Deep learning-based cutting force prediction for machining process using monitoring data
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...
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| Autor*in: |
Lee, Soomin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Enthalten in: Pattern Analysis & Applications - Springer-Verlag, 1999, 26(2023), 3 vom: 27. März, Seite 1013-1025 |
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| Übergeordnetes Werk: |
volume:26 ; year:2023 ; number:3 ; day:27 ; month:03 ; pages:1013-1025 |
| Links: |
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| DOI / URN: |
10.1007/s10044-023-01143-1 |
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| 520 | |a 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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10.1007/s10044-023-01143-1 doi (DE-627)SPR052343006 (SPR)s10044-023-01143-1-e DE-627 ger DE-627 rakwb eng Lee, Soomin verfasserin aut Deep learning-based cutting force prediction for machining process using monitoring data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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. Deep neural network (dpeaa)DE-He213 Long short-term memory (dpeaa)DE-He213 Machining process (dpeaa)DE-He213 Cutting force prediction (dpeaa)DE-He213 Virtual machining (dpeaa)DE-He213 Jo, Wonkeun aut Kim, Hyein aut Koo, Jeongin aut Kim, Dongil (orcid)0000-0001-7425-7579 aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 26(2023), 3 vom: 27. März, Seite 1013-1025 (DE-627)SPR008209189 nnns volume:26 year:2023 number:3 day:27 month:03 pages:1013-1025 https://dx.doi.org/10.1007/s10044-023-01143-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2023 3 27 03 1013-1025 |
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10.1007/s10044-023-01143-1 doi (DE-627)SPR052343006 (SPR)s10044-023-01143-1-e DE-627 ger DE-627 rakwb eng Lee, Soomin verfasserin aut Deep learning-based cutting force prediction for machining process using monitoring data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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. Deep neural network (dpeaa)DE-He213 Long short-term memory (dpeaa)DE-He213 Machining process (dpeaa)DE-He213 Cutting force prediction (dpeaa)DE-He213 Virtual machining (dpeaa)DE-He213 Jo, Wonkeun aut Kim, Hyein aut Koo, Jeongin aut Kim, Dongil (orcid)0000-0001-7425-7579 aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 26(2023), 3 vom: 27. März, Seite 1013-1025 (DE-627)SPR008209189 nnns volume:26 year:2023 number:3 day:27 month:03 pages:1013-1025 https://dx.doi.org/10.1007/s10044-023-01143-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2023 3 27 03 1013-1025 |
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10.1007/s10044-023-01143-1 doi (DE-627)SPR052343006 (SPR)s10044-023-01143-1-e DE-627 ger DE-627 rakwb eng Lee, Soomin verfasserin aut Deep learning-based cutting force prediction for machining process using monitoring data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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. Deep neural network (dpeaa)DE-He213 Long short-term memory (dpeaa)DE-He213 Machining process (dpeaa)DE-He213 Cutting force prediction (dpeaa)DE-He213 Virtual machining (dpeaa)DE-He213 Jo, Wonkeun aut Kim, Hyein aut Koo, Jeongin aut Kim, Dongil (orcid)0000-0001-7425-7579 aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 26(2023), 3 vom: 27. März, Seite 1013-1025 (DE-627)SPR008209189 nnns volume:26 year:2023 number:3 day:27 month:03 pages:1013-1025 https://dx.doi.org/10.1007/s10044-023-01143-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2023 3 27 03 1013-1025 |
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10.1007/s10044-023-01143-1 doi (DE-627)SPR052343006 (SPR)s10044-023-01143-1-e DE-627 ger DE-627 rakwb eng Lee, Soomin verfasserin aut Deep learning-based cutting force prediction for machining process using monitoring data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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. Deep neural network (dpeaa)DE-He213 Long short-term memory (dpeaa)DE-He213 Machining process (dpeaa)DE-He213 Cutting force prediction (dpeaa)DE-He213 Virtual machining (dpeaa)DE-He213 Jo, Wonkeun aut Kim, Hyein aut Koo, Jeongin aut Kim, Dongil (orcid)0000-0001-7425-7579 aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 26(2023), 3 vom: 27. März, Seite 1013-1025 (DE-627)SPR008209189 nnns volume:26 year:2023 number:3 day:27 month:03 pages:1013-1025 https://dx.doi.org/10.1007/s10044-023-01143-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2023 3 27 03 1013-1025 |
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10.1007/s10044-023-01143-1 doi (DE-627)SPR052343006 (SPR)s10044-023-01143-1-e DE-627 ger DE-627 rakwb eng Lee, Soomin verfasserin aut Deep learning-based cutting force prediction for machining process using monitoring data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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. Deep neural network (dpeaa)DE-He213 Long short-term memory (dpeaa)DE-He213 Machining process (dpeaa)DE-He213 Cutting force prediction (dpeaa)DE-He213 Virtual machining (dpeaa)DE-He213 Jo, Wonkeun aut Kim, Hyein aut Koo, Jeongin aut Kim, Dongil (orcid)0000-0001-7425-7579 aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 26(2023), 3 vom: 27. März, Seite 1013-1025 (DE-627)SPR008209189 nnns volume:26 year:2023 number:3 day:27 month:03 pages:1013-1025 https://dx.doi.org/10.1007/s10044-023-01143-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2023 3 27 03 1013-1025 |
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10.1007/s10044-023-01143-1 |
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| title_sort |
deep learning-based cutting force prediction for machining process using monitoring data |
| title_auth |
Deep learning-based cutting force prediction for machining process using monitoring data |
| abstract |
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. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
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. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
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. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Deep learning-based cutting force prediction for machining process using monitoring data |
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https://dx.doi.org/10.1007/s10044-023-01143-1 |
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| author2 |
Jo, Wonkeun Kim, Hyein Koo, Jeongin Kim, Dongil |
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Jo, Wonkeun Kim, Hyein Koo, Jeongin Kim, Dongil |
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2024-07-04T02:26:17.277Z |
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