Patel G. C. / Chate / Parappagoudar Machining of Hard Materials

A Comprehensive Approach to Experimentation, Modeling and Optimization
1. Auflage 2020
ISBN: 978-3-030-40102-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 137 Seiten, eBook

Reihe: SpringerBriefs in Applied Sciences and Technology

This book presents the potential applications of hard materials as well as the latest trends and challenges in machining hard materials. Models for online monitoring to adjust parameters to obtain desired machining characteristics (i.e. reverse modelling) are discussed in this book. The conflicting requirements (i.e. maximize: material removal rate, roundness and minimize: surface roughness, dimensional ovality, co axiality, tool wear) in machining for industry personal is solved using advanced optimization tools. In addition, the framework for experimental modelling, predictive physic-based forward and reverse process models and optimization for better machining characteristics applicable to industry are proposed.

Zielgruppe

Research

Autoren/Hrsg.

Patel G. C., Manjunath

Chate, Ganesh R.

Parappagoudar, Mahesh B.

Gupta, Kapil

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;1 Introduction to Hard Materials and Machining Methods;11
3.1;1.1 Introduction;11
3.2;1.2 Hard Materials;14
3.3;1.3 Machining Methods of Hard Materials;14
3.3.1;1.3.1 Hard Turning;15
3.3.2;1.3.2 Hard Broaching;16
3.3.3;1.3.3 Hard Boring;17
3.3.4;1.3.4 Hard Milling;18
3.4;1.4 Challenges in Machining of Hard Materials;19
3.4.1;1.4.1 Steels;19
3.4.2;1.4.2 Titanium and Its Alloys;19
3.4.3;1.4.3 Super-Alloys;20
3.4.4;1.4.4 Composite Materials and Metal Matrix Composites;20
3.4.5;1.4.5 Ceramics;22
3.5;1.5 Industrial Applications of Machined Hard Materials;22
3.6;1.6 Cutting Tool Materials;23
3.6.1;1.6.1 High-Speed Steel (HSS);24
3.6.2;1.6.2 Cemented Carbides;24
3.6.3;1.6.3 Ceramics;25
3.6.4;1.6.4 Carbon Boron Nitride (CBN) Tools;26
3.6.5;1.6.5 Polycrystalline Diamond (PCD);27
3.7;1.7 Selection of Cutting Tool Materials and Geometry;27
3.8;1.8 Advantages in Machining Hard Materials with Conventional Machining;29
3.9;References;30
4;2 Studies on Machining of Hard Materials;35
4.1;2.1 Hard Turning Process;35
4.2;2.2 Classical Engineering Experimental Approach or One Factor at a Time (OFAT);37
4.3;2.3 Numerical Modelling Approach;37
4.4;2.4 Input–Output and In-Process Parameter Relationship Modelling;40
4.4.1;2.4.1 Taguchi Method;41
4.4.2;2.4.2 Response Surface Methodology (RSM);46
4.4.3;2.4.3 Desirability Function Approach (DFA);48
4.4.4;2.4.4 Soft Computing Optimization Tools;49
4.5;2.5 Capabilities of Hard Turning Process;50
4.5.1;2.5.1 Variables of Hard Turning Process;50
4.6;2.6 Conclusion;55
4.7;References;55
5;3 Experimentation, Modelling, and Analysis of Machining of Hard Material;62
5.1;3.1 Selection of Experimental Design;64
5.2;3.2 Workpiece and Tool Material;66
5.3;3.3 Experiment Details;66
5.3.1;3.3.1 Material Removal Rate;67
5.3.2;3.3.2 Surface Roughness;68
5.3.3;3.3.3 Cylindricity and Circularity Error;68
5.4;3.4 Results and Discussion;69
5.4.1;3.4.1 Response: MRR;69
5.4.2;3.4.2 Response: SR;70
5.4.3;3.4.3 Response: CE;74
5.4.4;3.4.4 Response: Ce;76
5.5;3.5 Regression Model Validation;76
5.6;3.6 Concluding Remarks;79
5.7;References;80
6;4 Intelligent Modelling of Hard Materials Machining;81
6.1;4.1 Advantages of Artificial Intelligence Over Statistical Methods;81
6.2;4.2 Neural Networks;82
6.3;4.3 Modelling of Hard Turning Process;84
6.4;4.4 Data Collection for NN Modelling;85
6.4.1;4.4.1 Training Data;85
6.4.2;4.4.2 Testing Data;86
6.5;4.5 NN Modelling of Hard Turning Process;86
6.5.1;4.5.1 Forward Modelling;87
6.5.2;4.5.2 Reverse Modelling;87
6.6;4.6 Back-Propagation Neural Network (BPNN);89
6.6.1;4.6.1 Weights;90
6.6.2;4.6.2 Hidden Layers and Neurons;90
6.6.3;4.6.3 Learning Rate and Momentum Constant;90
6.6.4;4.6.4 Constants of Activation Function;91
6.6.5;4.6.5 Bias;91
6.7;4.7 Genetic Algorithm Neural Network (GA-NN);91
6.7.1;4.7.1 Selection;92
6.7.2;4.7.2 Crossover;92
6.7.3;4.7.3 Mutation;92
6.8;4.8 Results of Forward Mapping;93
6.8.1;4.8.1 BPNN;93
6.8.2;4.8.2 GA-NN;93
6.8.3;4.8.3 Summary Results of Forward Mapping;95
6.9;4.9 Reverse Mapping;100
6.9.1;4.9.1 Back-Propagation NN;101
6.9.2;4.9.2 Genetic Algorithm NN;101
6.9.3;4.9.3 Summary Results of Reverse Mapping;102
6.10;4.10 Conclusions;106
6.11;References;107
7;5 Optimization of Machining of Hard Material;111
7.1;5.1 Genetic Algorithm;112
7.2;5.2 Particle Swarm Optimization (PSO);114
7.3;5.3 Teaching–Learning-Based Algorithm (TLBO);115
7.3.1;5.3.1 Teacher Phase;116
7.3.2;5.3.2 Learner Phase;118
7.4;5.4 JAYA Algorithm;118
7.5;5.5 Modelling and Optimization for Machining Process;119
7.6;5.6 Mathematical Formulation for Multi-objective Optimization;124
7.7;5.7 Results of Parameter Study of Algorithms (GA, PSO, TLBO, and JAYA);126
7.7.1;5.7.1 Genetic Algorithm;126
7.7.2;5.7.2 Particle Swarm Optimization;127
7.7.3;5.7.3 Teaching–Learning-Based Optimization and JAYA Algorithm;127
7.8;5.8 Summary of Optimization Results;130
7.9;5.9 Validation Experiments;130
7.10;5.10 Tool Wear Studies;132
7.11;5.11 Conclusions;133
7.12;References;134
8;Index;137

Über Autor(innen)

Dr. Manjunath Patel GC is an Assistant Professor in the Mechanical Engineering Department at PES Institute of Technology and Management, Shivamogga, India. He obtained Ph.D. in Mechanical Engineering with specialization in Manufacturing from National Institute of Technology Karnataka, Surathkal, India in 2015. Modelling and Optimization of Advanced Metal Casting, Welding and Machining Processes are the areas of Interest and specialization. Currently, he is doing research in advanced machining,  and Hydrid Casting Processes. Mr. Ganesh R Chate is an Assistant Professor in Mechanical Engineering Department at KLS Gogte Institute of Technology Belagavi, Karnataka State, India. He holds a Master’s degree in Production Management from KLS Gogte Institute of Technology, Belagavi.His research areas include manufacturing process, 3D printing, CAD and automation. Prof. Mahesh B Parappagoudar joined Indian Institute of Technology, Kharagpur in 2004 asa research scholar, in the mechanical engineering department under the quality improvement program funded by MHRD, Govt. of India. Further, he obtained his PhD degree in Mechanical Engineering from Indian Institute of Technology, Kharagpur - 721302, India in 2008. Presently he is working as the principal and professor in Padre Conceicao College of Engineering, GOA, INDIA. His total experience (Industry, Teaching,Research, and Administration) extends over a period of 28 years. His research interests include applicationof statistical and soft computing tools in manufacturing and industrial engineering. Prof. Kapil Gupta is an Associate Professor in the Dept. of Mechanical and Industrial Engineering Technology at the University of Johannesburg. He obtained Ph.D. in mechanical engineering with specialization in Avanced Manufacturing from Indian Institute of Technology Indore, India in 2014. Advanced machining processes, sustainable manufacturing, precision engineering and gear technology are the areas of his interest and specialization. Currently, he is doing research in advanced/modern machining,  sustainable manufacturing and gear engineering.