Prediction of effect of thermo-mechanical parameters on mechanical properties and anisotropy of aluminum alloy AA3004 using artificial neural network

An artificial neural network model, using a back-propagation learning algorithm is utilized, to predict the yield stress, elongation, ultimate tension stress, image and ∣ΔR∣ during hot rolling, cold rolling and annealing of AA3004 aluminum alloy. Input nodes were chosen as the ratio of initial to final thicknesses, reduction, preheating time and temperature, finish rolling temperature and the final annealing temperature. The maximum error for predicted values was 6.35%, the average of absolute relative error was 0.57% and the RMS was 0.00998. It was found that the mechanical properties and anisotropy of AA3004 alloy sheets can be predicted by this approach.