ANN model for prediction of the effects of composition and process parameters on tensile strength and percent elongation of Si–Mn TRIP steels

The effects of composition and intercritical heat treatment parameters on tensile strength and percentage elongation of Si–Mn TRIP steels were modeled, using a neural network with a feed forward topology and a back propagation algorithm. It was found that a committee of nets models the experimental data more accurately than a single model. The trained network was then applied to a low-carbon low-silicon steel in order to estimate the appropriate heat treatment process conditions. To explain variations in the mechanical properties, the material was subjected to a typical two-stages intercritical annealing and bainitic holding treatment. According to the results of model, tempering of material for a shorter time results in higher tensile strength and percentage elongation values. This behavior was later confirmed by microstructural studies and was attributed to both higher austenite volume fraction and higher martensite content in the samples tempered for a shorter bainitic holding.