Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks

The study of material hardness is crucial for determining its quality, potential failures, and appropriate applications, as well as minimizing losses incurred during the production process. To achieve this, certain criteria must be met to ensure high quality. This process is typically performed manu...

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Autores:
Buitrago Diaz, Juan C.
Ortega-Portilla, Carolina
Mambuscay, Claudia L.
Piamba, 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:20.500.12313/5557
Acceso en línea:
https://hdl.handle.net/20.500.12313/5557
https://www.mdpi.com/2075-4701/13/8/1391
Palabra clave:
Redes Neuronales Convolucionales
Dureza Vickers en Acero D2
Recubrimiento TiNbN
Corner detection
D2 steel
Diagonal measurement
Indentation image analysis
Material hardness
Thermal treatment
Titanium niobium nitride (TiNbN) coating
Vickers hardness
Rights
openAccess
License
© 2023 by the authors.
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repository_id_str
dc.title.eng.fl_str_mv Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks
title Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks
spellingShingle Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks
Redes Neuronales Convolucionales
Dureza Vickers en Acero D2
Recubrimiento TiNbN
Corner detection
D2 steel
Diagonal measurement
Indentation image analysis
Material hardness
Thermal treatment
Titanium niobium nitride (TiNbN) coating
Vickers hardness
title_short Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks
title_full Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks
title_fullStr Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks
title_full_unstemmed Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks
title_sort Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks
dc.creator.fl_str_mv Buitrago Diaz, Juan C.
Ortega-Portilla, Carolina
Mambuscay, Claudia L.
Piamba, Jeferson Fernando
Forero, Manuel G
dc.contributor.author.none.fl_str_mv Buitrago Diaz, Juan C.
Ortega-Portilla, Carolina
Mambuscay, Claudia L.
Piamba, Jeferson Fernando
Forero, Manuel G
dc.subject.armarc.none.fl_str_mv Redes Neuronales Convolucionales
Dureza Vickers en Acero D2
Recubrimiento TiNbN
topic Redes Neuronales Convolucionales
Dureza Vickers en Acero D2
Recubrimiento TiNbN
Corner detection
D2 steel
Diagonal measurement
Indentation image analysis
Material hardness
Thermal treatment
Titanium niobium nitride (TiNbN) coating
Vickers hardness
dc.subject.proposal.eng.fl_str_mv Corner detection
D2 steel
Diagonal measurement
Indentation image analysis
Material hardness
Thermal treatment
Titanium niobium nitride (TiNbN) coating
Vickers hardness
description The study of material hardness is crucial for determining its quality, potential failures, and appropriate applications, as well as minimizing losses incurred during the production process. To achieve this, certain criteria must be met to ensure high quality. This process is typically performed manually or using techniques based on analyzing indentation image patterns produced through the Vickers hardness technique. However, these techniques require that the indentation pattern is not aligned with the image edges. Therefore, this paper presents a technique based on convolutional neural networks (CNNs), specifically, a YOLO v3 network connected to a Dense Darknet-53 network. This technique enables the detection of indentation corner positions, measurement of diagonals, and calculation of the Vickers hardness value of D2 steel treated thermally and coated with Titanium Niobium Nitride (TiNbN), regardless of their position within the image. By implementing this architecture, an accuracy of (Formula presented.) was achieved in accurately detecting the corner positions, with an average execution time of 6 seconds. The developed technique utilizes the network to detect the regions containing the corners and subsequently accurately determines the pixel coordinates of these corners, achieving an approximate relative percentage error between (Formula presented.) to (Formula presented.) in the hardness results.
publishDate 2023
dc.date.issued.none.fl_str_mv 2023-08
dc.date.accessioned.none.fl_str_mv 2025-08-29T13:52:47Z
dc.date.available.none.fl_str_mv 2025-08-29T13:52:47Z
dc.type.none.fl_str_mv Artículo de revista
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dc.identifier.citation.none.fl_str_mv Buitrago, J., Ortega-Portilla, C., Mambuscay, C., Piamba, J. y Forero, M. (2023). Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks. Metals, 13(8). DOI: 10.3390/met13081391
dc.identifier.doi.none.fl_str_mv 10.3390/met13081391
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12313/5557
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identifier_str_mv Buitrago, J., Ortega-Portilla, C., Mambuscay, C., Piamba, J. y Forero, M. (2023). Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks. Metals, 13(8). DOI: 10.3390/met13081391
10.3390/met13081391
url https://hdl.handle.net/20.500.12313/5557
https://www.mdpi.com/2075-4701/13/8/1391
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.citationissue.none.fl_str_mv 8
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dc.relation.citationvolume.none.fl_str_mv 13
dc.relation.ispartofjournal.none.fl_str_mv 20754701
dc.relation.references.none.fl_str_mv Castillo Gutiérrez, D.E.; Angarita Moncaleano, I.I.; Rodríguez Baracaldo, R. Microstructural and mechanical characterization of dual phase steels (ferrite-martensite), obtained by thermomechanical processes. Ingeniare Rev. Chil. Ing. 2018, 26, 430–439.
Arenas, W.; Martínez, O. Roughness and hardness optimization of 12L-14 steel using the response surface methodology. Ing. Ind. 2019, 37, 125–151.
Ageev, E.; Khardikov, S. Processing of Graphic Information in the Study of the Microhardness ofthe Sintered Sample of Chromium-containing Waste. In Proceedings of the CEUR Workshop, Pescaia, Italy, 16–19 June 2019; pp. 252–255
Koch, M.; Ebersbach, U. Experimental study of chromium PVD coatings on brass substrates for the watch industry. Surf. Eng. 1997, 13, 157–164.
ASTM E384-99; Standard Test Method for Microindentation Hardness of Materials. ASTM International: West Conshohocken, PA, USA, 2017; pp. 1–40.
ASTM E92-17; Standard Test Methods for Vickers Hardness and Knoop Hardness of Metallic Materials. ASTM International: West Conshohocken, PA, USA, 2017; pp. 1–27.
Buehler. Pruebas de Dureza Vickers. Available online: https://www.buehler.com/es/blog/pruebas-de-dureza-vickers/ (accessed on 23 June 2023).
Tanaka, Y.; Seino, Y.; Hattori, K. Automated Vickers hardness measurement using convolutional neural networks. Int. J. Adv. Manuf. Technol. 2020, 109, 1345–1355.
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 2018), Mexico City, Mexico, 5–7 September 2018; pp. 1–6.
Sugimoto, T.; Kawaguchi, T. Development of an automatic Vickers hardness testing system using image processing technology. IEEE Trans. Ind. Electron. 1997, 44, 696–702.
Polanco, J.D.; Jacanamejoy-Jamioy, C.; Mambuscay, C.L.; Piamba, J.F.; Forero, M.G. Automatic Method for Vickers Hardness Estimation by Image Processing. J. Imaging 2023, 9, 8.
Abiodun, O.I.; Jantan, A.; Omolara, A.E.; Dada, K.V.; Mohamed, N.A.; Arshad, H. State-of-the-art in artificial neural network applications: A survey. Heliyon 2018, 4, e00938.
Satat, G.; Tancik, M.; Gupta, O.; Heshmat, B.; Raskar, R. Object classification through scattering media with deep learning on time resolved measurement. Opt. Express 2017, 25, 17466–17479.
Salazar Guerrero, J.E. Implementación de un Prototipo de Sistema Autónomo de Visión Artificial para la Detección de Objetos en Vídeo Utilizando Técnicas de Aprendizaje Profundo. 2019. Available online: http://repositorio.espe.edu.ec/handle/21000/20995 (accessed on 23 June 2023).
Weiss, K.; Khoshgoftaar, T.M.; Wang, D.D. A survey of transfer learning. J. Big Data 2016, 3, 9.
Hussain, M.; Bird, J.J.; Faria, D.R. A Study on CNN Transfer Learning for Image Classification; Springer: Berlin/Heidelberg, Germany, 2019; Volume 840, pp. 191–202.
Jalilian, E.; Uhl, A. Deep Learning Based Automated Vickers Hardness Measurement. In Proceedings of the Computer Analysis of Images and Patterns, Virtual Event, 28–30 September 2021; 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.
Gonzalez-Carmona, J.M.; Mambuscay, C.L.; Ortega-Portilla, C.; Hurtado-Macias, A.; Piamba, J.F. TiNbN Hard Coating Deposited at Varied Substrate Temperature by Cathodic Arc: Tribological Performance under Simulated Cutting Conditions. Materials 2023, 16, 4531.
Redmon, J.; Divvala, S.K.; Girshick, R.B.; Farhadi, A. You Only Look Once: Unified, Real-Time Object Detection. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016.
Redmon, J.; Farhadi, A. YOLOv3: An Incremental Improvement. arXiv 2018, arXiv:1804.02767.
He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. arXiv 2015, arXiv:1512.03385
Zhao, L.; Li, S. Object Detection Algorithm Based on Improved YOLOv3. Electronics 2020, 9, 537.
Ammar, A.; Koubaa, A.; Ahmed, M.; Saad, A.; Benjdira, B. Vehicle Detection from Aerial Images Using Deep Learning: A Comparative Study. Electronics 2021, 10, 820.
Otomo, H.; Zhang, R.; Chen, H. Improved phase-field-based lattice Boltzmann models with a filtered collision operator. Int. J. Mod. Phys. 2018, 30, 1941009.
Gai, W.; Liu, Y.; Zhang, J.; Jing, G. An improved Tiny YOLOv3 for real-time object detection. Syst. Sci. Control. Eng. 2021, 9, 314–321
Tan, L.; Huangfu, T.; Wu, L.; Chen, W. Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification. BMC Med. Inform. Decis. Mak. 2021, 21, 324.
ZwickRoell. Durómetro ZHVμ. Available online: https://www.zwickroell.com/es/productos/equipos-de-ensayos-de-dureza/durometros-vickers/zhvm/
Lloyd Instruments. Microhardness Testing—Minimizing Common Problems. AZoM. Available online: https://www.azom.com/article.aspx?ArticleID=10807
Ebatco. Microindentation. Available online: https://www.ebatco.com/laboratory-services/mechanical/microindentation/ (accessed on 10 May 2022).
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spelling Buitrago Diaz, Juan C.c2b4c5d5-02a5-440e-8bc9-861167e45cba-1Ortega-Portilla, Carolina782a067f-b121-4e48-9d99-bd4b890c5268-1Mambuscay, Claudia L.ab20b931-69fc-4d19-932f-1ba5268bfeba-1Piamba, Jeferson Fernando4ce91b6d-87c1-4a1b-9fb3-be355b73f221-1Forero, Manuel Gd1814690-6764-437c-a01a-346c8f3436dc-12025-08-29T13:52:47Z2025-08-29T13:52:47Z2023-08The study of material hardness is crucial for determining its quality, potential failures, and appropriate applications, as well as minimizing losses incurred during the production process. To achieve this, certain criteria must be met to ensure high quality. This process is typically performed manually or using techniques based on analyzing indentation image patterns produced through the Vickers hardness technique. However, these techniques require that the indentation pattern is not aligned with the image edges. Therefore, this paper presents a technique based on convolutional neural networks (CNNs), specifically, a YOLO v3 network connected to a Dense Darknet-53 network. This technique enables the detection of indentation corner positions, measurement of diagonals, and calculation of the Vickers hardness value of D2 steel treated thermally and coated with Titanium Niobium Nitride (TiNbN), regardless of their position within the image. By implementing this architecture, an accuracy of (Formula presented.) was achieved in accurately detecting the corner positions, with an average execution time of 6 seconds. The developed technique utilizes the network to detect the regions containing the corners and subsequently accurately determines the pixel coordinates of these corners, achieving an approximate relative percentage error between (Formula presented.) to (Formula presented.) in the hardness results.application/pdfBuitrago, J., Ortega-Portilla, C., Mambuscay, C., Piamba, J. y Forero, M. (2023). Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks. Metals, 13(8). DOI: 10.3390/met1308139110.3390/met13081391https://hdl.handle.net/20.500.12313/5557https://www.mdpi.com/2075-4701/13/8/1391engMultidisciplinary Digital Publishing Institute (MDPI)Suiza813911320754701Castillo Gutiérrez, D.E.; Angarita Moncaleano, I.I.; Rodríguez Baracaldo, R. Microstructural and mechanical characterization of dual phase steels (ferrite-martensite), obtained by thermomechanical processes. Ingeniare Rev. Chil. Ing. 2018, 26, 430–439.Arenas, W.; Martínez, O. Roughness and hardness optimization of 12L-14 steel using the response surface methodology. Ing. Ind. 2019, 37, 125–151.Ageev, E.; Khardikov, S. Processing of Graphic Information in the Study of the Microhardness ofthe Sintered Sample of Chromium-containing Waste. In Proceedings of the CEUR Workshop, Pescaia, Italy, 16–19 June 2019; pp. 252–255Koch, M.; Ebersbach, U. Experimental study of chromium PVD coatings on brass substrates for the watch industry. Surf. Eng. 1997, 13, 157–164.ASTM E384-99; Standard Test Method for Microindentation Hardness of Materials. ASTM International: West Conshohocken, PA, USA, 2017; pp. 1–40.ASTM E92-17; Standard Test Methods for Vickers Hardness and Knoop Hardness of Metallic Materials. ASTM International: West Conshohocken, PA, USA, 2017; pp. 1–27.Buehler. Pruebas de Dureza Vickers. Available online: https://www.buehler.com/es/blog/pruebas-de-dureza-vickers/ (accessed on 23 June 2023).Tanaka, Y.; Seino, Y.; Hattori, K. Automated Vickers hardness measurement using convolutional neural networks. Int. J. Adv. Manuf. Technol. 2020, 109, 1345–1355.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 2018), Mexico City, Mexico, 5–7 September 2018; pp. 1–6.Sugimoto, T.; Kawaguchi, T. Development of an automatic Vickers hardness testing system using image processing technology. IEEE Trans. Ind. Electron. 1997, 44, 696–702.Polanco, J.D.; Jacanamejoy-Jamioy, C.; Mambuscay, C.L.; Piamba, J.F.; Forero, M.G. Automatic Method for Vickers Hardness Estimation by Image Processing. J. Imaging 2023, 9, 8.Abiodun, O.I.; Jantan, A.; Omolara, A.E.; Dada, K.V.; Mohamed, N.A.; Arshad, H. State-of-the-art in artificial neural network applications: A survey. Heliyon 2018, 4, e00938.Satat, G.; Tancik, M.; Gupta, O.; Heshmat, B.; Raskar, R. Object classification through scattering media with deep learning on time resolved measurement. Opt. Express 2017, 25, 17466–17479.Salazar Guerrero, J.E. Implementación de un Prototipo de Sistema Autónomo de Visión Artificial para la Detección de Objetos en Vídeo Utilizando Técnicas de Aprendizaje Profundo. 2019. Available online: http://repositorio.espe.edu.ec/handle/21000/20995 (accessed on 23 June 2023).Weiss, K.; Khoshgoftaar, T.M.; Wang, D.D. A survey of transfer learning. J. Big Data 2016, 3, 9.Hussain, M.; Bird, J.J.; Faria, D.R. A Study on CNN Transfer Learning for Image Classification; Springer: Berlin/Heidelberg, Germany, 2019; Volume 840, pp. 191–202.Jalilian, E.; Uhl, A. Deep Learning Based Automated Vickers Hardness Measurement. In Proceedings of the Computer Analysis of Images and Patterns, Virtual Event, 28–30 September 2021; 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.Gonzalez-Carmona, J.M.; Mambuscay, C.L.; Ortega-Portilla, C.; Hurtado-Macias, A.; Piamba, J.F. TiNbN Hard Coating Deposited at Varied Substrate Temperature by Cathodic Arc: Tribological Performance under Simulated Cutting Conditions. Materials 2023, 16, 4531.Redmon, J.; Divvala, S.K.; Girshick, R.B.; Farhadi, A. You Only Look Once: Unified, Real-Time Object Detection. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016.Redmon, J.; Farhadi, A. YOLOv3: An Incremental Improvement. arXiv 2018, arXiv:1804.02767.He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. arXiv 2015, arXiv:1512.03385Zhao, L.; Li, S. Object Detection Algorithm Based on Improved YOLOv3. Electronics 2020, 9, 537.Ammar, A.; Koubaa, A.; Ahmed, M.; Saad, A.; Benjdira, B. Vehicle Detection from Aerial Images Using Deep Learning: A Comparative Study. Electronics 2021, 10, 820.Otomo, H.; Zhang, R.; Chen, H. Improved phase-field-based lattice Boltzmann models with a filtered collision operator. Int. J. Mod. Phys. 2018, 30, 1941009.Gai, W.; Liu, Y.; Zhang, J.; Jing, G. An improved Tiny YOLOv3 for real-time object detection. Syst. Sci. Control. Eng. 2021, 9, 314–321Tan, L.; Huangfu, T.; Wu, L.; Chen, W. Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification. BMC Med. Inform. Decis. Mak. 2021, 21, 324.ZwickRoell. Durómetro ZHVμ. Available online: https://www.zwickroell.com/es/productos/equipos-de-ensayos-de-dureza/durometros-vickers/zhvm/Lloyd Instruments. Microhardness Testing—Minimizing Common Problems. AZoM. Available online: https://www.azom.com/article.aspx?ArticleID=10807Ebatco. Microindentation. Available online: https://www.ebatco.com/laboratory-services/mechanical/microindentation/ (accessed on 10 May 2022).© 2023 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/2075-4701/13/8/1391Redes Neuronales ConvolucionalesDureza Vickers en Acero D2Recubrimiento TiNbNCorner detectionD2 steelDiagonal measurementIndentation image analysisMaterial hardnessThermal treatmentTitanium niobium nitride (TiNbN) coatingVickers hardnessDetermination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural NetworksArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPublicationTEXTArtículo.pdf.txtArtículo.pdf.txtExtracted texttext/plain8412https://repositorio.unibague.edu.co/bitstreams/c9a0d2c7-8dcf-47d3-b6a6-8a581c0232a8/download403ff98ef4c54efcdfd474dc78684275MD53THUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg33204https://repositorio.unibague.edu.co/bitstreams/34dd46f4-89bb-4b16-b709-ae3130f864ee/download44306d895fd080b1eb6c018c29d117c0MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-8134https://repositorio.unibague.edu.co/bitstreams/fe04365b-dd65-4660-930b-cf63ba39d4cf/download2fa3e590786b9c0f3ceba1b9656b7ac3MD51ORIGINALArtículo.pdfArtículo.pdfapplication/pdf176448https://repositorio.unibague.edu.co/bitstreams/65082435-a4d6-44f3-91b6-8cc9547e05d1/download4d24373a729347d7bf34f349eb8accb3MD5220.500.12313/5557oai:repositorio.unibague.edu.co:20.500.12313/55572025-09-12 12:03:33.234https://creativecommons.org/licenses/by-nc/4.0/© 2023 by the authors.https://repositorio.unibague.edu.coRepositorio Institucional Universidad de Ibaguébdigital@metabiblioteca.comQ3JlYXRpdmUgQ29tbW9ucyBBdHRyaWJ1dGlvbi1Ob25Db21tZXJjaWFsLU5vRGVyaXZhdGl2ZXMgNC4wIEludGVybmF0aW9uYWwgTGljZW5zZQ0KaHR0cHM6Ly9jcmVhdGl2ZWNvbW1vbnMub3JnL2xpY2Vuc2VzL2J5LW5jLW5kLzQuMC8=

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