In the last years engineers have to deal with multiple, often conflicting targets, where improvement of one quantity leads to deterioration of others, therefore it is impossible to obtain simultaneous structure enhancements without automatic optimizations tools. The so-called trade-offs have to be applied, providing less efficient modifications, nevertheless, for all of the design objectives. The Pareto front is a method that helps to determine a set of equipotential designs.
In order to explore the entire design space, RSM supplemented by genetic algorithms is often used. In the work presented, the Gaussian Processes Methodology and an Adaptive Range Multi-Objective Genetic Algorithm - ARMOGA were implemented. Basing on the solutions obtained by the design of experiment, response surface methodology is used to predict the values of the measured outputs throughout the full range of interest. Since only a reduced number of tests is needed, the overall computational time is strongly reduced. Subsequent application of genetic algorithms, provide the possibility of detailed exploration of established metamodels.
The main goal of this kind of approach is to increase the product quality, by attaining multiple advances and reduce time-to-market. Simultaneous usage of RSM and genetic algorithm in purpose of conducting optimization, has improved all of the design objectives, keeping the resultant car body shape respect to the ergonomics constraints. Structural modifications were applied by means of the morphing technology, providing changes in the geometry in fully controllable manner.