During the manufacturing and maintenance of aerospace structures, surface contamination (such as mold release agent residues, oil stains, and fingerprints) and coating anomalies directly compromise adhesive bonding integrity and aerodynamic performance. Conventional visual inspection methods fail to identify thin-layer contaminants, whereas chemical wiping assays suffer from low operational throughput and incomplete area coverage. Hyperspectral imaging addresses this by capturing continuous surface reflectance spectra across the visible to near-infrared (VNIR, 400–1000 nm) and short-wave infrared (SWIR, 1000–2500 nm) spectra, facilitating the identification of diagnostic absorption peaks unique to specific molecular contaminants. For instance, the silicone oil component in mold release agents exhibits distinct C-H bond stretching vibrations in the near-infrared region, while aviation fuel residues display classic hydrocarbon spectral footprints. Utilizing spectral angle mapping (SAM) or spectral unmixing algorithms enables the automated localization and classification of surface contamination zones within the hyperspectral data cube, guiding localized cleaning operations to prevent adhesive bonding failure or coating delamination. For composite surface coating quality assessment (e.g., lightning strike protection copper meshes, anti-static barriers, and topcoats), hyperspectral imaging characterization tracks coating thickness uniformity, micro-pinholes, and regional spallation. Spatial variations in coating thickness alter the relative reflectance contribution between the top coat and the underlying composite substrate. By establishing quantitative thickness-to-spectral feature regression models, engineers can generate false-color thickness distribution maps to guide spray-coating optimization and quality sign-off. The HG-HyperLab laboratory hyperspectral imaging system, developed by Hagorun Technology Limited, integrates advanced spatial and spectral resolutions to deliver high-fidelity specimen scanning under laboratory configurations, providing foundational data matrices for aerospace material quality control workflows. In pre-bonding surface treatment evaluation, hyperspectral metrics assess the efficacy of surface activation treatments, including abrasive grit-blasting and atmospheric pressure plasma processing. Insufficiently activated surface areas display anomalous surface chemistry profiles that deviate spectrally from properly prepared zones, providing a non-destructive verification tool to ensure structural bond strength conforms to design parameters.

Carbon fiber reinforced polymers (CFRP) are extensively implemented in primary aerospace structural assemblies; however, internal flaws such as delamination, porosity, impact fractures, and foreign object inclusions pose severe structural safety risks. Hyperspectral imaging leverages the high sensitivity of near- and short-wave infrared wavebands to internal structural variations to achieve non-destructive diagnostics of surface and near-surface defects. The presence of internal delamination or micro-void clusters alters localized light scattering and refractive index distribution profiles, introducing measurable spectral anomalies in specific bands—most notably around the moisture and resin absorption bands near 1200 nm, 1400 nm, and 1900 nm. Although the penetration depth of optical hyperspectral sensors is physically constrained (typically spanning hundreds of micrometers to several millimeters depending on matrix properties), it demonstrates robust detectability for near-surface impact fractures, surface ply delamination, and adhesive layer voids, serving as a rapid wide-area pre-screening tool to guide localized ultrasonic testing. For barely visible impact damage (BVID), hyperspectral imaging can map the geometric extent and structural severity of the impact zone without stripping decorative surface coatings, capturing the subtle spectral shifts induced by subsurface fiber breakage, matrix cracking, and localized micro-voids. Compared to ultrasonic C-scans, which require liquid coupling agents and exhibit slow scanning velocities, hyperspectral systems complete large-area scans within seconds, rendering them uniquely suited for rapid pre-flight skin inspections of in-service aircraft. The HG-HyperLab hyperspectral imaging system from Hagorun Technology Limited can be configured with automated linear translation stages or rotational gantry mounts to accommodate curved aerodynamic geometries, outputting intuitive, spatially co-registered visualizations of structural damage zones. Regarding composite adhesive bond evaluation, hyperspectral imaging resolves uneven adhesive layer thickness, internal voids, and disbonding boundaries. Because the spectral response of the adhesive matrix displays diagnostic contrasts against the composite substrate within the SWIR spectrum, hyperspectral decomposition algorithms can quantitatively map adhesive coverage and verify bonding reliability.

During operational deployment, aerospace composites undergo prolonged exposure to solar ultraviolet (UV) radiation, hygrothermal cycling, and chemical fluid exposure, driving structural performance degradation through resin matrix aging and fiber/matrix interfacial degradation. Hyperspectral imaging quantifies this degradation by monitoring diagnostic spectral shifts linked to aging by-products, such as changes in the carbonyl index and modifications to the resin's foundational chemical architecture. Photo-oxidation of epoxy matrices induced by UV exposure yields carbonyl compounds, introducing new absorption bands or modifying the absorption amplitude of native polymer peaks within the SWIR region. Concurrently, hygrothermal aging induces moisture absorption within the resin, shifting the spectral reflectance footprint within the classic 1400 nm and 1900 nm water absorption bands. Characterizing these structural spectral variances against pristine reference baselines supports the development of predictive degradation models to estimate structural remaining useful life (RUL) and schedule optimal maintenance overhauls. Within aerospace composite patch repair validation, hyperspectral imaging verifies spectral consistency between the patch repair zone and the surrounding pristine structure, serving as a diagnostic indicator of resin curing completeness and interfacial bonding state. Insufficient cross-linking of the repair matrix or material mismatches manifest as distinct spectral anomalies, allowing quality inspectors to isolate substandard structural repairs. Furthermore, hyperspectral characterization plays a key role in composite raw material identification and supply chain traceability. Distinct carbon fiber grades and specific resin matrix formulations display unique spectral signatures. High-throughput hyperspectral screening verifies material lots and types rapidly, eliminating material mix-ups that could introduce catastrophic performance non-conformities into components. The HG-HyperLab laboratory hyperspectral imaging system from Hagorun Technology Limited is accompanied by a dedicated spectral data processing software suite that supports spectral feature extraction, classification model deployment, and automated inspection report generation, providing a robust analytical framework for aerospace composite research and industrial quality control.

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