Computational Intelligence In Manufacturing Handbook,9780849305924

Computational Intelligence In Manufacturing Handbook

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Edition: 1st
ISBN13: 9780849305924
ISBN10: 0849305926
Format: Hardcover
Pub. Date: 2000-12-27
Publisher(s): CRC Press
List Price: $272.84

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Summary

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.

Table of Contents

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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