A Knowledge-Embedded End-to-End Intelligent Reasoning Method for Processing Quality of Shaft Parts

The machining quality of a part is one of the most important factors affecting work effectiveness and service time, and it is closely related to multistage manufacturing processes (MMPs). State space model (SSM) is a typical method to analyze error propagation in MMPs, which contains the deep laws of error propagation, but the modeling process is complicated and the perception of quality is afterwards. In actual production, it is difficult to realize the pre-reasoning and control of processing quality. To address the above problems, an end-to-end intelligent reasoning method for processing quality with SSM knowledge embedding is proposed. On the one hand, the knowledge embedded in SSM is used for data simulation, and on the other hand, the end-to-end mapping between measured dimensions and processing quality of each process is realized by an Adaptive Network-based Fuzzy Inference System (ANFIS). In this paper, wall thickness difference (WTD) is used to describe the machining quality of shaft parts, and four sections of four processes are studied. SSM was constructed and validated using workshop data, and the average relative error for the six shafts was 5.54%. In the testing phase of the intelligent reasoning model, the maximum RMSE and MAE of the models for the four processes were 4.47 mu m and 3.23 mu m, respectively, satisfying the WTD prediction requirements.