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Online chatter detection in robotic machining based on adaptive variational mode decomposition

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Abstract

Chatter is the main problem that limits the application of industrial robots in the field of machining process. It is critically important to establish an adaptive chatter detection solution for robot machining process and realize the online detection of chatter. However, different from machine tool chatter, the chatter in robotic machining process is more complex to be detected due to the variable stiffness characteristics and weaker stiffness of normal industrial robot, and the existing literature has less research on this problem. This paper presents a comprehensive solution for online chatter detection in robotic machining process. Firstly, in order to detect the chatter in robotic machining process and avoid mode mixing problem in variational mode decomposition (VMD) process, an adaptive variational mode decomposition (AVMD) method based on kurtosis and instantaneous frequency is proposed, which realizes the adaptive selection of the decomposition parameter. Secondly, optimal decomposition parameters are calculated by using genetic algorithm. By optimizing the discrete step length of decomposition parameter, it greatly reduces the optimization time. Last but not least, approximate entropy, energy entropy, and proposed entropy drift coefficient are extracted to distinguish chatter and stable machining state. Simulation and experimental results show that the proposed method can meet the real-time requirements of online detection and detect the occurrence of chatter effectively.

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The data used or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 51875323) and the Key Technology Research and Development Program of Shandong (Grant No. 2019GGX104043).

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Authors and Affiliations

  1. School of Mechanical Engineering, Shandong University, Jinan, 250061, People’s Republic of China

    Qizhi Chen, Chengrui Zhang, Tianliang Hu, Yan Zhou & Teng Wang

  2. Key Laboratory of High Efficiency and Clean Mechanical Manufacture at Shandong University, Ministry of Education, Jinan, 250061, People’s Republic of China

    Qizhi Chen, Chengrui Zhang, Tianliang Hu & Teng Wang

  3. School of Mechanical and Electronic Engineering, Shandong Jianzhu University, Jinan, 250101, People’s Republic of China

    Hepeng Ni

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  1. Qizhi Chen
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  2. Chengrui Zhang
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  4. Yan Zhou
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Contributions

Qizhi Chen contributed to the idea and methodology development, software and validation, manuscript writing; Chengrui Zhang and Tianliang Hu contributed to the funding support, idea and methodology discussion, algorithm check and manuscript refinement; Yan Zhou contributed to the data analysis and visualization. Hepeng Ni and Teng Wang provided the support on the programming, writing, and experiments; all the authors contributed equally to the writing of the paper.

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Correspondence to Chengrui Zhang.

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Chen, Q., Zhang, C., Hu, T. et al. Online chatter detection in robotic machining based on adaptive variational mode decomposition. Int J Adv Manuf Technol 117, 555–577 (2021). https://doi.org/10.1007/s00170-021-07769-x

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