Accelerating the solving of mechanical equilibrium caused by lattice misfit through deep learning method

doi:10.1007/s40436-024-00494-0

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (3): 512-521.doi: 10.1007/s40436-024-00494-0

• • 上一篇

Accelerating the solving of mechanical equilibrium caused by lattice misfit through deep learning method

Chen-Xi Guo¹, Hui-Ying Yang¹, Rui-Jie Zhang^1,2

1. Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing, 100083, People's Republic of China;
2. Beijing Laboratory of Metallic Materials and Processing for Modern Transportation, University of Science and Technology Beijing, Beijing, 100083, People's Republic of China

收稿日期:2023-09-30 修回日期:2023-11-06 发布日期:2024-09-07
通讯作者: Rui-Jie Zhang,E-mail:zrj@ustb.edu.cn E-mail:zrj@ustb.edu.cn
作者简介:Chen-Xi Guo is currently pursuing the master’s degree with University of Science and Technology Beijing, Beijing, China. Her research focuses on the application of artificial intelligence to materials science;
Hui-Ying Yang received master’s degree from University of Science and Technology Beijing in 2022. She is currently working in the automotive electronics development industry;
Rui-Jie Zhang received Ph.D. degree in materials processing technology from Northwestern Polytechnical University, Xi’an, China, in 2004. He is currently an Associate Professor with University of Science and Technology Beijing, Beijing, China. His research interests include materials microstructure simulation and properties prediction.
基金资助:
This work was financially supported by the National Natural Science Foundation of China (Grant No. 52271019), and the Key Area Research and Development Program of Guangdong Province (Grant No. 2019B010942001).

Accelerating the solving of mechanical equilibrium caused by lattice misfit through deep learning method

Chen-Xi Guo¹, Hui-Ying Yang¹, Rui-Jie Zhang^1,2

1. Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing, 100083, People's Republic of China;
2. Beijing Laboratory of Metallic Materials and Processing for Modern Transportation, University of Science and Technology Beijing, Beijing, 100083, People's Republic of China

Received:2023-09-30 Revised:2023-11-06 Published:2024-09-07
Contact: Rui-Jie Zhang,E-mail:zrj@ustb.edu.cn E-mail:zrj@ustb.edu.cn
Supported by:
This work was financially supported by the National Natural Science Foundation of China (Grant No. 52271019), and the Key Area Research and Development Program of Guangdong Province (Grant No. 2019B010942001).

摘要/Abstract

摘要： Precipitation is a common phenomenon that occurs during heat treatments. There is internal stress around the precipitate owing to the lattice misfit between the precipitate and matrix. This internal stress has a significant influence not only on the precipitation kinetics but also on the material properties. The misfit stress can be obtained by numerically solving the mechanical equilibrium equations. However, this process is complex and time-consuming. We developed a new approach based on deep learning to accelerate the solution process. The training data were first generated by a phase-field model coupled with elastic mechanical equilibrium equations, which were solved using the finite difference method. The obtained precipitate morphologies and corresponding stress distributions were input data for training the physics-informed (PI) UNet model. The well-trained PI-UNet model can then be applied to predicting stress distributions with the precipitate morphology as the input. Prediction accuracy and efficiency are discussed in this study. The results showed that the PI-UNet model was an appropriate approach for quickly predicting the misfit stress between the precipitate and matrix.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00494-0

关键词: Precipitate, Mechanical equilibrium, Heat treatment, Deep learning

Abstract: Precipitation is a common phenomenon that occurs during heat treatments. There is internal stress around the precipitate owing to the lattice misfit between the precipitate and matrix. This internal stress has a significant influence not only on the precipitation kinetics but also on the material properties. The misfit stress can be obtained by numerically solving the mechanical equilibrium equations. However, this process is complex and time-consuming. We developed a new approach based on deep learning to accelerate the solution process. The training data were first generated by a phase-field model coupled with elastic mechanical equilibrium equations, which were solved using the finite difference method. The obtained precipitate morphologies and corresponding stress distributions were input data for training the physics-informed (PI) UNet model. The well-trained PI-UNet model can then be applied to predicting stress distributions with the precipitate morphology as the input. Prediction accuracy and efficiency are discussed in this study. The results showed that the PI-UNet model was an appropriate approach for quickly predicting the misfit stress between the precipitate and matrix.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00494-0

Key words: Precipitate, Mechanical equilibrium, Heat treatment, Deep learning

Chen-Xi Guo, Hui-Ying Yang, Rui-Jie Zhang. Accelerating the solving of mechanical equilibrium caused by lattice misfit through deep learning method[J]. Advances in Manufacturing, 2024, 12(3): 512-521.

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Accelerating the solving of mechanical equilibrium caused by lattice misfit through deep learning method

Accelerating the solving of mechanical equilibrium caused by lattice misfit through deep learning method

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