Automatic method for vickers hardness estimation by image processing
Hardness is one of the most important mechanical properties of materials, since it is used to estimate their quality and to determine their suitability for a particular application. One method of determining quality is the Vickers hardness test, in which the resistance to plastic deformation at the...
- Autores:
-
Polanco,Jonatan D.
Jacanamejoy-Jamioy, Carlos
Mambuscay, Claudia L.
Piamba, Jeferson F.
Forero, Manuel G.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2022
- Institución:
- Universidad de Ibagué
- Repositorio:
- Repositorio Universidad de Ibagué
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unibague.edu.co:20.500.12313/3883
- Acceso en línea:
- https://hdl.handle.net/20.500.12313/3883
- Palabra clave:
- Hardness estimation
Image processing
Mechanics of materials
Steel heat treating
Vickers hardness
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
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Automatic method for vickers hardness estimation by image processing |
| title |
Automatic method for vickers hardness estimation by image processing |
| spellingShingle |
Automatic method for vickers hardness estimation by image processing Hardness estimation Image processing Mechanics of materials Steel heat treating Vickers hardness |
| title_short |
Automatic method for vickers hardness estimation by image processing |
| title_full |
Automatic method for vickers hardness estimation by image processing |
| title_fullStr |
Automatic method for vickers hardness estimation by image processing |
| title_full_unstemmed |
Automatic method for vickers hardness estimation by image processing |
| title_sort |
Automatic method for vickers hardness estimation by image processing |
| dc.creator.fl_str_mv |
Polanco,Jonatan D. Jacanamejoy-Jamioy, Carlos Mambuscay, Claudia L. Piamba, Jeferson F. Forero, Manuel G. |
| dc.contributor.author.none.fl_str_mv |
Polanco,Jonatan D. Jacanamejoy-Jamioy, Carlos Mambuscay, Claudia L. Piamba, Jeferson F. Forero, Manuel G. |
| dc.subject.proposal.eng.fl_str_mv |
Hardness estimation Image processing Mechanics of materials Steel heat treating Vickers hardness |
| topic |
Hardness estimation Image processing Mechanics of materials Steel heat treating Vickers hardness |
| description |
Hardness is one of the most important mechanical properties of materials, since it is used to estimate their quality and to determine their suitability for a particular application. One method of determining quality is the Vickers hardness test, in which the resistance to plastic deformation at the surface of the material is measured after applying force with an indenter. The hardness is measured from the sample image, which is a tedious, time-consuming, and prone to human error procedure. Therefore, in this work, a new automatic method based on image processing techniques is proposed, allowing for obtaining results quickly and more accurately even with high irregularities in the indentation mark. For the development and validation of the method, a set of microscopy images of samples indented with applied forces of 5 and 10 on AISI D2 steel with and without quenching, tempering heat treatment and samples coated with titanium niobium nitride (TiNbN) was used. The proposed method was implemented as a plugin of the ImageJ program, allowing for obtaining reproducible Vickers hardness results in an average time of 2.05 seconds with an accuracy of 98.3% and a maximum error of 4.5% with respect to the values obtained manually, used as a golden standard. |
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2022 |
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2022-12-30 |
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2023-10-27T15:10:29Z |
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2023-10-27T15:10:29Z |
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Artículo de revista |
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Polanco, J. D., Jacanamejoy-Jamioy, C., Mambuscay, C. L., Piamba, J. F., & Forero, M. G. (2022). Automatic Method for Vickers Hardness Estimation by Image Processing. Journal of Imaging, 9(1), 8. MDPI AG. Retrieved from http://dx.doi.org/10.3390/jimaging9010008 |
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2313-433X |
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Polanco, J. D., Jacanamejoy-Jamioy, C., Mambuscay, C. L., Piamba, J. F., & Forero, M. G. (2022). Automatic Method for Vickers Hardness Estimation by Image Processing. Journal of Imaging, 9(1), 8. MDPI AG. Retrieved from http://dx.doi.org/10.3390/jimaging9010008 2313-433X |
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https://hdl.handle.net/20.500.12313/3883 |
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eng |
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eng |
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1 |
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9 |
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Journal of Imaging |
| dc.relation.references.none.fl_str_mv |
Callister, W.D. Introducción a la Ciencia E ingeniería de los Materiales: Tomo 1; Reverté: Barcelona, Spain, 1997. [Google Scholar] Askeland, D.R.; Fulay, P.P. The Science and Engineering of Materials; Cengage: Boston, MA, USA, 2016. [Google Scholar] Sydor, M.; Pinkowski, G.; Jasińska, A. The Brinell method for determining hardness of wood flooring materials. Forests 2020, 11, 878. [Google Scholar] [CrossRef] Zeng, X.; Xiao, Y.; Ji, X.; Wang, G. Mineral identification based on deep learning that combines image and Mohs hardness. Minerals 2021, 11, 506. [Google Scholar] [CrossRef] Rodríguez-Prieto, A.; Primera, E.; Frigione, M.; Camacho, A.M. Reliability prediction of acrylonitrile O-ring for nuclear power applications based on shore hardness measurements. Polymers 2021, 13, 943. [Google Scholar] [CrossRef] [PubMed] Schiavi, A.; Origlia, C.; Germak, A.; Prato, A.; Genta, G. Indentation modulus, indentation work and creep of metals and alloys at the macro-scale level: Experimental insights into the use of a primary Vickers hardness standard machine. Materials 2021, 14, 2912. [Google Scholar] [CrossRef] [PubMed] Hościło, B.; Molski, K.L. Determination of Surface Stresses in X20Cr13 Steel by the Use of a Modified Hardness Measurement Procedure with Vickers Indenter. Materials 2020, 13, 4844. [Google Scholar] [CrossRef] [PubMed] Albella, J.M. Láminas Delgadas y Recubrimientos. Preparación, Propiedades y Aplicaciones; Consejo Superior de Investigaciones Científicas: Madrid, Spain, 2003; p. 704. [Google Scholar] Jairo Florez Olaya, J.; Chipatecua, Y.L.G.; Rodil, S.E.P. Resistencia a la corrosión de recubrimientos de nitruros metálicos depositados sobre acero AISI M2 Corrosion resistance of transition metal nitride films deposited on AISI M2 steel. Ing. Y Desarro. 2012, 30, 1–22. [Google Scholar] Baptista, A.; Silva, F.; Porteiro, J.; Míguez, J.; Pinto, G. Sputtering Physical Vapour Deposition (PVD) Coatings: A Critical Review on Process Improvement and Market Trend Demands. Coatings 2018, 8, 402. [Google Scholar] [CrossRef][Green Version] Soffritti, C.; Fortini, A.; Sola, R.; Fabbri, E.; Merlin, M.; Garagnani, G.L. Influence of Vacuum Heat Treatments on Microstructure and Mechanical Properties of M35 High Speed Steel. Metals 2020, 10, 643. [Google Scholar] [CrossRef] Barrena-Rodríguez, M.d.J.; Acosta-González, F.A.; Téllez-Rosas, M.M. A Review of the Boiling Curve with Reference to Steel Quenching. Metals 2021, 11, 974. [Google Scholar] [CrossRef] Cicek, H.; Baran, O.; Keles, A.; Totik, Y.; Efeoglu, I. A comparative study of fatigue properties of TiVN and TiNbN thin films deposited on different substrates. Surf. Coatings Technol. 2017, 332, 296–303. [Google Scholar] [CrossRef] Sheppard, L.R.; Zhang, H.; Liu, R.; Macartney, S.; Murphy, T.; Wuhrer, R. Reactive sputtered Ti X Nb Y N Z thin films. I. Basic processing relationships. Mater. Chem. Phys. 2019, 224, 308–313. [Google Scholar] [CrossRef] Sheppard, L.R.; Zhang, H.; Liu, R.; Macartney, S.; Murphy, T.; Wainer, P.; Wuhrer, R. Reactive sputtered Ti x Nb y N coatings. II. Effect of common deposition parameters. Mater. Chem. Phys. 2019, 224, 320–327. [Google Scholar] [CrossRef] ASTM International Standards. Standard Test Method for Microindentation Hardness of Materials; ASTM International: West Conshohocken, PA, USA, 2017; Volume E384, pp. 1–40. [Google Scholar] [CrossRef] Sugimoto, T.; Kawaguchi, T. Development of an automatic Vickers hardness testing system using image processing technology. IEEE Trans. Ind. Electron. 1997, 44, 696–702. [Google Scholar] [CrossRef] Dominguez-Nicolas, S.M.; Wiederhold, P. Indentation Image Analysis for Vickers Hardness Testing. In Proceedings of the 2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), Mexico City, Mexico, 5–7 September 2018; pp. 1–6. [Google Scholar] [CrossRef] Fedotkin, A.; Laktionov, I.; Kravchuk, K.; Maslenikov, I.; Useinov, A. Automatic Processing of Microhardness Images Using Computer Vision Methods. Instruments Exp. Tech. 2021, 64, 357–362. [Google Scholar] [CrossRef] Privezentsev, D.; Zhiznyakov, A.; Kulkov, Y. Automation of Measuring Microhardness of Materials using Metal-Graphic Images. In Proceedings of the 2019 International Russian Automation Conference (RusAutoCon), Sochi, Russia, 8–14 September 2019; pp. 1–5. [Google Scholar] [CrossRef] Tanaka, Y.; Seino, Y.; Hattori, K. Automated Vickers hardness measurement using convolutional neural networks. Int. J. Adv. Manuf. Technol. 2020, 109, 1345–1355. [Google Scholar] [CrossRef] Jalilian, E.; Uhl, A. Deep Learning Based Automated Vickers Hardness Measurement. In Computer Analysis of Images and Patterns; Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M., Eds.; Springer International Publishing: Cham, Switzerland, 2021; pp. 3–13. [Google Scholar] [CrossRef] Li, Z.; Yin, F. Automated measurement of Vickers hardness using image segmentation with neural networks. Measurement 2021, 186, 110200. [Google Scholar] [CrossRef] Cheng, W.S.; Chen, G.Y.; Shih, X.Y.; Elsisi, M.; Tsai, M.H.; Dai, H.J. Vickers Hardness Value Test via Multi-Task Learning Convolutional Neural Networks and Image Augmentation. Appl. Sci. 2022, 12, 10820. [Google Scholar] [CrossRef] Chang, C.; Hwang, S.; Buehrer, D. A shape recognition scheme based on relative distances of feature points from the centroid. Pattern Recognit. 1991, 24, 1053–1063. [Google Scholar] [CrossRef] Mukhopadhyay, P.; Chaudhuri, B.B. A survey of Hough Transform. Pattern Recognit. 2015, 48, 993–1010. [Google Scholar] [CrossRef] |
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Polanco,Jonatan D.1beb0be2-b0e0-4875-9944-33e0caa72a4e-1Jacanamejoy-Jamioy, Carlosa350cdbd-f8b4-47c9-b17b-37b24d96a7ea-1Mambuscay, Claudia L.ab20b931-69fc-4d19-932f-1ba5268bfeba-1Piamba, Jeferson F.db9dba0a-904d-4cc1-90ec-b489ecd8648d-1Forero, Manuel G.221ba9eb-1b06-4908-aac9-b50cd974a391-12023-10-27T15:10:29Z2023-10-27T15:10:29Z2022-12-30Hardness is one of the most important mechanical properties of materials, since it is used to estimate their quality and to determine their suitability for a particular application. One method of determining quality is the Vickers hardness test, in which the resistance to plastic deformation at the surface of the material is measured after applying force with an indenter. The hardness is measured from the sample image, which is a tedious, time-consuming, and prone to human error procedure. Therefore, in this work, a new automatic method based on image processing techniques is proposed, allowing for obtaining results quickly and more accurately even with high irregularities in the indentation mark. For the development and validation of the method, a set of microscopy images of samples indented with applied forces of 5 and 10 on AISI D2 steel with and without quenching, tempering heat treatment and samples coated with titanium niobium nitride (TiNbN) was used. The proposed method was implemented as a plugin of the ImageJ program, allowing for obtaining reproducible Vickers hardness results in an average time of 2.05 seconds with an accuracy of 98.3% and a maximum error of 4.5% with respect to the values obtained manually, used as a golden standard.15 páginasapplication/pdfPolanco, J. D., Jacanamejoy-Jamioy, C., Mambuscay, C. L., Piamba, J. F., & Forero, M. G. (2022). Automatic Method for Vickers Hardness Estimation by Image Processing. Journal of Imaging, 9(1), 8. MDPI AG. Retrieved from http://dx.doi.org/10.3390/jimaging90100082313-433Xhttps://hdl.handle.net/20.500.12313/3883engSuiza19Journal of ImagingCallister, W.D. Introducción a la Ciencia E ingeniería de los Materiales: Tomo 1; Reverté: Barcelona, Spain, 1997. [Google Scholar]Askeland, D.R.; Fulay, P.P. The Science and Engineering of Materials; Cengage: Boston, MA, USA, 2016. [Google Scholar]Sydor, M.; Pinkowski, G.; Jasińska, A. The Brinell method for determining hardness of wood flooring materials. Forests 2020, 11, 878. [Google Scholar] [CrossRef]Zeng, X.; Xiao, Y.; Ji, X.; Wang, G. Mineral identification based on deep learning that combines image and Mohs hardness. Minerals 2021, 11, 506. [Google Scholar] [CrossRef]Rodríguez-Prieto, A.; Primera, E.; Frigione, M.; Camacho, A.M. Reliability prediction of acrylonitrile O-ring for nuclear power applications based on shore hardness measurements. Polymers 2021, 13, 943. [Google Scholar] [CrossRef] [PubMed]Schiavi, A.; Origlia, C.; Germak, A.; Prato, A.; Genta, G. Indentation modulus, indentation work and creep of metals and alloys at the macro-scale level: Experimental insights into the use of a primary Vickers hardness standard machine. Materials 2021, 14, 2912. [Google Scholar] [CrossRef] [PubMed]Hościło, B.; Molski, K.L. Determination of Surface Stresses in X20Cr13 Steel by the Use of a Modified Hardness Measurement Procedure with Vickers Indenter. Materials 2020, 13, 4844. [Google Scholar] [CrossRef] [PubMed]Albella, J.M. Láminas Delgadas y Recubrimientos. Preparación, Propiedades y Aplicaciones; Consejo Superior de Investigaciones Científicas: Madrid, Spain, 2003; p. 704. [Google Scholar]Jairo Florez Olaya, J.; Chipatecua, Y.L.G.; Rodil, S.E.P. Resistencia a la corrosión de recubrimientos de nitruros metálicos depositados sobre acero AISI M2 Corrosion resistance of transition metal nitride films deposited on AISI M2 steel. Ing. Y Desarro. 2012, 30, 1–22. [Google Scholar]Baptista, A.; Silva, F.; Porteiro, J.; Míguez, J.; Pinto, G. Sputtering Physical Vapour Deposition (PVD) Coatings: A Critical Review on Process Improvement and Market Trend Demands. Coatings 2018, 8, 402. [Google Scholar] [CrossRef][Green Version]Soffritti, C.; Fortini, A.; Sola, R.; Fabbri, E.; Merlin, M.; Garagnani, G.L. Influence of Vacuum Heat Treatments on Microstructure and Mechanical Properties of M35 High Speed Steel. Metals 2020, 10, 643. [Google Scholar] [CrossRef]Barrena-Rodríguez, M.d.J.; Acosta-González, F.A.; Téllez-Rosas, M.M. A Review of the Boiling Curve with Reference to Steel Quenching. Metals 2021, 11, 974. [Google Scholar] [CrossRef]Cicek, H.; Baran, O.; Keles, A.; Totik, Y.; Efeoglu, I. A comparative study of fatigue properties of TiVN and TiNbN thin films deposited on different substrates. Surf. Coatings Technol. 2017, 332, 296–303. [Google Scholar] [CrossRef]Sheppard, L.R.; Zhang, H.; Liu, R.; Macartney, S.; Murphy, T.; Wuhrer, R. Reactive sputtered Ti X Nb Y N Z thin films. I. Basic processing relationships. Mater. Chem. Phys. 2019, 224, 308–313. [Google Scholar] [CrossRef]Sheppard, L.R.; Zhang, H.; Liu, R.; Macartney, S.; Murphy, T.; Wainer, P.; Wuhrer, R. Reactive sputtered Ti x Nb y N coatings. II. Effect of common deposition parameters. Mater. Chem. Phys. 2019, 224, 320–327. [Google Scholar] [CrossRef]ASTM International Standards. Standard Test Method for Microindentation Hardness of Materials; ASTM International: West Conshohocken, PA, USA, 2017; Volume E384, pp. 1–40. [Google Scholar] [CrossRef]Sugimoto, T.; Kawaguchi, T. Development of an automatic Vickers hardness testing system using image processing technology. IEEE Trans. Ind. Electron. 1997, 44, 696–702. [Google Scholar] [CrossRef]Dominguez-Nicolas, S.M.; Wiederhold, P. Indentation Image Analysis for Vickers Hardness Testing. In Proceedings of the 2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), Mexico City, Mexico, 5–7 September 2018; pp. 1–6. [Google Scholar] [CrossRef]Fedotkin, A.; Laktionov, I.; Kravchuk, K.; Maslenikov, I.; Useinov, A. Automatic Processing of Microhardness Images Using Computer Vision Methods. Instruments Exp. Tech. 2021, 64, 357–362. [Google Scholar] [CrossRef]Privezentsev, D.; Zhiznyakov, A.; Kulkov, Y. Automation of Measuring Microhardness of Materials using Metal-Graphic Images. In Proceedings of the 2019 International Russian Automation Conference (RusAutoCon), Sochi, Russia, 8–14 September 2019; pp. 1–5. [Google Scholar] [CrossRef]Tanaka, Y.; Seino, Y.; Hattori, K. Automated Vickers hardness measurement using convolutional neural networks. Int. J. Adv. Manuf. Technol. 2020, 109, 1345–1355. [Google Scholar] [CrossRef]Jalilian, E.; Uhl, A. Deep Learning Based Automated Vickers Hardness Measurement. In Computer Analysis of Images and Patterns; Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M., Eds.; Springer International Publishing: Cham, Switzerland, 2021; pp. 3–13. [Google Scholar] [CrossRef]Li, Z.; Yin, F. Automated measurement of Vickers hardness using image segmentation with neural networks. Measurement 2021, 186, 110200. [Google Scholar] [CrossRef]Cheng, W.S.; Chen, G.Y.; Shih, X.Y.; Elsisi, M.; Tsai, M.H.; Dai, H.J. Vickers Hardness Value Test via Multi-Task Learning Convolutional Neural Networks and Image Augmentation. Appl. Sci. 2022, 12, 10820. [Google Scholar] [CrossRef]Chang, C.; Hwang, S.; Buehrer, D. A shape recognition scheme based on relative distances of feature points from the centroid. Pattern Recognit. 1991, 24, 1053–1063. [Google Scholar] [CrossRef]Mukhopadhyay, P.; Chaudhuri, B.B. A survey of Hough Transform. Pattern Recognit. 2015, 48, 993–1010. [Google Scholar] [CrossRef]info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Atribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/https://www.mdpi.com/2313-433X/9/1/8Hardness estimationImage processingMechanics of materialsSteel heat treatingVickers hardnessAutomatic method for vickers hardness estimation by image processingArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionPublicationORIGINALjimaging-09-00008.pdfjimaging-09-00008.pdfapplication/pdf31571890https://repositorio.unibague.edu.co/bitstreams/c2e3bff4-3e97-404b-8cc8-896794c29d4c/downloadb4e5c7ff0144bd5dbc331a24b0cfa924MD51TEXTjimaging-09-00008.pdf.txtjimaging-09-00008.pdf.txtExtracted texttext/plain37174https://repositorio.unibague.edu.co/bitstreams/40389e9d-d58a-47ca-b889-d4704405c7fd/download35c99312d5757f736ebf2270137ccb86MD53THUMBNAILjimaging-09-00008.pdf.jpgjimaging-09-00008.pdf.jpgGenerated Thumbnailimage/jpeg15414https://repositorio.unibague.edu.co/bitstreams/4a5ca624-4a57-488f-9c9e-924cbcec2913/download7077a3d39d17a6145a90c5320d828afbMD54LICENSElicense.txtlicense.txttext/plain; charset=utf-8134https://repositorio.unibague.edu.co/bitstreams/b9eb8ca1-b960-48c1-b8a1-09f65c7ec240/download2fa3e590786b9c0f3ceba1b9656b7ac3MD5220.500.12313/3883oai:repositorio.unibague.edu.co:20.500.12313/38832023-10-28 03:00:39.026https://creativecommons.org/licenses/by-nc-nd/4.0/https://repositorio.unibague.edu.coRepositorio Institucional Universidad de Ibaguébdigital@metabiblioteca.comQ3JlYXRpdmUgQ29tbW9ucyBBdHRyaWJ1dGlvbi1Ob25Db21tZXJjaWFsLU5vRGVyaXZhdGl2ZXMgNC4wIEludGVybmF0aW9uYWwgTGljZW5zZQ0KaHR0cHM6Ly9jcmVhdGl2ZWNvbW1vbnMub3JnL2xpY2Vuc2VzL2J5LW5jLW5kLzQuMC8= |