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Design optimization using Statistical Confidence Boundaries of response surfaces: Application to robust design of a biomedical implant

Abstract : This paper deals with the use of Statistical Confidence Boundaries (SCB) of response surfaces in robust design optimization. An empirical model is therefore selected to describe a real design constraint function. This constraint is thus approximated by a second order polynomial expansion which is fitted to numerical simulations that use a Finite Element Method (FEM). A technique is also proposed to analyze the effects of the uncertainties of the inputs of the simulations. This approach is employed to optimize the design of a biomedical wrist implant. A real optimized implant is then manufactured and tested to validate the numerical model.
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https://hal.archives-ouvertes.fr/hal-01303708
Contributor : Jean Mailhé <>
Submitted on : Thursday, January 19, 2017 - 1:51:25 PM
Last modification on : Tuesday, March 30, 2021 - 3:17:43 AM
Long-term archiving on: : Thursday, April 20, 2017 - 1:31:11 PM

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Laetitia Rossi, Jean-Marc Linares, Julien Chaves-Jacob, Jean Mailhé, Jean-Michel Sprauel. Design optimization using Statistical Confidence Boundaries of response surfaces: Application to robust design of a biomedical implant. CIRP Annals - Manufacturing Technology, Elsevier, 2014, 63 (1), pp.141--144. ⟨hal-01303708⟩

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