基于迁移学习的异工况下机床热误差建模方法

Thermal error modeling method for machine tool under different working conditions based on transfer learning

机床热误差预测模型在不同工况下难以保持高预测精度是导致热误差实际补偿效果差的重要原因,对此本文提出一种基于迁移学习的异工况下机床热误差建模方法。首先利用核均值匹配算法获取不同工况下机床温度数据间的迁移权重,从而提出基于迁移学习的热误差建模方法;对不同工况下热误差数据进行差异显著性检验,并利用本文所提方法建立热误差预测模型,分析建模效果;然后比对分析本文所提建模方法与常用建模方法的实际预测效果,最后进行补偿验证实验以证明本文所提方法的有效性。结果表明,本文所提基于迁移学习的建模方法能够有效提升建模效果,其中迁移学习结合LASSO算法针对不同工况下热误差数据的预测精度和稳健性分别达到3.73和1.14μm,补偿后机床X/Y/Z 3个方向热误差分别保持在-2.3~3.1μm、-3.4~3.9μm和-3.3~4.6μm范围内。

The difficulty in maintaining high prediction accuracy of machine tool thermal error prediction models under different working conditions is an important reason for thermal errors′poor actual compensation effect.This article proposes a modeling method for the thermal error of machine tools under different working conditions based on transfer learning.Firstly,the kernel mean matching algorithm is used to obtain the transfer weight between machine tool temperature data under different working conditions.And a thermal error modeling method based on transfer learning is proposed.Furthermore,the significance of differences in thermal error data under different working conditions is tested,and a thermal error prediction model is formulated by using the proposed method to analyze the modeling effect.Then,the actual prediction performance of the proposed modeling method and commonly used modeling methods are compared and analyzed.Finally,the compensation validation experiments are conducted to evaluate the effectiveness of the proposed method.The results show that the modeling method based on transfer learning proposed in this paper can effectively improve the modeling effect.The prediction accuracy and robustness of transfer learning combined with the LASSO algorithm under different working conditions reach 3.73 and 1.14μm,respectively.After compensation,the thermal errors in the X/Y/Z directions of the machine tool remain within-2.3~3.1μm,-3.4~3.9μm,and-3.3~4.6μm,respectively.