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Milling chatter detection using scalogram and deep convolutional neural network

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Abstract

In this paper, a novel approach of the real-time chatter detection in the milling process is presented based on the scalogram of the continuous wavelet transform (CWT) and the deep convolutional neural network (CNN). The cutting force signals measured from the stable and unstable cutting conditions were converted into two-dimensional images using the CWT. When chatter occurs, the amount of energy at the tooth passing frequency and its harmonics are shifted toward the chatter frequency. Hence, the scalogram images can serve as input to the CNN framework to identify the stable, transitive, and unstable cutting states. The proposed method does not require the subjective feature-generation and feature-selection procedures, and its classification accuracy of 99.67% is higher than the conventional machine learning techniques described in the existing literature. The result demonstrates that the proposed method can effectively detect the occurrence of chatter.

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Funding

This work was financially supported by the Center for Cyber-physical System Innovation from The Featured Area Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) of Taiwan. The Ministry of Science and Technology (MOST) of Taiwan partially funded the present work (grant number: MOST 107-2221-E-011-139).

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, 106, Taiwan

    Minh-Quang Tran & Meng-Kun Liu

  2. Center for Cyber-physical System Innovation, National Taiwan University of Science and Technology, Taipei, 106, Taiwan

    Minh-Quang Tran & Meng-Kun Liu

  3. Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, 106, Taiwan

    Quoc-Viet Tran

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  1. Minh-Quang Tran
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  2. Meng-Kun Liu
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  3. Quoc-Viet Tran
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Correspondence to Meng-Kun Liu.

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Tran, MQ., Liu, MK. & Tran, QV. Milling chatter detection using scalogram and deep convolutional neural network. Int J Adv Manuf Technol 107, 1505–1516 (2020). https://doi.org/10.1007/s00170-019-04807-7

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  • DOI: https://doi.org/10.1007/s00170-019-04807-7

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