Optimisation for crashworthiness is a critical part of the vehicle development process. Due to stringent regulations and increasing market demands, multiple factors must be considered within a limited timeframe. However, for optimal crashworthiness design, multiobjective optimisation is necessary, and for complex parts, multiple design parameters must be evaluated. This crashworthiness analysis requires computationally intensive finite element simulations. This challenge leads to the need for inverse multi-parameter multi-objective optimisation. This challenge leads to the need for multi-parameter, multi-objective inverse optimisation. This article investigates a machine learning-based method for this type of optimisation, focusing on the design optimisation of a multi-cell side sill to improve crashworthiness results. Furthermore, the optimiser is coupled with an FE solver to achieve improved results.

翻译：耐撞性优化是车辆开发过程中的关键环节。由于严格的法规要求和日益增长的市场需求，必须在有限时间内综合考虑多种因素。然而，为实现最优耐撞性设计，需要进行多目标优化；对于复杂部件，还需评估多个设计参数。此类耐撞性分析依赖于计算密集的有限元仿真。这一挑战催生了多参数、多目标逆向优化的需求。本文研究了一种基于机器学习的此类优化方法，重点针对多腔体侧围梁的设计优化以提升耐撞性结果。此外，该优化器与有限元求解器耦合，从而实现了更优的优化效果。