نور محمود مسعود
مدرس
Neural Network Regressor for Designing Biomedical Low Elastic Modulus Ti-Zr-Nb-Mo Medium Entropy Alloys
Key Engineering Materials • 2024
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.
