Computational intelligence in design and manufacturing

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Title:

Personal Author:

Kusiak, Andrew

Publication Information:

New York : John Wiley & Sons, 2000

ISBN:

9780471348795

Subject Term:

Production management

Computational intelligence

Computer integrated manufacturing systems

Engineering design

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010047011	TS155 K89 2000	Open Access Book	Book	Searching... Unknown

Take the next step in Integrated Product and Process Development
This pioneering book is the first to apply state-of-the-art computational intelligence techniques to all phases of manufacturing system design and operations. It equips engineers with a superior array of new tools for optimizing their work in Integrated Product and Process Development.
Drawing on his extensive experience in the field of advanced manufacturing, Andrew Kusiak has masterfully embedded coverage of data mining, expert systems, neural networks, autonomous reasoning techniques, and other computational methods in chapters that cover all key facets of integrated manufacturing system design and operations, including:
* Process planning
* Setup reduction
* Production planning and scheduling
* Kanban systems
* Manufacturing equipment selection
* Group technology
* Facilities and manufacturing cell layout
* Warehouse layout
* Manufacturing system product and component design
* Supplier evaluation
Each chapter includes questions and problems that address key issues on model integration and the use of computational intelligence approaches to solve difficulties across many areas of an enterprise. Examples and case studies from real-world industrial projects illustrate the powerfulapplication potential of the computational techniques.
Comprehensive in scope and flexible in approach, Computational Intelligence in Design and Manufacturing is right in step with the enterprise of the future: extended, virtual, model-driven, knowledge-based, and integrated in time and space. It is essential reading for forward-thinking students and professional engineers and managers working in design systems, manufacturing, and related areas.

Author Notes

Andrew Kusiak, Phd. is professor of Industrial Engineering at the University of Iowa.

Preface	p. xvii
1 Modern Manufacturing	p. 1
1.1 Introduction	p. 1
1.2 Integration	p. 3
1.3 Robotics	p. 4
1.4 Material Handling and Storage Technology	p. 5
1.4.1 Material Handling Technology	p. 5
1.4.2 Automated Storage Systems	p. 6
1.4.3 Control Systems	p. 7
1.5 Information Systems	p. 7
1.5.1 Network Compatibility	p. 7
1.5.2 Interface Standards	p. 8
1.5.3 Intelligent Data Systems	p. 10
1.6 Computational Intelligence	p. 11
1.7 Impact of Manufacturing Technology	p. 12
1.7.1 Product Life Cycle	p. 12
1.7.2 Management	p. 13
1.7.3 Human Dimension	p. 13
1.8 Design of Modern Manufacturing Systems	p. 13
1.8.1 Machining Systems	p. 14
1.8.2 Assembly Systems	p. 19
1.8.2.1 Process Planning for Assembly Systems	p. 20
1.9 Organization and Product Evaluation Standards	p. 22
1.10 The Future of Manufacturing Enterprises	p. 23
1.10.1 Enterprise Attributes	p. 23
1.10.2 Manufacturing Technology	p. 24
References	p. 26
Questions	p. 26
Problems	p. 26
2 Knowledge-Based Systems	p. 31
2.1 Introduction	p. 31
2.2 Knowledge Representation	p. 31
2.2.1 First-Order Logic	p. 32
2.2.2 Production Rules	p. 33
2.2.3 Frames	p. 34
2.2.4 Semantic Networks	p. 37
2.3 Inference Engine	p. 38
2.3.1 Basic Reasoning Strategies	p. 38
2.3.2 Uncertainty in Rule Bases	p. 41
2.3.3 Other Search Strategies	p. 44
2.3.3.1 Depth-First and Breadth-First Search Strategies	p. 44
2.3.3.2 Optimization and Knowledge-Based Systems	p. 44
2.4 Knowledge Acquisition	p. 46
2.5 Knowledge Consistency	p. 47
2.5.1 Detection of Anomaly Rules with Simple Action Clauses	p. 50
2.5.2 Grouping Rules with Compound Condition and Action Clauses	p. 54
2.5.3 Inference Anomalies in Rule Bases	p. 57
2.6 Summary	p. 61
References	p. 62
Questions	p. 62
Problems	p. 63
3 Features in Design and Manufacturing	p. 69
3.1 Introduction	p. 69
3.2 Fundamentals of Requirements, Features, and Functions	p. 71
3.2.1 Requirement Space	p. 71
3.2.2 Fundamentals of Features	p. 71
3.2.3 Classification of Feature-Related Functions	p. 72
3.2.4 Mapping of Requirements and Functions	p. 73
3.2.5 Relations	p. 73
3.3 Function Relations	p. 75
3.3.1 Problem Statement	p. 75
3.3.2 Classification of Function Relations	p. 76
3.3.3 Representation Scheme for Function Relations	p. 77
3.3.3.1 Representation of Explicit Function Relations	p. 77
3.3.3.2 Representation of Implicit Function Relations	p. 79
3.4 Feature Relations	p. 81
3.4.1 Basic Concepts	p. 81
3.4.2 Graph Representation of Feature Relations	p. 82
3.4.2.1 Geometry Relations	p. 82
3.4.2.2 Precision Relations	p. 84
3.4.3 Matrix Representation of Feature Relations	p. 84
3.4.3.1 Geometry Relations	p. 84
3.5 Representation of Function--Feature Relations	p. 85
3.6 Summary	p. 87
References	p. 87
Questions	p. 88
Problems	p. 88
4 Reason Maintenance in Product Modeling	p. 89
4.1 Introduction	p. 89
4.2 Product Modeling	p. 91
4.3 Truth-Maintained Multiple Worlds	p. 95
4.4 Model Synthesis	p. 97
4.5 Model Analysis	p. 102
4.6 Discussion	p. 106
4.7 Summary	p. 107
References	p. 107
Questions	p. 108
Problems	p. 109
5 Process Planning	p. 110
5.1 Introduction	p. 110
5.2 Phases of Process Planning	p. 111
5.3 Interpreation of Part Design Data	p. 112
5.3.1 Feature-Based Part Modeling	p. 112
5.3.2 Syntactic Pattern Recognition	p. 114
5.3.3 State Transition Diagrams	p. 115
5.3.4 Decomposition Approach	p. 115
5.3.5 Knowledge-Based Approach	p. 116
5.3.6 Constructive Solid Geometry Approach	p. 116
5.3.7 Graph-Based Approach	p. 116
5.4 Selection of Processes	p. 119
5.5 Selection of Machines, Tools, and Fixtures	p. 120
5.6 Process Optimization	p. 121
5.6.1 Single-Pass Model	p. 121
5.6.2 Multipass Model	p. 123
5.7 Decomposition of Material Volume to be Removed	p. 124
5.8 Selection of Manufacturing Features	p. 125
5.9 Generation of Precedence Constraints	p. 128
5.10 Sequencing Manufacturing Features	p. 129
5.11 Object-Oriented System for Process Planning	p. 131
5.11.1 Part Modeling and Generation of Elementary Manufacturing Features	p. 132
5.11.2 Grouping Elementary Manufacturing Features	p. 135
5.11.3 Selection of Manufacturing Features	p. 137
5.11.4 Generation of Precedence Constraints and Sequencing Machining Features	p. 139
5.12 Process Planning Shell	p. 140
5.12.1 Process Planning Domain	p. 141
5.12.2 Part Description Language	p. 141
5.12.3 System Architecture	p. 145
5.12.4 Knowledge Organization	p. 145
5.12.5 Reasoning Mechanism	p. 147
5.13 Summary	p. 148
Appendix Model Listing for Example 5.1	p. 148
References	p. 149
Questions	p. 151
Problems	p. 151
6 Setup Reduction	p. 156
6.1 Introduction	p. 156
6.2 Characteristics of Setup Activities	p. 158
6.3 Scheduling Model	p. 160
6.3.1 Example of Scheduling Setup Activities	p. 160
6.3.2 Project Scheduling Model	p. 163
6.4 Minimizing Internal Setup Time	p. 166
6.4.1 Setup Scheduling Model	p. 166
6.4.2 Model Extensions	p. 171
6.5 Computational Experience	p. 172
6.5.1 Numerical Example	p. 172
6.5.2 Comparative Analysis of Models	p. 176
6.5 Summary	p. 177
References	p. 177
Questions	p. 179
Problems	p. 179
7 Production Planning and Scheduling	p. 180
7.1 Production Planning	p. 180
7.1.1 Manufacturing Resource Planning	p. 180
7.1.1.1 Processing Frequency	p. 185
7.1.1.2 MRP Nervousness	p. 185
7.1.2 Optimized Production Technology System	p. 187
7.1.3 Just-in-Time System	p. 188
7.1.3.1 Kanban System Concept	p. 190
7.1.3.2 Kanban Rules	p. 191
7.2 Capacity Balancing	p. 192
7.3 Assembly Line Balancing	p. 197
7.4 Manufacturing Scheduling	p. 199
7.4.1 Scheduling n Operations on a Single Machine	p. 201
7.4.2 Scheduling Flexible Forging Machine	p. 202
7.4.2.1 Features of Flexible Forging Machine Scheduling Model	p. 203
7.4.2.2 Model without Precedence Constraints	p. 204
7.4.2.3 Model with Precedence Constraints	p. 208
7.4.3 Two-Machine Flowshop Model	p. 211
7.4.4 Two-Machine Job Shop Model	p. 213
7.4.5 Special Case of Three-Machine Flow Shop Model	p. 214
7.4.6 Scheduling Model for m Machines and n Operations	p. 215
7.4.7 Heuristic Scheduling of Multiple Resources	p. 223
7.4.8 Resource-Based Scheduling Rule	p. 228
7.5 Rescheduling	p. 231
7.6 Summary	p. 231
Appendix Integer Programming Formulation of the Problem in Example 7.6	p. 232
References	p. 233
Questions	p. 235
Problems	p. 235
8 Kanban Systems	p. 245
8.1 Introduction	p. 245
8.1.1 Operations Principles	p. 246
8.1.2 Kanban Functions	p. 246
8.1.3 Kanban Types	p. 247
8.1.4 Auxiliary Equipment	p. 247
8.1.5 Kanban Operations	p. 248
8.1.6 Kanban Control	p. 249
8.1.6.1 Production Line	p. 249
8.1.6.2 Receiving Area	p. 252
8.1.6.3 Determining the Number of Kanbans	p. 253
8.1.6.4 Kanban System Adjustments	p. 255
8.2 Modeling Kanban Systems	p. 256
8.2.1 Basic Kanban Models	p. 256
8.2.2 Control Approaches	p. 257
8.2.3 Scheduling Approaches	p. 257
8.2.4 Comparing Kanban Systems with Other Systems	p. 257
8.3 Modified Kanban Systems	p. 257
8.3.1 Constant Work-in-Process Model	p. 258
8.3.2 Generic Kanban System	p. 259
8.3.3 Modified Kanban System for Semiconductor Manufacturing	p. 261
8.3.4 Integrated Push-Pull Manufacturing Strategy	p. 261
8.3.5 Periodic Pull System	p. 262
8.3.6 Case Study	p. 263
8.4 Summary	p. 263
References	p. 264
Questions	p. 269
Problems	p. 270
9 Selection of Manufacturing Equipment	p. 272
9.1 Design of Manufacturing Systems	p. 272
9.1.1 Manufacturing Equipment Selection	p. 272
9.1.2 Machine Cell Formation	p. 273
9.1.3 Machine Layout	p. 273
9.1.4 Machine Cell Layout	p. 274
9.2 Selection of Machines and Material Handling Equipment	p. 274
9.2.1 Machine Selection	p. 274
9.2.2 Selection of Machines and Material Handling Systems	p. 276
9.2.3 Special Case of the Equipment Selection Model	p. 278
9.3 Selection of Manufacturing Resources Based on Process Plans	p. 279
9.3.1 Model Background	p. 280
9.3.2 Integer Programming Model	p. 282
9.3.3 Construction Algorithm	p. 284
9.4 Summary	p. 287
Appendix 9.1 Input File of Example 9.2	p. 287
Appendix 9.2 Input File of Example 9.5	p. 288
References	p. 289
Questions	p. 290
Problems	p. 290
10 Group Technology	p. 294
10.1 Introduction	p. 294
10.1.1 Visual Method	p. 295
10.1.2 Coding Method	p. 295
10.2 Cluster Analysis Method	p. 296
10.2.1 Matrix Formulation	p. 297
10.2.1.1 Similarity Coefficient Methods	p. 300
10.2.1.2 Sorting-Based Algorithms	p. 301
10.2.1.3 Cluster Identification Algorithm	p. 302
10.2.1.4 Extended CI Algorithm	p. 305
10.2.2 Mathematical Programming Formulation	p. 310
10.2.2.1 The p-Median Model	p. 311
10.2.2.2 Generalized p-Median Model	p. 313
10.2.3 Innovative Applications of Group Technology	p. 315
10.2.3.1 Data Mining	p. 316
10.3 Branching Algorithms	p. 316
10.4 Assignment of Parts to the Existing Machine Cells	p. 333
10.5 Summary	p. 336
Appendix 10.1 Model Listing for Example 10.4	p. 337
Appendix 10.2 Model Listing for Example 10.5	p. 338
References	p. 341
Questions	p. 342
Problems	p. 343
11 Neural Networks	p. 347
11.1 Introduction	p. 347
11.2 Neural Networks versus Other Intelligent Approaches	p. 350
11.2.1 Knowledge-Based Systems	p. 350
11.2.2 Fuzzy-Logic-Based Systems	p. 352
11.3 Learning	p. 353
11.3 Learning Rules	p. 355
11.3.1.1 Learning by Analogy	p. 357
11.3.1.2 Learning by Induction	p. 357
11.4 Back-Propagation Neural Network	p. 358
11.4.1 Back-Propagation Learning	p. 359
11.4.1.1 Back-Propagation Learning Algorithm	p. 362
11.5 Self-Learning Neural Network	p. 367
11.5.1 ART Neural Network	p. 367
11.5.1.1 Vigilance in ART Network	p. 370
11.5.2 Learning in ART Network	p. 371
11.5.3 Computational Experience	p. 374
11.6 Summary	p. 375
References	p. 377
Questions	p. 379
Problems	p. 379
12 Layout of Machines and Facilities	p. 382
12.1 Introduction	p. 382
12.2 Single-Row Machine Layout	p. 383
12.3 Double-Row Machine Layout	p. 390
12.4 Multirow Facility and Machine Layout	p. 399
12.4.1 Quadratic Assignment Model	p. 399
12.4.2 CRAFT Algorithm	p. 402
12.5 Summary	p. 404
References	p. 404
Questions	p. 405
Problems	p. 405
13 Inventory Space Allocation	p. 412
13.1 Introduction	p. 412
13.2 Related Models	p. 412
13.3 Space Allocation Model	p. 413
13.3.1 Basic Model	p. 413
13.4 Model Formulation	p. 415
13.4.1 Definitions	p. 415
13.4.2 System Description	p. 416
13.4.3 Case Study Objective	p. 416
13.4.4 Constructing the Space Allocation Model	p. 419
13.4.5 Solving the Space Allocation Model	p. 421
13.5 Summary	p. 425
References	p. 426
Questions	p. 427
Problems	p. 427
14 Layout of a Warehouse	p. 428
14.1 Introduction	p. 428
14.2 Related Literature	p. 429
14.3 Procedure for Warehouse Layout	p. 430
14.3.1 Class-Based Storage Rationale	p. 430
14.3.2 Computational Procedure	p. 431
14.3.2.1 The Procedure	p. 431
14.4 Pallet Storage and Retrieval System Case Study	p. 438
14.4.1 Case Study Background	p. 438
14.4.2 Application of the Computational Procedure	p. 440
14.4.3 Computational Results	p. 442
14.4.4 Comparison of Existing and Proposed Designs	p. 442
14.4.5 Results	p. 445
14.5 Summary	p. 445
References	p. 446
Questions	p. 447
Problems	p. 447
15 Design for Agility	p. 448
15.1 Introduction	p. 448
15.2 Design Rules	p. 449
15.2.1 Rule 1 (Modular Design): Decomposing a Complex System into Several Independent Units	p. 449
15.2.2 Rule 2: Designing a Product with Robust Scheduling Characteristics	p. 452
15.2.2.1 Designing and Scheduling an Assembly Line	p. 454
15.2.2.2 Algorithm for Designing and Scheduling Assembly Lines	p. 456
15.2.2.3 Robust Characteristics	p. 459
15.2.3 Rule 3: Streamlining the Flow of Products in an Assembly Line	p. 460
15.2.3.1 Scheduling a Streamlined Assembly Line	p. 460
15.2.3.2 Designing a Streamlined Assembly Line	p. 460
15.2.4 Rule 4: Reduce the Number of Stations in an Assembly Line	p. 466
15.2.4.1 Development of Design Approaches for Short Lines	p. 467
15.3 Product Differentiation	p. 470
15.3.1 Delayed Product Differentiation	p. 470
15.3.2 Early Product Differentiation	p. 471
15.3.3 Manufacturing Performance and the Design of Products	p. 473
15.4 Summary	p. 474
References	p. 475
Questions	p. 477
Problems	p. 477
16 Supplier Evaluation	p. 479
16.1 Introduction	p. 479
16.2 Key Characteristics of the Supplier-Customer Relationship	p. 480
16.2.1 Information Collection Process	p. 480
16.2.2 Key Characteristics	p. 481
16.2.3 Importance of Characteristics	p. 482
16.2.4 Potential for Evaluating Characteristics	p. 482
16.2.5 Discussion	p. 482
16.3 Building a Comprehensive Model	p. 483
16.3.1 Supplier Capabilities	p. 483
16.3.1.1 Past Performance	p. 484
16.3.1.2 Engineering Capabilities	p. 484
16.3.1.3 Manufacturing Capabilities	p. 484
16.3.1.4 Management Capabilities	p. 484
16.3.1.5 Price	p. 485
16.3.1.6 Environmental Awareness	p. 486
16.3.2 Supplier Rating Matrices	p. 486
16.3.2.1 Technology Life-Cycle/Supplier Capability Matrix	p. 486
16.3.2.2 Relationship Life-Cycle/Supplier Capability Matrix	p. 488
16.3.2.3 Supplier Rating Matrix	p. 489
16.4 System Implementation	p. 490
16.4.1 Tool for Commodity Teams	p. 490
16.4.1.1 Supplier Evaluation	p. 490
16.4.1.2 Source Selection	p. 491
16.4.1.3 Monitoring the Progress of a Supplier-Customer Alliance	p. 492
16.4.2 Bulletin Board	p. 493
16.4.3 Intelligent Supplier Evaluation System	p. 493
16.5 Summary	p. 494
Appendix Key Characteristics of the Supplier-Customer Relationship	p. 495
References	p. 496
Questions	p. 497
Problems	p. 497
17 Data Mining	p. 498
17.1 Introduction	p. 498
17.2 Background and Definitions	p. 499
17.3 Reduct Generation Algorithm	p. 501
17.4 Feature Extraction Algorithm	p. 504
17.4.1 Integer Number Case	p. 505
17.4.2 Real Number Case	p. 507
17.5 Feature Extraction Model	p. 511
17.5.1 Integer Programming Formulation	p. 511
17.5.2 Equal-Weight Case	p. 512
17.5.3 Unequal-Weight Case	p. 514
17.5.4 Discussion	p. 517
17.6 Decision Making	p. 518
17.7 Data Farming	p. 519
17.8 Summary	p. 522
Appendix 17.1 Input Data and Corresponding o-Reducts	p. 522
Appendix 17.2 Input File of Example 17.4	p. 524
References	p. 524
Questions	p. 526
Problems	p. 526
Index	p. 529

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