Proposing Method for Automotive Structural Component Cross-Sectional Designs through Diffusion Model

Abstract

In the development of automotive structural components, there is a demand to ensure high energy absorption performance while designing lightweight structures within a short timeframe. Surrogate models are effective means for efficiently conducting structural investigations; however, structural generation is often parametric, limiting the freedom of shape variation. In this study, we apply a diffusion model to propose cross-sectional shapes that meet the target energy absorption performance. By employing AI to suggest component structures, innovation in automotive parts design is anticipated.