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.

Adhesive and its aging can have influence on the Piezoelectric (PZT) elements in structural health monitoring (SHM) systems. However, the current research pays little attention to the effect of adhesives in SHM systems, and the mechanism of adhesive aging on monitoring signals is still unclear. In the present study, the relationship between types of adhesives, adhesive thickness, accelerated aging, and monitoring signal was analyzed in detail. The study was carried out with three kinds of epoxy resin AB adhesive (AW106, E-30CL, and E-120HP) and bonding thicknesses (0.01, 0.05, and 0.12 mm), and the elevated temperature was 100 °C for 45 days. The signal-based monitoring method was used to extract the characteristic parameters. The experimental results indicated that the standard shear strength and thickness of the adhesive may have a combined effect on the monitoring signal. Increasing the thickness may enhance the shear strength between PZT and the matrix. For the effectiveness of monitoring the signal propagation, however, increase in thickness may have a negative influence. Moreover, the elevated temperature will bring the signal amplitude to a peak in the first period of aging, and the highest point appears in 10 to 15 days. Nevertheless, with the increase of aging time, the adhesive will finally be degraded, resulting in the decrease of the signal amplitude. The experimental results may provide useful information for practical monitoring in the SHM field.

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.

Corrosion damage presents significant challenges to the safety and reliability of connected vehicles. However, traditional non-destructive methods often fall short when applied to complex automotive structures, such as bolted lap joints. To address this limitation, this study introduces a novel corrosion monitoring approach using Lamb wave-based weighted fusion imaging methods. First, the Minimum Variance Distortionless Response (MVDR) is utilized to process Lamb wave signals collected under bolt-loosening and bolt-tightening conditions to image the bolt locations. Second, based on the identified bolt positions, the weighted Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID) is applied to the Lamb wave signals acquired before and after corrosion, enabling precise imaging of the actual positions of the corroded bolts. Experiments are conducted on three-bolt lap joints in cases of single-corrosion and two-corrosion using A0 mode Lamb waves and piezoelectric sensor networks. The results demonstrate that the proposed method effectively images multiple types of damage and achieves maximum location deviations of 7.43 mm. This approach enables precise and visual multi-damage assessment, particularly in hard-to-access regions. When integrated with V2X-enabled (Vehicle-to-Everything) systems, the method offers potential for incorporation into automotive structural health monitoring systems for remote diagnosis in complex structures, thereby enhancing monitoring efficiency and accuracy.