Weighted Self-Regulation Complex Network-Based Variation Modeling and Error Source Diagnosis of Hybrid Multistage Machining Processes

Abstract

Manufacturing of complex parts is often finished in a hybrid multistage machining process, in which various error transfers and accumulates generally happen in multiple processes. To identify the key processing features and error sources in the processing of complex parts, a complex network-based method is proposed in this paper. First, a weighted self-regulation variation propagation network (WSRVPN) is developed to describe the relationship between the actual error and the machining process in serial–parallel multistage manufacturing processes (SP-MMP). Then, the weighted LeaderRank algorithm is employed to recognize the key processing features in the constructed WSRVPN. Finally, the traveling algorithm is proposed to search the error propagation paths for potential problem nodes. A quantifying index called ContributionIndex is proposed to identify the error source from the propagation paths to determine the out-of-control machining nodes. An industrial case (i.e., machining process of the main bearing cap) based on SP-MMP is used to verify the effectiveness of the proposed method. The experimental results indicate that the proposed method can effectively identify key nodes and variation sources in SP-MMP.