数控机床热变形误差超前预测研究

针对当前数控机床热变形误差实施补偿存在的预测值滞后实际补偿值问题,提出基于长短期记忆(LSTM)神经网络算法热误差超前预测解决方案,详细探讨LSTM神经网络算法的解析流程,建立基于LSTM神经网络算法热误差超前预测模型,并进行关键温测点及热变形误差超前预测实验。实验结果表明：热变形误差实际值与预测值的最大残差、平均值和均方差均在可控范围内,超前预测的准确度为86.3%,进一步论证了机床热变形误差超前预测的有效性。

Research on the Thermal Deformation Errors Prediction of CNC Machine Tools

Aiming at the problem that the prediction value lags behind the actual compensation value in the CNC machine tool thermal deformation error compensation, a thermal error advance prediction solution based on the long and short-term memory (LSTM) neural network algorithm was proposed, and the analytical process of LSTM neural network algorithm was discussed in detail. The thermal error advance prediction model of the neural network algorithm based on LSTM was established, and the key temperature measurement points and the thermal deformation error advance prediction experiment were carried out. The experimental results show that the maximum residual, the average value and the mean square error of the actual value of the thermal deformation error and the predicted value are all within the controllable range, and the accuracy of the advance prediction is 86.3%, which further demonstrates the effectiveness of the advance prediction of the thermal deformation error of the machine tool.