Abstract
This paper proposes a method to develop a design assistance tool dedicated to preliminary design. This tool increases interactivity and enables the design space to be explored. The user-friendly interface proposes to define design parameters with interval values. In addition, an objective has to be selected and the tool offers an optimum design. To take full advantage of all the exploration’s capabilities, it is suggested that all the potential optimization functions be considered as design parameters and that each design parameter be considered as a potential objective function. This method was applied to build a software mock-up dedicated to compression spring design. The industrialization process leading to the commercial software is detailed.
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References
Johnson R.C.: Optimum Design of Mechanical Elements. Wiley, New York (1980)
Metwalli S., Radwan A., Elmeligy A.A.: CAD and optimization of helical torsion springs. Comput. Eng. 2, 767–773 (1994)
Deford, R.: Iterative logic for nested compression spring design. Int. Mag. Spring Des. Manuf. June, 27–28 (2003)
Tumkor S.: Internet-based design catalogue for the shaft and bearing. Res. Eng. Des. 12, 163–171 (2000)
Dudley D.W.: When splines need stress control. Prod. Eng. NY 28, 56–61 (1957)
Serna L., Fischer X., Bennis F.: Cognitive virtual exploration for optimization model reduction. Int. j. appl. sci. eng. technol. 4, 79–87 (2008)
Palesi, M., Givargis, T.: Multi-objective design space exploration using genetic algorithms. In: CODES’02, Estes Park (2002)
Yannou, B., Simpson, T.W., Barton, R.R.: Towards a conceptual design explorer using metamodeling approaches and constraint programming. In: Proceedings of the 2003 Design Engineering Technical Conference, Chicago (2003)
Rajagopal S., Cavallaro J.R., Rixner S.: Design space exploration for real time embedded stream processors. IEEE Micro Special Issue Embed. Syst. 24(4), 54–66 (2004)
Ekpanyapong, M., Lim S.K., Ballapuram, C. Lee, H.-H.S.: Wire-driven microarchitectural design space exploration. In: IEEE International Symposium on Circuits and Systems, ISCAS 2005, 23–26 May 2005, vol. 2, 1867–1870 (2005)
Kunzli S.: Efficient design space exploration for embedded systems. Shaker Verlag, Aachen (2006)
Dore R., Pailhes J., Fischer X., Nadeau J.P.: Identification of design variables and criterion variables towards the integration of user requirements into preliminary design. Int. j. Prod. Dev. 4(5), 508–529 (2007)
Wong, L.M., Wang, G.G.: Development of an automatic design and optimization system for industrial silencers. In: Proceedings of the 2002 Design Engineering Technical Conference, Montreal (2002)
Deb, K.: Multi-objective evolutionary optimization: past, present, and future evolutionnary design and manufacture, selected papers from ACDM’00. pp. 225–236. Springer (2000)
Vanderplats G.N.: Numerical Optimization Techniques for Engineering Design. McGraw-Hill, New York (1984)
Goldberg D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)
Back T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York (1996)
Haykin S.: Neural Networks, A Comprehensive Fundation. Prentice Hall PTR, Upper Saddle River (1994)
Giraud-Moreau L., Lafon P.: A comparison of evolutionary algorithms for mechanical design components. Eng. Optim. 34, 307–322 (2002)
Meckesheimer M., Barton R.R., Simpson T., Limayem F., Yannou B.: Metamodeling of combined discrete/continuous responses. AIAA J. 39(10), 1950–1959 (2001)
Moore R.E.: Methods and Applications of Interval Analyzis. SIAM, Philadelphia (1979)
Hyvonen E.: Constraint reasoning based on interval arithmetic: the tolerance propagation approach. Artif. Intell. 58, 71–112 (1992)
Yao Z., Johnson A.L.: On estimating the feasible solution space of design. Comput. Aided Des. 29, 649–655 (1997)
Sandgren E.: Nonlinear integer and discrete programming in mechanical design optimization. J. Mech. Des. 112, 223–229 (1990)
Kannan B.K., Kramer S.N.: An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its application to mechanical design. J. Mech. Des. 116, 405–411 (1994)
Deb K., Goyal M.: A flexible optimization procedure for mechanical component design based on genetic adaptative search. J. Mech. Des. 120, 162–164 (1998)
Paredes M., Sartor M., Daidié A.: Advanced assistance tool for optimal compression spring design. Eng. Comput. 21, 140–150 (2005)
Paredes M., Sartor M., Fauroux J.C.: Stock spring selection tool. Int. Mag. Spring Des. Manuf. 39, 53–67 (2000)
Coleman T.F., Li Y.: An interior, trust region approach for nonlinear minimization subject to bounds. SIAM J. Optim. 6, 418–445 (1996)
Powell, M.J.D.: A fast algorithm for nonlineary constrained optimization calculations, numerical analyzis, Lecture Notes in Mathematics, vol. 630 (1978)
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Paredes, M. Methodology to build an assistance tool dedicated to preliminary design: application to compression springs. Int J Interact Des Manuf 3, 265–272 (2009). https://doi.org/10.1007/s12008-009-0079-3
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DOI: https://doi.org/10.1007/s12008-009-0079-3