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Abstract The rapid increase in use of polymer matrix composites in different industries underscores the need for reliable non‐destructive evaluation techniques to characterize small‐scale damage and prevent structural failure. A novel dielectric technique exploits moisture‐polymer interactions to identify and track damage, leveraging differences in dielectric properties between free and bound water. This technique has demonstrated the ability to detect low levels of damage, but the localization accuracy has not yet been evaluated. This work utilizes unsupervised machine learning to assess the technique's ability to identify the damage boundary following a low‐velocity impact event. Bismaleimide/quartz and E‐glass/epoxy laminates were impacted via drop tower to induce varying levels of damage, and subsequently inspected via dielectric technique at several moisture levels by weight. Resulting data was processed via k‐means clustering and the identified damage boundary was compared to a boundary obtained from backlit images and scanning electron microscopy. Accuracy was quantified using developed metrics for damage centroid and boundary identification. The technique averaged 93.9% accuracy in determining the damage center and 77.5% accuracy in identifying the damage boundary. Results indicated the technique's effectiveness across varying moisture levels, particularly in damage centroid identification. Localization accuracy was shown to be insensitive to moisture content, improving the technique's practical capabilities. Further analysis revealed potential for delineation of delaminations. HighlightsLow‐velocity impact of two material architectures.Damage boundary determined and validated via scanning electron microscopy.Detected damage site via dielectric technique compared to damage boundary.High technique accuracy revealed; >90% centroid localization accuracy.Potential for delamination delineation observed. 

Largely due to superior properties compared to traditional materials, the use of polymer matrix composites (PMC) has been expanding in several industries such as aerospace, transportation, defense, and marine. However, the anisotropy and nonhomogeneity of these structures contribute to the difficulty in evaluating structural integrity; damage sites can occur at multiple locations and length scales and are hard to track over time. This can lead to unpredictable and expensive failure of a safety-critical structure, thus creating a need for non-destructive evaluation (NDE) techniques which can detect and quantify small-scale damage sites and track their progression. Our research group has improved upon classical microwave techniques to address these needs; utilizing a custom device to move a sample within a resonant cavity and create a spatial map of relative permittivity. We capitalize on the inevitable presence of moisture within the polymer network to detect damage. The differing migration inclinations of absorbed water molecules in a pristine versus a damaged composite alters the respective concentrations of the two chemical states of moisture. The greater concentration of free water molecules residing in the damage sites exhibit highly different relative permittivity when compared to the higher ratio of polymer-bound water molecules in the undamaged areas. Currently, the technique has shown the ability to detect impact damage across a range of damage levels and gravimetric moisture contents but is not able to specifically quantify damage extent with regards to impact energy level. The applicability of machine learning (ML) to composite materials is substantial, with uses in areas like manufacturing and design, prediction of structural properties, and damage detection. Using traditional NDE techniques in conjunction with supervised or unsupervised ML has been shown to improve the accuracy, reliability, or efficiency of the existing methods. In this work, we explore the use of a combined unsupervised/supervised ML approach to determine a damage boundary and quantification of single-impact specimens. Dry composite specimens were damaged via drop tower to induce one central impact site of 0, 2, or 3 Joules. After moisture exposure, Entrepreneur Dr, Raleigh, North Carolina 27695, U.S.A. 553 each specimen underwent dielectric mapping, and spatial permittivity maps were created at a variety of gravimetric moisture contents. An unsupervised K-means clustering algorithm was applied to the dielectric data to segment the levels of damage and define a damage boundary. Subsequently, supervised learning was used to quantify damage using features including but not limited to thickness, moisture content, permittivity values of each cluster, and average distance between points in each cluster. A regression model was trained on several samples with impact energy as the predicted variable. Evaluation was then performed based on prediction accuracy for samples in which the impact energies are not known to the model. 

Safety has long been a key factor in the design, manufacturing and maintenance of products that are made from composite materials. The exceptional properties these materials exhibit compared to their metal counterparts is enabling widespread adoption across civil infrastructure, oil & gas, marine, automotive, and aerospace industries. But the lack of a definitive and accurate technique to predict damage progression in a polymer-matrix composite (PMC) during their service life continues to pose a major risk and creates a gap in the long-term integrity of the structures produced. Although there is widespread consensus regarding the deleterious effects of the ingressed moisture on the overall properties of a composite, recent studies have revealed that the inevitable presence of moisture in a PMC can be leveraged for damage characterization. This work aims to employ Near-Infrared spectroscopy for quantifying molecular moisture in polymer composites for submicron scale damage detection. Prior to moisture absorption, a drop tower was used to induce a barely visible impact damage (BVID) in the center of dry E-glass/epoxy specimens. Three different specimens were subjected to 1J, 1.5J, and 2J of damage, respectively. The NIR Nano EVM Spectrophotometer was used to obtain spectral scans between wavelengths of 900-1700 nm for each of the three damaged samples, as well as an undamaged sample, in their dry state. The samples were then exposed to moisture contamination via water bath, and subsequent spectral scans were acquired at consistent intervals of gravimetric moisture gain. The spatial variation of the moisture content was evaluated from the characteristic peak for water in the damaged samples at various levels of absorbed moisture. The absorbance area obtained from the NIR spectral shows quantitative values to represent increasing damage and spatial maps indicating different states of absorbed moisture in each sample.