Artificial neural network prediction model for impact energy of thermal aged cast stainless steel

Abstract EN

Impact energy prediction of thermal aged cast stainless steel from impact test was studied using artificial neural network (ANN) modeling.

Impact energy data for specimens from eleven cast stainless steel alloys at different aging times and temperatures, were used to evaluate possible artificial neural network architecture for prediction impact energy.

These data are taken from Argonne National Laboratories (ANL) in USA that involved impact test results of cast stainless steel after aging between 200 and 400 oC for up to 30000 hour.

The ANN model exhibited excellent comparison with experimental results of ANL i.

e.

correlation coefficient (R = 0.9451) and mean square error (MSE=1.2*10-5).

Since a large number of variables were used during training the ANN model, a reliable and useful predictor for impact energy in thermal aged cast stainless steel was provided.