Machine Learning Based Methods for Obtaining Correlations between Microstructures and Thermal Stresses
Abstract
:1. Introduction
2. Methodology
2.1. Dataset
2.1.1. Classification and Clustering
2.1.2. Regression between Microstructures and Stresses
3. Results and Discussion
3.1. Classification and Clustering
3.2. Regression between Microstructures and Stresses
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Regression Analysis Performed Based on the CNN Model Using Stress Components σxx and σxy
References
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Type | Filter Size | Number of Filters | Output Size |
---|---|---|---|
Convolution | 3 × 3 | 264 | 222 × 222 × 264 |
Average Pooling | 2 × 2 | - | 111 × 111 × 264 |
Convolution | 3 × 3 | 128 | 109 × 109 × 128 |
Average Pooling | 2 × 2 | - | 54 × 54 × 128 |
Convolution | 3 × 3 | 64 | 52 × 52 × 64 |
Average Pooling | 2 × 2 | - | 26 × 26 × 64 |
Convolution | 3 × 3 | 32 | 24 × 24 × 32 |
Average Pooling | 2 × 2 | - | 12 × 12 × 32 |
Flatten | - | - | 4608 |
Dense | - | - | 32 |
Dense | - | - | 6 |
Type | Filter Size | Number of Filters | Output Size |
---|---|---|---|
Convolution | 3 × 3 | 264 | 222 × 222 × 264 |
Average Pooling | 2 × 2 | - | 111 × 111 × 264 |
Convolution | 3 × 3 | 128 | 109 × 109 × 128 |
Average Pooling | 2 × 2 | - | 54 × 54 × 128 |
Convolution | 3 × 3 | 64 | 52 × 52 × 64 |
Average Pooling | 2 × 2 | - | 26 × 26 × 64 |
Flatten | - | - | 43,264 |
Type | Hidden Units | Output Shape |
---|---|---|
Dense | 2064 | 2064 |
Dropout | - | 2064 |
Dense | 2064 | 2064 |
Dropout | - | 2064 |
Dense | 2064 | 2064 |
Dropout | - | 2064 |
Dense | 3 | 3 |
Target | ||||||
---|---|---|---|---|---|---|
Training Data | Validation Data | Test Data | Training Data | Validation Data | Test Data | |
0.9990 | 0.8380 | 0.8665 | 0.005 | 0.0613 | 0.0573 | |
0.9993 | 0.8581 | 0.8753 | 0.0038 | 0.0538 | 0.0496 | |
0.9993 | 0.8605 | 0.8735 | 0.0038 | 0.0523 | 0.0483 | |
0.9992 | 0.8522 | 0.8717 | 0.0042 | 0.0558 | 0.0517 |
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Bhutada, A.; Kumar, S.; Gunasegaram, D.; Alankar, A. Machine Learning Based Methods for Obtaining Correlations between Microstructures and Thermal Stresses. Metals 2021, 11, 1167. https://0-doi-org.brum.beds.ac.uk/10.3390/met11081167
Bhutada A, Kumar S, Gunasegaram D, Alankar A. Machine Learning Based Methods for Obtaining Correlations between Microstructures and Thermal Stresses. Metals. 2021; 11(8):1167. https://0-doi-org.brum.beds.ac.uk/10.3390/met11081167
Chicago/Turabian StyleBhutada, Akshay, Sunni Kumar, Dayalan Gunasegaram, and Alankar Alankar. 2021. "Machine Learning Based Methods for Obtaining Correlations between Microstructures and Thermal Stresses" Metals 11, no. 8: 1167. https://0-doi-org.brum.beds.ac.uk/10.3390/met11081167