scholarly journals Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation

Crystals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 46
Author(s):  
Uttam Bhandari ◽  
Congyan Zhang ◽  
Congyuan Zeng ◽  
Shengmin Guo ◽  
Aashish Adhikari ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Experimental Validation ◽  
Essential Property ◽  
High Entropy Alloys ◽  
Alternative Route ◽  
The Novel ◽  
High Entropy ◽  
Hardness Prediction ◽  
First Time

Hardness is an essential property in the design of refractory high entropy alloys (RHEAs). This study shows how a neural network (NN) model can be used to predict the hardness of a RHEA, for the first time. We predicted the hardness of several alloys, including the novel C0.1Cr3Mo11.9Nb20Re15Ta30W20 using the NN model. The hardness predicted from the NN model was consistent with the available experimental results. The NN model prediction of C0.1Cr3Mo11.9Nb20Re15Ta30W20 was verified by experimentally synthesizing and investigating its microstructure properties and hardness. This model provides an alternative route to determine the Vickers hardness of RHEAs.

scholarly journals Forest Fire Detection via Feature Entropy Guided Neural Network

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 128
Author(s):  
Zhenwei Guan ◽  
Feng Min ◽  
Wei He ◽  
Wenhua Fang ◽  
Tao Lu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Forest Fire ◽  
State Of The Art ◽  
Fire Detection ◽  
High Entropy ◽  
Training Samples ◽  
Feature Information ◽  
Forest Fire Detection ◽  
Entropy Source

Forest fire detection from videos or images is vital to forest firefighting. Most deep learning based approaches rely on converging image loss, which ignores the content from different fire scenes. In fact, complex content of images always has higher entropy. From this perspective, we propose a novel feature entropy guided neural network for forest fire detection, which is used to balance the content complexity of different training samples. Specifically, a larger weight is given to the feature of the sample with a high entropy source when calculating the classification loss. In addition, we also propose a color attention neural network, which mainly consists of several repeated multiple-blocks of color-attention modules (MCM). Each MCM module can extract the color feature information of fire adequately. The experimental results show that the performance of our proposed method outperforms the state-of-the-art methods.

scholarly journals Deep learning-based phase prediction of high-entropy alloys: Optimization, generation, and explanation

2021 ◽  
Vol 197 ◽  
pp. 109260
Author(s):  
Soo Young Lee ◽  
Seokyeong Byeon ◽  
Hyoung Seop Kim ◽  
Hyungyu Jin ◽  
Seungchul Lee
Keyword(s):  
Deep Learning ◽  
High Entropy Alloys ◽  
High Entropy ◽  
Phase Prediction

Mechanical Behavior of Novel Suction Cast Ti-Cu-Fe-Co-Ni High Entropy Alloys

2014 ◽  
Vol 790-791 ◽  
pp. 503-508 ◽  
Author(s):  
Sumanta Samal ◽  
Sutanuka Mohanty ◽  
Ajit Kumar Misra ◽  
Krishanu Biswas ◽  
B. Govind
Keyword(s):  
Mechanical Properties ◽  
Vacuum Arc ◽  
High Entropy Alloys ◽  
The Novel ◽  
Electron Microscopic ◽  
High Entropy ◽  
Alloy Chemistry ◽  
Arc Melting ◽  
Vacuum Arc Melting ◽  
Ductile Mode

The present investigation reports mechanical properties of novel multicomponent TixCuyFe20Co20Ni20 high entropy alloys (HEAs) with different alloy chemistry (x/y = 1/3, 3/7, 3/5, 9/11, 1, 11/9 and 3/2). The alloy cylinders were prepared by vacuum arc melting-cum-suction casting route. The detailed electron microscopic observations reveal the presence of three different solid solution phases; FCC (a1) phase, FCC (a2) phase and BCC (b) phase for all the investigated alloys, whereas ultrafine eutectic between FCC (a1) phase, and Ti2 (Co, Ni) - type Laves phase has been observed for the HEAs with x/y = 9/11, 1, 11/9 and 3/2. Room temperature compression test of the suction cast cylinders with aspect ratio of 2/1 has been conducted to obtain mechanical properties of the HEAs. The optimum combination of strength (~ 1.88 GPa) and plasticity (~ 21 %) is obtained for x/y = 9/11; indicating simultaneous improvement of strength as well as plasticity of the novel HEAs. Fractographic analysis of the fractured surfaces reveals mixed mode of fracture for x/y = 1/3, 3/7 and 3/5, ductile mode for x/y = 9/11 and 1, whereas brittle mode of fracture for x/y = 11/9 and 3/2.

scholarly journals Deep Learning-based Phase Prediction of High-Entropy Alloys

2020 ◽  
Vol 987 ◽  
pp. 012025
Author(s):  
Zeyad Yousif Abdoon Al-Shibaany ◽  
Nadia Alkhafaji ◽  
Yaser Al-Obaidi ◽  
Alaa Abdulhasan Atiyah
Keyword(s):  
Deep Learning ◽  
High Entropy Alloys ◽  
High Entropy ◽  
Phase Prediction
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