Computational intelligence techniques for new product design:
Gespeichert in:
| Beteiligte Personen: | , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2012
|
| Schriftenreihe: | Studies in computational intelligence
403 |
| Schlagwörter: | |
| Links: | http://deposit.dnb.de/cgi-bin/dokserv?id=3932015&prov=M&dok%5Fvar=1&dok%5Fext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025096963&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XIII, 239 S. Ill., graph. Darst. |
| ISBN: | 9783642274756 3642274757 |
Internformat
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Datensatz im Suchindex
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|---|---|
| adam_text | IMAGE 1
CONTENTS
1 INTEGRATED PRODUCT DESIGN 1
1.1 INTRODUCTION 1
1.2 DETERMINATION O F IMPORTANCE O F CUSTOMER REQUIREMENTS 4
1.3 IDENTIFICATION O F NEW PRODUCT OPPORTUNITIES 9
1.4 FUNCTIONAL MODELING O F THE RELATIONSHIPS BETWEEN CUSTOMER
REQUIREMENT AND DESIGN ATTRIBUTES 11
1.4.1 LINEAR MODELING METHODS 14
1.4.2 NONLINEAR MODELING METHODS 15
1.5 MAXIMIZATION O F OVERALL CUSTOMER SATISFACTION AND DETERMINATION O F
DESIGN ATTRIBUTE SETTING OF A NEW PRODUCT 15 1.6 DEVELOPMENT O F
MANUFACTURING PROCESS MODELS FOR QUALITY PREDICTION O F MANUFACTURED
PRODUCTS 19
1.7 CONCLUSION 21
REFERENCES 22
2 COMPUTATIONAL INTELLIGENCE TECHNOLOGIES FOR PRODUCT DESIGN 25 2.1
INTRODUCTION 25
2.2 MODELING APPROACHES 2 6
2.2.1 FUZZY REGRESSION 28
2.2.1.1 TANAKA S FUZZY REGRESSION 3 0
2.2.1.2 PETERS FUZZY REGRESSION 30
2.2.2 NEURAL NETWORKS 33
2.2.2.1 DIFFERENT CONFIGURATIONS O F NEURAL NETWORKS 34 2.2.2.2 LEARNING
ALGORITHMS FOR NEURAL NETWORK WEIGHTS 4 0
2.3 STOCHASTIC OPTIMIZATION APPROACHES 43
2.3.1 SIMULATED ANNEALING 4 3
2.3.2 EVOLUTIONARY ALGORITHM 4 6
2.3.3 PARTICLE SWARM OPTIMIZATION 48
2.4 SUMMARY O F THIS CHAPTER 52
2.5 APPLICATION O F COMPUTATIONAL INTELLIGENCE TECHNIQUES TO PRODUCT
DESIGN WITHIN THIS BOOK 53
REFERENCES 55
HTTP://D-NB.INFO/1017779775
IMAGE 2
X CONTENTS
3 DETERMINATION OF IMPORTANCE OF CUSTOMER REQUIREMENTS USING
THE FUZZY AHP METHOD 59
3.1 INTRODUCTION 59
3.2 HIERARCHICAL STRUCTURE FOR THE DEVELOPMENT O F CUSTOMER REQUIREMENTS
6 0
3.3 FUZZY REPRESENTATION O F PAIRWISE COMPARISON 61
3.4 FUZZY AHP 63
3.5 CASE STUDY: REMOVABLE MOUNTAIN BICYCLE SPLASHGUARD 65
3.5.1 DEVELOPING A HIERARCHICAL STRUCTURE O F CUSTOMER REQUIREMENTS FOR
BICYCLE SPLASH-GUARD DESIGN 65
3.5.2 CONSTRUCTING FUZZY COMPARISON MATRICES 66
3.5.3 COMPUTING IMPORTANCE WEIGHTS O F CUSTOMER REQUIREMENTS 68
3.7 CONCLUSION 75
REFERENCES 76
4 AN ENHANCED FUZZY AHP METHOD WITH EXTENT ANALYSIS FOR DETERMINING
IMPORTANCE OF CUSTOMER REQUIREMENTS 79
4.1 INTRODUCTION 79
4.2 OVERALL CUSTOMER SATISFACTION ON HAIR DRYER DESIGN 79
4.2.1 DEVELOPMENT OF THE FUZZY MATRIX 80
4.2.2 PAIRWISE COMPARISON O F CUSTOMER REQUIREMENTS 81
4.2.3 CALCULATION O F THE CONSISTENCY INDEX AND CONSISTENCY RATIO 85
4.2.4 DETERMINATION O F WEIGHT VECTORS FOR CUSTOMER SATISFACTIONS 86
4.2.5 COMPARISON O F FUZZY NUMBERS 87
4.3 CONCLUSION 92
REFERENCES 92
5 DEVELOPMENT OF PRODUCT DESIGN MODELS USING CLASSICAL EVOLUTIONARY
PROGRAMMING 95
5.1 INTRODUCTION 95
5.2 CLASSICAL GENETIC PROGRAMMING 9 6
5.2.1 MODEL REPRESENTATION 98
5.2.2 FITNESS FUNCTION 9 9
5.2.3 CROSSOVER AND MUTATION 100
5.2.4 SELECTION AND CONVERGENCE 101
5.3 A CASE STUDY OF DIGITAL CAMERA DESIGN 102
5.4 CONCLUSION 107
REFERENCES 107
IMAGE 3
CONTENTS X I
6 DEVELOPMENT OF PRODUCT DESIGN MODELS USING FUZZY
REGRESSION BASED GENETIC PROGRAMMING I L L
6.1 INTRODUCTION I L L
6.2 FUZZY REGRESSION BASED GENETIC PROGRAMMING 112
6.2.1 SPECIFICATION O F THE FORM O F THE FUZZY REGRESSION MODEL 112
6.2.2 DETERMINATION O F FUZZY COEFFICIENTS 113
6.2.3 PSEUDOCODE O F ALGORITHM 113
6.2.3.1 FUNCTIONAL MODEL REPRESENTATION 115
6.2.3.2 FITNESS FUNCTION 116
6.2.3.3 EVOLUTIONARY OPERATIONS 117
6.3 AN ILLUSTRATIVE EXAMPLE 117
6.3.1 MOBILE PHONE DESIGN 117
6.3.2 FUNCTIONAL MODEL DEVELOPMENT 120
6.3.3 OPTIMIZATION O F AFFECTIVE DESIGN 124
6.4 CONCLUSION 125
REFERENCES 126
7 GENERALIZED FUZZY LEAST SQUARE REGRESSION FOR GENERATING CUSTOMER
SATISFACTION MODELS 129
7.1 INTRODUCTION 129
7.2 THEORETICAL BACKGROUND OF GENERALIZED FUZZY LEAST SQUARES REGRESSION
130
7.3 MODELING FUNCTIONAL RELATIONSHIPS USING GENERALIZED FUZZY
LEAST-SQUARES REGRESSION (GFLSR) 133
7.4 AN ILLUSTRATIVE CASE: PACKING MACHINE DESIGN 138
7.4.1 ESTABLISHING A HOQ FOR PACKING MACHINE DESIGN 138
7.4.2 NORMALIZING ENGINEERING PERFORMANCE VALUES OF ENGINEERING
CHARACTERISTICS 138
7.4.3 DEVELOPMENT OF FUNCTIONAL MODELS REGARDING QFD 140 7.5 CONCLUSION
142
REFERENCES 142
8 AN ENHANCED NEURO-FUZZY APPROACH FOR GENERATING CUSTOMER SATISFACTION
MODELS 145
8.1 INTRODUCTION 145
8.2 AN ENHANCED NEURAL FUZZY NETWORK APPROACH 145
8.2.1 DEVELOPMENT O F NEURAL FUZZY NETWORK MODELS 146
8.2.2 - EXTRACTION O F SIGNIFICANT FUZZY RULES AND THE CORRESPONDING
INTERNAL MODELS USING A PROPOSED RULE EXTRACTION METHOD 148
8.3 CASE STUDY. NOTEBOOK COMPUTER 150
8.4 CONCLUSION 160
REFERENCES 161
IMAGE 4
XII CONTENTS
9 OPTIMIZATION OF CUSTOMER SATISFACTION USING AN IMPROVED
SIMULATION ANNEALING 163
9.1 INTRODUCTION 163
9.2 DEVELOPMENT O F NEIGHBOURHOOD FUNCTION BASED ON ORTHOGONAL
EXPERIMENTAL DESIGN FOR PRODUCT DESIGN PURPOSES 164
9.2.1 ORTHOGONAL ARRAY BASED NEIGHBOURHOOD FUNCTION (ONF) 164
9.2.2 AN IMPROVED ORTHOGONAL ARRAY BASED NEIGHBOURHOOD FUNCTION 166
9.3 A CASE STUDY: EMULSIFIED DYNAMITE PACKING MACHINE 168
9.4 CONCLUSION 173
REFERENCES 174
10 AN ENHANCED GENETIC ALGORITHM INTEGRATED WITH ORTHOGONAL DESIGN 177
10.1 INTRODUCTION 177
10.2 ORTHOGONAL ARRAY BASED CROSSOVERS 178
10.2.1 ORTHOGONAL CROSSOVER (OC) 179
10.2.2 MAIN EFFECT CROSSOVER (MC) 182
10.3 INTERACTION CROSSOVER (IC) 184
10.4 A CASE STUDY: CAR DOOR DESIGN 186
10.5 CONCLUSION 194
REFERENCES 195
11 A NONLINEAR FUZZY REGRESSION FOR DEVELOPING MANUFACTURING PROCESS
MODELS 199
11.1 INTRODUCTION 199
11.2 NONLINEAR FUZZY REGRESSION 200
11.2.1 MODEL REPRESENTATION 202
11.2.2 FITNESS FUNCTION 203
11.2.3 CROSSOVER AND MUTATION 204
11.2.4 SELECTION AND CONVERGENCE 204
11.3 VALIDATION O F GENETIC PROGRAMMING BASED FUZZY REGRESSION APPROACH
TO MODELING MANUFACTURING PROCESSES 205
11.4 CONCLUSION 210
REFERENCES 211
12 RULE EXTRACTION FROM EXPERIMENTAL DATA FOR MANUFACTURING PROCESS
DESIGN 213
12.1 INTRODUCTION 213
12.2 FLUID DISPENSING FOR MICROCHIP ENCAPSULATION 214
12.3 GA-BASED RULE DISCOVERY SYSTEM 215
12.3.1 GENERATION O F RANDOM STRINGS 216
12.3.2 FITNESS EVALUATION 216
12.3.3 SELECTION AND CONVERGENCE 218
12.3.4 CROSSOVER AND MUTATION 219
IMAGE 5
CONTENTS XIII
12.3.5 RULE INDUCTION 220
12.4 RESULTS VERIFICATION 221
12.5 CONCLUSION 226
REFERENCES 226
13 CONCLUSION AND FUTURE WORK 229
13.1 CONCLUSIONS 229
13.1.1 DETERMINATION O F IMPORTANCE WEIGHTS FOR CUSTOMER REQUIREMENTS
230
13.1.2 DEVELOPMENT O F CUSTOMER SATISFACTION MODELS 231 13.1.3
OPTIMIZATION O F OVERALL CUSTOMER SATISFACTION 233
13.1.4 DEVELOPMENT O F MANUFACTURING PROCESS MODELS FOR QUALITY
PREDICTION O F PRODUCTS 233
13.2 FUTURE WORKS 234
13.2.1 COLLECTION O F CUSTOMER SURVEY DATA USING WEB MINING 234
13.2.2 INVESTIGATION O F INNOVATIVE COMPUTATIONAL INTELLIGENCE
APPROACHES 235
REFERENCES 235
INDEX 237
|
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| author | Chan, Kit Yan Kwong, C. K. Dillon, Tharam 1943- |
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| dewey-ones | 658 - General management |
| dewey-raw | 658.5752 |
| dewey-search | 658.5752 |
| dewey-sort | 3658.5752 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Informatik Wirtschaftswissenschaften |
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| id | DE-604.BV040240754 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T16:10:33Z |
| institution | BVB |
| isbn | 9783642274756 3642274757 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025096963 |
| oclc_num | 794494868 |
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| owner | DE-11 |
| owner_facet | DE-11 |
| physical | XIII, 239 S. Ill., graph. Darst. |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spellingShingle | Chan, Kit Yan Kwong, C. K. Dillon, Tharam 1943- Computational intelligence techniques for new product design Studies in computational intelligence Industriedesign (DE-588)4072788-9 gnd Soft Computing (DE-588)4455833-8 gnd Produktentwicklung (DE-588)4139402-1 gnd |
| subject_GND | (DE-588)4072788-9 (DE-588)4455833-8 (DE-588)4139402-1 |
| title | Computational intelligence techniques for new product design |
| title_auth | Computational intelligence techniques for new product design |
| title_exact_search | Computational intelligence techniques for new product design |
| title_full | Computational intelligence techniques for new product design Kit Yan Chan ; C.K. Kwong, and Tharam S. Dillon |
| title_fullStr | Computational intelligence techniques for new product design Kit Yan Chan ; C.K. Kwong, and Tharam S. Dillon |
| title_full_unstemmed | Computational intelligence techniques for new product design Kit Yan Chan ; C.K. Kwong, and Tharam S. Dillon |
| title_short | Computational intelligence techniques for new product design |
| title_sort | computational intelligence techniques for new product design |
| topic | Industriedesign (DE-588)4072788-9 gnd Soft Computing (DE-588)4455833-8 gnd Produktentwicklung (DE-588)4139402-1 gnd |
| topic_facet | Industriedesign Soft Computing Produktentwicklung |
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| volume_link | (DE-604)BV020822171 |
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