An Improved Robust Thermal Error Prediction Approach for CNC Machine Tools

Thermal errors significantly affect the accurate performance of computer numerical control (CNC) machine tools. In this paper, an improved robust thermal error prediction approach is proposed for CNC machine tools based on the adaptive Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) algorithms. Specifically, the adaptive LASSO method enjoys the oracle property of selecting temperature-sensitive variables. After the temperature-sensitive variable selection, the XGBoost algorithm is further adopted to model and predict thermal errors. Since the XGBoost algorithm is decision tree based, it has natural advantages to address the multicollinearity and provide interpretable results. Furthermore, based on the experimental data from the Vcenter-55 type 3-axis vertical machining center, the proposed algorithm is compared with benchmark methods to demonstrate its superior performance on prediction accuracy with 7.05 mu m (over 14.5% improvement), robustness with 5.61 mu m (over 12.9% improvement), worst-case scenario predictions with 16.49 mu m (over 25.0% improvement), and percentage errors with 13.33% (over 10.7% improvement). Finally, the real-world applicability of the proposed model is verified through thermal error compensation experiments.

Automated summary

This paper proposes an improved robust thermal error prediction approach for CNC machine tools based on the adaptive LASSO and XGBoost algorithms. The approach selects temperature-sensitive variables and models thermal errors using the XGBoost algorithm, resulting in improved prediction accuracy and robustness. Experimental data demonstrates its superior performance compared to benchmark methods.