Generating artificial displacement data of cracked specimen using physics-guided adversarial networks
Digital image correlation (DIC) has become a valuable tool to monitor and evaluate mechanical experiments of cracked specimen, but the automatic detection of cracks is often difficult due to inherent noise and artefacts. Machine learning models have been extremely successful in detecting crack paths...
| Главные авторы: | , , |
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| Формат: | Статья |
| Язык: | English |
| Опубликовано: |
IOP Publishing
2024-01-01
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| Серии: | Machine Learning: Science and Technology |
| Предметы: | |
| Online-ссылка: | https://doi.org/10.1088/2632-2153/ad15b2 |