Multi-criteria structural optimization of a hood inner structure using machine learning technique

Abstract

This article presents an efficient multi-criteria structural optimization process based on machine learning for shape and size optimization of the hood inner. For this purpose, a low dimensional parameterization based on two-dimensional mapping and dimensionality reduction is used to decrease the number of design variables. There are many design parameters to be considered for designing this structure. In this study, the effect of three important criteria consisting of head injury, eigenfrequency, and static load were analyzed. A full vehicle finite element model is prepared for a newly developed product because head injury analysis requires a full vehicle model due to contact with the inner components of the engine compartment. Then, an experimental study is performed based on the real test scenario for model verification. A new rating method for analyzing the head injury criteria evaluated in different points is proposed and different machine learning techniques are compared in the approximation of the objective function. Finally, the problem is solved as both single and multi-objective optimization problems. Results present considerable improvement in accuracy and efficiency in the identified structure while the method has a low computational cost and the implementation is not difficult.