## Fuzzy Sets in Engineering Design and ConfigurationAs 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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Dr. Dr. h.c. Hans-Jürgen Zimmermann, Editor European Laboratory for Intelligent

Dr. Dr. h.c. Hans-Jürgen Zimmermann, Editor European Laboratory for Intelligent

**Techniques**Engineering Aachen, Germany Other books in the series: Applied Research in Fuzzy Technology by Anca L. Ralescu Analysis and Evaluation of Fuzzy ... Página vi

... 164 5 Solving Applications Using the Integrated Approach of KnowledgeBased

... 164 5 Solving Applications Using the Integrated Approach of KnowledgeBased

**Techniques**with Fuzzy Logic and Fuzzy MCDM 181 6 Summary and Conclusions 226 4 MANAGEMENT OF UNCERTAIN INFORMATION IN SIMULTANEOUS ENGINEERING W. Eversheim, ... Página 1

... U.S.A. ABSTRACT The background, terminology, and theory of a

... U.S.A. ABSTRACT The background, terminology, and theory of a

**technique**(called the Method of Imprecision or MoI) to formally represent and manipulate imprecise information in engineering design calculations is reviewed here [3, 29, ... Página 2

A

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**technique**(called the Method of Imprecision or MOJ) has been developed by the authors to represent and manipulate this intrinsic imprecision by incorporating designers' and customers' preferences as fuzzy membership functions [3, 29, ... Página 3

Reference [40] describes the restrictions applied to the problem to permit it to be solved using crisp constraints and various optimization

Reference [40] describes the restrictions applied to the problem to permit it to be solved using crisp constraints and various optimization

**techniques**(monotonicity analysis, non-linear programming). The example was chosen for the ...### Opinião das pessoas - Escrever uma crítica

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### Índice

1 | |

7 | |

8 | |

10 | |

11 | |

12 | |

Flat and Hemi Head Tank TradeOff Strategy Results | 17 |

Chapter 3 | 20 |

Representation of objects by frames | 160 |

Representation of Concepts by SlotFiller notation to repre sent the attributes | 161 |

Example of a specialization chain | 162 |

47 | 169 |

Problems and Solvers | 171 |

Linguistic variable Style of object car | 174 |

Linguistic variable sizeengine of object car | 175 |

Chen and Huangs scale to represent imprecise linguistic terms | 180 |

Design Variable Probability Distributions | 22 |

Applied Pressure Possibility | 24 |

Preferences for the Flat and Hemi Head Tanks | 28 |

Drum Brake System | 30 |

Disk Brake System | 31 |

Induced Preferences | 37 |

Torque Performance Specification Tr | 38 |

Induced Preferences Force and Torque | 40 |

Induced Preferences Temperature | 42 |

Chapter 2 | 48 |

MULTIPLE OBJECTIVE DESIGN | 53 |

Path generating planar fourbar mechanism | 73 |

Driving torque as a function of input link orientation | 75 |

An ACOSSFOUR flexible space structure | 80 |

LOS error versus time for minimum weight design | 83 |

LOS error versus time for minimum control effort design | 84 |

LOS error for case a of fuzzy game theory formulation | 85 |

INTELLIGENT SYSTEMS | 89 |

Areas of Application of IDSS | 90 |

Relevant Methods for IDSS | 91 |

Simple illustrative example of a configuration problem | 92 |

An Architecture of an IDSS | 94 |

Conceptual Hierarchy Car | 97 |

Conceptual Hierarchy a general view | 98 |

Conceptual Hierarchy an example | 99 |

Conceptual Hierarchies Hierarchies of Objects described by Attributes | 101 |

Classes Subclasses | 102 |

Example FRAME TUBE | 103 |

Specialization of a Class | 104 |

Fuzzy Sets for specific linguistic terms | 108 |

Fuzzy Set FastCar | 110 |

Example for an Aggregation | 112 |

Crisp Approaches for MultiQbjective Optimization | 114 |

Algorithm to specify a Fuzzy MultiCriteria Design Model | 115 |

optimizationdirected perspective | 117 |

10 | 120 |

Optimization in the case of Fuzziness multicriteria several realization of SPEC | 121 |

Two Substitute Criteria in the MADM Case | 122 |

Modifications of DECON1 | 126 |

Basic Algorithm of Sequential Configuration | 132 |

A particular Branch and Bound Strategy | 139 |

Cabin Layout that was configured with XKL | 142 |

26 | 143 |

Customers Requirement for a certain Seat Pitch | 144 |

A selection of existing space launch systems | 145 |

Specialization Hierarchy of Space Launch Systems | 146 |

Partof Hierarchy for STSObjects | 148 |

Example Logistic Layout | 150 |

Transportation of letters an example | 151 |

Graphical User Interface | 152 |

Subgraph to represent a PC | 154 |

Europe | 158 |

The Conceptual Hierarchy of the RAINSmodel incomplete because of the complexity | 159 |

Fuzzy Set ComfortableDistance | 181 |

Fuzzy Set LargeSeatNumber related to a business class | 182 |

Illustration of the BestFirstSearch Part of the BaB Algo | 188 |

rithm | 191 |

An algorithm to estimate the start mass | 192 |

Part of the goal hierarchy and alternatives STSs | 195 |

Linguistic Variable Noise Intensity | 197 |

Specialization Hierarchy of Logisit Objects | 198 |

Simplified Compositional Hierarchy | 199 |

Membership function of a fuzzy set preferredservicetime mailbox | 201 |

53 | 210 |

Costfunction fix for a sourceregion k | 211 |

Membership function for AcceptableGridDeposition | 212 |

Membership function for AcceptableCost | 213 |

Fuzzy Sets to model EmissionlevelofSourceregionk | 214 |

Membership function to model an objective as fuzzy constraint | 216 |

Architecture of the Overall Modelling Approach | 219 |

A special requirement model | 222 |

Fuzzy constraint Desired Cost Level | 224 |

Aggregation tree | 225 |

Chapter 4 | 227 |

MANAGEMENT OF UNCERTAIN | 233 |

Aspects for Modeling Uncertain Information | 235 |

73 | 237 |

Presentation of uncertainties on a contents level described in EXPRESSG | 238 |

Determining the informationmaturity | 239 |

EXPRESSG Model for structuring information using an object oriented schema | 245 |

First step for the use of informationmaturity in Simultaneous Engineering | 246 |

Second and third step for the use of informationmaturity in Simultaneous Engineering | 247 |

APPLICATION OF THE FUZZY AHP METHOD FOR ASSESSING ALTERNATIVE PRODUCTION CYCLES M Weck F Klocke H Schell and E Ri... | 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 |

166 | 278 |

### 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

actuating force aggregation algorithm alternatives ANTONSSON application approach assessment attributes brake configuration Branch and Bound cabin layout components computed conceptual constraints conceptual hierarchy consider criteria defined Defuzzification denotes described design and configuration design problems design variables Dhingra disk brake domain drum brake engineering design evaluation evolutionary algorithm example extension module Figure finite set formulation fuzzy constraints fuzzy game fuzzy goals Fuzzy Logic fuzzy objective functions fuzzy optimization fuzzy set theory game theory goal programming hubs imprecise input integral is-a knowledge base knowledge-based KONWERK KSAOs linguistic variables mailbox matrix maximal membership functions method minimized MOO problem objective functions optimization problem optimum solution overall parameter Pareto-optimal performance specification performance variable possible preference production cycle relations requirement model restrictions Section selection solve space launch systems SPEC strategy structure techniques tion torque trade-off uncertain information uncertainty values vector weighting y-level measure