Prediction of elastic modulus, yield strength, and tensile strength in biocompatible titanium alloys
Biocompatible titanium alloys possess a balanced set of improved mechanical properties and good biocompatibility, making them crucial materials in biomedical engineering. There is an increasing demand for these new alloys with superior properties. Furthermore, there is a need to understand the relat...
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| Format: | Article |
| Language: | English |
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University of Belgrade, Technical Faculty, Bor
2024-01-01
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| Series: | Journal of Mining and Metallurgy. Section B: Metallurgy |
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| Online Access: | https://doiserbia.nb.rs/img/doi/1450-5339/2024/1450-53392400019M.pdf |
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| author | Marković G. Ružić J. Sokić M. Milojkov D. Manojlović V.D. |
| author_facet | Marković G. Ružić J. Sokić M. Milojkov D. Manojlović V.D. |
| author_sort | Marković G. |
| collection | DOAJ |
| description | Biocompatible titanium alloys possess a balanced set of improved mechanical properties and good biocompatibility, making them crucial materials in biomedical engineering. There is an increasing demand for these new alloys with superior properties. Furthermore, there is a need to understand the relationship between parameters and properties, and machine learning is being applied to make the whole process cheaper and more efficient. The aim of this study is to develop accurate machine learning models for predicting mechanical properties: modulus of elasticity, tensile strength, and yield strength, specifically using the Extra Trees Regressor model. Compared to the previous results, an improvement of the elastic modulus prediction model was observed after the inclusion of data on heat treatment parameters and Poisson’s ratio, as seen in the reduced MAE from 7.402 to 7.160 GPa. Models were built to predict the values of tensile strength and yield strength, where iron and tin were shown as most important features respectively, while the correlation coefficients for the test set were 0.893 and 0.868. |
| format | Article |
| id | doaj-art-46b23743c8de4adb89d3f0eb40891382 |
| institution | Kabale University |
| issn | 1450-5339 2217-7175 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | University of Belgrade, Technical Faculty, Bor |
| record_format | Article |
| series | Journal of Mining and Metallurgy. Section B: Metallurgy |
| spelling | doaj-art-46b23743c8de4adb89d3f0eb408913822025-02-03T12:00:12ZengUniversity of Belgrade, Technical Faculty, BorJournal of Mining and Metallurgy. Section B: Metallurgy1450-53392217-71752024-01-0160227328210.2298/JMMB240221019M1450-53392400019MPrediction of elastic modulus, yield strength, and tensile strength in biocompatible titanium alloysMarković G.0Ružić J.1Sokić M.2Milojkov D.3Manojlović V.D.4Institute for Technology of Nuclear and Other Mineral Raw Materials, Belgrade, SerbiaDepartment of Materials, “Vinča” Institute of Nuclear Sciences—National Institute of the Republic of Serbia, University of Belgrade, Belgrade, SerbiaInstitute for Technology of Nuclear and Other Mineral Raw Materials, Belgrade, SerbiaInstitute for Technology of Nuclear and Other Mineral Raw Materials, Belgrade, SerbiaFaculty of Technology and Metallurgy, University of Belgrade, Belgrade, SerbiaBiocompatible titanium alloys possess a balanced set of improved mechanical properties and good biocompatibility, making them crucial materials in biomedical engineering. There is an increasing demand for these new alloys with superior properties. Furthermore, there is a need to understand the relationship between parameters and properties, and machine learning is being applied to make the whole process cheaper and more efficient. The aim of this study is to develop accurate machine learning models for predicting mechanical properties: modulus of elasticity, tensile strength, and yield strength, specifically using the Extra Trees Regressor model. Compared to the previous results, an improvement of the elastic modulus prediction model was observed after the inclusion of data on heat treatment parameters and Poisson’s ratio, as seen in the reduced MAE from 7.402 to 7.160 GPa. Models were built to predict the values of tensile strength and yield strength, where iron and tin were shown as most important features respectively, while the correlation coefficients for the test set were 0.893 and 0.868.https://doiserbia.nb.rs/img/doi/1450-5339/2024/1450-53392400019M.pdfmodulus of elasticitytensile strengthyield strengthbiocompatibilitymachine learning |
| spellingShingle | Marković G. Ružić J. Sokić M. Milojkov D. Manojlović V.D. Prediction of elastic modulus, yield strength, and tensile strength in biocompatible titanium alloys Journal of Mining and Metallurgy. Section B: Metallurgy modulus of elasticity tensile strength yield strength biocompatibility machine learning |
| title | Prediction of elastic modulus, yield strength, and tensile strength in biocompatible titanium alloys |
| title_full | Prediction of elastic modulus, yield strength, and tensile strength in biocompatible titanium alloys |
| title_fullStr | Prediction of elastic modulus, yield strength, and tensile strength in biocompatible titanium alloys |
| title_full_unstemmed | Prediction of elastic modulus, yield strength, and tensile strength in biocompatible titanium alloys |
| title_short | Prediction of elastic modulus, yield strength, and tensile strength in biocompatible titanium alloys |
| title_sort | prediction of elastic modulus yield strength and tensile strength in biocompatible titanium alloys |
| topic | modulus of elasticity tensile strength yield strength biocompatibility machine learning |
| url | https://doiserbia.nb.rs/img/doi/1450-5339/2024/1450-53392400019M.pdf |
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