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

  • Industrial and Commercial Application
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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 \(R^{2}\) 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.

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Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 34134, Republic of Korea

    Soomin Lee & Wonkeun Jo

  2. Smart Manufacturing System R&D Department, Korea Institute of Industrial Technology, 89 Yangdaegiro-gil, Cheonan, 31056, Republic of Korea

    Hyein Kim & Jeongin Koo

  3. Department of Data Science, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul, 03760, Republic of Korea

    Dongil Kim

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  1. Soomin Lee
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Correspondence to Dongil Kim.

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Publisher's Note

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.

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Lee, S., Jo, W., Kim, H. et al. Deep learning-based cutting force prediction for machining process using monitoring data. Pattern Anal Applic 26, 1013–1025 (2023). https://doi.org/10.1007/s10044-023-01143-1

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