Neural network prediction of aluminum-silicon carbide tensile strength from acoustic emission rise angle data
| Autor: | Carlo Santulli, T. Sasikumar, Fragassa Cristiano, C Mahil Loo Christopher |
|---|---|
| Přispěvatelé: | Christopher, C. Mahil Loo, Sasikumar, T., Santulli, C., Fragassa, C. |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Materials science
0211 other engineering and technologies chemistry.chemical_element 02 engineering and technology Acoustic emission Rise angle chemistry.chemical_compound Aluminium 021105 building & construction Ultimate tensile strength Silicon carbide Feed forward neural network riseangle Mechanics of Material Composite material Artificial neural network Mechanical Engineering 021001 nanoscience & nanotechnology Strength of materials Felicity ratio chemistry lcsh:TA1-2040 Mechanics of Materials Failure prediction Feedforward neural network lcsh:Engineering (General). Civil engineering (General) lcsh:Mechanics of engineering. Applied mechanics lcsh:TA349-359 0210 nano-technology |
| Zdroj: | FME Transactions, Vol 46, Iss 2, Pp 253-258 (2018) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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