Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of post-irradiation swelling rate of 316L stainless steel based on Variational Autoencoders and interpretable machine learning
In the field of materials science, accurately predicting the swelling rate of materials in irradiated environments is crucial for ensuring safety and reliability. This study aims to enhance the predictive accuracy of the swelling rate of irradiated 316L stainless steel, particularly in high-tech applications such as nuclear energy. By comparing various machine learning models, it was found that the Extreme Trees Regression (ETR) model performed best on the test set, achieving an R2 of 0.79 and a Root Mean Square Error (RMSE) of 1.65 %. Although it demonstrated strong generalization capabilities, the limited data volume restricted its predictive accuracy. To address this issue, the study employed Variational Autoencoders (VAEs) for data augmentation, generating an additional 400 synthetic data points to expand the original dataset. This enhancement increased the R2 on the test set to 0.91 and reduced the RMSE to 1.11 %. Following data augmentation, feature selection was conducted, resulting in Si, C, IrF, T, and Dd being identified as the optimal feature combination. SHapley Additive exPlanations (SHAP) was then utilized for interpretability analysis, revealing the significant effects of these features on the swelling rate. The findings provide essential insights for understanding and optimizing the swelling behavior of materials following irradiation.
Prediction of post-irradiation swelling rate of 316L stainless steel based on Variational Autoencoders and interpretable machine learning
In the field of materials science, accurately predicting the swelling rate of materials in irradiated environments is crucial for ensuring safety and reliability. This study aims to enhance the predictive accuracy of the swelling rate of irradiated 316L stainless steel, particularly in high-tech applications such as nuclear energy. By comparing various machine learning models, it was found that the Extreme Trees Regression (ETR) model performed best on the test set, achieving an R2 of 0.79 and a Root Mean Square Error (RMSE) of 1.65 %. Although it demonstrated strong generalization capabilities, the limited data volume restricted its predictive accuracy. To address this issue, the study employed Variational Autoencoders (VAEs) for data augmentation, generating an additional 400 synthetic data points to expand the original dataset. This enhancement increased the R2 on the test set to 0.91 and reduced the RMSE to 1.11 %. Following data augmentation, feature selection was conducted, resulting in Si, C, IrF, T, and Dd being identified as the optimal feature combination. SHapley Additive exPlanations (SHAP) was then utilized for interpretability analysis, revealing the significant effects of these features on the swelling rate. The findings provide essential insights for understanding and optimizing the swelling behavior of materials following irradiation.
Prediction of post-irradiation swelling rate of 316L stainless steel based on Variational Autoencoders and interpretable machine learning
Chengcheng Liu (Autor:in) / Hang Su (Autor:in)
2025
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
Swelling of 316L austenitic stainless steel induced by plasma nitriding
| British Library Online Contents | 2011
Fatigue Properties of 316L Stainless Steel
| British Library Conference Proceedings | 2012
Unusual strain rate sensitive behaviour of AISI 316L austenitic stainless steel
| British Library Online Contents | 2004
Thermomechanical fatigue evaluation and life prediction of 316L(N) stainless steel
| British Library Online Contents | 2009