Artificial neural networks modeling of mechanical property and microstructure evolution in the Tempcore process

In this study, the microstructures and the mechanical properties of steel bars treated by the Tempcore process have been investigated. In the Tempcore process, AISI 1020 steel bars of various diameters were used. In bars, unlike the self-tempering temperature and the extent of elongation, an increase in the amount of martensite was observed, which caused a consequential increase in yield and tensile strength as a function of quenching duration. The amounts of martensite, bainite, pearlite and the values of elongation, self-tempering temperature, yield and tensile strength could be obtained by a new and fast method, by using artificial neural networks. A pascal computer program has been developed for this study. In the numerical method, bar diameters and quenching durations were chosen as variable parameters. The numerical results obtained via the neural networks were compared with the experimental results. It appears that the agreement is reasonably good.