IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v35y2024i1d10.1007_s10845-022-02045-5.html
   My bibliography  Save this article

Digital modeling-driven chatter suppression for thin-walled part manufacturing

Author

Listed:
  • Guo Zhou

    (Tsinghua University)

  • Kai Zhou

    (Tsinghua University)

  • Jing Zhang

    (Tsinghua University)

  • Meng Yuan

    (Tsinghua University)

  • Xiaohao Wang

    (Tsinghua University
    Tsinghua University)

  • Pingfa Feng

    (Tsinghua University
    Tsinghua University)

  • Min Zhang

    (Tsinghua University)

  • Feng Feng

    (Tsinghua University)

Abstract

Thin-walled parts are widely used in various industries such as aerospace and automotive, but the manufacturing processes are often harmed by chatter which is a self-excited vibration because of the poor rigidity in the direction perpendicular to the wall surface. The traditional stability lobe diagram (SLD) method can predict chatter based on the manufacturing system and workpiece parameters. However, these parameters could vary along with the manufacturing execution, compromising SLD's accuracy and even feasibility. To enable effective chatter suppression in thin-walled part milling, this study proposes a digital twin model, where two sub-models including the cutting parameters optimization and chatter detection are established. In the sub-model of cutting parameters optimization, a real-time SLD considering the time-varying modal parameters at the cutting region of the workpiece is generated as the optimization criteria. The sub-model of chatter detection can recognize chatter by a fusional analysis of the multiple sensors' signals, including vibration, force, and sound. Considering the bias of real-time SLD, these two sub-models are combined to output optimized cutting parameters to avoid chatter. Besides, a monitoring window to visualize the milling scenario and a database to record the manufacturing data are implemented in the digital twin model. According to the milling experiments, the digital twin model is validated to perform more effectively in chatter suppression than the traditional stationary SLD method.

Suggested Citation

  • Guo Zhou & Kai Zhou & Jing Zhang & Meng Yuan & Xiaohao Wang & Pingfa Feng & Min Zhang & Feng Feng, 2024. "Digital modeling-driven chatter suppression for thin-walled part manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 289-305, January.
  • Handle: RePEc:spr:joinma:v:35:y:2024:i:1:d:10.1007_s10845-022-02045-5
    DOI: 10.1007/s10845-022-02045-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-022-02045-5
    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/s10845-022-02045-5?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. 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.
    2. Konstantinos Mykoniatis & Gregory A. Harris, 2021. "A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1899-1911, October.
    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. Antonio Caputi & Davide Russo, 2021. "The optimization of the control logic of a redundant six axis milling machine," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1441-1453, June.
    2. Ayman AboElHassan & Soumaya Yacout, 2023. "A digital shadow framework using distributed system concepts," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3579-3598, December.
    3. Chi Ma & Hongquan Gui & Jialan Liu, 2023. "Self learning-empowered thermal error control method of precision machine tools based on digital twin," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 695-717, February.
    4. Zeqing Yang & Mingxuan Zhang & Yingshu Chen & Ning Hu & Lingxiao Gao & Libing Liu & Enxu Ping & Jung Il Song, 2024. "Surface defect detection method for air rudder based on positive samples," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 95-113, January.
    5. Benno Gerlach & Simon Zarnitz & Benjamin Nitsche & Frank Straube, 2021. "Digital Supply Chain Twins—Conceptual Clarification, Use Cases and Benefits," Logistics, MDPI, vol. 5(4), pages 1-24, December.
    6. Neto, Anis Assad & Ribeiro da Silva, Elias & Deschamps, Fernando & do Nascimento Junior, Laercio Alves & Pinheiro de Lima, Edson, 2023. "Modeling production disorder: Procedures for digital twins of flexibility-driven manufacturing systems," International Journal of Production Economics, Elsevier, vol. 260(C).

    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:35:y:2024:i:1:d:10.1007_s10845-022-02045-5. 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.