Abstract Acoustic emission offers various data concerning the fracture behavior of different materials. In this paper AE study was conducted on 14 numbers of tensile specimens made of Al6061 reinforced with silicon carbide particles in which tensile loading is done up to failure with a 100 kN universal testing machine. AE parameters are filtered from the specimens and solely 60 % of actual failure loading 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 three individual artificial neural networks generated with the AE parameters like hits, felicity ratio and rise angle were trained properly towards the ultimate strength as the anticipated output. The network was able to predict the worst case error of – 3.51 %,-4.73 %, and -2.73 %. The failure prediction accuracy by using rise angle as input found to be better, however the three trained artificial neural networks have also proven its significance towards the prediction exercise.
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Crack Growth Detection on Al/Sicp Using Acoustic Monitoring and Artificial Neural Network
Semantic Scholar · Materials Science · 2019
Abstract
Abstract Acoustic emission offers various data concerning the fracture behavior of different materials. In this paper AE study was conducted on 14 numbers of tensile specimens made of Al6061 reinforced with silicon carbide particles in which tensile loading is done up to failure with a 100 kN universal testing machine. AE parameters are filtered from the specimens and solely 60 % of actual failure loading 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 three individual artificial neural networks generated with the AE parameters like hits, felicity ratio and rise angle were trained properly towards the ultimate strength as the anticipated output. The network was able to predict the worst case error of – 3.51 %,-4.73 %, and -2.73 %. The failure prediction accuracy by using rise angle as input found to be better, however the three trained artificial neural networks have also proven its significance towards the prediction exercise.