Novel method for predicting the cracks of oxide scales during high temperature oxidation of metals and alloys by using machine learning

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dc.contributor.authorChawuthai, Rathachai
dc.contributor.authorPromchan, Teeratat
dc.contributor.authorRojsanga, Jularak
dc.contributor.authorChandra-ambhorn, Somrerk
dc.contributor.authorNilsonthi, Thanasak
dc.contributor.authorWongpromrat, Patthranit
dc.contributor.authorBumrungthaichaichan, Eakarach
dc.contributor.authorAnantpinijwatna, Amata
dc.date.accessioned2026-08-06T10:52:42Z
dc.date.available2026-08-06T10:52:42Z
dc.date.issued2025-12-01
dc.description.abstractMaterial degradation is one of the main problems in various high-temperature processes, directly resulting in the failure of the material. Crack and protective oxide film spallation caused either by mechanical stress development in the oxidation process or thermal stress due to a mismatch of the thermal expansions of the formed oxide and alloy are common forms of failure in high-temperature processes. Typically, the Pilling-Bedworth ratio (PBR) is employed to predict crack and spallation of the oxide by determining the volume changes of oxide and alloy because of its simplicity. However, this approach provides poor crack and spallation predictions. Hence, machine learning was adopted in the present work to predict oxide formation and spallation in the temperature range of 600-1,200 °C. The inputs for the present developed model were alloy compositions, oxide formed during oxidation, and oxidation conditions and periods. Furthermore, the predicted results of the present developed machine learning model were compared to those obtained by the PBR method. The present results revealed that the accuracy of the oxide spallation prediction of the present model was better than that of the PBR method. The random forest with 15 estimators was the best machine learning model. Finally, it can be concluded that the machine learning model is essential for accurate material failure prediction.
dc.identifier.citationScientific Reports, 15(1), 2025
dc.identifier.doi10.1038/s41598-025-91449-3
dc.identifier.issn20452322
dc.identifier.other2-s2.0-86000110918
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17372
dc.sourceScientific Reports
dc.subjectHigh temperature oxidation
dc.subjectMachine learning
dc.subjectPrediction
dc.subjectSpallation
dc.titleNovel method for predicting the cracks of oxide scales during high temperature oxidation of metals and alloys by using machine learning
dc.typeArticle

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