基于温度相似性评价的机床热误差重构模型  

Thermal Error Reconstruction Model of Machine Tools Based onTemperature Similarity Evaluation

作　　者：徐凯 李喆裕 李国龙[2] 苗恩铭 庹军波 XU Kai;LI Zheyu;LI Guolong;MIAO Enming;TUO Junbo(College of Mechanical Engineering,Chongqing University of Technology,Chongqing 400054;State Key Laboratory of Mechanical Transmission,Chongqing University,Chongqing 400044;College of Mechanical Engineering,Chongqing Technology and Business University,Chongqing 400067)

机构地区：[1]重庆理工大学机械工程学院,重庆400054 [2]重庆大学机械传动国家重点实验室,重庆400044 [3]重庆工商大学机械工程学院,重庆400067

出　　处：《机械工程学报》2024年第5期288-295,共8页Journal of Mechanical Engineering

基　　金：国家自然科学基金(52005065,U22B2084)资助项目。

摘　　要：数据驱动方法广泛用于热误差建模,然而缺乏机理支撑的开环黑箱运行模式难以保证模型在新工况中的鲁棒性,模型极易失效。为进一步提升机床热误差模型的精度和稳定性,提出了一种基于温度相似性评价的机床热误差重构模型。通过对预测组温度均值向量与建模组温度均值向量的相似性评价,从原始建模批次中筛选出与预测组温度相似的特定批次,并基于偏最小二乘算法进行热误差模型重构。为验证重构模型的有效性,进行了累计31批次的热误差预测,结果表明重构模型在保证稳定性的前提下,可将预测结果的均方根误差均值进一步降低至2.2μm,相比常规的模糊聚类结合多元线性回归模型与偏最小二乘模型,预测精度可分别提高15%、35%,效果显著。方法对于评价数据驱动热误差模型的适用性,提高模型预测精度具有参考价值。Data driven methods are widely used in thermal error modeling,but the open-loop and black-box operation mode without mechanism support is difficult to ensure the robustness of the model in new operating conditions,resulting in the failure of the model.To further improve the accuracy and stability of the thermal error model of machine tools,this paper proposes a thermal error reconstruction model based on temperature similarity evaluation.By evaluating the similarity between the temperature mean vector of the prediction group and the temperature mean vector of the modeling group,a specific batch similar to the temperature of the prediction group is selected from the original modeling batch,and the thermal error model is reconstructed based on the partial least squares algorithm.To verify the effectiveness of the reconstruction model,the thermal error prediction of 31 batches was conducted.The results show that the reconstruction model can further reduce the mean root mean square error of prediction results to 2.2μm.And on the premise of ensuring stability,the prediction accuracy can be improved by 15%and 35%respectively compared with the conventional multiple linear regression model combining fuzzy clustering and the partial least squares model,showing the significant effect.The method has reference value for evaluating the applicability of data-driven thermal error models and improving model prediction accuracy.

关 键 词：相似性评价 重构模型 偏最小二乘算法 稳定性 

分 类 号：TH161[机械工程—机械制造及自动化]