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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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A series of 18 tensile coupons were monitored with an acoustic emission (AE) system, while loading them up to failure. AE signals emitted due to different failure modes in tensile coupons were recorded. Amplitude, duration, energy, counts, etc., are the effective parameters to classify the different failure modes in composites, viz., matrix crazing, fiber cut, and delamination, with several subcategories such as matrix splitting, fiber/matrix debonding, fiber pullout, etc. Back propagation neural network was generated to predict the failure load of tensile specimens. Three different networks were developed with the amplitude distribution data of AE collected up to 30%, 40%, and 50% of the failure loads, respectively. Amplitude frequencies of 12 specimens in the training set and the corresponding failure loads were used to train the network. Only amplitude frequencies of six remaining specimens were given as input to get the output failure load from the trained network. The results of three independent networks were compared, and we found that the network trained with more data was having better prediction performance.

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