Thermal Error Modeling of Spindle for Precision CNC Machine Tool Based on AO-CNN

To accurately predict the thermal error of the spindle of a CNC machine tool and avoid serious impacts of such thermal error on gear machining accuracy, a thermal error model of grinding machine spindle based on AO-CNN is proposed by combining the convolutional neural network (C N N) with strong self-learning and self-adaptive abilities and the aquila optimizer (AO) with strong ability to solve the optimal solution. Firstly, the thermal deformation principle of the spindle and the grinding process were analyzed, and it is found that the thermal error in X direction is the main factor affecting the machining accuracy. Then, the key temperature points were selected by use of the fuzzy C-means clustering (FCM) algorithm with relevant coefficients. The convolution kernel of CNN structure was optimized by AO algorithm and the X-direction thermal error prediction model was established for the spindle of the CNC machine tool based on AO-CNN. Finally, the performance of the model was verified under two experimental conditions at different speeds. And the results show that the thermal error prediction accuracy of AO-CNN model in X direction of the CNC machine tool improved by 15 % compared with the CNN model providing superior prediction accuracy. © 2022 Xi'an Jiaotong University. All rights reserved.