Abstract
The aim of this study was to investigate the Type-I hot corrosion performance of Ti–48Al–1Mo–2Cr and Ti–48Al–1Mo–2Mn (at.%) alloys in the Na2SO4–25K2SO4 (wt.%) molten salt environment at 900C for 180 h under cyclic conditions, and the applicability of artificial neural network (ANN) modeling for the prediction of the corrosion behavior of these alloys. In addition, this study elucidates the effect of Cr and Mn additions on corrosion resistance of the TiAl in the molten salt mixture. Corrosion kinetics indicated that the Mn-added alloy has poor corrosion resistance compared to Cr-added alloy. X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive spectroscopy (EDS) were conducted to characterize the corrosion scale formed on the alloys. The XRD patterns showed the corrosion products are composed of TiO2, Al2O3 and Na2Ti3O7. The inspects of surface and cross-section showed that the corrosion products of both alloys have porous and loose attributes due to corrosive molten salt mixture. The modeling results revealed a good agreement between experimental and prediction results.
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