IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i2d10.1007_s10845-023-02293-z.html
   My bibliography  Save this article

Intelligent G-code-based power prediction of ultra-precision CNC machine tools through 1DCNN-LSTM-Attention model

Author

Listed:
  • Zhicheng Xu

    (University of Wisconsin-Madison
    Wuhan University of Technology)

  • Vignesh Selvaraj

    (University of Wisconsin-Madison)

  • Sangkee Min

    (University of Wisconsin-Madison)

Abstract

As the most promising and advanced technology, ultra-precision machining (UPM) has dramatically increased its production volume for wide-range applications in various high-tech fields such as chips, optics, microcircuits, biotechnology, etc. The concomitantly negative environmental impact resulting from huge-volume UPM has attracted unprecedented attention from both academia and industry. Accurate energy prediction of ultra-precision machine tools (UPMTs) can provide significant insight into energy planning, machining strategy, and energy conservation. Data-driven models for predicting energy have become increasingly popular due to their high accuracy and low modeling difficulty. However, existing data-driven models only focus on ordinary precision machine tools, and their applications on UPMTs are hardly studied. To fill the gap, this paper proposed a data-driven model constructed with 1DCNN-LSTM-Attention layers for predicting the instantaneous power profile of a five-axes UPMT. In the data-preparation phase, an advanced G-code interpreter was developed to generate the working status dataset from the G-code command and accurately match them with the power data collected. Random hyperparameters searching method was adopted to tune the 1DCNN-LSTM-Attention structure for better accuracy in the model creation phase. Finally, the sensitivity of these hyperparameters on the model performance was analyzed. Results demonstrate that the learning rate, 1DCNN, LSTM and dense layer numbers are identified as critical parameters affecting the model performance. The optimized 1DCNN-LSTM-Attention model outperforms other models, achieving an R2 value of 0.93. This work first validate the feasibility of utilizing advanced machine learning techniques for predicting energy consumption in UPM field, which can further promoting energy-efficient and sustainable UPM practices by digitalizing the energy consumption process.

Suggested Citation

  • Zhicheng Xu & Vignesh Selvaraj & Sangkee Min, 2025. "Intelligent G-code-based power prediction of ultra-precision CNC machine tools through 1DCNN-LSTM-Attention model," Journal of Intelligent Manufacturing, Springer, vol. 36(2), pages 1237-1260, February.
  • Handle: RePEc:spr:joinma:v:36:y:2025:i:2:d:10.1007_s10845-023-02293-z
    DOI: 10.1007/s10845-023-02293-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-023-02293-z
    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/s10845-023-02293-z?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.

    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:36:y:2025:i:2:d:10.1007_s10845-023-02293-z. 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.