A Novel Quality Defects Diagnosis Method for the Manufacturing Process of Large Equipment Based on Product Gene Theory

• Main Authors: Wenxiang Xu, Chen Guo, Shunsheng Guo, Xixing Li
• Format: Article
• Language: English
• Published: MDPI AG 2019-05-01
• Series: Symmetry
• Subjects: quality defects diagnosis; manufacturing process; product gene; large equipment; similarity evaluation
• Online Access: https://www.mdpi.com/2073-8994/11/5/685

Focusing on the problems of quality information management and quality defects diagnosis in the manufacturing process of large equipment, a novel quality defects diagnosis method based on product gene theory and knowledge base was developed. First, a product gene model and a sectional encoding method for the quality control of the manufacturing process of large equipment were proposed. In that model, the processing surface was the minimum information granularity to meet the production characteristics of large equipment and to improve the flexibility of the product gene model. Then, a similarity evaluation rule and an optimization method of the weights of elements based on particle swarm optimization (PSO) were addressed to filter the available knowledge of product gene from the product gene knowledge base. Aiming at the characteristic of many-to-many between quality defects and quality influence factors in some cases, a fuzzy comprehensive evaluation (FCE) method was developed for the further localization of diagnosis knowledge. Finally, an experiment of bearing spacer was applied to illustrate the proposed quality diagnosis approach. In the experiment, the data from the target gene and knowledge genes were described reasonably. On this basis, available knowledge genes could be accurately filtered with the proposed similarity rule and the method of filtration, where the PSO was proved to be effective. The diagnosis results of the experiment show that multiple factors lead to the defects that were verified. Therefore, the proposed quality defects diagnosis method is an effective way for quality control.