Cutting Force Similarity Calculation in Milling Process Using Siamese LSTM Structure

Juheon Kwak; Wonkeun Jo; Soomin Lee; Hyein Kim; Jeongin Koo; Dongil Kim

doi:10.1109/MEEE57080.2023.10126810

Cutting Force Similarity Calculation in Milling Process Using Siamese LSTM Structure

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

Department of Data Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Cutting force is a key factor in machining processes. Cutting force similarity is required for several important issues, namely: stability evaluation, process control, and process parameter setting. This study employed a long short-term memory (LSTM) with Siamese architecture to measure the similarity of the cutting forces in a milling process. The Siamese LSTM was trained with time series data of the vertical cutting force collected from a cutting tool during the milling process to calculate the similarity. For evaluation, dynamic time warping (DTW), a common approach used to calculate the similarity of time series data, was employed for comparison with the Siamese LSTM. Experimental results showed that the proposed Siamese LSTM outperformed the conventional DTW-based similarity calculation.

Original language	English
Title of host publication	2023 2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	54-58
Number of pages	5
ISBN (Electronic)	9781665474450
DOIs	https://doi.org/10.1109/MEEE57080.2023.10126810
State	Published - 2023
Event	2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023 - Abu Dhabi, United Arab Emirates Duration: 10 Feb 2023 → 12 Feb 2023

Publication series

Name	2023 2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023

Conference

Conference	2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023
Country/Territory	United Arab Emirates
City	Abu Dhabi
Period	10/02/23 → 12/02/23

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This study was supported by the Korea Institute of Industrial Technology’s learning-based process ability diagnostic control system development project (Kitech EO-19-0043) for self-optimization of production systems. In addition, it received support from the Information and Communication Planning and Evaluation Institute (IITP-2019-0-01343 (Graduate School of Convergence Security)) and the National Research Foundation (No. 2020R1F1A1075781) with the finances of the Ministry of Science and ICT.

Publisher Copyright:
© 2023 IEEE.

Keywords

cutting force
dynamic time warping
long short-term memory
Manhattan distance
Siamese neural network
similarity

Access to Document

10.1109/MEEE57080.2023.10126810

Cite this

Kwak, J., Jo, W., Lee, S., Kim, H., Koo, J., & Kim, D. (2023). Cutting Force Similarity Calculation in Milling Process Using Siamese LSTM Structure. In 2023 2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023 (pp. 54-58). (2023 2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MEEE57080.2023.10126810

Kwak, Juheon ; Jo, Wonkeun ; Lee, Soomin et al. / Cutting Force Similarity Calculation in Milling Process Using Siamese LSTM Structure. 2023 2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 54-58 (2023 2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023).

@inproceedings{c3564aa0e6534546b9fc0942bc45d5aa,

title = "Cutting Force Similarity Calculation in Milling Process Using Siamese LSTM Structure",

abstract = "Cutting force is a key factor in machining processes. Cutting force similarity is required for several important issues, namely: stability evaluation, process control, and process parameter setting. This study employed a long short-term memory (LSTM) with Siamese architecture to measure the similarity of the cutting forces in a milling process. The Siamese LSTM was trained with time series data of the vertical cutting force collected from a cutting tool during the milling process to calculate the similarity. For evaluation, dynamic time warping (DTW), a common approach used to calculate the similarity of time series data, was employed for comparison with the Siamese LSTM. Experimental results showed that the proposed Siamese LSTM outperformed the conventional DTW-based similarity calculation.",

keywords = "cutting force, dynamic time warping, long short-term memory, Manhattan distance, Siamese neural network, similarity",

author = "Juheon Kwak and Wonkeun Jo and Soomin Lee and Hyein Kim and Jeongin Koo and Dongil Kim",

note = "Funding Information: ACKNOWLEDGMENT This study was supported by the Korea Institute of Industrial Technology{\textquoteright}s learning-based process ability diagnostic control system development project (Kitech EO-19-0043) for self-optimization of production systems. In addition, it received support from the Information and Communication Planning and Evaluation Institute (IITP-2019-0-01343 (Graduate School of Convergence Security)) and the National Research Foundation (No. 2020R1F1A1075781) with the finances of the Ministry of Science and ICT. Publisher Copyright: {\textcopyright} 2023 IEEE.; 2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023 ; Conference date: 10-02-2023 Through 12-02-2023",

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language = "English",

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Kwak, J, Jo, W, Lee, S, Kim, H, Koo, J & Kim, D 2023, Cutting Force Similarity Calculation in Milling Process Using Siamese LSTM Structure. in 2023 2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023. 2023 2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023, Institute of Electrical and Electronics Engineers Inc., pp. 54-58, 2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023, Abu Dhabi, United Arab Emirates, 10/02/23. https://doi.org/10.1109/MEEE57080.2023.10126810

Cutting Force Similarity Calculation in Milling Process Using Siamese LSTM Structure. / Kwak, Juheon; Jo, Wonkeun; Lee, Soomin et al.
2023 2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 54-58 (2023 2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kim, Dongil

N1 - Funding Information: ACKNOWLEDGMENT This study was supported by the Korea Institute of Industrial Technology’s learning-based process ability diagnostic control system development project (Kitech EO-19-0043) for self-optimization of production systems. In addition, it received support from the Information and Communication Planning and Evaluation Institute (IITP-2019-0-01343 (Graduate School of Convergence Security)) and the National Research Foundation (No. 2020R1F1A1075781) with the finances of the Ministry of Science and ICT. Publisher Copyright: © 2023 IEEE.

PY - 2023

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N2 - Cutting force is a key factor in machining processes. Cutting force similarity is required for several important issues, namely: stability evaluation, process control, and process parameter setting. This study employed a long short-term memory (LSTM) with Siamese architecture to measure the similarity of the cutting forces in a milling process. The Siamese LSTM was trained with time series data of the vertical cutting force collected from a cutting tool during the milling process to calculate the similarity. For evaluation, dynamic time warping (DTW), a common approach used to calculate the similarity of time series data, was employed for comparison with the Siamese LSTM. Experimental results showed that the proposed Siamese LSTM outperformed the conventional DTW-based similarity calculation.

AB - Cutting force is a key factor in machining processes. Cutting force similarity is required for several important issues, namely: stability evaluation, process control, and process parameter setting. This study employed a long short-term memory (LSTM) with Siamese architecture to measure the similarity of the cutting forces in a milling process. The Siamese LSTM was trained with time series data of the vertical cutting force collected from a cutting tool during the milling process to calculate the similarity. For evaluation, dynamic time warping (DTW), a common approach used to calculate the similarity of time series data, was employed for comparison with the Siamese LSTM. Experimental results showed that the proposed Siamese LSTM outperformed the conventional DTW-based similarity calculation.

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BT - 2023 2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023

Y2 - 10 February 2023 through 12 February 2023

ER -

Kwak J, Jo W, Lee S, Kim H, Koo J, Kim D. Cutting Force Similarity Calculation in Milling Process Using Siamese LSTM Structure. In 2023 2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 54-58. (2023 2nd International Conference on Mechatronics and Electrical Engineering, MEEE 2023). doi: 10.1109/MEEE57080.2023.10126810

Cutting Force Similarity Calculation in Milling Process Using Siamese LSTM Structure

Abstract

Publication series

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Bibliographical note

Keywords

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