Development of LSTM and TCN Spindle Thermal Compensation Model by Using the Laser R-Test System

The thermal error of machine tools is a key factor which affects machining accuracy. Currently, most inspection methods build a set of 3-axis or 5-axis non-contact measurement system using capacitance probes. However, since the equipment is expensive and not easy to set up, most thermal error model of machine tools can only be modeled beforehand. Therefore, once the AI model fails, it is often impossible to repair, or the equipment may be required to be brought to the manufacturing site again for installation, set-up, data collection and model building. In view of this, the study uses an optical non-contact spindle temperature measurement system previously developed by the team, which includes a 3D position sensing module, a standard glass ball (mounted on a standard tool holder interface), a PT100 temperature sensing module, an edge computer, and a human-machine interface. During the verification process, the system can effectively collect machine tool thermal data, including XYZ displacements, spindle speed, temperature, etc. By designing a quick tool holder jig, the center of the standard glass ball can be placed at the center of the 3D position sensor, significantly reducing the setup time. As for model building, this study uses XGBoost to establish correlation between temperature parameters and displacement in order to perform preliminary sensor selection. The RMSE and MSE of remaining sensors were then compared. After sensor selection, this study reduces the number of sensors used to 5, 7, 10, and 14. Then, LSTM and TCN is applied to build the thermal error model, with data from Day-1 (2022/07/15) as the training dataset. Using software and hardware modules mentioned in the study, thermal error for the test datasets Day-2 (2022/07/17) and Day-3 (2022/08/15) were decreased by more than 70%, which is also applicable to other dates.