2026 Volume 39 Issue 1 Pages 1-9
In machining, errors occur due to the complex effects of multiple factors, such as thermal deformation of tools and workpieces, deformation due to cutting forces, and deterioration of machine tools. To reduce machining errors, it is therefore necessary to consider the interaction of these multiple factors. In recent years, studies have been conducted on the prediction of machining errors by combining machine learning with monitoring data that can be obtained through digitization. Most of these studies, however, only predict machining errors and do not analyze the error factors. In this study, we developed a system that enables factor analysis by identifying the variables that have the highest accuracy in predicting machining errors in machine learning models. We then visualize the reasons behind error prediction using an interpretation method for the machine learning models created from the variables. We demonstrate the effectiveness of the proposed system in an experiment using an end-milling machine.
