Dimensioning a product in preliminary design through different exploration techniques

Once a design concept has been chosen and parameterised, the embodiment design stage consists of choosing materials and dimensions to ensure a 'good matching' with the expected performances. In this context of preliminary design stages, several approaches exist, which correspond to slightly different complexities and issues and must consequently be used at different moments. We consider in this paper three families of approaches: 1) exploring design (parametric) dimensioning under uncertainty (through constraint programming techniques, representations of feasible design points or Pareto frontiers); 2) robust design and multidisciplinary optimisation; 3) design for reliability. We advocate and state in this paper that these approaches must be used in that order of increasing complexity. Indeed, applying an approach allows one to quickly figure out inadequacies with performance specifications or initial allowable bounds of design parameters and then to backtrack or to refine the design issue before proceeding to the next stage or approach. We illustrate that phenomenon by successively applying the three approaches on a dimensioning issue of a two-member truss structure. We clearly show that the successive optimal designs obtained are notably different, but that the optimal point obtained in a given approach is used to explore its surroundings within the next approach.

[1]  A. Messac,et al.  The normalized normal constraint method for generating the Pareto frontier , 2003 .

[2]  G. Kharmanda,et al.  Efficient reliability-based design optimization using a hybrid space with application to finite element analysis , 2002 .

[3]  Kristin L. Wood,et al.  Computations with Imprecise Parameters in Engineering Design: Background and Theory , 1989 .

[4]  Alaa Chateauneuf,et al.  Reliability-based optimization of structural systems by adaptive target safety – Application to RC frames , 2008 .

[5]  Erik K. Antonsson,et al.  Imprecision in Engineering Design , 1995 .

[6]  Nam P. Suh,et al.  Axiomatic Design: Advances and Applications , 2001 .

[7]  Durward K. Sobek,et al.  Set-based concurrent engineering and Toyota , 1994 .

[8]  Bruce D'Ambrosio,et al.  Taxonomy for classifying engineering decision problems and support systems , 1995, Artif. Intell. Eng. Des. Anal. Manuf..

[9]  Basem Said El-Haik,et al.  Axiomatic Quality: Integrating Axiomatic Design with Six-Sigma, Reliability, and Quality Engineering , 2005 .

[10]  Jon H Sims Williams,et al.  An object-oriented modeling framework for representing uncertainty in early variant design , 2003 .

[11]  Ramon E. Moore Methods and applications of interval analysis , 1979, SIAM studies in applied mathematics.

[12]  Bernard Yannou,et al.  TRUSS DIMENSIONING WITH AN UNCERTAINTY REDUCTION PARADIGM , 2004 .

[13]  William W. Finch,et al.  A SET-BASED SYSTEM FOR ELIMINATING INFEASIBLE DESIGNS IN ENGINEERING PROBLEMS DOMINATED BY UNCERTAINTY , 1997 .

[14]  William Y. Fowlkes,et al.  Engineering Methods for Robust Product Design: Using Taguchi Methods in Technology and Product Development , 1995 .

[15]  Bernard Yannou,et al.  Faster Generation of Feasible Design Points , 2005, DAC 2005.

[16]  Panos Y. Papalambros,et al.  The optimization paradigm in engineering design: promises and challenges , 2002, Comput. Aided Des..

[17]  Jay D. Martin,et al.  The ARL Trade Space Visualizer: An Engineering Decision-Making Tool , 2004 .

[18]  Bernard Yannou,et al.  Use of Constraint Programming for Design , 2006 .

[19]  E. Antonsson,et al.  USING INDIFFERENCE POINTS IN ENGINEERING DECISIONS , 2000 .

[20]  Frédéric Goualard,et al.  Revising Hull and Box Consistency , 1999, ICLP.