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 Jiménez, Jeferson Fernando
Forero, Manuel G.
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2023
- Institución:
- Universidad de Ibagué
- Repositorio:
- Repositorio Universidad de Ibagué
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unibague.edu.co:20.500.12313/5540
- Palabra clave:
- Procedimiento de imágenes
Mecánica de materiales
Acero - Tratamiento térmico
Hardness estimation
Image processing
Mechanics of materials
Steel heat treating
Vickers hardness
- Rights
- openAccess
- License
- © 2022 by the authors.
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| dc.title.eng.fl_str_mv |
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 Procedimiento de imágenes Mecánica de materiales Acero - Tratamiento térmico 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 Jiménez, Jeferson Fernando Forero, Manuel G. |
| dc.contributor.author.none.fl_str_mv |
Polanco, Jonatan D. Jacanamejoy-Jamioy, Carlos Mambuscay, Claudia L. Piamba Jiménez, Jeferson Fernando Forero, Manuel G. |
| dc.subject.armarc.none.fl_str_mv |
Procedimiento de imágenes Mecánica de materiales Acero - Tratamiento térmico |
| topic |
Procedimiento de imágenes Mecánica de materiales Acero - Tratamiento térmico Hardness estimation Image processing Mechanics of materials Steel heat treating Vickers hardness |
| dc.subject.proposal.eng.fl_str_mv |
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 (Formula presented.) and (Formula presented.) 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 (Formula presented.) seconds with an accuracy of (Formula presented.) and a maximum error of (Formula presented.) with respect to the values obtained manually, used as a golden standard. |
| publishDate |
2023 |
| dc.date.issued.none.fl_str_mv |
2023-01 |
| dc.date.accessioned.none.fl_str_mv |
2025-08-25T16:39:58Z |
| dc.date.available.none.fl_str_mv |
2025-08-25T16:39:58Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Polanco, J., Jacanamejoy-Jamioy, C., Mambuscay, C., Piamba, J. y Forero, M. (2023). Automatic Method for Vickers Hardness Estimation by Image Processing. Journal of Imaging, 9(1). DOI: 10.3390/jimaging9010008 |
| dc.identifier.doi.none.fl_str_mv |
10.3390/jimaging9010008 |
| dc.identifier.issn.none.fl_str_mv |
2313433X |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12313/5540 |
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https://www.mdpi.com/2313-433X/9/1/8 |
| identifier_str_mv |
Polanco, J., Jacanamejoy-Jamioy, C., Mambuscay, C., Piamba, J. y Forero, M. (2023). Automatic Method for Vickers Hardness Estimation by Image Processing. Journal of Imaging, 9(1). DOI: 10.3390/jimaging9010008 10.3390/jimaging9010008 2313433X |
| url |
https://hdl.handle.net/20.500.12313/5540 https://www.mdpi.com/2313-433X/9/1/8 |
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eng |
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eng |
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1 |
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8 |
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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 Askeland, D.R.; Fulay, P.P. The Science and Engineering of Materials; Cengage: Boston, MA, USA, 2016. Sydor, M.; Pinkowski, G.; Jasińska, A. The Brinell method for determining hardness of wood flooring materials. Forests 2020, 11, 878. Zeng, X.; Xiao, Y.; Ji, X.; Wang, G. Mineral identification based on deep learning that combines image and Mohs hardness. Minerals 2021, 11, 506. 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. 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. 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. Albella, J.M. Láminas Delgadas y Recubrimientos. Preparación, Propiedades y Aplicaciones; Consejo Superior de Investigaciones Científicas: Madrid, Spain, 2003; p. 704. 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. 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. 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. 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. 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. 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. 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. ASTM International Standards. Standard Test Method for Microindentation Hardness of Materials; ASTM International: West Conshohocken, PA, USA, 2017; Volume E384, pp. 1–40. Sugimoto, T.; Kawaguchi, T. Development of an automatic Vickers hardness testing system using image processing technology. IEEE Trans. Ind. Electron. 1997, 44, 696–702. 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. 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. 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. Tanaka, Y.; Seino, Y.; Hattori, K. Automated Vickers hardness measurement using convolutional neural networks. Int. J. Adv. Manuf. Technol. 2020, 109, 1345–1355. 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. Li, Z.; Yin, F. Automated measurement of Vickers hardness using image segmentation with neural networks. Measurement 2021, 186, 110200. 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. 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. Mukhopadhyay, P.; Chaudhuri, B.B. A survey of Hough Transform. Pattern Recognit. 2015, 48, 993–1010 |
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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 Jiménez, Jeferson Fernando2542324e-59b6-44cb-8577-2da79c47599a600Forero, Manuel G.d1814690-6764-437c-a01a-346c8f3436dc-12025-08-25T16:39:58Z2025-08-25T16:39:58Z2023-01Hardness 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 (Formula presented.) and (Formula presented.) 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 (Formula presented.) seconds with an accuracy of (Formula presented.) and a maximum error of (Formula presented.) with respect to the values obtained manually, used as a golden standard.application/pdfPolanco, J., Jacanamejoy-Jamioy, C., Mambuscay, C., Piamba, J. y Forero, M. (2023). Automatic Method for Vickers Hardness Estimation by Image Processing. Journal of Imaging, 9(1). DOI: 10.3390/jimaging901000810.3390/jimaging90100082313433Xhttps://hdl.handle.net/20.500.12313/5540https://www.mdpi.com/2313-433X/9/1/8engMDPISuiza189Journal of ImagingCallister, W.D. Introducción a la Ciencia E ingeniería de los Materiales: Tomo 1; Reverté: Barcelona, Spain, 1997Askeland, D.R.; Fulay, P.P. The Science and Engineering of Materials; Cengage: Boston, MA, USA, 2016.Sydor, M.; Pinkowski, G.; Jasińska, A. The Brinell method for determining hardness of wood flooring materials. Forests 2020, 11, 878.Zeng, X.; Xiao, Y.; Ji, X.; Wang, G. Mineral identification based on deep learning that combines image and Mohs hardness. Minerals 2021, 11, 506.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.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.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.Albella, J.M. Láminas Delgadas y Recubrimientos. Preparación, Propiedades y Aplicaciones; Consejo Superior de Investigaciones Científicas: Madrid, Spain, 2003; p. 704.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.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.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.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.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.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.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.ASTM International Standards. Standard Test Method for Microindentation Hardness of Materials; ASTM International: West Conshohocken, PA, USA, 2017; Volume E384, pp. 1–40.Sugimoto, T.; Kawaguchi, T. Development of an automatic Vickers hardness testing system using image processing technology. IEEE Trans. Ind. Electron. 1997, 44, 696–702.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.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.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.Tanaka, Y.; Seino, Y.; Hattori, K. Automated Vickers hardness measurement using convolutional neural networks. Int. J. Adv. Manuf. Technol. 2020, 109, 1345–1355.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.Li, Z.; Yin, F. Automated measurement of Vickers hardness using image segmentation with neural networks. Measurement 2021, 186, 110200.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.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.Mukhopadhyay, P.; Chaudhuri, B.B. A survey of Hough Transform. Pattern Recognit. 2015, 48, 993–1010© 2022 by the authors.info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial 4.0 Internacional (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/https://www.mdpi.com/2313-433X/9/1/8Procedimiento de imágenesMecánica de materialesAcero - Tratamiento térmicoHardness estimationImage processingMechanics of materialsSteel heat treatingVickers hardnessAutomatic Method for Vickers Hardness Estimation by Image ProcessingArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPublicationORIGINALArtículo.pdfArtículo.pdfapplication/pdf108441https://repositorio.unibague.edu.co/bitstreams/5fc9d95a-c24c-41c4-853f-568576fd7f54/downloade9d00878f2056355e933263b49715c8fMD52TEXTArtículo.pdf.txtArtículo.pdf.txtExtracted texttext/plain3297https://repositorio.unibague.edu.co/bitstreams/85979f68-1bbf-4503-a80a-37d4a18e31bd/download0ccaecc0ee89ae6bf6b63d8607e2aa06MD53THUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg31956https://repositorio.unibague.edu.co/bitstreams/c9ac76b5-958b-4191-a0f1-b9181c05fb7b/download56819adf05dadd3fffd10a2ada4ae22cMD54LICENSElicense.txtlicense.txttext/plain; charset=utf-8134https://repositorio.unibague.edu.co/bitstreams/b4cdc2ce-40ec-41e2-9fb5-7f13e4044e01/download2fa3e590786b9c0f3ceba1b9656b7ac3MD5120.500.12313/5540oai:repositorio.unibague.edu.co:20.500.12313/55402025-09-12 11:49:59.872https://creativecommons.org/licenses/by-nc/4.0/© 2022 by the authors.https://repositorio.unibague.edu.coRepositorio Institucional Universidad de Ibaguébdigital@metabiblioteca.comQ3JlYXRpdmUgQ29tbW9ucyBBdHRyaWJ1dGlvbi1Ob25Db21tZXJjaWFsLU5vRGVyaXZhdGl2ZXMgNC4wIEludGVybmF0aW9uYWwgTGljZW5zZQ0KaHR0cHM6Ly9jcmVhdGl2ZWNvbW1vbnMub3JnL2xpY2Vuc2VzL2J5LW5jLW5kLzQuMC8= |