以TX1600 G镗铣加工中心镗削系统主轴部件为研究对象，针对其热误差问题，提出一种基于小波神经网络的预测方法。首先根据镗铣加工中心主轴部件的结构特点建立其有限元模型，基于该模型进行热-结构耦合分析，进而选取热关键点并获取其样本数据；然后利用小波神经网络建立主轴热误差预测模型，并与BP神经网络预测结果相对比；最后结果表明小波神经网络预测精度高，为该加工中心的主轴热误差预测提供了理论依据，该方法同样适用于其它主轴热误差的前期预测。%Taking the boring spindle system of TX1600G boring-milling machining center as the research ob-ject, a wavelet neural network-based prediction method is proposed to solve the thermal error problem. First-ly a finite element model of the spindle is established according to the structural characteristics of the boring-milling machining center, thus the thermal key points are selected and the sample data are obtained after the thermal-structure coupling analysis is processed based on the model above; secondly, with the method of wavelet neural network, the prediction model of spindle thermal error is built up, which compared with the prediction results of BP neural network;finally, the results indicate that the prediction based on wavelet neu-ral network is of higher precision, which provides a theory evidence for the thermal error prediction of the machining center spindle and this method is also applicable to what predicts the spindle error of other types.
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