[en] High-entropy alloys (HEAs) distinguish themselves from other multi-component alloys through their unique nanostructures and mechanical properties. This study employs molecular dynamics (MD) simulations and machine learning to investigate the deformation mechanisms of AlCoCuCrFeNi HEA under varying temperatures, strain rates, and average grain sizes. The modeling results show that interactions between partial dislocations in AlCoCrCuFeNi HEA during tension and compression deformation cause various lattice disorders. The effect of temperature, strain rates, and grain boundaries on lattice disorder, plastic deformation behavior, dislocation density, and von-Mises stress (VMS) is disclosed. This study offers new insights into the atomic-scale deformation mechanisms governing the mechanical behavior of AlCoCrCuFeNi HEAs. It also presents a comprehensive workflow for predicting the mechanical properties of this HEA using machine learning models. The proposed approach provides several advantages, including significantly reduced simulation time and robust model validation. By employing the machine learning model trained in Stage 1, the time needed to simulate mechanical properties in Stage 2 is significantly decreased. Additionally, the framework ensures that the machine learning model effectively captures and understands the underlying representations of the mechanical properties of HEAs, thereby enhancing both the efficiency and accuracy of the predictions.
Disciplines :
Materials science & engineering
Author, co-author :
Nguyen, Hoang-Giang; Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan ; Department of Electronic Engineering, National United University, Miaoli City, Taiwan ; Faculty of Engineering, Kien Giang University, Viet Nam
Young, Sheng-Joue; Department of Electronic Engineering, National United University, Miaoli City, Taiwan
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
CHATZINOTAS, Symeon ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
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 :
Deformation mechanisms of AlCoCrCuFeNi: A molecular dynamics and machine learning approach
Bộ Giáo dục và Ðào tạo National Science and Technology Council
Funding text :
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Te-Hua Fang reports financial support was provided by National Kaohsiung University of Science and Technology. Sheng-Joue Young reports financial support was provided by National United University. Hoang-Giang Nguyen reports financial support was provided by Kien Giang University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.The authors acknowledge the support by the National Science and Technology Council, Taiwan, under grant numbers NSTC 113-2221-E-992-067-MY3, NSTC 113-2811-E239-002, NSTC 114-2221-E-992 -029 -MY3, NSTC 114-2221-E-992 -030 -MY3, the Industry Cooperation Project No. 113A00262, and the Ministry of Education and Training, Vietnam, under grant number B2026-TKG-01.The authors acknowledge the support by the National Science and Technology Council , Taiwan, under grant numbers NSTC 113-2221-E-992-067-MY3 , NSTC 113-2811-E239-002 , NSTC 114-2221-E-992 -029 -MY3 , NSTC 114-2221-E-992 -030 -MY3 , the Industry Cooperation Project No. 113A00262 , and the Ministry of Education and Training , Vietnam, under grant number B2026-TKG-01 .
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