Application of artificial neural networks for quantitative damage detection in unidirectional composite structures based on Lamb waves
| Autor: | Weifang Zhang, Rongqiao Wang, Cheng Qian, Bo Sun, Yunmeng Ran, Jingjing He, Yi Ren |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Damage detection
Artificial neural network Computer science Mechanical Engineering Acoustics lcsh:Mechanical engineering and machinery Composite number Artificial neural network model 02 engineering and technology 021001 nanoscience & nanotechnology 020303 mechanical engineering & transports Lamb waves 0203 mechanical engineering lcsh:TJ1-1570 0210 nano-technology |
| Zdroj: | Advances in Mechanical Engineering, Vol 12 (2020) |
| ISSN: | 1687-8140 |
| Popis: | This article provides a quantitative nondestructive damage detection method through a Lamb wave technique assisted by an artificial neural network model for fiber-reinforced composite structures. For simulating damages with a variety of sizes, rectangular Teflon tapes with different lengths and widths are applied on a unidirectional carbon fiber–reinforced polymer composite plate. Two characteristic parameters, amplitude damage index and phase damage index, are defined to evaluate effects by the shape of the rectangular damage in the carbon fiber–reinforced polymer composite plate. The relationships between the amplitude damage index and phase damage index parameters and the damage sizes in the carbon fiber–reinforced polymer composite plate are quantitatively addressed using a three-layer artificial neural network model. It can be seen that a reasonable agreement is achieved between the pre-assigned damage lengths and widths and the corresponding predictions provided by the artificial neural network model. This shows the great potential of using the proposed artificial neural network model for quantitatively detecting the damage size in fiber-reinforced composite structures. |
| Databáze: | OpenAIRE |
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