## Abstract Typically in the analysis of industrial data for product/process optimization, there are many response variables that are under investigation at the same time. Robustness is also an important concept in industrial optimization. Here, robustness means that the responses are not sensitive
Evaluation of response surface methodologies used in crashworthiness optimization
β Scribed by J. Forsberg; L. Nilsson
- Publisher
- Elsevier Science
- Year
- 2006
- Tongue
- English
- Weight
- 587 KB
- Volume
- 32
- Category
- Article
- ISSN
- 0734-743X
No coin nor oath required. For personal study only.
β¦ Synopsis
Optimization of car structures is of great interest to the automotive industry. This work is concerned with structural optimization of a car body with the intent to increase the crashworthiness properties of the vehicle or decrease weight with the crashworthiness properties unaffected. In this work two different methodologies of constructing an intermediate approximation to the optimization problem are investigated, i.e. classical response surface methodology and Kriging. The major difference between the two methodologies is how the residuals between the true function value and the polynomial surface approximation value at a design point are treated.
Several different optimization problems have been investigated, both analytical problems as well as finite element impact problems.
The major conclusion is that even if the same kind of updating scheme is used both for Kriging and linear classic response surface methodology, Kriging improves the sequential behaviour of the optimization algorithm in the beginning of the optimization process. Problems may occur if a constraint is violated after several iterations and then classic response surface methodology seems to more easily be able to find a design point which satisfies the constraint.
π SIMILAR VOLUMES