Robust optimization of front members in a full frontal car impact

In the search for lightweight automobile designs, it is necessary to assure that robust crashworthiness performance is achieved. Structures that are optimized to handle a finite number of load cases may perform poorly when subjected to various dispersions. Thus, uncertainties must be accounted for i...

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Bibliographic Details
Published in:Engineering optimization 2013-03, Vol.45 (3), p.245-264
Main Authors: Aspenberg (né Lönn), David, Jergeus, Johan, Nilsson, Larsgunnar
Format: Article
Language:English
Subjects:
Approximation
Crashworthiness
Design engineering
dual response surface
genetic algorithm
Impact strength
Mathematical analysis
Mathematical models
metamodel
Neural networks
Optimization
Polynomials
robust optimization
Robustness
Searching
TECHNOLOGY
TEKNIKVETENSKAP
Transportation research
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Summary:In the search for lightweight automobile designs, it is necessary to assure that robust crashworthiness performance is achieved. Structures that are optimized to handle a finite number of load cases may perform poorly when subjected to various dispersions. Thus, uncertainties must be accounted for in the optimization process. This article presents an approach to optimization 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 optimization approach are displayed in an analytical example, and further demonstrated in a large-scale design example of front side members of a car. Different optimization formulations are applied and it is shown that the proposed approach works well. It is also concluded that a robust optimization puts higher demands on the finite element model performance than normally.
ISSN:0305-215X
1029-0273
1029-0273
DOI:10.1080/0305215X.2012.669380