Valence Electron Structures and Mechanical and Thermal Properties of Single-Phase Refractory High-Entropy Alloys

2021 ◽  
Author(s):  
Xinpeng Guo ◽  
Yongquan Guo ◽  
Linhan Yin
Keyword(s):  
Thermal Properties ◽  
Valence Electron ◽  
Single Phase ◽  
Mechanical And Thermal Properties ◽  
High Entropy Alloys ◽  
High Entropy

A review on phase prediction in high entropy alloys

2021 ◽  
pp. 095440622110089
Author(s):  
Vinay Kumar Soni ◽  
S Sanyal ◽  
K Raja Rao ◽  
Sudip K Sinha
Keyword(s):  
Solid Solution ◽  
Phase Formation ◽  
Single Phase ◽  
High Entropy Alloy ◽  
High Entropy Alloys ◽  
Modeling Tools ◽  
High Entropy ◽  
Recent Developments ◽  
Phase Prediction ◽  
The Stability

The formation of single phase solid solution in High Entropy Alloys (HEAs) is essential for the properties of the alloys therefore, numerous approach were proposed by many researchers to predict the stability of single phase solid solution in High Entropy Alloy. The present review examines some of the recent developments while using computational intelligence techniques such as parametric approach, CALPHAD, Machine Learning etc. for prediction of various phase formation in multicomponent high entropy alloys. A detail study of this data-driven approaches pertaining to the understanding of structural and phase formation behaviour of a new class of compositionally complex alloys is done in the present investigation. The advantages and drawbacks of the various computational are also discussed. Finally, this review aims at understanding several computational modeling tools complying the thermodynamic criteria for phase formation of novel HEAs which could possibly deliver superior mechanical properties keeping an aim at advanced engineering applications.

scholarly journals Al, Cu and Zr Addition to High Entropy Alloys: The Effect on Recrystallization Temperature

2018 ◽  
Vol 941 ◽  
pp. 1137-1142
Author(s):  
Elena Colombini ◽  
Andrea Garzoni ◽  
Roberto Giovanardi ◽  
Paolo Veronesi ◽  
Angelo Casagrande
Keyword(s):  
Solid Solution ◽  
Grain Size ◽  
Single Phase ◽  
Boundary Energy ◽  
Cold Deformation ◽  
Recrystallization Temperature ◽  
High Entropy Alloys ◽  
High Entropy ◽  
Ni Alloy ◽  
Sluggish Diffusion

The equimolar Cr, Mn, Fe, Co and Ni alloy, first produced in 2004, was unexpectedly found to be single-phase. Consequently, a new concept of materials was developed: high entropy alloys (HEA) forming a single solid-solution with a near equiatomic composition of the constituting elements. In this study, an equimolar CoCrFeMnNi HEA was modified by the addition of 5 at% of either Al, Cu or Zr. The cold-rolled alloys were annealed for 30 minutes at high temperature to investigate the recrystallization kinetics. The evolution of the grain boundary and the grain size were investigated, from the as-cast to the recrystallized state. Results show that the recrystallized single phase FCC structures exhibits different twin grains density, grain size and recrystallization temperatures as a function of the at.% of modifier alloying elements added. In comparison to the equimolar CoCrFeMnNi, the addition of modifier elements increases significantly the recrystallization temperature after cold deformation. The sluggish diffusion (typical of HEA alloys), the presence of a solute in solid solution as well as the low twin boundary energy are responsible for the lower driving force for recrystallization.

scholarly journals Shear localization of fcc high-entropy alloys

2018 ◽  
Vol 183 ◽  
pp. 03028 ◽  
Author(s):  
Marc A. Meyers ◽  
Zezhou Li ◽  
Shiteng Zhao ◽  
Bingfeng Wang ◽  
Yong Liu ◽  
...  
Keyword(s):  
Shear Strain ◽  
Single Phase ◽  
Dynamic Compression ◽  
Impact Resistance ◽  
Shear Localization ◽  
Solid Solution Hardening ◽  
High Entropy Alloys ◽  
High Entropy ◽  
Work Hardening Rate ◽  
High Work

Dynamic behavior of the single phase (fcc) Al0.3CoCrFeNi and CoCrFeMnNi high-entropy alloys (HEAs) was examined. The combination of multiple strengthening mechanisms such as solid solution hardening, cutting forest dislocation, as well as mechanical nano-twinning leads to a high work-hardening rate, compared with conventional alloys. The resistance to shear localization was studied by dynamicallyloading hat-shaped specimens to induce forced shear localization. However, no adiabatic shear band could be observed for Al0.3CoCrFeNi HEA at a large shear strain ~1.1. Additionally, shear localization of the CoCrFeMnNi HEA was only found at an even larger shear strain ~7 under dynamic compression. It is therefore proposed that the combination of the excellent strain-hardening ability and modest thermal softening of these two kinds of high-entropy alloys gives rise to remarkable resistance to shear localization, which makes HEAs excellent candidates for impact resistance applications.

scholarly journals Prediction of Equilibrium Phase, Stability and Stress-Strain Properties in Co-Cr-Fe-Ni-Al High Entropy Alloys Using Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1569
Author(s):  
Nicolae Filipoiu ◽  
George Alexandru Nemnes
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Equilibrium Phase ◽  
Al Alloy ◽  
Mechanical And Thermal Properties ◽  
High Entropy Alloys ◽  
Huge Number ◽  
Stress Strain ◽  
High Entropy ◽  
Artificial Neural

High entropy alloys (HEAs) are still a largely unexplored class of materials with high potential for applications in various fields. Motivated by the huge number of compounds in a given HEA class, we develop machine learning techniques, in particular artificial neural networks, coupled to ab initio calculations, in order to accurately predict some basic HEA properties: equilibrium phase, cohesive energies, density of states at the Fermi level and the stress-strain relation, under conditions of isotropic deformations. Known for its high tensile ductility and fracture toughness, the Co-Cr-Fe-Ni-Al alloy has been considered as a test candidate material, particularly by adjusting the Al content. However, further enhancement of the microstructure, mechanical and thermal properties is possible by modifying also the fractions of the base alloy. Using deep neural networks, we map structural and chemical neighborhood information onto the quantities of interest. This approach offers the possibility for an efficient screening over a huge number of potential candidates, which is essential in the exploration of multi-dimensional compositional spaces.

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