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A novel Chebyshev-wavelet-based approach for accurate and fast prediction of milling stability

Chengjin Qin,Jianfeng Tao,Haotian Shi,Dengyu Xiao,Bingchu Li,Chengliang Liu

Published 2020 in Precision Engineering-journal of The International Societies for Precision Engineering and Nanotechnology

ABSTRACT

Abstract Currently, semi-analytical stability analysis methods for milling processes focus on improving prediction accuracy and simultaneously reducing computing time. This paper presents a Chebyshev-wavelet-based method for improved milling stability prediction. When including regenerative effect, the milling dynamics model can be concluded as periodic delay differential equations, and is re-presented as state equation forms via matrix transformation. After divide the period of the coefficient matrix into two subintervals, the forced vibration time interval is mapped equivalently to the definition interval of the second kind Chebyshev wavelets. Thereafter, the explicit Chebyshev–Gauss–Lobatto points are utilized for discretization. To construct the Floquet transition matrix, the state term is approximated by finite series second kind Chebyshev wavelets, while its derivative is acquired with a simple and explicit operational matrix of derivative. Finally, the milling stability can be semi-analytically predicted using Floquet theory. The effectiveness and superiority of the presented approach are verified by two benchmark milling models and comparisons with the representative existing methods. The results demonstrate that the presented approach is highly accurate, fast and easy to implement. Meanwhile, it is shown that the presented approach achieves high stability prediction accuracy and efficiency for both large and low radial-immersion milling operations.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    Precision Engineering-journal of The International Societies for Precision Engineering and Nanotechnology

  • Publication date

    2020-03-01

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1016/j.precisioneng.2019.11.016
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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