An ExtensionDecision Tree Algorithm for Lightweight Design of Autobody Structure

Abstract

The lightweight design of autobody involves multiple objectives and requires the collaboration between various disciplines. To improve the existing autobody lightweight designs, it is necessary to establish an accurate and objective evaluation method for lightweight effect. This paper proposes an extension decision tree (EDT) algorithm for lightweight design of autobody. The algorithm solves the contradictions in the autobody lightweight design were solved through divergence and convergence. The workflow of the solving process is shaped like a scalable diamond. Specifically, the knowledge of autobody structure was described accurately by extension modeling and extension divergence reasoning. Then, the lightweight design of autobody structure and material was achieved through extension transforms. Next, the EDT algorithm was constructed based on extension theory and the DT algorithm, and used to evaluate the lightweight effect of autobody. Finally, the effectiveness of the proposed algorithm as verified through a case study and a computer-aided engineering (CAE) simulation. The results show that our algorithm can accurately predict the weight reduction effect of autobody based on the case data, and generate a set of intelligent strategies to optimize the current design. The research results shed new light on intelligent evaluation of autobody lightweight design with multiple objectives and constraints.