Study of static thermal deformation modeling based on a hybrid CNN-LSTM model with spatiotemporal correlation

The thermal error of a machine tool is one of the main factors affecting the machining accuracy. By establishing the error model and compensating the error, the accuracy can be improved effectively. This paper presents a novel static thermal deformation modeling method based on a hybrid CNN-LSTM model with spatiotemporal correlation (ST-CLSTM). Firstly, by organizing the temperature data into a specific matrix, a sample set with spatiotemporal characteristics is constructed. Secondly, using convolutional neural network (CNN) to extract spatiotemporal features in the sample set, the problem of selecting temperature-sensitive points in thermal error modeling can be solved. Thirdly, the long short-term memory (LSTM) network is used to capture the characteristics of temperature change abstractly from the perspective of the time series of temperature data. Finally, the ST-CLSTM model is verified at different working conditions and compared with other traditional methods, such as the multiple linear regression (MLR) model, the back propagation neural network (BPNN) model, the CNN model, and the LSTM model. The experimental results show that the ST-CLSTM model obtains higher prediction accuracy in X, Y, and Z directions, which guarantees the stability of prediction performance. The proposed model possesses strong robustness and shows a preliminary industrial application prospect.