Artificial neural networks to estimate the thermal properties of an experimental micro-alloyed steel and their application to the welding thermal analysis

Abstract

The effect of welding thermal cycles on the microstructure and micro-hardness of the heat-affected zone (HAZ) of an experimental microalloyed steel was studied. Due to the experimental difficulties involved in acquiring the thermal cycles, these were determined by applying the solutions of Rosenthal's equations for thick and thin plates. However, to perform this thermal analysis, it requires knowledge of the thermal properties of the micro-alloyed steel; therefore, the implementation of two artificial neural networks (ANNs) was proposed as tool to estimate the thermal conductivity and the heat capacity as a function of the chemical composition and temperature. The ANNs were trained with information obtained from the literature review and then tested with steels that were not used for the training step, but with thermal properties known. A good approximation between the actual and the estimated properties was observed. It was determined that the microstructural characteristics of the welding zone are a function of the thermal cycles, although there is no great difference in micro-hardness. Martensite was not observed in the welding zone; therefore, the welds of this steel, under these welding conditions, could not be susceptible to hydrogen induced cracking (HIC).