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E-raamat: Computational Intelligence Techniques for New Product Design

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Applying computational intelligence for product design is a fast-growing and promising research area in computer sciences and industrial engineering. However, there is currently a lack of books, which discuss this research area. This book discusses a wide range of computational intelligence techniques for implementation on product design. It covers common issues on product design from identification of customer requirements in product design, determination of importance of customer requirements, determination of optimal design attributes, relating design attributes and customer satisfaction, integration of marketing aspects into product design, affective product design, to quality control of new products. Approaches for refinement of computational intelligence are discussed, in order to address different issues on product design. Cases studies of product design in terms of development of real-world new products are included, in order to illustrate the design procedures, as well as the effectiveness of the computational intelligence based approaches to product design. This book covers the state-of-art of computational intelligence methods for product design, which provides a clear picture to post-graduate students in industrial engineering and computer science. It is particularly suitable for researchers and professionals working on computational intelligence for product design. It provides concepts, techniques and methodologies, for product designers in applying computational intelligence to deal with product design.

Using computational intelligence in product design is a fast-growing and promising research area in computer sciences and industrial engineering. These assembled papers from the CIMA 2010 workshop cover the full spectrum of topics in this exciting field.
1 Integrated Product Design
1(24)
1.1 Introduction
1(3)
1.2 Determination of Importance of Customer Requirements
4(5)
1.3 Identification of New Product Opportunities
9(2)
1.4 Functional Modeling of the Relationships between Customer Requirement and Design Attributes
11(4)
1.4.1 Linear Modeling Methods
14(1)
1.4.2 Nonlinear Modeling Methods
15(1)
1.5 Maximization of Overall Customer Satisfaction and Determination of Design Attribute Setting of a New Product
15(4)
1.6 Development of Manufacturing Process Models for Quality Prediction of Manufactured Products
19(2)
1.7 Conclusion
21(4)
References
22(3)
2 Computational Intelligence Technologies for Product Design
25(34)
2.1 Introduction
25(1)
2.2 Modeling Approaches
26(17)
2.2.1 Fuzzy Regression
28(2)
2.2.1.1 Tanaka's Fuzzy Regression
30(1)
2.2.1.2 Peters' Fuzzy Regression
30(3)
2.2.2 Neural Networks
33(1)
2.2.2.1 Different Configurations of Neural Networks
34(6)
2.2.2.2 Learning Algorithms for Neural Network Weights
40(3)
2.3 Stochastic Optimization Approaches
43(9)
2.3.1 Simulated Annealing
43(3)
2.3.2 Evolutionary Algorithm
46(2)
2.3.3 Particle Swarm Optimization
48(4)
2.4 Summary of This
Chapter
52(1)
2.5 Application of Computational Intelligence Techniques to Product Design within This Book
53(6)
References
55(4)
3 Determination of Importance of Customer Requirements Using the Fuzzy AHP Method
59(20)
3.1 Introduction
59(1)
3.2 Hierarchical Structure for the Development of Customer Requirements
60(1)
3.3 Fuzzy Representation of Pairwise Comparison
61(2)
3.4 Fuzzy AHP
63(2)
3.5 Case Study: Removable Mountain Bicycle Splashguard
65(10)
3.5.1 Developing a Hierarchical Structure of Customer Requirements for Bicycle Splash-Guard Design
65(1)
3.5.2 Constructing Fuzzy Comparison Matrices
66(2)
3.5.3 Computing Importance Weights of Customer Requirements
68(7)
3.7 Conclusion
75(4)
References
76(3)
4 An Enhanced Fuzzy AHP Method with Extent Analysis for Determining Importance of Customer Requirements
79(16)
4.1 Introduction
79(1)
4.2 Overall Customer Satisfaction on Hair Dryer Design
79(13)
4.2.1 Development of the Fuzzy Matrix
80(1)
4.2.2 Pairwise Comparison of Customer Requirements
81(4)
4.2.3 Calculation of the Consistency Index and Consistency Ratio
85(1)
4.2.4 Determination of Weight Vectors for Customer Satisfactions
86(1)
4.2.5 Comparison of Fuzzy Numbers
87(5)
4.3 Conclusion
92(3)
References
92(3)
5 Development of Product Design Models Using Classical Evolutionary Programming
95(16)
5.1 Introduction
95(1)
5.2 Classical Genetic Programming
96(6)
5.2.1 Model Representation
98(1)
5.2.2 Fitness Function
99(1)
5.2.3 Crossover and Mutation
100(1)
5.2.4 Selection and Convergence
101(1)
5.3 A Case Study of Digital Camera Design
102(5)
5.4 Conclusion
107(4)
References
107(4)
6 Development of Product Design Models Using Fuzzy Regression Based Genetic Programming
111(18)
6.1 Introduction
111(1)
6.2 Fuzzy Regression Based Genetic Programming
112(5)
6.2.1 Specification of the Form of the Fuzzy Regression Model
112(1)
6.2.2 Determination of Fuzzy Coefficients
113(1)
6.2.3 Pseudocode of Algorithm
113(2)
6.2.3.1 Functional Model Representation
115(1)
6.2.3.2 Fitness Function
116(1)
6.2.3.3 Evolutionary Operations
117(1)
6.3 An Illustrative Example
117(8)
6.3.1 Mobile Phone Design
117(3)
6.3.2 Functional Model Development
120(4)
6.3.3 Optimization of Affective Design
124(1)
6.4 Conclusion
125(4)
References
126(3)
7 Generalized Fuzzy Least Square Regression for Generating Customer Satisfaction Models
129(16)
7.1 Introduction
129(1)
7.2 Theoretical Background of Generalized Fuzzy Least Squares Regression
130(3)
7.3 Modeling Functional Relationships Using Generalized Fuzzy Least-Squares Regression (GFLSR)
133(5)
7.4 An Illustrative Case: Packing Machine Design
138(4)
7.4.1 Establishing a HOQ for Packing Machine Design
138(1)
7.4.2 Normalizing Engineering Performance Values of Engineering Characteristics
138(2)
7.4.3 Development of Functional Models Regarding QFD
140(2)
7.5 Conclusion
142(3)
References
142(3)
8 An Enhanced Neuro-fuzzy Approach for Generating Customer Satisfaction Models
145(18)
8.1 Introduction
145(1)
8.2 An Enhanced Neural Fuzzy Network Approach
145(5)
8.2.1 Development of Neural Fuzzy Network Models
146(2)
8.2.2 Extraction of Significant Fuzzy Rules and the Corresponding Internal Models Using a Proposed Rule Extraction Method
148(2)
8.3 Case Study: Notebook Computer
150(10)
8.4 Conclusion
160(3)
References
161(2)
9 Optimization of Customer Satisfaction Using an Improved Simulation Annealing
163(14)
9.1 Introduction
163(1)
9.2 Development of Neighbourhood Function Based on Orthogonal Experimental Design for Product Design Purposes
164(4)
9.2.1 Orthogonal Array Based Neighbourhood Function (ONF)
164(2)
9.2.2 An Improved Orthogonal Array Based Neighbourhood Function
166(2)
9.3 A Case Study: Emulsified Dynamite Packing Machine
168(5)
9.4 Conclusion
173(4)
References
174(3)
10 An Enhanced Genetic Algorithm Integrated with Orthogonal Design
177(22)
10.1 Introduction
177(1)
10.2 Orthogonal Array Based Crossovers
178(6)
10.2.1 Orthogonal Crossover (OC)
179(3)
10.2.2 Main Effect Crossover (MC)
182(2)
10.3 Interaction Crossover (IC)
184(2)
10.4 A Case Study: Car Door Design
186(8)
10.5 Conclusion
194(5)
References
195(4)
11 A Nonlinear Fuzzy Regression for Developing Manufacturing Process Models
199(14)
11.1 Introduction
199(1)
11.2 Nonlinear Fuzzy Regression
200(5)
11.2.1 Model Representation
202(1)
11.2.2 Fitness Function
203(1)
11.2.3 Crossover and Mutation
204(1)
11.2.4 Selection and Convergence
204(1)
11.3 Validation of Genetic Programming Based Fuzzy Regression Approach to Modeling Manufacturing Processes
205(5)
11.4 Conclusion
210(3)
References
211(2)
12 Rule Extraction from Experimental Data for Manufacturing Process Design
213(16)
12.1 Introduction
213(1)
12.2 Fluid Dispensing for Microchip Encapsulation
214(1)
12.3 GA-Based Rule Discovery System
215(6)
12.3.1 Generation of Random Strings
216(1)
12.3.2 Fitness Evaluation
216(2)
12.3.3 Selection and Convergence
218(1)
12.3.4 Crossover and Mutation
219(1)
12.3.5 Rule Induction
220(1)
12.4 Results Verification
221(5)
12.5 Conclusion
226(3)
References
226(3)
13 Conclusion and Future Work
229(8)
13.1 Conclusions
229(5)
13.1.1 Determination of Importance Weights for Customer Requirements
230(1)
13.1.2 Development of Customer Satisfaction Models
231(2)
13.1.3 Optimization of Overall Customer Satisfaction
233(1)
13.1.4 Development of Manufacturing Process Models for Quality Prediction of Products
233(1)
13.2 Future Works
234(3)
13.2.1 Collection of Customer Survey Data Using Web Mining
234(1)
13.2.2 Investigation of Innovative Computational Intelligence Approaches
235(1)
References
235(2)
Index 237
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