Bulk modulus prediction of austenitic stainless steel using a hybrid GA–ANN as a data mining tools

Abstract

In the current paper, the hybrid model based on genetic algorithm (GA) and artificial neural network (ANN) was used as a data mining tool to synthesize the optimal concentration of manganese (Mn). The aim is to achieve the optimal bulk modulus of FeCrNiMn austenitic stainless steel alloy. An ANN model has been developed to analyze and simulate the correlation between the elastic properties and chemical composition. The ANN training has been carried out upon three inputs, namely (Cr, Ni, and Mn), with an alloy weight percentage each, while the bulk modulus is the output target. The fitness function of GA was obtained from trained ANN model. The Mn concentration value has been obtained by the GA–ANN algorithm corresponding at the maximum bulk modulus. More ever, the given result through the GA–ANN was compared to the obtained from quantum mechanical simulation, based on the first principal calculations implemented in the Vienna Ab-initio Simulation Package (VASP). It was found that the relative error is within 2.89%. The results averred that the data mining tool based on the combination of GA–ANN is a useful, efficient, strong and adequate.