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

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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.

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Acknowledgements

This research is supported by National Natural Science Foundation of China under Grant No. 51805189, National Natural Science Foundation of China under Grant No. 51625502, and Science Fund for Creative Research Groups of the National Natural Science Foundation of China under Grant No. 51721092.

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Authors and Affiliations

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

    Yu Wang, Mingkai Zhang, Xiaowei Tang, Fangyu Peng & Rong Yan

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  1. Yu Wang
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  2. Mingkai Zhang
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  3. Xiaowei Tang
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  4. Fangyu Peng
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  5. Rong Yan
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Correspondence to Xiaowei Tang.

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Wang, Y., Zhang, M., Tang, X. et al. A kMap optimized VMD-SVM model for milling chatter detection with an industrial robot. J Intell Manuf 33, 1483–1502 (2022). https://doi.org/10.1007/s10845-021-01736-9

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  • DOI: https://doi.org/10.1007/s10845-021-01736-9

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