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TECHNICAL PAPERS

Fuzzy Approaches to Evaluation in Engineering Design

[+] Author and Article Information
Libardo V. Vanegas

Mechanical Engineering Department, Universidad Technologica de Pereira, A. A. 97 Pereira, Columbiae-mail: ivanegas@utp.edu.co

Ashraf W. Labib

School of Mechanical, Aerospace & Civil Engineering, University of Manchester, P.O. Box 88, Manchester M60 1QD, UKe-mail: ashraf.labib@manchester.ac.uk

J. Mech. Des 127(1), 24-33 (Mar 02, 2005) (10 pages) doi:10.1115/1.1814639 History: Received January 26, 2003; Revised April 21, 2004; Online March 02, 2005
Copyright © 2005 by ASME
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References

Wood,  K. L., and Antonsson,  E. K., 1990, “Modeling Imprecision and Uncertainty in Preliminary Engineering Design,” Mech. Mach. Theory, 25(3), pp. 305–324.
Antonsson,  E. K., and Otto,  K. N., 1995, “Imprecision in Engineering Design,” ASME J. Mech. Des., 117B, pp. 25–32.
Giachetti,  R. E., Young,  R. E., Roggatz,  A., Eversheim,  W., and Perrone,  G., 1997, “A Methodology for the Reduction of Imprecision in the Engineering Process,” Eur. J. Oper. Res., 100, pp. 277–292.
Vanegas,  L. V., and Labib,  A. W., 2001, “A Fuzzy Quality Function Deployment (FQFD) Model for Deriving Optimum Targets,” Int. J. Prod. Res., 39(1), pp. 99–120.
Thurston,  D. L., and Carnahan,  J. V., 1992, “Fuzzy Ratings and Utility Analysis in Preliminary Design Evaluation of Multiple Attributes,” ASME J. Mech. Des., 114, pp. 648–658.
Kaymak, U., and Van Nauta Lemke, H. R., 1993, “A Parametric Generalized Goal Function for Fuzzy Decision Making With Unequally Weighted Objectives,” IEEE International Conference on Fuzzy Systems, March, pp. 1156–1160.
Carnahan,  J. V., Thurston,  D. L., and Liu,  T., 1994, “Fuzzing Ratings for Multiattribute Design Decision-Making,” ASME J. Mech. Des., 116, pp. 511–521.
Durr,  H., and Schramm,  M., 1997, “Feature Based Feedback Into the Early Stages of Design,” Eur. J. Oper. Res., 100, pp. 338–350.
Muller,  K., and Sebastian,  H.-J., 1997, “Intelligent Systems for Engineering Design and Configuration Problems,” Eur. J. Oper. Res., 100, pp. 315–326.
Hsiao,  S.-W., 1998, “Fuzzy Logic Based Decision Model for Product Design,” Int. J. Ind. Ergonom., 21, pp. 103–116.
Vanegas,  L. V., and Labib,  A. W., 2001, “Application of New Fuzzy-Weighted Average (NFWA) Method to Engineering Design Evaluation,” Int. J. Prod. Res., 39(6), pp. 1147–1162.
Saaty, T. L., 1994, Fundamentals of Decision Making and Priority Theory With the Analytical Hierarchy Process, RWS Publications, Pittsburgh.
Chen,  Y. H., 1996, “Fuzzy Ratings in Mechanical Engineering Design—Application to Bearing Selection,” Proc. Inst. Mech. Eng., Part B: J. Eng. Manuf.,210, pp. 49–53.
Khoo,  L. P., and Ho,  N. C., 1996, “Framework of a Fuzzy Quality Function Deployment System,” Int. J. Prod. Res., 34, pp. 299–311.
Zhou,  M., 1998, “Fuzzy Logic and Optimization Models for Implementing QFD,” Comput. Ind. Eng.,35, pp. 237–240.
Zhou, M., 1997, “Fuzzy Logic Based Models for Quality Planning and Improvement,” ASME Conference: Intelligent Engineering Systems Through Artificial Neural Networks, ASME, New York, Vol. 7, pp. 311–316.
Wang,  J., 1999, “Fuzzy Outranking Approach to Prioritize Design Requirements in Quality Function Deployment,” Int. J. Prod. Res., 37, pp. 899–916.
Klir, G. J., and Floger, T. A., 1988, Fuzzy Sets, Uncertainty and Information, Prentice-Hall, Englewood Cliffs, NJ.
Klir, G. J., and Yuan, B., 1995, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice-Hall, Englewood Cliffs, NJ.
Zadeh, L. A., 1965, “Fuzzy Sets,” Information and Control, Academic Press, 8 , pp. 338–353.
Wang,  J., 1997, “A Fuzzy Outranking Method for Conceptual Design Evaluation,” Int. J. Prod. Res., 35, pp. 995–1010.
Chen,  S. H., 1985, “Ranking Fuzzy Numbers With Maximising Set and Minimizing Set,” Fuzzy Sets Syst., 17, pp. 113–129.
Masud, A. S. M., and Dean, E. B., 1993, “Using Fuzzy Sets in Quality Function Deployment,” Proc. the 2nd Industrial Engineering Research Conference, pp. 270–274.
Farhang-Mehr,  A., and Azarm,  S., 2003, “An Information-Theoretic Entropy Metric for Assessing Multi-Objective Optimization Solution Set Quality,” ASME J. Mech. Des., 125(4), pp. 655–663.
McAdams,  D. A., and Wood,  K. L., 2002, “A Quantitative Similarity Metric for Design-by-Analogy,” ASME J. Mech. Des., 124(2), pp. 173–182.
Wu,  J., and Azarm,  S., 2001, “Metrics for Quality Assessment of a Multiobjective Design Optimization Solution Set,” ASME J. Mech. Des., 123(1), pp. 18–25.
Kota,  S., Sethuraman,  K., and Miller,  R., 2000, “A Metric for Evaluating Design Commonality in Product Families,” ASME J. Mech. Des., 122(4), pp. 403–410.
Shah,  J. J., Kulkarni,  S. V., and Vargas-Hernandez,  N., 2000, “Evaluation of Idea Generation Methods for Conceptual Design: Effectiveness Metrics and Design of Experiments,” ASME J. Mech. Des., 122(4), pp. 377–384.
Buckley,  J. J., 1984, “The Multiple-Judge, Multiple Criteria Ranking Problem: A Fuzzy-Set Approach,” Fuzzy Sets Syst., 13, pp. 25–37.
Weck,  M., Klocke,  F., Schell,  H., and Ruenauver,  E., 1997, “Evaluating Alternative Production Cycles Using the Extended Fuzzy AHP Method,” Eur. J. Oper. Res., 100, pp. 351–366.
Jones,  J. D., and Hua,  Y., 1998, “A Fuzzy Knowledge Base to Support Routine Engineering Design,” Fuzzy Sets Syst., 98, pp. 267–278.
Giachetti,  R. E., 1998, “A Decision Support System for Material and Manufacturing Process Selection,” J. Intel. Manuf.,9, pp. 265–276.
Esawi,  A. M. K., and Ashby,  M. F., 1998, “Computer-Based Selection of Manufacturing Processes: Methods, Software and Case Studies,” Proc. Inst. Mech. Eng., Part B: J. Eng. Manuf.,212(B8), pp. 595–610.

Figures

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Fuzzy number representing
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Fuzzy numbers for predicted and required performances
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Desirability m(r) of a design for the criterion “weight”
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Required performance of appearance and predicted performance of an alternative
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Weights of criteria of the example in Table 1
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Overall performance of the alternative in Table 1, calculated through the NFWA
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Desirability mA(r) with respect to the criterion weight of a value r with possibility represented by mB(r)
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Jones and Hua 26 metric for finding desirability
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Necessity measure of the predicted durability under its requirement
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Required versus predicted performance
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A new metric for approach 3
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Aggregate fuzzy sets, overall performances of two design solutions
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Examples of a new type of fuzzy goal for linguistic predicted performances
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Trapezoidal fuzzy numbers that represent the required performance for the attribute “weight” and the predicted performance of machining with respect to this attribute
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Fuzzy numbers for predicted and required performances for the attribute “appearance”
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Ranking of manufacturing options

Tables

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