IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v30y2019i2d10.1007_s10845-016-1260-8.html
   My bibliography  Save this article

A geometric error budget method to improve machining accuracy reliability of multi-axis machine tools

Author

Listed:
  • Ziling Zhang

    (Beijing University of Technology)

  • Ligang Cai

    (Beijing University of Technology)

  • Qiang Cheng

    (Beijing University of Technology
    Central South University)

  • Zhifeng Liu

    (Beijing University of Technology)

  • Peihua Gu

    (Shantou University)

Abstract

Machining accuracy reliability is considered to be one of the most important indexes in the process of performance evaluation and optimization design of the machine tools. Geometric errors, thermal errors and tool wear are the main factors to affect the machining accuracy and so affect the machining accuracy reliability of machine tools. This paper proposed a geometric error budget method that simultaneously considers geometric errors, thermal errors and tool wear to improve the machining accuracy reliability of machine tools. Homogeneous transformation matrices, neural fuzzy control theory and a tool wear predictive approach were employed to develop a comprehensive error model, which shows the influence of the geometric, thermal errors and tool wear to the machining accuracy of a machine tool. Based on Rackwite–Fiessler and Advanced First Order and Second Moment, a reliability model and a sensitivity model were put forward, which can deal with the errors of a machine tool drawn from any distribution. Then, a geometric error budget method of multi-axis NC machine tool was developed and formed into a mathematical model. In such method, the minimum cost of machine tool was the optimization objective, the reliability of the machining accuracy was the constraint, and the sensitivity was to identify the geometric errors to be optimized. An example conducted on a five-axis NC machine tool was used to explain and validate the proposed method.

Suggested Citation

  • Ziling Zhang & Ligang Cai & Qiang Cheng & Zhifeng Liu & Peihua Gu, 2019. "A geometric error budget method to improve machining accuracy reliability of multi-axis machine tools," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 495-519, February.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:2:d:10.1007_s10845-016-1260-8
    DOI: 10.1007/s10845-016-1260-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-016-1260-8
    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/s10845-016-1260-8?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Antoni Świć & Arkadiusz Gola & Łukasz Sobaszek & Natalia Šmidová, 2021. "A thermo-mechanical machining method for improving the accuracy and stability of the geometric shape of long low-rigidity shafts," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1939-1951, October.
    2. 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.
    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. Ye, Zhenggeng & Yang, Hui & Cai, Zhiqiang & Si, Shubin & Zhou, Fuli, 2021. "Performance evaluation of serial-parallel manufacturing systems based on the impact of heterogeneous feedstocks on machine degradation," Reliability Engineering and System Safety, Elsevier, vol. 207(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:30:y:2019:i:2:d:10.1007_s10845-016-1260-8. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.