Contour error modeling and compensation of CNC machining based on deep learning and reinforcement learning

Abstract

Contour error compensation of the Computer Numerical Control (CNC) machine tool is a vital technology that can improve machining accuracy and quality. To achieve this goal, the tracking error of a feeding axis, which is a dominant issue incurring the contour error, should be firstly modeled and then a proper compensation strategy should be determined. However, building the precise tracking error prediction model is a challenging task because of the nonlinear issues like backlash and friction involved in the feeding axis; besides, the optimal compensation parameter is also difficult to determine because it is sensitive to the machining tool path. In this paper, a set of novel approaches for contour error prediction and compensation is presented based on the technologies of deep learning and reinforcement learning. By utilizing the internal data of the CNC system, the tracking error of the feeding axis is modeled as a Nonlinear Auto-Regressive Long-Short Term Memory (NAR-LSTM) network, considering all the nonlinear issues of the feeding axis. Given the contour error as calculated based on the predicted tracking error of each feeding axis, a compensation strategy is presented with its parameters identified efficiently by a Time-Series Deep Q-Network (TS-DQN) as designed in our work. To validate the feasibility and advantage of the proposed approaches, extensive experiments are conducted, testifying that, our approaches can predict the tracking error and contour error with very good precision (better than about 99% and 90% respectively), and the contour error compensated based on the predicted results and our compensation strategy is significantly reduced (about 70%~85% reduction) with the machining quality improved drastically (machining error reduced about 50%).

A Hybrid-Model-Based CNC Machining Trajectory Error Prediction and Compensation Method

“…To improve the machining accuracy and enhance the machining performance of CNC systems, researchers have proposed a variety of CNC machining trajectory error prediction and compensation methods, which can be categorized into model-based and data-based methods [11,12]. A model-based method mainly describes the error characteristics by establishing a mathematical model, such as using polynomials, wavelet functions, etc., to represent the error change curve, and, through the optimal fitting of the model parameters, the machining trajectory error can be predicted and compensated for in the subsequent processing.…”

B-Spline-Based Curve Fitting to Cam Pitch Curve Using Reinforcement Learning

“…[4] proposed a method that takes into account the arc-length-based parameter setting, G2 continuity, and the numerical measure. In another study, Jiang put forward a set of approaches for contour error prediction and compensation on the basis of deep learning and reinforcement learning (RL) [5]. Under the chord error constraint, Bi [6] presented a general, fast and robust B-spline fitting scheme for high-speed interpolation of a micro-line tool path.…”

Incremental Learning of LSTM-AutoEncoder Anomaly Detection in Three-Axis CNC Machines

“…These algorithms have been effective for implementing advanced capabilities ranging from tool wear monitoring [8] to cybersecurity of industrial machines [12] [9]. Machine learning techniques have also been implemented effectively for anomaly detection within the manufacturing environment [11] [13] [16] [19] [18] [20], showing their promise as a solution to this problem.…”