Optimum design of an automotive inner door panel with a tailor-welded blank structure

This paper defines an integrated approach using finite element analysis, an artificial neural network (ANN), and a genetic algorithm (GA) for the optimization design of an inner door panel with a tailor-welded blank (TWB) structure, aiming at reducing the weight and enhancing the crashworthiness performances in side-impact collisions. In addition, strength and deformation resistance of the inner door panel are taken into account in the form of constraints in the optimization. First the governing equation of the central processing unit calculation time and the meshing method using the transition region is presented prior to the optimization process. Thus the dimension of the crash model can be controlled efficiently to prepare a fast-speed finite element model required for the later optimization. Then, in the initial design stage, a rough profile of the TWB structure is determined according to the distributions of the removed reinforcements around the inner door panel. Finally, the detailed design combines the ANN and the GA properly to provide an optimal combination of variables selected in the complicated multi-disciplinary problem. The optimal results indicate that the design framework presented here is outstanding with respect to the weight reduction and crashworthiness improvement.