Fuzzy Sets in Engineering Design and ConfigurationHans-Jürgen Sebastian, Erik K. Antonsson Springer Science & Business Media, 06/12/2012 - 282 páginas As understanding of the engineering design and configuration processes grows, the recognition that these processes intrinsically involve imprecise information is also growing. This book collects some of the most recent work in the area of representation and manipulation of imprecise information during the syn thesis of new designs and selection of configurations. These authors all utilize the mathematics of fuzzy sets to represent information that has not-yet been reduced to precise descriptions, and in most cases also use the mathematics of probability to represent more traditional stochastic uncertainties such as un controlled manufacturing variations, etc. These advances form the nucleus of new formal methods to solve design, configuration, and concurrent engineering problems. Hans-Jurgen Sebastian Aachen, Germany Erik K. Antonsson Pasadena, California ACKNOWLEDGMENTS We wish to thank H.-J. Zimmermann for inviting us to write this book. We are also grateful to him for many discussions about this new field Fuzzy Engineering Design which have been very stimulating. We wish to thank our collaborators in particular: B. Funke, M. Tharigen, K. Miiller, S. Jarvinen, T. Goudarzi-Pour, and T. Kriese in Aachen who worked in the PROKON project and who elaborated some of the results presented in the book. We also wish to thank Michael J. Scott for providing invaluable editorial assis tance. Finally, the book would not have been possible without the many contributions and suggestions of Alex Greene of Kluwer Academic Publishers. 1 MODELING IMPRECISION IN ENGINEERING DESIGN Erik K. Antonsson, Ph.D., P.E. |
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... Section 3 illustrates trade - offs ( aggregations ) that can be made between design and performance variables , all without considering noise . Noise and its consequences for engi- neering decision - making are presented in Section 4 ...
... Section 3 illustrates trade - offs ( aggregations ) that can be made between design and performance variables , all without considering noise . Noise and its consequences for engi- neering decision - making are presented in Section 4 ...
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... section . ) v ( p ) is the combined design preference induced onto the PVS by use of the extension principle . The process of mapping the design preference μ ( d ) from the DVS to v ( p ) on the PVS is referred to as inducing the design ...
... section . ) v ( p ) is the combined design preference induced onto the PVS by use of the extension principle . The process of mapping the design preference μ ( d ) from the DVS to v ( p ) on the PVS is referred to as inducing the design ...
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Índice
Aggregation tree | 225 |
4 | 227 |
1 | 234 |
Aspects for Modeling Uncertain Information | 235 |
Presentation of uncertainties on a contents level described in EXPRESSG | 238 |
Determining the informationmaturity | 239 |
сл | 241 |
EXPRESSG Model for structuring information using an object oriented schema | 245 |
MULTIPLE OBJECTIVE DESIGN | 53 |
Numerical Examples | 71 |
Objective function values for single objective optimizations | 82 |
3 | 85 |
Chapter 3 | 89 |
Conceptual Hierarchy an example | 98 |
11 | 104 |
15 | 111 |
17 | 117 |
21 | 125 |
Basic Algorithm of Sequential Configuration | 132 |
23 | 138 |
A selection of existing space launch systems | 145 |
32 | 151 |
37 | 160 |
40 | 168 |
5 | 169 |
Linguistic variable Style of object car | 174 |
44 | 181 |
6 | 182 |
46 | 191 |
51 | 201 |
Fuzzy Sets to model EmissionlevelofSourceregionk | 212 |
Architecture of the Overall Modelling Approach | 219 |
A special requirement model | 222 |
Fuzzy constraint Desired Cost Level | 224 |
First step for the use of informationmaturity in Simultaneous Engineering | 246 |
Second and third step for the use of informationmaturity in Simultaneous Engineering | 247 |
Conclusion | 249 |
པ APPLICATION OF THE FUZZY AHP METHOD FOR ASSESSING ALTERNATIVE PRODUCTION CYCLES M Weck F Klocke H Schell and E ... | 251 |
Introduction | 252 |
Selecting which Assessment Method to Apply | 253 |
Structuring the decisionmaking problem and deriving a suit able method of assessment | 254 |
Extract of the hierarchy in a decisionmaking problem | 255 |
Methods of Weighting and Assessing Stages of Production | 256 |
Determining Kv the number relating to a specific process | 258 |
Assessing Production Cycles | 259 |
Example of an Assessment Process | 260 |
Cycles for manufacturing a gear shaft | 261 |
Result of the assessment of the alternatives | 263 |
A METHOD FOR PERSONNEL SELECTION IN CONCURRENT ENGINEERING USING FUZZY SETS J Stahl | 265 |
Introduction | 266 |
Integrated Personnel Planning in CE | 267 |
Hierarchical Structure of KSAOS | 268 |
Hierarchical Structure of KSAOS | 269 |
Example for Membership Functions of Ordinal Criteria | 271 |
Derivation of the Membership Functions for the Linguistic Description | 272 |
Nominal Criteria | 273 |
Metric Criteria | 274 |
Conclusion | 275 |
INDEX | 277 |
Outras edições - Ver tudo
Fuzzy Sets in Engineering Design and Configuration Hans-Jürgen Sebastian,Erik K. Antonsson Visualização de excertos - 1996 |
Fuzzy Sets in Engineering Design and Configuration Hans-Jürgen Sebastian,Erik K. Antonsson Pré-visualização indisponível - 2011 |
Fuzzy Sets in Engineering Design and Configuration Hans-Jurgen Sebastian,Erik K Antonsson Pré-visualização indisponível - 1996 |
Palavras e frases frequentes
Aachen actuating force aggregation algorithm alternatives ANTONSSON application approach assessment attributes brake configuration Branch and Bound components concept conceptual constraints conceptual hierarchy consider crisp criteria decision defined Defuzzification denotes described design problems design variables Dhingra disk brake drum brake engineering design evaluation evolutionary algorithm example F₁ Figure finite formulation fuzzy constraints fuzzy game fuzzy goals Fuzzy Logic fuzzy objective functions fuzzy set theory game theory global optimum goal programming imprecise induced performance induced preference input integral is-a knowledge-based KONWERK KSAOS linguistic variables MADM matrix maximal membership functions method minimized MOO problem multiobjective multiple objective optimum solution overall parameter Pareto-optimal solutions performance specification performance variable possible production cycle Requirement Model restrictions RWTH Aachen Section selection solve space launch systems SPEC structure techniques temperature decay profile temperature rise tion torque uncertain information uncertainty values vector weighting y-level measure