IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v34y2023i5d10.1007_s10845-022-01912-5.html
   My bibliography  Save this article

Efficient stability prediction of milling process with arbitrary tool-holder combinations based on transfer learning

Author

Listed:
  • Congying Deng

    (Chongqing University of Posts and Telecommunications
    Chongqing University of Posts and Telecommunications)

  • Jielin Tang

    (Chongqing University of Posts and Telecommunications)

  • Jianguo Miao

    (Sichuan University)

  • Yang Zhao

    (Chongqing University of Posts and Telecommunications
    Chongqing University of Posts and Telecommunications)

  • Xiang Chen

    (Chongqing University of Posts and Telecommunications
    Chongqing University of Posts and Telecommunications)

  • Sheng Lu

    (Chongqing University of Posts and Telecommunications
    Chongqing University of Posts and Telecommunications)

Abstract

Chatter occurring in the milling process can seriously deteriorate the machining efficiency and surface quality. The stability diagram predicted using the tool tip frequency response functions (FRFs) is an effective approach to avoid the chatter vibration. The tool tip FRFs highly depend on the characteristics of the tool-holder-spindle-machine tool frame assembly. Thus, when the tool-holder assembly or only the tool overhang length changed, the FRFs will be reobtained to plot the stability diagrams. Considering this time-consuming situation, this paper introduces the transfer learning to efficiently predict the milling stability of arbitrary tool-holder combinations. First, a source tool-holder assembly is selected to measure sufficient overhang length-dependent tool tip FRFs and then predict the limiting axial cutting depth aplim values under different process parameters for forming the source data. For a new tool-holder assembly, impact tests are only performed under a few key tool overhang lengths to measure the tool tip FRFs and then predict the aplim values to form the target data. Combining the target data and the source data, the transfer learning containing the domain adaptation and adaptative weighting is introduced to train an overhang length-dependent milling stability prediction model of a target tool-holder assembly. A case study has been performed on a vertical machine tool with four different tool-holder assemblies to validate the feasibility of the proposed transfer learning-based milling stability prediction method.

Suggested Citation

  • Congying Deng & Jielin Tang & Jianguo Miao & Yang Zhao & Xiang Chen & Sheng Lu, 2023. "Efficient stability prediction of milling process with arbitrary tool-holder combinations based on transfer learning," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2263-2279, June.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:5:d:10.1007_s10845-022-01912-5
    DOI: 10.1007/s10845-022-01912-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-022-01912-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-022-01912-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. M. Pour & M. A. Torabizadeh, 2016. "Improved prediction of stability lobes in milling process using time series analysis," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 665-677, June.
    2. Minglong Guo & Zhaocheng Wei & Minjie Wang & Shiquan Li & Jia Wang & Shengxian Liu, 2021. "Modal parameter identification of general cutter based on milling stability theory," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 221-235, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Minglong Guo & Zhaocheng Wei & Minjie Wang & Shiquan Li & Jia Wang & Shengxian Liu, 2021. "Modal parameter identification of general cutter based on milling stability theory," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 221-235, January.
    2. Jianfeng Tao & Chengjin Qin & Dengyu Xiao & Haotian Shi & Xiao Ling & Bingchu Li & Chengliang Liu, 2020. "Timely chatter identification for robotic drilling using a local maximum synchrosqueezing-based method," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1243-1255, June.
    3. Feng Feng & Meng Yuan & Yousheng Xia & Haoming Xu & Pingfa Feng & Xinghui Li, 2022. "Roughness Scaling Extraction Accelerated by Dichotomy-Binary Strategy and Its Application to Milling Vibration Signal," Mathematics, MDPI, vol. 10(7), pages 1-17, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:34:y:2023:i:5:d:10.1007_s10845-022-01912-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.