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

arXiv (Cornell University)

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.

Authors 3

1. Aditya Borse Aachen Institute of General Mechanics

   RWTH Aachen University

   Affiliation as printed

   Institute of General Mechanics (IAM) , RWTH Aachen , Eilfschornsteinstraße 18 , 52062 Aachen , Germany

2. Rutwik Gulakala Aachen Institute of General Mechanics

   RWTH Aachen University

   Affiliation as printed

   Institute of General Mechanics (IAM) , RWTH Aachen , Eilfschornsteinstraße 18 , 52062 Aachen , Germany

3. Marcus Stoffel Aachen Institute of General Mechanics

   RWTH Aachen University

   Affiliation as printed

   Institute of General Mechanics (IAM) , RWTH Aachen , Eilfschornsteinstraße 18 , 52062 Aachen , Germany