Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Russlan Jaafreh"'
Publikováno v:
Journal of Magnesium and Alloys, Vol 12, Iss 8, Pp 3216-3228 (2024)
Magnesium (Mg) based materials hold immense potential for various applications due to their lightweight and high strength-to-weight ratio. However, to fully harness the potential of Mg alloys, structured analytics are essential to gain valuable insig
Externí odkaz:
https://doaj.org/article/381dbb72109c44b8b2066b90d62b6b09
Autor:
Surjeet Kumar, Russlan Jaafreh, Subhajit Dutta, Jung Hyeon Yoo, Santiago Pereznieto, Kotiba Hamad, Dae Ho Yoon
Publikováno v:
Journal of Magnesium and Alloys, Vol 12, Iss 4, Pp 1540-1553 (2024)
This study presents a transfer learning approach for discovering potential Mg-based superconductors utilizing a comprehensive target dataset. Initially, a large source dataset (Bandgap dataset) comprising approximately ∼75k compounds is utilized fo
Externí odkaz:
https://doaj.org/article/3cf93f83c7734d17856e52375a2e8cbd
Publikováno v:
Journal of Magnesium and Alloys, Vol 11, Iss 1, Pp 392-404 (2023)
In the present work, we have employed machine learning (ML) techniques to evaluate ductile-brittle (DB) behaviors in intermetallic compounds (IMCs) which can form magnesium (Mg) alloys. This procedure was mainly conducted by a proxy-based method, whe
Externí odkaz:
https://doaj.org/article/5f00351f7f1c410b8fa9f5538f3f5520
Publikováno v:
Journal of Materiomics, Vol 8, Iss 3, Pp 678-684 (2022)
In this work, a machine learning (ML) model was created to predict intrinsic hardness of various compounds using their crystal chemistry. For this purpose, an initial dataset, containing the hardness values of 270 compounds and counterpart applied lo
Externí odkaz:
https://doaj.org/article/6e9fdbc93bab442a992a65fed465d77d
Publikováno v:
Crystals, Vol 12, Iss 9, p 1247 (2022)
In the present work, machine learning (ML) was employed to build a model, and through it, the microstructural features (parameters) affecting the stress concentration (SC) during plastic deformation of magnesium (Mg)-based materials are determined. A
Externí odkaz:
https://doaj.org/article/113d3903daaa42438faa3970dfa5b6ef
Publikováno v:
Mathematics, Vol 10, Iss 5, p 766 (2022)
In this study, isothermal compression tests of highly ductile AZ31-0.5Ca Mg alloys were conducted at different strain rates (0.001–0.1 s−1) and temperatures (423–523 K) along with extruded direction. The flow stress characteristics were evaluat
Externí odkaz:
https://doaj.org/article/8a15683854544736a05fbb088201473e
Publikováno v:
The International Journal of Advanced Manufacturing Technology. 122:1143-1166
Publikováno v:
Journal of Materiomics. 8:678-684
In this work, a machine learning (ML) model was created to predict intrinsic hardness of various compounds using their crystal chemistry. For this purpose, an initial dataset, containing the hardness values of 270 compounds and counterpart applied lo
Publikováno v:
Materials Letters. 337:133926
Publikováno v:
ACS applied materialsinterfaces. 13(48)
In the present work, we used machine learning (ML) techniques to build a crystal-based model that can predict the lattice thermal conductivity (LTC) of crystalline materials. To achieve this, first, LTCs of 119 compounds at various temperatures (100-