Enhancing creep rupture life prediction of high‐temperature titanium alloys using convolutional neural networks
Year: 2024
Type: article
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.
DOI: 10.1002/mgea.68
Authors: Bangtan Zong, Jinshan Li, Changlu Zhou, Ping Wang, Bin Tang, Ruihao Yuan
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid:
OpenAlex ID: W4404664666