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

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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 60~85% reduction) with the machining quality improved drastically (machining error reduced about 50%).

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Funding

This work is supported by the Major Science and Technology Project of Hubei Province (Grant No. 2020AGA018-01).

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Authors and Affiliations

  1. National Numerical Control System Engineering Research Center, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yakun Jiang, Jihong Chen, Huicheng Zhou, Jianzhong Yang, Pengcheng Hu & Junxiang Wang

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  1. Yakun Jiang
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  2. Jihong Chen
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  3. Huicheng Zhou
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  4. Jianzhong Yang
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  5. Pengcheng Hu
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Contributions

Yakun Jiang implemented all the algorithms and drafted the paper; Jihong Chen, Huicheng Zhou, and Jianzhong Yang helped design the algorithm and experiments of this paper; Pengcheng Hu proposed the original idea and revised the paper; Junxiang Wang helped implement the experiments.

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Correspondence to Pengcheng Hu.

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Appendix

Appendix

1.1 Geometric parameters of four contours

Heart contour: NURBS curve with order k = 4, knot vector: {0, 0, 0, 0.15, 0.5, 0.5, 0.85, 1, 1, 1}; control points (x, y): {(0.0, 0.0), (−20.0, 50.0), (40.0, 20.0), (75.0, 0.0), (40.0, −20.0), (−20.0, −50.0), (0.0, 0.0)}; weights: {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0}.

Goggles contour: NURBS curve with order k = 4, knot vector: {0, 0, 0, 0.15, 0.3, 0.45, 0.6, 0.75, 0.9, 1, 1, 1}; control points (x, y): {(0.0, 0.0), (10.0, −40.0), (40.0, −10.0), (70.0, −40.0), (80.0, 0.0), (70.0, 10.0), (40.0, 20.0), (10.0,10.0), (0.0, 0.0)}; weights: {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0}.

Rhombus contour: Polygon with 4 points: {(350.0, −200.0), (380.0, −200.0), (400.0, −150.0), (370.0, −150.0), (350.0, −200.0)}.

Star contour: NURBS curve with order k = 4, knot vector: {0, 0, 0, 0.111, 0.222, 0.333, 0.444, 0.555, 0.666, 0.777, 0.888, 1, 1, 1}; control points (x, y): {(0.0, 60.0), (30.0, 20.0), (80.0, 20.0), (40.0, −20.0), (50.0, −60.0), (0.0, −30.0), (−50.0, −60.0), (−40.0, −20.0), (−80.0, 20.0), (−30.0, 20.0), (0.0, 60.0)}; weights: {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0}.

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Jiang, Y., Chen, J., Zhou, H. et al. Contour error modeling and compensation of CNC machining based on deep learning and reinforcement learning. Int J Adv Manuf Technol 118, 551–570 (2022). https://doi.org/10.1007/s00170-021-07895-6

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