Roof-Crush Protection Design of Automotive Bodies Using Clustering and Pattern Recognition

Abstract

Computer-aided engineering (CAE) tools play an indispensable role in the vehicle development process. However, it is difficult to accurately predict the relationships and behavior of automotive bodies in vehicle crashes owing to high-order nonlinearity and numerous design variables of the automotive body structure. In this study, clustering and pattern recognition techniques were used to develop a novel optimization design of an automotive body considering roof crushing by vehicle rollover. The large-scale data were clustered to find the strong and weak clusters, and new response surface models were acquired by clustering analysis to achieve better performance than the response surface model of traditional optimization. For an efficient robust design, clusters with weak performance were excluded from the optimum solution. Finally, it was confirmed that the solutions by the proposed optimization technique were better than those obtained by the traditional optimum method based on a comparative analysis by various cluster combinations.

Keywords

• clustering
• pattern recognition
• machine learning
• optimum design
• vehicle
• roof crush