FEM and ANN investigation of A356 composites reinforced with B4C particulates

In this research, the mechanical properties of A356 matrix reinforced with B4C particulates were first experimentally investigated and then a combination of artificial neural network (ANN) and finite element technique was implemented in order to model the mechanical properties including yield stress, UTS, hardness and elongation percentage. Microstructural characterization revealed that the B4C particles were distributed between the dendrite branches. The strain-hardening behavior and elongation to fracture of the composite materials appeared very different from that of un-reinforced Al alloy. It was noted that the elastic constant, strain hardening and UTS of the MMCs is higher than that of the un-reinforced Al alloy and increase with increasing of B4C content. It is also revealed that predictions of ANN are consistent with experimental measurements for A356 composite and considerable savings in terms of cost and time could be obtained by using neural network model. The results of this research were used for solidification codes of SUT CAST software.