In this work, the ultimate strength of aluminum/silicon carbide (Al/SiC) composites was predicted by using acoustic emission (AE) parameters through artificial neural network (ANN) analysis. With this aim, a series of fourteen Al/SiC tensile samples were loaded up to the failure to investigate the amplitude distribution of AE events detected during loading. A back propagation ANN was prepared to correlate the amplitude values generated during loading up to 60% of known ultimate strength with ultimate failure strength of the samples. Three individual neural networks generated with parameters like hits, the Felicity ratio and rise angle were trained towards anticipating the ultimate strength value, which was predicted within the worst case error of-3.51 %,-4.73 %, and-2.73 %, respectively. The failure prediction accuracy by using rise angle as input was found to be slightly better, although the three neural networks all proved effective.

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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.