1 Introduction

In just over a century, the aviation industry has undergone remarkable transformations, evolving from the inception of flight to achieving unprecedented feats in speed, distance, and capacity. Today, with over 100,000 commercial flights taking off worldwide every day, equating to more than 400 departures per hour. Aviation stands as one of the safest and most reliable modes of transportation globally. This progress has been fueled by cutting-edge technologies, including autonomous devices and ultralight materials, as well as innovations such as unmanned aircraft and artificial intelligence [1,2,3,4]. In order to accommodate the increasing number of aircraft worldwide, enhancing onboard diagnostics and real-time structural health monitoring of aircraft components is essential. This ensures reliable and safe flights by aligning scheduled and planned maintenance of the aircraft components. DIC emerges as a potential solution for monitoring the structural health of aircraft components. The present review sheds light on the current state of DIC in the aviation industry, ongoing advancements and innovations, and future prospects. DIC plays a crucial role in the field of aerospace engineering, offering a versatile and non-destructive method for assessing structural deformations and material responses. In the aerospace industry, DIC is applied to monitor and analyse various components, ensuring the structural integrity and reliability of the aircraft.

Fig. 1
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Structural Health Monitoring of aircraft components using DIC

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The illustrated process in DIC application for monitoring the structural health of aerospace components, as depicted in Fig. 1, typically involves stages of image acquisition, processing, and DIC analysis aimed at identifying potential structural health concerns. This approach encompasses examining stress distribution, strain patterns, and overall deformation characteristics across various flight phases and operational conditions [5,6,7]. By providing detailed insights into the mechanical response of materials, DIC aids in optimizing designs, enhancing safety, and contributing to the overall efficiency of aerospace systems. Wings constitute a crucial element of an aircraft, responsible for generating lift. Aircraft wings undergo dynamic loading and environmental stresses during flight, making them a critical component to monitor for structural health [8,9,10]. Therefore, to gain a comprehensive understanding of this phenomenon, conducting a structural analysis of the aircraft wing is one of the important exercises [11, 12]. Engineers can analyse the structural performance of the wings under various conditions thanks to sophisticated engineering methods and cutting-edge technologies like finite element analysis, and DIC techniques. Aviation experts can spot possible problem areas, decide on necessary maintenance and repairs, and eventually improve the aircraft’s overall performance and safety by conducting thorough structural evaluations. This proactive strategy not only protects the crew and passengers, but it also increases the aircraft’s dependability and lifespan, guaranteeing that it can continue to play a crucial role in the aviation industry’s constant change [13, 14]. According to McCormick et al. [15], DIC is applied for the examination of a broad spectrum of material specimens. This includes assessing the evolution and uniformity of strain in materials testing, studying crack tip and crack propagation, detecting damage development in composites, analyzing structural deflections, mapping high-temperature strain, and conducting dynamic vibrational analysis. Collaborating with various partners such as Airbus, Atomic Weapons Establishment (AWE), Stresscraft, and British Energy, National Physical Laboratory (NPL) has actively engaged in implementing DIC solutions tailored for the measurement of residual stress.

Fig. 2
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Schematic of DIC of a typical aircraft wing, adapted from Pan et al. [23]

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The evolution of DIC arose from the challenges faced in the experimental testing and study of structures. Since the assessment of stress and strain is crucial, several approaches were attempted. Strain guages were used to measure strain which later proved to be inefficient since aerospace components are exposed to wide temperature variations, vibrations and pressures which affect the accuracy and reliability of strain guages. Additionally, strain guages can also be susceptible to electromagnetic interference which necessitate proper shielding for the strain guages [16,17,18]. Optical approaches have become crucial due to the demand for more thorough strain measurements, particularly those offering full-field data [19]. To precisely measure in-plane displacement and strain, DIC was devised in the early 1980s [20]. DIC was a non-contact, whole-field imaging technique that uses tracking and image registration to accurately detect strain and deformation in both two- and three-dimensional (3D) spaces. Through DIC, data on structural displacement and deformations provided insights on how to maintain structural integrity under in-flight circumstances. DIC could be used to carry out prospective improvements to maximize the wing effectiveness. Due to its relative simplicity of use and cost-effectiveness in producing detailed displacement maps of actual engineering components, DIC has become increasingly popular in the field of experimental mechanics [21]. DIC, which was initially created to record surface displacements and deformations, depends on the availability of a digital image system to capture an optical image of the surface both before and after deformation. The measurement of displacement and deformation gradients is then made possible by quantifying the difference in grey levels between the pre-deformation and post-deformation images utilizing a variety of image correlation algorithms. Notably, this technique differs from other methods by only requiring that the surfaces have a visibly speckled pattern. Chu et al. [22] formulated an algorithm for DIC analysis within the field of experimental mechanics. Optical measurements of macroscopic parameters, including strain and displacement, have evolved as a recognized domain within experimental stress analysis. Techniques such as holography, speckle interferometry, speckle photography, speckle-shearing interferometry, white-light speckle, and moiré have been extensively utilized for analyzing a diverse array of engineering problems. Pan [23] introduced a parameter called the "mean intensity gradient" for assessing the quality of the speckle pattern, and stressed on the importance of the relevant correlation criteria for comparing the similarities of the reference and deformed subsets. Figure 2 shows the region of interest and the subsets considered for a typical wing. A comparison between the iterative least squares (ILS) algorithm, designed for precise subpixel motion detection, and the already established Newton–Raphson algorithm was provided in [23].

In summary, DIC has emerged as one of the prospective approaches for modern aerospace structural health monitoring. It can be used for real-time tracking of structural integrity of the vital aircraft components. DIC, being a non-destructive technique, coupled with its accuracy and cost-effectiveness, can ensure the safety, reliability, and efficiency of aircraft in the rapidly evolving and fast-growing aviation industry.

2 Digital image correlation technique

Fig. 3
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3D DIC method for structural analysis of airfoil, adapted from Reyes et al. [25]

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In this section, the concept of digital image correlation has been dealt with in detail. The fundamental principle underlying DIC involves the comparison of images captured before and after deformation using an image correlation algorithm. This algorithm examines the variations within the speckled patterns to identify and analyze the deformation’s specific characteristics. Remarkably, DIC is capable of precisely scrutinizing even subtle deformations [24]. However, it’s important to note that this method necessitates a substantial setup, including a high-resolution camera and a robust computational system for its execution. DIC can be implemented in two distinct modes: 2D DIC and 3D DIC. Historically, 2D DIC was the sole option available, but recent advancements in the field have ushered in the era of 3D DIC applications. This innovation empowers us to gain a more profound understanding of the intricate deformations. The 3D DIC method employs a unique approach in which the specimen surfaces are observed from two distinct computer-generated perspectives, with the images then combined on a single Charge-Coupled Device (CCD) sensor [25]. To achieve this, a sequence of mirrors is strategically employed to create these dual views of the object and subsequently project them onto a single CCD sensor. The optical path for each of these orientations, as projected onto the solitary CCD, is depicted in Fig. 3.

Fig. 4
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Image capture sequence for the Helicopter rotor blade, adapted from Sutton et al. [21]

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The optical setup employs first surface mirrors coated with a specific wavelength pattern and carefully placed within precision machined mounts. The initial set of mirrors, positioned at a 45°angle to the lens, are secured using mounts featuring alignment holes. These mirrors are then affixed to a mounting pattern with specified dimensions. The calibration process of the analysis software will include a magnification factor, which utilizes changes in the line segment’s length to determine the panel’s relative orientation in space, as illustrated in the Fig. 4. Image correlation techniques offer a versatile and valuable tool for analyzing deformations. By employing two cameras, three-dimensional investigations become feasible. DIC operates on a straightforward principle and boasts a wide array of applications. The applicability of DIC extends across diverse domains and fields of study. Its versatility finds utility in various areas, including aerospace applications Chu and Poudel [11], and the analysis of rotor blade operations in helicopters Rizo-Patron et al. [26], among others. Moreover, it is useful for detecting cracks across different materials and offers understanding into stress and strain deformations in nearly all material types.

3 Softwares used in DIC

DIC requires advanced computational facilities to process real-time data on deformation along 2D or 3D spaces, and output as strain and stress fields necessary for health monitoring of the specific aircraft components. DIC makes used of correlation software, among which GOM Correlation distinguishes itself with a comprehensive suite of technologies, encompassing DIC, Photogrammetry, and Computer Tomography. Demonstrating the potential to supersede existing sensor-based technologies [26, 27], this software excels in 2D and 3D DIC algorithms, Image mapping, CAD file imports from widely used software like CATIA™, and SolidWorks™. It can conduct Thermography for material inspection too. GOM Correlation introduces indispensable solutions, notably the ARAMIS correlation algorithm catering to 3D DIC challenges. Additional tools, including PONTOS Live (real-time Co-ordinate Measurement Machine), TRITOP (Portable Optical CMM), ARGUS (Optical Forming Analysis system), and RVAT, contribute to its comprehensive functionality. GOM Correlation finds extensive utility across diverse sectors like automotive, aerospace, defence, biomechanics, medical fields, civil engineering, microelectronics, energy technologies, and transportation. DIC applications, ranging from material testing to high-strain rate testing, ballistic analysis, composite material testing, vibration analysis, thermography, and sheet metal analysis, further underline its versatility. The applications of DIC are diverse and encompass material testing, validation of finite element analysis and simulation, high-temperature testing, high-strain rate testing, ballistic analysis, composite material testing, vibration analysis, thermography, and sheet metal analysis, among others.

Fig. 5
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Application of DIC for aircraft wing analysis, adapted from Pan et al. [23]

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DIC operates based on the principle of image correlation, involving a comparison between images captured before and after deformation. This process entails the acquisition, digitization, and storage of two sets of patterns: the primary set captured before deformation and the secondary set obtained after deformation. Subsequently, a sub-image from the undeformed set is selected, and its corresponding location in the deformed set is determined, as illustrated in Fig. 6. Once these locations are identified, local displacements are quantified. The estimation of strains and in-plane surface displacements is accomplished through a three-step procedure within a MATLAB environment [28, 29]. In the initial step, a 2D cross-correlation coefficient is computed to derive initial estimates of full-field planar displacements. Bicubic interpolation is then applied to ensure that the peak of the correlation function attains sub-pixel accuracy, typically at 1/16th of a pixel level precision. This process is iteratively repeated across the entire image to yield comprehensive in-plane displacement data. Using the displacement values obtained in the first step as initial guesses, the subsequent stage employs an iterative technique grounded in nonlinear least-squares minimization to forecast both the displacements and their gradients. The chosen method in this step is the Newton–Raphson method. Specifically, it involves the utilization of the Broyden, Fletcher, Goldfarb, and Shanno algorithm or the BFGS algorithm, along with a line search, for the purpose of updating an inverse Hessian matrix [30, 31]. From this second step, displacement gradients are extracted, representing the average values for each subset, but they tend to exhibit some level of noise. Consequently, to obtain a coherent displacement field (u, v) for subsequent strain extraction, the implementation of smoothing algorithms becomes imperative [32, 33]. It is important to highlight that crack-opening displacements exhibit discontinuities along the crack’s path. Near the crack tip, the accuracy of deformation interpretation tends to diminish due to the inherent smoothing effect of conventional methods, which inadvertently smooths the displacements across the crack surfaces. Hence, there arises a critical need for the introduction of a smoothing technique capable of preserving the discontinuity in crack-opening displacements along the crack faces.

Fig. 6
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Comparison of deformed and undeformed sub-images before and after deformation, adapted from Chen et al. [27]

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3.1 Computing algorithms in DIC

The basic correlation algorithm is employed to analyze two sets of image data, often represented as speckle patterns, with the goal of extracting deformation profiles resulting from subtle alterations in the images [33]. Bornert et al. [34] explored Gray levels interpolation in their study. The computation of correlations often involves estimating image gray levels for non-integer pixel locations. The interpolation methods employed in their work include polynomial interpolation (bilinear or bi-cubic) and B-spline interpolation (bi-cubic or bi-quintic). While other interpolations based on Fourier or wavelet transforms could be potential options, they were not specifically investigated in the current study. In terms of optimization algorithms, Newton–Raphson and Levenberg–Marquard for full optimization techniques were employed. Whereas the partial optimization is focused on a restricted set of parameters. After conducting this partial optimization for a specific set of subsets, the reevaluation of higher-order coefficients concerning a given subset is carried out using explicit expressions derived from the relative displacements of the centers of neighboring subsets. Moreover, the Bi-parabolic interpolation of the correlation coefficient was applied when only identification of translation components was required. This interpolation method involves computing the correlation coefficient for integer values of the translation components and interpolating it using a bi-parabolic function in the vicinity of its maximum and the eight nearest neighbors. The optimization of this process can be achieved analytically. When considering the patterns in Fig. 7, subjected to various load conditions, the DIC technique necessitates the use of two images: one captured after deformation (during loading) and another taken before deformation occurs (after removal of loading). Strain and displacement measurements are then derived by scrutinizing these two images. The primary objective of this approach is to determine the deformations and displacements of small segments within the second image concerning the first one. This task is achieved by comparing the intensity levels, which typically range from 0 to 255 [35], within these image segments. The complexity of this search process can vary; it is straightforward if the second image only exhibits pure displacement but becomes intricate when deformation in the segment arises due to load conditions. To initiate the analysis, a subset is selected from the digitized intensity pattern of the unaltered object’s surface, commencing from point P, which changes to \(P'\) after the deformation. To achieve sub-pixel precision, an interpolation method, typically bilinear, is applied to the chosen subset. The cross-correlation coefficient (C) provides a means of comparing these two subsets. Equation 1 shows relation for the cross-correlation coefficient, where \(\Delta \)M is the subset of the undeformed image and \(\Delta \) M\(^\star \) is the subset of the deformed image. The x- and y- displacement vectors \(\xi \) and \(\eta \) are represented in terms of the local deformation gradients as given in Eq. 2 and Eq. 3, respectively.

Fig. 7
figure 7

Speckled patterns, a spray-painted pattern, b Naturally present pattern, c Acoustography fiber pattern, adapted from Quino et al. [36]

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$$\begin{aligned}{} & {} C\left( \xi \left( u,\frac{\partial u}{\partial x},\frac{\partial u}{\partial y}\right) ,\eta \left( v,\frac{\partial v}{\partial x},\frac{\partial v}{\partial y}\right) \right) \nonumber \\{} & {} \quad = \frac{\int _{\Delta M^*} f(x,y) f^*(x+\xi ,y+\eta )dA}{\sqrt{\int _{\Delta M} [f(x,y)]^2 dA \int _{\Delta M^*} [f^*(x+\xi ,y+\eta )]^2 dA}}\nonumber \\ \end{aligned}$$
(1)
$$\begin{aligned} \xi= & {} u+\frac{\partial u}{\partial x}\Delta x+\frac{\partial u}{\partial y}\Delta y \end{aligned}$$
(2)
$$\begin{aligned} \eta= & {} v+\frac{\partial v}{\partial x}\Delta x+\frac{\partial v}{\partial y}\Delta y \end{aligned}$$
(3)

In the work of Pan [23], the ILS algorithm was employed with a first-order displacement mapping function to optimize the correlation criterion, obtaining the desired deformation parameters for each point. The system automatically eliminated invalid points within each subset surrounding the boundary points. Consequently, the displacements of the points situated at or near the boundaries of the region of interest could be determined reliably and accurately. Figure 8 shows the results of the DIC measurements made for a deformation of the panel. Pan et al. [37] also proposed a rapid DIC method for efficient deformation measurement. The proposed method achieves this by effectively eliminating redundant calculations inherent in conventional Newton Raphson-algorithm-based DIC methods. Specifically, it employs a reliability-guided displacement scanning strategy to bypass time-consuming integer-pixel displacement searches for each calculation point. Additionally, a pre-computed global interpolation coefficient look-up table is utilized to eliminate repetitive interpolation calculations at sub-pixel locations. Through these two innovative approaches, the fast DIC method significantly enhances the computational efficiency of the traditional NR-algorithm-based DIC method.

Fig. 8
figure 8

Results of DIC technique applied to deformation, adapted from Pan [23]

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Pilch et al. [38] introduced an intelligent digital image correlation technique utilizing genetic algorithms to measure surface displacements and strains. The authors use subpixel resolution to derive two displacements and four deformation gradients for a specific subset under investigation. The results served as a proof of concept, demonstrating measurements of rigid-body displacement, 1-D, and 2-D strain fields.

4 Aerospace applications of DIC

This section covers the different applications of DIC in aerospace. DIC enables precise and non-contact measurement of deformation, strain, and displacement in aerospace components, such as aircraft wings, fuselages, and engine parts. It finds applications in structural health monitoring, fatigue analysis, and quality control during manufacturing and maintenance processes. Lei-Gang and colleagues [5] presented a non-contact scheme, based on 3D DIC technology, for measuring the wing deformation of cantilever monoplanes during flight. Two camera groups were rigidly installed on the aircraft’s vertical fin and calibrated to record measurements. They designed pre-stretched targets and speckled patterns for accurate detection. A real-time camera position self-correction method was devised, and a novel dual-temporal matching approach was proposed to address image correlation challenges. The authors developed a system and validated it through a laboratory simulation test.

Loutas et al. [39] utilized Four fiber Bragg grating (FBG) optical sensors to record the dynamic response of an aerospace composite structure. The authors used multi-sensor data fusion approach, advanced signal processing and pattern recognition techniques, including discrete wavelet transform (DWT) and support vector machines (SVM), integrated into the system. The findings demonstrated that SVMs with non-linear kernels present a robust and promising pattern recognition approach for damage diagnosis. As part of the EU-funded project AIM (Advanced In-flight Measurement Techniques), the DLR and Piaggio Aero Industries employed an IPCT (Image Pattern Correlation Technique) to study in-flight deformations of the wing and propeller blades of the Piaggio P180 aircraft. Boden and team [8] higlighted the stereoscopic camera setup for flight tests, and presented results from pretests conducted on the Piaggio P180, along with potential solutions to major challenges in optical in-flight testing. Du et al. [40] conducted a groundbreaking study that quantitatively investigated the fracture behavior of a large aircraft panel (\(4.8 \, \text {m} \times 1.4 \, \text {m}\)) using DIC for the first time. They evaluated mixed-mode (\(I+II\)) stress intensity factors by combining DIC with a multi-point over-deterministic method, analyzing more than 800 images captured during a 10-minute monotonic loading test. The research revealed that the crack propagated through the panel’s skin at a relatively low speed, altering its path due to structural reinforcement. In the later stages of the test, substantial shear lips were observed, indicating a plane stress state. The study found high stress intensity factor values, suggesting the remarkable resistance to crack growth in the panel’s structural components, emphasizing the utility of DIC in understanding complex structural behaviors. Veerman and colleagues [7] conducted aircraft integration tests in a hangar, accompanied by verification measurements comparing the aircraft-integrated IPCT system with a micrometer. The ground-based verification showcased the inherent high accuracy of the method. In-flight trials of the IPCT system successfully measured wing deformations under various load conditions, ranging from 0 to 2.5 g. Optical displacements of a randomly speckled wing section were determined through cross-correlation techniques and converted to geometrical wing deformations in a reference frame. A wing deflection model, capturing parameters like change in wing heave, dihedral, and torsion, was fitted based on these results. The study demonstrated the IPCT technique’s utility for high-accuracy, static, and dynamic in-flight wing deflection measurements, including the analysis of dynamic behaviors during events such as landing. Wu et al. [10] emphasized the role of airplane wing deformation and its significance during certification tests for stutter. The authors introduced a real-time detection method utilizing three-dimensional speckle image correlation technology. Speckle patterns, responsive to aircraft vibrations, were applied to the wing and captured by CCD cameras inside the aircraft. To enhance computational efficiency, they proposed a matching technique based on Geodetic Systems Incorporated coded points and classical epipolar constraints. This approach allowed the derivation of a displacement vector map for the aircraft wing by comparing speckle point coordinates before and after deformation. Verification experiments, including static and dynamic tests with an aircraft wing model, demonstrated the accuracy and effectiveness of the proposed method. Di Lorenzo et al. [41] utilized the DIC technique in their study to capture full-field displacement information from the tested structure. This data served as a foundation for extracting the structure’s modal characteristics, such as natural frequencies, damping ratios, and complete mode shapes. To validate these findings, conventional sensors like strain gauges and accelerometers or alternative optical methods such as laser Doppler vibrometers were employed. The research encompassed various test cases, employing two distinct approaches to amalgamate data acquired during vibration tests. The first approach involved aligning the time histories of input (shaker) and output using reference signals for calculating Frequency Response Functions (FRFs) before any further analysis. An alternative method, applicable in cases of broadband excitation, entailed processing time data into auto and crosspowers and subsequently identifying modal parameters through Operational Modal Analysis (OMA).

Fig. 9
figure 9

DIC during bending test of composite wing (Dardo Aspect aircraft), adapted from Pagani et al. [42]

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In one of the earlier studies, Pagani et al. [42] explored the application of DIC for measuring displacement and strain in a wet lay-up composite wing. Unlike traditional strain gauges, DIC enables comprehensive strain analysis across the entire surface of structural components, ranging from simple to intricate geometries. The research involves conducting wing-up bending tests and strain measurements on CFM Air’s composite wing for the Dardo Aspect aircraft, utilizing a custom test rig and the Q-400 DIC system developed by Dantec Dynamics. Figure 9 shows the DIC cameras capturing the random pattern on main spar web of wing root, and the deformation along x- and y-directions, respectively. Furthermore, the obtained results are employed to validate the accuracy of a finite element model representing the structure under examination. Continuing their work, Pagani and colleagues [43] present significant findings derived from a test campaign performed on the Dardo Aspect, a very-light airplane (VLA) constructed from wet-laminate full-composite materials. The study encompasses both static and dynamic experimental analyses, all conducted with strict adherence to certification requirements. The paper places particular emphasis on showcasing the reliability of innovative techniques, including DIC, operational modal analysis (OMA), and the taxing vibration test (TVT), even though they are not yet approved for certification purposes. Notably, these methods offer substantial time and cost savings, albeit with potential challenges related to calibration and test preparation.

Table 1 Summary table of DIC in aerospace applications
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Wood et al. [44] explore the Fluid–Structure interaction (FSI) of a wing with two degrees of freedom (pitch and heave) in the transitional Reynolds number regime, a classic configuration in aeroelasticity crucial for assessing flutter stability. Unlike past approaches relying on analytic methods and simplifying assumptions like potential flow, this study aims to provide experimental reference data for well-defined configurations under clear operating conditions. The researchers utilize modern numerical simulations with fully coupled approaches, known for their broad applicability and enhanced power, to capture instantaneous physical phenomena such as flutter. The experiments conducted in a wind tunnel employ digital-image correlation (DIC) for measurements on a straight wing featuring a symmetric NACA 0012 airfoil. The wing is mounted in a frame using bending and torsional springs to emulate its elastic behavior, with three configurations altering the flutter stability by shifting the center of gravity along the chord line of the airfoil. This comprehensive investigation contributes valuable insights into the complex dynamics of fluid–structure interaction in aeroelastic systems. Doan et al. [45] introduced a novel DIC method designed to mitigate the impact of painted speckle patterns on thin flexible structures in vibration measurements. In this investigation, they successfully explored the dynamic vibration characteristics of a thin, flexible artificial wing utilizing the virtual speckle pattern based DIC method, and successfully employed modal analysis coupled with DIC. According to Hild et al. [46], comprehensive full-field displacement and strain measurements are frequently necessary. The recent advancement of reliable, rapid, and cost-effective tools for full-field kinematic measurements has increasingly captured the attention of experimentalists. A closed-form solution for displacements can be obtained through the Kolossov-Muskhelishvili potentials. Banks et al. [47] applied DIC to a curved aerofoil. The application of DIC for measuring full-field deformation in fluid–structure interaction experiments conducted within a wind tunnel was investigated. The study highlighted the necessity of enclosed camera fairings to minimize error arising from wind-induced camera vibration during aerodynamic loading. The effectiveness of the methodology was demonstrated through testing a high-performance curved foil from a NACRA F20 sailing catamaran in the University of Southampton’s RJ Mitchell wind tunnel. The evaluation focused on the coupled deflection and blade twist in the tip region (80\(\rightarrow \)100% span, measured from the root) across various wind speeds and angles of attack. While steady deformations at low angles of attack were accurately captured, the study observed increased variability in unsteady deformations at higher angles of attack, attributed to hardware limitations in the current DIC system. In their investigation, Reagan et al. [48] explored a pioneering method that integrates an unmanned aerial vehicle with three-dimensional digital image correlation for non-contact, optical measurements aimed at monitoring the structural health of bridges. These studies highlight the crucial role of DIC technology in advancing aerospace research, facilitating precise data collection for structural analysis, and certification compliance, while pushing the boundaries of testing efficiency.

Xin et al. [49] introduced a novel method for subset assignment and correlation aimed at measuring complex deformations. This innovative approach simplifies complex deformations into translational deformations within a logarithmic coordinate system, leading to significantly enhanced accuracy and stability in deformation calculations. In the application of a non-centered warpage deformation to the reference image, correlation and strain calculations are conducted using both the traditional and proposed methods. Figure 10 shows the DIC measurement during warpage stage.

Fig. 10
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DIC measurement during warpage deformation stage, adapted from Xin et al. [49]

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Fig. 11
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Crack growth monitoring using DIC, adapted from Chen et al. [50]

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Chen et al. [50] examined the impact of salt spray and salt solution on the fatigue characteristics of 2024-T3 aluminum alloy plates utilized in aircraft fuselage structures. They conducted investigations by integrating DIC with in-situ corrosion fatigue tests. The DIC technology was employed to monitor the crack propagation process, while also assessing the effects of NaCl concentration on fatigue life and crack growth. In Fig. 11, the progression of crack growth is depicted within a salt solution environment. As the crack extended horizontally, its length was determined by measuring the difference between the horizontal coordinate of the crack tip and its initial position, and tracking the vertical strain field using VIC-3D software. Similar studies on applications of DIC for fatigue characterization of aircraft components were conducted in [51, 52]. Development of materials for the aircraft structures would have to be in line with the DIC systems in place for continuous monitoring for planned maintenance [53].

DIC offers several advantages over alternative techniques for strain measurement in aerospace applications. Its capability to provide full-field measurements enables a comprehensive understanding of complex deformations across entire components, essential for assessing structural integrity. Being non-contact, DIC avoids altering the behavior of tested structures and allows measurements in challenging areas. Moreover, its high spatial resolution enables precise detection of small-scale deformations and imperfections critical for aerospace materials. However, DIC systems can be complex to set up and require careful calibration. Environmental factors such as lighting variations and vibrations may affect measurement accuracy, demanding stringent control measures. Additionally, DIC generates large datasets needing sophisticated processing, which can be time-consuming. Despite these challenges, DIC’s effectiveness in aerospace is hindered by its sensitivity to temperature variations, susceptibility to vibrations, and potential electromagnetic interference, necessitating advanced mitigation strategies for reliable performance.

5 Conclusions

DIC emerges as an invaluable tool for comprehending the impact of stress and strain on materials and for guiding aircraft design to mitigate stress-induced damage. Aircraft, by nature, endure substantial stress and strain variations due to factors such as pressure differentials above and below the wings, crosswinds, and friction. Hence, conducting a DIC study on aircraft wing conditions becomes imperative, offering vital insights into design considerations. Operating on the principles of image correlation, DIC compares speckled images before and after deformation to discern alterations in the object under scrutiny. The algorithm then detects these disparities, providing a detailed report on the observed changes. This report, in turn, informs aircraft design modifications and maintenance efforts to rectify any issues. Furthermore, DIC facilitates an understanding of an aircraft’s life cycle by extrapolating results to assess material load-bearing capacities. In sum, this process serves as an indispensable and crucial tool for enhancing our comprehension of the ramifications of stress and strain on aircraft wings, ultimately contributing to safer and more efficient aircraft design and maintenance practices.