基于改进神经网络算法的数控钻攻中心进给轴热误差预测

为降低数控机床热误差对数控钻攻中心的影响,提高工件的加工精度,解决不同工况下热误差预测精度不佳的问题。 在进给速度为 10 m/ min、环境温度 20°的工作条件下进行数控机床进给系统热误差测量实验,采用鹈鹕优化算法对神经网络进 行优化,确定 BP 神经网络的最优权值和阈值,建立进给系统热误差的 POA-BP 预测模型,并与传统 BP 神经网络和 GA-BP 神经 网络以及 SCN 随机配置网络进行实验对比分析。 结果表明,传统 BP 神经网络预测平均相对误差为 12. 23%,GA-BP 神经网络 平均相对误差为 11. 5%,SCN 预测模型预测平均相对误差为 12. 71%,POA-BP 预测模型预测平均相对误差为 9. 93%,精度有所 提升。 结论:提出的鹈鹕优化算法改进的神经网络在热误差预测中具有较强的有效性和精确性,可以提高进给运动精度,为热 误差补偿的实现提供理论指导。

Prediction of thermal error of CNC drilling center feed axis based on improved neural network algorithm

To reduce the impact of thermal errors on CNC machine tools, improve the machining accuracy of workpieces, and solve the problem of poor thermal error prediction accuracy under different working conditions. The thermal error measurement experiment of the CNC machine tool feed system is conducted under working conditions of a feed speed of 10 m/ min and an ambient temperature of 20°. The Pelican optimization algorithm is used to optimize the neural network, determine the optimal weight and threshold of the BP neural network, and the thermal error of the feed system prediction model of POA-BP is established. The experiment is compared and analyzed with traditional BP neural network, GA-BP neural network and the SCN random configuration network. The results show that the average relative error of traditional BP neural network prediction is 12. 23%, the average relative error of GA-BP neural network is 11. 5%, the average relative error of SCN prediction model is 12. 71%, and the average relative error of POA-BP prediction model is 9. 93%, which improves the accuracy. Conclusion: The neural network improved by the proposed Pelican optimization algorithm has strong effectiveness and accuracy in thermal error prediction, which can improve the accuracy of feed motion and provide theoretical guidance for the realization of thermal error compensation.