[This article belongs to Volume - 53, Issue - 06]
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-12-01-2022-98

Title : Deep Learning Prediction for Thermal Error of CNC Machine Tools Based on Attention Mechanism
DU Liuqing, LI Renjie, LI Baochuan,

Abstract :

Thermal error prediction and compensation of CNC machine tools is an important technology to improve the machining accuracy and reliability of CNC machine tools. The thermal error of machine tool is time-varying and nonlinear. To improve the accuracy and robustness of thermal error prediction, a numerical control machine tool thermal error prediction model based on attention mechanism and deep learning network was proposed. Using the data conversion strategy, the original temperature data of CNC machine tool was transformed into temperature image, which could be directly used as the input of deep learning network. The complete information of the temperature field of the machine tool was retained by converting the temperature field data into the temperature image points. At the same time, the nonlinear and coupling problems between the temperature measuring points were avoided by using the deep learning modeling method. A recognition network of temperature sensitive points based on attention mechanism was proposed. According to the correlation degree between temperature measuring points and thermal error, different weights were given to each temperature measuring point to avoid the disadvantages of artificial selection of temperature measuring points. A 12–layer deep CNN learning prediction network was established to mine the nonlinear mapping relationship between temperature image and thermal error by using its powerful image feature learning ability. This method does not need to preselect the key temperature points, retained more relationship between thermal error and machine temperature characteristics, and can significantly improve the prediction accuracy of the model. In order to improve the accuracy and generalization ability of thermal error model, dropout regularization method and Adam optimization algorithm were introduced to optimize the structure and parameters of deep convolution neural network. The method shows high prediction accuracy in the thermal error verification of G460L CNC lathe. Compared with the thermal error models based on BP neural network, multiple regression and CNN network, the proposed method performs better in generalization performance.