A method for monitoring machining errors of complex thin-walled parts based on the fusion of physical information and CNN

Machining errors of complex thin-walled parts have a direct impact on product quality and performance, making their monitoring essential. The monitoring of machining errors of such parts often depends on relevant physical information. However, time-varying physical information (TVPI) is influenced by the dynamic response of the measurement system, and the spatial dynamic relationship between the physical information and machining errors is highly intricate, posing significant challenges for monitoring. To address these challenges, a method based on the fusion of physical information and Convolutional Neural Network (CNN) is proposed for monitoring machining errors of complex thin-walled parts. Initially, a TVPI identification method based on physical theory is introduced, and the spectrum amplitudes of cutting forces are extracted as the TVPI for monitoring machining errors. The feature extraction and nonlinear regression modeling capabilities of the CNN are then leveraged to filter the physical information intelligently and learn the complex relationship between the physical information and machining errors. Ultimately, a monitoring method for machining errors based on the fusion of physical information and the CNN is proposed and experimentally validated on complex thin-walled parts such as blades. Compared with traditional feature identification methods, the TVPI identification method provides enhanced physical interpretability. Additionally, the fusion of physical information and the CNN notably improves the monitoring performance. Compared with monitoring methods based on Gaussian Process Regression, Deep Neural Network and Long Short-Term Memory, the monitoring method based on the fusion of physical information and the CNN results in at least a 21.74 % reduction in the RMSE. The method not only provides valuable feedback on machining errors of complex thin-walled parts but also offers technical support for the subsequent optimization and adjustment of machining strategies.