Crack Growth Detection on Al/Sicp Using Acoustic Monitoring and Artificial Neural Network
| Autor: | C. Mahil Loo Christopher, T. Sasikumar, Tom Page |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
010302 applied physics
Universal testing machine Materials science Artificial neural network 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Shear (sheet metal) Cracking Acoustic emission Rise time 0103 physical sciences Ultimate tensile strength Fracture (geology) Composite material 0210 nano-technology |
| Zdroj: | Materials Today: Proceedings. 16:604-611 |
| ISSN: | 2214-7853 |
| DOI: | 10.1016/j.matpr.2019.05.135 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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