Alloys innovation through machine learning: a statistical literature review
ABSTRACTThis review systematically analyzes over 200 publications to explore the growing role of data-driven methods and their potential benefits in accelerating alloy development. The review presents a comprehensive overview of different aspects of alloy innovation by machine learning and other com...
Main Authors: | Alireza Valizadeh, Ryoji Sahara, Maaouia Souissi |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2024-12-01
|
Series: | Science and Technology of Advanced Materials: Methods |
Subjects: | |
Online Access: | https://www.tandfonline.com/doi/10.1080/27660400.2024.2326305 |
Similar Items
-
Automatic knowledge acquisition from superconductivity information in literature
by: Kento Mitsui, et al.
Published: (2023-12-01) -
Semi-automatic staging area for high-quality structured data extraction from scientific literature
by: Luca Foppiano, et al.
Published: (2023-12-01) -
Machine-Learning-Based Composition Analysis of the Stability of V–Cr–Ti Alloys
by: Katsuaki Tanabe
Published: (2023-04-01) -
A framework for computer-aided high performance titanium alloy design based on machine learning
by: Suyang An, et al.
Published: (2024-04-01) -
Editorial: Innovators in structural materials—alloys and metals
by: Jiangshan Zhang, et al.
Published: (2023-08-01)