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Dynamic error prediction of CNC machine tools based on GA-BP network
Published:2024-10-30 author:LI Shuaijie, CHEN Guangsheng. Browse: 126 Check PDF documents
Dynamic error prediction of CNC machine tools based on GA-BP network


LI Shuaijie, CHEN Guangsheng

(College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China)


Abstract: 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 model‘’s 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.

Key words:  high-speed and high-precision computer numerical control machine tools; dynamic error; nonlinear characteristics; back propagation neural network optimized by genetic algorithm (GA-BP); contour error estimation


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