Study on the Coupling Mechanism of Machining Process Parameters and Part Machining Accuracy Prediction in Intelligent Production Line

This paper studies part machining accuracy prediction. Firstly, the coupling mechanism of intelligent production line machining process parameters is studied, and a parameter coupling model based on correlation analysis is proposed, which can consider the interaction and influence between different parameters and optimize the machining process to a certain extent. Then, a machine learning-based part machining accuracy prediction method is proposed for the actual situation of intelligent production line machining. The method combines error network diagrams for multi-process machining units and enables the prediction of part machining accuracy by combining the production results of the previous process with the process parameters of the current process. Finally, the effectiveness and feasibility of the proposed parameter coupling model and accuracy prediction method are verified through experiments. The experimental results show that the proposed method can significantly improve the accuracy and stability of part machining, and also provide a certain theoretical and practical basis for the optimization of intelligent production lines. This study has important theoretical and practical significance for improving machining quality, reducing costs and improving enterprise competitiveness.