Development of a machine learning-based design optimization method for crashworthiness analysis

Abstract

This article investigates design optimisation in the automotive field using machine learning (ML). A thin-walled crash box under axial impact is studied and the design parameters are optimised for front-impact crash tests. This study is based on geometrically and physically nonlinear shell theory, finite element analysis (FEA), dynamic buckling analysis and design optimisation using ML. An artificial neural network framework consisting of various ML methods is developed. A generative adversarial network is established for data generation and reinforcement learning is implemented to automate exploration of the design parameter. This ML framework is proven to determine optimal parameters under predefined crashworthiness constraints.