Digital-twin-driven intelligent tracking error compensation of ultra-precision machining

Abstract

In ultra-precision machining (UPM), linear axis tracking affects contour accuracy and final machining quality. Traditional error modeling is complicated by the identification of numerous unknown parameters linked to nonlinear characteristics in the linear feed axes. To fill this gap, this study proposed a digital-twin-driven framework integrating the developed G-code interpreter and the deep learning model to achieve real-time tracking error compensation for UPM. To enhance the prediction accuracy of the tracking error of each axis of UPM machines, Bayesian hyperparameter optimization and feature importance analysis were conducted in the proposed TCN-BiLSTM model using high-quality training datasets from well-designed experiments. Ultimately, validation of the proposed system on a three-axis ultra-precision milling machine demonstrated its excellent performance. The experimental results showed that the optimized TCN-BiLSTM model exhibited an excellent capacity to predict the tracking error of the X-axis and Y-axis with minimal mean absolute error values of 0.000009 and 0.000023, respectively. Implementing the customized application reduced X-axis and Y-axis tracking errors by approximately 45–75% and 40–70%, respectively. This study first validates the feasibility of deep learning to improve accuracy in the UPM field, which will provide significant insight into speeding up the digitalization and intellectualization of the UPM scenario.