Inverse Design of Fe-Based Bulk Metallic Glasses Using Machine Learning

Metals

Hyunkyu Lim¹  Namhyuk Seo²  Seungbae Son²  Seokjae Lee²  Junhyub Jeon²  Minha Lee³  Hwijun Kim⁴ 

[1] Advanced Process and Materials R&D Group, Korea Institute of Industrial Technology, Incheon 21999, Korea;Division of Advanced Materials Engineering, Jeonbuk National University, Jeonju 54896, Korea;KITECH North America, Korea Institute of Industrial Technology, 2833 Junction Ave. Suite 207, San Jose, CA 95134, USA;Liquid Processing & Casting R&D Group, Korea Institute of Industrial Technology, Incheon 21999, Korea;

关键词: Fe-based bulk metallic glasses;    materials design;    machine learning;    artificial neural network;    inverse design;   

DOI  :  10.3390/met11050729

来源: DOAJ

【 摘 要 】

Fe-based bulk metallic glasses (BMGs) are a unique class of materials that are attracting attention in a wide variety of applications owing to their physical properties. Several studies have investigated and designed the relationships between alloy composition and thermal properties of BMGs using an artificial neural network (ANN). The limitation of the wide-scale use of these models is that the required composition is yet to be found despite numerous case studies. To address this issue, we trained an ANN to design Fe-based BMGs that predict the thermal properties. Models were trained using only the composition of the alloy as input and were created from a database of more than 150 experimental data of Fe-based BMGs from relevant literature. We adopted these ANN models to design BMGs with thermal properties to satisfy the intended purpose using particle swarm optimization. A melt spinner was employed to fabricate the designed alloys. X-ray diffraction and differential thermal analysis tests were used to evaluate the specimens.

【 授权许可】

Unknown