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• W4313893311 abstract "Abstract The oxidation resistance of FeCrAl based on alloying composition and oxidizing conditions is predicted using a combinatorial experimental and artificial intelligence approach. A neural network (NN) classification model was trained on the experimental FeCrAl dataset produced at GE Research. Furthermore, using the SHapley Additive exPlanations (SHAP) explainable artificial intelligence (XAI) tool, we explore how the NN can showcase further material insights that are unavailable directly from a black-box model. We report that high Al and Cr content forms protective oxide layer, while Mo in FeCrAl creates thick unprotective oxide scale that is vulnerable to spallation due to thermal expansion. Graphical abstract" @default.
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• W4313893311 date "2023-01-09" @default.
• W4313893311 modified "2023-10-14" @default.
• W4313893311 title "Understanding oxidation of Fe-Cr-Al alloys through explainable artificial intelligence" @default.
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• W4313893311 doi "https://doi.org/10.1557/s43579-022-00315-0" @default.
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