PREDICTING THERMAL CONDUCTIVITY OF STEELS USING ARTIFICIAL NEURAL NETWORKS

The data on the physical properties of steels which depend on temperature are needed for the calculation and simulation of heating and cooling processes. A method for predicting thermal conductivity of steels at elevated temperatures (up to 700 degrees C) from the known steel chemical composition has been developed, and the results obtained by the simulation are given. A static multi-layer feed-forward artificial neural network with the back propagation training function and Levenberg-Marquardt optimization was used to predict the coefficient of thermal conductivity of steels. In order to prevent the over fitting the early stopping method was applied. The following groups of steel were included in the model: structural steels, hot-work tool steels, high-speed steels, stainless steels, heat resistant steels austenitic steels for elevated temperatures, and cobalt alloyed steels and alloys for elevated temperatures. The mean absolute error in predicting thermal conductivity and the standard deviation were found to be very acceptable.