数控机床主轴热误差的数据驱动模型研究

当实际工况与建模工况存在差异时,传统的热误差模型往往表现出较差的鲁棒性和预测精度,主要原因在于建模数据的局限性和模型的未建模动态。为了改善上述状况,提出了一种基于数据驱动的数控机床主轴补偿模型。此模型采用无模型自适应控制算法建模,结合机床运行中生成的数据(温度数据和误差数据)对热误差模型进行实时修正,使模型能快速适应新的加工工况,从而提高模型的鲁棒性。在一台数控车床主轴上进行了试验验证,结果表明:无模型自适应控制与多元回归模型比较,其标准差、最大残差和误差平方和分别提高了41%、62%和56%,此模型的鲁棒性和预测效果好。同时,此方法为大数据在机床主轴热误差补偿中的应用奠定了基础。

Study on Thermal Error Model of CNC Machine Tool Spindle Based on Data-Driven

Traditional thermal error model often shows a poor robustness and prediction accuracy when there are differences between actual condition and modeling condition. The main reasons are the limitation of modeling data and the unmodeled dynamics of the model. In order to improve the above phenomenon, a thermal error compensation modeling method based on data-driven is proposed for the computer numerical control (CNC) machine spindle. The method was utilized of data (temperature and error) collected in process of machining and a model-free adaptive control algorithm to build up model and to modify in real time, which could rapidly adapt to various operating conditions and then to improve its robustness. An experiment was develop to verify its effect on a CNC lathe. The results show that, compared to Multiple Linear Regression (MLR) model, the method improves the standard deviation, maximum residual and error squares by 41, 62 and 56 percent, respectively, with good effect in robustness and forecast. In addition, the method lay a foundation for big data to apply to thermal error compensation of the CNC machine spindle.