XGBoost-based thermal error prediction and compensation of ball screws

Abstract

The ball screw is an essential component in feed drive systems whose accuracy is seriously affected by machine tool internal and external heat sources. In this paper, a thermal error compensation method for ball screws is proposed based on the extreme gradient boosting (XGBoost) algorithm and thermal expansion principle. An XGBoost predictive model is established using the time series temperature data collected from thermal characteristics experiments. Furthermore, the predictive performance between the XGBoost algorithm and BP neural network is compared to validate the effectiveness and robustness of the proposed model. The results show that the XGBoost model has better predictive performance. Based on this, the temperature of key points on the ball screw can be obtained, and thermal error (which is useful for pulse compensation) is predicted. Simultaneously, thermal error compensation experiments are carried out on the ball screw bench with average results of more than 45%. The presented thermal error compensation method proved effective and can provide a foundation for precision machining.