Prediction and Compensation of Contour Error of CNC Systems Based on LSTM Neural-Network
Jiangang Li, Changgui Qi, Yanan Li, Zenghao Wu
Abstract
This article proposes a contour error estimation (CEE) and compensation method for computer numerical control (CNC) systems based on the long short-term memory neural network. This is achieved by performing modeling of each axis to predict the tracking error, calculating the actual trajectory, estimating the contour error, and modifying the reference trajectory. First, linear feature selection based on a simplified single-axis model and nonlinear feature selection based on a circular test are performed to achieve tracking error prediction. Then, a spline-approximation-based CEE method is proposed to estimate the contour error between the reference trajectory and the predicted trajectory. Finally, contour error compensation is performed on the reference trajectory before it is run on CNC systems. The proposed method is validated through experiments on a three-axis CNC system.