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The inevitable presence of moisture within a polymer composite has allowed for the development of a novel dielectric nondestructive evaluation (NDE) technique which capitalizes on the behavior of moisture under an applied electromagnetic field. Relative permittivity of water which is bound to the polymer network differ significantly from that of water which is not bound to the network, and the preferential diffusion of this “free” water to damage sites permits the creation of spatial permittivity maps. Presently, this technique has shown capability for damage detection but has not achieved quantification, which is crucial for industry use. The introduction of machine learning algorithms to existing techniques in this field has proven valuable, thus, a machine learning approach for data processing and damage quantification to the existing dielectric technique was developed and applied in this work. BMI/Quartz samples and S2-Glass/Epoxy samples were fabricated and subjected to impact damage via drop tower. The BMI samples were impacted centrally at 9 J and the S2-Glass samples were subjected to two impact events of differing energies, 5 and 3 J. An unsupervised K-means clustering algorithm was applied to the acquired dielectric scans at different gravimetric moisture contents which has provided promising results for all samples. Specifically, within the two impact samples, the algorithm assigned a higher cluster center to the site with more damage, indicating the technique has the capability to both detect and quantify impact damage at all moisture levels examined. 

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.

 

Despite recent advances, the need for improved non-destructive evaluation (NDE) techniques to detect and quantify early-stage damage in polymer matrix composites remains critical. A recently developed microwave based NDE technique which capitalizes on the ubiquitous presence of moisture within a polymer matrix has yielded positive results. The chemical state of moisture directly affects dielectric properties of a polymer matrix composite. Thus, the preferential diffusion of ‘free’ water into microcracks and voids associated with physical damage allows for damage detection through spatial permittivity mapping using techniques that are sensitive to moisture content and molecular water state. While it has been demonstrated that the method can detect damage at low levels of moisture and impact damage, the specific parameters under which the technique will accurately and reliably capture damage within a composite are unknown. The three variables affecting the performance of the method to detect impact damage are moisture content, extent of damage, and resolution of the dielectric scanning technique. Here, we report on the impact of the latter as a function of the two environmental variables (moisture and damage extent). To understand limits and optimize execution of the technique, the interrelationships between each of the variables must be explored. This study investigates the relationship between moisture content and scan resolution. Two BMI/quartz laminates were impacted at 9 Joules to induce barely visible impact damage. The specimens were inspected at a variety of gravimetric moisture levels, and several variations of the spatial permittivity map were created for each moisture level. Detection standards for the technique were investigated based on moisture content and desired scan accuracy; findings showed at 0.05-0.4% moisture content (by wt.) the technique can detect damage location and size with a minimum of 88% accuracy. Pareto frontiers were generated at each moisture level to optimize scan speed and accuracy.

 

Leveraging the state of absorbed moisture within a polymer network to identify physical and chemical features of the host material is predicated upon a clear understanding of the interaction between the polymer and a penetrant water molecule; an understanding that has remained elusive. Recent work has revealed that a novel damage detection method that exploits the very low baseline levels of water typically found in polymer matrix composites (PMC) may be a valuable tool in the composite NDE arsenal, provided that a clear understanding of polymer–water interaction can be obtained. Precise detection, location, and possible quantification of the extent of damage can be performed by characterizing the physical and chemical states of moisture present in an in-service PMC. Composite structures have a locally elevated dielectric constant near the damage sites due to a higher fraction of bulk (“free”) water, which has a higher dielectric constant when compared to water molecules bound to the polymer network through secondary bonding interactions. In this study, we aim to get a clear atomistic scale picture of the interactions which drive the dielectric signature variations necessary for tracking damage. Molecular Dynamics (MD) simulations were used to explore the effect of temperature on the state of moisture in two epoxy matrices with identical chemical constituents but different morphologies. The motivation was to understand whether higher polarity binds a greater fraction of moisture even at higher temperatures, leading to suppressed dielectric activity. Consequently, the influence of secondary bonding interactions was investigated to understand the impact of temperature on the absorbed water molecules in a composite epoxy matrix.