Developement of Reinforcement Learning based Optimisation Method for Side-Sill Design

TL;DR

This paper introduces a reinforcement learning-based optimization method for designing vehicle side sills to enhance crashworthiness, integrating machine learning with finite element simulations for efficient multi-parameter, multi-objective optimization.

Contribution

It presents a novel reinforcement learning approach coupled with FE simulations for multi-parameter, multi-objective crashworthiness optimization of vehicle side sills.

Findings

Improved crashworthiness design outcomes.

Efficient multi-parameter optimization process.

Successful integration of RL with FE simulations.

Abstract

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.