Application of Box-Behnken method based ANN-GA to prediction of wt.% of doping elements for incoloy 800H coated by aluminizing chromizing

Abstract EN

In this work , an effective procedure of Box-Behnken based-ANN (Artificial Neural Network) and GA (Genetic Algorithm) has been utilized for finding the optimum conditions of wt.% of doping elements (Ce,Y, and Ge) doped-aluminizing-chromizing of Incoloy 800H .

ANN and Box-Behnken design method have been implanted for minimizing hot corrosion rate kp (10-12g2.cm-4.s-1) in Incoloy 800H at 900oC .

ANN was used for estimating the predicted values of hot corrosion rate kp (10-12g2.cm-4.s-1) .

The optimal wt.% of doping elements combination to obtain minimum hot corrosion rate was calculated using genetic algorithm approach .

The predicted optimal values for minimizing hot corrosion rate for Incoloy 800H coated by (Ce-Y-Ge) doped-aluminizing-chromizing are (3wt.%Ce, 3wt.%Y, and 3wt.%Ge) , the hot corrosion rate kp (10-12g2.cm-4.s-1) value at these conditions was found to be 71.701 .

The results have been verified by confirmation experiment , results obtained by GA method match closely with experimental values (R2=98.30) .

EDS and XRD results show that the formation of protective layers Al2O3 and Cr2O3 during hot corrosion tests.