Robust optimisation of front members in a full frontal car impact
(English)Manuscript (preprint) (Other academic)
In the search of a lightweight design of automobiles, it is necessary to assure that a robust crashworthiness performance is achieved. Structures that are optimised to handle a finite number of load cases may perform poorly when subjected to various dispersions. Thus, uncertainties must be accounted for in the optimisation process. This paper presents an approach to optimisation where all design evaluations include an evaluation of the robustness. Metamodel approximations are applied both to the design space and the robustness evaluations, using Artifical Neural Networks and polynomials, respectively. The features of the robust optimisation approach are displayed in an analytical example, and further demonstrated in a large scale design example of front side members of a car. Different optimisation formulations are applied and it is shown that the proposed approach works well. It is also concluded that a robust optimisation puts higher demands on the FE model performance than normally.
Robust optimisation; dual response surface; metamodel; genetic algorithm
IdentifiersURN: urn:nbn:se:liu:diva-70192OAI: oai:DiVA.org:liu-70192DiVA: diva2:436681