Spindle Thermal Error Prediction Based on LSTM Deep Learning for a CNC Machine Tool

Applied Sciences

Yao-Cheng Tsai¹  Yu-Chi Liu¹  Kun-Ying Li² 

[1] Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan;Graduate Institute of Precision Manufacturing, National Chin-Yi University of Technology, Taichung 411030, Taiwan;

关键词: spindle thermal error;    elbow method;    long short-term memory (LSTM);   

DOI  :  10.3390/app11125444

来源: DOAJ

【 摘 要 】

In the precision processing industry, maintaining the accuracy of machine tools for an extensive period is crucial. Machining accuracy is affected by numerous factors, among which spindle thermal elongation caused by an increase in machine temperature is the most common. This paper proposed a key temperature point selection algorithm and thermal error estimation method for spindle displacement in a machine tool. First, highly correlated temperature points were clustered into groups, and the characteristics of small differences within groups and large differences between groups were realized. The optimal number of key temperature points was then determined using the elbow method. Meanwhile, the long short-term memory (LSTM) modeling method was proposed to establish the relationship between the spindle thermal error and changes of the key temperature points. The results show the largest root mean square errors (RMSEs) of the proposed LSTM model and the key temperature point selection algorithm were within 0.6 µm in the spindle thermal displacement experiments with different temperature changes. The results demonstrated that the combined methodology can provide improved accuracy and robustness in predicting the spindle thermal displacement.

【 授权许可】

Unknown