A new artificial neural network model for predicting fatigue limit and fracture toughness values of some stainless steels

Abstract

Aim of this paper is to present the possibility for application of Artificial Intelligence for determining fracture toughness and fatigue limit values of some grades of stainless steels. Experimental procedures for both, fracture toughness and fatigue limit determination are time consuming, thus application of artificial intelligence instead of long, time exhausting experiment could result in less time spent waiting on experimental results. For this purpose, two Artificial Neural Networks (ANN) with same architecture were created and applied. Above mentioned properties are determined for the austenitic stainless steel X5CrNiMo17-12-2 and ferritic stainless steel X6Cr17. Complete work regarding ANN was conducted in Mathworks MATLAB 2017 software using nntool module. After completed training of ANN when adequate regression levels were reached, simulations were conducted using chemical composition of X5CrNiMo17-12-2 and X6Cr17 steels. Obtained results were compared with existing data. Conclusion that was drawn is that ANN that predicts KIC values has greater precision than ANN for fatigue limit. Potential reason for that could be that input layer needs more input data to increase precision.