Deep learning-based cutting force prediction for machining process using monitoring data

Soomin Lee, Wonkeun Jo, Hyein Kim, Jeongin Koo, Dongil Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Machining is a critical process in manufacturing industries. With the increase in the complexity and precision of machining, computer systems, such as computerized numerical control, machining monitoring systems (MMSs), and virtual machining (VM), have been incorporated in modern machining processes. In this study, a deep learning-based cutting force prediction method was proposed. MMS and VM data were collected from real-world machining processes. Next, the prediction of the cutting force using five deep learning-based methods, including the long short-term memory (LSTM) and temporal convolutional networks, were analyzed and compared with values measured with a tool dynamometer. The experimental results revealed that the proposed LSTM model, including bidirectional and residual structures, outperformed other benchmark models in terms of predicting the cutting force. Furthermore, the proposed method trained only with MMS data exhibited excellent performance with a root-mean-square error of 12.55 and R2 of 0.99 on average. Thus, the cutting force required at each point can be predicted accurately, and the method can become a reference for further studies.

Original languageEnglish
Pages (from-to)1013-1025
Number of pages13
JournalPattern Analysis and Applications
Volume26
Issue number3
DOIs
StatePublished - Aug 2023

Bibliographical note

Funding Information:
I submitted my acknowledgement commnets as in the title page via the editorial manager. I think the acknowledgement is missing in this proof. So, I'd like to add the acknowledgment as below: This work was supported by Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2022-0-01200, Training Key Talents in Industrial Convergence Security), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2020R1F1A1075781), and Korea Institute of Industrial Technology (Kitech EO-19-0043). Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

Keywords

  • Cutting force prediction
  • Deep neural network
  • Long short-term memory
  • Machining process
  • Virtual machining

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