Visible Impact Damage Research Articles

Machine learning based segmentation of delamination patterns from sparse ultrasound data of barely visible impact damage in composites

Composite laminates, known for their strength and flexibility, are widely used but can delaminate under certain barely visible impact. Ultrasound Transmission (UT) scans can detect such failures, but the noise in the scans makes automation of delamination morphology challenging. Machine learning could solve this, but it requires substantial training data. Furthermore, given the considerable time and cost of conducting impact tests on composite laminates, such data remains sparse. This study overcomes data sparsity by enlarging UT scan dataset using image augmentation techniques and synthetic data generation methods. A combination of rotation and elastic deformation of real UT images produced augmented data. Synthetic data was generated by mimicking the statistical variations in real data, including delamination length and rotation per ply, and adding gradients to match the original image noise. A U-Net based machine learning segmentation approach was utilized to capture local and global information through a series of convolutions to apply per-pixel classification. Systematic experimentation was conducted to study the influence of adding augmented and synthetic data on segmentation accuracy. The results show that the use of augmented data increases accuracy significantly. With the addition of synthetic data to the augmented data, there is a marginal improvement in accuracy. However, feature edge accuracy is significantly improved. The preliminary results presented here show the promising use of machine learning techniques for complex, non-destructive inspection methods.

Predictive modeling for hat-stiffener delamination during low velocity impact

Barely visible impact damage poses a significant threat to composite structures, particularly prevalent in the aerospace industry. The occurrence of low velocity impact (LVI) events can lead to substantial deformations in composite structures, causing interlaminar delamination and diminishing their compression load carrying capacity. Beyond LVI-induced interlaminar delamination, it is crucial to investigate failures occurring between co-cured composite structures. This study focuses on hat-stiffened coupon structures, to investigate the delaminating phenomenon between the hat and skin components. The outcomes of experimental results inform the development of a finite element model, enabling to capture the structural response and delamination dynamics. The primary objective of the experiments is to understand the delamination process which occurs suddenly upon impact. LVI experiments are conducted to understand the structural behavior at varied impact locations. A mesh study establishes the relationship between cohesive parameters and element size in the model to enable computational efficiency by avoiding the need for a highly refined mesh. When the mesh is relatively coarse, the finite element model’s capability to resolve stresses at near singular locations is limited, however the computational time is lower. Therefore, the balance between computational efficiency on the one hand, and resolving gradients on the other, requires a fine balance in order to carry out a large number of design iterations efficiently. This research significantly contributes to advancing our understanding of delamination mechanisms at free edges in composite structures, offering valuable insights for computational modeling.

Accelerated strategies for impact identification of composite laminate using sparse sensor networks and model-based virtual time reversal

Barely visible impact damages are notorious threats to the health of composite structures. Detection of the impact is critical to promise the performance of composite structures. Recently, the authors proposed an impact identification method based on sparse sensor networks and virtual time reversal, which is verified on composite laminates. However, the computational demands of virtual time reversal simulations pose a notable limitation to this approach. To improve the efficiency of the method, several acceleration measures are developed in this article. First, the re-emitted wavefield analysis is accelerated using a frequency-domain method rather than the time-domain simulation, which largely reduces the computational costs. Subsequently, the GPU parallel acceleration algorithm is configured to further improve the efficiency. The proposed acceleration strategy is experimentally validated by various impact events on a composite laminate. The results render that the proposed acceleration strategy can promise the accuracy of the results of impact identification while significantly promoting computational efficiency, in which the time consumption plummets to only 1.94 s. Moreover, factors affecting the efficiency of the proposed method are discussed. The time consumption is positively correlated with the number of sensors involved in the identification. In addition, as the number of candidate points increases, the GPU encounters difficulty in handling all complex matrix operations simultaneously, leading to a substantial rise in computation time. Furthermore, the effect of signal lengths on the efficiency is minimal and largely negligible.

Broadband nonlinear delay and sum (NL-DAS) for baseline-free damage imaging using a sparse sensor network

The Delay and Sum (DAS) method is a well-known damage imaging technique in sparse array guided wave imaging. However, the DAS method requires prior knowledge on the stable baseline state, which makes it ineffective under different operational or environmental conditions, e.g. temperature and moisture, and altered experimental settings, e.g. degraded bonding quality of sensors.This paper proposes a Nonlinear Delay and Sum (NL-DAS) method which does not require prior knowledge about the baseline state, but instead exploits the presence of nonlinear wave/defect interactions. Relatively high-power broadband sweep sine signals are supplied to a sparse sensor array to activate a multitude of nonlinear wave/defect interactions. Application of time–frequency filtering to the response signals allows the isolation of damage-induced broadband nonlinear responses. The isolated broadband nonlinear response signals are subsequently decomposed into a series of narrowband tone burst responses from which a set of damage maps are constructed. To improve the quality of the constructed damage maps, an automated framework is proposed to obtain the frequency-dependent directional group velocities without requiring prior knowledge on material properties. Finally, the resulting set of damage maps is fused into a single broadband NL-DAS damage map.The proposed broadband NL-DAS approach is demonstrated on a simulation dataset, generated with 3D Finite Element method, which is representative for a cross-ply carbon fiber reinforced polymer (CFRP) containing a delamination defect. The damage imaging performance is studied for different test conditions in terms of (i) signal-to-noise ratio, (ii) number of sensors and (iii) excitation bandwidth. Experimental validation is illustrated on a CFRP plate containing a barely visible impact damage, and on a CFRP A320 component with a disbond at one of the stiffeners.

In-situ detection of delamination reinitiation in carbon fiber reinforced polymers post barely visible impact damage

In this study, advancements are presented in the in-situ detection of delamination reinitiation from Barely Visible Impact Damage (BVID) in composite materials, utilizing enhancements in Digital Image Correlation (DIC) techniques during a Compression After Impact (CAI) test. The study measured strain fields in the longitudinal, transverse, and shear directions, focusing specifically on the point of highest out-of-plane displacement to identify the onset of delamination propagation from BVID sites generated at different impact energy levels. By correlating the measured strains with the peak out-of-plane displacement, a unique determination of onset damage reinitiation associated with BVID during CAI testing was achieved. This method introduces a refined in-situ assessment technique for structural integrity, allowing for the early detection of critical damage propagation in composite materials.

Bridging the Gap: Correlating Ultrasonically Quantified BVID with the Compressive Strength of CFRP Composites

ABSTRACT Carbon fiber laminates are susceptible to impact damage due to the lack of fibers in the direction of impact. Often such damage can be difficult to find with visual assessment, prompting the need for detection via nondestructive testing techniques. This study quantifies the ultrasonically characterized impact damage diameters in 22 layered CFRP samples from 16 J, 18 J, 20 J, 22 J, and 24 J impact energies. These energies result in damage often defined as barely visible impact damage. This study utilizes x-ray computed tomography as a baseline to verify the results of the ultrasonic testing. Compression after impact test measurements are used in this study to find the ultimate compressive residual strength of the impacted carbon fiber laminates and to investigate the negative correlation between impact energy and residual mechanical properties of the damaged carbon fiber laminates. Many studies focus on the characterization of impact damage from various impact energies and setups via ultrasonic testing or, separately, the effect of the impact setups on the residual strength of the composite. This study strengthens the bridge between the barely visible impact damage quantified via ultrasonic testing and the residual compressive strength of the affected composite. Overall, this paper offers further insights into the relationship between internal damage size obtained using ultrasonic testing and correlates the characterized internal damage geometry to the resultant compressive strength reduction, providing a path for future studies in predictive modeling of the residual strength after impact.

Characterisation and Application of Bio-Inspired Hybrid Composite Sensors for Detecting Barely Visible Damage under Out-of-Plane Loadings.

Traditional inspection methods often fall short in detecting defects or damage in fibre-reinforced polymer (FRP) composite structures, which can compromise their performance and safety over time. A prime example is barely visible impact damage (BVID) caused by out-of-plane loadings such as indentation and low-velocity impact that can considerably reduce the residual strength. Therefore, developing advanced visual inspection techniques is essential for early detection of defects, enabling proactive maintenance and extending the lifespan of composite structures. This study explores the viability of using novel bio-inspired hybrid composite sensors for detecting BVID in laminated FRP composite structures. Drawing inspiration from the colour-changing mechanisms found in nature, hybrid composite sensors composed of thin-ply glass and carbon layers are designed and attached to the surface of laminated FRP composites exposed to transverse loading. A comprehensive experimental characterisation, including quasi-static indentation and low-velocity impact tests alongside non-destructive evaluations such as ultrasonic C-scan and visual inspection, is conducted to assess the sensors' efficacy in detecting BVID. Moreover, a comparison between the two transverse loading types, static indentation and low-velocity impact, is presented. The results suggest that integrating sensors into composite structures has a minimal effect on mechanical properties such as structural stiffness and energy absorption, while substantially improving damage visibility. Additionally, the influence of fibre orientation of the sensing layer on sensor performance is evaluated, and correlations between internal and surface damage are demonstrated.

Experimental and numerical investigation on detection of BVID in CFRP composite plates using vibro-acoustic modulation

ABSTRACT Carbon fibre reinforced plastic (CFRP) composite has gained popularity due to the advantages such as its high strength-to-weight ratio and high corrosion resistance, but susceptibility to barely visible impact damage limits their use in engineering. This study experimentally and numerically investigates the detection of BVID in CFRP composite plates using Vibro-acoustic modulation (VAM) technique. An experimental system of VAM was constructed for measuring sidebands induced by the nonlinear effects of BVID. An integrated finite element (FE) model is developed to further explore the nonlinear interaction between the BVID, low-frequency pump, and high-frequency probe waves. To address this issue of the mesh size and computational time step mismatch caused by the large difference in frequency between the pump and probe waves, a regionally refined mesh was employed at the BVID region. Furthermore, the effects of BVID’s size and depth on the modulation were discussed. Through the Hilbert-Huang Transform (HHT), the amplitude modulation (AM) and frequency modulation (FM) indexes were extracted from the VAM responses to quantitatively characterise the impact damages. The results demonstrate that the MIA index increases steadily with the exciting voltage and damage degree, making it a suitable indicator to accurately assess the impact damage in CFRP composites.

Low‐velocity impact characterization of polyurethane rubber/nano‐clay enriched sustainable sandwich composites: Synergy of experimentation and simulations

AbstractThis research proposes a novel sustainable composite using basalt (B), hemp (H) and polyurethane rubber (Pu) reinforced in nano clay functionalised epoxy matrix for sacrificial structural applications prone to low‐velocity impact (LVI). To this end, symmetric and asymmetric laminates such as HHHH, BBBB, BHHB, BHPuHB, BBPuHH and BPuBPuHPuH are fabricated using compression molding techniques and subjected to LVI at three different impact energies of 19.66, 39.39, and 59.05 J. The hemp fibers were treated with NaOH solution before fabrication. Material characterization such as X‐ray diffraction, Raman spectroscopy and morphological studies has been carried out. The impact and post‐impact properties of the proposed composites are experimentally evaluated and validated with the finite element (FE) results. The effect on the residual tensile strength degradation of laminates at different interlayers and energy levels is also investigated using the Caprino analytical model. The barely visible impact damages (BVID) are investigated through non‐destructive dye‐penetration tests, which facilitate easy identification of the prominent LVI damages like “Plateau” and “Cliff‐drop” impressions. Based on the impact energy absorption and residual tensile strength, proposed laminates followed BHPuHB > BBBB > BHHB > HHHH. The experimentation suggests that Polyurethane core laminates support maximum impact energy absorption by favoring a structural change in interlayers. Also, the residual tensile strength decreases as impact velocity increases.Highlights Low‐velocity impact behavior of sustainable composites is experimented. Basalt, hemp and polyurethane rubber are reinforced in nano clay epoxy matrix. A FE framework to validate the experimentation is proposed. Dye penetrant NDT is adopted to investigate the damages. Morphological studies are conducted to understand LVI responses.

A Composite Fuselage under Mechanical Load: a case study for Guided Wave-based SHM

The introduction of composite materials in aeronautics has brought numerous advantages, along with unique damage and failure modes. The structure health is currently ensured by a damage-tolerant design and non-destructive inspections. Among other techniques, Guided Wave-based Structural Health Monitoring (GW-SHM) has gained interest as a cost and time effective alternative to traditional non-destructive techniques. One of the main challenges for GW-SHM is the influence of environmental and operational conditions on the damage identification capability. Aircraft structures undergo a broad range of mechanical load conditions, affecting the GW-SHM system. The study of load influence is meaningful in view of applications where the SHM system is expected to work under varying load conditions. Such applications may be in-flight monitoring, but also on-ground only usage and structural test monitoring. A full-scale CFRP door surrounding structure was instrumented with a GW-SHM system and tested under mechanical load. A hydraulic test rig was used to apply three representative load cases in quasi-static conditions on the structure. A network of robust piezocomposite transducers to monitor the structure has been designed and manufactured. The network is organized in arrays, which include the transducers, cabling and a connecting base plate for optimized sensor installation. A multiplexing module is directly connected to the base plate enabling a fast and reliable sensor connection, a drastic cable weight reduction, and a modular design. The combined influence of load and temperature on the propagation of Guided Waves has been assessed. Finally, a data-driven approach to damage identification has allowed the detection and localization of barely visible impact damage introduced during the test campaign.

Consequences of thermal aging on the detection of impact damage in composite structures with PZT/FBG guided wave systems

In the context of recent reusable launch vehicles (RLV) developments, SHM could help fast and economic revalidation of RLVs between launches, provided that the transducers keep their functionality and reliability after repeating launches being exposed to harsh flight conditions. This paper studies the detection of barely visible impact damage in composite plates with guided waves (GW) based SHM systems. More precisely, it focuses on how this detection can be affected by the degradation of the sensors potentially induced by the exposition to flight conditions. The aging conditions in this work mainly consist of thermal cycling with high maximum temperature (150°C), expected for RLV structures and close to the operational limit of the thermoset-matrix composite. The response of the sensors to such environmental conditions depends on the sensor itself and on its bonding to the structure. Thus, in this paper two different types of sensors are tested (commonly used PZT transducers and FBG sensors) along with two different bonding methods (classic bonding with a secondary adhesive and cobonding on composite surface during the curing process). The sensors are setup in pairs (PZT-PZT or PZT-FBG) with GW signal paths going through the impact location. After a baseline acquisition, incremental impact tests (with increasing energy) are performed and GW signals are acquired after each impact to assess the damage signature on the waveform. Then after submitting the plate to thermal cycling, GW signals are recorded again to assess its effect on the damage detection performance and on the risk of false alarms (mistaking sensor degradation for structural damage). Sensor degradations are monitored through non-destructive techniques and impedance self-diagnostic measurements for PZT. Additionally to this aging test, the responses of degraded sensors previously studied by the authors are compared with the impact signature to assess how those degraded sensors could harm the impact detection.

Guided Ultrasonic Wave Monitoring of Damage in Anisotropic Composites

Lightweight, carbon fibre reinforced composites are often employed for the manufacture of aerospace components due to their good strength to weight ratio. However, due to poor interlaminar strength, composite components are prone to barely visible impact damage, caused by low velocity impacts, during aircraft operation. Guided-wave-based structural health monitoring (SHM) techniques, using a network of distributed sensors, is an important Structural Health Monitoring (SHM) tool for the detection and localization of in-service impact damage in composite structures. However, the material anisotropy of composite laminates influences guided wave propagation and scattering. These anisotropic effects, if unaccounted for, could lead to inaccurate localization of damage, and potential regions of the structure where guided waves cannot propagate with sufficient amplitude, reducing damage sensitivity. Defect characterization can be improved by considering the scattering characteristics of various damage types for the sparse array signal processing. Guided wave scattering (A0 Lamb wave mode) was investigated around an artificial insert delamination in a quasi-isotropic CFRP panel. Permanent magnets, mounted on an undamaged region of the plate, were also used as scattering targets and compared to the delamination case. Full 3D Finite Element (FE) simulations were performed for both the delamination and magnet cases and compared to wavefield data obtained from non-contact laser Doppler vibrometer (LDV) measurements. Individual ply layers were modelled using unidirectional composite material properties to accurately capture the anisotropy effects. Good agreement was found between the experimental measurements and simulations. Scattered guided wave amplitudes around each damage type show strong directional dependency with energy focusing along the fiber directions of the outer ply layers of the laminate. Distinct scattering behavior was observed for each damage type. Implications of anisotropy and angular scattering on sparse array SHM of different defect types are discussed.

CRFP Mechanical Properties—Stated Values Versus Experimental Data

Abstract This study thoroughly examines the mechanical properties of carbon fiber-reinforced polymers (CFRPs), motivated by the critical need for accurate composite property data in investigating fracture control measures for structures subjected to barely visible impact damage. We compared experimental results with manufacturer-stated values, focusing on discrepancies in fiber volume fraction and its impact on elastic modulus. Experimental findings showed an increase in elastic modulus to 190 GPa for 0 deg orientation samples, compared to the manufacturer's stated value of 159.27 GPa. The recalculated fiber volume fraction increased from the expected 57% to an actual value of 60.96%. This increase in fiber content, determined through the Voigt modulus equation and corroborated by SEM image analysis, directly contributed to the observed variations in elastic modulus. Tension tests at 0 deg and 90 deg angles exhibited average percentage errors of 14.83% and 11.57%, respectively, while compression tests at 0 deg displayed a deviation of approximately −13.92% after adjusting for values beyond 0.05% compressive strain. The study underscores the critical impact of fiber volume fraction on CFRP properties and highlights the importance of precise empirical evaluation for accurate CFRP characterization in applications such as aerospace engineering.

Drop-weight impact and compressive properties of repeatable self-healing carbon fiber reinforced composites

Herein, repeated self-healing of impact damage in carbon fiber reinforced composites is successfully achieved by using hollow glass fibers containing self-healing resin and two types of microcapsules (HGFs-Ms). The HGFs-Ms and microcapsules containing hardener were incorporated to fabricate repeatable self-healing carbon fiber reinforced polymer composite laminates (RSH-CFRPs). Low-velocity drop-weight impact tests and compression tests were performed to investigate the impact resistance, energy absorption, residual compressive strength and repeated self-healing performances of the RSH-CFRPs. The results revealed that the energy absorption capacity of the RSH-CFRPs could be repeatedly recovered and the barely visible impact damage (BVID) zone was almost fully healed after every healing process. Moreover, the RSH-CFRPs successfully minimized the reduction in compressive strength after repeated drop-weight impact tests due to their efficient repeated self-healing ability in impact induced damage.

Advances in Embedded Sensor Technologies for Impact Monitoring in Composite Structures

Embedded sensor technologies have emerged as pivotal tools in redefining structural health monitoring (SHM) within composite materials, addressing a critical need in the composite structure industry. Composites, by their layered nature, are particularly vulnerable to internal delamination and micro-cracks from impacts, which can propagate and lead to catastrophic failures. Traditional inspection methods often fail to detect internal damage and these undetected damages can lead to reduced performance and potential system failures. Embedded sensors offer a solution capable of detecting a spectrum of damages, from barely visible impact damages (BVID) and subtle low-energy impacts to pronounced impact-related deformations, all in real-time. Key sensors, such as Piezoelectric transducers (PZTs), Fiber Bragg Gratings (FBGs), and other potential sensors, have been discussed as potential detection techniques in this review. This review discusses a comprehensive picture of the progress and current scenario of different embedded sensors for SHM of composite structures. The growth of embedded sensor technologies, current limitations, and future requirements focusing on sensor materials have been discussed in this review. Finally, challenges and opportunities for the development of a sustainable SHM system have been discussed in this paper.

Detecting of Barely Visible Impact Damage on Carbon Fiber Reinforced Polymer Using Diffusion Ultrasonic Improved by Time-Frequency Domain Disturbance Sensitive Zone.

Based on the decorrelation calculation of diffusion ultrasound in time-frequency domain, this paper discusses the repeatability and potential significance of Disturbance Sensitive Zone (DSZ) in time-frequency domain. The experimental study of Barely Visible Impact Damage (BVID) on Carbon Fiber Reinforced Polymer (CFRP) is carried out. The decorrelation coefficients of time, frequency, and time-frequency domains and DSZ are calculated and compared. It has been observed that the sensitivity of the scattered wave disturbance caused by impact damage is non-uniformly distributed in both the time and frequency domains. This is evident from the non-uniform distribution of the decorrelation coefficient in time-domain and frequency-domain decorrelation calculations. Further, the decorrelation calculation in the time-frequency domain can show the distribution of the sensitivity of the scattered wave disturbance in the time domain and frequency domain. The decorrelation coefficients in time, frequency, and time-frequency domains increase monotonically with the number of impacts. In addition, in the time-frequency domain decorrelation calculation results, stable and repetitive DSZ are observed, which means that the specific frequency component of the scattered wave is extremely sensitive to the damage evolution of the impact region at a specific time. Finally, the DSZ obtained from the first 15 impacts is used to improve the decorrelation calculation in the 16-th to 20-th impact. The results show that the increment rate of the improved decorrelation coefficient is 10.22%. This study reveals that the diffusion ultrasonic decorrelation calculation improved by DSZ makes it feasible to evaluate early-stage damage caused by BVID.

Barely visible impact damage detection in CFRP composite using vibrothermography with narrowband sweep excitation

The extensive use of carbon fiber-reinforced plastic (CFRP) composite materials in various industries has posed new challenges for conducting effective structural health monitoring and nondestructive evaluation techniques. Based on local defect resonance (LDR), the vibrothermography with narrowband sweep excitation is proposed to detect BVIDs in the CFRP composite. The response spectrum of displacement at the impact crater was collected by a laser Doppler vibrometer, which was used to determine the frequency range of narrowband sweep excitation. The frequency range was further validated by the analysis of local defect resonance for thermal responses, and the sweep duration was also optimized. Subsequently, the proposed vibrothermography was employed to detect BVIDs with varying severity. The impact damage could be efficiently activated and a significant temperature rise was generated in the defective areas, which enhances the temperature contrast of the thermal images and facilitates the quantification of defects.

Compression after impact (CAI) failure mechanisms and damage evolution in large composite laminates: High-fidelity simulation and experimental study

This study focuses on developing and validating a high-fidelity finite element model for predicting damage evolution and residual strength in fiber-reinforced composite panels. Impact and compression after impact (CAI) tests were conducted at both barely visible impact damage (BVID) and clearly visible impact damage (CVID) levels. The ASTM D7137 standard 100 mm × 150 mm CAI coupons were inadequate to cover the range of experimental studies required for model validation. Therefore, larger 254 mm × 304.8 mm laminates were investigated under two CAI testing conditions: one a scaled-up version of ASTM standard coupon, and the other with additional anti-buckling support plates to reduce unsupported areas to 127 mm × 177.8 mm. The model captured inter- and intra-laminar failure modes, including fiber breakage, splitting, kinking, pull-out, and crushing as well as matrix cracking, delamination, and their interactions. This was achieved by cohesive zone modeling technique and enhancement of the LaRC05 failure criteria through modeling the fiber damage evolution and utilizing an efficient search algorithm to determine the matrix fracture plane and fiber kink band angle. This study underscores the efficacy of the high-fidelity modeling approach in accurately predicting both impact damage and CAI strength in typical aircraft impact damage scenarios. Additionally, it provides insights into complex CAI failure mechanisms and energy release associated with various damage modes and highlights the effect of global buckling on the failure behavior and compressive strength of composite laminates. Furthermore, it shows that the proposed fixture with support plates is suitable for testing a broader range of impact scenarios without experiencing global buckling.

Attention mechanism enhanced spatiotemporal-based deep learning approach for classifying barely visible impact damages in CFRP materials

Most existing machine learning approaches for analysing thermograms mainly focus on either thermal images or pixel-wise temporal profiles of specimens. To fully leverage useful information in thermograms, this article presents a novel spatiotemporal-based deep learning model incorporating an attention mechanism. Using captured thermal image sequences, the model aims to better characterise barely visible impact damages (BVID) in composite materials caused by different impact energy levels. This model establishes the relationship between patterns of BVID in thermography and their corresponding impact energy levels by learning from spatial and temporal information simultaneously. Validation of the model using 100 composite specimens subjected to five different low-velocity impact forces demonstrates its superior performance with a classification accuracy of over 95%. The proposed approach can contribute to Structural Health Monitoring (SHM) community by enabling cause analysis of impact incidents based on predicting the potential impact energy levels. This enables more targeted predictive maintenance, which is especially significant in the aviation industry, where any impact incidents can have catastrophic consequences.

A simple image correlation technique for imaging subsurface damage from low-velocity impacts in composite structures

A robust computer vision system is proposed to visualize subsurface barely visible impact damage (BVID) in composite structures through a simple image correlation technique together with a damage imaging condition. This system uses a digital camera to record a video of the surface motion, capturing micron-scale dynamic movement from guided waves propagating on the surface of the structure generated via a sweeping frequency excitation (chirp) up to the ultrasonic frequency range. As the excitation frequency changes during the chirp, waves become trapped within this damaged region, forming standing waves to generate local resonance at specific frequencies. This localized resonance accumulates high wave energy in the region, generating a higher transverse displacement at the damage site compared to the remaining part of the structure. In this work, a simple image correlation technique is proposed to correlate each filtered video frame with the temporal mean of the filtered wavefield video to highlight standing waves at localized damage. The proposed image correlation technique is distinct from digital image correlation (DIC) since it does not use correlation for subset matching to track the movement of surface patterns between two images (video frames). Instead, it uses correlation to directly quantify similarities between corresponding image pixels (windows). Utilizing a single camera greatly simplifies system complexity and hence enhances the practicality and potential for real-time performance over a recently developed technique that utilized 3D DIC with a stereo camera for vision-based BVID detection. To realize the aim, work was conducted in two sequential steps: (1) off-axis 2D DIC was employed rather than 3D DIC to examine the potential of employing a single camera to capture images and extract not only in-plane displacement but also a fraction of transverse displacement in which local resonance is dominant and (2) image correlation was then employed, supplanting the off-axis 2D DIC image processing involved in the first step, to highlight the damage with significantly less processing complexity. This proposed technique using a zero-mean normalized cross-correlation imaging condition, rather than the total wave energy used for DIC-based approaches, is efficient and effective for identifying regions of minute surface movement from local resonance within the damage region without the use of the computationally intensive interpolation to get the sub-pixel gray level information followed by subset matching employed in DIC-based image processing. Two geometrically identical CFRP composite honeycomb panels that had been subjected to low-velocity impacts were used for verification and validation, and two excitation location configurations were tested for each panel. Damaged images produced with correlation for a 100 mm × 100 mm field of view using a single 3-s video, or a total of 19,200 video frames, show accurate damage imaging capabilities regardless of excitation location that exceed that of DIC techniques and is comparable to benchmark damage images obtained from laser Doppler vibrometry, ultrasonic C-scans, and X-ray CT scans. This success of correlation-based imaging of subsurface BVID demonstrates substantial improvements in efficiency and practicality and shows high potential for in situ and real-time computer vision-based nondestructive inspection or structural health monitoring of subsurface BVID in composite aircraft and other critical structures.