Quality control in multistage machining processes based on a machining error propagation event-knowledge graph

Abstract

In multistage machining processes (MMPs), a clear understanding of the error accumulation, propagation, and evolution mechanisms between different processes is crucial for improving the quality of machining products and achieving effective product quality control. This paper proposes the construction of a machining error propagation event-knowledge graph (MEPEKG) for quality control in MMPs, inspired by the application of knowledge graphs to data, information, and knowledge organization and utilization. Initially, a cyber-physical system (CPS)-based production process data acquisition sensor network is constructed, and process flow-oriented process monitoring is achieved through the radio frequency identification (RFID) production event model. Secondly, the process-related quality feature and working condition data are preprocessed; features are extracted from the distributed CPS nodes; and the production event model is used to achieve the dynamic mapping and updating of feature data under the guidance of the MEPEKG schema layer. Moreover, the mathematical model of machining error propagation based on the second-order Taylor expansion is used to quantitatively analyze the quality control in MMPs based on the support of MEPEKG data. Finally, the efficacy and reliability of the MEPEKG for error propagation analysis and quality control of MMPs were verified using a case study of a specially shaped rotary component.