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Tool-path continuity determination based on machine learning method

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Abstract

Computer-aided manufacturing (CAM) software outputs machining data by encoding a tool-path into a series of G-codes which are composed of various lengths of line segments. The discontinuities of these line segments may cause inefficiency for computer numerical control (CNC) system. To achieve high-speed continuous motions, corner smoothing algorithms based on look-ahead methods are widely used. However, it is difficult to meet smoothing trajectories in real-time requirements. Based on machine learning method, in this paper, a support vector machine (SVM) system is presented for directly outputting classification results of the various geometric continuities at the transition corners. The feature values used for generating continuity classification model are extracted from sampling paths of the previous publication work: the machining parameters, length, fairness criteria, the root mean square (RMS) contour errors, and dominant stage type of movement of each sampling path are calculated. The acceleration/deceleration (ACC/DEC) feedrate planning scheme is used to determine the feedrate at the transition corners. Simulations and experiments show that the proposed algorithm can realize accurately and efficiently continuity classification in real-time requirements under the conditions of machining accuracy.

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All data generated or analyzed during this study are included in this published article.

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Funding

The authors are grateful for the support provided by National Key Research and Development Project (grant no. 2018YFB1105300), National Natural Science Foundation of China (grant no. 51605475) and The central government guides the local science and technology development fund plan (grant no. 2021JH6/10500123).

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Authors and Affiliations

  1. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Bo Zhou, Tongtong Tian, Jibin Zhao & Dianhai Liu

  2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China

    Bo Zhou, Tongtong Tian, Jibin Zhao & Dianhai Liu

Authors
  1. Bo Zhou
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  2. Tongtong Tian
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  3. Jibin Zhao
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  4. Dianhai Liu
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Contributions

Bo Zhou performed the algorithm design, code writing, and was a major contributor in writing the manuscript. Tongtong Tian analyzed and interpreted the experimental data. Jibin Zhao presented the experimental site and equipment, and proposed the revise opinions. Dianhai Liu corrected the grammars of the revised manuscript. All authors read and approved the final manuscript.

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Correspondence to Bo Zhou or Jibin Zhao.

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Zhou, B., Tian, T., Zhao, J. et al. Tool-path continuity determination based on machine learning method. Int J Adv Manuf Technol 119, 403–420 (2022). https://doi.org/10.1007/s00170-021-08156-2

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  • DOI: https://doi.org/10.1007/s00170-021-08156-2

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