Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments

Hardness is one of the most crucial mechanical properties, serving as a key indicator of a material’s suitability for specific applications and its resistance to fracturing or deformation under operational conditions. Machine learning techniques have emerged as valuable tools for swiftly and accurat...

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Autores:: Ortega-Portilla, Carolina
Mambuscay, Claudia Lorena
Piamba Jiménez, Jeferson Fernando
Forero Vargas, Manuel Guillermo

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2024

Institución:: Universidad de Ibagué

Repositorio:: Repositorio Universidad de Ibagué

Idioma:: eng

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dc.title.eng.fl_str_mv	Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments
title	Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments
spellingShingle	Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments Dureza Vickers - Modelado predictivo Acero D2 - Aprendizaje automático Coating Indentation imprint Machine learning Regression Titanium Niobium Nitride (TiNbN) Vickers hardness
title_short	Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments
title_full	Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments
title_fullStr	Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments
title_full_unstemmed	Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments
title_sort	Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments
dc.creator.fl_str_mv	Ortega-Portilla, Carolina Mambuscay, Claudia Lorena Piamba Jiménez, Jeferson Fernando Forero Vargas, Manuel Guillermo
dc.contributor.author.none.fl_str_mv	Ortega-Portilla, Carolina Mambuscay, Claudia Lorena Piamba Jiménez, Jeferson Fernando Forero Vargas, Manuel Guillermo
dc.subject.armarc.none.fl_str_mv	Dureza Vickers - Modelado predictivo Acero D2 - Aprendizaje automático
topic	Dureza Vickers - Modelado predictivo Acero D2 - Aprendizaje automático Coating Indentation imprint Machine learning Regression Titanium Niobium Nitride (TiNbN) Vickers hardness
dc.subject.proposal.eng.fl_str_mv	Coating Indentation imprint Machine learning Regression Titanium Niobium Nitride (TiNbN) Vickers hardness
description	Hardness is one of the most crucial mechanical properties, serving as a key indicator of a material’s suitability for specific applications and its resistance to fracturing or deformation under operational conditions. Machine learning techniques have emerged as valuable tools for swiftly and accurately predicting material behavior. In this study, regression methods including decision trees, adaptive boosting, extreme gradient boosting, and random forest were employed to forecast Vickers hardness values based solely on scanned monochromatic images of indentation imprints, eliminating the need for diagonal measurements. The dataset comprised 54 images of D2 steel in various states, including commercial, quenched, tempered, and coated with Titanium Niobium Nitride (TiNbN). Due to the limited number of images, non-deep machine learning techniques were utilized. The Random Forest technique exhibited superior performance, achieving a Root Mean Square Error (RMSE) of 0.95, Mean Absolute Error (MAE) of 0.12, and Coefficient of Determination ((Formula presented.)) ≈ 1, surpassing the other methods considered in this study. These results suggest that employing machine learning algorithms for predicting Vickers hardness from scanned images offers a promising avenue for rapid and accurate material assessment, potentially streamlining quality control processes in industrial settings.
publishDate	2024
dc.date.issued.none.fl_str_mv	2024-05
dc.date.accessioned.none.fl_str_mv	2025-11-06T20:23:59Z
dc.date.available.none.fl_str_mv	2025-11-06T20:23:59Z
dc.type.none.fl_str_mv	Artículo de periódico
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dc.identifier.citation.none.fl_str_mv	Mambuscay, C.L.; Ortega-Portilla, C.; Piamba, J.F.; Forero, M.G. Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments. Materials 2024, 17, 2235. https://doi.org/ 10.3390/ma17102235
dc.identifier.doi.none.fl_str_mv	https://doi.org/ 10.3390/ma17102235
dc.identifier.issn.none.fl_str_mv	19961944
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/20.500.12313/5915
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identifier_str_mv	Mambuscay, C.L.; Ortega-Portilla, C.; Piamba, J.F.; Forero, M.G. Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments. Materials 2024, 17, 2235. https://doi.org/ 10.3390/ma17102235 19961944
url	https://doi.org/ 10.3390/ma17102235 https://hdl.handle.net/20.500.12313/5915 https://www.mdpi.com/1996-1944/17/10/2235
dc.language.iso.none.fl_str_mv	eng
language	eng
dc.relation.citationissue.none.fl_str_mv	10
dc.relation.citationvolume.none.fl_str_mv	17
dc.relation.ispartofjournal.none.fl_str_mv	Materials
dc.relation.references.none.fl_str_mv	Albella, J.M. Láminas Delgadas y Recubrimientos. Preparación, Propiedades y Aplicaciones; Consejo Superior de Investigaciones Científicas: Madrid, Spain, 2003; p. 704. 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. Ohkubo, I.; Hou, Z.; Lee, J.; Aizawa, T.; Lippmaa, M.; Chikyow, T.; Tsuda, K.; Mori, T. Realization of closed-loop optimization of epitaxial titanium nitride thin-film growth via machine learning. Mater. Today Phys. 2021, 16, 100296. Wuest, T.; Weimer, D.; Irgens, C.; Thoben, K.D. Machine learning in manufacturing: Advantages, challenges, and applications. Prod. Manuf. Res. 2016, 4, 23–45 Lenz, B.; Hasselbruch, H.; Mehner, A. Automated evaluation of Rockwell adhesion tests for PVD coatings using convolutional neural networks. Surf. Coat. Technol. 2020, 385, 125365 Martins, L.A.; Pádua, F.L.; Almeida, P.E. Automatic detection of surface defects on rolled steel using Computer Vision and Artificial Neural Networks. In Proceedings of the IECON Proceedings (Industrial Electronics Conference), Glendale, AZ, USA, 7–10 November 2010; pp. 1081–1086. Dobrzański, L.; Staszuk, M.; Honysz, R. Application of artificial intelligence methods in PVD and CVD coatings properties modelling. Arch. Mater. Sci. Eng. 2012, 58, 152–157 Mohamad, M.A.; Ali, N.A.; Haron, H. Computational Intelligence Approach for Predicting the Hardness Performances in Titanium Aluminium Nitride (TiA1N) Coating Process. Int. J. Artif. Intell. Expert Syst. 2014, 5, 1–14. Wen, C.; Zhang, Y.; Wang, C.; Xue, D.; Bai, Y.; Antonov, S.; Dai, L.; Lookman, T.; Su, Y. Machine learning assisted design of high entropy alloys with desired property. Acta Mater. 2019, 170, 109–117. 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. 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. El-Garaihy, W.H.; Alateyah, A.I.; Shaban, M.; Alsharekh, M.F.; Alsunaydih, F.N.; El-Sanabary, S.; Kouta, H.; El-Taybany, Y.; Salem, H.G. A Comparative Study of a Machine Learning Approach and Response Surface Methodology for Optimizing the HPT Processing Parameters of AA6061/SiCp Composites. J. Manuf. Mater. Process. 2023, 7, 148. Fu, K.; Zhu, D.; Zhang, Y.; Zhang, C.; Wang, X.; Wang, C.; Jiang, T.; Mao, F.; Zhang, C.; Meng, X.; et al. Predictive Modeling of Tensile Strength in Aluminum Alloys via Machine Learning. Materials 2023, 16, 7236. Dovale-Farelo, V.; Tavadze, P.; Lang, L.; Bautista-Hernandez, A.; Romero, A.H. Vickers hardness prediction from machine learning methods. Sci. Rep. 2022, 12, 22475. Jeon, J.; Seo, N.; Son, S.B.; Lee, S.J.; Jung, M. Application of machine learning algorithms and shap for prediction and feature analysis of tempered martensite hardness in low-alloy steels. Metals 2021, 11, 1159 Swetlana, S.; Khatavkar, N.; Singh, A.K. Development of Vickers hardness prediction models via microstructural analysis and machine learning. J. Mater. Sci. 2020, 55, 15845–15856. Privezentsev, D.G.; Zhiznyakov, A.L.; Kulkov, Y.Y. Automation of measuring microhardness of materials using metal-graphic images. In Proceedings of the 2019 International Russian Automation Conference, RusAutoCon 2019, 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. 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 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 Loh, W.Y. Classification and regression trees. In Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery; John Wiley & Sons: Hoboken, NJ, USA, 2011; Volume 1. Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825–2830. Friedman, J.; Hastie, T.; Tibshirani, R. Additive logistic regression: A statistical view of boosting (With discussion and a rejoinder by the authors). Ann. Stat. 2000, 28, 337–407. Chen, T.; Guestrin, C. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, San Francisco, CA, USA, 13–17 August 2016. Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32
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spelling	Ortega-Portilla, Carolina782a067f-b121-4e48-9d99-bd4b890c5268-1Mambuscay, Claudia Lorena9aecdb75-6d0d-48bf-8249-26f370774c41-1Piamba Jiménez, Jeferson Fernando2542324e-59b6-44cb-8577-2da79c47599a600Forero Vargas, Manuel Guillermo816a18a4-450c-4a3e-8275-84beb65a90ec6002025-11-06T20:23:59Z2025-11-06T20:23:59Z2024-05Hardness is one of the most crucial mechanical properties, serving as a key indicator of a material’s suitability for specific applications and its resistance to fracturing or deformation under operational conditions. Machine learning techniques have emerged as valuable tools for swiftly and accurately predicting material behavior. In this study, regression methods including decision trees, adaptive boosting, extreme gradient boosting, and random forest were employed to forecast Vickers hardness values based solely on scanned monochromatic images of indentation imprints, eliminating the need for diagonal measurements. The dataset comprised 54 images of D2 steel in various states, including commercial, quenched, tempered, and coated with Titanium Niobium Nitride (TiNbN). Due to the limited number of images, non-deep machine learning techniques were utilized. The Random Forest technique exhibited superior performance, achieving a Root Mean Square Error (RMSE) of 0.95, Mean Absolute Error (MAE) of 0.12, and Coefficient of Determination ((Formula presented.)) ≈ 1, surpassing the other methods considered in this study. These results suggest that employing machine learning algorithms for predicting Vickers hardness from scanned images offers a promising avenue for rapid and accurate material assessment, potentially streamlining quality control processes in industrial settings.application/pdfMambuscay, C.L.; Ortega-Portilla, C.; Piamba, J.F.; Forero, M.G. Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments. Materials 2024, 17, 2235. https://doi.org/ 10.3390/ma17102235https://doi.org/ 10.3390/ma1710223519961944https://hdl.handle.net/20.500.12313/5915https://www.mdpi.com/1996-1944/17/10/2235engMultidisciplinary Digital Publishing Institute (MDPI)Suiza1017MaterialsAlbella, J.M. Láminas Delgadas y Recubrimientos. Preparación, Propiedades y Aplicaciones; Consejo Superior de Investigaciones Científicas: Madrid, Spain, 2003; p. 704.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.Ohkubo, I.; Hou, Z.; Lee, J.; Aizawa, T.; Lippmaa, M.; Chikyow, T.; Tsuda, K.; Mori, T. Realization of closed-loop optimization of epitaxial titanium nitride thin-film growth via machine learning. Mater. Today Phys. 2021, 16, 100296.Wuest, T.; Weimer, D.; Irgens, C.; Thoben, K.D. Machine learning in manufacturing: Advantages, challenges, and applications. Prod. Manuf. Res. 2016, 4, 23–45Lenz, B.; Hasselbruch, H.; Mehner, A. Automated evaluation of Rockwell adhesion tests for PVD coatings using convolutional neural networks. Surf. Coat. Technol. 2020, 385, 125365Martins, L.A.; Pádua, F.L.; Almeida, P.E. Automatic detection of surface defects on rolled steel using Computer Vision and Artificial Neural Networks. In Proceedings of the IECON Proceedings (Industrial Electronics Conference), Glendale, AZ, USA, 7–10 November 2010; pp. 1081–1086.Dobrzański, L.; Staszuk, M.; Honysz, R. Application of artificial intelligence methods in PVD and CVD coatings properties modelling. Arch. Mater. Sci. Eng. 2012, 58, 152–157Mohamad, M.A.; Ali, N.A.; Haron, H. Computational Intelligence Approach for Predicting the Hardness Performances in Titanium Aluminium Nitride (TiA1N) Coating Process. Int. J. Artif. Intell. Expert Syst. 2014, 5, 1–14.Wen, C.; Zhang, Y.; Wang, C.; Xue, D.; Bai, Y.; Antonov, S.; Dai, L.; Lookman, T.; Su, Y. Machine learning assisted design of high entropy alloys with desired property. Acta Mater. 2019, 170, 109–117.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.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.El-Garaihy, W.H.; Alateyah, A.I.; Shaban, M.; Alsharekh, M.F.; Alsunaydih, F.N.; El-Sanabary, S.; Kouta, H.; El-Taybany, Y.; Salem, H.G. A Comparative Study of a Machine Learning Approach and Response Surface Methodology for Optimizing the HPT Processing Parameters of AA6061/SiCp Composites. J. Manuf. Mater. Process. 2023, 7, 148.Fu, K.; Zhu, D.; Zhang, Y.; Zhang, C.; Wang, X.; Wang, C.; Jiang, T.; Mao, F.; Zhang, C.; Meng, X.; et al. Predictive Modeling of Tensile Strength in Aluminum Alloys via Machine Learning. Materials 2023, 16, 7236.Dovale-Farelo, V.; Tavadze, P.; Lang, L.; Bautista-Hernandez, A.; Romero, A.H. Vickers hardness prediction from machine learning methods. Sci. Rep. 2022, 12, 22475.Jeon, J.; Seo, N.; Son, S.B.; Lee, S.J.; Jung, M. Application of machine learning algorithms and shap for prediction and feature analysis of tempered martensite hardness in low-alloy steels. Metals 2021, 11, 1159Swetlana, S.; Khatavkar, N.; Singh, A.K. Development of Vickers hardness prediction models via microstructural analysis and machine learning. J. Mater. Sci. 2020, 55, 15845–15856.Privezentsev, D.G.; Zhiznyakov, A.L.; Kulkov, Y.Y. Automation of measuring microhardness of materials using metal-graphic images. In Proceedings of the 2019 International Russian Automation Conference, RusAutoCon 2019, 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.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, 1391Gonzalez-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, 4531Loh, W.Y. Classification and regression trees. In Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery; John Wiley & Sons: Hoboken, NJ, USA, 2011; Volume 1.Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825–2830.Friedman, J.; Hastie, T.; Tibshirani, R. Additive logistic regression: A statistical view of boosting (With discussion and a rejoinder by the authors). Ann. Stat. 2000, 28, 337–407.Chen, T.; Guestrin, C. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, San Francisco, CA, USA, 13–17 August 2016.Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32© 2024 by the authors.info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Atribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/Dureza Vickers - Modelado predictivoAcero D2 - Aprendizaje automáticoCoatingIndentation imprintMachine learningRegressionTitanium Niobium Nitride (TiNbN)Vickers hardnessPredictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various TreatmentsArtículo de periódicohttp://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/plain4238https://repositorio.unibague.edu.co/bitstreams/9f237d4c-0cfc-4843-b84b-08961b8148ba/download773df21f77f656387353417dc2c69473MD53THUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg25623https://repositorio.unibague.edu.co/bitstreams/1b5bf0ec-f55f-4814-bec2-649a17f4fb83/download14ab23937942e99c969dcb9522df7a55MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-8134https://repositorio.unibague.edu.co/bitstreams/dcebbbd3-0577-479a-a95b-7df5b701355a/download2fa3e590786b9c0f3ceba1b9656b7ac3MD51ORIGINALArtículo.pdfArtículo.pdfapplication/pdf88233https://repositorio.unibague.edu.co/bitstreams/83072559-d4fd-4c99-9146-7f0fdcaa8332/downloadd7e80400f297728c2b1d8580d50d7ce3MD5220.500.12313/5915oai:repositorio.unibague.edu.co:20.500.12313/59152025-11-07 03:01:26.861https://creativecommons.org/licenses/by/4.0/© 2024 by the authors.https://repositorio.unibague.edu.coRepositorio Institucional Universidad de Ibaguébdigital@metabiblioteca.comQ3JlYXRpdmUgQ29tbW9ucyBBdHRyaWJ1dGlvbi1Ob25Db21tZXJjaWFsLU5vRGVyaXZhdGl2ZXMgNC4wIEludGVybmF0aW9uYWwgTGljZW5zZQ0KaHR0cHM6Ly9jcmVhdGl2ZWNvbW1vbnMub3JnL2xpY2Vuc2VzL2J5LW5jLW5kLzQuMC8=

Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments

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