Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation
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 C<sub>0.1</sub>C...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Crystals |
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| Online Access: | https://www.mdpi.com/2073-4352/11/1/46 |
| _version_ | 1827603962937737216 |
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| author | Uttam Bhandari Congyan Zhang Congyuan Zeng Shengmin Guo Aashish Adhikari Shizhong Yang |
| author_facet | Uttam Bhandari Congyan Zhang Congyuan Zeng Shengmin Guo Aashish Adhikari Shizhong Yang |
| author_sort | Uttam Bhandari |
| collection | DOAJ |
| description | 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 C<sub>0.1</sub>Cr<sub>3</sub>Mo<sub>11.9</sub>Nb<sub>20</sub>Re<sub>15</sub>Ta<sub>30</sub>W<sub>20</sub> using the NN model. The hardness predicted from the NN model was consistent with the available experimental results. The NN model prediction of C<sub>0.1</sub>Cr<sub>3</sub>Mo<sub>11.9</sub>Nb<sub>20</sub>Re<sub>15</sub>Ta<sub>30</sub>W<sub>20</sub> 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. |
| first_indexed | 2024-03-09T05:50:13Z |
| format | Article |
| id | doaj.art-6de3c32e97bf40a5a16b8f5aeb8dcdff |
| institution | Directory Open Access Journal |
| issn | 2073-4352 |
| language | English |
| last_indexed | 2024-03-09T05:50:13Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Crystals |
| spelling | doaj.art-6de3c32e97bf40a5a16b8f5aeb8dcdff2023-12-03T12:18:12ZengMDPI AGCrystals2073-43522021-01-011114610.3390/cryst11010046Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental ValidationUttam Bhandari0Congyan Zhang1Congyuan Zeng2Shengmin Guo3Aashish Adhikari4Shizhong Yang5Department of Computer Science, Southern University and A&M College, Baton Rouge, LA 70813, USADepartment of Computer Science, Southern University and A&M College, Baton Rouge, LA 70813, USADepartment of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, USADepartment of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, USASchool of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USADepartment of Computer Science, Southern University and A&M College, Baton Rouge, LA 70813, USAHardness 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 C<sub>0.1</sub>Cr<sub>3</sub>Mo<sub>11.9</sub>Nb<sub>20</sub>Re<sub>15</sub>Ta<sub>30</sub>W<sub>20</sub> using the NN model. The hardness predicted from the NN model was consistent with the available experimental results. The NN model prediction of C<sub>0.1</sub>Cr<sub>3</sub>Mo<sub>11.9</sub>Nb<sub>20</sub>Re<sub>15</sub>Ta<sub>30</sub>W<sub>20</sub> 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.https://www.mdpi.com/2073-4352/11/1/46high entropy alloysneural networkshardness-predictionmicrostructure |
| spellingShingle | Uttam Bhandari Congyan Zhang Congyuan Zeng Shengmin Guo Aashish Adhikari Shizhong Yang Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation Crystals high entropy alloys neural networks hardness-prediction microstructure |
| title | Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation |
| title_full | Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation |
| title_fullStr | Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation |
| title_full_unstemmed | Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation |
| title_short | Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation |
| title_sort | deep learning based hardness prediction of novel refractory high entropy alloys with experimental validation |
| topic | high entropy alloys neural networks hardness-prediction microstructure |
| url | https://www.mdpi.com/2073-4352/11/1/46 |
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