Enhancing creep rupture life prediction of high‐temperature titanium alloys using convolutional neural networks
Abstract Prediction of creep rupture life of high‐temperature titanium alloys is crucial for their practical applications. The efficient representations (features) of the information encoded in the data are essential to achieve an accurate prediction model. Here, using convolutional neural networks...
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| 主要な著者: | , , , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Wiley-VCH
2024-12-01
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| シリーズ: | Materials Genome Engineering Advances |
| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1002/mgea.68 |
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