Digital Twin-Driven Thermal Error Prediction for CNC Machine Tool Spindle

Abstract

Traditional methods for predicting thermal error ignore the correlation between physical world data and virtual world data, leading to the low prediction accuracy of thermal errors and affecting the normal processing of the CNC machine tool (CNCMT) spindle. To solve the above problem, we propose a thermal error prediction approach based on digital twins and long short-term memory (DT-LSTM). DT-LSTM combines the high simulation capabilities of DT and the strong data processing capabilities of LSTM. Firstly, we develop a DT system for the thermal characteristics analysis of a spindle. When the DT system is implemented, we can obtain the theoretical value of thermal error. Then, the experimental data is used to train LSTM. The output of LSTM is the actual value of thermal error. Finally, the particle swarm optimization (PSO) algorithm fuses the theoretical values of DT with the actual values of LSTM. The case study demonstrates that DT-LSTM has a higher accuracy than the single method by nearly 11%, which improves the prediction performance and robustness of thermal error.