This paper presents a systematic and general method for Lamb wave-based crack size quantification using finite element simulations and Bayesian updating. The method consists of construction of a baseline quantification model using finite element simulation data and Bayesian updating with limited Lamb wave data from target structure. The baseline model correlates two proposed damage sensitive features, namely the normalized amplitude and phase change, with the crack length through a response surface model. The two damage sensitive features are extracted from the first received S0 mode wave package. The model parameters of the baseline model are estimated using finite element simulation data. To account for uncertainties from numerical modeling, geometry, material and manufacturing between the baseline model and the target model, Bayesian method is employed to update the baseline model with a few measurements acquired from the actual target structure. A rigorous validation is made using in-situ fatigue testing and Lamb wave data from coupon specimens and realistic lap-joint components. The effectiveness and accuracy of the proposed method is demonstrated under different loading and damage conditions.

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.

Corrosion is a critical issue for engineered metallic components in mechanical and aerospace industries. Due to the complexity of aerospace aluminum alloy structure, corrosion is particularly tend to occur and expand in stress concentration areas, such as the edge of a hole, which causes the overall structure to be more likely to fail. In this paper, a Lamb wave-based active sensing method with improved sensors network was used to detect the hole-edge corrosion expansion. A0 wave packet of Lamb wave is extracted from signals, and two damage factors are used as characteristics of the signals. Probabilistic imaging algorithm is used to imaging and quantify the hole-edge corrosion area. Five corrosion extension tests show that the proposed method can effectively locate and quantify the hole-edge corrosion damage expansion of a single-hole structure; furthermore, the normalized amplitude damage index and phase change damage index can be used to predict hole-edge corrosion expansion effectively.

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