Study on generation of abrasive protrusion height based on projection information–driven intelligent algorithm
Abstract Abrasive protrusion height (APH) is the core parameter of abrasive tools, and it is also a principal parameter for modeling and simulation of the surface topography of abrasive tools. Because of the difficulty in obtaining the 3D feature information of abrasives and the limited measured dat...
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| Autor*in: |
Li, Hongyang [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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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| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - Springer London, 1985, 123(2022), 11-12 vom: 19. Nov., Seite 4309-4320 |
|---|---|
| Übergeordnetes Werk: |
volume:123 ; year:2022 ; number:11-12 ; day:19 ; month:11 ; pages:4309-4320 |
| Links: |
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| DOI / URN: |
10.1007/s00170-022-10474-y |
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| Katalog-ID: |
OLC2080059998 |
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| 520 | |a Abstract Abrasive protrusion height (APH) is the core parameter of abrasive tools, and it is also a principal parameter for modeling and simulation of the surface topography of abrasive tools. Because of the difficulty in obtaining the 3D feature information of abrasives and the limited measured data; this study proposes an APH model based on the spatial projection relationship. Combined with the gradient boosting decision tree (GBDT) intelligent algorithm, the APH characteristics of the abrasive tool are quickly generated by the data-fusion learning model. Through the corresponding experiments, we acquired the 2D image of the abrasive tool and extracted the area information of the abrasive projection area. The result shows that the fitting degree R2 of the algorithm model reaches 0.911. Comparing the APH generated based on the algorithm model with the actual one, the average accuracy is about 94.69% and the mean absolute error of generated protrusion height MAE is 5.31 μm, which validates the proposed model. The results of this study demonstrate that the data-driven approach can effectively establish the generation model of the protrusion height of abrasive grains, which not only enable the measurement of the protrusion height of abrasive but also provide a reference for accurate modeling of abrasive tools. | ||
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10.1007/s00170-022-10474-y doi (DE-627)OLC2080059998 (DE-He213)s00170-022-10474-y-p DE-627 ger DE-627 rakwb eng 670 VZ Li, Hongyang verfasserin aut Study on generation of abrasive protrusion height based on projection information–driven intelligent algorithm 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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 Abrasive protrusion height (APH) is the core parameter of abrasive tools, and it is also a principal parameter for modeling and simulation of the surface topography of abrasive tools. Because of the difficulty in obtaining the 3D feature information of abrasives and the limited measured data; this study proposes an APH model based on the spatial projection relationship. Combined with the gradient boosting decision tree (GBDT) intelligent algorithm, the APH characteristics of the abrasive tool are quickly generated by the data-fusion learning model. Through the corresponding experiments, we acquired the 2D image of the abrasive tool and extracted the area information of the abrasive projection area. The result shows that the fitting degree R2 of the algorithm model reaches 0.911. Comparing the APH generated based on the algorithm model with the actual one, the average accuracy is about 94.69% and the mean absolute error of generated protrusion height MAE is 5.31 μm, which validates the proposed model. The results of this study demonstrate that the data-driven approach can effectively establish the generation model of the protrusion height of abrasive grains, which not only enable the measurement of the protrusion height of abrasive but also provide a reference for accurate modeling of abrasive tools. Abrasive tool Protrusion height Abrasive image Data-fusion Intelligent algorithm Fang, Congfu aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 123(2022), 11-12 vom: 19. Nov., Seite 4309-4320 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:123 year:2022 number:11-12 day:19 month:11 pages:4309-4320 https://doi.org/10.1007/s00170-022-10474-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_2018 GBV_ILN_2333 AR 123 2022 11-12 19 11 4309-4320 |
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10.1007/s00170-022-10474-y doi (DE-627)OLC2080059998 (DE-He213)s00170-022-10474-y-p DE-627 ger DE-627 rakwb eng 670 VZ Li, Hongyang verfasserin aut Study on generation of abrasive protrusion height based on projection information–driven intelligent algorithm 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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 Abrasive protrusion height (APH) is the core parameter of abrasive tools, and it is also a principal parameter for modeling and simulation of the surface topography of abrasive tools. Because of the difficulty in obtaining the 3D feature information of abrasives and the limited measured data; this study proposes an APH model based on the spatial projection relationship. Combined with the gradient boosting decision tree (GBDT) intelligent algorithm, the APH characteristics of the abrasive tool are quickly generated by the data-fusion learning model. Through the corresponding experiments, we acquired the 2D image of the abrasive tool and extracted the area information of the abrasive projection area. The result shows that the fitting degree R2 of the algorithm model reaches 0.911. Comparing the APH generated based on the algorithm model with the actual one, the average accuracy is about 94.69% and the mean absolute error of generated protrusion height MAE is 5.31 μm, which validates the proposed model. The results of this study demonstrate that the data-driven approach can effectively establish the generation model of the protrusion height of abrasive grains, which not only enable the measurement of the protrusion height of abrasive but also provide a reference for accurate modeling of abrasive tools. Abrasive tool Protrusion height Abrasive image Data-fusion Intelligent algorithm Fang, Congfu aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 123(2022), 11-12 vom: 19. Nov., Seite 4309-4320 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:123 year:2022 number:11-12 day:19 month:11 pages:4309-4320 https://doi.org/10.1007/s00170-022-10474-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_2018 GBV_ILN_2333 AR 123 2022 11-12 19 11 4309-4320 |
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10.1007/s00170-022-10474-y doi (DE-627)OLC2080059998 (DE-He213)s00170-022-10474-y-p DE-627 ger DE-627 rakwb eng 670 VZ Li, Hongyang verfasserin aut Study on generation of abrasive protrusion height based on projection information–driven intelligent algorithm 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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 Abrasive protrusion height (APH) is the core parameter of abrasive tools, and it is also a principal parameter for modeling and simulation of the surface topography of abrasive tools. Because of the difficulty in obtaining the 3D feature information of abrasives and the limited measured data; this study proposes an APH model based on the spatial projection relationship. Combined with the gradient boosting decision tree (GBDT) intelligent algorithm, the APH characteristics of the abrasive tool are quickly generated by the data-fusion learning model. Through the corresponding experiments, we acquired the 2D image of the abrasive tool and extracted the area information of the abrasive projection area. The result shows that the fitting degree R2 of the algorithm model reaches 0.911. Comparing the APH generated based on the algorithm model with the actual one, the average accuracy is about 94.69% and the mean absolute error of generated protrusion height MAE is 5.31 μm, which validates the proposed model. The results of this study demonstrate that the data-driven approach can effectively establish the generation model of the protrusion height of abrasive grains, which not only enable the measurement of the protrusion height of abrasive but also provide a reference for accurate modeling of abrasive tools. Abrasive tool Protrusion height Abrasive image Data-fusion Intelligent algorithm Fang, Congfu aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 123(2022), 11-12 vom: 19. Nov., Seite 4309-4320 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:123 year:2022 number:11-12 day:19 month:11 pages:4309-4320 https://doi.org/10.1007/s00170-022-10474-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_2018 GBV_ILN_2333 AR 123 2022 11-12 19 11 4309-4320 |
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10.1007/s00170-022-10474-y doi (DE-627)OLC2080059998 (DE-He213)s00170-022-10474-y-p DE-627 ger DE-627 rakwb eng 670 VZ Li, Hongyang verfasserin aut Study on generation of abrasive protrusion height based on projection information–driven intelligent algorithm 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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 Abrasive protrusion height (APH) is the core parameter of abrasive tools, and it is also a principal parameter for modeling and simulation of the surface topography of abrasive tools. Because of the difficulty in obtaining the 3D feature information of abrasives and the limited measured data; this study proposes an APH model based on the spatial projection relationship. Combined with the gradient boosting decision tree (GBDT) intelligent algorithm, the APH characteristics of the abrasive tool are quickly generated by the data-fusion learning model. Through the corresponding experiments, we acquired the 2D image of the abrasive tool and extracted the area information of the abrasive projection area. The result shows that the fitting degree R2 of the algorithm model reaches 0.911. Comparing the APH generated based on the algorithm model with the actual one, the average accuracy is about 94.69% and the mean absolute error of generated protrusion height MAE is 5.31 μm, which validates the proposed model. The results of this study demonstrate that the data-driven approach can effectively establish the generation model of the protrusion height of abrasive grains, which not only enable the measurement of the protrusion height of abrasive but also provide a reference for accurate modeling of abrasive tools. Abrasive tool Protrusion height Abrasive image Data-fusion Intelligent algorithm Fang, Congfu aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 123(2022), 11-12 vom: 19. Nov., Seite 4309-4320 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:123 year:2022 number:11-12 day:19 month:11 pages:4309-4320 https://doi.org/10.1007/s00170-022-10474-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_2018 GBV_ILN_2333 AR 123 2022 11-12 19 11 4309-4320 |
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10.1007/s00170-022-10474-y doi (DE-627)OLC2080059998 (DE-He213)s00170-022-10474-y-p DE-627 ger DE-627 rakwb eng 670 VZ Li, Hongyang verfasserin aut Study on generation of abrasive protrusion height based on projection information–driven intelligent algorithm 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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 Abrasive protrusion height (APH) is the core parameter of abrasive tools, and it is also a principal parameter for modeling and simulation of the surface topography of abrasive tools. Because of the difficulty in obtaining the 3D feature information of abrasives and the limited measured data; this study proposes an APH model based on the spatial projection relationship. Combined with the gradient boosting decision tree (GBDT) intelligent algorithm, the APH characteristics of the abrasive tool are quickly generated by the data-fusion learning model. Through the corresponding experiments, we acquired the 2D image of the abrasive tool and extracted the area information of the abrasive projection area. The result shows that the fitting degree R2 of the algorithm model reaches 0.911. Comparing the APH generated based on the algorithm model with the actual one, the average accuracy is about 94.69% and the mean absolute error of generated protrusion height MAE is 5.31 μm, which validates the proposed model. The results of this study demonstrate that the data-driven approach can effectively establish the generation model of the protrusion height of abrasive grains, which not only enable the measurement of the protrusion height of abrasive but also provide a reference for accurate modeling of abrasive tools. Abrasive tool Protrusion height Abrasive image Data-fusion Intelligent algorithm Fang, Congfu aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 123(2022), 11-12 vom: 19. Nov., Seite 4309-4320 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:123 year:2022 number:11-12 day:19 month:11 pages:4309-4320 https://doi.org/10.1007/s00170-022-10474-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_2018 GBV_ILN_2333 AR 123 2022 11-12 19 11 4309-4320 |
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Li, Hongyang Fang, Congfu |
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Li, Hongyang |
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study on generation of abrasive protrusion height based on projection information–driven intelligent algorithm |
| title_auth |
Study on generation of abrasive protrusion height based on projection information–driven intelligent algorithm |
| abstract |
Abstract Abrasive protrusion height (APH) is the core parameter of abrasive tools, and it is also a principal parameter for modeling and simulation of the surface topography of abrasive tools. Because of the difficulty in obtaining the 3D feature information of abrasives and the limited measured data; this study proposes an APH model based on the spatial projection relationship. Combined with the gradient boosting decision tree (GBDT) intelligent algorithm, the APH characteristics of the abrasive tool are quickly generated by the data-fusion learning model. Through the corresponding experiments, we acquired the 2D image of the abrasive tool and extracted the area information of the abrasive projection area. The result shows that the fitting degree R2 of the algorithm model reaches 0.911. Comparing the APH generated based on the algorithm model with the actual one, the average accuracy is about 94.69% and the mean absolute error of generated protrusion height MAE is 5.31 μm, which validates the proposed model. The results of this study demonstrate that the data-driven approach can effectively establish the generation model of the protrusion height of abrasive grains, which not only enable the measurement of the protrusion height of abrasive but also provide a reference for accurate modeling of abrasive tools. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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 Abrasive protrusion height (APH) is the core parameter of abrasive tools, and it is also a principal parameter for modeling and simulation of the surface topography of abrasive tools. Because of the difficulty in obtaining the 3D feature information of abrasives and the limited measured data; this study proposes an APH model based on the spatial projection relationship. Combined with the gradient boosting decision tree (GBDT) intelligent algorithm, the APH characteristics of the abrasive tool are quickly generated by the data-fusion learning model. Through the corresponding experiments, we acquired the 2D image of the abrasive tool and extracted the area information of the abrasive projection area. The result shows that the fitting degree R2 of the algorithm model reaches 0.911. Comparing the APH generated based on the algorithm model with the actual one, the average accuracy is about 94.69% and the mean absolute error of generated protrusion height MAE is 5.31 μm, which validates the proposed model. The results of this study demonstrate that the data-driven approach can effectively establish the generation model of the protrusion height of abrasive grains, which not only enable the measurement of the protrusion height of abrasive but also provide a reference for accurate modeling of abrasive tools. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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 Abrasive protrusion height (APH) is the core parameter of abrasive tools, and it is also a principal parameter for modeling and simulation of the surface topography of abrasive tools. Because of the difficulty in obtaining the 3D feature information of abrasives and the limited measured data; this study proposes an APH model based on the spatial projection relationship. Combined with the gradient boosting decision tree (GBDT) intelligent algorithm, the APH characteristics of the abrasive tool are quickly generated by the data-fusion learning model. Through the corresponding experiments, we acquired the 2D image of the abrasive tool and extracted the area information of the abrasive projection area. The result shows that the fitting degree R2 of the algorithm model reaches 0.911. Comparing the APH generated based on the algorithm model with the actual one, the average accuracy is about 94.69% and the mean absolute error of generated protrusion height MAE is 5.31 μm, which validates the proposed model. The results of this study demonstrate that the data-driven approach can effectively establish the generation model of the protrusion height of abrasive grains, which not only enable the measurement of the protrusion height of abrasive but also provide a reference for accurate modeling of abrasive tools. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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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Study on generation of abrasive protrusion height based on projection information–driven intelligent algorithm |
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https://doi.org/10.1007/s00170-022-10474-y |
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