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A kMap optimized VMD-SVM model for milling chatter detection with an industrial robot

Author

Listed:
  • Yu Wang

    (Huazhong University of Science and Technology)

  • Mingkai Zhang

    (Huazhong University of Science and Technology)

  • Xiaowei Tang

    (Huazhong University of Science and Technology)

  • Fangyu Peng

    (Huazhong University of Science and Technology)

  • Rong Yan

    (Huazhong University of Science and Technology)

Abstract

Industrial robots play an important role in the milling of large complex parts. However, the robot is less rigid and prone to vibration-related problems; chatter, which affects machining quality and efficiency, is more complex and difficult to monitor. In this paper, a variational mode decomposition-support vector machine (VMD-SVM) model based on information entropy (IE) is built to detect chatter in robotic milling. Significantly, the vibration signals are classified into four states for the first time: stable, transition, regular chatter, and irregular chatter. To improve the accuracy of the identification model based on VMD-SVM, a novel hyper-parameter optimization strategy—the kMap method—is proposed in this paper for optimizing three-dimensional hyper-parameters in the VMD-SVM model. The hyper-parameters of VMD-SVM are jointly optimized by the kMap method, with constant step sizes. As an improved grid search (GS), kMap reduces the operation time to the same order of magnitude as the heuristic algorithm (HA) [comprising particle swarm optimization (PSO) and genetic algorithm (GA)]. The VMD-SVM model with the hyper-parameters optimized by kMap exhibits higher accuracy and better stability than the hyper-parameters optimized by PSO and GA. The results of the validation experiments show that the kMap-optimized identification model is effective in industrial robotic milling.

Suggested Citation

  • Yu Wang & Mingkai Zhang & Xiaowei Tang & Fangyu Peng & Rong Yan, 2022. "A kMap optimized VMD-SVM model for milling chatter detection with an industrial robot," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1483-1502, June.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:5:d:10.1007_s10845-021-01736-9
    DOI: 10.1007/s10845-021-01736-9
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    References listed on IDEAS

    as
    1. M. Musselman & H. Xie & D. Djurdjanovic, 2019. "Nonstationary signal analysis and support vector machine based classification for vibration based characterization and monitoring of slit valves in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1099-1110, March.
    2. Yang Fu & Yun Zhang & Huang Gao & Ting Mao & Huamin Zhou & Ronglei Sun & Dequn Li, 2019. "Automatic feature constructing from vibration signals for machining state monitoring," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 995-1008, March.
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