Machine learning alloying design of biodegradable zinc alloy for bone implants using XGBoost and Bayesian optimization
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| Názov: | Machine learning alloying design of biodegradable zinc alloy for bone implants using XGBoost and Bayesian optimization |
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| Autori: | Deif, Mohanad, Attar, Hani, Aljaidi, Mohammad, Alsarhan, Ayoub, Al-Fraihat, Dimah, Solyman, Ahmed |
| Zdroj: | Deif, M, Attar, H, Aljaidi, M, Alsarhan, A, Al-Fraihat, D & Solyman, A 2025, 'Machine learning alloying design of biodegradable zinc alloy for bone implants using XGBoost and Bayesian optimization', Intelligent Systems with Applications, vol. 27, 200549. https://doi.org/10.1016/j.iswa.2025.200549 |
| Rok vydania: | 2025 |
| Predmety: | Bayesian optimization, Biodegradable alloy, Bone implant, Machine learning alloying, XGBoost, Zinc alloy, /dk/atira/pure/subjectarea/asjc/1700/1701, name=Computer Science (miscellaneous), /dk/atira/pure/subjectarea/asjc/1700/1711, name=Signal Processing, /dk/atira/pure/subjectarea/asjc/1700/1707, name=Computer Vision and Pattern Recognition, /dk/atira/pure/subjectarea/asjc/1700/1706, name=Computer Science Applications, /dk/atira/pure/subjectarea/asjc/1700/1702, name=Artificial Intelligence |
| Popis: | Developing implants using biodegradable materials eliminates the need for secondary surgery, improves both mechanical and biological properties, and enhances biocompatibility. This study proposes a machine learning approach based on Bayesian optimization (BO) and an eXtreme Gradient Boosting (XGBoost) algorithm to design a biodegradable Zinc (Zn) alloy and forecast percentage of elements in the Zn alloy for bone implants. The dataset employed in this study comprised 1182 samples of Zn alloys obtained from supplementary articles from Google Scholar and the mat web database. For forecasting the mechanical parameters Yield Stress (YS), Ductility, and Ultimate Tensile Strength (UTS), the suggested method got maximum R 2 values of 0.85, 0.87, and 0.81 demonstrating its exceptional predictive capacity. In addition, the model created a Zn biodegradable alloy with UTS of 363.55 Mpa, YS of 318.93 Mpa, and Ductility of 14 %, which are regarded as good mechanical characteristics meet bone implant criteria. The BO-XGBoost model can expedite the production of the proper alloy for several medical applications, saving time, money, and effort. |
| Druh dokumentu: | article in journal/newspaper |
| Popis súboru: | application/pdf |
| Jazyk: | English |
| Relation: | info:eu-repo/semantics/altIdentifier/pissn/2667-3053 |
| DOI: | 10.1016/j.iswa.2025.200549 |
| Dostupnosť: | https://researchonline.gcu.ac.uk/en/publications/3fe1e829-0078-4589-a6f0-d45083573bba https://doi.org/10.1016/j.iswa.2025.200549 https://researchonline.gcu.ac.uk/ws/files/103967130/103900889.pdf https://www.scopus.com/pages/publications/105009000623 |
| Rights: | info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by-nc/4.0/ |
| Prístupové číslo: | edsbas.C273D5E |
| Databáza: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | Developing implants using biodegradable materials eliminates the need for secondary surgery, improves both mechanical and biological properties, and enhances biocompatibility. This study proposes a machine learning approach based on Bayesian optimization (BO) and an eXtreme Gradient Boosting (XGBoost) algorithm to design a biodegradable Zinc (Zn) alloy and forecast percentage of elements in the Zn alloy for bone implants. The dataset employed in this study comprised 1182 samples of Zn alloys obtained from supplementary articles from Google Scholar and the mat web database. For forecasting the mechanical parameters Yield Stress (YS), Ductility, and Ultimate Tensile Strength (UTS), the suggested method got maximum R 2 values of 0.85, 0.87, and 0.81 demonstrating its exceptional predictive capacity. In addition, the model created a Zn biodegradable alloy with UTS of 363.55 Mpa, YS of 318.93 Mpa, and Ductility of 14 %, which are regarded as good mechanical characteristics meet bone implant criteria. The BO-XGBoost model can expedite the production of the proper alloy for several medical applications, saving time, money, and effort. |
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| DOI: | 10.1016/j.iswa.2025.200549 |