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...
- Autores:
-
Buitrago Diaz, Juan C.
Ortega-Portilla, Carolina
Mambuscay, Claudia L.
Piamba, Jeferson Fernando
Forero, Manuel G.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2023
- Institución:
- Universidad de Ibagué
- Repositorio:
- Repositorio Universidad de Ibagué
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unibague.edu.co:20.500.12313/3843
- Acceso en línea:
- https://hdl.handle.net/20.500.12313/3843
- Palabra clave:
- Material hardness
Indentation image analysis
Vickers hardness
Corner detection
Diagonal measurement
D2 steel
Thermal treatment
Titanium niobium nitride (TiNbN) coating
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
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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 Material hardness Indentation image analysis Vickers hardness Corner detection Diagonal measurement D2 steel Thermal treatment Titanium niobium nitride (TiNbN) coating |
| 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.proposal.eng.fl_str_mv |
Material hardness Indentation image analysis Vickers hardness Corner detection Diagonal measurement D2 steel Thermal treatment Titanium niobium nitride (TiNbN) coating |
| topic |
Material hardness Indentation image analysis Vickers hardness Corner detection Diagonal measurement D2 steel Thermal treatment Titanium niobium nitride (TiNbN) coating |
| 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 92% 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 0.17% to 5.98% in the hardness results. |
| publishDate |
2023 |
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2023-10-17T21:19:23Z |
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2023-10-17T21:19:23Z |
| dc.date.issued.none.fl_str_mv |
2023-08-02 |
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Artículo de revista |
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Buitrago Diaz, J.C.; Ortega-Portilla, C.; Mambuscay, C.L.; Piamba, J.F.; Forero, M.G. Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks. Metals 2023, 13, 1391. https://doi.org/10.3390/met13081391 |
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2075-4701 |
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https://hdl.handle.net/20.500.12313/3843 |
| identifier_str_mv |
Buitrago Diaz, J.C.; Ortega-Portilla, C.; Mambuscay, C.L.; Piamba, J.F.; Forero, M.G. Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks. Metals 2023, 13, 1391. https://doi.org/10.3390/met13081391 2075-4701 |
| url |
https://hdl.handle.net/20.500.12313/3843 |
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eng |
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eng |
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20 |
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1391 |
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1 |
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Metals |
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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. [Google Scholar] [CrossRef] Arenas, W.; Martínez, O. Roughness and hardness optimization of 12L-14 steel using the response surface methodology. Ing. Ind. 2019, 37, 125–151. [Google Scholar] [CrossRef] 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. [Google Scholar] [CrossRef] Koch, M.; Ebersbach, U. Experimental study of chromium PVD coatings on brass substrates for the watch industry. Surf. Eng. 1997, 13, 157–164. [Google Scholar] [CrossRef] ASTM E384-99; Standard Test Method for Microindentation Hardness of Materials. ASTM International: West Conshohocken, PA, USA, 2017; pp. 1–40. [CrossRef] 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. [CrossRef] 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. [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 2018), Mexico City, Mexico, 5–7 September 2018; pp. 1–6. [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] 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. [Google Scholar] [CrossRef] [PubMed] 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. [Google Scholar] [CrossRef] [PubMed][Green Version] 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. [Google Scholar] [CrossRef] [PubMed][Green Version] 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. [Google Scholar] [CrossRef][Green Version] 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. [Google Scholar] [CrossRef] 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. [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] 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. [Google Scholar] [CrossRef] [PubMed] 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. [Google Scholar] Redmon, J.; Farhadi, A. YOLOv3: An Incremental Improvement. arXiv 2018, arXiv:1804.02767. [Google Scholar] He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. arXiv 2015, arXiv:1512.03385. [Google Scholar] Zhao, L.; Li, S. Object Detection Algorithm Based on Improved YOLOv3. Electronics 2020, 9, 537. [Google Scholar] [CrossRef][Green Version] 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. [Google Scholar] [CrossRef] 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. [Google Scholar] [CrossRef][Green Version] 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. [Google Scholar] [CrossRef] 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. [Google Scholar] [CrossRef] [PubMed] ZwickRoell. Durómetro ZHVμ. Available online: https://www.zwickroell.com/es/productos/equipos-de-ensayos-de-dureza/durometros-vickers/zhvm/ (accessed on 15 May 2022). Lloyd Instruments. Microhardness Testing—Minimizing Common Problems. AZoM. Available online: https://www.azom.com/article.aspx?ArticleID=10807 (accessed on 15 May 2022). Ebatco. Microindentation. Available online: https://www.ebatco.com/laboratory-services/mechanical/microindentation/ (accessed on 10 May 2022). |
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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 G.d1814690-6764-437c-a01a-346c8f3436dc-12023-10-17T21:19:23Z2023-10-17T21:19:23Z2023-08-02The 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 92% 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 0.17% to 5.98% in the hardness results.application/pdfBuitrago Diaz, J.C.; Ortega-Portilla, C.; Mambuscay, C.L.; Piamba, J.F.; Forero, M.G. Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks. Metals 2023, 13, 1391. https://doi.org/10.3390/met130813912075-4701https://hdl.handle.net/20.500.12313/3843engSuiza201391113MetalsCastillo 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. [Google Scholar] [CrossRef]Arenas, W.; Martínez, O. Roughness and hardness optimization of 12L-14 steel using the response surface methodology. Ing. Ind. 2019, 37, 125–151. [Google Scholar] [CrossRef]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. [Google Scholar] [CrossRef]Koch, M.; Ebersbach, U. Experimental study of chromium PVD coatings on brass substrates for the watch industry. Surf. Eng. 1997, 13, 157–164. [Google Scholar] [CrossRef]ASTM E384-99; Standard Test Method for Microindentation Hardness of Materials. ASTM International: West Conshohocken, PA, USA, 2017; pp. 1–40. [CrossRef]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. [CrossRef]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. [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 2018), Mexico City, Mexico, 5–7 September 2018; pp. 1–6. [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]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. [Google Scholar] [CrossRef] [PubMed]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. [Google Scholar] [CrossRef] [PubMed][Green Version]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. [Google Scholar] [CrossRef] [PubMed][Green Version]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. [Google Scholar] [CrossRef][Green Version]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. [Google Scholar] [CrossRef]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. [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]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. [Google Scholar] [CrossRef] [PubMed]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. [Google Scholar]Redmon, J.; Farhadi, A. YOLOv3: An Incremental Improvement. arXiv 2018, arXiv:1804.02767. [Google Scholar]He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. arXiv 2015, arXiv:1512.03385. [Google Scholar]Zhao, L.; Li, S. Object Detection Algorithm Based on Improved YOLOv3. Electronics 2020, 9, 537. [Google Scholar] [CrossRef][Green Version]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. [Google Scholar] [CrossRef]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. [Google Scholar] [CrossRef][Green Version]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. [Google Scholar] [CrossRef]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. [Google Scholar] [CrossRef] [PubMed]ZwickRoell. Durómetro ZHVμ. Available online: https://www.zwickroell.com/es/productos/equipos-de-ensayos-de-dureza/durometros-vickers/zhvm/ (accessed on 15 May 2022).Lloyd Instruments. Microhardness Testing—Minimizing Common Problems. AZoM. Available online: https://www.azom.com/article.aspx?ArticleID=10807 (accessed on 15 May 2022).Ebatco. Microindentation. 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