A numerical control machining tool path step error prediction method based on BP neural network

Abstract

Step error calculation of numerical control (NC) machining tool path is a premise for generating high-quality tool path and promoting its application. At present, iterative methods are generally used to calculate step error, and the computation time increases when accuracy improves. Neural networks can be calculated on GPUs and cloud platforms, which is conducive to reducing computation time and improving accuracy through continuous learning. This article innovatively introduces a BP neural network model to predict step error values. Firstly, the core parameters required for step error calculation are taken as the data samples to construct the neural network model, and map to the same scale through Z-score normalization to eliminate the adverse effects of singular parameters on the calculation results. Then, considering only a small number of parameters determine theoretical values of step error, the Dropout technique can drop hidden layer neurons with a certain probability, which is helpful to avoid overfitting and used in the neural network model design. In the neural network model training, this paper adds the Stochastic Gradient Descent with Momentum (SGDM) optimizer to the back propagation of network training in order to improves the network’ stability and accuracy. The proposed neural network predicts step error of samples from three surface models, the results show that the prediction error decreases as sample training increases. After trained by 15% of the surface samples, the neural network predicts the step errors of the remaining samples. Compared with theoretical values, more than 99% of the predicted values have an absolute error less than 1 μm. Moreover, the cost time is only one-third of the geometric method, which verifies the effectiveness and efficiency of our method.

An integrated method for compensating and correcting nonlinear error in five-axis machining utilizing cutter contacting point data

“…On this basis, five measurement trajectories were designed to identify the geometric error of the C-axis. Zhang 14 introduced a BP neural network model to predict step error values. Sang 15 proposed a simplified calculation model for the deviation between the ideal cutter trajectory and the actual cutter trajectory caused by the nonlinear motion of the rotary and translational axes and an improved interpolation algorithm considering geometric deviation and motion constraints, which improves the precision of machining to a certain extent.…”