Microhardness Profile Prediction of Functionally Graded Steels by Artificial Neural Networks

ABSTRACT: In this study, the Vickers microhardness profile of ferritic and aus-tenitic functionally graded steel produced by electroslag remelting process has been modeled by artificial neural networks. To produce functionally graded steels, two slices of plain carbon steel and austenitic stainless steels were spot-welded and used as electroslag remelting electrode. Functionally graded steel containing graded layers of ferrite and austenite may be fabricated via diffusion of alloying elements during the remelting stage. To build the model for predicting the Vickers micro-hardness of graded ferritic and austenitic steels, training, testing, and validation using, respectively, 174 and 120 experimental data were conducted. The utilized data in the multilayer feed-forward neural network models were arranged in a format of seven input parameters that cover the chromium concentration at the first of each layer, chromium concentration at the end of each layer, nickel con-centration at the first of each layer, nickel concentration at the end of each layer, carbon concentration at the first of each layer, carbon concentration at the end of each layer, and the distance of the middle of each layer from the specimen edge. The training, testing, and validation results in the neural network models have shown a strong potential for predicting microhardness profile of both graded ferritic and austenitic steels