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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. 

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.