scholarly journals High-Entropy Alloys Properties Prediction Model by Using Artificial Neural Network Algorithm

Metals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1559
Author(s):  
Sanggyu Choi ◽  
Sung Yi ◽  
Junghan Kim ◽  
Byungsue Shin ◽  
Soongkeun Hyun
Keyword(s):  
Neural Network ◽  
Yield Strength ◽  
Mole Fraction ◽  
Prediction Error ◽  
Strength Prediction ◽  
High Entropy Alloys ◽  
Process Data ◽  
High Entropy ◽  
Post Process ◽  
The Mean

A new approach method has been studied for the efficient and accurate prediction of high-entropy alloys (HEAs) properties. The artificial neural network (ANN) algorithm was employed to predict the mechanical properties such as yield strength, microstructure, and elongation of the alloy by training from the mole fraction and post-process information that has an influence on the mechanical properties. The mean error rate of prediction for the yield strength was 19.6%. Microstructure predictions were consistent for all test data. On the other hand, the ANN model trained only with mole fraction data had a yield strength prediction error of 33.9%. Omission of post-process data caused a decrease in the accuracy. In addition, the prediction was performed with the lasso regression model in the same way. The mean error rate of the lasso model trained with only a mole fraction was 26.1%. The lasso model trained with a mole fraction and post-process data had a yield strength prediction error of 31.1%. The linear regression equation showed limitations, as the accuracy decreased as the number of independent variables increased. As there are more variables affecting metal properties, the ANN approach is more advantageous, and the more data there are, the more accuracy increases, making it possible to design HEAs alloys that are simpler and more efficient than conventional methods. This approach predicted HEAs properties using only mole fraction and post-processing information, without the need to use conventional physicochemical theories or perform derived complex calculations.

scholarly journals Machine Learning-Based Strength Prediction for Refractory High-Entropy Alloys of the Al-Cr-Nb-Ti-V-Zr System

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7213
Author(s):  
Denis Klimenko ◽  
Nikita Stepanov ◽  
Jia Li ◽  
Qihong Fang ◽  
Sergey Zherebtsov
Keyword(s):  
Machine Learning ◽  
Yield Strength ◽  
Strength Prediction ◽  
Support Vector ◽  
High Entropy Alloys ◽  
High Entropy ◽  
Average Prediction Error ◽  
Calphad Modeling ◽  
Wide Range ◽  
Alloys Of The Al

The aim of this work was to provide a guidance to the prediction and design of high-entropy alloys with good performance. New promising compositions of refractory high-entropy alloys with the desired phase composition and mechanical properties (yield strength) have been predicted using a combination of machine learning, phenomenological rules and CALPHAD modeling. The yield strength prediction in a wide range of temperatures (20–800 °C) was made using a surrogate model based on a support-vector machine algorithm. The yield strength at 20 °C and 600 °C was predicted quite precisely (the average prediction error was 11% and 13.5%, respectively) with a decrease in the precision to slightly higher than 20% at 800 °C. An Al13Cr12Nb20Ti20V35 alloy with an excellent combination of ductility and yield strength at 20 °C (16.6% and 1295 MPa, respectively) and at 800 °C (more 50% and 898 MPa, respectively) was produced based on the prediction.

Yield strength prediction of high-entropy alloys using machine learning

2020 ◽  
pp. 101871
Author(s):  
Uttam Bhandari ◽  
Md. Rumman Rafi ◽  
Congyan Zhang ◽  
Shizhong Yang
Keyword(s):  
Machine Learning ◽  
Yield Strength ◽  
Strength Prediction ◽  
High Entropy Alloys ◽  
High Entropy

scholarly journals Laser Beam Welding of Feconicrmn High-Entropy Alloys with Preplaced Powders

Metals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1402
Author(s):  
Ziyi Zhou ◽  
Feng Zhang ◽  
Jili Wu ◽  
Jinhong Pi ◽  
Fei Chen
Keyword(s):  
Tensile Strength ◽  
Ultimate Tensile Strength ◽  
Aspect Ratio ◽  
Yield Strength ◽  
Base Alloy ◽  
Laser Beam Welding ◽  
High Entropy Alloys ◽  
High Entropy ◽  
Engineering Practices ◽  
Equiaxed Grains

In this paper, as-annealed FeCoNiCrMn plates were laser-welded with preplaced FeCoNiCrMn and FeCoNiCrAl powders, respectively. The grains in the fusion zone of the weld with FeCoNiCrMn powder have a reduced aspect ratio compared to those without preplaced powders and the weld with FeCoNiCrAl powder presents relative equiaxed grains. The yield strength of each weld has been remarkably enhanced when referring to the base alloy, and the ultimate tensile strength of each weld with preplaced powder exceeds 80% of that of the base and the maximum reaches 88.5% when referring to the weld with preplaced FeCoNiCrMn powder. Cleavage fractography was observed in the welds. The finding of this work will service the engineering practices of high-entropy alloys.

Tensile strength prediction of dual-phase Al0.6CoCrFeNi high-entropy alloys

2020 ◽  
Vol 27 (10) ◽  
pp. 1341-1346 ◽  
Author(s):  
Min Zhang ◽  
Jin-xiong Hou ◽  
Hui-jun Yang ◽  
Ya-qin Tan ◽  
Xue-jiao Wang ◽  
...  
Keyword(s):  
Tensile Strength ◽  
Strength Prediction ◽  
Dual Phase ◽  
High Entropy Alloys ◽  
High Entropy

scholarly journals Computational property predictions of Ta–Nb–Hf–Zr high-entropy alloys

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shashank Mishra ◽  
Soumyadipta Maiti ◽  
Beena Rai
Keyword(s):  
Shear Stress ◽  
Yield Strength ◽  
Configurational Entropy ◽  
Annealing Treatment ◽  
Nuclear Radiation ◽  
Solid Solution Alloy ◽  
High Entropy Alloys ◽  
High Entropy ◽  
Principal Axes ◽  
Edge Dislocations

AbstractRefractory high entropy alloys (R-HEAs) are becoming prominent in recent years because of their properties and uses as high strength and high hardness materials for ambient and high temperature, aerospace and nuclear radiation tolerance applications, orthopedic applications etc. The mechanical properties like yield strength and ductility of TaNbHfZr R-HEA depend on the local nanostructure and chemical ordering, which in term depend on the annealing treatment. In this study we have computationally obtained various properties of the equimolar TaNbHfZr alloy like the role of configurational entropy in the thermodynamic property, rate of evolution of nanostructure morphology in thermally annealed systems, dislocation simulation based quantitative prediction of yield strength, nature of dislocation movement through short range clustering (SRC) and qualitative prediction of ductile to brittle transition behavior. The simulation starts with hybrid Monte Carlo/Molecular Dynamics (MC/MD) based nanostructure evolution of an initial random solid solution alloy structure with BCC lattice structure created with principal axes along [1 1 1], [− 1 1 0] and [− 1 − 1 2] directions suitable for simulation of ½[1 1 1] edge dislocations. Thermodynamic properties are calculated from the change in enthalpy and configurational entropy, which in term is calculated by next-neighbor bond counting statistics. The MC/MD evolved structures mimic the annealing treatment at 1800 °C and the output structures are replicated in periodic directions to make larger 384,000 atom structures used for dislocation simulations. Edge dislocations were utilized to obtain and explain for the critically resolved shear stress (CRSS) for the structures with various degrees of nanostructure evolution by annealing, where extra strengthening was observed because of the formations of SRCs. Lastly the MC/MD evolved structures containing dislocations are subjected to a high shear stress beyond CRSS to investigate the stability of the dislocations and the lattice structures to explain the experimentally observed transition from ductile to brittle behavior for the TaNbHfZr R-HEA.

Sign in / Sign up

Export Citation Format

Share Document