Search Results (20)

Composite materials are extensively used in the aeronautical and aerospace industries for their high strength-to-weight ratios but are vulnerable to barely visible impact damage (BVID), which can severely compromise structural integrity. Digital shearography (DS) provides a non-contact, full-field solution for subsurface inspection; however, low signal-to-noise ratios in raw phase maps often hinder precise damage identification. This study explores a post-processing methodology utilizing a band-pass filtering algorithm and temporal summation to isolate damage-related spatial frequencies. An in-house digital shearography system was used to inspect a carbon-fiber-reinforced polymer (CFRP) plate subjected to 13.5 J and 26.2 J impacts. Twelve phase maps, acquired during the thermal cooling stage, were processed using a multi-pass filters to systematically analyze different frequency ranges. Results demonstrate that summing multiple filtered phase maps significantly enhances the contrast of damage signatures compared to single phase maps or traditional unwrapping techniques. Furthermore, quantitative assessment using image quality metrics, such as the generalized contrast-to-noise ratio (gCNR), confirmed that optimal frequency selection is essential for an accurate damage delineation. This approach provides a robust framework for improving the reliability and sensitivity of non-destructive testing in composite structures. Full article

Long bone fractures are breaks or cracks in a long bone of the body typically caused by trauma like a fall, sport injury, accidents etc. This study investigates the effectiveness of experimental methods for fast and safe healing of long bone fractures in humans, highlighting both their advantages and disadvantages, respectively finding the most effective and safe methods for evaluating the types of fixators that can be used in the consolidation of fractured long bones. As for the preliminary data, numerical methods and applied mathematics were used to address this problem. After collecting of preliminary data there were performed a series of experimental analysis as follows: Electrical Strain Gauges (ESGs); the Moiré Fringes method; Photo-Elasticity, with the particular technique thereof, the so-called Photo-Stress method; Holographic Interferometry (HI); Speckle Pattern Interferometry (ESPI) and Shearography; and Video Image Correlation (VIC), which is also called Digital Image Correlation (DIC). By analyzing different methods, the following two methods resulted to be widely applicable, namely, ESG and DIC/VIC. The findings highlight the net advantages regarding the objective choice of these types of fixators, thereby contributing to a possible extension of these approaches for the benefit of medical surgical practice Full article

This paper presents a relevant and timely study on the application of thermal loaded digital shearography for crack classification in glass fiber reinforced plastic (GFRP) structures, particularly air-cooled condenser (ACC) fan blades. A thermal loaded digital shearography system was applied to measure strain concentration caused by the cracks at different fatigue cycles. A thermomechanical model was introduced to estimate the heating temperature and the time to ensure heat can reach to the desired depth and that both shallow and deep cracks can be detected. In order to correlate the information of strain concentration in the shearograms to the different stages of cracks, fatigue testing with dynamic three-point bending was conducted. The fatigue tests demonstrated how the strain concentration evolved in the shearograms, while the crack developed from the early (no noticeable strain concentration), to the middle (strain concentration is forming), to the late stage (significant strain concentration is found). The relationships between the degrees of strain concentration in the shearograms and the different stages of cracks can be obtained from testing of the artificial cracks. Using the rules and experimental results obtained from artificial samples, digital shearography was applied to classify the crack stages in parts of ACC fan blades from industry. The combination of artificial crack testing, fatigue loading experiments, and validation with CT scans demonstrates a comprehensive approach and provides potential guidance for industry to determine criticality and maintenance criteria. Full article

This paper presents a novel method for damage identification in aluminum beams using the contourlet transform. Four aluminum beams were used in the study: one was undamaged, while the other three had different damage scenarios. The damage included middle and side slots with depth-to-thickness ratios of 7% and 28%. Damage is identified using the proposed index of contourlet transform of the modal rotations and modal curvatures of the beams for the free-free condition. The beam’s first three modal rotations are directly measured with digital shearography, and the corresponding modal curvatures are obtained through their numerical differentiation. The results indicated that to detect the exact locations and identify damage severities using the proposed damage indices, instead of modal rotations, the modal curvatures should be introduced as the input. Moreover, they revealed that the proposed damage indices need modal data of the undamaged state as a baseline to identify smaller damage. In addition, comparing the proposed contourlet-based damage indices with previously suggested wavelet-based damage detection methods revealed that, although the wavelet-based damage index is more sensitive to damage severity, it also exhibits higher noise levels in undamaged locations. The Tukey windowing process was introduced to address the boundary effect problem. Full article

This study comprehensively compares the performance of two non-destructive testing (NDT) techniques—active thermography (AT) and digital shearography (DS)—for identifying various damage types in composite structures. Three distinct composite specimens were inspected: a carbon-fiber-reinforced polymer (CFRP) plate with flat-bottom holes, an aluminum honeycomb core sandwich plate with a circular skin-core disbond, and a CFRP plate with two low-energy impacts damage. The research highlights the significant role of post-processing methods in enhancing damage detectability. For AT, algorithms such as fast Fourier transform (FFT) for temperature phase extraction and principal component thermography (PCT) for identifying significant temperature components were employed, generally making anomalies brighter and easier to locate and size. For DS, a novel band-pass filtering approach applied to phase maps, followed by summing the filtered maps, remarkably improved the visualization and precision of damage-induced anomalies by suppressing background noise. Qualitative image-based comparisons revealed that DS consistently demonstrated superior performance. The sum of DS filtered phase maps provided more detailed and precise information regarding damage location and size compared to both pulsed thermography (PT) and lock-in thermography (LT) temperature phase and amplitude. Notably, DS effectively identified shallow flat-bottom holes and subtle imperfections that AT struggled to clearly resolve, and it provided a more comprehensive representation of the impacts damage location and extent. This enhanced capability of DS is attributed to the novel phase map filtering approach, which significantly improves damage identification compared to the thermogram post-processing methods used for AT. Full article

Speckle-shearing technology is widely used in defect detection due to its high precision and non-contact characteristics. However, the wrapped-phase recording defect information is often accompanied by a lot of speckle noise, which affects the evaluation of defect information. To solve the problems of traditional denoising algorithms in suppressing speckle noise and preserving the texture features of wrapped phases, this study proposes a speckle denoising algorithm called a speckle denoising convolutional neural network (SDCNN). The proposed method reduces the loss of texture information and the blurring of details in the denoising process by optimizing the loss function. Different from the previous simple assumption that the speckle noise is multiplicative, this study proposes a more realistic wrapped image-simulation method, which has better training results. Compared with representative algorithms such as BM3D, SDCNN can handle a wider range of speckle noise and has a better denoising effect. Simulated and real speckle-noise images are used to evaluate the denoising effect of SDCNN. The results show that SDCNN can effectively reduce the speckle noise of the speckle-shear wrapping phase and retain better texture details. Full article

Carbon fiber-reinforced composites are popular due to their high strength and light weight; thus, the structures demonstrate high performance and specific strength. However, these composites are susceptible to impact damage. The objective of this research was to study the behavior of carbon fiber-reinforced laminates based on a polyetheretherketone (PEEK) matrix with six stacking sequences under static and impact loading. Four-point bending, short-beam bending, drop weight impact, and compression after impact tests were carried out. The results were complemented with digital shearography to estimate the damaged areas. Finite element modeling served to assess the failure mechanisms, such as fiber and matrix failure, in different layers due to tension of compression. Three behavior pattern of layups under drop-weight impact were found: (i)—energy redistribution due to mostly linear behavior (like a trampoline) and thus lower kinetic energy absorption for damage initiation, (ii)—moderate absorption of energy with initiation and propagation of concentrated damage with depressed redistribution of energy in the material, (iii)—moderate energy absorption with good redistribution due to initiation of small, dispersed damage. The results can be used to predict the mechanical behavior of composites with different stacking sequences in materials for proper structural design. Full article

Dry fruits and nutshells are biological capsules of outstanding toughness and strength with biomimetic potential to boost fiber-reinforced composites and protective structures. The strategies behind the Betholletia excelsa fruit mechanical performance were investigated with C-ring and compression tests. This last test was monitored with shearography and simulated with a finite element model. Microtomography and digital and scanning electron microscopy evaluated crack development. The fruit geometry, the preferential orientation of fibers involved in foam-like sclereid cells, promoted anisotropic properties but efficient energy dissipating mechanisms in different directions. For instance, the mesocarp cut parallel to its latitudinal section sustained higher forces (26.0 ± 2.8 kN) and showed higher deformation and slower crack propagation. The main toughening mechanisms are fiber deflection and fiber bridging and pullout, observed when fiber bundles are orthogonal to the crack path. Additionally, the debonding of fiber bundles oriented parallel to the crack path and intercellular cracks through sclereid and fiber cells created a tortuous path. Full article

In digital shearography, the speckle noise of the phase fringe pattern has a negative effect on the accuracy and reliability of the phase unwrapping procedure. A novel and optimized sine–cosine transform wavelet threshold denoising method is proposed to suppress speckle noise. Fast phase denoising can be achieved by using the proposed method while preserving the phase reversal information. The details of the selected wavelet basis function, the optimal decomposition level, the threshold function, and the denoising threshold are also provided in this manuscript. In particular, the decomposition level is analyzed and optimized through simulation analysis according to the speckle suppression index and the adaptive denoising method. The experimental results show that the proposed method has more adaptive ability in practical application than the sine–cosine transform average denoising method with the selected mask and iterative procedure, which speeds the denoising process up and takes better-unwrapped phase patterns. Full article

A feasible and precise method to measure ligament strain during surgical interventions could significantly enhance the quality of ligament reconstructions. However, all existing scientific approaches to measure in vivo ligament strain possess at least one significant disadvantage, such as the impairment of the anatomical structure. Seeking a more advantageous method, this paper proposes defining medical and technical requirements for a non-destructive, optical measurement technique. Furthermore, we offer a comprehensive review of current optical endoscopic techniques which could potentially be suitable for in vivo ligament strain measurement, along with the most suitable optical measurement techniques. The most promising options are rated based on the defined explicit and implicit requirements. Three methods were identified as promising candidates for a precise optical measurement of the alteration of a ligaments strain: confocal chromatic imaging, shearography, and digital image correlation. Full article

Digital shearography is a technique that has recently been applied to material inspections that cannot be performed by the naked eyes, including the detection of air bubble defects in tires. Although digital shearography detects bubbles that are not visible to the naked eyes, the process of determining tire defects still relies on field operators, with inconsistent results depending on the experiences of the field operator personnel. New or different types of bubble defects that AI models have not previously recognized are often missed, resulting in an inadequate quality detection model. In this paper, we propose a bubble defect detection method based on an incremental YOLO architecture. The data for this research was provided by the largest tire manufacturer in Taiwan. In our research, we classify the defects into six distinct categories, pre-process the images to allow better detections of less-noticeable defects, increase the amount of training data used, and generate an initial training model with the YOLO framework. We also propose an incremental YOLO method using small-model training for previously unobserved defects to improve the model detection rate. We have observed detection accuracy and sensitivity of 98% and 90% in the experimental results, respectively. The methods proposed in this paper can assist tire manufacturers in achieving semi-automatic quality inspections and labor cost reductions. Full article

A two-dimensional (2D) shearography with source displacement is proposed to measure object contours. Using a dual-shear shearographic setup with two movable laser sources, the full-field slopes along a pair of orthogonal shear directions were obtained. The contour was then obtained by performing 2D integration of the surface slopes. Theoretical derivations and experimental results are presented to demonstrate the performance of the proposed method. The experimental results show that contour of objects with various types of surfaces, such as spherical and hyperbolic paraboloid surfaces, can be effectively measured. The measurement of the contour aids in the precision measurement of strain and the precision location of defects. Full article

Since real experimental shearography images are usually few, the application of deep learning for defect detection in digital shearography is limited. A simulation dataset preparation method of shearography images is proposed in this paper. Firstly, deformation distributions are estimated by finite element analysis (FEA); secondly, phase maps are calculated according to the optical shearography system; finally, simulated shearography images are obtained after 2π modulus and gray transform. Various settings in the parameters of object, defect, load and shearing in those three steps could prepare a diverse simulation dataset for deep learning. Together with the real experimental images taken from a shearography setup, hybrid trainings of deep learning for defect detection are performed and discussed. The results show that a simulation dataset, generated without any real defective specimen, shearography system or manual experiment, can greatly improve the generalization of a deep learning network when the number of experimental training images is small. Full article

The growing demand and diversity in the application of industrial composites and the current inability of present non-destructive evaluation (NDE) methods to perform detailed inspection of these composites has motivated this comprehensive review of sensing technologies. NDE has the potential to be a versatile tool for maintaining composite structures deployed in hazardous and inaccessible areas, such as offshore wind farms and nuclear power plants. Therefore, the future composite solutions need to take into consideration the niche requirements of these high-value/critical applications. Composite materials are intrinsically complex due to their anisotropic and non-homogeneous characteristics. This presents a significant challenge for evaluation and the associated data analysis for NDEs. For example, the quality assurance, certification of composite structures, and early detection of the failure is complex due to the variability and tolerances involved in the composite manufacturing. Adapting existing NDE methods to detect and locate the defects at multiple length scales in the complex materials represents a significant challenge, resulting in a delayed and incorrect diagnosis of the structural health. This paper presents a comprehensive review of the NDE techniques, that includes a detailed discussion of their working principles, setup, advantages, limitations, and usage level for the structural composites. A comparison between these techniques is also presented, providing an insight into the future trends for composites’ prognostic and health management (PHM). Current research trends show the emergence of the non-contact-type NDE (including digital image correlation, infrared tomography, as well as disruptive frequency-modulated continuous wave techniques) for structural composites, and the reasons for their choice over the most popular contact-type (ultrasonic, acoustic, and piezoelectric testing) NDE methods is also discussed. The analysis of this new sensing modality for composites’ is presented within the context of the state-of-the-art and projected future requirements. Full article

Shearography, as a novel non-destructive evaluation technique, has shown notable ability in the detection of composite materials. However, in current shearography practices, the phase shifting and loading methods applied are mainly static. For instance, vacuum hood or force loading facilities are often used in phase-shifting shearography, and these are hard to realise with robotic control, especially for on-board inspection. In this study, a dynamic process for detecting defects in the subsurface of a wind turbine blade (WTB) using spatial phase shift with dynamic thermal loading was proposed. The WTB sample underwent a dynamic thermal loading operation, and its status is captured by a Michelson interferometric-based spatial phase shift digital shearography system using a pixelated micro-polarisation array sensor. The captured images were analysed in a 2D frequency domain and low-pass filtered for phase map acquisition. The initial phase maps underwent a window Fourier filtering process and were integrated to produce a video sequence for realisation of visualising the first derivative of the displacement in the process of thermal loading. The approach was tested in experimental settings and the results obtained were presented and discussed. A comparative assessment of the approach with shearography fringe pattern analysis and temporal phase shift technique is also presented and discussed. Full article