Neural network based smart damage deduction using a fiber optic sensor for aluminium 6063 cantilever beam
| Autor: | A.S. Wali, Amit Tyagi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
010302 applied physics
Optical fiber Cantilever Materials science Artificial neural network Acoustics Physics::Optics 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences law.invention Amplitude Transmission (telecommunications) law Fiber optic sensor Frequency domain 0103 physical sciences 0210 nano-technology Beam (structure) |
| Zdroj: | Materials Today: Proceedings. 21:1412-1416 |
| ISSN: | 2214-7853 |
| DOI: | 10.1016/j.matpr.2019.08.181 |
| Popis: | This paper presents the experimental measurement of frequency domain parameters like real part, amplitude and phase change with the help of optical fiber sensor by using surface mounted transmission type optical fiber. The aluminum beam is considered as a host material in this study. Frequency domain optical parameters are used as input to the designed neural network and the output parameter is considered as the ratio of the notch location distance to the total length of the beam. Variation of the notch location is studied under different static loadings. The excellent performance of the model is observed which confirms the notch location prediction capability of the developed model. |
| Databáze: | OpenAIRE |
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