第23卷第11期 2015年11月
文章编号
1004-924X(2015)11-3176-07
光学精密工程
Optics and Precision Engineering
数控机床热误差补偿最佳转速选择
苗恩铭*，吕玄玄，苗继超，党连春
（合肥工业大学仪器科学与光电工程学院，安徽合肥230009）
Vol. 23No.11
Nov.2015
摘要：为提高数控机床热误差补偿模型在实际工程应用中的补偿精度和稳健性，研究了热误差补偿建模时机床最佳转速状态的选择方法。首先，以LeaderwayV-450数控加工中心主轴Z向为研究对象，控制机床主轴在空转状态下，以图谱和恒定转速两种方式进行了多批次实验。然后，采用模糊聚类结合灰色关联度选择温度敏感点并建立多元线性回归模型。最后，分析不同转速类型下模型的预测效果并对同种转速类型下模型预测效果进行相对评价，从而给出热误差补偿建模时机床最佳转速状态的选择方法。实验结果表明，根据国际标准中不同主轴转速类型建立的热误差补偿模型，对于
机床热误差预测效果存在较大差异。根据实际工程应用选择的最佳转速状态建立的补偿模型有较好的预测效果。关键调：数控机床；热误差；主轴转速；多元国归模型；预测效果
中图分类号：TG659;TH161
文献标识码：A
doi;10.3788/OPE.20152311.3176
Selectionofoptimumspindlespeedtothermal
errorcompensationof machinetools
MIAOEn-ming',LU Xuan-xuan,MIAO Ji-chao,DANG Lian-chun(School of Instrument Science and Opto-electronics Engineering,
HefeiUniversityofTechnology，Hefei230009,China)*Correspondingquthor，E-mail:miaoem@163.com
Abstract: To improve the compensation accuracy and robustness of a thermal error compensation model for Computer Numerical Control(CNC) machine center in practical engineering applications, a selection method for the optimal speed of CNC machine center was presented. For the Z-axis of Leaderway V-45o CNC machining center, several batches of experiments were carried out at a variable speed spectrum and a constant speed by controlling the spindle in an idling condition. Then, fuzzy clustering method combined with the gray correlation was used to select the temperature sensitive point and establish multiple linear regression models. Finally, by analysis of the model predictive effect in different type speeds and comparison of the model predictive effect in the same speed type, a selection method for the optimum spindle speed of CNC center in modeling of thermal error compensation was given. The research results show that thermal error compensation models established by different spindle speed regimes based on the international standard have great differences in the thermal error predictive effect of machine tools. It suggests that the compensation
收稿日期：2015-03-07；修订日期：2015-04-15，
基金项目：国家自然科学基金重大项目（No.51490660/51490661）；国家自然科学基金资助项目（No.51175142/
E051102)