Vibration Based Delamination Detection in Fiber Metal Laminates Composite Beam
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Abstract
Delamination is typical damage in the Fiber Metal Laminate composite structures, usually hidden from the outer side that can reduce the structural stiffness. The delamination is undoubtedly an important topic as it causes to worsen the performance of the Fiber metal laminates (FMLs)structures in the service. The detection and severity analysis of delamination in a field like the aviation industry is vital for safety and economic considerations. The existence of delamination varies the vibration characteristics, such as natural frequencies, mode shapes, etc., of composites. Hence, this indication can be effectively used to locate and quantify the delamination. The changes in vibration characteristics are inputs for the inverse problem to determine the location and size of delamination. This paper used a machine-learning and regression model to determine the locations and severity of the delamination in the Fiber Metal laminate cantilever beams. The dataset related to delamination location, severity, and bending natural frequencies was obtained using the Finite Element Analysis. From this study, it is found that, the machine learning and regression model results related to predictions of delamination locations and severity are close to each other and give good agreement with actual delamination locations and delamination areas.
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