Analysis of failure mode and fracture behavior by using acoustic emission parameter and artificial neural network

Abstract

Acoustic emission (AE) provide snumerous statistics regarding the fracture attitude of distinct materials. In this research AE investigation was handled on 14 numbers of tensile specimens built of aluminum 6061 strengthened by silicon carbide particles at which 100 KN. Universal testing machine is used for tensile testing. The AE parameters are refined from the specimens and only 60% of actual tensile strength parameters are considered for further analysis. Parameters like count, energy, duration, rise time and amplitude are used to characterize the fractures occurred on metal matrix composites due to the low matrix cavitations, particle cracking, interfacial debonding and the transition of mode from tensile to shear. The two individual artificial neural networks generated with the parameters rise angle and by theshear mode of rise angle. The ultimate strength is considered as the anticipated output by training it properly. The structure has the capable to predict the absolute fault up to 2.74% and 1.31%. The shear mode values which occurred at few cycles before the failure of the specimen as inputs are found to be better than the rise angle(RA) data entered, however the prediction exercise proved by the two trained artificial neural network(ANN) has proved its significances.