多传感器融合的数控机床热误差回归建模新方法

A New Regression Modeling Method for Thermal Error of Numerical Control Machine Tool Based on Multi-Sensor Fusion

热误差对机床的加工精度影响很大,高性能的补偿系统依赖于多传感器融合建立的三维模型的精度、鲁棒性和合适的温度进行反馈输入。使用温度与位移传感器的模糊聚类进行温度分类,基于评价模型比对分析最优的温度分类,从每个分类中选择具有代表性温度作为候选温度。归纳试验数据,使用分段逆回归SIR模型进行热误差建模,SIR模型将高维前移回归问题转化为多个一维的回归问题,并且进一步消除了候选温度之间的耦合。热误差试验表明,SIR模型具有泛化能力强、预测精度高及鲁棒性好的特点,能够准确地描述多种典型工况条件下的实际热误差特性。

Thermal error significantly influences the accuracy of CNC machine tool.A high-performance compensation system depends upon the accuracy and robustness of the model and appropriate model inputs of temperatures.In this paper,fuzzy clustering is conducted in temperature classification.Based upon an evaluation model,an optimal temperature classification can be found.The representative temperatures from each group construct temperature candidates.Then,a sliced inverse regression(SIR)model is introduced in thermal error modeling,which can change the problem of high-dimensional forward regression into several one-dimensional regression and meanwhile further eliminate the coupling among temperatures candidates.A series of experiments for thermal error modeling verify the validity of this method.SIR model has good generalization ability,accurate prediction and robustness,so it can describe the actual thermal error characteristics more accurately under multiple work conditions.