Mechanical properties of 3D-Printed aluminum, nickel, and titanium using a hybrid machine learning and computational mechanics approach

Mechanical properties of 3D-Printed aluminum, nickel, and titanium using a hybrid machine learning and computational mechanics approach

This study presents a hybrid computational framework designed to accurately predict the mechanical properties of essential 3D printing materials, namely, Aluminum (Al), Titanium (Ti), and Nickel (Ni). By integrating first-principles simulations via the CASTEP code—grounded in Density Functional Theo...

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Bibliographic Details
Main Authors: Sara Samine, Mohamed Karouchi, Maria Zemzami, Nabil Hmina, Soufiane Belhouideg
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-06-01
Series:Frontiers in Materials
Subjects:
artificial intelligence (AI)
machine learning
DFT
mechanical properties
3D printing
elastic constants
Online Access:https://www.frontiersin.org/articles/10.3389/fmats.2025.1584896/full
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https://www.frontiersin.org/articles/10.3389/fmats.2025.1584896/full

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