Kumar / Hussein AI Applications in Sheet Metal Forming

1. Auflage 2017
ISBN: 978-981-10-2251-7
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 298 Seiten

Reihe: Topics in Mining, Metallurgy and Materials Engineering

This book comprises chapters on research work done around the globe in the area of artificial intelligence (AI) applications in sheet metal forming. The first chapter offers an introduction to various AI techniques and sheet metal forming, while subsequent chapters describe traditional procedures/methods used in various sheet metal forming processes, and focus on the automation of those processes by means of AI techniques, such as KBS, ANN, GA, CBR, etc. Feature recognition and the manufacturability assessment of sheet metal parts, process planning, strip-layout design, selecting the type and size of die components, die modeling, and predicting die life are some of the most important aspects of sheet metal work. Traditionally, these activities are highly experience-based, tedious and time consuming. In response, researchers in several countries have applied various AI techniques to automate these activities, which are covered in this book. This book will be useful for engineers working in sheet metal industries, and will serve to provide future direction to young researchers and students working in the area.


Dr. Shailendra Kumar is an Associate Professor in the Mechanical Engineering Department at S.V. National Institute of Technology, Surat, India. He received his Bachelor's degree in Production Engineering from the Regional Institute of Technology (presently National Institute of Technology), Jamshedpur, India in 1999 and his PhD from the Faculty of Engineering & Technology, Maharshi Dayanand University, Rohtak, India in 2007. His main research interests are in the area of press tool design, AI applications in manufacturing, KBS/expert systems for die design, sheet metal forming, CAPP, CAD/CAM and non-traditional manufacturing. He has successfully completed one research project for the Department of Science and Technology, Government of India and engaged in three more research projects sanctioned by national and international agencies. Three PhD scholars and 22 M. Tech. students have completed their research work under his supervision. Dr. Kumar has more than 120 research papers in reputed journals and conferences to his credit. He serves as a reviewer for many reputed journals and is a life member of the Indian Society of Mechanical Engineers (ISME), International Association of Engineers (IAENG), and World Academy of Science, Engineering & Technology (WASET), and Senior member of Universal Association of Mechanical and Aeronautical Engineers (UAMAE), The IRED, New York, USA  Dr. H.M.A. Hussein is currently serving at the Faculty of Engineering, Mechanical Engineering Department, Helwan University, Cairo, Egypt. He received his PhD in Mechanical (Production) Engineering from Helwan University, Egypt in 2008. He has also served in the tool design department of many companies. His research interests include computer aided sheet metal die design, AI applications to sheet metal forming, CAD/CAM, AutoCAD application and customization, and CAPP. He has completed many research projects in the area of design and manufacturing sanctioned by various funding agencies. He is a member of the Egyptian Syndicate of Professional Engineers, and of the Egyptian Mechanical Engineers Associations.

Autoren/Hrsg.

Kumar, Shailendra

Hussein, Hussein M. A.

Inhaltsverzeichnis

1;Foreword;6
2;Preface;8
3;Contents;10
4;Editors and Contributors;12
5;1 An Overview of Applications of Artificial Intelligence (AI) in Sheet Metal Work;14
5.1;1 Introduction;14
5.2;2 Feature Modeling: Concepts and Techniques;15
5.3;3 Modeling and Planning for Progressive Cutting Operations;16
5.3.1;3.1 Bending and Forming Operations in Progressive Die Design;16
5.3.2;3.2 Process Planning of Progressive Dies;20
5.3.3;3.3 Other Works Using AI Tools for Progressive Die Design and Planning;21
5.3.4;3.4 Summary;24
5.4;References;25
6;2 Generic Classification and Representation of Shape Features in Sheet-Metal Parts;27
6.1;1 Introduction;27
6.2;2 Sheet-Metal Parts;33
6.3;3 Sheet-Metal Features;33
6.4;4 Volumetric Sheet-Metal Features;35
6.4.1;4.1 Classification Based on Placement of 2D Profile;36
6.4.2;4.2 Classification Based on Shape of the 2D Profile;36
6.5;5 Deformation Sheet-Metal Features;39
6.5.1;5.1 Classification of Feature Faces for Deformation Sheet-Metal Features;43
6.5.2;5.2 Classification of Deformation Sheet-Metal Features;45
6.5.2.1;5.2.1 Number and Arrangement of Boundary Shell Faces;45
6.5.2.2;5.2.2 Number of Interior Shell Faces in a Deformation Feature;46
6.5.2.3;5.2.3 Type of Bends in a Deformation Feature;47
6.6;6 Conclusion;49
6.7;References;49
7;3 Feature Extraction and Manufacturability Assessment of Sheet Metal Parts;52
7.1;1 Introduction;52
7.2;2 Literature Review;55
7.2.1;2.1 Feature Extraction/Recognition of Sheet Metal Parts;55
7.2.2;2.2 Manufacturability Assessment of Sheet Metal Parts;56
7.3;3 Computer-Aided System for Automatic Feature Extraction;58
7.4;4 Knowledge-Based System for Manufacturability Assessment of Sheet Metal Parts;58
7.4.1;4.1 Procedure for Development of the Proposed System;63
7.5;5 Validation of the Proposed Systems FE and MCKBS;67
7.6;6 Conclusion;75
7.7;References;76
8;4 Knowledge-Based System for Design of Blanking Dies;78
8.1;1 Introduction;78
8.2;2 Knowledge-Based Design Rules for Blanking Dies;80
8.2.1;2.1 Strip Thickness;80
8.2.2;2.2 Contour Length;80
8.2.3;2.3 Main Part Dimension (Length/Width/Diameter);81
8.3;3 Parametric Design in 2D;84
8.3.1;3.1 Blank Layout;84
8.3.2;3.2 Die Block Boundary;85
8.3.3;3.3 Die Block Parametres;88
8.3.4;3.4 Fasteners and Dowel Pin Position;88
8.3.5;3.5 Strip Boundary;90
8.3.6;3.6 Parametric Relation of Die Holder Plate;91
8.3.7;3.7 Parametric Relation Between Die Holder Dimension and Die-Set Selection;92
8.4;4 Parametric Design in 3D;96
8.5;5 Conclusion;101
8.6;References;102
9;5 Knowledge-Based System for Design of Deep Drawing Die for Axisymmetric Parts;104
9.1;1 Introduction;104
9.2;2 Literature Review;106
9.2.1;2.1 Computer-Aided Process Planning;106
9.2.2;2.2 Computer-Aided Die Design;107
9.2.3;2.3 Knowledge-Based Deep Drawing Die Design;108
9.3;3 Considerations for Design of Deep Drawing Die;109
9.3.1;3.1 Process Planning;109
9.3.2;3.2 Strip-Layout Design;110
9.3.3;3.3 Selection of Die Components;110
9.3.4;3.4 Modeling of Die Components and Die Assembly;111
9.4;4 Intelligent Design System: INTDDD;111
9.4.1;4.1 Methodology for Development of Proposed System;111
9.4.2;4.2 Organization of the Proposed System;114
9.4.2.1;4.2.1 Subsystem PPDDP;114
9.4.2.2;4.2.2 Subsystem ISDSL;118
9.4.2.3;4.2.3 Subsystem DDCOMP;119
9.4.2.4;4.2.4 Subsystem AUTODDMOD;119
9.5;5 Validation of the System INTDDD;122
9.6;6 Conclusion;127
9.7;References;127
10;6 An Integrated Approach for Optimized Process Planning of Multistage Deep Drawing;131
10.1;1 Introduction;131
10.2;2 Literature Review;132
10.3;3 Integrated AI Approach;135
10.4;4 Shape Recognition;136
10.5;5 Process Design: The Governing Rules;140
10.5.1;5.1 Part Geometry in Drawing Stages;141
10.5.1.1;5.1.1 Corner Radius;141
10.5.1.2;5.1.2 Cross Section;142
10.5.1.3;5.1.3 Part Height;142
10.5.2;5.2 Tool Design;143
10.5.2.1;5.2.1 Punch Cross Section;143
10.5.2.2;5.2.2 Die Cross Section;143
10.5.2.3;5.2.3 Die and Punch Nose Radii;144
10.5.2.4;5.2.4 Blank Holder Dimensions;144
10.5.3;5.3 Operating Parameters;144
10.5.3.1;5.3.1 Punch Force;144
10.5.3.2;5.3.2 Blank Holder Pressure;145
10.6;6 Optimization and Validation for Process Planning;145
10.6.1;6.1 Dynamic Programming Approach;146
10.6.2;6.2 Finite Element Modeling and Analysis;150
10.7;7 Case Studies;157
10.7.1;7.1 Square Box;158
10.7.2;7.2 Rectangular Box with Extreme Aspect Ratio;162
10.8;8 Concluding Remarks;167
10.9;References;168
11;7 Knowledge-Based System for Design of Deep Drawing Die for Elliptical Shape Parts;171
11.1;1 Introduction;171
11.2;2 Constitutions of the Knowledge-Based System;173
11.2.1;2.1 Recognition of Shape Module;173
11.2.2;2.2 Three-Dimensional Modeling Module;175
11.2.3;2.3 Blank Design Module;177
11.2.4;2.4 Process Planning Module;178
11.3;3 Production Rules of the Knowledge-Based System;180
11.4;4 Results and Discussion;183
11.4.1;4.1 The Surface Area Calculation;183
11.4.2;4.2 Drawing Coefficient;184
11.4.3;4.3 Punch and Die Radii;185
11.5;5 Conclusion;188
11.6;References;189
12;8 An Expert System for Automatic Design of Compound Dies;192
12.1;1 Introduction;192
12.2;2 Literature Review;194
12.3;3 Proposed Expert System: ESIDCD;197
12.3.1;3.1 Subsystem PPCD;199
12.3.2;3.2 Subsystem CDCOMP;203
12.3.3;3.3 Subsystem AUTOMODCD;206
12.4;4 Validation of the Proposed System;206
12.5;5 Conclusion;222
12.6;Acknowledgments;222
12.7;References;222
13;9 Prediction of Life of Compound Die Using Artificial Neural Network;226
13.1;1 Introduction;226
13.2;2 Proposed ANN Model for Prediction of Life of Compound Die;231
13.3;3 Validation of the Proposed ANN Model;234
13.4;4 Conclusion;249
13.5;References;250
14;10 Knowledge-Based System for Automatic Design of Bending Dies;253
14.1;1 Introduction;253
14.1.1;1.1 Design of Bending Dies;254
14.1.2;1.2 Knowledge-Based System;255
14.2;2 Literature Review;256
14.2.1;2.1 Process Planning of Bending Parts;256
14.2.2;2.2 Bending Die Design;257
14.3;3 Proposed KBS for Automatic Design of Bending Dies;258
14.3.1;3.1 Subsystem PPBP;259
14.3.2;3.2 Subsystem BDCOMP;270
14.3.3;3.3 Subsystem AUTOBDMOD;272
14.4;4 Validation of System ASDBD;275
14.5;5 Conclusions;296
14.6;References;297