Machine Learning Predicts Porosity Effects on Zr Alloys Corrosion ML Machinelearning corrosion alloy alloys MaterialsToday
By Shaheer RehanSep 14 2022Reviewed by Megan Craig, M.Sc. The oxidation kinetics and corrosion of Zr alloys are highly dependent on the porosity exhibited by the zirconia layers.
Zircaloy-4 offers the highest resistance against corrosion and minimum hydrogen uptake among the Zr alloys. A shift in the corrosion kinetics, referred to as the transition, occurs at the threshold oxide thickness level. Diffusion may take place at the grain boundaries and the accompanying pores on the oxide layer; therefore, the texture of the oxide layer is a key element that influences the diffusion mechanism. The oxide texture is linked with the texture of the Zr alloy.
Machine learning approaches, as substitutes to human observation descriptors, have been widely employed to determine nanoscale characteristics via automatic extraction of these characteristics from multidimensional data sources. The nanoscale porosity and oxide texture were finally examined with respect to the corrosion rate, corrosion temperature, and texture of the substrate. The goal was to comprehend their correlations and determine the best strategy to enhance the resistance against corrosion in Zr alloys.
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