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Machine learning-based predictions of yield strength for neutron-irradiated ferritic/martensitic steels

Sai, NJ and Rathore, P and Sridharan, K and Chauhan, A (2023) Machine learning-based predictions of yield strength for neutron-irradiated ferritic/martensitic steels. In: Fusion Engineering and Design, 195 .

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Official URL: https://doi.org/10.1016/j.fusengdes.2023.113964


Due to their superior resistance to radiation-induced damage, Ferritic/Martensitic (F/M) steels are promising structural materials for advanced nuclear reactors. This study investigates machine learning (ML) methodologies to predict the yield strength of neutron-irradiated F/M steels. Popular ML algorithms, such as Random Forest (RF), Extreme Gradient Boosting (XGBOOST), Gradient Boosting (GBOOST), and Support Vector Regression (SVR), were trained on a experimental dataset to understand the relationship between the input variables (e.g., irradiation dose, irradiation temperature, tensile test condition, heat treatment conditions and steels composition) and the output variable (yield strength). Further, the SHapley Additive exPlanations (SHAP) algorithm was employed to obtain the importance hierarchy of the input variables for their selection. Post-training and testing ML algorithms, their performance was evaluated by assessing their ability to predict the unseen/validation dataset. Among the algorithms tested, XGBOOST demonstrated the highest performance in predicting the validation dataset, followed by RF, GBOOST, and SVR. Synthetic experiments show that the trained ML algorithms can capture the trends between the irradiation input variables and the yield strength. Overall, the trained ML algorithms overcame challenges such as data uncertainties, smaller and sparser datasets, and other complexities and predicted almost 70 to 75 of the datapoints in the unseen/validation dataset within one standard deviation. © 2023 Elsevier B.V.

Item Type: Journal Article
Publication: Fusion Engineering and Design
Publisher: Elsevier Ltd
Additional Information: The copyright for this article belongs to the Elsevier Ltd.
Keywords: Ability testing; Ferrite; Forecasting; Forestry; Neutron irradiation; Neutrons; Statistical tests; Support vector regression; Tensile testing, Ensemble algorithms; Ferritic/martensitic steel; Gradient boosting; Input variables; Machine learning algorithms; Machine-learning; Neutron irradiated; Neutron irradiation, ferritic/martensitic steel; Random forests; Support vector regressions, Yield stress
Department/Centre: Division of Interdisciplinary Sciences > Robert Bosch Centre for Cyber Physical Systems
Division of Mechanical Sciences > Materials Engineering (formerly Metallurgy)
Date Deposited: 08 Nov 2023 03:27
Last Modified: 08 Nov 2023 03:27
URI: https://eprints.iisc.ac.in/id/eprint/83028

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