Research on the rapid design technology of automotive inspection structure based on MBD model. (July 2022)

• Record Type: Journal Article
• Authors: Wang, Shuchun; Cheng, Qi; Wang, Lina; Xu, Jingying; Fang, Xifeng; Kang, Jie
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 236:Number 8(2022)
• Page Start: 1670
• Page End: 1686
• Publication Date: 2022-07
• Subjects: BP neural network algorithm -- FCM clustering algorithm -- skeleton structure -- automobile inspection fixture -- deformation design; Mechanical engineering -- Congresses; Transportation engineering -- Congresses; 629.2
• DOI: 10.1177/09544070211065201
• Languages: English
• ISSNs: 0954-4070

Abstract

In view of the specificity and low efficiency of the design of automobile inspection fixture, a deformation design method of inspection fixture based on BP neural network algorithm is proposed. BP neural network algorithm is used to realize the learning and classification of case knowledge, and FCM (fuzzy c-means) algorithm and kernel principal component analysis are used to optimize the information source to improve the retrieval efficiency and accuracy. Based on the analysis of the existing fixture design case structure, the case structure is skeletonized to increase the applicability of the case structure. At the same time, the case frame structure is associated with the size chain, the priority deformation rule is proposed, and the relationship of each size chain is established to realize the mutual adjustment of each size chain. From the similarity of retrieval cases, the paper proposes the design scheme of inspection tools to improve the design efficiency. Finally, taking the front bumper model as the experimental object, the deformation rules are compared, and the priority deformation rule is more accurate than the ordinary basic rule. Compared with the manual design, the design efficiency of this method is improved by 55.71%, which proves the feasibility of this method.