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Ass. Lect. Nour Mahmoud Massoud :: Publications: |
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| Title: | Neural Network Regressor for Designing Biomedical Low Elastic Modulus Ti-Zr-Nb-Mo Medium Entropy Alloys |
| Authors: | Nour Mahmoud Eldabah, Amin Shoukry, Wael M Khair-Eldeen, Sengo Kobayashi, Mohamed Abdel Hady Gepreel |
| Year: | 2024 |
| Keywords: | Medium entropy alloys, β – Titanium alloys, Artificial neural networks, Low elastic modulus, CALPHAD. |
| Journal: | Key Engineering Materials |
| Volume: | 967 |
| Issue: | Not Available |
| Pages: | 89-94 |
| Publisher: | Trans Tech Publications Ltd |
| Local/International: | International |
| Paper Link: | Not Available |
| Full paper | Not Available |
| Supplementary materials | Not Available |
| Abstract: |
The excellent biocompatibility of Ti and Zr alloys makes them the best candidates for orthopedic implantations. The design of high Ti and Zr-containing alloys that show low Young's modulus for implant manufacturing is the objective of this work. Here, a feed-forward-back propagation neural network was used to speed up the design process and optimize alloy composition. The β-typeTi45-Zr39-Nb12-Mo4 alloy is designed and showed promising properties. The alloy showed a low elastic modulus of 78 GPa and a high yield strength of 891 MPa resulting in a high elastic admissible strain that made it suitable for orthopedic applications. |
