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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley-VCH
2024-12-01
|
| Series: | Materials Genome Engineering Advances |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/mgea.68 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841543276314755072 |
|---|---|
| author | Bangtan Zong Jinshan Li Changlu Zhou Ping Wang Bin Tang Ruihao Yuan |
| author_facet | Bangtan Zong Jinshan Li Changlu Zhou Ping Wang Bin Tang Ruihao Yuan |
| author_sort | Bangtan Zong |
| collection | DOAJ |
| description | 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 (CNN) enhanced features, we obtain largely improved prediction models for creep rupture life. Comparison of CNN‐based features with the original features in describing different samples reveals that the former, by assigning more individualized labels, outperforms the latter and underpins improved prediction models. This work suggests that beyond images, CNN is also suitable for numerical data to obtain enhanced features and surrogate models. |
| format | Article |
| id | doaj-art-bfa623472e6748f39a07cf192147bde4 |
| institution | Kabale University |
| issn | 2940-9489 2940-9497 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Materials Genome Engineering Advances |
| spelling | doaj-art-bfa623472e6748f39a07cf192147bde42025-01-13T15:15:31ZengWiley-VCHMaterials Genome Engineering Advances2940-94892940-94972024-12-0124n/an/a10.1002/mgea.68Enhancing creep rupture life prediction of high‐temperature titanium alloys using convolutional neural networksBangtan Zong0Jinshan Li1Changlu Zhou2Ping Wang3Bin Tang4Ruihao Yuan5State Key Laboratory of Solidification Processing Northwestern Polytechnical University Xi'an ChinaState Key Laboratory of Solidification Processing Northwestern Polytechnical University Xi'an ChinaState Key Laboratory of Solidification Processing Northwestern Polytechnical University Xi'an ChinaState Key Laboratory of Solidification Processing Northwestern Polytechnical University Xi'an ChinaState Key Laboratory of Solidification Processing Northwestern Polytechnical University Xi'an ChinaState Key Laboratory of Solidification Processing Northwestern Polytechnical University Xi'an ChinaAbstract 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 (CNN) enhanced features, we obtain largely improved prediction models for creep rupture life. Comparison of CNN‐based features with the original features in describing different samples reveals that the former, by assigning more individualized labels, outperforms the latter and underpins improved prediction models. This work suggests that beyond images, CNN is also suitable for numerical data to obtain enhanced features and surrogate models.https://doi.org/10.1002/mgea.68convolutional neural networkscreep rupture lifehigh‐temperature titanium alloys |
| spellingShingle | Bangtan Zong Jinshan Li Changlu Zhou Ping Wang Bin Tang Ruihao Yuan Enhancing creep rupture life prediction of high‐temperature titanium alloys using convolutional neural networks Materials Genome Engineering Advances convolutional neural networks creep rupture life high‐temperature titanium alloys |
| title | Enhancing creep rupture life prediction of high‐temperature titanium alloys using convolutional neural networks |
| title_full | Enhancing creep rupture life prediction of high‐temperature titanium alloys using convolutional neural networks |
| title_fullStr | Enhancing creep rupture life prediction of high‐temperature titanium alloys using convolutional neural networks |
| title_full_unstemmed | Enhancing creep rupture life prediction of high‐temperature titanium alloys using convolutional neural networks |
| title_short | Enhancing creep rupture life prediction of high‐temperature titanium alloys using convolutional neural networks |
| title_sort | enhancing creep rupture life prediction of high temperature titanium alloys using convolutional neural networks |
| topic | convolutional neural networks creep rupture life high‐temperature titanium alloys |
| url | https://doi.org/10.1002/mgea.68 |
| work_keys_str_mv | AT bangtanzong enhancingcreeprupturelifepredictionofhightemperaturetitaniumalloysusingconvolutionalneuralnetworks AT jinshanli enhancingcreeprupturelifepredictionofhightemperaturetitaniumalloysusingconvolutionalneuralnetworks AT changluzhou enhancingcreeprupturelifepredictionofhightemperaturetitaniumalloysusingconvolutionalneuralnetworks AT pingwang enhancingcreeprupturelifepredictionofhightemperaturetitaniumalloysusingconvolutionalneuralnetworks AT bintang enhancingcreeprupturelifepredictionofhightemperaturetitaniumalloysusingconvolutionalneuralnetworks AT ruihaoyuan enhancingcreeprupturelifepredictionofhightemperaturetitaniumalloysusingconvolutionalneuralnetworks |