Thermal Error Prediction and Compensation of Digital Twin Laser Cutting Based on T-XGBoost.

Abstract

Laser cutting belongs to non-contact processing, which is different from traditional turning and milling. In order to improve the machining accuracy of laser cutting, a thermal error prediction and dynamic compensation strategy for laser cutting is proposed. Based on the time-varying characteristics of the digital twin technology, a hybrid model combining the thermal elastic–plastic finite element (TEP-FEM) and T-XGBoost algorithms is established. The temperature field and thermal deformation under 12 common working conditions are simulated and analyzed with TEP-FEM. Real-time machining data obtained from TEP-FEM simulation is used in intelligent algorithms. Based on the XGBoost algorithm and the simulation data set as the training data set, a time-series-based segmentation algorithm (T-XGBoost) is proposed. This algorithm can reduce the maximum deformation at the slit by more than 45%. At the same time, by reducing the average volume strain under most working conditions, the lifting rate can reach 63% at the highest, and the machining result is obviously better than XGBoost. The strategy resolves the uncontrollable thermal deformation during cutting and provides theoretical solutions to the implementation of the intelligent operation strategies such as predictive machining and quality monitoring.