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  • Chatter detection for milling using novelp-leader multifractal features

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    Embargoed until: 2021-09-05
    Author(s)
    Chen, Yun
    Li, Huaizhong
    Hou, Liang
    Bu, Xiangjian
    Ye, Shaogan
    Chen, Ding
    Griffith University Author(s)
    Li, Huaizhong
    Year published
    2020
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    Abstract
    Chatter in machining results in poor workpiece surface quality and short tool life. An accurate and reliable chatter detection method is needed before its complete development. This paper applies a novel p-leader multifractal formalism for chatter detection in milling processes. This novel formalism can discover internal singularities rising on unstable signals due to chatter without prior knowledge of the natural frequencies of the machining system. The p-leader multifractal features are selected by using a multivariate filter method for feature selection, and verified by both numerical simulations and experimental studies ...
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    Chatter in machining results in poor workpiece surface quality and short tool life. An accurate and reliable chatter detection method is needed before its complete development. This paper applies a novel p-leader multifractal formalism for chatter detection in milling processes. This novel formalism can discover internal singularities rising on unstable signals due to chatter without prior knowledge of the natural frequencies of the machining system. The p-leader multifractal features are selected by using a multivariate filter method for feature selection, and verified by both numerical simulations and experimental studies with detailed parameter selection discussions when applying this formalism. The proposed method is assessed in terms of their dynamic monitoring abilities and classification accuracies under wide cutting conditions. The results show that the multifractal features can successfully detect chatter with high accuracies and short computation time. For further verification, the proposed method is compared with two commonly-used methods, which indicates that the proposed method gives better classification accuracies, especially when identifying unstable tests.
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    Journal Title
    Journal of Intelligent Manufacturing
    DOI
    https://doi.org/10.1007/s10845-020-01651-5
    Copyright Statement
    © 2020 Springer. This is an electronic version of an article published in the Journal of Intelligent Manufacturing, 2020. The Journal of Intelligent Manufacturing is available online at: http://link.springer.com/ with the open URL of your article.
    Subject
    Artificial Intelligence and Image Processing
    Other Information and Computing Sciences
    Manufacturing Engineering
    Science & Technology
    Technology
    Computer Science, Artificial Intelligence
    Engineering, Manufacturing
    Computer Science
    Publication URI
    http://hdl.handle.net/10072/397850
    Collection
    • Journal articles

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