High-entropy alloys; Long Short-Term Memory; Machine learning; Molecular dynamics; Tensile stress; Dynamic learning; Dynamics simulation; Grainsize; High entropy alloys; Machine-learning; Mechanical; Multi-component alloy; Property; Short term memory; Strain-rates; Materials Science (all); Mechanics of Materials; Mechanical Engineering
Abstract :
[en] High-entropy alloys (HEAs) stand out from multi-component alloys due to their attractive microstructures and mechanical properties. In this investigation, molecular dynamics (MD) simulation and machine learning (ML) were used to ascertain the deformation mechanism of AlCoCrCuFeNi HEAs under the influence of temperature, strain rate, and grain sizes. First, the MD simulation shows that the yield stress decreases significantly as the strain and temperature increase. In other cases, changes in strain rate and grain size have less effect on mechanical properties than changes in strain and temperature. The alloys exhibited superplastic behavior under all test conditions. The deformity mechanism discloses that strain and temperature are the main sources of beginning strain, and the shear bands move along the uniaxial tensile axis inside the workpiece. Furthermore, the fast phase shift of inclusion under mild strain indicates the relative instability of the inclusion phase of hexagonal close-packed (HCP). Ultimately, the dislocation evolution mechanism shows that the dislocations are transported to free surfaces under increased strain when they nucleate around the grain boundary. Surprisingly, the ML prediction results also confirm the same characteristics as those confirmed from the MD simulation. Hence, the combination of MD and ML reinforces the confidence in the findings of mechanical characteristics of HEA. Consequently, this combination fills the gaps between MD and ML, which can significantly save time, human power, and cost to conduct real experiments for testing HEA deformation in practice.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Nguyen, Hoang-Giang; Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan ; Faculty of Engineering and Technology, Kien Giang University, Viet Nam
LE, Thanh-Dung ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom ; Department of Electrical Engineering, Écolede Technologie Supérieure, University of Québec, Montréal, Canada
Nguyen, Hong-Giang; Institute of Testing and Quality Assurance in Education, Hue University, Hue City, Viet Nam
Fang, Te-Hua; Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan ; Department of Fragrance and Cosmetic Science, Kaohsiung Medical University, Kaohsiung, Taiwan
External co-authors :
yes
Language :
English
Title :
Mechanical properties of AlCoCrCuFeNi high-entropy alloys using molecular dynamics and machine learning
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