Abstract
This paper analyses the advantages of using a stock spring selection tool that manages the uncertainty of designer requirements. Firstly, the manual search and its main drawbacks are described. Then a computer assisted stock spring selection tool is presented which performs all necessary calculations to extract the most suitable spring from within a database. The algorithm analyses data set with interval values using both multi-criteria analysis and fuzzy logic. Two examples, comparing manual and assisted search, are presented. They show not only that the results are significantly better using the assisted search but it helps designers to detail easily and precisely their specifications and thus increase design process flexibility.
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References
Yuyi L, Kok-Keong T and Ljangxi W, 1995. Application of Expert System for Spring Design and Procurement. Springs march: 66–80. From the Spring Manufacturer Institute, http://www.smihq.org
Sandgren E, 1990. Nonlinear Integer and Discrete Programming in Mechanical Design Optimization. ASME Journal of Mechanical Design112: 223–229.
Kannan B.K. and Kramer S.N., 1994. An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and its Applications to Mechanical Design. ASME Journal of Mechanical Design116: 405–411.
Deb K and Goyal M, 1998. A Flexible Optimization Procedure for Mechanical Component Design Based on Genetic Adaptive Search. ASME Journal of Mechanical Design120: 162–164.
G.B. Innomech, 1994. SPEC Select, Spring Selection.
Harmer Q.J., Weaver, P.M., Wallace, K.M., 1998. Design-led component selection. Computer-Aided Design, 30: 391–405.
IST®, Institute of Spring Technology, Henry Street Sheffield S3 TEQ United Kingdom, http://www.istec.demon.co.uk
Wahl A. M., 1963. Mechanical Springs. McGraw-Hill, New York.
Bradley S.R. and Agogino A.M., 1994. An Intelligent Real Time Methodology for Component Selection: An Approach to Managing Uncertainty. ASME Journal of Mechanical Design116: 980–988.
Vogwell J and Culley S.J., 1991. A strategy for selecting engineering components. Proc. Imeche, Journal of Manufacturing systems205: 11–17.
Pardes M, Sartor M and Masclet C, 2000. Stock Spring Selection Tool. Springs Winter. From the Spring Manufacturer Institute, http://www.smihq.org
Mamdani E.H., 1974. Application of fuzzy algorithms for control of simple dynamic plant. Proc. Institution of electrical engineers, Control and science121: 1585–1588.
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© 2000 Springer-Verlag London
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Paredes, M., Sartor, M., Masclet, C. (2000). Advantages in Using a Stock Spring Selection Tool that Manages the Uncertainty of the Designer Requirements. In: Parmee, I.C. (eds) Evolutionary Design and Manufacture. Springer, London. https://doi.org/10.1007/978-1-4471-0519-0_6
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DOI: https://doi.org/10.1007/978-1-4471-0519-0_6
Publisher Name: Springer, London
Print ISBN: 978-1-85233-300-3
Online ISBN: 978-1-4471-0519-0
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