The impact of self-learning thermal error compensation models on the accuracy of 4-axis thermal test pieces

Abstract

Self-learning thermal error compensation models are a cost-effective and robust approach to reduce thermal errors of machine tools. This paper evaluates a self-learning thermal error compensation strategy under cutting conditions with and without metal working fluid using thermal test pieces mapping the most relevant thermal errors of a rotary table. The experimental results indicate that the self-learning thermal error compensation models reduce the deviations of the thermal test pieces for both cutting conditions between 63% and 73%. Consequently, the machining results on the thermal test pieces verify the ability of the self-learning thermal compensation models to reduce robustly thermal errors of machine tools.