Benha University

نور محمود مسعود

مدرس

كلية الهندسة ببنها الهندسة الميكانيكية

Neural Network Regressor for Designing Biomedical Low Elastic Modulus Ti-Zr-Nb-Mo Medium Entropy Alloys

Key Engineering Materials • 2024
العودة
معلومات البحث
المؤلفون Nour Mahmoud Eldabah, Amin Shoukry, Wael M Khair-Eldeen, Sengo Kobayashi, Mohamed Abdel Hady Gepreel
الكلمات المفتاحية Medium entropy alloys, β – Titanium alloys, Artificial neural networks, Low elastic modulus, CALPHAD.
المجلة العلمية Key Engineering Materials
الناشر Trans Tech Publications Ltd
المجلد 967
العدد Not Available
الصفحات 89-94
publication.type International
رابط البحث Not Available
المواد المرفقة Not Available
الملخص
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.
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