머신러닝 및 베이지안 최적화를 이용한 타이어 최적 설계

Abstract

Product design optimization plays an important role in the manufacturing industry. In the tire manufacturing industry, design optimization process traditionally involves generation of tire design candidates and quality prediction by using finite element analysis (FEA). However, this traditional process requires expert’s experiences to derive design candidates that satisfies target quality. In addition, FEA requires a lot of time to obtain the prediction results although it provides accurate predictive performance. To overcome these issues, we propose Bayesian optimization based on a predictive model for the tire design. We train a model that can predict multiple quality variables and perform Bayesian optimization that can optimize numerical and categorical variables simultaneously. Results show that the proposed method can effectively predict and optimize the tire design with reduced time complexity.