Microstructural characterization and hardness prediction of AlCuCrFeNi high entropy alloys using Transformer-based Physics-Informed Neural Networks (T-PINN)
This research investigated a novel approach to generating dual-phase high entropy alloys (HEAs) from recycled waste materials, with the objective of attaining sustainable manufacturing while maintaining material performance. The AlCuCrFeNi composition was synthesized using vacuum arc melting (VAM) t...
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Elsevier
2025-09-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S223878542501974X |
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| author | Mohamed Yasin Abdul Salam Enoch Nifise Ogunmuyiwa Victor Kitso Manisa Abid Yahya Irfan Anjum Badruddin |
| author_facet | Mohamed Yasin Abdul Salam Enoch Nifise Ogunmuyiwa Victor Kitso Manisa Abid Yahya Irfan Anjum Badruddin |
| author_sort | Mohamed Yasin Abdul Salam |
| collection | DOAJ |
| description | This research investigated a novel approach to generating dual-phase high entropy alloys (HEAs) from recycled waste materials, with the objective of attaining sustainable manufacturing while maintaining material performance. The AlCuCrFeNi composition was synthesized using vacuum arc melting (VAM) to achieve the desired equiatomic composition. The HEAs were characterized using X-ray diffraction (XRD) and field emission scanning electron microscopy (FESEM). Synthesized alloys were heat-treated at different temperatures to enhance phase stability, induce microstructural evolution and hardness performance. The findings indicate that the AlCuCrFeNi HEAs have a dual-phase composition, characterized by a Body-Centered Cubic (BCC) and Face-Centered Cubic (FCC) structure, with a dendritic and interdendritic microstructure. Microstructural alterations were detected at each heat treatment temperature. Grain formation commenced at 950 °C, and the BCC phase dissolved around 1100 °C. The highest microhardness value of 353.4 HV was achieved for the alloy subjected to heat treatment at 950 °C. To predict the hardness properties quantitatively, a Machine Learning (ML) model using a Transformer-based Physics-Informed Neural Network (T-PINN) was developed. The T-PINN model demonstrated high accuracy, with a Root Mean Square Error (RMSE) - 0.048, Mean Absolute Error (MAE) of- 0.036, and R-square (R2) - 0.97 values, respectively. This research has highlighted the potential of ML to advance materials with enhanced mechanical properties. |
| format | Article |
| id | doaj-art-175e1f6e3989449abf81ddd4587d2f38 |
| institution | DOAJ |
| issn | 2238-7854 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj-art-175e1f6e3989449abf81ddd4587d2f382025-08-20T03:02:59ZengElsevierJournal of Materials Research and Technology2238-78542025-09-01381934194610.1016/j.jmrt.2025.08.021Microstructural characterization and hardness prediction of AlCuCrFeNi high entropy alloys using Transformer-based Physics-Informed Neural Networks (T-PINN)Mohamed Yasin Abdul Salam0Enoch Nifise Ogunmuyiwa1Victor Kitso Manisa2Abid Yahya3Irfan Anjum Badruddin4Department of Chemical, Materials & Metallurgical Engineering, School of Earth Science & Engineering, Botswana International University of Science and Technology, Private Bag 16, Palapye, BotswanaDepartment of Chemical, Materials & Metallurgical Engineering, School of Earth Science & Engineering, Botswana International University of Science and Technology, Private Bag 16, Palapye, Botswana; Corresponding author.Department of Chemical, Materials & Metallurgical Engineering, School of Earth Science & Engineering, Botswana International University of Science and Technology, Private Bag 16, Palapye, BotswanaDepartment of Electrical, Computer and Telecommunications Engineering, Botswana International University of Science and Technology, School of Electrical & Mechanical Engineering, Private Bag 16, Palapye, BotswanaDepartment of Mechanical Engineering, College of Engineering, King Khalid University, Abha, 61421, Saudi ArabiaThis research investigated a novel approach to generating dual-phase high entropy alloys (HEAs) from recycled waste materials, with the objective of attaining sustainable manufacturing while maintaining material performance. The AlCuCrFeNi composition was synthesized using vacuum arc melting (VAM) to achieve the desired equiatomic composition. The HEAs were characterized using X-ray diffraction (XRD) and field emission scanning electron microscopy (FESEM). Synthesized alloys were heat-treated at different temperatures to enhance phase stability, induce microstructural evolution and hardness performance. The findings indicate that the AlCuCrFeNi HEAs have a dual-phase composition, characterized by a Body-Centered Cubic (BCC) and Face-Centered Cubic (FCC) structure, with a dendritic and interdendritic microstructure. Microstructural alterations were detected at each heat treatment temperature. Grain formation commenced at 950 °C, and the BCC phase dissolved around 1100 °C. The highest microhardness value of 353.4 HV was achieved for the alloy subjected to heat treatment at 950 °C. To predict the hardness properties quantitatively, a Machine Learning (ML) model using a Transformer-based Physics-Informed Neural Network (T-PINN) was developed. The T-PINN model demonstrated high accuracy, with a Root Mean Square Error (RMSE) - 0.048, Mean Absolute Error (MAE) of- 0.036, and R-square (R2) - 0.97 values, respectively. This research has highlighted the potential of ML to advance materials with enhanced mechanical properties.http://www.sciencedirect.com/science/article/pii/S223878542501974XHigh-entropy alloysMachine learningRecycled materialsVacuum arc meltingMicrostructure optimizationSustainable manufacturing |
| spellingShingle | Mohamed Yasin Abdul Salam Enoch Nifise Ogunmuyiwa Victor Kitso Manisa Abid Yahya Irfan Anjum Badruddin Microstructural characterization and hardness prediction of AlCuCrFeNi high entropy alloys using Transformer-based Physics-Informed Neural Networks (T-PINN) Journal of Materials Research and Technology High-entropy alloys Machine learning Recycled materials Vacuum arc melting Microstructure optimization Sustainable manufacturing |
| title | Microstructural characterization and hardness prediction of AlCuCrFeNi high entropy alloys using Transformer-based Physics-Informed Neural Networks (T-PINN) |
| title_full | Microstructural characterization and hardness prediction of AlCuCrFeNi high entropy alloys using Transformer-based Physics-Informed Neural Networks (T-PINN) |
| title_fullStr | Microstructural characterization and hardness prediction of AlCuCrFeNi high entropy alloys using Transformer-based Physics-Informed Neural Networks (T-PINN) |
| title_full_unstemmed | Microstructural characterization and hardness prediction of AlCuCrFeNi high entropy alloys using Transformer-based Physics-Informed Neural Networks (T-PINN) |
| title_short | Microstructural characterization and hardness prediction of AlCuCrFeNi high entropy alloys using Transformer-based Physics-Informed Neural Networks (T-PINN) |
| title_sort | microstructural characterization and hardness prediction of alcucrfeni high entropy alloys using transformer based physics informed neural networks t pinn |
| topic | High-entropy alloys Machine learning Recycled materials Vacuum arc melting Microstructure optimization Sustainable manufacturing |
| url | http://www.sciencedirect.com/science/article/pii/S223878542501974X |
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