Despite the large volume of publications devoted to neural networks, fuzzy logic, and evolutionary programming, few address the applications of computational intelligence in design and manufacturing. Computational Intelligence in Manufacturing Handbook fills this void as it covers the most recent advances in this area and state-of-the-art applications. This comprehensive handbook contains an excellent balance of tutorials and new results, that allows you to:
obtain current information
understand technical details
assess research potentials, and
define future directions of the field
Manufacturing applications play a leading role in progress, and this handbook gives you a ready reference to guide you easily through these developments.
PART I Overview Computational Intelligence for Manufacturing D. T. Pham P. T. N. Pham Introduction 1(1) Knowledge-Based Systems 1(3) Fuzzy Logic 4(3) Inductive Learning 7(4) Neural Networks 11(4) Genetic Algorithms 15(4) Some Applications in Engineering and Manufacture 19(6) Conclusion 25 Neural Network Applications in Intelligent Manufacturing: An Updated Survey Jun Wang Wai Sum Tang Catherine Roze Introduction 1(2) Modeling and Design of Manufacturing Systems 3(7) Modeling, Planning, and Scheduling of Manufacturing Processes 10(4) Monitoring and Control of Manufacturing Processes 14(4) Quality Control, Quality Assurance, and Fault Diagnosis 18(5) Concluding Remarks 23 Holonic Metamorphic Architectures for Manufacturing: Identifying Holonic Structures in Multiagent Systems by Fuzzy Modeling Michaela Ulieru Dan Stefanoiu Douglas Norrie Introduction 1(1) Agent-Oriented Manufacturing Systems 2(1) The MetaMorph Project 3(6) Holonic Manufacturing Systems 9(2) Holonic Self-Organization of MetaMorph via Dynamic Virtual Clustering 11(3) Automatic Grouping of Agents into Holonic System: Simulation Results 14(12) MAS Self-Organization as a Holonic System: Simulation Results 26(10) Conclusions 36 PART II Manufacturing System Modeling and Design Neural Network Applications for Group Technology and Cellular Manufacturing Nallan C. Suresh Introduction 1(2) Artificial Neural Networks 3(2) A Taxonomy of Neural Network Application for GT/CM 5(14) Conclusions 19 Application of Fuzzy Set Theory in Flexible Manufacturing System Design A. Kazerooni K. Abhary L. H. S. Luong F. T. S. Chan Introduction 1(1) A Multi-Criterion Decision-Making Approach for Evaluation of Scheduling Rules 2(2) Justification of Representing Objectives with Fuzzy Sets 4(1) Decision Points and Associated Rules 4(1) A Hierarchical Structure for Evaluation of Scheduling Rules 4(7) A Fuzzy Approach to Operation Selection 11(4) Fuzzy-Based Part Dispatching Rules in FMSs 15(2) Fuzzy Expert System-Based Rules 17(4) Selection of Routing and Part Dispatching Using Membership Functions and Fuzzy Expert System-Based Rules 21 Genetic Algorithms in Manufacturing System Design L. H. S. Luong M. Kazerooni K. Abhary Introduction 1(1) The Design of Cellular Manufacturing Systems 2(2) The Concepts of Similarity Coefficients 4(3) A Genetic Algorithm for Finding the Optimum Process Routings for Parts 7(3) A Genetic Algorithm to Cluster Machines into Machine Groups 10(2) A Genetic Algorithm to Cluster Parts into Part Families 12(1) Layout Design 13(1) A Genetic Algorithm for Layout Optimization 14(2) A Case Study 16(3) Conclusion 19 Intelligent Design Retrieving Systems Using Neural Networks C. Alec Chang Chieh-Yuan Tsai Introduction 1(1) Characteristics of Intelligent Design Retrieval 2(1) Structure of an Intelligent System 3(2) Performing Fuzzy Association 5(1) Implementation Example 5 PART III Process Planning and Scheduling Soft Computing for Optimal Planning and Sequencing of Parallel Machining Operations Yuan-Shin Lee Nan-Chieh Chiu Shu-Cherng Fang Introduction 1(2) A Mixed Integer Program 3(2) A Genetic-Based Algorithm 5(4) Tabu Search for Sequencing Parallel Machining Operations 9(3) Two Reported Examples Solved by the Proposed GA 12(6) Two Reported Examples Solved by the Proposed Tabu Search 18(4) Random Problem Generator and Further Tests 22(4) Conclusion 26 Application of Genetic Algorithms and Simulated Annealing in Process Planning Optimization Y. F. Zhang A. Y. C. Nee Introduction 1(2) Modeling Process Planning Problems in an Optimization Perspective 3(10) Applying a Genetic Algorithm to the Process Planning Problem 13(5) Applying Simulated Annealing to the Process Planning Problem 18(5) Comparison between the GA and the SA Algorithm 23(1) Conclusions 24 Production Planning and Scheduling Using Genetic Algorithms Runwei Cheng Mitsuo Gen Introduction 1(1) Resource-Constrained Project Scheduling Problem 1(8) Parallel Machine Scheduling Problem 9(8) Job-Shop Scheduling Problem 17(8) Multistage Process Planning 25(3) Part Loading Scheduling Problem 28 PART IV Manufacturing Process Monitoring and Control Neural Network Predictive Process Models: Three Diverse Manufacturing Applications Sarah S. Y. Lam Alice E. Smith Introduction to Neural Network Predictive Process Models 1(1) Ceramic Slip Casting Application 2(2) Abrasive Flow Machining Application 4(5) Chemical Oxidation Application 9(2) Concluding Remarks 11 Neural Network Applications to Manufacturing Processes: Monitoring and Control Hyung Suck Cho Introduction 1(1) Manufacturing Process Monitoring and Control 2(4) Neural Network-Based Monitoring 6(4) Quality Monitoring Applications 10(9) Neural Network-Based Control 19(3) Process Control Applications 22(9) Conclusions 31 Computational Intelligence in Microelectronics Manufacturing Gary S. May Introduction 1(1) The Role of Computational Intelligence 2(9) Process Modeling 11(8) Optimization 19(13) Process Monitoring and Control 32(9) Process Diagnosis 41(11) Summary 52 Monitoring and Diagnosing Manufacturing Processes Using Fuzzy Set Theory R. Du Yangsheng Xu Introduction 1(1) A Brief Description of Fuzzy Set Theory 2(6) Monitoring and Diagnosing Manufacturing Processes Using Fuzzy Sets 8(15) Application Examples 23(4) Conclusions 27 Fuzzy Neural Network and Wavelet for Tool Condition Monitoring Xiaoli Li Introduction 1(1) Fuzzy Neural Network 2(5) Wavelet Transforms 7(3) Tool Breakage Monitoring with Wavelet Transforms 10(2) Identification of Tool Wear States Using Fuzzy Method 12(11) Tool Wear Monitoring with Wavelet Transforms and Fuzzy Neural Network 23 PART V Quality Assurance and Fault Diagnosis Neural Networks and Neural-Fuzzy Approaches in an In-Process Surface Roughness Recognition System for End Milling Operations Joseph C. Chen Introduction 1(1) Methodologies 2(6) Experimental Setup and Design 8(3) The In-Process Surface Roughness Recognition Systems 11(3) Testing Results and Conclusions 14 Intelligent Quality Controllers for On-Line Parameter Design Ratna Babu Chinnam Introduction 1(5) An Overview of Certain Emerging Technologies Relevant to On-Line Parameter Design 6(3) Design of Quality Controllers for On-Line Parameter Design 9(5) Case Study: Plasma Etching Process Modeling and On-Line Parameter Design 14(7) Conclusion 21 A Hybrid Neural Fuzzy System for Statistical Process Control Shing I Chang Statistical Process Control 1(2) Neural Network Control Charts 3(1) A Hybrid Neural Fuzzy Control Chart 4(12) Design, Operations, and Guidelines for Using the Proposed Hybrid Neural Fuzzy Control Chart 16(2) Properties of the Proposed Hybrid Neural Fuzzy Control Chart 18(1) Final Remarks 19 RClass*: A Prototype Rough-Set and Genetic Algorithms Enhanced Multi-Concept Classification System for Manufacturing Diagnosis Li-Pheng Khoo Lian-Yin Zhai Introduction 1(1) Basic Notions 2(5) A Prototype Multi-Concept Classification System 7(3) Validation of RClass* 10(2) Application of RClass* to Manufacturing Diagnosis 12(4) Conclusions 16 Index I-1
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