512 16 0.344 1.000 17 0.412 0.543 18 0.344 0.412

Grey relational grade (ci Þ

0.562 0.646 0.781 0.551 0.623 0.776 0.630 0.492 0.879 0.646 0.469 0.470 0.440 0.637 0.457 0.672 0.478 0.378

3.1.3 Calculation of Grey Relational Grade The grey relational grade exposed in the final column of Table 6 is the average value of the grey relational coefficients, and these values are determined by the following formula [20, 21]: ci ¼

1 Xm c ðk Þ i¼1 i m

ð6Þ

Where m is the number of output targets. Based on Eq. (6), they are calculated as illustrated in Table 6. 3.2

Determining Optimum Input Parameters

Based on the above results, it has been observed that experiment trial No. 9 has the highest value of grey relational grade ðcmax ¼ c9 ¼ 0:879Þ. Thus, it indicates the best combination of multi-objective responses. However, MRR at this setting is low. To identify the optimal level of parameters, the analysis of variance is used on grey relational grade (GRG). Because higher GRG is desirable, the higher-the-better S/N quality feature is used to get the optimal combination for multi-objective optimization.

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Thus, S/N ratios of multi response features are determined by using Eq. (1) and illustrated in Table 7. The level of a parameter with the largest S/N ratio shows the optimal level. Table 7. Main effects on grey relation grades Level Ton 1 −4.794 2 −7.080 3 Delta 2.286 Rank 1

Cp −5.961 −5.974 −5.877 0.096 5

Toff −6.279 −6.725 −4.808 1.917 2

IP −5.493 −7.006 −5.313 1.693 3

SV −6.020 −6.536 −5.255 1.281 4

Fig. 2. Main effects plot for grey relational grade

Figure 2 illustrates the main influence for the grey relational grade. The deviation from the mean line indicates the significance of factors on quality [22, 23]. It can be clearly seen that the optimum input factors are the 1st level of Ton (6 ls), the 3rd levels of Cp (0.06 g/l), Toff (30 ls), IP(4 A) and SV(5 V). These optimum values of the input parameters would help in minimizing the surface roughness and maximizing MRR. 3.3

Analysis of Variance (ANOVA)

This method indicates the numerical contribution of factors on measures [24, 25]. The significant contribution of each input factor on the responses in PMSEDM process is studied by using ANOVA. The results of the analysis are displayed in Table 8. It is revealed that Ton is the most influential element among the five input factors studied in this work. The impact steadily decreased in the following sequence order of the five input factors: Ton, IP, SV, Toff, and CP. Accordingly, the smallest influential element is CP.

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Table 8. Results of ANOVA on grey relational grade Source DF Ton 1 CP 2 Toff 2 IP 2 SV 2 Residual Error 8 Total 17

3.4

Seq SS 0.093151 0.000680 0.013235 0.060121 0.027483 0.116660 0.311331

Adj SS 0.093151 0.000680 0.013235 0.060121 0.027483 0.116660

Adj MS 0.093151 0.000340 0.006618 0.030060 0.013742 0.014583

F 6.39 0.02 0.45 2.06 0.94

P 0.035 0.977 0.651 0.190 0.429

Determining Predict Model

From the above experimental data, the predicted optimum value of GRG is found as: lop ¼ Ton1 þ Cp3 þ Toff 3 þ IP3 þ SV 3  4gm

ð7Þ

In which: ηm is the average of grey relation grades of the whole experiment (ηm = 0.588); Ton1 ; Cp3 ; Toff 3 ; IP3 , and SV3 are the mean values of the grey relational grade where Ton is at level 1, CP, Toff, IP and SV are at level 3. Therefore: lop ¼ 0:66 þ 0:5881 þ 0:6235 þ 0:6673 þ 0:6411  40:588 ¼ 0:828 From the optimum level of input factors, the optimal value of the Ra and MRR outputs is determined by: ðRa; MRRÞop ¼ Ton1 þ Cp3 þ Toff 3 þ IP3 þ SV3  4T

ð8Þ

Where: Ton1 ; Cp3 ; Toff 3 ; IP3 ; SV3 are the average surface roughness or MRR with Ton at level 1, CP, Toff, IP and SV at level 3. T is the average surface roughness or MRR of the whole experiment. Therefore: ðRaÞToiuu ¼ 2:896 þ 4:673 þ 4:163 þ 5:259 þ 4:142  44:401 ¼ 3:527 lm ðMRRÞop ¼ 0:026141 þ 0:029129 þ 0:036545 þ 0:064031 þ 0:037276  40:0301 ¼ 0:0724 g=min From the above equation, the predicted values of output parameters have been determined with optimal parameter settings as 3.527 (Ra) and 0.0724 (MRR).

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Evaluating the Predict Model

To evaluate the accuracy of the predict model, a verified experiment with three repetitions has been carried out. The experimental input factors are the optimum input parameters including Ton = 6 ls, Cp = 0.06 g/l, Toff = 30 ls, IP = 4 A, and SV = 5 V. Table 9 shows the experimental and calculated values of output factors corresponding to the optimal input value. The obtained result shows that the difference between calculation results and experiments is within 8.56% of the range, indicating that the models which are proposed in the study are reliable. Table 9. For evaluating the proposed model Output parameters

Surface roughness – Ra (µm) MRR (g/min) Grey relation grade value ( lop Þ

Optimum parameters Prediction value Ton1/Cp3/Toff3/IP3/SV3 3.527 0.0724 0.828

Experimental value Ton1/Cp3/Toff3/IP3/SV3 3.367

Error (%)

0.0786

8.56

4.54

4 Conclusions In this work, the Taguchi-Grey method has been utilized to determine the optimal values of input parameters in PMSEDM process for achieving better multi-objective responses. The research has obtained the following optimum parameters: Ton = 6 ls, Toff = 30 ls, IP = 12 A, SV = 5 V, CP = 6 g/l. In the optimal technology condition, the optimized surface roughness is 3.527 µm and the optimized MRR is 0.0724 g/min. In addition, it has been found that Ton is the most influential element among the five input parameters. The factor with the smallest influence is Cp. Such research results would help manufacturers to determine and set up optimal input parameters in order to enhance economic and technical effectiveness of PMSEDM process. Acknowledgments. This work was financially supported by the scientific project No. B2019TNA-03.

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3. Ojha, K., Garg, R.K., Singh, K.K.: Parametric optimization of PMEDM process using chromium powder mixed dielectric and triangular shape electrodes. J. Miner. Mater. Charact. Eng. 10, 1087–1102 (2011) 4. Straka, L’., Corný, I., Pitel’, J., Hašová, S.: Statistical approach to optimize the process parameters of HAZ of tool steel EN X32CrMoV12-28 after die-sinking EDM with SF-Cu electrode. Metals 07(35), 01–22 (2017) 5. Salcedo, A.T., Arbizu, I.P., Pérez, C.J.L.: Analytical modelling of energy density and optimization of the EDM machining parameters of inconel 600. Metals 07(166), 01–21 (2017) 6. Abidi, M.H., Al-Ahmari, A.M., Siddiquee, A.N., Mian, S.H., Mohammed, M.K., Rasheed, M.S.: An investigation of the micro-electrical discharge machining of nickel-titanium shape memory alloy using grey relations coupled with principal component analysis. Metals 07 (486), 01–15 (2017) 7. Hoang, T.T., Duc, T.A., Cuong, N.M., Tung, L.A., Hung, L.X., Pi, V.N.: Modelling surface finish in electrical discharge machining tablet shape punches using response surface methodology. SSRG Int. J. Mech. Eng. 04(09), 28–30 (2017) 8. Hung, L.X., Hung, L.X., Hoang, T.T., Pi, V.N.: A study on modelling surface finish in electrical discharge machining tablet shape punches using response surface methodology. J. Environ. Sci. Eng. 06, 387–390 (2017) 9. Nguyen, P.H., Banh, L.T., Bui, V.D., Hoang, D.T.: Multi-response optimization of process parameters for powder mixed electro-discharge machining according to the surface roughness and surface micro-hardness using Taguchi-TOPSIS. Int. J. Data Netw. Sci. 02, 109–119 (2018) 10. Nguyen, M.C., Tung, L.A., Danh, B.T., Cuong, N.V., Hong, T.T., Linh, N.H., Quy, L.T., Pi, V.N.: Influence of input factors on material removal rate in PMEDM cylindrical shaped parts with silicon carbide powder suspended dielectric. In: Key Engineering Materials, vol. 861, pp. 129–135. Trans Tech Publications Ltd. (2020) 11. Ky, L.H., Danh, B.T., Cuong, N.V., Hong, T.T., Nguyen, T.V., Nguyen, T.Q.D., Le, T.P.T., Nguyen, M.C.: Effect of input parameters on electrode wear in PMEDM cylindrical shaped parts. In: Key Engineering Materials, vol. 861, pp. 136–142. Trans Tech Publications Ltd. (2020) 12. Pi, V.N., Tam, D.T., Cuong, N.M., Tran, T.-H.: Multi-objective optimization of PMEDM process parameters for processing cylindrical shaped parts using Taguchi method and grey relational analysis. Int. J. Mech. Prod. Eng. Res. Dev. 10(2), 669–678 (2020) 13. Huo, J., Liu, S., Wang, Y., Muthuramalingam, T., Pi, V.N.: Influence of process factors on surface measures on electrical discharge machined stainless steel using TOPSIS. Mater. Res. Express 06, 01–08 (2019) 14. Lan, T.-S., Chuang, K.-C., Chen, Y.-M.: Optimization of machining parameters using fuzzy Taguchi method for reducing tool wear. Appl. Sci. 08(1011), 01–13 (2018) 15. Niu, W., Mo, R., Chang, Z., Wan, N.: Investigating the effect of cutting parameters of Ti– 6Al–4 V on surface roughness based on a SPH cutting model. Appl. Sci. 09(654), 01–23 (2019) 16. Ngoc-Pi, V., Nguyen, Q.-T., Tran, T.-H., Le, H.-K., Nguyen, A.-T., Luu, A.-T., Nguyen, V.T., Le, X.-H.: Optimization of grinding parameters for minimum grinding time when grinding tablet punches by CBN wheel on CNC milling machine. Appl. Sci. 09(957), 01–09 (2019) 17. Patil, A.N., Walke Gaurish, A., Mahesh, G.: Grey relation analysis methodology and its application. Res. Rev. Int. J. Multidisciplinary 04(02), 409–411 (2019)

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Numerical Identification of the Mechanical Behaviour of a Fluoroelastomer (FKM) Using Nanoindentation Test Florent Chalon1(&), Julie Pepin1, Nathan Le Pennec1, Tien-Dung Do2, Stéphane Meo1, Clémence Fradet1, Gaelle Berton1, and Florian Lacroix1 1

Université de Tours, Université d’Orléans, INSA Centre val de Loire, Laboratoire de Mécanique Gabriel Lamé, 7 Avenue Marcel Dassault, 37000 Tours, France [email protected] 2 Thai Nguyen University of Technology, Thai Nguyen City, Vietnam

Abstract. The aim of the paper is to provide a numerical model of nanoindentation tests carried out on a synthetic elastomer. Some works deal with such numerical model but on classical elastomer like silicon rubber. Our study focuses on a filled fluoro-elastomer (FKM). At first, a 2D numerical model equivalent to a Berkovich test is built. The law of behaviour used in the simulation is obtained from the results of a single traction test. Then a numerical nanoindentation test can be carried out and compared with experimental nanoindentation curves of a previous study. The relevance of the most suitable laws of behaviour is deduced. Keywords: Nanoindentation


Numerical simulation
Elastomer
FKM

1 Introduction Nanoindentation is now commonly used to obtain local mechanical properties of materials. The metal or classical material with an elasto-plastic behavior are widely studied. Numerical models are elaborated to provide tools which allow to lead easier numerical campaign of tests. The relevance of the numerical approach is also applied for example to bio-sourced material [1], human bones [2], hard-brittle material [3]…. Hereby, the point is to focus on rubbery materials. Some studies can be found in the literature and it often concerns unfilled rubber without a viscous component [4] or unfilled and/or silicone rubber [5, 6]. A 2D model, which saves computing time, can be used to lead our study. Firstly, the parameters of the mechanical behavior are extracted by a comparison with experimental uniaxial tests. Then these properties are used to simulate a nanoindentation test and the numerical indentation curves are compared with the experimental ones. The difficulty in this study is to match with the behavior of a synthetic filled elastomer, the fluoroelastomer (FKM) in the case of a nanoindentation tests. The future aim of the study is to model the viscosity of the FKM by comparison with nanoindentation curves following the profile loading-holding-unloading. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 607–612, 2021. https://doi.org/10.1007/978-3-030-64719-3_66

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2 Material Fluoroelastomer (FKM) is a special rubber designed to withstand severe chemical and thermal conditions. The raw rubber blend was elaborated by introducing all the ingredients into an internal mixer and was then pressed and cured so as to get 2-mmthick sheets. The curing process was carried out for 20 min at 180 °C within the mold of the press and was followed by a post-curing step realized in an oven for 2 h at 200 ° C. Samples were finally extracted from the 2-mm-thick sheets.

3 Experimental Nanoindentation Tests Indentation tests have been performed [7] using a Bruker’s Hysitron TI98O Triboindenter allowing forces and displacements up to 10 mN and 15 µm respectively, on which a diamond Berkovich tip was mounted. To limit measurements artifacts linked to the surface quality, it has been decided to carry out indentation test on surfaces prepared with razor blades. It allows to reduce five times the roughness in comparison with a molded sample (molded sample: Ra = 547 ± 100 nm, razor blade sample: Ra = 100 ± 32 nm). Samples were glued on a support which was itself maintained on the machine by vacuum. All tests were made with the force controlled quasi-static mode applying a trapezoidal load function. A maximal force Pmax of 1 mN has been chosen which is small enough to extract the local mechanical response. The load function was as follow: Loading time 100 s Holding time 300 s Unloading time 100 s Fives tests are carried out to verify the reproducibility (Fig. 1).

Fig. 1. Reproducibility of the indentation hysteresis for FKM. Maximal force of 1 mN, loading rate 0.01 mN/s, holding time 300 s and unloading rate 0.01 mN/s.

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4 Numerical Models The objective is here to provide a numerical model of a nanoindentation test which will be used to determine the mechanical parameters of the FKM. To obtain an accurate evaluation of the parameters of the mechanical model, it is usually necessary to proceed to four tests on the material: an uniaxial test, a planar test, a biaxial test and a volumetric test. The aim of the approach is to predict the indentation test using the macroscopic law of behavior obtained by identification from uniaxial tests. Then these coefficients could be improved by an optimization scheme. To rise this objective a numerical simulation of a nanoindentation test is carried out. To reduce the time of simulation, a 2D axisymmetric model is realized. Indeed, it has been shown, in previous studies, that the 3D indentation test with a Berkovich tip can be carried out with an axisymmetric configuration [8, 9]. To perform the simulation, the FEM software Abaqus® 6.14 is used. The indenter is assumed to be a rigid body which is a commonly hypothesis for indentation of soft polymers. The geometrical size of the sample is a radius of 100 µm and 100 µm of thickness which is large enough to avoid the boundary effects [9] (see Fig. 2). A 2D finite element mesh is built with quadrangle elements with quadratic shape function. To obtain an accurate description of the sample deformation, it is important that the density of nodes under the indenter is high enough. The rigid indenter is fixed in the horizontal direction and a load is applied in the vertical direction. The bottom nodes of the sample are fixed and the boundary conditions to consider the axis symmetry are applied. The maximum displacement rises less than 6% at the maximum load. The friction between the indenter and the elastomer is neglected [10].

Fig. 2. Representation (a) of the tip and mesh of the sample, (b) zoom of the mesh near the tip

To carry out the simulation, uniaxial tests are realized and the mean is drawn on the Fig. 3.

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5 Comparison of the Numerical and Experimental Results

Fig. 3. Uniaxial tests

In help with the module of Abaqus® it is possible to evaluate the coefficients of different hyperelastic models (Fig. 3). There are numerical specific forms of strainenergy functions to describe hyperelastic properties. We only focus on isotropic and nearly incompressible ones such as Neo Hooke, Mooney-Rivlin, Ogden…. To validate the numerical model, a load of 1mN is applied. The depth of the tip is measured and the curve of the load as a function of displacement is drawn. On the Fig. 4, the experimental curve [7] is superimposed with the numerical ones. We can observe that two models feat well with experimental data: Neo-Hooke and Mooney-Rivlin. The Neo-Hooke curve is closer to the experimental curve at the end of the loading whereas the Mooney-Rivlin curve has a good fitting at the beginning of the loading but the gap is more important at the end of the loading. Let focus on these two models. The Mooney-Rivlin free energy is written as follow: W¼

X2 i þ j¼1

 i  j 1 Cij I 1  3 I 2  3 þ ð J  1Þ 2 D1

ð1Þ

And for Neo-Hooke:   1 W ¼ C10 I 1  3 þ ð J  1Þ 2 D1 Where I 1 and I 2 are the first and second principle invariants

ð2Þ

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  T I 1 ¼ tr FF     1   T 2 T 2 tr FF I2 ¼ tr FF 2

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ð3Þ ð4Þ

F is the deformation gradient, J the jacobian, F ¼ JF and Cij and D1 are material constants.

Fig. 4. Numerical and experimental loading curves

Table 1. Mooney-Rivlin and Neo-Hooke coefficients. Model C10 C01 C11 C20 C02 D1 Neo-Hooke 0,2348 – – – – 0,1726 Mooney-Rivlin −0,5923 0,906 9,65E−2 −1,88E−2 0,1656 0,1291

The value of the parameters estimated by the module of Abaqus® are listed in the Table 1. As seen on the Fig. 4, the second order Mooney-Rivlin model does not provide a better description than the Neo-Hooke model. It shows that an uniaxial test is insufficient to provide a satisfying description of a nanoindentation test even if it is acceptable. The discrepancy between the numerical and experimental nanoindentation curves comes from the approximation of the simulation (conical tip, frictionless) and imperfect identification of the mechanical behavior.

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6 Conclusion Using a single and simple uniaxial traction test allows to carry out a numerical nanoindentation test on a fluoroelastomer. But the prediction is not enough accurate. To improve the model, the next step will be to optimize the coefficients of the law of behaviour. To achieve this aim, a script could calculate the difference between numerical and experimental nanoindentation curves and minimize it by correcting the parameters of the law. To improve the model the viscosity has also to be considered. To validate the viscosity effect a simulation of the loading-holding-unloading will be performed and compared to the experimental unloading nanoindentation curves.

References 1. Girard, M., Gaillard, Y., Burr, A., Darque-Ceretti, E., Felder, E.: Nanoindentation of biosourced adhesive 75% rosin/25% beeswax: experiment results and modelisation. Mech. Mater. 69, 185–194 (2014) 2. Remache, D., Semaan, M., Rossi, J.M., Pithioux, M., Milan, J.L.: Application of the Johnson-Cook model in the finite element simulations of the nanoindentation of the cortical bone. Biomed. Mater. 101, 103426 (2020) 3. Youn, S.W., Kang, C.G.: FEA study on nanodeformation behaviors of amorphous silicon and borosilicate considering tip geometry for pit array fabrication. Mater. Sci. Eng., A 390, 233–239 (2005) 4. Le Saux, V., et al.: Identification of constitutive model for rubber elasticity from microindentation tests on natural rubber and validation by macroscopic tests. Mech. Mater. 43, 775–786 (2011) 5. Kohl, J.G., Singer, I.L., Simonson, D.L.: Determining the viscoelastic parameters of thin elastomer based on materials using continuous microindentation. Polym. Test. 27(6), 679– 682 (2008) 6. Bles, G., Le Saux, V., Castro-Lopez, C., Morvan, B., Marco, Y., Prisacariu, G.: Instrumented micro-hardness measurements used to identify the local visco-hyper-elastic parameters of Polyurethane elastomers. In: Gil-Negrete, N., Alonso, A. (eds.) Constitutive Models for Rubber VIII, pp. 177–182. Taylort & Francis Group, London (2013) 7. Fradet, C., Lacroix, F., Berton, G., Méo, S., Le Bourhis, E.: Instrumented indentation of an elastomeric material, protocol and application to vulcanization gradient. Polym. Test. 81, 106278 (2020) 8. Lichinchi, M., Lenardi, C., Haupt, J., Vitali, R.: Simulation of Berkovich nanoindentation experiments on thin films using finite element method. Thin Solid Films 312, 240–248 (1998) 9. Chen, Z., Scheffer, T., Seibert, H., Diebels, S.: Macroindentation of a soft polymer: identification of hyperelasticity and validation by uni/biaxial tensile tests. Mech. Mater. 64, 111–127 (2013) 10. Chen, Z., Diebels, S., Schmitt, J.: Frictional nanoindentation of hyperelastic polymer layers: a numerical study. In: Proceeding of 3rd ECCOMAS, Thematic Conference on the Mechanical Response of Composites, pp 229–236 (2011)

On Room-Temperature Electrodeposition of Cobalt from a Deep Eutectic Solvent: A Study of Electronucleation and Growth Mechanisms Thao Dao Vu Phuong1,2, Hoang Thi Thanh Thuy3, Phuong Dinh Tam1, and Tu Le Manh1(&) 1

Faculty of Materials Science and Engineering, Phenikaa University, Hanoi 12116, Vietnam [email protected] 2 Advanced Institute for Science and Technology (AIST), Hanoi University of Science and Technology (HUST), No 01, Dai Co Viet Road, Hanoi, Vietnam 3 Faculty of Industrial Chemistry, Hanoi University of Industry, Cau Dien StreetBac Tu Liem District, Hanoi, Vietnam

Abstract. This paper demonstrates the capability of the cobalt electrodeposition from a eutectic mixture of choline chloride and urea at room temperature. Thermodynamic and kinetic aspects of cobalt electronucleation and growth mechanisms onto glassy carbon electrode were investigated. The cobalt electrodeposition, at 303 K, is dominated by progressive nucleation. The behavior of current density transients of the Co electrodeposition can be described by a model comprising the contribution due to three-dimensional nucleation and diffusion-controlled growth from metallic nuclei combined with the effect of the induction – time. The diffusion coefficient of cobalt ions in deep eutectic solvent was determined by cyclic voltammetry and best-fit parameters obtained using the proposed model. Both values were in agreement with the results published in the literature. SEM and EDS verified the presence and the nucleation type of cobalt onto glassy carbon electrode. Keywords: Cobalt nucleation and growth solvent


Induction-time
Deep eutectic

1 Introduction Cobalt has been attracted by the scientists because of its strong magnetic and catalytic properties, which are applied in numerous fields such as magnetic sensors, chemical sensors, and catalysts [1, 2]. The cobalt electrodeposition is extensively studied in the aqueous media [2]. It is known that the use of aqueous media causes many problems (i.e. liberation of hydrogen, evaporation) and technological concerns (toxic solutions), which reduces the cobalt electrodeposition process efficiency [2]. Dealing with these problems, the deep eutectic solvent (DES) based on choline chloride [3] is a prospective candidate. Various metals and their alloys have been synthesized by © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 613–618, 2021. https://doi.org/10.1007/978-3-030-64719-3_67

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electrochemical method from DESs such as Ni [4], Zn [5], Co [6, 7], etc. They verified that the early stage of cobalt nuclei formation onto glassy carbon electrode (GCE) plays an important role in the cobalt electrodeposition from DES [6, 7]. However, since the viscosity of the DES is high, these works suggested to perform the experiment at relatively high temperature (above 343 K) to reduce the effect of DES’s viscosity. This causes the following problems: i) it requires more accessories for heating the DES and ii) it decreases the volume of DES due to the thermal decomposition [8], which results more difficult in controlling the metal ions concentration during the electrodeposition process and decreasing the efficiency. Therefore, it is important to study the capability of the Co electrodeposition at room temperature and its thermodynamic and kinetic aspects. This paper focuses on studying mechanisms and kinetics of cobalt nucleation and growth onto glassy carbon from choline chloride-based DES at 303 K by means of cyclic voltammetry (CV) and chronoamperometry (CA) techniques. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) were used to characterize the morphology and chemical composition of surface electrodeposit, respectively.

2 Materials and Methods 2.1

Electrolyte Preparation

Choline Chloride and Urea are used to prepare DES in a 1:2 molar ratio at 363 K. This mixture was magnet-stirred. The electrolyte solution was obtained by adding cobalt (II) chloride hexahydrate salt CoCl2.6H2O 50 mM to the DES kept stirring for 12 h at 343 K. 2.2

Electrochemical Experiments and Surface Characterization

The cobalt nucleation and growth mechanisms onto a glassy carbon electrode from DES were studied through CV and CA at room temperature. These tests were performed in a cell comprising three electrodes, using a VersaSTAT Potentiostat/Galvanostat, coupled to the VersaStudio software running on a PC to facilitate experimental control and data collection. The electrochemical cell was composed of a polished GCE with a surface area of 0.0707 cm2 as the working electrode, a platinum wire as counter, and a silver wire as quasi reference electrode. The surface morphology of the Co deposit onto GCE from DES was characterized by a field emission scanning electron microscope (SEM JEOL 7000). The elementary analysis of the surface deposit was determined by EDS.

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3 Results and Discussion 3.1

Potentiodynamic Study

Figure 1 shows the CVs recorded on the GCE of DES and 50 mM Co(II) in DES at 303 K. No peak formation can be observed in black curve (in the absence of Co(II)) in both scan directions, while in the system GCE/50 mM Co(II) in DES, typical reduction and oxidation peaks are clearly formed. The quality of these peaks also verified the capability of DES to electrodeposit Co at room temperature. In the forward scan direction of CV, the reduction peak is located in the potential range from – 0.95 V to – 1.20 V. In the opposite direction, the oxidation peak located in the potential range of – 0.4 V to 0.1 V is supposed to the oxidation of Co metallic nuclei into Co (II) ion. The increase of current density at the potential lower than – 1.20 V is attributed to the decomposition of the solvent. To assess the thermodynamic and kinetic behavior of the system GCE/Co (II) in DES, CVs were performed at different scan rates (from 10 to 90 mVs−1). The inset of Fig. 1 indicates that the current density of cathode peak (jcp) displays a linear behavior with square root of the scan rate (v1/2), which demonstrates that the Co nucleation onto GCE from DES follows a diffusion – controlled mechanism described by the Randles – Sevcik equation [7]:

ð1Þ where n is the total number of electrons transferred during the overall electrochemical process, C0 (mol cm−3) is the reduced species bulk concentration, DCo(II) (cm2 s−1) is the diffusion coefficient of Co(II) ions and T (K) is the temperature of medium. Also, the 3D nucleation behavior can be described by the model developed by Scharifker – Mostany (SM) [9]. From Eq. (1), DCo(II) was calculated to be 2.79  10−7 cm2 s−1, which is consistent with of the results published by Manh et al. [6]. 0.0015

DES Co(II) in DES

0.0000 0.005 1/2

-2

-0.0030

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-jcp = 0.0205 v - 0.0014; R = 0.992

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-0.0075 -1.2

-1.0

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Fig. 1. Comparison between CVs recorded in the system GCE/DES in the absence (black curve) and in the presence of 50 mM Co (II) (red curve). In both cases the potential scan started at 0 V in the negative direction with a scan rate of 90 mVs−1. The inset displays jcp of the CVs as a function of v1/2.

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3.2

Nucleation and Growth Mechanisms of Co Electrodeposition

From CVs, experimental current-time transients (j-t plots) were recorded at – 0.95 V and – 0.96 V at 313 K. The recorded data from experiment was normalized through the maximum of current density for each curve (jm, tm) and then compared with theoretical plots for instantaneous and progressive nucleation [9] to investigate the Co nucleation and growth mechanism. Figure 2 shows the influence of the induction time by comparing the experimental normalized plot and the theoretical one in both cases (with and without t0). If ignoring the influence of t0, the normalized experimental curve is under the progressive curve (Fig. 2a), out of the validated zone that follows the behavior of the 3D nucleation and diffusion-controlled growth described by the SM model [10]. 1.0

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Fig. 2. Comparison of the non-dimensional plots of the experimental current density transients (black line) with the theoretical ones for instantaneous (blue) and progressive (red) nucleation at 303 K at -0.95 V (a) without induction-time compensation and (b) with induction-time compensation.

Therefore, in this situation, the classical form of the SM model cannot depict the nucleation and growth mechanism of Co. For this condition, it is significant to consider the consequence of t0 in the SM model by replacing t = t – t0 [11]. As shown in Fig. 2b, after eliminating the induction-time, the new normalized experimental curve is in the validated zone of SM model, and nearly fit well to the progressive curve. This also evidences that the induction time plays the important part during the early stage of the Co electrodeposition process. Then, the SM model after modifying is below [11]:

ð2Þ where t = t – t0, q is the density of the Co deposit and M is its atomic mass, N0 is the number density of active sites on the electrode surface, and A is the nucleation frequency per active site.

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- 0.96 V - 0.95 V

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Fig. 3. (a) Comparison between the j – t plots of experimental CAs obtained in the system GCE/50 mM Co(II) in DES and the theoretical ones (solid lines) fitting to the experimental data after subtracting the induction-time at 303 K (b) The pictures of Co(II) salt solution in DES and Co electrodeposition on copper electrode, (c) SEM images of Co electrodeposited on the copper substrate at −0.96 V at 303 K and (d) EDS analysis of the electrode surface.

Figure 3a shows the comparison between the experimental j – t current density transients and the theoretical plots fitting to the experimental data at 303 K using Eq. (2). It presents a good concordance in the shapes with the experimental j – t plots, which demonstrate that the modified SM model by adding the induction-time correction can depict the nucleation and growth mechanism of Co. Electrodeposition of Co was performed at − 0.96 V at 303 K on copper electrode (Fig. 3b). The grey color able to see in naked eye of copper sheet indicates the formation of Co on the electrode, which is confirmed by the presence of the Cu peak shown in EDS spectrum (Fig. 3d). Furthermore, SEM image in Fig. 3c reveals that the cobalt particles exhibit a hexagonal structure, with a relatively homogeneous distribution, which is consistent with the observation done in [8]. This evidences the ability of Co electrodeposition from DES at room temperature.

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4 Conclusions The capability of Co electrodeposition, at room temperature, onto glassy carbon from DES based on choline chloride was studied through the CV and CA analyses. It was found that the Co nucleation onto the GCE from the DES occurred by a diffusion – controlled mechanism, which can be described by adding the contribution of the induction-time to the classic SM model. The diffusion coefficient of Co(II) ions was determined to be 2.79  10−7 cm2 s−1. Based on the results, it was obvious to conclude that the early stage of the Co electrodeposition is dominant by progressive nucleation mechanism at 303 K. Finally, the ability of cobalt electrodeposition in DES at low temperature can expand the potential applications of DES in different engineering fields. Acknowledgement. This research is funded by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 103.02-2019.28.

References 1. Leslie-Pelecky, D.L., Rieke, R.D.: Magnetic properties of nanostructured materials. Chem. Mater. 8, 1770–1783 (1996) 2. Rios-Reyes, C.H., Granados-Neri, M., Mendoza-Huizar, L.H.: Kinetic study of the cobalt electrodeposition onto glassy carbon electrode from ammonium sulfate solutions. Quim. Nova 32(9), 2382–2386 (2009) 3. Abbott, A.P., Capper, G., Davies, D.L., Rasheed, R.K., Tambyrajah, V.: Novel solvent properties of choline chloride/urea mixtures. Chem. Commun. (Camb), (1) 70–1 (2003) 4. Huang, P., Zhang, Y.: Electrodeposition of nickel coating in choline chloride-urea deep eutectic solvent. Int. J. Electrochem. Sci. 13, 10798–10808 (2018) 5. Vieira, L., Whitehead, A.H., Gollas, B.: Mechanistic studies of zinc electrodeposition from deep eutectic electrolytes. J. Electrochem. Soc. 161(1), D7–D13 (2014) 6. Manh, T.L., Arce-Estrada, E.M., Mejía-Caballero, M.I., Aldana-González, J., RomeroRoma, M., Palomar-Pardavé, M.: Electrochemical synthesis of cobalt with different crystal structures from a deep eutectic solvent. J. Electrochem. Soc. 165, 285–290 (2018) 7. Cao, X., Xu, L., Shi, Y., Wang, Y., Xue, X.: Electrochemical behavior and electrodeposition of cobalt from choline chloride- urea deep eutectic solvent. Electrochim. Acta 295, 550–557 (2019) 8. Yizhak, M.: Properties of Deep Eutectic Solvents, pp. 45–110. Springer International Publishing (2019) 9. Scharifker, B., Hills, G.: Theoretical and experimental studies of multiple nucleation. Electrochim. Acta 28(7), 879–889 (1983) 10. Scharifker, B.R., Mostany, J.: Three-dimensional nucleation with diffusion-controlled growth. J. Electroanal. Chem. 177, 13–23 (1984) 11. Manh, T.L., Arce-Estrada, E.M., Mejıa-Caballero, I., Rodrıguez-Clemente, E., Sanchez, W., Aldana-Gonzalez, J., Lartundo-Rojas, L., Romero-Romo, M., Palomar-Pardave, M.: Iron electrodeposition from Fe(II) ions dissolved in a choline chloride: urea eutectic mixture. J. Electrochem. Soc. 165(16), D808–D812 (2018)

Optimal Design of Cab’s Isolation System for a Single-Drum Vibratory Roller Le Van Quynh2(&), Nguyen Tien Duy2, Nguyen Van Liem1, Bui Van Cuong1, and Le Xuan Long1 1

2

Faculty of Automotive and Power Machinery Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam Faculty of Electronics Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected]

Abstract. The goal of this paper is to find out the optimal parameters of cab’s isolation system to improve the ride comfort of the vibratory roller. A halfvehicle ride dynamic model of a single drum vibratory roller is established under various operating conditions. The design parameters of cab’s isolation system are optimized by the root mean square (rms) values of acceleration responses of the vertical driver’s seat (aws) and cab’s pitch angle angle (awcphi) according to the ISO 2631:1997(E) standard. A genetic algorithm (GA) and a multi-objective optimization algorithm are used for searching for the optimal design parameters of cab’s isolation systems. The study results indicate that the aws and awcphi values with GA optimal parameters reduce by 31.88% and 31.27%, respectively in comparison with the original parameters of cab’s isolation systems when the vehicle moves on the ISO class D road surface at the vehicle speed v = 5 km/h, which shows that the performance of the optimal parameters of cab’s isolation system is better than that of cab’s original isolation system in improving the ride comfort of a single drum vibratory roller at all operating conditions. Keywords: Single–drum vibratory roller
Cab
Isolation system Optimization
Genetic algorithm
Ride comfort




1 Introduction The construction machine always works in the harsh working environment. Machine vibration and noise always exist while the machine is operating. For vibratory rollers, the vibration sources are transmitted to the driver via cab’s isolation and the vehicle is not usually equipped with the suspension systems. Therefore, cab’s isolation system plays an important role in reducing the vibration transmitted to the driver. Three different cab’s isolation mounts for a single-drum vibratory roller were proposed to analyze the effectiveness of mounts in the direction of improving vehicle ride comfort using a three-dimensional nonlinear dynamic model [1]. The performance of the design parameters of cab’s isolation system of a single-drum vibratory roller was analyzed the effects of its on vehicle ride comfort using a 3-D nonlinear dynamic model [2]. The © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 619–627, 2021. https://doi.org/10.1007/978-3-030-64719-3_68

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design parameters of cab’s original isolation system of a single–drum vibratory roller were used to evaluate the effects of the different operating conditions on vehicle ride comfort [3]. The design parameters of the isolation system as well as various operating conditions of a single-drum compactor were surveyed to find out the effects of its on vehicle ride comfort using a 12-degrees-of-freedom in-plane ride dynamic model [5]. The hydraulic mounts of cab were proposed and equipped on earth-moving machinery, and the performance of the hydraulic mounts was analyzed and compared with the rubber mounts of cab [7]. The design parameters of a single-drum vibratory roller were analyzed via the theoretical analysis, experimental research and simulation analysis methods [11, 14]. In order to have a perfect design for the isolation systems of the vibratory rollers in the direction of improving vehicle ride comfort, the design parameters of cab’s main isolation system [4] and cab’s auxiliary isolation system [6] were reviewed and optimized based on the finite element method, and other optimization algorithms such as the genetic algorithms [8] and multi-objective genetic algorithm [9]. In order to improve the cab’s ride comfort, the control strategies are applied to control the characteristics of the isolation systems. The combined control method of Fuzzy and PID control was proposed to control the cab isolation system of a single-drum vibratory roller using a half-vehicle ride dynamic model [10]. A kind of Magneto-rheological (MR) damper was proposed to control the vibration sources from drum transferred to vehicle frame by using the fuzzy control. The rest of this paper is organized as follows: A half-vehicle ride dynamic model of a single drum vibratory roller established under various operating conditions is presented in Sect. 2. In Sect. 3, the genetic algorithm (GA) is used to find the optimal design parameters of cab’s isolation system and the objective functions of the weighted root mean square (rms) of acceleration responses of the vertical driver’s seat and cab’s pitch angle and the constraints are proposed when the vehicle operates under different conditions. Section 4 presents the results as well as discussion and the conclusions are given in Sect. 5.

2 Half-Vehicle Ride Dynamic Model To analyze and find out the optimal design parameters of cab’s isolation system for a single –drum vibratory roller, a half-vehicle ride dynamic model based on the obtained results by our research team is as shown in Fig. 1. In Fig. 1, md, mff, mfr, mc and ms are the mass of the dynamic drum, front vehicle body (frame), rear vehicle body, cab and driver’s seat, respectively; Iff, Ifr, and Ic are the moment of inertia of the front vehicle body, and cab, respectively; ks and cs are the stiffness and damping of driver’s seat suspension system; kcf and ccr are the stiffness and damping of the front and rear cab’s isolation systems, respectively; kt and ct are the stiffness and damping of the tire; kd and cd are the stiffness and damping of the cab’s isolation system, respectively; zd, zff, zfr, zc and zs are the vertical displacements at centre of gravity of the drum, the front vehicle body, the rear vehicle body, cab and driver’s seat, respectively; uff, ufr and uc are the pitch angle displacements of the front vehicle body and rear vehicle body and cab, respectively; qt and qd are the excitation of road surface roughness at drum and tire, respectively; lt, ld, lt1,lt2, lc1, lc1, lcf, lfr, ls are

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Fig. 1. Half-vehicle ride dynamic model of a single-drum vibratory roller [16]

the distances; F = F0sin(xt) is the force excitation of the vibrating drum; F0 is the amplitude of force excitation; x is the angular frequency of the vibrator; e is the eccentricity of the rotating mass; Fp and Mp are the coupling force in the vertical direction and the coupling moments in the front direction at the point of intersection, respectively; v is the vehicle speed. Equations of Motion: The equations of motion for a single-drum vibratory roller using Newton’s second law of motion are written in two operating conditions below. From Fig. 1, the motion equations of vehicle mass are written as follows: ms€zs ¼ Fs

ð1Þ

where, Fs ¼ ½ks ðzs  zc þ ls uc Þ þ cs ð_zs  z_ c þ ls u_ c  mc€zc ¼ Fs  Fcf  Fcr

ð2Þ

where, Fcf ¼ ½kcf ðzc þ lc1 uc  zfr  lfr uc Þ þ ccf ð_zc þ lc1 u_ c  z_ fr  lfr u_ c Þ; Fcr ¼ ½kcr ðzc  lc2 uc  zfr  lcf uc Þ þ ccr ð_zc  lc2 u_ c  z_ fr  lcf u_ c Þ Ic uc ¼ Fs ls  Fcf lc1 þ Fcr lc2

ð3Þ

mfr€zfr ¼ Fcf þ Fcr  Ft  Fd

ð4Þ

where, Ft ¼ ½kt ðzfr  lt2 ufr  qt Þ þ ct ð_zfr  lt2 u_ fr  q_ t Þ; Fd ¼ ½kd ðzff  zd Þ þ cd ð_zff  z_ d 

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Ifr€zfr ¼ Fcr lfr þ Fcf lcf  Ft lt2  Fd lt1  Md

ð5Þ

where, Md ¼ Fd ld Case 1: Vehicle moves into the workshop: The drum of vibratory roller in contact with the rigid road surface is the contact point which is considered in this study. The vertical force of cab’s isolation system is defined as:

 Fd ¼ kd zff  qd þ cd z_ ff  q_ d

ð6Þ

where, qt and qd are the excitation of road surface roughness at drum and tire. The road surface roughness according to the International Standards Organization (ISO) 8608 [12] is road excitation which is simulated in space domain and acts as an input to the vehicle-road model. Case 2: Vehicle operates in the workshop: In this study, the properties of deformable ground soil are linear by the stiffness and damping coefficients of the elastic soil ground. The equation of motion for the dynamic drum is written as follows md€zd ¼ Fe þ Fd  kse zd  cse z_ d

ð7Þ

3 Optimization via Genetic Algorithm and Objective Function Optimization via Genetic Algorithm: There are many optimal algorithms such as Genetic algorithm (GA) [18], Artificial neural network(ANN) [19], Particle swarm optimization algorithm (PSO) [20] are used to find out the optimal parameters of vehicle suspensions to improve vehicle ride comfort as well as reduce the negative impacts on the road surface. In this study, genetic algorithm is used to search the optimal design parameters of cab’s isolation system for a single-drum vibratory roller. The Genetic Algorithm (GA) is a technique that mimics the evolutionary adaptation of biological populations based on Darwinism. GA is a method of finding the optimal randomization by simulating the evolution of humans or organisms. The idea of genetic algorithm is to simulate natural phenomena, to inherit and fight for survival. A simple genetic algorithm consists of the following steps: [Step 1]: Initialize an initial population of chromosome chains; [Step 2]: Determine target values for each respective chromosome; [Step 3]: Create new chromosomes based on genetic operators; [Step 5]: Determine target functions for the new chromosomes and introduce them to the population; [Step 4]: Remove low adaptive chromosomes and [Step 6]: Check that the stop condition is satisfied. If the conditions are correct, remove the best chromosome, the algorithm is stopped; otherwise, go back to step 3. The flowchart of the algorithm is shown in Fig. 2.

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Start

Define limit constraints of the model parameters

GA Process

Randomly generated initial population

Called Simulink model

Evaluate the population of each objective function value Selection Crossver Mulation

Terminate?

Fig. 2. Schematic diagram of a genetic algorithm

Objective Function: The process of optimizing design parameters of cab’s isolation system is carried out by using a multi-objective optimization function formed by the root mean square (rms) values of acceleration responses of the vertical driver’s seat (aws) and cab’s pitch angle (awcphi) according to the ISO 2631:1997(E) standard [13]. To obtain the optimal design variable values, the objective is to minimize a multiobjective function shown below   F ð X Þ ¼ w1 faws ð X Þg þ w2 awphic ð X Þ ! min

ð8Þ

where, wn (n = 12) are the weighting coefficients of sub-objective functions; X = [kcf, kcr, ccf, ccr] is the design parameter vector of cab’s isolation systems. Boundary Conditions: The objective function Eq. (8) must satisfy the following boundary conditions: 8 > > > >
3:4  105  kci  3:45  106 > > > : 2:46  103  cci  8:9  103

i = f,r

ð9Þ

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4 Simulation and Discussion In order to find out the optimal design parameters of cab’s isolation system for a singledrum vibratory roller, Matlab/Simulink environment with the initial vehicle design parameters is applied as shown in Table 1. A program of genetic algorithm is written in Matlab to declare input parameters such as objective function Eq. 8 with w1 = 0.7, w2 = 0.3, boundary conditions Eq. 9, and GA parameters such as population size as 100 and generation as 200, which called by Simulink module function using the sim function. The optimal values of the parameters of cab’s isolation systems are obtained by GA method in comparison with the original parameters, as shown in Table 2 when the vehicle moves on the ISO class D road surface at the vehicle speed v = 5 km/h (Case1). The acceleration responses of the vertical driver’s seat (as) and cab’s pitch angle (acphi) with GA optimal parameters in comparison with the original parameters of cab’s isolation systems are shown Fig. 3 in Case 1. From the results of Fig. 3, we can determine the aws and awcphi values are 0.7938 m/s2 and 0.7571 rad/s2 with the original parameters and 0.6047 m/s2 and 0.5741 rad/s2 with GA optimal parameters of cab’s isolation systems. The results show that the aws and awcphi values with GA optimal parameters greatly reduce by 31.88% and 31.27% in comparison with the original parameters of cab’s isolation systems, which means that the performance optimization of cab’s isolation systems is better than the original isolation systems of drums in improving the ride comfort.

Table 1. Parameters of a single-drum vibrating roller Parameters ms/kg mc/kg mff/kg mfr/kg md/kg Ic/kg m2 Ifr/kg m2 kd/(N/m) cd/(Ns/m)

Values 85 890 2822 4464 4378 568 3054 3980000 29000

Parameters kt/(N/m) ct/(Ns/m) kse/(N/m) cse/(Ns/m) kcf/(N/m) ccr/(Ns/m) kcr/(N/m) ccr/(Ns/m) F0/N

Values 500000 4000 6440000 70000 170000 4500 170000 4000 280/190

Parameters f/Hz ls/m lc1/m lc2/m lfr/m lt1/m lt2/m ld/m lcf/m

Values 30/35 0.383 0.383 0.240 0.71 1.01 0.50 1.50 0.09

Verifying the optimal design parameters when the vehicle operates at Case 2: The acceleration responses of the vertical driver’s seat (as) and cab’s pitch angle (acphi) with GA optimal parameters in comparison with the original parameters of cab’s isolation systems are shown in Fig. 4 when the vehicle compacts on the original place with the parameters of the elastic soil grounds in Table 1 at the front drum with the excitation force F0 = 0.28  106/N and f = 30 Hz and the rear wheel moves on the ISO class E road surface at the vehicle speed v = 5 km/h (Case 2). From the results of Fig. 4, we can determine the aws and awcphi values are 1.0093 m/s2 and

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0.2493 rad/s2 with the original parameters and 0.7902 m/s2 and 0.2005 rad/s2 with GA optimal parameters of cab’s isolation systems. The results show that the aws and awcphi values with GA optimal parameters greatly reduce by 27.73% and 24.34% in comparison with the original parameters of cab’s isolation systems. Similarly, the as and acphi in time domain are shown Fig. 4 when vehicle operates at Case 2 F0 = 0.19  106 N and f = 35 Hz. The aws and awcphi values with GA optimal parameters greatly reduce by 27.05% and 26.99% in comparison with the original parameters of cab’s isolation systems, which means that the performance optimization of cab’s isolation systems in improving the ride comfort is better than the original isolation systems of drums (Fig. 5).

Table 2. GA optimization parameters of cab’s isolation systems Parameters kcf/(N/m) ccf/(Ns/m) kcr/(N/m) ccr/(Ns/m) Original values 170000 4500 170000 4000 Optimal values by GA 125000 6800 125000 6800

4

2

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Fig. 3. The comparison of acceleration responses at Case 1

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Fig. 4. The comparison of acceleration responses at Case 2 with F0 = 0.28  106, f = 30 Hz

L. Van Quynh et al. 2

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Fig. 5. The comparison of acceleration responses at Case 2 with F0 = 0.19  106, f = 35 Hz

5 Conclusions In this study, a half-vehicle dynamic model of a single – drum vibratory roller is established to find out the optimal parameters of cab’s isolation system to improve the ride comfort when the vehicle moves on the ISO class D road surface at the vehicle speed v = 5 km/h, and the optimal design parameters are verified when the vehicle operates at Case 2. The major conclusions drawn from the analysis results are: (1) The aws and awcphi values with GA optimal parameters greatly reduce by 31.88% and 31.27% in comparison with the original parameters of cab’s isolation systems; (2) The aws and awcphi values with GA optimal parameters greatly reduce by 27.73% and 24.34% in comparison with the original parameters of cab’s isolation systems and (3) The aws and awcphi values with GA optimal parameters greatly reduce by 27.05% and 26.99% in comparison with the original parameters of cab’s isolation systems. Acknowledgment. This research was supported financially by Thai Nguyen University of Technology, TNUT, Viet Nam.

References 1. Liem, N.V., Run, Z.J., et al.: Vibration analysis and modeling of an off-road vibratory roller equipped with three different cab’s isolation mounts. Shock Vibr. 2018, 8527574 (2018) 2. Van Quynh, L., Thao, V.T.P., et al.: Influence of design parameters of cab’s isolation system on vibratory roller ride comfort under the deformed ground surfaces. Int. Res. J. Eng. Technol. (IRJET) 6(6), 1974–1978 (2019) 3. Le, V.Q., Zhang, J.R., et al.: Ride comfort evaluation of vibratory roller under different soil ground. Tran. Chin. Soc. Agric. Eng. 29, 39–47 (2013) 4. Quynh, L.V., Jianrun, Z., et al.: Experimental modal analysis and optimal design of cab’s isolation system for a single drum vibratory roller. Vibroeng. Procedia 31, 52–56 (2020) 5. Kordestani, A., Rakheja, S., et al.: Analysis of ride vibration environment of soil compactors. SAE Int. J. Commercial Veh. 3(1), 259–272 (2010) 6. Quynh, L.V., Zhang, J.R., et al.: Vibration analysis and optimal design for cab’s isolation system of vibratory roller. Adv. Mater. Res. 199(200), 936–940 (2011) 7. Sun, X., Zhang, J.: Performance of earth-moving machinery cab with hydraulic mounts in low frequency. J. Vibr. Control 20(5), 724–735 (2012) 8. Quynh, L.V., Thao, V.T.P., Phong, T.T.: Optimal design parameters of cab’s isolation system for a double-drum vibratory roller. Vibroeng. Procedia 31, 74–79 (2020)

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9. Le, V.Q., Nguyen, K.T.: Optimal design parameters of cab’s isolation system for vibratory roller using a multi-objective genetic algorithm. Appl. Mech. Mater. 875, 105–112 (2018) 10. Nguyen, V., Jiao, R., et al.: Performance of PID-Fuzzy control for cab isolation mounts of soil compactors. Math. Models Eng. 5(4), 137–145 (2019) 11. Li, J., Zhang, Z., et al.: Dynamic characteristics of the vibratory roller test-bed vibration isolation system: simulation and experiment. J. Terramech. 56, 139–156 (2014) 12. ISO 8068. Mechanical vibration-road surface profiles-reporting of measured data. International Organization for Standardization (1995) 13. ISO 2631-1. Mechanical Vibration and Shock-Evaluation of Human Exposure to WholeBody Vibration, Part I: General Requirements. The International Organization for Standardization (1997) 14. Le, V.: Ride comfort analysis of vibratory roller via numerical simulation and experiment. DEStech Transactions on Engineering and Technology Research (2017) 15. Liu, S.N., Yan, S.R., et al.: Dynamic and control of vibratory road roller based on magnetorheological semi-active damper. Adv. Mater. Res. 479(481), 1200–1204 (2017) 16. Van Quynh, L., Cong, N.T., et al.: Effect of cab’s excitation frequency on ride comfort of a single-drum vibratory roller. World J. Res. Rev. (WJRR) 11(1), 7–10 (2020) 17. Le, V.: Vibration Study and Control for Cab of Vibratory Roller. Southeast University (2013) 18. Sun, Lu: Optimum design of road friendly vehicle suspension systems subjected to rough pavement surfaces. Appl. Math. Model. 26, 635–652 (2002) 19. Gohari, M., Rahman, R.A., et al.: Off-Road vehicle seat suspension optimisation, part I: derivation of an artificial neural network model to predict seated human spine acceleration in vertical vibration. J. Low Freq. Noise, Vib. Active Control 33(4), 429–441 (2014) 20. Drehmer, L.R.C., Paucar, C.W.J., et al.: Parameters optimisation of a vehicle suspension system using a particle swarm optimisation algorithm. Veh. Syst. Dyn. 53(4), 449–474 (2015)

Optimization of Cutting Parameters and Nanoparticle Concentration in Hard Milling for Surface Roughness of JIS SKD61 Steel Using Linear Regression and Taguchi Method Thanh-Dat Phan(&), The-Vinh Do, Thanh-Long Pham, and Huong-Lam Duong Thai Nguyen University of Technology, Thai Nguyen Province, Vietnam [email protected]

Abstract. Minimum Quantity Lubricant (MQL) technique with nanoparticles application has become one of the most effective approaches in cutting hard materials. In this present work, SiO2 particles based on cutting oil CT232 were applied in milling JIS SDK61 steel under MQL condition. The two main targets were to build a mathematical model form machining parameters to predict the surface roughness Ra of machined surface and find the optimum value of Ra. The cutting speed, feed rate, and depth of cut together with nanoparticle concentration were chosen to validate the experiments, which were designed by L27 orthogonal of the Taguchi DOE method. A fitted linear regression model was established with the coefficient of determination R-sq of 88.33%. The minimum Ra of 0.094 µm verified the predictive ability of the model. Further investigation with S/N ratio and analysis of variance (ANOVA) showed that the most significant factor was the feed rate followed by the nanoparticle concentration. Keywords: Hard milling
Nanoparticle concentration
SiO2 nanoparticle
JIS SKD61

1 Introduction Surface roughness is one of the most important factors to evaluate machined parts. Machining of hard materials used to rely on the finish process of grinding to obtain the demanded roughness. However, since the mid-1980 s hard machining like cutting methods using one or more cutting tools has eventually developed and used widely [1, 2] to reduce the cost and machining time. Look back at the time of the 1990 s, W. Konig et al. compared the surface integrity of turning with grinding on 16MnCr5 hardened steel [3]. They discussed the white layer occurred during hard machining and compared it to grinding burn. In conclusion, they saw the potential of using PCBN tools in the precision machining of hard material with the surface roughness Rtm below 2 µm. In 2005, a similar comparison was made by F. Klocke et al. [4]. They did various experiments to come up with a qualitative overview of the capability of hard cutting and grinding process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 628–635, 2021. https://doi.org/10.1007/978-3-030-64719-3_69

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The experimental result of machining 16MnCr5 showed that the surface roughness Ra after turning was under 0.2 µm while it was between 0.08 and 0.12 µm after grinding. Actually, the turning process did present the ability of getting the Ra as small as 0.12 µm. However, that achievement was not consistent due to the increasing of tool wear as the experiments went on. Nevertheless, the overview suggested suitable applications for each type of process. Debabrata Samantaraya et al. did their comparison of grinding and hard turning of automotive gears made of 20Mn5Cr5 in 2018 [5]. The surface roughness attained from turning was slightly higher (i.e., 0.56 µm compared to 0.37 µm), but it satisfied the requirement of 0.8 µm. On the other hand, the manufacturing was 52% lower leading to 60% of cost saving. Milling is similar to turning in terms of using a cutting tool to remove a large amount of material. The primary difference between the two techniques is that in turning, the work-piece rotates while in milling, the tools rotate. Different applications require a specific technique. Working with JIS SKD61 (i.e., AISI H13) as in molds and dies manufacturing traditionally requires milling annealed steel, heat treatment, and electro discharge machining (EDM) or finishes with grinding/hand polishing [6]. This time consuming procedure could also be improved by using hard milling with minimum quantity lubricant (MQL) enhanced[7–9]. In addition, researchers used nanoparticles in MQL technique to achieve a more satisfactory result [10–13]. ShyhChour Huang et al. set a multi-objective optimization on hard milling AISI H13 steel [14]. They used graphite nanoparticles and found that the cutting energy can be saved up to 14%. The-Vinh Do et al. optimized the cutting parameters for better surface roughness in hard milling AISI H13[15]. They performed experiments in 3 conditions: dry, MQL, and MQL with Silica nanofluid and got better results of surface roughness with Silica nanofluid. In this present work, SiO2 nanoparticle assisted MQL was used in milling JIS SKD61 with the hardness of 50HRC. In order to find the optimum value of surface roughness, a linear regression model was built based on cutting parameters: cutting speed, feed rate, the depth of cut and nanoparticle concentration using the Taguchi method. Further analysis with S/N ratio and ANOVA showed details of the way that the cutting parameters affect the surface roughness.

2 Experiment Setup A short description of experimental setup is shown in Table 1. Each JIS SKD61 block with the dimension of 200 mm x 100 mm x 50 mm was attached on the Victor V Center 4 Vertical Machining Center (Victor Taichung Machinery Works Co., Ltd., Taichung, Taiwan). Cutting tool for slot milling process was U10 TiAlN coated end mill made by CMTec Company (Taiwan). A new cutter was used for each run to reduce the effects of tool ware on the result. An MC 1700 cooling system manufactured by Noga Engineering Ltd. (Shlomi, Israel) was used to perform MQL spay. An SJ-401 surf-test instrument from Mitutoyo Corporation (Japan) was prepared to measure the surface roughness of machined parts. The description of MQL setup is on Table 2 The MQL was CT232 oil which is mixed with SiO2 particle with a size of 100 nm. The flow rate was set at 50 ml/h under

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3 kg/cm2 of air pressure. The mixture was put into a magnetic stirring device for 12 h to ensure the dispersion of SiO2 particles. Table 1. Experiment setup Items Description CNC Machine Victor V-Center-4 Surface roughness measuring instrument Sj-401 Cutting tool U10 TiAlN Work-piece material SKD61 50HRC Work-piece dimensions 200 mm x 100 mm x 50 mm MQL nozzle Noga - MC 1700

Table 2. MQL setup Items Fluid flow (mL/h) Pressure (kg/cm2) Based Lubricant Nanoparticle

Description 50 3 Cutting oil CT232 SiO2 particle with a size of 100 nm

Four parameters used to determine the surface roughness are presented in Table 3. In order to minimize the experimental error, each experiment was repeated thrice.

Table 3. Parameters levels Items

Levels 1 Nanoparticles concentration (wt%) 0 Cutting speed (m/min) 40 Feed-rate (mm/tooth) 0.01 Depth-of-cut (mm) 0.2

2 3 2 4 60 80 0.02 0.03 0.4 0.6

3 Results and Discussions The surface roughness Ra and signal to noise ratio for each run of L27 orthogonal Taguchi array are presented in Table 4. The average values of Ra were used as the response for the input parameters. The linear regression equation model was built using Minitab 19 software as follow: Ra = 0.1577 - 0.01439 c - 0.000728 v + 0.0447 d + 5.189 f (1)

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Table 4. Experiment results Run c v 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

0 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 2 4 4 4 4 4 4 4 4 4

40 40 40 60 60 60 80 80 80 40 40 40 60 60 60 80 80 80 40 40 40 60 60 60 80 80 80

d 0.2 0.4 0.6 0.2 0.4 0.6 0.2 0.4 0.6 0.2 0.4 0.6 0.2 0.4 0.6 0.2 0.4 0.6 0.2 0.4 0.6 0.2 0.4 0.6 0.2 0.4 0.6

f 0.01 0.02 0.03 0.02 0.03 0.01 0.03 0.01 0.02 0.02 0.03 0.01 0.03 0.01 0.02 0.01 0.02 0.03 0.03 0.01 0.02 0.01 0.02 0.03 0.02 0.03 0.01

Ra (µm) Trial 1 Trial 2 0.198 0.198 0.245 0.248 0.313 0.31 0.226 0.226 0.27 0.278 0.181 0.183 0.258 0.259 0.166 0.169 0.218 0.213 0.184 0.18 0.29 0.3 0.176 0.181 0.27 0.244 0.186 0.191 0.251 0.26 0.122 0.129 0.21 0.21 0.272 0.272 0.261 0.265 0.133 0.137 0.179 0.191 0.096 0.097 0.159 0.159 0.211 0.213 0.172 0.175 0.205 0.204 0.13 0.124

S/N Trial 3 0.204 0.251 0.313 0.22 0.28 0.179 0.254 0.166 0.211 0.173 0.295 0.171 0.251 0.19 0.278 0.133 0.15 0.272 0.254 0.144 0.186 0.101 0.183 0.206 0.184 0.191 0.124

Average 0.200 0.248 0.312 0.224 0.276 0.181 0.257 0.167 0.214 0.179 0.295 0.176 0.255 0.189 0.263 0.128 0.190 0.272 0.260 0.138 0.185 0.098 0.167 0.210 0.177 0.200 0.126

13.979 12.111 10.117 12.995 11.182 14.846 11.801 15.546 13.392 14.943 10.604 15.090 11.869 14.471 11.601 17.856 14.425 11.309 11.701 17.202 14.641 20.175 15.546 13.556 15.041 13.979 17.993

Accordingly, the minimum value of predicted Ra found at 0.103 µm with the nanoparticle concentration of 4%, the cutting speed of 80 m/min, the depth of cut of 0.2 mm and the feed rate of 0.01 mm/s. Another experiment run was made to verify the model. The measured Ra of 0.094 µm was the best surface roughness achieved. The residual plot in Fig. 1 showed the fitness of the model to actual Ra. All of the residual values lied within the 95% confidence curve. Further analysis with the signal to noise ratio of the 4 parameters where Ra was the response found the most effective factor - the feed rate, following by the nanoparticle concentration, the cutting speed and the depth of cut. The ranks of these parameters showed in Table 5. Since minimizing the surface roughness was the target, the smaller

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Fig. 1. Residual plot of the predicted versus the actual Ra Table 5. Response table for Signal to Noise Ratios - smaller is better Level 1 2 3 Delta Rank

c 12.885 13.574 15.537 2.652 2

v 13.376 14.027 14.593 1.217 3

D 14.484 13.896 13.616 0.869 4

f 16.351 13.855 11.791 4.560 1

and better type of S/N ratio was selected. The Eq. (2) presents the calculation of S/N ratio: n S 1 X ¼ 10log ð y2 Þ N n i¼1 i

ð2Þ

where: yi is the observed data, n is the number of experiments repeated The significance of the model as well as input factors (i.e., cutting parameters) are defined by analysis of variance (ANOVA). The P-value in Table 6 indicates the factors which have significant influence on the surface roughness, while the percentage of contribution (%C) concludes the contribution of each factor and the contribution of the whole model. The R-sq assumed that the overall predictable ability of the model was 88.33%. The P-value is significant if it is smaller than 0.05. Therefore, the feed rate, the nanoparticle concentration, and the cutting speeds are the three significant factors

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which have the contribution of 62.38%, 19.19%, and 4.91% respectively. The S/N ratio plot (Fig. 2) visualizes the impact of each factor. The steeper of the slope means the more impact of the factor. Hence, the feed rate (f) and the nanoparticle concentration (c) are the two main factors contributing to the change of surface roughness value Ra. It is noticeable that the slope of factor (c) has a diverse change from 2 wt% to 4 wt%. Table 6. ANOVA Table Source DF Regression 4 c 1 v 1 d 1 f 1 Error 22 Total 26 R-sq = 88.33% a significant

Adj SS 0.068625 0.014907 0.003814 0.00144 0.048464 0.009067 0.068625

Adj MS F-Value P-Value 0.017156 41.63 0.00a 0.014907 36.17 0.00a 0.003814 9.25 0.006a 0.00144 3.49 0.075 0.048464 117.59 0.00a 0.000412

%C 88.33% 19.19% 4.91% 1.85% 62.38%

Main Effects Plot for SN ratios Data Means c

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4 Conclusion This research aimed to build a mathematical model to predict the surface roughness when milling JIS SKD61 steel of 50HRC is under the condition of nanofluid based on MQL technique. Then, an optimum value of surface roughness could be found using the built model. Four parameters were used to vary the cutting condition: cutting speed, the depth of cut, feed rate, and nanoparticle concentration. The experiment results can be interpreted as follow: • The model of linear regression built from four parameters including cutting speed, feed rate, the depth of cut, and nanoparticle concentration moderately fit the measured surface roughness. • The minimum value of surface roughness Ra found by the model was 0.103 µm while the measured value was 0.094 µm. The error was 9%. • The nanoparticle concentration is an important factor in milling hardened steel like JIS SKD61. However, the most affecting parameter is the feed rate. Acknowledgments. The authors wish to thank Thai Nguyen University of Technology. This work was supported by Thai Nguyen University of Technology

References 1. Davim, J.P.: Machining of hard materials: Springer Science & Business Media (2011) 2. Do, T.-V., Hsu, Q.-C.: Optimization of minimum quantity lubricant conditions and cutting parameters in hard milling of AISI H13 steel. Appl. Sci. 6, 83 (2016) 3. König, W., Berktold, A., Koch, K.-F.: Turning versus grinding–a comparison of surface integrity aspects and attainable accuracies. CIRP Ann. 42, 39–43 (1993) 4. Klocke, F., Brinksmeier, E., Weinert, K.: Capability profile of hard cutting and grinding processes. CIRP Ann. 54, 22–45 (2005) 5. Samantaraya, D., Lakade, S., Keche, A.: An alternate machining method for hardened automotive gears. Procedia Manuf. 20, 517–522 (2018) 6. Ding, T., Zhang, S., Wang, Y., Zhu, X.: Empirical models and optimal cutting parameters for cutting forces and surface roughness in hard milling of AISI H13 steel. Int. J. Adv. Manuf. Technol. 51, 45–55 (2010) 7. Do, T.-V., Vu, N.-C., Nguyen, Q.-M.: Optimization of cooling conditions and cutting parameters during hard milling of AISI H13 steel by using Taguchi method. In: 2018 IEEE International Conference on Advanced Manufacturing (ICAM), pp. 396-398 (2018) 8. Do, T.-V., Le, N.-A.-V.: Optimization of surface roughness and cutting force in MQL hardmilling of AISI H13 steel. In: Advances in Engineering Research and Application: Proceedings of the International Conference, ICERA 2018, pp. 448–454 (2019) 9. Boswell, B., Islam, M.N., Davies, I.J., Ginting, Y., Ong, A.K.: A review identifying the effectiveness of minimum quantity lubrication (MQL) during conventional machining. Int. J. Adv. Manuf. Tech. 92, 321–340 (2017) 10. Dong, L., Li, C., Bai, X., Zhai, M., Qi, Q., Yin, Q., et al.: Analysis of the cooling performance of Ti–6Al–4 V in minimum quantity lubricant milling with different nanoparticles. Int. J. Adv. Manuf. Technol. 103, 2197–2206 (2019)

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11. Li, M., Yu, T., Li, H., Yang, L., Shi, J., Wang, W.: Research on surface integrity in graphene nanofluid MQL milling of TC21 alloy. Int. J. Abras. Technol. 9, 49–59 (2019) 12. Bayat, M., Abootorabi, M.M.: Estimation of energy consumption in milling process with minimum quantity lubrication and comparison with wet cutting state. Modares Mech. Eng. 20, 1701–1708 (2020) 13. Do, T.-V.: Empirical model for surface roughness in hard milling of AISI H13 steel under nanofluid-MQL condition based on analysis of cutting parameters. J. Mech. Eng. Res. Develop. 43, 89–94 (2020) 14. Vu, N.-C., Dang, X.-P., Huang, S.-C.: Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition. Measurement and Control, pp. 0020294020919457 (2020) 15. Do, T.V., Nguyen, Q.M., Pham, M.T.: Optimization of cutting parameters for improving surface roughness during hard milling of AISI H13 steel. In: Key Engineering Materials, pp. 35–39 (2020)

Optimization of Dressing Parameters in Surface Grinding SKD11 Tool Steel by Using Taguchi Method Tran Thi Hong1, Nguyen Thanh Tu2, Nguyen Anh Tuan3, Tran Ngoc Giang2, Nguyen Thi Quoc Dung2, Le Xuan Hung2, Bui Thanh Danh4, and Luu Anh Tung2(&) 1

Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam 2 Faculty of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen 23000, Vietnam [email protected] 3 University of Economic and Technical Industries, Hanoi 100000, Vietnam 4 University of Transport and Communications, Hanoi, Vietnam

Abstract. Nowadays, surface grinding plays an important role in industry due to the growing demand for increasingly accurate parts with a low production cost. The efficiency of this process is affected by the process parameters such as dressing feed rate (S), rough dressing depth (ar), rough dressing times (nr), fine dressing depth (af), fine dressing times (nf), and non-feeding dressing (nnon). etc. In this paper, the optimization of dressing parameters in surface grinding SKD11 tool steel is presented. The aim of the study is to find the most appropriate value set of dressing parameters to minimize the normal cutting force (Fy). In order to solve the problem, the Taguchi method is employed. Based on an orthogonal array L16(44
22), sixteen experiments have been conducted. By analyzing the experimental results, an optimal solution of the optimization problem has been solved, presenting the most appropriate dressing parameters as follows: ar = 0.015 mm, nr = 1 times, af = 0 mm, nf = 0 times, nnon = 0 times, S = 1.8 m/min. The discovered technology mode has been applied into the actual machining process and the outcome shows a much better result in comparison with the default setting modes with the difference between the model values and the real values of the mean normal cutting force controlled within 3.01% of the ranges. Keywords: Grinding wheel  Dressing parameters  Taguchi method  SKD11

1 Introduction Grinding is a widely used machining method for high quality finishing operations in today’s industry. However, there are various factors that affect the efficiency of the grinding process [1–8]. Among them the grinding wheel plays a key role in the grinding process for obtaining high machining accuracy and good surface roughness of the work-piece [9]. An important stage in this process is wheel preparation whereby the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 636–647, 2021. https://doi.org/10.1007/978-3-030-64719-3_70

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grinding wheel is prepared for cutting by truing and dressing. The efficiency of dressing process directly affects the cutting capacity of grinding stone, the accuracy and quality of ground part. This is one of the crucial factors for improving the efficiency of grinding process. A number of previously published studies have focused on increasing the efficiency of dressing process [9–13]. Hong-Tsu Young and Der-Jen Chen [9] studied on an online dressing system to determine the timing and amount of dressing. Their results indicated that the proposed system is capable of determining the timing of dressing and its optimal number of dressing [9]. Fritz Klocke et al. [10] presented an analyticalempirical model of the wear of the grinding wheel as a function of the dressing process. They indicated that the dressing process has strong influence on the initial radial wear of the grinding wheel in the grinding process. Similarly, Novak Mar-tin et al. [11] studied the influence of dressing process on the surface roughness of machined part and cutting forces. Based on the experimental results, they showed that resizing grinding wheel dressing has a significant effect on surface roughness. Furthermore, when the value of grinding tool dressing increases, the components of the total cutting force rise [11]. Also, an experimental investigation was performed by P. Puertoa et al. [12] and it revealed a close relationship between surface roughness and grinding forces as well as a relationship between surface roughness and the dressing parameters [12]. It has been found that aggressive dressing conditions make coarse topographies of grinding wheel, so initial roughness tends to be high and reduces gradually in grinding process. In addition, forces and roughness tend to stabilize at the same time [12]. Recently, Le Xuan Hung et al. [13] presented an experimental study on the impacts of the dressing parameters on the surface roughness and material removal rate in internal cylindrical grinding process for 9CrSi tool steel. They showed that the minimum surface roughness and the maximize material removal rate were found with the coarse dressing depth of 0.02 mm, the coarse dressing times of 1 time, the fine dressing depth of 0.005 mm, the fine dressing times of 3 times, the non-feeding dressing of 5 times and the dressing feed rate of 1.4 m/min. From the extensive review of the above cited literatures, it is evident that there is a keen interest among researchers in the direction of performance enhancement of dressing process. To the best of author’s knowledge, many published researches are available to improve the efficiency of dressing process, but there are few studies on the problem of optimizing dressing parameters for grinding SKD11 steel work-piece on a surface grinding machine. For these reasons, this paper proposes a method to optimize six dressing parameters in surface grinding SKD11 tool steel. The dressing parameters including dressing feed rate (S), rough dressing depth (ar), rough dressing times (nr), fine dressing depth (af), fine dressing times (nf), and non-feeding dressing (nnon) were selected as input process parameters in this study. Sixteen experiments have been conducted. The experimental data would be the basis to analyze the influence of process parameters on normal cutting force (Fy) and obtain the optimal dressing parameters in surface grinding SKD11 steel. Also, the Taguchi method has been commonly applied to investigate the influences of input parameters on the output results. It has been used effectively in many experimental works [14–20]. For that reason, this method has been chosen to design and analyze experimental results in this study.

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2 Methodology 2.1

Experimental Machine and Equipment

Fig. 1. Surface grinding process.

A surface grinding machine equipped with a Cn46TB2GV1.300.32.127.30 grinding wheel was used for the grinding tests as shown in Fig. 1. The grinding wheel was dressed using a single-point diamond pen dresser. The work-pieces consisting of SKD11 steel were used as the test specimen and subjected to a heat treatment prior to the grinding tests. The dimensions of the specimen were 70
40
25 mm. The processing equipment is shown in Table 1. 2.2

Design of Experiment

The experiments were carried out with the following set grinding parameters: t the grinding wheel velocity of 26.7 m/sec, the depth of cut of 0.01 mm, the work-piece velocity of 8 m/min, and the feed rate of the machine table of 8 m/min. Also, six dressing parameters (S, ar, nr, af, nf, nnon) were selected as input process parameters in this study to explore their effects on material removed rate. The assigned values of input parameters at different levels and their designation are tabulated in Table 2. The Taguchi method was applied for the design of experiment. An orthogonal array L16 (44
22) with fractional parametrical designs was selected and sixteen experiments have been conducted as shown in the first seven columns in Table 3. After each experiment, the normal cutting force was determined three times by dynamometer Kistler 9257BA. These values are presented as in the last four columns in Table 3.

Optimization of Dressing Parameters in Surface Grinding SKD11 Table 1 Experimental machine and equipment Machine and equipment Machine for grinding Dressing tool Grinding wheel

Units MOTO – YOKOHAMA (Japan) 3908-0088C (type 2, Russia) Cn46TB2GV1.300.32.127.30 m/s (Hai Duong, Vietnam) SKD11 70
40
25 mm3 Kistler 9257BA piezoelectric dynamometer SJ201 – Mitutoyo (Japan)

Work-piece material Work-piece dimensions Dynamometer Roughness measurement machine

Table 2. Dressing parameters and levels Dressing parameters Unit S ard nr af nf nnon

m/min mm Times mm Times Times

Levels 1 2 1.6 1.8 0.015 0.02 1 2 0.005 0.01 0 1 0 1

3 – 0.025 3 – 2 2

4 – 0.03 4 – 3 3

Table 3. L16 Orthogonal array with factors and responses No. trd

nrd nnon nfd tfd

S

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

1.6 1.8 1.6 1.8 1.8 1.6 1.8 1.6 1.8 1.6 1.8 1.6 1.6 1.8 1.6 1.8

0.015 0.015 0.015 0.015 0.02 0.02 0.02 0.02 0.025 0.025 0.025 0.025 0.03 0.03 0.03 0.03

0 1 2 3 1 0 3 2 2 3 0 1 3 2 1 0

0 1 2 3 2 3 0 1 3 2 1 0 1 0 3 2

0.005 0.005 0.01 0.01 0.01 0.01 0.005 0.005 0.005 0.005 0.01 0.01 0.01 0.01 0.005 0.005

Fy (N) T1 T2 138 143 176 182 162 160 153 160 138 140 167 169 148 151 180 189 179 182 193 198 147 143 138 143 143 146 158 162 183 185 157 158

T3 145 174 163 150 142 173 153 187 183 201 149 142 148 163 189 161

Mean 142.00 177.33 161.67 154.33 140.00 169.67 150.67 185.33 181.33 197.33 146.33 141.00 145.67 161.00 185.67 158.67

S/N −43.05 −44.98 −44.17 −43.77 −42.92 −44.59 −43.56 −45.36 −45.17 −45.91 −43.31 −42.99 −43.27 −44.14 −45.38 −44.01

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3 Results and Discussion To evaluate the influence of six dressing parameters on the normal cutting force (Fy) and to determine the optimal value, the analysis of variance (ANOVA) was applied. Regarding the goal of the experiment, the smaller the normal cutting force is, the better the result is achieved. Therefore, according to Taguchi method, the signal-to-noise ratio (S/N) for this target is calculated by formula (1) [21, 22]. The obtained material removal rate results of the conducted experiments and the corresponding signal-tonoise ratios (S/N) are also presented in Table 3. S=N ¼ 10:Log10½MSD

ð1Þ


 MSD ¼ y21 þ y22 þ    þ y2n =n

ð2Þ

With:

Where: n is the number of experiments under the same input parameters; yi is the material removal rate value for the ith experiment (i = 1, 2, 3). 3.1

Determination the Influence of Dressing Parameters

The ANOVA values of the medium normal cutting force (Fy ) are shown in Table 4, Table 5 and Fig. 2. The analysis results show the influence level of dressing parameters on medium normal cutting force as follows: the impact levels of the fine dressing depth (af), the rough dressing times (nf), the fine dressing times (nf), the non-feeding dressing times (nnon), the dressing feed rate (S), and the rough dressing depth (ar) on the normal cutting force are 31.13%, 24.17%, 23.85%, 13.34%, 4.26%, 2.42%, respectively. Thus, the influence of the fine dressing depth (af) on the normal cutting force is the greatest, and the effect of the rough dressing depth (ar) on the normal cutting force is the smallest.

Table 4. The ANOVA values of medium normal cutting force (F y ) Source DF Seq SS Adj SS Adj MS F ar 3 122.47 122.47 40.82 0.97 nr 3 1221.92 1221.92 407.31 9.64 nnon 3 674.31 674.31 224.77 5.32 nf 3 1205.58 1205.58 401.86 9.51 af 1 1573.44 1573.44 1573.44 37.24 S 1 215.11 215.11 215.11 5.09 Re. error 1 42.25 42.25 42.25 Total 15 5055.08

P C% 0.616 2.42 0.231 24.17 0.306 13.34 0.233 23.85 0.103 31.13 0.266 4.26 0.84 100.00

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Table 5. The influence level of dressing parameters on medium normal cutting force (F y ) Level 1 2 3 4 Delta Rank

ar 158.8 161.4 166.5 162.7 7.7 5

nr 152.3 176.3 161.1 159.8 24.1 1.5

nnon 154.2 161.0 172.3 162.0 18.2 4

nf 148.7 163.7 164.4 172.8 24.1 1.5

af S 172.3 166.0 152.5 158.7

19.8 3

7.3 6

Fig. 2. Main effect plots of dressing parameters for means of normal cutting force

According to Fig. 2, in the beginning the normal cutting force (Fy) increases, but when the number of rough dressing times (nr) increases, it decreases. It reaches the smallest value at the rough dressing times of 1 (equivalent to the rough dressing times value at the first level). Similarly, the normal cutting force initially rises but decreases when the non-feeding dressing times (nnon) or the rough dressing depth (ar) grows. It reaches the smallest value at the non-feeding dressing times of 0 (equivalent to the nonfeeding dressing times value at the first level). It is minimum at the rough dressing depth of 0.015 (equivalent to the rough dressing depth value at the first level). In addition, the normal cutting force increases when the number of fine dressing times (nf) increases. Its value is minimum at the fine dressing times (nf) of 0 (equivalent to the value of fine dressing times at the first level). In contract, the normal cutting force decreases when the fine dressing depth (af) or the dressing feed rate (S) increases. Its value is minimum at the fine dressing depth (af) of 0.01 mm (equivalent to the value of

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fine dressing depth at the second level). It reaches the smallest value at the dressing feed rate (S) of 1.8 m/min (equivalent to dressing feed rate value at the second level). 3.2

Determination the Influence of Dressing Parameters

For determination of optimal dressing parameters, the ANOVA values for signal-tonoise ratios (S/N) are determined (as shown in Table 6 and Fig. 3).

Table 6. The ANOVA values for signal-to-noise ratios (S/N) Source DF Seq SS 3 0.2608 ar nr 3 3.5316 nnon 3 1.9578 nf 3 3.4571 af 1 4.2518 S 1 0.5072 Re. error 1 0.0746 Total 15 14.0410

Adj SS 0.26083 3.53163 1.95777 3.45714 4.25184 0.50716 0.07460

Adj MS 0.08694 1.17721 0.65259 1.15238 4.25184 0.50716 0.07460

F 1.17 15.78 8.75 15.45 56.99 6.80

P 0.577 0.182 0.242 0.184 0.084 0.233

Table 6 and Fig. 3 indicate that the signal-to-noise ratio (S/N) reaches the greatest value with the following dressing parameters: ar = 0.015 mm, nr = 1 times, nnon = 0 times, nf = 0 times, af = 0.01 mm, S = 1.8 m/min. These values of the dressing parameters are optimum, which would help to get the best normal cutting force. 3.3

Determination of Optimal Normal Cutting Force Value

The optimal normal cutting force value F y;OP is determined by the levels of the dressing parameters that strongly affect the signal-to-noise ratio (S/N) as follows: F y;OP ¼ nr1 þ nnon1 þ nf 1 þ af 2 þ S2  4  T Fy

ð3Þ

Where: nr1 is the mean normal cutting force value corresponding to the rough dressing times value at the first level (nr1 ¼ 152:3NÞ; nnon1 is the mean normal cutting force value corresponding to the non-feeding dressing times value at the first level (nnon1 ¼ 154:2NÞ; nf1 is the mean normal cutting force value corresponding to the fine dressing times value at the first level (nf1 ¼ 148:7NÞ; af2 is the mean normal cutting force value corresponding to the fine dressing depth value at the second level (af2 ¼ 152:5NÞ; S2 is the mean normal cutting force value corresponding to the dressing feed rate value at the second level (S2 ¼ 158; 7NÞ;

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Fig. 3. Main effect plots of dressing parameters for signal-to-noise ratios (S/N)

Tg is the mean normal cutting force value of the total experiment; Thus: P16 T Fy ¼

i¼1

RMRI þ

P16

MR þ 48

i¼1

P16 i¼1

RMRIII

¼ 162:38 N

Therefore: F yOP ¼ 152:3 þ 154:2 þ 148:7 þ 152:5 þ 158:7  4  162:38 ¼ 116:88 N

3.4

Determining Confidence Interval

The confidence interval (CI) is determined by: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   1 1 þ CI ¼ Fa ð1; fe Þ:Ve : Ne R

ð4Þ

Where: fe is the freedom degrees of error (fe = 4); Ve is the average error (Ve = 41.18); F/ ð1; 2Þ is the Fisher coefficient corresponding to the confidence level (a) of 90% (F/ ð1; 4Þ ¼ 4:5448Þ; R is the number of iterations of an experiment (R = 3);

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Ne is the number of effective iterations which can be computed as follows:
 Ne ¼ SSum = 1 þ Af ¼ 48=ð1 þ 3 þ 3 þ 3 þ 1 þ 1Þ ¼ 3:2 In which, Ssum is the total number of experiments and Af is the total freedom of all averaged parameters. Substituting F/ , Ve, R and Ne into (4), we have: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   1 1 þ ¼ 11 CI ¼  4:5448  41:18  3:2 3 Accordingly, at the confidence level (a) of 90% the normal cutting force value is predicted with the optimum level of the input parameters ar1/nr1/nnon1/nf1/af2/S2 as follows: ð116:88  11ÞN  Fyop  ð116:88 þ 11ÞN Finally, the confirmation experiment which takes the parameters found above as the dressing mode has been conducted to compare the experimental values of the normal cutting force to those which are calculated by the mathematical models (3). The result of the experiment, which is presented in Table 7, indicates that the difference between experimental values and mathematical model values of the normal cutting force is within 3.01% of the range. This means that the models proposed in the study are reliable.

Table 7. The results from model and experiment Optimal parameters Output responds

The normal cutting force – Fy (N)

3.5

Prediction value ar1, nr1, nnon1, nf1, af2, s2 116.88

Experimental value ar1, nr1, nnon1, nf1, af2, s2 120.5

Error (%) 3.01

Determining the Influence of Dressing Parameters

Figure 4 shows the normal distribution graph of the Fy data set. It can be seen from the Figure that the distributions of empirical data points are located along the standard line and within a range. Thus, the empirical data can be regarded as following the standard distribution law. In addition, the statistical information of the Fy data set presented in Fig. 4 shows that the allowable p value of 0.237 is much larger than the significance level a of 0.05. Therefore, it can be concluded that Fy data set follows standard distribution law.

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Fig. 4. The normal distribution graph of the Fy data set.

4 Conclusions We propose herein a method based on Taguchi technique to optimize the dressing parameters in surface grinding operation for SKD11 steel. Based on the experimental investigation, the following points can be concluded: – Among the six dressing parameters, the most influential parameter on normal cutting force is the fine dressing depth (af). The descending sequence order of the influence of the input parameters on normal cut-ting force is the fine dressing depth (af), the rough dressing times (nf), the fine dressing times (nf), the non-feeding dressing times (nnon), the dressing feed rate (S), and rough dressing depth (ar). – By applying Taguchi technique and analysis of variance (ANOVA), with the proposed target function of the normal cutting force, the optimum dressing parameters for the minimum normal cutting force have been found as follows: ar = 0.015 mm, nr = 1 times, nnon = 0 times, nf = 0 times, S = 1.8 m/min. – The difference between the experimental values and mathematical model values of the normal cutting force is within 3.01% of the range, which proves that the models proposed in the study are reliable. Acknowledgements. The work was supported by Thai Nguyen University of Technology.

References 1. Hung, L.X., Ky, L.H., Hong, T.T., Cuong, N.V., Trung, D.D., Phan, N.H., Tung, L.A., Vu, N.P.: Optimization of Manufacturing Timein Internal Grinding. ICERA 2019, 557–565 (2020)

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2. Tu, H.X., Thao, L.P., Hong, T.T., Nga, N.T.T., Trung, D.D., Gong, J., Pi, V.N.: Influence of dressing parameters on surface roughness of workpiece for grinding hardened 9XC tool steel. IOP Conf. Series: Mater. Sci. Eng. 542 (2019) 3. Hung, L.X., Pi, V.N., Tung, L.A., Tu, H.X., Jun, G., Long, B.T.: Determination of optimal exchanged grinding wheel diameter when internally grinding alloy tool steel 9CrSi. In: IOP Conference Series: Materials Science and Engineering. IOP Publishing (2018) 4. Tu, H.X., Jun, G., Hung, L.X., Tung, L.A., Pi, V.N.: Calculation of optimum exchanged grinding wheel diameter when external grinding tool steel 9CrSi. Int. J. Mech. Eng. Robot. Res. 8(1), 59–64 (2019) 5. Thang, V.T., Tuan, N.A., Tiep, N.V.: Application of pneumatic measuring probe to determine appropriate time for dressing grinding wheel in profile grinding for the inner ring groove of ball bearing. J. Eng. Sci. Res. 1(2), 222–227 (2017) 6. Winter, M., Li, W., Kara, S., Herrmann, C.: Determining optimal process parameters to increase the eco-efficiency of grinding processes. J. Clean. Prod. 66, 644–654 (2013) 7. Hung, L.X., Lien, V.T., Pi, V.N., Long, B.T.: A study on coolant parameters in internal grinding of 9CrSi Steel. Mater. Sci. Forum 950, 24–31 (2019). https://doi.org/10.4028/www. scientific.net/msf.950.24 8. Tuan, N.A., Ky, L.H., Hong, T.T., Cuong, N.V., Tung, L.A., Tu, N.T., Tu, H.X., Vu, N.P.: Optimization of exchanged grinding wheel diameter for minimum cost in external grinding. In: Sattler, K.-U., Nguyen, D.C., Vu, N.P., Tien Long, B., Puta, H. (eds.) ICERA 2019. LNNS, vol. 104, pp. 546–556. Springer, Cham (2020). https://doi.org/10.1007/978-3-03037497-6_62 9. Young, H.-T., Chen, D.-J.: Online dressing of profile grinding wheels. Int. J. Adv. Manuf. Technol. 27, 27883–888 (2006) 10. Klocke, F., Thiermann, J., Mattfeld, P.: Influence of the dressing process on grinding wheel wear. Prod. Eng. 9(5) 563–568 (2015) 11. Martin, N., Nataša, N., Hiroshi, K.: Influence of grinding wheel dressing on the roughness of final surface and cutting force during GGG60 grinding. Key Eng. Mater. 686, 218–223 (2016) 12. Puertoa, P., et al.: Evolution of surface roughness in grinding and its relationship with the dressing parameters and the radial wear. The Manufacturing Engineering Society International Conference, MESIC 2013. Procedia Eng. 63, 174–182 (2013) 13. Hung, L.X., Pi, V.N., Hong, T.T., Ky, L.H., Lien, V.T., Tung, L.A., Long, B.T.: Multiobjective optimization of dressing parameters of internal cylindrical grinding for 9CrSi aloy steel using Taguchi method and grey relational analysis. Mater. Today: Proc. 18(7), 2257– 2264 (2019) 14. Hoang, X.T., Pi, V.N., Jun, G.: A study on determination of optimum parameters for lubrication in external cylindrical grinding base on Taguchi method. Key Eng. Mater. 796, 97–102 (2019). https://doi.org/10.4028/www.scientific.net/KEM.796.97 15. Hung, L.X., Hong, T.T., Ky, L.H., Tung, L.A., Nga, N.T.T., Pi, V.N.: Optimum dressing parameters for maximum material removal rate when internal cylindrical grinding using Taguchi method. Int. J. Mech. Eng. Technol. 9(12), 123–129 (2018) 16. Tran, T.H., Hoang, T.D., Ky, H.L., Do, T.T., Bui, T.H., Cuong, N.M., Tung, L.A., Pi, N.V.: Analysis of effects of machining parameters on surface roughness in electrical discharge machining tablet shape punches using Taguchi method. Mater. Sci. Forum 977, 12–17 (2020) 17. Hung, L.X., Pi, V.N., Hong, T.T., Ky, L.H., Lien, V.T., Tung, L.A., Long, B.T.: Multiobjective optimization of dressing parameters of internal cylindrical grinding for 9CrSi aloy steel using Taguchi method and grey relational analysis. Mater. Today: Proc. 18, 2257–2264 (2019)

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18. Hong, T.T., Cuong, N.V., Ky, L.H., Nguyen, Q.T., Long, B.T., Tung, L.A., Nguyen, T.T., Pi, V.N.: Multi-criteria optimization of dressing parameters for surface grinding 90CrSi tool steel using Taguchi method and grey relational analysis. Mater. Sci. Forum 998, 61–68 (2020). https://doi.org/10.4028/www.scientific.net/msf.998.61 19. Tung, L.A., et al.: Optimization of dressing parameters of grinding wheel for 9CrSi tool steel using the Taguchi method with grey relational analysis, IOP Conference Series: Materials Science and Engineering, vol. 635, 10th International Conference on Mechatronics and Manufacturing (ICMM 2019) 21–23 January 2019, Bangkok, Thailand 20. Hong, T.T., Cuong, N.V., Ky, L.H., Nguyen, Q.T., Long, B.T., Tung, L.A., Pi, V.N.: Multicriteria optimization of dressing parameters for surface grinding 90CrSi tool steel using Taguchi method and grey relational analysis. In: Materials Science Forum, vol. 998, pp. 61– 68. Trans Tech Publications Ltd (2020) 21. Chekole, N., Deshpande, V.: Review analysis on optimization of cylindrical grinding process parameters by using Taguchi technique. Ind. Eng. J. 14, 35–39 (2018) 22. Byun, H.-S., Lee, S.-H.: Design of a piston forging process using a hybrid Taguchi method and multiple criteria decision-making. J. Mech. Sci. Technol. 31(4), 1869–1876 (2017). https://doi.org/10.1007/s12206-017-0334-7

Optimization of PMEDM Parameters for Improving MMR in Machining 90CrSi Steel - A Taguchi Approach Tran Thi Hong1, Do Thi Tam2, Do The Vinh2, Luu Anh Tung2, Le Thu Quy3, Thangaraj Muthuramalingam4, Vu Ngoc Pi2, and Nguyen Manh Cuong2(&) 1

3

Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam 2 Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] National Research Institute of Mechanical Engineering, Ha Noi City, Vietnam 4 Department of Mechatronics Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India

Abstract. To overcome the limits of the electrical discharge machining (EDM) process, the nanoscale fine powder is added to the dielectric in a new machining method called powder mixed electrical discharge machining (PMEDM). In this research, the Taguchi approach has been applied to determine the effects of PMEDM parameters such as the powder concentration, the pulseon-time, the pulse-off-time, the pulse current, and the server voltage to material removal rate (MRR) in hardened 90CrSi steel processing. L18 orthogonal array, signal to noise (S/N) ratio, and ANOVA were employed to plan and analyze the experiment. The pulse current was determined to be the factor that had the strongest impact on MRR. Moreover, an optimal EDM condition was found to improve MRR. The powder concentration of 3.5 g/l, the pulse-on-time of 6 µs, the pulse-off-time of 30 µs, the pulse current of 12 A, and the server voltage of 5 V resulted in maximum MRR. Keywords: EDM powder


PMEDM
MRR
Taguchi method
ANOVA
SiC

1 Introduction EDM is an advanced machining method that is commonly applied in industry. The advantage of EDM lies in the ability to process complex geometric shapes and materials that are difficult to machine by conventional machining methods. During EDM, the metal is removed by spark erosion principle [1, 2]. Sparks appear on a narrow gap between the electrode and the workpiece that takes place in the dielectric fluid [3–6]. Low processing productivity is a major problem for the EDM process. In addition, low surface quality, high roughness, and high tool wear rate are limitations of this machining method. In 1981, A. Erden and S. Bilgin published their scientific work on the role of impurities in electric discharge machining [7]. The authors suggested that © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 648–657, 2021. https://doi.org/10.1007/978-3-030-64719-3_71

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the increase in the concentration of impurities led to a significant improvement in removal rate. Their findings are key to improving the performance of the EDM process. PMEDM has been proposed with a number of advantages such as improved roughness, high MRR, and low tool wear rate to replace the traditional EDM method [8–10]. During PMEDM, nano-sized fine powder is mixed in dielectric. EDM dielectric fluid functions as a spark conductor and discharge medium, and it is also used to remove eroded metal particles. The mixing of fine powder into the dielectric changed the discharge gap size, discharge transitivity, breakdown strength, and deionization of dielectric [6, 9]. The size of the discharge gap between the electrode and the machining surface is increased due to the decrease of the insulating strength of dielectric when adding the powder [11–16]. The enlarged size of the discharge gap facilitates the flushing of debris and short-circuiting. The uniform distribution of sparks and the stability of the discharge process lead to an increase in MRR [11, 17–19]. In a study conducted by Paramjit Singh et al. [19], the authors concluded that the PMEDM process parameters such as electric current, the material of the electrode, and the concentration of powder had a strong impact on MRR. Ojha, K. et al. conducted an investigation to determine the effects of PMEDM parameters on MRR and tool wear [20]. The conclusion was that pulse current, concentration of powder and electrode diameter were main factors affecting MRR and tool wear. In a study of Long, B. T. et al., an optimization of PMEDM parameters to improve the MRR was carried out. The authors confirmed that electric current, electrode material, and concentration of powder had the biggest impact on MRR [21]. PMEDM process of SKD61 steel was conducted in a research of Kobayashi et al. [22]. The results showed that a suitable amount of suspended silicon powder added increased MRR and reduced surface roughness. Similarly, H.K. Kansal optimized the PMEDM process by using the Taguchi method. He found that a sufficient amount of graphite powder added to the dielectric greatly increased MRR, reduced tool wear and improved roughness [23]. SiC powder added to the EDM dielectric has been applied in several studies. In [18], SiC powder concentration was reported as the main factor affecting particle transfer. A positive result was achieved in the study of Al-Khazraji when the author investigated the effect of PMEDM process on SiC powder on white layer, heat flux, and fatigue life in machining of AISI D2 steel [24]. In another research [25], Kuriachen, B. and J. Mathew emphasized that lower powder concentration results in an increase in MRR and an improvement of tool wear during machining of Ti-6Al-4 V. A similar result was provided in a study by Razak, M. A. et al. The authors argued that the application of SiC powder in the EDM process improved surface roughness, increased MRR, and reduced processing costs and time [2]. Various research works have been performed for enhancing the machinability of difficult-to-cut materials using various forms of EDM processes [26–36]. It was observed that the materials could be effectively produced using EDM processes. Mechanical products with complex shapes and difficult-to-machine materials are challenges for traditional machining processes. In actual production, cylindrical-shaped parts are those that are difficult to machine. A typical example of this type is the tablet press punch as shown in Fig. 1. The face of the tablet press punch is concave with a complex profile. In advanced industrial countries, the tablet press punch is often machined by micro-milling. In developing countries, this punch is made by the

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benchwork with rather low productivity. Therefore, a machining method for making the tablet press punch proposed by our team is to use EDM with a hole-shaped electrode. The productivity of this process is also enhanced by the application of PMEDM [37].

Fig. 1. Tablet press punches [37]

In this work, an attempt was made to determine the influence of PMEDM parameters on the MRR in processing cylindrical shape parts made of 90CrSi steel (as tablet press punches) by using Taguchi approach. The optimum values of PMEDM conditions have been determined to achieve the maximum MRR. In addition, the efficiency of EDM with SiC powder-mixed dielectric has been found predominant when compared that of traditional EDM processes.

2 Experimental Procedure In the study, the Taguchi method is used to find the optimal condition of the PMEDM process for maximizing MRR in hardened 90CrSi steel processing. In addition, an orthogonal array is selected to organize the experiment. The parameters of PMEDM process include the powder concentration (Cp), the pulse-on-time (Ton), the pulse-offtime (Toff), the pulse current (IP), and the server voltage (SV) as shown in Table 1. With the parameters and their levels, L18 array is chosen to design the experiment. To measure the performance characteristics and calculate the impact of each input factor, a suitable type of signal to noise (S/N) and ANOVA analysis are employed. With the goal of maximizing MRR, the-bigger-is-the-better S/N type is applied and calculated as the following equation: n S 1X 1 ¼ 10 log N n i¼1 y2i

ð1Þ

Where yi is the data received by the experiments, n is the number of experiments.

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Table 1. PMEDM parameters and their levels Levels Parameters Cp (g/l) Ton (µs) Toff (µs) IP (A) 1 0 6 14 4 2 2 10 21 8 3 2.5 14 30 12 4 3.5 – – – 5 4 – – – 6 4.5 – – –

SV (V) 3 4 5 – – –

All experiments are performed using a die-sinking EDM platform, model Sodick A30 Machine. The EDM process takes place in a 300 mm  250 mm  250 mm container as shown in Fig. 2. The stirring rotates at a speed of 90 r/min to maintain the uniformity of SiC powder in the dielectric fluid. A nozzle is used to pump liquid into the work zone to eject chips and maintain the stability of the discharge process. The task of the magnetic plate is to collect steel debris generated during machining to prevent them from re-entering the machining area. To determine MRR, a highprecision scale (precision of 0.001 g) is used to measure workpiece mass before and after each experiment repeats three times. The material of the electrode is copper, and Diel MS 7000 oil of Total company is utilized as dielectric fluid. The work material is 90CrSi tool steel with the hardness of 58–62 HRC. The employed SiC powder has a size of 500 nm. The statistical analysis is realized using the Minitab 18 software.

Fig. 2. Schematic diagram of the experiment

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3 Results and Discussions The results of the experiments and S/N ratio are shown in Table 2. The values of PMEDM parameters such as the Cp, Ton, Toff, IP, and SV are based on the arrangement of the L18 array. MRR values are obtained from 3 trials in each run. The means of MRR are distributed in the range from 0.001787 g/h (Run 7) to 0.452930 g/h (Run 10). The S/N is obtained by Minitab software. Table 2. Results of experiments Run Cp Ton Toff IP SV MRR [g/h] Trial 1 Trial 2 1 0 6 14 4 3 0.01921 0.01925 2 0 10 21 8 4 0.01011 0.01012 3 0 14 30 12 5 0.24745 0.24651 4 2 6 14 8 4 0.00341 0.00339 5 2 10 21 12 5 0.33044 0.33016 6 2 14 30 4 3 0.03213 0.03213 7 2.5 6 21 4 5 0.00179 0.00178 8 2.5 10 30 8 3 0.03924 0.03916 9 2.5 14 14 12 4 0.33835 0.33835 10 3.5 6 30 12 4 0.45369 0.45233 11 3.5 10 14 4 5 0.05054 0.05058 12 3.5 14 21 8 3 0.00254 0.00253 13 4 10 30 4 4 0.32517 0.32488 14 4 14 14 8 5 0.00777 0.00775 15 4 6 30 8 5 0.00886 0.00883 16 4.5 10 14 12 3 0.31019 0.30989 17 4.5 14 21 4 4 0.00702 0.00701 18 4.5 14 30 8 3 0.00799 0.00798

Trial 3 0.01925 0.01012 0.24698 0.00340 0.33016 0.03216 0.00179 0.03908 0.33808 0.45278 0.05049 0.00253 0.32547 0.00774 0.00885 0.31019 0.00700 0.00800

Mean 0.019241 0.010121 0.246981 0.003404 0.330254 0.032142 0.001787 0.039163 0.338263 0.452930 0.050535 0.002536 0.325175 0.007752 0.008847 0.310090 0.007013 0.007989

S/N −34.3156 −39.8957 −12.1468 −49.3611 −9.6230 −29.8586 −54.9584 −28.1426 −9.4149 −6.8794 −25.9281 −51.9155 −9.7577 −42.2115 −41.0643 −10.1703 −43.0820 −41.9507

Figure 3 shows the main effects plot for S/N ratio. As shown in the figure, the levels giving the maximum S/N values for each parameter include the fourth level of the powder concentration, the first level of the pulse-on-time, the third level of the pulse-off-time, the pulse current, and the server voltage. Therefore, the optimal input parameters for maximum MRR are the powder concentration of 3.5 g/l, the Ton of 6 µs, the pulse-off-time of 30 µs, the pulse current of 12 A, and the server voltage of 5 V. Figure 4 shows the effect of nanopowder concentration on the MMR. An increase in powder concentration results in a growth in MRR. MRR expands by over 83% from 0.09211 g/h to 0.16867 g/h when SiC powder concentration rises from 0 g/l to 3.5 g/l. However, when the powder concentration is applied at a higher level, the MRR decreases. The increase in Cp which leads to the increase of MRR is explained by the fact that mixing SiC nanopowder into the dielectric widens the discharge gap and

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Fig. 3. Main effects for S/N ratio

increases the size of the plasma stream, which leads to an enhancement of MRR. However, when a powder concentration of more than 3.5 g/l is applied, the MRR decreases due to the short-circuit phenomenon causing the instability of the spark discharge.

0.16867

0.18 0.16

MRR (g/h)

0.14 0.12 0.1

0.12193

0.1264

0.11392

0.10836

4

4.5

0.09211

0.08 0.06 0.04 0.02 0 0

2

2.5

3.5

Cp (g/lit)

Fig. 4. Effect of nanopowder concentration on MRR

ANOVA for MRR is indicated in Table 3. It can be realized that the pulse current is the most prominent factor affecting MRR. The impact of the pulse current on MRR accounts for 42.61% of the total impact. The second strongest impact factor is

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pulse-off-time with 13.79% of the total effect. The effect of other factors is below 5% of the total effect. Table 3. ANOVA for the MRR Source DF Seq SS AdjSS 5 28.27 28.27 Cp Ton 2 55.15 55.15 Toff 2 647.85 647.85 IP 2 2001.07 2001.07 SV 2 178.01 178.01 Residual-error 4 1786.01 1786.01 Total 17 4696.36 –

AdjMS 5.65 27.57 323.92 1000.54 89.01 446.50 –

F 0.01 0.06 0.73 2.24 0.20 – –

P 1.000 0.941 0.538 0.222 0.827 – –

%C 0.60 1.17 13.79 42.61 3.79 – –

The predicted average MMR (MRROP ) is determined by Eq. (2): MRROP ¼ C p4 þ T on1 þ T off 3 þ IP3 þ SV 3  4  T MRR

ð2Þ

Where Cp4 , T on1 , T off 3 ; IP3 , SV 3 , T MRR is the average MRR for Cp at level 4, Ton at level 1, Toff at level 3, IP at level 3, SV at level 3, and the average MMR, respectively Based on Table 2, the values of parameters in Eq. (2) can be determined as the following: C p4 ¼ 0; 16867g=h T on1 ¼ 0; 18544g=h T off 3 ¼ 0; 18151g=h IP3 ¼ 0; 28344g=h SV 3 ¼ 0; 15808g=h P18 T MRR ¼

i¼1

MRRI þ

P18 i¼1

MRRII þ 54

P18 i¼1

MRRIII

¼ 0; 121901g=h

By the Eq. (2): MRROP ¼ 0; 16867 þ 0; 18544 þ 0; 18151 þ 0; 28344 þ 0; 15808  4  0; 121901 ¼ 0; 48954g=h A verification test is conducted with the optimal condition of PMEDM including the powder concentration of 3.5 g/l, the Ton of 6 ls the pulse-off-time of 30 ls, the pulse current of 12 A, and the server voltage of 5 V. The MMR obtained from the verification test is 0.656 lm. This result is 9.71% different from the predicted average MRR calculated by Eq. (2), which proves the reliability of the model developed by the

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research. In addition, MMR is greatly improved by the optimal PMEDM conditions when compared to machining by conventional EDM processes. Figure 5 illustrates the normal probability plot of MMR. From the figure, MRR data description points are distributed nearby to the center line. It means that the input factors selected in the study have a statistically significant effect on the output response.

Fig. 5. Normal probability plot of MRR

4 Conclusion In this study, Taguchi method and ANOVA were used to find the effect of powder mixed EDM parameters such as the powder concentration, Ton, the pulse-off-time, the pulse current, and the server voltage on MMR during machining of hardened 90CrSi steel. An optimal condition of the PMEDM process for maximizing MRR has been proposed. Some noteworthy conclusions of the study can be drawn as follows: – Pulse current is the factor that has the most dominant impact on MMR followed by pulse-off-time. They contribute 42.61% and 13.79% to the total influence, respectively. – The optimal condition of the PMEDM process for maximizing MRR includes the powder concentration of 3.5 g/l, the Ton of 6 ls the pulse-off-time of 30 ls, the pulse current of 12 A, and the server voltage of 5 V. – The excellent effect of mixing SiC nanopowder on the dielectric fluid of the EDM process has been demonstrated. When applying the appropriate concentration of nanopowder (Cp = 3.5 g/l), MRR was enhanced by 83% compared to EDM process without mixing powder. Acknowledgments. This work was financially supported by the scientific project No. B2019TNA-03.

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References 1. Singh, D.: Fundamentals of Manufacturing Engineering. CRC Press, USA (2008) 2. Razak, M., Abdul-Rani, A., Nanimina, A.: Improving EDM efficiency with silicon carbide powder-mixed dielectric fluid. Int. J. Mater. Mech. Manufact. 3(1), 40–43 (2015) 3. Groover, M.P.: Fundamentals of Modern Manufacturing: Materials Processes, and Systems. John Wiley & Sons, New Jersey (2007) 4. Kumar, A., et al.: Research developments in additives mixed electrical discharge machining (AEDM): a state of art review. Mater. Manufact. process. 25(10), 1166–1180 (2010) 5. Ho, K., Newman, S.: State of the art electrical discharge machining (EDM). Int. J. Mach. Tools Manufact. 43(13), 1287–1300 (2003) 6. Ekmekci, B., et al.: Suspended SiC particle deposition on plastic mold steel surfaces in powder-mixed electrical discharge machining. Proc. Inst. Mech. Eng. Part B: J. Eng. Manufact. 229(3), 475–486 (2015) 7. Erden, A., Bilgin, S.: Role of impurities in electric discharge machining. In: 21st Proceedings of International Machine Tool Design and Research Conference. Springer, New York (1981) 8. Zhang, Y., et al.: A review of the current understanding and technology of powder mixed electrical discharge machining (PMEDM). In: 2012 IEEE International Conference on Mechatronics and Automation, IEEE (2012) 9. Jawahar, M., Reddy, C.S., Srinivas, C.: A review of performance optimization and current research in PMEDM. Mater. Today: Proc. 19, 742–747 (2019) 10. Khan, M., et al.: Current research trends on dry, near-dry and powder mixed electrical discharge machining. In: Advanced Materials Research. Trans Tech Publications Ltd. (2011) 11. Wong, Y., et al.: Near-mirror-finish phenomenon in EDM using powder-mixed dielectric. J. Mater. Process. Technol. 79(1–3), 30–40 (1998) 12. Tzeng, Y.-F., Lee, C.-Y.: Effects of powder characteristics on electrodischarge machining efficiency. Int. J. Adv. Manufact. Technol. 17(8), 586–592 (2001) 13. Mohri, N., et al.: A new process of finish machining on free surface by EDM methods. CIRP Ann. 40(1), 207–210 (1991) 14. Uno, Y., Okada, A.: Surface generation mechanism in electrical discharge machining with silicon powder mixed fluid. Int. J. Electric. Mach. 2, 13-18 (1997) 15. Kansal, H., Singh, S., Kumar, P.: Parametric optimization of powder mixed electrical discharge machining by response surface methodology. J. Mater. Process. Technol. 169(3), 427-436 (2005) 16. Zhao, W., Meng, Q., Wang, Z.: The application of research on powder mixed EDM in rough machining. J. Mater. Process. Technol. 129(1–3), 30–33 (2002) 17. Kansal, H., Singh, S., Kumar, P.: Performance parameters optimization (multicharacteristics) of powder mixed electric discharge machining (PMEDM) through Taguchi’s method and utility concept. Indian J. Eng. Mater. Sci. 13(3), 209–216 (2006) 18. Öpöz, T.T., et al.: Particle migration and surface modification on Ti6Al4 V in SiC powder mixed electrical discharge machining. J. Manufact. Process. 31, 744–758 (2018) 19. Singh, P., et al.: Influence of electrical parameters in powder mixed electric discharge machining (PMEDM) of hastelloy. J. Eng. Res. Stud. 1(2), 93–105 (2010) 20. Ojha, K., Garg, R., Singh, K.: Experimental investigation and modeling of PMEDM process with chromium powder suspended dielectric. Int. J. Appl. Sci. Eng. 9(2), 65–81 (2011) 21. Long, B.T., et al.: Optimization of PMEDM process parameter for maximizing material removal rate by Taguchi’s method. The Int. J. Adv. Manufact. Technol. 87(5–8), 1929–1939 (2016)

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22. Kobayashi, K., et al.: The present and future developments of electrical discharge machining. In: 2nd in Proceedings of International Conference on Die and Mould Technology, Singapore (1992) 23. Kansal, H., Singh, S., Kumar, P.: Application of Taguchi method for optimisation of powder mixed electrical discharge machining. Int. J. Manufact. Technol. Manage. 7(2–4), 329–341 (2005) 24. Al-Khazraji, A., Amin, S.A., Ali, S.M.: The effect of SiC powder mixing electrical discharge machining on white layer thickness, heat flux and fatigue life of AISI D2 die steel. Eng. Sci. Technol. Int. J. 19(3), 1400–1415 (2016) 25. Kuriachen, B., Mathew, J.: Effect of powder mixed dielectric on material removal and surface modification in microelectric discharge machining of Ti-6Al-4V. Mater. Manufact. Process. 31(4), 439–446 (2016) 26. Muthuramalingam, T., Ramamurthy, A., Moiduddin, K., Alkindi, M., Ramalingam, S., Alghamdi, S.: Enhancing the surface quality of micro titanium alloy specimen in WEDM. Process by adopting TGRA-based optimization. Materials 13, 1440 (2020) 27. Muthuramalingam, T.: Effect of diluted dielectric medium on spark energy in green EDM process using TGRA approach. J. Cleaner Prod. 238, 117894 (2019) 28. Geethapriyan, T., Kalaichelvan, K., Muthuramalingam, T.: Multi performance optimization of electrochemical micro-machining process surface related parameters on machining Inconel 718 using Taguchi-grey relational analysis. La Metallurgia Italiana 2016(4), 13–19 (2016) 29. Huu-Phan, N., Tien-Long, B., Quang-Dung, L., Duc-Toan, N., Muthuramalingam, T.: Multicriteria decision making using preferential selection index in titanium based die-sinking PMEDM. J. Korean Soc. Precision Eng. 36(9), 793–802 (2019) 30. Vu, N.-P., et al.: Optimization of grinding parameters for minimum grinding time when grinding tablet punches by CBN wheel on CNC milling machine. Appl. Sci. 9(5), 957 (2019) 31. Hung, L.X., Hoang, T.T., Pi, V.N.: A study on modelling surface finish in electrical discharge machining tablet shape punches using response surface methodology. J. Environ. Sci. Eng. B 6, 387–390 (2017) 32. Hoang, T.T., et al.: Modelling Surface Finish in Electrical Discharge Machining Tablet Shape Punches using Response Surface Methodology. SSRG Int. J. Mech. Eng. 4(9) (2017) 33. Muthuramalingam, T., Mohan, B., Rajadurai, A., Antony, M.D.: Experimental investigation of iso energy pulse generator on performance measures in EDM. Mater. Manufact. Process. 28(10), 1137–1142 (2013) 34. Muthuramalingam, T, Ramamurthy, A., Sridharan, K., Ashwin, S.: Analysis of surface performance measures on WEDM processed titanium alloy with coated electrodes. Mater. Res. Express 126503 (2018) 35. Tam, D.T., Tuan, N.Q., Ky, L.H., Hong, T.T., Pi, V.N.: Modelling surface finish in the inner circle machining of 90CrSi tool steel using wire cut EDM. Int. J. Mech. Produc. Eng. Res. Dev. 9(3), 119–124 (2019) 36. Pi, V.N., Tam, D.T., Cuong, N.M., Tran, T.-H.: Multi-objective optimization of PMEDM process parameters for processing cylindrical shaped parts using Taguchi method and grey relational analysis. Int. J. Mech. Prod. Eng. Res. Dev. 10(2), 669–678 (2020) 37. Tran, T.-H., et al.: Electrical discharge machining with SiC powder-mixed dielectric: an effective application in the machining process of hardened 90CrSi steel. Machines 8(3), 36 (2020)

Overshoot and Settling Time Assignment for Second-Order Systems with Time Delay Nam Hoai Nguyen(&) and Phuoc Doan Nguyen Department of Automatic Control, School of Electrical Engineering, Hanoi University of Science and Technology, Hanoi 11615, Vietnam [email protected]

Abstract. In this work, an overshoot and settling time assignment (OSA) method with filter PID is proposed for second-order system, and a control strategy is also come up with for linear time-delay systems. Then, a PID based controller design method, aimed at as small overshoot and settling time as desired, is developed for second-order systems with time delay. Some numerical simulations are given to illustrate the proposed method. Keywords: PID tuning


Overshoot
Settling time

1 Introduction In industry, there are many processes with time delay, which can be modelled as a firstorder or higher order system with time delay. There have been abundant PID controller design methods for this type of system. For general linear systems, a classical experimental PID tuning method [1] was often used. For high-order systems with time delay, there were PID tuning method [4], stable space for PID parameters [11], PI-PD controller [18]. Some Smith based approaches were proposed in [5, 20]. There have been a lot of optimal design methods such as time-optimal PID control [2], optimal PID tuning [7, 17], PID tuning [3] for desired requirements, PID tuning options [13, 14] using the IAE criteria, model reduction [6], optimal PID using LQR approach [8], optimal H2 and IMC approach [12]. Other methods consist of robust PID [9], automatic PID tuning [10], delay margins for unstable systems [15, 16], and time-delayed regulator [19]. However, there has not been any PID tuning method concurrently dealing with both desired settling time and overshoot assignment for second-order systems with time delay, even when all parameters of the system are known. Recently, a PID tuning method [21] was proposed to achieve desired settling time and overshoot as small as possible. This motivates us to propose a PID controller design method for a secondorder system with delay to aim at not only desired settling time but also required overshoot as small as possible. The paper contains 4 sections. In next section, a filter PID tuning method is proposed for a second-order system. The proposed controller for the system with delay is also provided. Then, an example is used to illustrate the proposed method and compare with the genetic algorithm (GA) method in Sect. 3. The final section will draw some conclusions and future works.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 658–664, 2021. https://doi.org/10.1007/978-3-030-64719-3_72

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2 Main Results Given a plant with transfer function of Gp ðsÞ ¼ SðsÞess ;

ð1Þ

where Sð s Þ ¼

k ðT1 s þ 1ÞðT2 s þ 1Þ

ð2Þ

s is a time delay, k is a gain coefficient, T1 and T2 are time constants with arbitrary sign. The control objective is to design a controller such that the Fig. 1. Response in p domain closed-loop system has both desired overshoot r% and settling time Ta% as small as expected. 2.1

Proposed Filter PID Tuning Method for Second-Order Systems

The filter PID controller is represented as follows   1 Td s þ RðsÞ ¼ kp 1 þ Ti s ds þ 1

ð3Þ

where d is a time constant of the filter. Theorem 1. Given a control system consisting of a plant (2) and a controller (3). If the parameters of the controller are computed as d = xT/(1 + x), Ti= T1 + T2 − d, Td= T1T2/(T1 + T2 − d) − d, kp= (T1+ T2 −d)d/(kxT2) in which T = Ta%/p, 0  x < 180 w ¼ 180 if ðX [ 0Þ and ðcs  hA;axis Þ\0 > : 0 other

X¼

ð10Þ

Tilt angle of a solar module surface with a solar tracker is expressed as: hT ¼ cos1 ½cos R cos hT;axis 

ð11Þ

Consequently, the azimuth angles of a solar module surface are expressed as: hA ¼ hA;axis þ sin1



 sin R For hT 6¼ 0 sin hT

ð12Þ

3 Result and Discussion The study has conducted measurements from July 2018 to September 2018 and selected sunny, scattered cloudy and rainy days to be the three specific case studies. 3.1

Reference Yield

From the GHI, DNI, DHI data obtained from Solcast, and combined with the calculation results of angle of incidence of a solar module surface of fixed- tilted systems and the tracking system, we calculated the total in-plane solar irradiance for the two systems. The reference yield for the two systems is calculated according to formula (3) and shown in Fig. 2. In addition, the average air temperature from 5:00 to 18:00 is collected and presented in Fig. 2.

Air temperatura

without tracker

with tracker

32

Temperatura (oC)

Yr (kWh/kW)

12 10

30

8

28

6

26

4

24

2

22

0

20

Date Fig. 2. Air temperature and reference yield of a system with and without tracking

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The cloudless sunny days can include the 2–7th July, 20th August and 4–5th September obtained reference yield higher than 8.5 kWh/kW for the GCPVS with asolar tracker and greater than 4.6 for GCPVS without asolar tracker (fixed- tilted system). On 3rd July, the highest reference yield was reached over 9.0 kWh/kW for the GCPVS with a solar tracker and more than 7.5 kWh/kW for GCPVS without a solar tracker. On 22nd July and 9th August, the lowest reference yield was less than 0.9 kWh/kW for two systems. 3.2

Performance Ratio of GCPVS on a Sunny Day

90

Power improvement

Gt without tracker

Gt with tracker

1000

70

800

50

600

30

400

10

200

-10

Gt (W/m2)

Power improvement (W)

In the experimental results, 2 typical sunny days were selected during the study period to analyze and evaluate the PR of the two systems. Figures 3 and 4 show the total in-plane solar irradiance and power improvement on a sunny day. Power improvement is the difference of net power output of two GCPVS with and without a solar tracker. Experimental results have shown that the GCPVS with a solar tracker has a total inplane solar irradiance and the net power output is better than the GCPVS without a tracker in the morning time until early noon, and late noon to late afternoon. At noon

0

Time

90

Power improvement

Gt without tracker

Gt with tracker

1000

70

800

50

600

30

400

10

200

Gt (W/m2)

Power improvement (W)

Fig. 3. Power improvement and total in-plane solar irradiance on a sunny day (9/5/2018).

0

-10

Time Fig. 4. Power improvement and in-plane solar irradiance total on a sunny day (7/3/2018).

PR Analysis Using Experimental Combining Historical Weather Data

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around 11:00 to 13:00, two systems have the same tilt angle and azimuth angle, so the sum of isolation is equal, resulting in little difference in the power transmitted to the grid. Table 1 shows that the PR of the GCPVS with a tracker higher than GCPVS without a tracker and the highest is 79.8%. In addition, on July 3rd, 2018 the reference yield was higher, but the PR was smaller than that on September 5th, 2018. This can be explained by the effect of the air temperature on the PR index. Table 1. Performance ratio on typical sunny days Date Air temperature, °C Cloud opacity, % GCPVS with a tracker

7/3/2018 31.80 1.60 9.05 Yr, kWh/kW Yf, kWh/kWp 6.35 PR, % 70.1 GCPVS without a tracker Yr, kWh/kW 7.50 Yf, kWh/kWp 4.90 PR, % 64.9

3.3

9/5/2018 29.60 1.00 8.51 6.78 79.80 6.74 5.30 78.70

Performance Ratio of GCPVS on a Scattered Cloudy Day

90

Power improvement

Gt without tracker

Gt with tracker

1000

70

800

50

600

30

400

10

200

-10

Gt (W/m2)

Power improvement (W)

In the experimental results, two scattered cloudy days were selected during the study period to analyze and evaluate the PR of the two systems. Figures 5 and 6 show the total in-plane solar irradiance and power improvement on a scattered cloudy day. At the time of cloud cover, the total in-plane solar irradiance decreased sharply, because there were only ground-reflected irradiance and sky-diffuse irradiance components, so the output power of the two GCPVS is almost equal and power improvement is close to 0.

0

Time Fig. 5. Power improvement and total in-plane solar irradiance on a scattered cloudy day (7/25/2018).

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7/11/2018 25.90 53.80 Yr, kWh/kW 3.61 Yf, kWh/kWp 2.67 PR, % 73.80 GCPVS without a tracker Yr, kWh/kW 3.72 Yf, kWh/kWp 2.58 PR, % 69.50

7/25/2018 28.40 41.80 4.73 3.00 63.50 4.74 2.82 59.60

90

Power improvement

Gt without tracker

Gt with tracker

1000

70

800

50

600

30

400

10

200

Gt (W/m2)

Power improvement (W)

The data in Table 2 indicate that GCPVS with a solar tracker allows to obtain higher PR. In addition, on July 25th, 2018 a higher reference yield was obtained, but PR is smaller than that on July 11th, 2018. It is proving that temperature affects PR of the system.

0

-10

Time Fig. 6. Power improvement and in-plane solar irradiance total on a scattered cloudy day (11/07/2018).

3.4

Performance Ratio of GCPVS on a Rainy Day

In the experimental results, in order to analyze and evaluate the PR of the two systems, we selected many scattered cloudy and rainy days. Figure 7 shows the total of in-plane solar irradiance and power improvement on a scattered cloudy day. Clearly, the total inplane solar irradiance was less than 0.4 kW/m2 and power improvement was not significantly different, average air temperature was 25 °C, cloud opacity was 71.6%, PR was 61.9 and 60.7 for GCPVS with and without a solar tracker, respectively.

50

Power improvement

Gt without tracker

Gt with tracker

673

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40

500

30

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20

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100

Gt (W/m2)

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PR Analysis Using Experimental Combining Historical Weather Data

0

-10

Time

9

90

8

80

7

70

6

60

5

50

4 3 2 1

Yr without tracker Yr with tracker PR without tracker PR with tracker

0

40

PR, %

Yr, kWh/kW

Fig. 7. Power improvement and in-plane solar irradiance total on a rainy day (7/28/2018).

30 20 10 0

Fig. 8. PR of GCPVS under different weather conditions

Figure 8 shows average PR and PR under different weather conditions of GCPVS with and without a solar tracker. Once again it indicates that the PR of GCPVS depends not only on the total in-plane solar irradiance but also on the air temperature.

4 Conclusion The present study shows PR calculation method of the GCPVS which is used experimentally data combination with weather data. The proposed method based on the calculations of the total in-plane solar irradiance on tilt surfaces suitable for GCPVS with and without a solar tracker and given reference yield during the study period. The results show that the PR of GCPVS with a solar tracker is larger than that of GCPVS without a solar tracker. On sunny days, GCPVS with a solar tracker gets more total inplane solar irradiance, and generates better energy output into the grid than GCPVS without a solar tracker in the morning and afternoon. The largest PR received 79.8%,

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an average of 66.0% and 69.2% for the GCPVS without and with a solar tracker, respectively. The analysis also shows that the PR of GCPVS depends not only on the total in-plane solar irradiance, but also on the environmental temperature, which will be the premise for further studies.

References 1. Jordan, D.C., Deline, C., Kurtz, S.R., Kimball, G.M., Anderson, M.: Robust PV degradation methodology and application. IEEE J. Photovolt. 8(2), 525–531 (2017) 2. Vasisht, M.S., Srinivasan, J., Ramasesha, S.K.: Performance of solar photovoltaic installations: effect of seasonal variations. Solar Energy 131, 39–46 (2016) 3. Dolara, A., Grimaccia, F., Leva, S., Mussetta, M., Faranda, R.: Gualdoni, M: Performance analysis of a single-axis tracking PV system. IEEE J. Photovolt. 2(4), 524–531 (2012) 4. Jamil, I., Zhao, J., Zhang, L., Jamil, R., Rafique, S.F.: Evaluation of energy production and energy yield assessment based on feasibility, design, and execution of 3  50 MW gridconnected solar PV pilot project in Nooriabad. Int. J. Photoenergy 2017 (2017) 5. Kymakis, E., Kalykakis, S., Papazoglou, T.M.: Performance analysis of a grid connected photovoltaic park on the island of Crete. Energy Convers. Manag. 50(3), 433–438 (2009) 6. Kazem, H.A., Khatib, T.: Techno-economical assessment of grid connected photovoltaic power systems productivity in Sohar, Oman. Sustain. Energy Technol. Assess. 3, 61–65 (2013) 7. Ngo, X.C., et al.: Grid-connected photovoltaic systems with single-axis sun tracker: case study for central Vietnam. Energies 13(6), 1457 (2020) 8. Solcast: Solar Irradiance Data. https://solcast.com.au. Accessed 30 July 2020 9. Maleki, M., Abbas, S., Hizam, H., Gomes, C.: Estimation of hourly, daily and monthly global solar radiation on inclined surfaces: models re-visited. Energies 10(1), 134 (2017) 10. Marion, W.F., Dobos, A.P.: Rotation angle for the optimum tracking of one-axis trackers. No. NREL/TP-6A20-58891. National Renewable Energy Lab. (NREL), Golden, CO (United States) (2013)

Power Control of Andronov-Hopf Oscillator Based Distributed Generation in GridConnected Microgrids Tobias Heins1, Trung Tran1,2(&), David Raisz1,3, and Antonello Monti1 1

3

Institute for Automation of Complex Power System, RWTH Aachen University, Aachen, Germany {tobias.heins,ttrung}@eonerc.rwth-aachen.de 2 Thai Nguyen University of Technology, Thai Nguyen University, Thai Nguyen, Vietnam Budapest University of Technology and Economics, Budapest, Hungary

Abstract. This paper proposes a new control method to enable simultaneous active and reactive power control of the Andronov-Hopf Oscillator Controlbased VSC in grid-connected mode. An analysis based on the well-known power flow equations is implemented to determine the power tracking problem of conventional AHO. The solution of the power flow equations for given power set-points are used to change the internal parameters of the AHO to regulate the VSC power outputs. A simple control law is also used to compensate for the remaining errors caused by system uncertainties. A single AHO-controlled VSC connected to a non-stiff grid is simulated in MATLAB/Simulink to demonstrate the effectiveness of the proposed method. The simulation results show that the proposed method exhibits a very good dynamic and steady-state performance, in comparison with conventional AHO. Keywords: Power converter


Virtual oscillator control
Microgrid

1 Introduction Microgrids (MGs) are a new concept in the power system domain that comprise power electronics-based Distributed Generation (DG), Control Systems, Local Loads, Energy Storage Systems, and Protection Devices. MGs improve the reliability, resiliency, stability and quality of power generation for local customers, as well as reduce the stress on transmission systems and provide auxiliary services for conventional distribution systems. MGs can be operated either autonomously in islanded mode or in gridconnected mode [1]. In islanded operation, the power converter interface of DGs (also known as Voltage source converter - VSC) employs a grid-forming control strategy that stabilizes the voltage and frequency at the DG bus and guarantees power-sharing among parallel-connected DGs in proportion to the VSC rated powers. In this paper, the grid-connected operation of an MG is considered, in which the MGs voltage and frequency are regulated by the main grid and the VSCs are used to control the power output of DGs according to the reference control signals from the grid operator in a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 675–687, 2021. https://doi.org/10.1007/978-3-030-64719-3_74

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grid-feeding control strategy. Specifically, grid-feeding strategies control the VSCs active and reactive power output simultaneously. Thereby, they can serve to achieve a power flow that is optimized for the MGs main objectives in grid-connected mode, like maximizing renewable in-feed, minimizing operating costs, or delivering power to the main grid as determined by the energy market. This paper seeks to design a controller structure that can accommodate both islanded and grid-connected operation by switching between supporting voltage and frequency regulation in a grid-forming strategy, and controlling power output with a grid-feeding strategy. Preferably, the control method is realized in a decentralized fashion, due to the advantages of reliability and modularity. A well-known decentralized method to control VSCs with a grid-forming strategy is Droop control [2–4]. This approach emulates the steady-state output characteristic of a synchronous generator to stabilize the system voltage and frequency, and enables plug-and-play capabilities that allow DGs to be simply added to or removed from an existing system. Droop controlled VSCs alter their power output based on set-points, but do not guarantee accurate tracking of these power set-points. Instead, the power output is traded off against deviations of bus voltage and frequency. Virtual Inertia Control (VIC) is an advanced control method for VSC control that provides virtual inertia to the system [5, 6]. In VIC, the swing equation of synchronous generators (SG) is emulated to mimic the transient characteristic of conventional synchronous machines. There are various models for VIC implementations in the literature, such as Synchronverter, the Ise Lab’s model, or Linear Swing Dynamics. The steady-state output regulation of VIC adopts the droop characteristics and supports voltage and frequency in a decentralized grid-forming strategy with power set-points. Therefore, it also suffers the problem of not being able to guarantee simultaneous tracking of the active and reactive power set-point. Droop control and VIC are based on the calculation of phasors of active and reactive power, which have the additional disadvantage of requiring lowpass filters and synchronous steady state to be well defined. In recent years, the concept of a new VSC control technology, namely Virtual Oscillator Control (VOC) has emerged and is expected to provide better overall performance than the conventional droop control method. VOCs provide a way to synchronize and control VSCs by emulating the dynamics of weakly non-linear oscillators, such as dead zone [7], Van der Pol [8], or Andronov-Hopf [9]. By implementing only a single control layer, VOC can: 1) guarantee synchronization of a parallel-connected electrical network from arbitrary initial conditions; 2) provide voltage and frequency control, and 3) guarantee power-sharing among parallel-connected DGs in proportion to their rated powers. Initially, VOC was designed to operate in a grid-forming method in islanded MGs and realize a more tightly regulated voltage and frequency range than conventional droop controllers achieved. There is little research on controlling the power output of VOC-based DGs in a grid-connected mode in the literature, since the dead-zone and Van der Pol implementations do not provide set-point functionality and the Andronov-Hopf oscillators set-points alter the output similar to power set-points in droop control. A complex gain K ¼ jK j\# is introduced into the control structure of VOCs [10] that modifies the magnitude and phase angle of the current input to control both VSC active and reactive power output. The active and reactive power output of a VOC-based VSC is controlled via the voltage gain Kv and current gain Ki respectively

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in [11, 12] by applying two PI-controllers. However, these implementations are based on the Van der Pol oscillator, which, as opposed to the Andronov-Hopf oscillator, has a limit cycle that is by design distorted from being perfectly circular and thereby introduces harmonic content in the output voltage. Additionally, the oscillator design for Van der Pol systems carries a design trade-off between a tight voltage and frequency regulation and harmonic content. The dispatchable virtual oscillator control (dVOC) presented in [9, 13–15] is based on the Andronov-Hopf Oscillator (AHO), which has a perfectly circular limit cycle and includes power set-points for active and reactive power dispatching. Accurate set-point tracking has been shown for active power in an inductive grid setting. However, VSC based generation is typically located at the medium or low voltage level of the grid, where distribution lines are more resistive. The assumption of an inductive grid where active and reactive power control is decoupled is thus not appropriate. Simultaneous tracking of active and reactive power and the application of AHO in a grid with a high R/X-ratio have not been addressed and need further evaluation. This work proposes a modification to the conventional AHO that enables simultaneous control of active and reactive power of an AHO based VSC in the gridconnected mode in a resistive grid setting. The AHO implementation of [9] is adopted and applied in a resistive low voltage (LV) network. The dispatchability of the VSC is analyzed and the voltage regulation is modified by the introduction of a new voltage set-point. Then, a decentralized power flow calculation that provides the initial voltage value and an additional integral controller is used to eliminate the steady-state errors. The rest of the paper is organized as follows. In Sect. 2 the AHO control structure and its dispatchability are presented. Section 2 also introduces the proposed modification of the AHO and Sect. 3 presents simulation results to compare the new controller with the unmodified version. The paper is concluded in Sect. 4.

2 Analysis of the Grid-Connected AHO-Based DG In this section, the fundamentals of the AHO are briefly presented. A possible problem related to power reference tracking control is investigated to prove that the conventional structure of AHO experiences a steady-state error if the power control is related to voltage regulation. In an inductive grid, this refers to reactive power control and with the resistive setting in this paper to active power control. It is also shown that power control related to frequency regulation (here: reactive power) does not have a steadystate error. Then, a necessary modification based on integral control is proposed so that an AHO-based DG can eliminate the active power error and track both active and reactive power references simultaneously. 2.1

AHO Control Model

Figure 1 shows the AHO controller and circuit model applied to a three-phase VSC. The AHO circuit model comprises a parallel LC-tank that sets the system frequency pffiffiffiffiffiffi xn ¼ 1= LC . The filter inductor current and capacitor voltage x ¼ ½vc ; eil T are used as

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pffiffiffiffiffiffiffiffiffi the control states of the model, where e ¼ L=C. The desired oscillator dynamics are achieved through state-dependent non-linear voltage and current sources defined as  n  2 2Xn  k xk2 eiL xn  n  2 im ¼ 2Xn  kxk2 vC exn vm ¼

ð1Þ ð2Þ

pffiffiffi where 2Xn determines the amplitude of the oscillator limit cycle. Also, Vn ¼ jv Xn and xn denote the nominal system voltage and frequency, n governs the speed of convergence to steady-state and k
k is the Euclidean norm. The filter inductor current ia;b is measured and compared to a reference current signal ia;b . The resulting current error, Di ¼ ia;b  ia;b , is used as an input of the oscillator circuit after scaling with the current gain ji and applying an axis rotation with the rotation matrix RðwÞ with the rotation angle w. The rotation matrix is given by 

cosðwÞ RðwÞ ¼ sinðwÞ

{

vc

im

 ð3Þ

L

iL vm

C

sinðwÞ cosðwÞ

+ +

Ki

iL Kv PWM

abc

Current calculation

αβ Z

Rf

Rg

Lf

Lg

Rl

Ll

Rn

Ln

VG

VDC Cf

Fig. 1. Diagram of three-phase AHO-based DG in grid-connected operation mode.

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Based on the fundamental definitions for voltage magnitude, phase angle, active and reactive power in ab-coordinates, the dynamic model of AHO in terms of voltage magnitude VC and phase angle h is derived as follows [9]:   jj      v i V_ C ¼ jn2 VC 2Vn2  2V2C  3CV sinðwÞ Q  Qref þ cosðwÞ P  Pref C v

h_ ¼ xn 

jv ji 2 3C VC



    sinðwÞ P  Pref  cosðwÞ Q  Qref

ð4Þ ð5Þ

The active and reactive power set-points, Pref and Qref , are determined from a higher control level. In steady-state, where V_ ¼ 0 and h_ ¼ x, the equilibria of the voltage and frequency of VSC are defined as follows: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi     12 2ji j3v  VC ¼ pVnffiffi2 1 þ 1  3CnV þ cosðwÞ P  Pref 4 sinðwÞ Q  Qref

ð6Þ

n

x ¼ xn 

jv ji 2 3C VC



    sinðwÞ P  Pref  cosðwÞ Q  Qref

ð7Þ

In (6) and (7) it can be seen that the relation of active and reactive power to voltage magnitude and frequency is determined by the angle w of the rotation matrix RðwÞ. Choosing w according to the grid impedance decouples voltage and frequency control by matching the regulating response of voltage and frequency with the grid characteristics of the power flow. For ease of analysis, in this paper, the AHO is connected to a resistive grid and the angle is chosen as w ¼ 0, which results in P  V and Q  x droop relationships. However, without the loss of generality, a similar conclusion as the results shown in the below section can be obtained for inductive networks. 2.2

Power Tracking Problem Determination

By choosing w ¼ 0, the output relationships P  V and Q  x are obtained, which allow to separately control P and Q by regulating voltage and frequency, respectively. These relations represent the control of power infeed in a resistive grid. The power flow between the VSC and the main grid is given by: 2

j jVAHO jjVG j P ¼ jVAHO cosðh þ dÞ jZ j jZ j cosðhÞ  2

j jVAHO jjVG j sinðh þ dÞ Q ¼ jVAHO jZ j jZ j sinðhÞ 

ð8Þ ð9Þ

where Z ¼ jZ j\h is the equivalent impedance between the VSC and the main grid, d represents the voltage angle difference, and VAHO and VG denote the VSC and grid voltages. For h ¼ 0, (8) and (9) are simplified to: j P ¼ jVAHO R ½jVAHO j  jVG j cosðdÞ

ð10Þ

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Q ¼  jVAHORjjVG j sinðdÞ

ð11Þ

Assuming a small voltage angle d, one can see that the active power flow is mostly affected by the difference in voltage amplitude between the grid and the VSC, while the reactive power flow is controlled by the voltage angle difference. Control of frequency and phase angle are connected through the following relationship: Z

Dd ¼ Dxdt

ð12Þ

In grid-connected mode, the VSC frequency is governed by the main grid and therefore regarded as a constant x ¼ xn . Equation (12) shows that a change of the VSC output frequency adjusts the voltage angle difference in an integral relation, which thereby changes the reactive power output as described in (11). Figure 2 illustrates the behavior of VSC reactive power output during a set-point update.

AHO regulaƟon Grid regulaƟon Set point A

B

Fig. 2. Q  x relation of AHO under a reactive power set-point change.

As seen in Fig. 2, when the reactive power set-point increases from Q1;ref to Q2;ref , the droop curve shifts horizontally to the right. The AHO forces the VSC to decrease its output frequency by an amount of Dx. According to (12), the voltage angle difference is changed by the amount of Dd. Consequently, the VSC reactive power output reaches an equilibrium point Q ¼ Q2;ref . The integral relation to the phase angle enables frequency control to accurately track the reactive power set-point without causing a steady-state error. On the other hand, Eq. (10) describes the VSC active power output as a function of its terminal voltage. Assuming that the main grid is stiff, i.e., VG ¼ Vn , the active power output increases when the VSC terminal voltage increases and vice versa. This relation is shown as the green curve in Fig. 3, whereas the blue curve represents the equivalent voltage droop curve of the AHO. When the active power set-point changes from P1;ref to P2;ref , since AHO tends to maintain the VSC terminal voltage at a nominal value Vn ,

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AHO regulaƟon AcƟve power flow Set point Equilibrium

A

B

Fig. 3. P  V relation of AHO under an active power set-point change

the VSC is not able to increase its voltage output to the value where P ¼ P2;ref . Instead, the system converges to an equilibrium that is defined as an intersection of the voltage droop and the active power flow curve, where Peq \P2;ref . This steady state error occurs because the power set-point is defined at nominal system voltage and does not consider the necessary increase in terminal voltage from (10). Thus, the active power set-point is not reachable and voltage regulation fails to track the related power setpoint. The above analysis shows that the conventional AHO cannot track both active and reactive power references simultaneously. Depending on the characteristic of the main grid, either resistive or inductive type, the steady-state error will happen on active or reactive power tracking, respectively. 2.3

Proposed Simultaneous Power Tracking Control

As mentioned in the previous section, the steady-state power tracking error occurs because the AHO is intendedly designed to keep VSC output voltage at a nominal value. In order to eliminate the tracking error, the structure of the AHO must be modified in such a way that it controls the VSC voltage output to follow the solution of the power flow equations. We introduce a voltage set-point Vref that defines the voltage amplitude the oscillator generates at the set-point Pref , which was previously characterized by the design parameter Vn . The AHO has a circular limit cycle with a radius of pffiffiffi 2Xn during an unforced oscillation, i.e., when P ¼ Pref and Q ¼ Qref . We use the equations given in [9] to parameterize the AHO and with this design, the oscillator states provide normalized RMS amplitudes with Xn ¼ 1 V. However, the oscillator output voltage is defined as Vn ¼ jv Xn . The oscillator is interfaced with the system via the voltage and current scaling factors, which are chosen as jv ¼ Vn and ji ¼ 3Vn =Sr respectively, where Sr is the VSC rated apparent power. Therefore, replacing jv and ji

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with their formula expression and replacing the static value of Vn with the dynamic reference signal Vref enables control over the oscillator amplitude and the associated voltage at the active power set-point. The aim is to control this amplitude so that the voltage reference matches the power flow solution of the physical system and the active power set-point is reachable, i.e., the voltage set-point Vref moves the voltage droop curve vertically to correctly align with the active power flow in Fig. 3. The steady state voltage regulation of (6), with w ¼ 0, is thus altered to:  qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  ffi12 V 2 VC ¼ prefffiffi2 1 þ 1  CnS P  P ref r

ð13Þ

One can see that if P ¼ Pref the terminal voltage is VC ¼ Vref . The value of Vref is determined as Vref ¼ jVAHO j þ DVref , where jVAHO j is the solution of the power flow Eqs. (8) and (9) with respect to the VSC output voltage jVAHO j, and DVref is the correction term used to eliminate the error caused by system uncertainties. The value of jVAHO j is defined as follows: jVAHO j ¼

jVG j2 2

þ

QX þ PR 3

þ

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi!12 r  jVG j2 þ 23ðQX þ PRÞ 2

2

 ðP

2

þ Q2 ÞðR2 þ X 2 Þ 32

ð14Þ

Here R and X are the real and imaginary parts of the equivalent impedance Z, which consists of the LCL filter, a distribution line, and the grid impedance. Generally, the exact value of Z is hard to obtain or even unknown, which reduces the accuracy of the solution of (14). To eliminate this steady-state error, the following control law is used: d dt DVref

  ¼ KI  P  Pref

ð15Þ

where KI is the controller gain and P is the measured active power output of the VSC. By using (14) and (15), the voltage reference Vref corresponding to Pref is obtained and used to regulate the VSC voltage output. Thus, the active power output of the VSC follows precisely the set-point Pref . Figures 4 and 5 illustrate the control strategy to determine Vref and the modified scaling factors of the AHO circuit respectively.

P calculation

Equation (14)

Fig. 4. Algorithm to determine the voltage set-point

Power Control of Andronov-Hopf Oscillator

{

vc

im

iL vm

C

683

L + +

iL

Fig. 5. Modified AHO structure

This proposed method is used for the resistive network. Without loss of generality, the application of the proposed method for the inductive network can be obtained.

3 Simulation Results and Discussion In this section, the AHO with the proposed control method is compared with the conventional AHO by using time-domain simulations in MATLAB/Simulink. The physical system and control parameters are given in Tables 1 and 2, respectively.

Table 1. Parameters of the physical system. Description DC voltage (VDC) Phase-to-phase rated voltage (Vn) VSC rated active power (Pr) VSC rated reactive power (Qr) Nominal frequency (fn) Grid capacity (SSC), X/R-ratio Line impedance Output filter

Parameters 800 V 400 V 15 kW 10 kVAr 50 Hz 1 MVA, 0.1 Rl = 0.5 X, Ll = 159.15 lH Rf = 0.1 X, Lf = 1200 lH, Cf = 15 lF, Rg = 0.1 X, Lg = 300 lH

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T. Heins et al. Table 2. Controller parameters. Description Nominal oscillation amplitude (Xn) Speed constant (n) AHO virtual capacitance (C) AHO virtual inductance (L) Integral gain (KI)

Parameters 1V 16.1107 1/sV2 0.2494 F 40.627 lH 30

(a)

(b) Fig. 6. Active and reactive power output of the VSC with conventional AHO.

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Figures 6 and 7 show the response of the active and reactive power output of the grid-connected VSC under the set-points changes, with conventional and proposed AHO, respectively. The power set-points are changed every 2 s.

(a)

(b)

Fig. 7. Active and reactive power output of the VSC with the proposed AHO.

As seen in Fig. 6, while the conventional AHO forces the VSC to track the reactive power set-point accurately, the active power cannot follow the set-points. Meanwhile, with the proposed AHO, both active and reactive power output of the VSC properly track the set-point without steady-state errors. The simulation results prove the effectiveness of the proposed method in the resistive network. As discussed in the previous section, similar results can be obtained in the case of an inductive network.

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4 Conclusion This paper introduced a modified AHO that simultaneously controls the active and reactive power output of the VSC in grid-connected mode. The proposed control method uses the solution of the power flow equations and an integral control law to determine the value of the added voltage set-point. Thereby, the VSC terminal voltage can be regulated according to the power flow and the steady-state power tracking errors are eliminated while keeping the simple structure of the AHO. Acknowledgement. The authors gratefully acknowledge funding by the German Federal Ministry of Education and Research (BMBF) within the Kopernikus Project ENSURE ‘New ENergy grid StructURes for the German Energiewende’.

References 1. Marnay, C., et al.: Microgrid evolution roadmap. In: 2015 International Symposium on Smart Electric Distribution Systems and Technologies (EDST), pp. 139–144 (2015) 2. Guerrero, J.M., Vasquez, J.C., Matas, J., Vicuna, L.G.d., Castilla, M.: Hierarchical control of droop-controlled AC and DC microgrids—a general approach toward standardization. IEEE Trans. Ind. Electron. 58(1), 158–172, (2011) 3. Zhong, Q.: Robust droop controller for accurate proportional load sharing among VSCs operated in parallel. IEEE Trans. Ind. Electron. 60(4), 1281–1290 (2013) 4. Brabandere, K.D., Bolsens, B., Keybus, J.V.d., Woyte, A., Driesen, J., Belmans, R.: A voltage and frequency droop control method for parallel VSCs. IEEE Trans. Power Electron. 22(4), 1107–1115 (2007) 5. Driesen, J., Visscher, K.: Virtual synchronous generators. In: 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, pp. 1–3 (2008) 6. Liu, J., Miura, Y., Ise, T.: Comparison of dynamic characteristics between virtual synchronous generator and droop control in VSC-based distributed generators. IEEE Trans. Power Electron. 31(5), 3600–3611 (2016) 7. Johnson, B., Rodriguez, M., Sinha, M., Dhople, S.: Comparison of virtual oscillator and droop control. In: 2017 IEEE 18th Workshop on Control and Modeling for Power Electronics (COMPEL), pp. 1–6 (2017) 8. Johnson, B.B., Sinha, M., Ainsworth, N.G., Dörfler, F., Dhople, S.V.: Synthesizing virtual oscillators to control islanded VSCs. IEEE Trans. Power Electron. 31(8), 6002–6015 (2016) 9. Lu, M., Dutta, S., Purba, V., Dhople, S., Johnson, B.: A grid-compatible virtual oscillator controller: analysis and design. In: 2019 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 2643–2649 (2019) 10. Raisz, D., Thai, T.T., Monti, A.: Power control of virtual oscillator controlled VSCs in gridconnected mode. IEEE Trans. Power Electron. 34(6), 5916–5926 (2019) 11. Ali, M., Li, J., Callegaro, L., Nurdin, H.I., Fletcher, J.E.: Regulation of active and reactive power of a virtual oscillator controlled VSC. IET Gener. Transm. Distrib. 14(1), 62–69 (2020) 12. Ali, M., Nurdin, H.I., Fletcher, J.: Dispatchable virtual oscillator control for single-phase islanded VSCs: analysis and experiments. IEEE Trans. Ind. Electron. 1 (2020)

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13. Colombino, M., Groß, D., Brouillon, J., Dörfler, F.: Global phase and magnitude synchronization of coupled oscillators with application to the control of grid-forming power VSCs. IEEE Trans. Autom. Control 64(11), 4496–4511 (2019) 14. Groß, D., Colombino, M., Brouillon, J., Dörfler, F.: The effect of transmission-line dynamics on grid-forming dispatchable virtual oscillator control. IEEE Trans. Control Netw. Syst. 6 (3), 1148–1160 (2019) 15. Seo, G., Colombino, M., Subotic, I., Johnson, B., Groß, D., Dörfler, F.: Dispatchable virtual oscillator control for decentralized VSC-dominated power systems: analysis and experiments. In: 2019 IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 561–566 (2019)

Prediction of Cutting Force When Surface Milling Using Face Milling Tool Nguyen Van Thien1, Do Duc Trung1, Vilaivanh Xaixavang1, Tran Thi Hong2, Nguyen Thanh Tu3, Tran Ngoc Giang3, and Le Xuan Hung3(&) 1

2

3

Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi, Vietnam Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam Faculty of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected]

Abstract. The aim of the study is to predict the cutting force when surface milling with a face milling tool. Based on theoretical published research on cutting force modeling, this study predicted the cutting force when milling 40Cr steel with a face milling tool. The results show that the deviation of predicted cutting force was approximately 15.7% in comparison with the test, and about 9.9% compared to the predicted value using regression model, while the cutting force predicted using regression model deviated 9.6% from the test results. Also, development directions for further studies are suggested. Keywords: Surface milling steel


Face milling tool
Cutting force model
40Cr

1 Introduction For mechanical processing methods in general and surface processing by face milling tools in particular, cutting force is one of the parameters that greatly affects the efficiency of the machining process. This parameter not only affects the vibration of the technological system, thereby greatly affecting the machining precision, but it also has an impact on the tool life as well as the microstructure of surface layers. In addition, cutting force has great influence on power consumption during machining. For that reason, different studies on cutting forces have been conducted by many scientists as the basis for controlling milling processes. One of the most commonly used methods to determine the effects of machining process parameters on cutting forces is experimental study [1–8]. However, these experimental studies are often costly, which causes a negative effect on the economic and technical efficiency of the milling process. Moreover, the results of experimental studies can only be applied to some specific cases. Another method considered by scientists when studying cutting forces is the prediction of cutting forces by the response surface method. The essence of this method is to build the relationship between cutting force and machining process parameters in © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 688–694, 2021. https://doi.org/10.1007/978-3-030-64719-3_75

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the form of regression equation which can be used to calculate the value of cutting force. So, this can be considered as a method of ‘backwards’ compared to the experimental method, however, the relationship is constructed based on the data used for experimental study [3, 9–12]. Thus, it can be seen that the response surface method reveals many limitations as analyzed above. In order to overcome the above-mentioned limitations of experimental study methods, some scientists studied the prediction of cutting forces when milling based on the theoretical analysis of the cutting process [13– 16]. In this paper, one of these studies is utilized to predict cutting force when milling and applied to the case of 40Cr steel plane milling with a face milling cutter.

2 Cutting Force Model Analysis model of the components of forces during milling is shown in Fig. 1.

Fig. 1. Analysis model of the components of forces

Assuming that during the milling process, at the instant when the back of the cutting tool has an amount of wear hr , there will be a friction force component between the workpiece surface and the wear part of the cutting tool, Ffed . Due to the elasticity of the material, this force will produce a normal force component Ned . In particular, the friction force Ffed is on the surface of the tool wear area, perpendicular to Ned .

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During machining, the front of the cutting tool also produces a friction force component Fffr , which is located on the front surface of the cutting tool. At that time, a normal force component Nfr which is perpendicular to Fffr also appears. The cutting force Pc is used for removing materials on the machining surface. This component is analyzed into two components Pz and Px in two directions of OZ and OX, respectively. In particular, the OZ axis is parallel to the direction of the cutting velocity vector V, while OX is perpendicular to OZ. At that time, cutting force Pc is described as follows: Pc ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P2z þ P2x

ð1Þ

The cutting force Pc will also produce bending and compressive forces acting on the cutting tool in two directions yk and xk , respectively. Herein yk is in the bisector plane of the nose angle, perpendicular to xk . Therefore, the component Pn which is coinciding with the direction of the yk axis is the component that causes compressive stress on the part surface during milling. The component Pr has the same direction as the xk axis, which causes residual bending stress on the part surface. This makes the cutting force Pc match the direction of cutting velocity V to create an angle u. Observing Fig. 2, the value of u is determined as follows:   Px u ¼ arctg Pz

ð2Þ

The components Pn and Pr create compressive stress rn and bending stress rr with the corresponding quantities as follows: Pn Pcut :sinðu þ c þ b=2Þ ¼ B:hr B:hr

ð3Þ

Pr Pcut : cosðu þ c þ b=2Þ ¼ B:hr : sinc B : hr : sinc

ð4Þ

rn ¼ rr ¼

In which: hr is the length of wear part on the back of the cutting tool, mm; B is the width of the cutting edge, mm; c is the rake angle of the cutting tool; b is the sharpening angle of the cutting edge. From Fig. 1, we have: Pz ¼ Nfr : cosc þ Fffr : sinc þ Ffed

ð5Þ

Px ¼ Ned  Ffed : cosc þ Nfr : sinc

ð6Þ

Ffed ¼ Ned : f

ð7Þ

Prediction of Cutting Force When Surface Milling

Fffr ¼ Nfr : f

691

ð8Þ

In which: f is the friction coefficient between the cutting tool and the surface of workpiece. Components Ned and Nfr are typical quantities for elasticity of a material, determined by the following formulas: Ned ¼ rel : Scut

ð9Þ

Nfr ¼ rcom : Spr

ð10Þ

In which: rel is the compressive strength of the processed material, MPa; rcom is the elastic stress arising from the cutting material, MPa; Scut is the sectional area of a cutting element in the direction, mm2 , perpendicular to Ned ; Spr is the sectional area of a cut-off stratum in the direction, mm2 , perpendicular to Nfr . Scut ¼ B : hr

ð11Þ

Spr ¼ B : t0

ð12Þ

where t0 is the maximum thickness of the slice, mm. The value of t0 is determined as follows: t0 ¼

S :t  z   R : sin arccos 1  Rt

ð13Þ

In which, Sz is the feed to the cutter tooth, mm/tooth; R is the cutter radius, mm; t is the depth of cut, mm. The cutting force Pc will be calculated using a combination of equations from (1) to (13).

3 Comparison of Cutting Force When Calculating, Testing and Predicting Using Regression Model The results of the experimental study on milling 40Cr steel with PVD-coated milling cutter by Nguyen Hong Son [16] will be used to compare the cutting force values between the calculation results and the test results. The parameters used during the test will also be employed during the calculation in this study as shown in Table 1. PcðregressionÞ ¼ 28:20612  0:09981  v þ 123:2  Sz þ 23:91597  t

ð14Þ

692

N. Van Thien et al. Table 1. Parameters to determine cutting force Parameter v t R Sz c B hr rpr el rpr com

Unit m/min Mm Mm mm/tooth Degree mm mm MPa MPa

Value 185; 223.65; 250 0.281; 0.4; 0.519 62.5 0.08; 0.1; 0.13; 0.16; 0.18 25 10 0 (By using the new cutter without flank tool wear) 200 400

Table 2. Cutting force when calculating, testing and predicting using regression model No. Cutting parameters v, Sz, t, m/min mm/tooth mm

1 223.65 2 223.65 3 250 4 185 5 185 6 185 7 185 8 185 Average

0.16 0.1 0.13 0.08 0.18 0.13 0.13 0.13

0.281 0.519 0.4 0.4 0.4 0.4 0.4 0.4

Cutting force, N Pc Pc

Pc

Deviation, % Between Pc Between Pc

(calculated)

(regression)

(calculated)

(regression)

(calculated)

and Pc

and Pc

and Pc

(measured)

(measured)

(regression)

12.5% 0.9% 21.9% 12.1% 31.8% 14.2% 9.8% 22.1% 15.7%

0.1% 0.1% 0.2% 18.3% 12.2% 14.7% 9.5% 21.8% 9.6%

12.3% 0.8% 22.1% 25.7% 17.5% 0.4% 0.4% 0.4% 9.9%

36.30 30.86 35.20 21.66 48.74 35.20 35.20 35.20

(measured)

32.28 30.58 28.88 24.65 36.97 30.81 39.03 45.19

32.32 30.62 28.84 29.16 41.48 35.32 35.32 35.32

Between Pc

Fig. 2. Chart of cutting force when calculating, testing and predicting using regression model

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Table 2 and Fig. 2 show that the cutting force when calculating is relatively accurate compared to the test and the prediction using regression model. The average deviation between the calculated values is about 15.7% compared to the test values, and about 9.9% compared to the values predicted using the regression model. If using the regression model to predict, the deviation compared to the test data is about 9.6%. Although the use of the equations from (1) to (13) to predict cutting forces is not as accurate as the regression model Eq. (14), the degree of difference between the calculated results, the results predicted using the regression model and the test results is slight. Hence, the calculation results according to the formulas from (1) to (13) which are performed based on the theoretical study method are much more widely applicable than when using the regression model (14).

4 Conclusion A model for determining the cutting force when surface milling with a face milling tool has been applied to predict the cutting force when milling 40Cr steel. The average deviation in the value of cutting force when milling of the calculated results is only about 15.7% compared to the test results. Although compared to the method using regression model, the method of calculating cutting force used in this study has lower accuracy, the difference between them is insignificant. In terms of economic indicators and applicability, it is clear that the method of calculating cutting force has more advantages compared to the method using regression model. The application of the model analyzed in this study allows the prediction of cutting force, which is the basis for controlling the milling process to reduce machine adjustment time - test machining time, contributing to improve the efficiency of machining process. In order that the cutting force values are more accurate compared to the test values, it is necessary to include in the cutting force model parameters that have a significant effect on the components of force such as parameters of cutting tool material, cooling and lubrication parameter, … These are also the orientations for further studies. Acknowledgements. This work was supported by Thai Nguyen University of Technology.

References 1. Tsai1, J.-C., Kuo1, C.-Y., Liu1, Z.-P., Hsiao, K.H.-H.: An investigation on the cutting force of milling Inconel 718. In: MATEC Web of Conferences 169 (2018). https://doi.org/10. 1051/matecconf/201816901039 2. Patwari, A.U., Nurul Amin, A.K.M., Faris, W.F.: Prediction of tangential cutting force in end milling of medium carbon steel by coupling design of experiment and respose surface methodology. J. Mech. Eng. 40(2), 95–103 (2009) 3. Kilickap, E., Yardimeden, A., Çelik, Y.H.: Mathematical modelling and optimization of cutting force, tool wear and surface roughness by using artificial neural network and response surface methodology in milling of Ti-6242S. Appl. Sci. 7(1064) (2017). https://doi. org/10.3390/app7101064

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4. Hoang, T.D., Nguyen, N.-T., Tran, D.Q., Nguyen, V.T.: Cutting forces and surface roughness in face-milling of SKD61 hard steel. Strojniški vestnik – J. Mech. Eng. 65(6), 375–385 (2019) 5. Pathak, B.N., Sahoo, K.L., Mishra, Madhawanand: Effect of machining parameters on cutting forces and surface roughness in Al-(1-2) Fe-1V-1Si alloys. Mater. Manuf. Process. 28, 463–469 (2013) 6. Zhao, Z., Xiao, Y., Zhu, Y., Liu, B.: Influence of cutting speed on cutting force in highspeed milling. Adv. Mater. Res. 139–141, 835–838 (2010) 7. Ali, M.H., Ansari, M.N.M.: The effect of nose radius on cutting force and temperature during machining titanium alloy (Ti-6Al-4V). Int. J. Mech. Aerosp. Ind. Mechatron. Eng. 8 (12), 2054–2057 (2014) 8. Khanna, N., Sangwan, K.S.: Interrupted machining analysis for Ti6Al4V and Ti5553 titanium alloys using physical vapor deposition (PVD)–coated carbide inserts. Proc. IMechE Part B: J. Eng. Manuf. 227(3), 465–470 (2013) 9. Halim, N.H.A., Haron, C.H.C., Ghani, J.A., Azhar, M.F.: Prediction of cutting force for milling of Inconel 718 under cryogenic condition by response surface methodology. J. Mech. Eng. 16(1), 1–16 (2019) 10. Subramanian, M., Sakthivel, M., Sooryaprakash, K., Sudhakaran, R.: Optimization of cutting parameters for cutting force in shoulder milling of Al7075-T6 using response surface methodology and genetic algorithm. Procedia Eng. 64, 690–700 (2013) 11. Dikshit, M.K., Puri, A.B., Maity, A., Banerjee, A.J.: Analysis of cutting forces and optimization of cutting parameters in high speed ball-end milling using response surface methodology and genetic algorithm. Procedia Mater. Sci. 5, 1623–1632 (2014) 12. Raman, N.A., Sharif, S., Sudin, I.: Mathematical modeling of cutting force in milling of medium density fibreboard using response surface method. Adv. Mater. Res. 445, 51–55 (2012) 13. Wu, B., Yan, X., Luo, M., Gao, G.: Cutting force prediction for circular end milling process. Chin. J. Aeronaut. 26, 1057–1063 (2013). https://doi.org/10.1016/j.cja.2013.04.003 14. Rychkov, D.A., Yanyushkin, A.S.: The methodology of calculation of cutting forces when machining composite materials. In: IOP Conference on Series: Materials Science and Engineering, no. 142, p. 012088 (2016). https://doi.org/10.1088/1757-899X/142/1/012088 15. Dung, D.C., Son, T.H.: Develop model of cutting force when milling 3D surfaces by ballend mill. Sci. Technol. J. HaNoi Univ. Ind. 51, 50–55 (2019). (Written in Vietnamese) 16. Son, N.H.: Effect of cutting parameters on cutting force and surface roughness of workpiece when milling 40Cr steel using PVD-coated cutter. Int. J. Sci. Eng. Invest. 9(96), 13–18 (2020)

Research Method for Calculating Additional Power Losses, Considering the Asymmetric Loads in the Low-Voltage Power Supply System Vietnam Pham Trung Son(&) Electrification Department, Faculty of Electromechanics, Hanoi University of Mining and Geology, No. 18 Vien Street - Duc Thang Ward - Bac Tu Liem District, Hanoi, Vietnam [email protected]

Abstract. The efficiency of the use of electric energy is determined by the creation of consumption conditions under which the required quality of electric energy and a minimum of losses are ensured. The urgency of improving the quality and reducing losses of electric energy is especially growing in the power supply system with voltage up to 1 kV, due to the fact that he power supply system in the Vietnam, such a mode of operation as asymmetrical is widespread. This is due to the distribution of electricity consumers in three-phase power supply systems, the symmetric multiphase design of which is either impossible or impractical for technical and economic reasons. Voltage asymmetry is characterized by the presence of reverse or zero sequence voltages in a threephase power supply system, significantly smaller in magnitude of the corresponding voltage components of the forward (main) sequence. In the presence of reverse and zero sequence currents, the total currents in individual phases of the elements in the low voltage power supply system increase, which leads to an increase in active power losses and may be unacceptable from the point of view of heating and operating costs. The paper is devoted to methods of calculating additional power losses in the low voltage power supply system in Vietnam. Through coefficient kadd , with any low voltage power supply system and with any operating mode can accurately calculate the amount of additional power loss caused by asymmetric loads. Keywords: Power supply system
Power loss Asymmetric load
Symmetric load


Additional power losses


Voltage asymmetry is characterized by the presence of reverse or zero sequence voltages in a three-phase power supply system, significantly smaller in magnitude of the corresponding voltage components of the forward (main) sequence. In the presence of reverse and zero sequence currents, the total currents in individual phases of the elements in the low voltage power supply system increase, which leads to an increase

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.-U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 695–707, 2021. https://doi.org/10.1007/978-3-030-64719-3_76

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in active power losses and may be unacceptable from the point of view of heating and operating costs. The paper is devoted to methods of calculating additional power losses in the low voltage power supply system in Vietnam. Through coefficient kadd , with any low voltage power supply system and with any operating mode can accurately calculate the amount of additional power loss caused by asymmetric loads.

1 Introduction The results of measurement and testing of electrical energy quality carried out by various measurement and testing centers, universities and research institutes, etc. show that in most cases, one of the factors that increase losses in the grids and power distribution devices is the significant asymmetry of the long-term nature of the current in the electrical grid, among which is the lower-voltage power supply system 0.38 kV. The long-term asymmetry of the low voltage grid is caused mainly by the following reasons: 1) distribution of load unevenly to the phases of the grid, common singlephase load in the lower-voltage power supply system, including: electric loads of industrial enterprises, loads at electromechanical workshops, electro-mechanical repair workshops, administrative office loads, civil electric load; 2) the complete asymmetrical of the three-phase loads themselves. The measurement results at Substation 1600 kVA–6/0.4 kV of typical industrial enterprises in Vietnam as shown in Fig. 1 and Tables 1, 2 below will indicate the current status of electricity quality as well as the asymmetry in three-phase electricity grid. Voltage asymmetry at the terminals of threephase loads, caused by the presence of asymmetric mode of current in the grid, in most cases exceeds the permissible values according to the current standards [1]. The asymmetry of the current is one of the factors that increases the electrical energy loss in the grids and power distribution elements of the low voltage power supply systems, causing huge economic losses. Economic losses caused by the effects of asymmetric currents and voltages due to a decrease in the efficiency of the work and the life of electrical equipment, a decrease in reliability in the operation of electrical grids, an increase in losses of active energy and increased active and reactive energy consumption on loads, can even threaten fire safety. Among the results expected in the documents of Vietnam’s long-term energy development programs strategic, the goal of reducing power losses during transmission to 2020 will be reduced to 6.5%, for industrial enterprises this is even more stringent. Because of the power industry’s strategies and the need to minimize production costs, industrial enterprises need to consider measures to reduce losses due to poor electricity quality. Over the years, quite several studies have been done by scientists [2–15], focusing on the asymmetric operation of electrical installations and power supply systems, in order to develop measures to enhance the quality of electrical energy effectively and reduce energy losses. However, studies for low-voltage power supply system in industrial enterprises

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are very limited. Moreover, the methods that the scientists provide mainly to calculate the characteristics of the electrical power system in general do not take into account the phase difference factor, phase random asymmetry in loads with a high rate of change over time. At the same time, the random measurement results at the industrial enterprises have indicated phase differences, phase random asymmetry in the loads as shown in Fig. 1 and Tables 1, 2. Therefore, an important research for the low-voltage power supply system is to develop methods, improve and develop mathematical models to calculate and analyze indicators of the quality of electrical energy. Among them, first of all is the study of asymmetric coefficients of current and voltage, caused by asymmetric loads on the busbars of substations, in nodes and on system elements of low-voltage power supply systems, aiming to: set the level of additional power and energy loss; improve accuracy in calculating power losses and energy losses. This paper is devoted to methods of calculating additional power losses through coefficient kadd in the low voltage power supply system.

Table 1. The random measurement results of current harmonic ratio Parameter Measurement results (%) A3 ATHD (%) A B C A B 24.3 24.3 24.7 1.2 1.3 A9 A11 A B C A B 0.9 0.9 1.0 5.9 5.9

A5 C A 1.3 19.1 A13 C A 6.1 4.4

A7 B C A 19.2 19.5 11.0 A15 B C A 4.2 4.2 0.5

B C 10.8 10.9 B 0.5

C 0.5

Table 2. The random measurement results of voltage harmonic ratio Parameter Measurement VTHD (%) A B C 9.5 9.9 9.9 V9 A B C 0.3 0.3 0.3

results (%) V3 A B C 0.2 0.2 0.2 V11 A B C 2.2 2.4 2.3

V5 A B 3.7 3.7 V13 A B 2.0 2.0

V7 C A B 3.6 2.5 2.6 V15 C A B 2.1 0.2 0.3

C 2.7 C 0.3

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Fig. 1. Measurement results at Substation 1600 kVA–6/0.4 kV of typical industrial enterprises in Vietnam

2 Research Methods The asymmetry of currents in existing low voltage power supply system 0.38 kV consists of two components - random and nonrandom asymmetry of currents. Overloading of one and underloading of other phases due to the uneven distribution of single-phase electrical loads in phases, as well as due to putting into use new electrical loads without considering their symmetric distribution in phases, determines a systematic (nonrandom) asymmetry of currents. In addition, there is a probabilistic (random) asymmetry of currents caused by random switching on and off of individual single-phase power loads. Thus, violations of the rules for symmetrical connection of power loads cause significant deviations in the quality of electricity due to the asymmetry of currents and voltages. However, even with a phase-to-uniform connection, an asymmetric mode of operation of the low voltage power supply system 0.38 kV occurs, due to random reasons.

Research Method for Calculating Additional Power Losses

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The asymmetry of the currents causes the appearance of current in the neutral wire, and at the same time, the bias voltage of the neutral point of the phase voltage system. As a result, the voltage phases at the terminals of the electrical loads become asymmetrical. Voltage asymmetry, as a result of current asymmetry, has a negative effect on electrical equipment. In particular, this worsens the operation of capacitor units, has a negative effect on the operation of the protection of the units, leads to significant errors in accounting for electricity, and worsens the operation of electric motors. The asymmetry of the currents is also the reason for the appearance of additional power losses, which is one of the causes of thermal damage to the insulation. In addition, the asymmetry of the currents determines the magnetic influence of the 0.38 kV lines on the ones passing near the communication line. Among the main consequences of the asymmetry of currents can also be called a decrease in the reliability of the power supply system. Existing methods for determining symmetrical components are based on the laws of the parameters of the changing load and the predetermined parameters of the balancing device. Research results and discussion are based on the method of symmetrical components, the system of equations for currents will look like this [16–18]: 8 < I_A ¼ I_1 þ I_2 þ I_0 _IB ¼ a2
I_1 þ a
I_2 þ I_0 ð1Þ :_ IC ¼ a
I_1 þ a2
I_2 þ I_0 Solving the system of equations for I1, I2, I0, received:   8 1 2 < I_1 ¼ 3 I_A þ a
I_B þ a
I_C  I_ ¼ 1 I_ þ a2
I_B þ a
I_C  : 2 _ 3 A1  _ I0 ¼ 3 IA þ I_B þ I_C

ð2Þ

Where I_A ; I_B ; I_C - the corresponding current of the phases A, B, C; I_1 ; I_2 ; I_0 – the corresponding components of the forward - reverse and zero current sequences; a phase rotation operators. Similar relations can be obtained for the symmetrical components of the forward, reverse, and zero voltage sequences [16–18]: 8 < U_ A ¼ U_ 1 þ U_ 2 þ U_ 0 _ ð3Þ U ¼ a2
U_ 1 þ a
U_ 2 þ U_ 0 : _B UC ¼ a
U_ 1 þ a2
U_ 2 þ U_ 0 It is determined that the problem of determining the asymmetry coefficients of currents and voltages in the reverse and zero sequence is solved by representing the asymmetric system of vectors in the form of three symmetric systems using the method of symmetric components (Fig. 2).

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2

a U2

I0

U0

aU1

a2I2

UC

aI1 UA

0 U1

U0 U2

I0 aI2

2

UB

IC

a U1

IB 0

a2I1

I1

IA I2 I0

U0 aU2 Fig. 2. An example of the application of the method of symmetrical components for voltages and currents of phases A, B, C [16–18].

Using the possibility of representing an asymmetric system of vectors in the form of three symmetric systems, write Eq. (3) for each of the indicators characterizing the asymmetry of stresses of the coefficients k2U and k0U: 8 jU_ j jU_ þ a2
U_ B þ a
U_ C j > < k2U ¼ _ 2
100% ¼ _ A
100% jU1 j jUA þ a
U_ B þ a2
U_ C j _ _ _ _ > : k0U ¼ jU_ 0 j
100% ¼ _ jUA þ U_ B þ U2 C_j
100% jU1 j jUA þ a
UB þ a
UC j

ð4Þ

The equation for the indicators determining the level of asymmetry of the currents k2I and k0I in the following form, considering: 8 jI_ j jI_ þ a2
I_ þ a
I_ j > < k2I ¼ _2
100% ¼ _A _ B 2 _C
100% jI1 j jIA þ a
IB þ a
IC j _ _ _ I_0 j j > : k0I ¼ _
100% ¼ _ jIA þ_IB þ I2C j_
100% jI1 j jIA þ a
IB þ a
IC j

ð5Þ

A generalization of the methods used to take into account the effect of asymmetry of load currents on power losses led to the conclusion that the currently used algorithms (methods) do not fully allow for the mode of present (actual) asymmetry in the elements of the low voltage power supply system, thereby not allowing adequately assess the effect of asymmetry of currents and voltages on the magnitude of losses. Based on the results of practical measurements, it can be argued that the phenomenon of prolonged phase asymmetry of the load is quite common in both previous and present electrical grids. Long asymmetrical modes occur primarily with a phase-wise difference in system parameters, or in the case of non-phase modes of electrical equipment or when connecting asymmetrical loads.

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With such modes of operation in the grid, there is both amplitude and angular asymmetry of currents and, accordingly, voltages, which in turn lead to the appearance of currents and voltages with a phase sequence different from the forward - reverse and zero sequence. Consider the most widely known and used methods of considering the influence of asymmetry of phase loads when calculating the power loss. In general, losses in the current-carrying parts (in this case, in a four-wire cable line) are the sum of the losses in each conductor: DPP ¼ DPA þ DPB þ DPC þ DPN ¼ IA2 RA þ IB2 RB þ IC2 RC þ IN2 RN

ð6Þ

where I_N ¼ I_A þ I_B þ I_C is the current of the neutral conductor; RA (B, C)- active resistances of cable cores; RN - resistance of the neutral wire. From the relations of the method of symmetrical components, the zero-sequence current is determined as:  I_N 1 I_0 ¼ I_A þ I_B þ I_C ¼ ) I_N ¼ 3I_0 3 3

ð7Þ

Considering expressions (4) and (5), write to determine the phase-by-phase excess of power losses in asymmetric mode over power losses in symmetric, the system of equations: 8 2 2 2 DPAasym >  ¼ I1RA þ I2 RA þ I0 RA > > > < DPBasym ¼ ða2 I1 Þ2 RB þ ðaI2 Þ2 RB þ I02 RB      2 > > DPCasym ¼ ðaI1 Þ2 RC þ ða2 I2 Þ RC þ I02 RC > > : DPNasym ¼ 3
I02 RN

ð8Þ

Express the power loss (8) in terms of the coefficients k2i and k0i, determined from relations (5), while considering the equalities of resistances RA = RB = RC = R (for the circuit under consideration):   8 2 2 2 DP ¼ I R 1 þ k þ k Aasym > 1 2I 0I >    > > < DPBasym ¼ I 2 R a2  2 þ ðjajk2I Þ2 þ k 2 1 0I    2  2 2 2 > a k2I þ k 2 > DP ¼ I R ð a Þ þ j j Casym > 1 0I > : 2 2 I1 R DPNasym ¼ 3
k0I

ð9Þ

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In the case of a symmetrical load, there are no zero sequence currents (Fig. 2), the current does not flow through the neutral wire, therefore the system of Eq. (9) will take the form: 8 >
 2 : DPCsym ¼ jaj
I1sym
R

ð10Þ

where I1sym is the forward sequence current in the symmetric mode. To determine the excess power losses in the asymmetric mode, compared to the symmetric mode, sums the values of each phase of (9) and (10): DPP asym ¼ I12 R



    2   2  2 2 1 þ a2  þ ðjajÞ2 þ k2I þ ðjajk2I Þ2 þ a2 k2I þ 6k0I    2 2 R 1 þ a2  þ ðjajÞ2 DPP sym ¼ I1sym

DPP asym I12 R
¼ DPP



sym

    2   2  2 2 1 þ a2  þ ðjajÞ2 þ k2I þ ðjajk2I Þ2 þ a2 k2I þ 6k0I   2 I1sym R
1 þ ðja2 jÞ2 þ ðjajÞ2 ð11Þ

After transformations (11), write the system of equations in the final form: DPP asym ¼

I12 R




    2   2  2 2 1 þ a2  þ ðjajÞ2 þ k2I þ ðjajk2I Þ2 þ a2 k2I þ 6k0I   DPP sym 2 2 2 2 I1sym R
1 þ ðja jÞ þ ðjajÞ

ð12Þ . 2 ¼ i21 i21sym is the ratio of the currents of the forward sequence in the where k1sym . asymmetric and symmetric mode of operation; kr powerloss ¼ DPP asym DPP sym - the ratio of power losses in the asymmetric mode compared to the symmetric mode of operation. Thus, taking (10) as the basis, obtain the expression for determining the magnitude of power losses in the asymmetric mode, using the relationships between the currents of different sequences, that is, taking into account the presence of not only amplitude, but also phase (angular) asymmetry of currents: DPP asym ¼ DPP sym
kadd

ð13Þ

where DPsym - power losses in symmetric mode, i.e. only in the presence of currents of forward sequence;

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kadd - coefficient of additional power losses, calculated depending on the selected calculation method. The kadd coefficient is determined according to (12):

kadd

     2   2  2 2 1 þ a2  þ ðjajÞ2 þ k2I þ ðjajk2I Þ2 þ a2 k2I þ 6k0I 2   ¼ k1sym 1 þ ð j a2 j Þ 2 þ ð j aj Þ 2

ð14Þ

In the case of a distribution grid circuit without a neutral wire, the additional excess of power losses in asymmetric mode is calculated using the coefficient of additional power losses determined from the expression:  2 kadd ¼ k1sym

   2   2   2 1 þ a2  þ ðjajÞ2 þ k2I þ ðjajk2I Þ2 þ a2 k2I   1 þ ð j a2 j Þ 2 þ ð j aj Þ 2

ð15Þ

Thus, it follows from expressions (14) and (15) that, in addition to the ratio of direct sequence currents in various operating modes (symmetric and asymmetric), the value of power losses during a long asymmetric mode is affected by the ratio of the reverse and zero sequence currents to the forward sequence current, as well as the magnitude of the active resistances of the forward and zero sequence of the investigated section of the power supply system. Coefficient kadd can use to accurately calculate the amount of additional power loss, makes the calculation of technical and economic conditions more accurate.

3 Research Results and Discussion Based on the method of symmetrical components, the system of equations for currents will look like (1), (2): Consider the case where the load node consists of three-phase symmetrical power loads connected to the interphase voltage, forming a three-phase symmetric load, and single-phase power load connected to the phase voltage and forming a three-phase unbalanced load. Assume that the symmetrical device is included directly in the load node. The power supply circuit of such a node is shown in Fig. 3. T1

10kV

T2

0,4kV 1

3

2

Fig. 3. Scheme of a section of the electric grid with loads and a balancing device: 1 - Ssymload the full power of a three-phase symmetrical load; 2 - Ssymdevice - the full power of the symmetrical device; 3 - Sasymload - full power of a three-phase asymmetric load

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An example of calculating the parameters of the symmetrical device in a distribution grid of 0.38 kV. For the above method of calculating the parameters of the symmetrical device, perform the calculation using the example of the circuit in Fig. 3. The calculation was made with the following initial data. The calculations of power losses and asymmetry index of currents and voltages in the grid of 0.38 kV without the symmetrical device showed that with an average value of the statistical voltage unbalance at the terminals of power loads of 7.3%, the asymmetry of the secondary voltage of the transformer of the 35/10 kV distribution substation does not exceed 2%. Therefore, in calculating the asymmetry of currents and voltages and power losses, a 35/10 kV distribution substation can be taken as a conditionally symmetric power source. The length of the overhead line 10 kV is taken according to the standards of reliability of power supply, equal to 16.7 km. The complex resistance of the forward (reverse) sequence of this line, made by wire brand AC-35, is reduced to a voltage of 0.4 kV: 0

Zolhv1 ¼ Zolhv2 ¼ 0:0243 þ j0:01 ¼ 0:0263
ei22:45 ; Ohm The load is supplied from a substation 10/0.4 kV with a transformer with a starstar-to-zero winding connection scheme with a power of Snom = 40 kVA. Resistance of the forward (reverse) transformer sequence: 0

ZT1 ¼ ZT2 ¼ 0:09 þ j0:156 ¼ 0:18
ei60:02 ; Ohm The complex resistance of the zero sequence of the transformer: 0

ZT0 ¼ 1:133 þ j1:73 ¼ 2:068
ei56:78 ; Ohm The length of the overhead line 0.38 kV is 0.5 km; the line is made by wire brand 4A-50. The cross section of the phase and neutral wires are assumed to be the same. The complex resistances of the forward (reverse) sequences of the 0.38 kV line are: 0

Zollv1 ¼ Zollv2 ¼ 0:315 þ j0:148 ¼ 0:348
ei25:24 ; Ohm 0

Zollv0 ¼ 1:26 þ j0:47 ¼ 1:345
ei20:46 ; Ohm The load node at the end of the overhead line 0.38 kV contains three-phase symmetrical power loads - asynchronous electric motors with a total power Ssymload with cosu = 0.8 and single-phase power loads with a power Sasymload with cosu = 0.9, unevenly distributed over three phases.

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The technique is applicable for various power ratios Ssymload and Sasymload at the rated power of the transformer, i.e. The following condition is met: psymload þ pasymload ¼ const C A B Where pasymload ¼ pA þ pB þ pC ¼ SSnom þ SSnom þ SSnom . The total capacities and conductivity complexes of the individual phases of threephase symmetric and asymmetric loads are determined based on the statistical characteristics of the asymmetry of currents in a rural grid using analytical expressions in accordance with [16–18]. The relative power values of symmetric and asymmetric loads are changed in accordance with the Table 3.

Table 3. Relative values of the power of three-phase symmetric and asymmetric loads pB pC Psymload pA 0,0177 0,00425 0,003 0,225

The phase angles of three-phase symmetric and asymmetric loads are taken in accordance with the average statistics of the asymmetry of currents in grid and are accordingly equal: usymload = 36.870 ; uA ¼ uB ¼ uC = 25.840 Based on the calculations of [16–18], the values of the asymmetry coefficients are as follows: 0

k2U ¼ 0:00237
ej217:9745 ¼ 0:0019  j0:0015 0

k0U ¼ 0:03329
ej254:5942 ¼ 0:0088  j0:0321 0

k2I ¼ 0:03065
ej27:3581 ¼ 0:0272 þ j0:0141 0

k0I ¼ 0:05192
ej20:8342 ¼ 0:0485 þ j0:0185 Thus, the phase voltages will be equal: 8