基于GA-BP网络的数控机床动态误差预测研究

Dynamic error prediction of CNC machine tools based on GA-BP network

动态误差是高速高精度数控机床的重要误差源,针对实际加工过程中动态误差对工件精度影响较大的问题,提出了一种基于遗传算法优化的反向传播(GA-BP)神经网络以预测动态误差。首先,为了提高神经网络对动态误差的预测精度,从线性特征与非线性特征两方面对动态误差影响因素进行了深入分析,确定了神经网络输入输出参数;然后,采用了遗传算法对BP神经网络进行了优化,建立了动态误差模型,获得了最优网络学习参数,从而实现了对动态跟随误差的精准预测;之后,采用三次样条插值的方法对理想轨迹与实际轨迹之间的轮廓误差进行了计算,有效提高了轮廓误差估算精度;最后,采用了五轴数控机床进行了实验,对模型的有效性进行了验证。研究结果表明:所建神经网络模型可以精准预测机床反向越冲特性对轮廓误差的影响,各轴的动态误差预测精度为±3μm,复杂轨迹轮廓误差预测精度为±1.5μm。实验结果验证了所建模型的可靠性,为后续机床动态误差建模与控制研究提供了一定的参考价值。

Dynamic error is an important source of error in the machining process of high-speed and high-precision computer numerical control machine tools.Aiming at the problem that dynamic errors have a significant impact on workpiece accuracy in actual machining,a method for predicting dynamic errors using a back propagation neural network optimized by genetic algorithm(GA-BP)was proposed.Firstly,in order to improve the prediction accuracy of neural networks for dynamic errors,the in-depth analysis was conducted on the influencing factors of machine tool dynamic errors from both linear and nonlinear features,and the input and output parameters of the neural network were determined.Then,genetic algorithm was used to optimize the parameters of the BP neural network and establish a dynamic error model to obtain the optimal learning parameters of the neural network,thereby achieving accurate prediction of dynamic tracking errors.Subsequently,the contour error between the ideal trajectory and the actual trajectory was calculated using cubic spline interpolation,effectively improving the accuracy of contour error estimation.Finally,the model was experimentally validated on a five-axis computer numerical control machine tool.The experimental results show that the neural network model can accurately predict the impact of reverse overshoot machining characteristics on workpiece contour errors,with a dynamic error prediction accuracy of±3μm for each axis.In the prediction of complex trajectory contour errors,the models prediction accuracy reaches±1.5μm.The experimental results verify the reliability of the constructed model,and can provide a certain reference for subsequent research on dynamic error modeling and control of machine tools.