This paper proposes a novel dynamic linearization modeling method for machine tool thermal errors based on data-driven control theory, with improved accuracy and robustness under various practical working conditions of machine tool. The nonlinear, quasi-static and pseudo-hysteric characteristics of the machine tool temperature field are identified as the main causes for poor robustness in conventional thermal error mathematical models. The theoretical and practical difficulties in applying conventional modeling approaches based on the model-based control theory are demonstrated using two types of CNC machine tools as examples. The data-driven control theory is applied to dynamic linearization modeling and the developed data model has shown significant improvement over the general dynamic model in terms of model accuracy and robustness. The feasibility and effectiveness of the proposed dynamic linearization modeling method has been verified using two experiments, demonstrating excellent robustness and ability to adapt to various machining conditions and to improve machining accuracy.

本文提出了一种基于数据驱动控制理论的机床热误差动态线性化建模方法，提高了机床各种实际工况下的精度和鲁棒性。在传统的热误差数学模型中，机床温度场的非线性，准静态和伪磁滞特性被认为是鲁棒性差的主要原因。以两种类型的数控机床为例，说明了在基于模型控制理论的基础上应用常规建模方法的理论和实践困难。数据驱动控制理论被应用于动态线性化建模，并且所开发的数据模型在模型准确性和鲁棒性方面已显示出比一般动态模型显着的改进。