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This paper describes a methodology for the modeling and simulation of a pulsed thermography inspection of carbon fiber-reinforced composites. A thermal–numerical model based on the 3D transient heat conduction equation for heterogeneous media is proposed to model the thermal response of the composite medium when a thermal pulse is applied to its surface. The solution of the model is developed using the finite volume method, in which the approximated equations are obtained by performing energy balances for each elementary volume. The experimental validation of the thermal–numerical model consisted of a comparison of the thermal decay curves for defective areas obtained through numerical simulation and with a pulsed thermography inspection of a laminated composite specimen with simulated defects (Teflon inserts). A parametric study was carried out in order to analyze the influence of the irradiation power density, non-uniform heating and characteristics associated with the defects on quantitative variables such as the onset time and thermal contrast. Numerical results showed that the variable thermal contrast is highly sensitivity to changes in the defect thickness, aspect ratio and parameters related to the external stimulation. Furthermore, an increase in the detectability of defects – especially those with lower aspect ratios – can be achieved by increasing the irradiation power. [ABSTRACT FROM AUTHOR]

Coplanar capacitive sensors are employed in Non-destructive Testing (NDT) methods to measure the difference in dielectric properties of the materials. The most important design parameters for a coplanar capacitive sensor include the shape, size, and separation distance of the electrodes which affect the sensor performance. In addition, the impact of the shielding plate and guard electrode should be considered. In the framework of this paper, numerical simulations and physical experiments are studied for two shapes of electrodes, triangular and rectangular, by examining different sizes and different separation distances between electrodes to assess and analyze the important features of the coplanar capacitive electrodes, such as the penetration and strength of the electric field as a function of sensor geometrical properties. Therefore, a detailed analysis of numerical simulation using Finite Element Modelling (FEM) is provided to study these geometric parameters. In addition, the influence of the different frequencies, lift-off, and the presence or absence of a metal shielding plate and guard electrode on the output result is analyzed. Finally, sensors were manufactured and several experiments were carried out under different configurations. Comparison of the numerical simulation results and physical experiments illustrate that they are in good qualitative agreement. [ABSTRACT FROM AUTHOR]

The non-uniformity of non-planar object inspection data makes their analysis challenging. This paper reports a study of the use of recurrent neural network and artificial feed-forward neural network in pulsed thermography during the automated inspection of non-planar carbon fiber reinforced plastic samples. The time series, including the raw temperature–time series and sequenced signals obtained from the first derivative after thermographic signal reconstruction was used to train and test the models respectively. Quantitative comparisons of testing results showed that the long short-term memory recurrent neural network model was more accurate in handling time dependent information compared to the artificial feed-forward neural network model. [ABSTRACT FROM AUTHOR]

This study investigates the effect of electrospun nylon nanofibres modification on the low-velocity impact resistance of carbon fibre reinforced polymer (CFRP) laminates. Integration of multiple excitation infrared thermography using advanced image processing techniques is used to characterize the damage as a function of impact energy. The results show an enhanced damage resistance for the modified specimens, which is also validated by force – displacement curves. The interlaminar cracks show a maximum reduction of 24.8%, while delaminated areas and defect depths experience reductions of up to 28.7% and 24.3%, respectively. Interestingly, thermal diffusivity is introduced to identify the damage shape successfully. Finally, a correlation study is conducted, and a novel damage prediction model based on thermal diffusivity is proposed for further quantitative analysis. [Display omitted] • Electrospun nylon nanofibre veils are applied for interlayer toughening to enhance impact resistance. • Infrared thermography is firstly used to quantitatively evaluate severity of impact damage in composite. • Force – displacement curve is used to validate experimental results. • Thermal diffusivity map is produced by a novel quantitative model to establish a link to the impact energy. [ABSTRACT FROM AUTHOR]

Passive infrared thermography (IRT) has been introduced as a faster, safer, and contactless alternative for the nondestructive inspection of subsurface delamination in concrete infrastructure. However, some uncertainties remain, such as the absence of an inspection protocol to inspect multiple concrete components and the dependence of the technique's performance on environmental conditions and solar energy. This study presents a proof of concept about the capabilities of passive IRT in detecting subsurface damages in multiple concrete components under variate solar exposure. The passive IRT capabilities are explored by analyzing the thermal sequences obtained from samples of artificially damaged concrete structures inspected over 24 h in various environmental conditions over three seasons. As a result, damages with a size-to-depth (S/D) ratio between 1.0 and 2.7 were detected using the thermal contrast method. Furthermore, the implementation of signal processing techniques yielded an improvement in capabilities ranging from 15% to 52%. Finally, a procedure for acquiring data while inspecting multiple concrete components using passive IRT is proposed. • Infrared thermography detection of concrete delamination under varying solar exposure. • Procedure to optimize data collection in concrete under varying solar exposure. • Detection of damages up to 8.0 cm depth in concrete with a 1.0 width-to-depth ratio. • The specific capability varied based on the component type and solar orientation. • Signal processing mostly improved contrast-to-noise ratio and detection capabilities. [ABSTRACT FROM AUTHOR]

Abstract In order to quantitatively compare the reliability of pulsed thermography and ultrasonic testing techniques, a set of thirty-five Carbon Fiber Reinforced Plastic (CFRP) composite panels with impact damages are inspected by pulsed thermography and ultrasonic C-scan. The comparative experimental results and Probability of Detection (PoD) analysis results are presented. The quantitative comparison shows that pulsed thermography testing has smaller defect size at 90% PoD with 95% confidence level, i.e. a 90/95 values than ultrasonic testing for the parameters and setup used in the inspections of these thirty-five CFRP composite panels. [ABSTRACT FROM AUTHOR]

Highlights • The solar loading was used as thermal stimulus. • Buildings without heating systems in operation were analyzed. • Iterative filtering minimized the influence of the shadows projected on the facade. • Sparse principal component thermography was used to detect thermal bridges. • The measurement accuracy improved after the application of the iterative filtering. Abstract The detection of thermal bridges in buildings is one of the key points to be taken into account in energy saving procedures during refurbishment works. Passive infrared thermography (IRT) has been applied for years to detect thermal bridges by referring to the International Organization for Standardization (ISO) 6781:1983. However, the successfulness of this norm is strongly affected by the detection accuracy of the thermal imprint produced on the facade by a conductive material called as "defect" in this work. The drop shadow effect, also produced by the surrounding environment on the facade under inspection, plays indeed an important role during the defect evaluation procedure since it can mask/modify the natural thermal evolution due to diffusion. Many real-life signals acting in the space physics domain exhibit variations across different temporal scales. This work presents an application of a new multiscale data analysis method, the Iterative Filtering (IF), which allows to describe the multiscale nature of an electromagnetic signal working in the long-wave infrared (LWIR) region. IF appears to be a promising method minimizing the influence of the shadows projected on the facade under inspection; subsequently, it allows the optimization of the detection of thermal bridges via sparse principal component thermography (SPCT) technique. The latter inherits the advantages of PCT allowing more flexibility by introducing a penalization term. Here is shown how the accuracy of the defect detection increases after the application of the IF mathematical procedure. Results are discussed on the basis of a couple of case studies referring to dissimilar buildings. Finally, a signal-to-noise-ratio (SNR) comparison with raw data is added to the discussion of the results. Graphical abstract Image, graphical abstract [ABSTRACT FROM AUTHOR]

• We conducted probability of detection and false positive analyses to quantitatively evaluate and compare the reliability of the automated and human-based evaluations. • We compared three image-processing-based segmentation and three sequential-signal-based deep learning algorithms for automatic data analysis. • We demonstrated the potential of using advanced deep-learning techniques to solve complex non-destructive examination data interpretation tasks. Nowadays, the reliability of data analysis and decision-making based on deep learning (DL) remains a primary concern in promoting DL technology for industrial non-destructive testing and evaluation (NDT&E). This study focuses on the quantitative assessment of the reliability of various automated data analysis techniques in NDT&E. To achieve this, optical pulsed thermography was employed to inspect three non-planar carbon-fiber-reinforced polymer (CFRP) samples, each containing embedded Teflon to simulate debonding defects. After applying thermographic signal reconstruction and first-order derivation processing to the raw thermal data, automated analysis was performed using three image-processing-based segmentation methods and three sequential-signal-based DL algorithms. Additionally, an experienced inspector manually analyzed the data for comparison purposes. Subsequently, the probability of detection and false positive analyses were conducted to quantitatively evaluate and compare the reliability of the automated and human-based evaluations. The comparison results demonstrated that optimal DL classification and advanced image-processing-based segmentation techniques could achieve performance levels close to that of human inspectors in defect detection, even for challenging non-planar CFRP samples. [ABSTRACT FROM AUTHOR]

The restoration of Cultural Heritage wouldn't be possible without the financial resources to meet Cultural Heritage needs. A smart procedure to reduce in time and funds spent for restoration is linked to a planning of the diagnostic interventions acting to predict incipient defects undetectable to the naked eye. One of the main methods to fulfil this task is infrared thermography (IRT). The aim of this study is to examine the efficiency of various mathematical techniques in thermographic data processing, with respect to the thermal excitation procedure and the type of artificial defect in a panel painting sample. One of the thermographic analyses performed was based on the pixelwise algorithm for time-derivative of temperature (PATDT). With this algorithm, Newton's cooling law was applied pixel per pixel, resulting in the computation of the cooling rate of each pixel. In addition, the capabilities of the multivariate statistical analysis methods, independent component thermography (ICT) and sparse principal component thermography (SPCT) were also investigated. In the present case study, the authors inspected possible pathologies resembling splitting areas ( i.e. , detachments) in real panel paintings, with the consequent change in the heat transfer coefficient and the heat capacity. The feasibility of the different analysis methods was illustrated with the application results. [ABSTRACT FROM AUTHOR]

The investigation of cultural heritage objects is commonly carried out by using non-destructive inspection techniques. In fact, due to the artistic peculiarities of cultural heritage objects, conventional contact-type inspection techniques such as ultrasonic scan cannot always be applied. The objective of this study is to develop a new autonomous dynamic line-scan continuous-wave terahertz (CW THz) non-destructive inspection system combined with long-wave infrared (LWIR) thermography. The newly developed system is aimed at producing clear external and internal maps for wooden objects inherent to the cultural patrimony. Additionally, a new unsupervised fusion algorithm is also proposed for multi-energy density data fusion to correct the inaccurate image acquisition caused by unbalanced line-scan exposure source. The algorithm is designed in an encoder-decoder deep learning structure with dense blocks. Finally, it is worth mentioning that the newly developed system has the capability of speedy detection (up to 49.2 mm/s) and fast in-line processing (of the order of seconds). • A new autonomous dynamic line-scan non-destructive inspection system combining with continuous-wave terahertz (CW THz) imaging and unsupervised exposure fusion was developed. • Line-scanner CW THz technology for inspection was poorly documented in the open literature. The newly developed system is aimed at producing clear external and internal maps for industrial non-destructive inspection. • A new unsupervised fusion algorithm UDFF was proposed for multi-focus image fusion to correct the inaccurate image acquisition caused by unbalanced line-scan exposure source. • The comparisons exhibit the superiority of UDFF to the state-of-the-art algorithms, especially for background noise suppression and defect details preserving. • Experiments and analyses certify the capability of the speedy detection (up to 49.2 mm/s) for the newly developed system. [ABSTRACT FROM AUTHOR]

In addition to being a serious threat to aircraft safety, kiss-bonding is also difficult to detect using conventional non-destructive testing techniques. It is the objective of this study to develop an autonomous high resolution technique to detect kiss-bonds and disbonds between carbon nanotube (CNT) skins and the host substrate of large-scale nanocomposite structures. The newly developed automated dynamic line-scan thermography (LST) approach is capable of speedy detection (up to 50 mm/s) of contact kiss-bonds as well as disbonds in laminated CNT-reinforced nanocomposite. • A novel autonomous dynamic line-scan thermographic inspection approach. • Fast high resolution inspection for large-scale components (up to 50 mm/s). • Results immediately available for analysis while the scanning process continues. • First reported research of infrared thermography for examining kiss-bonds. • Kiss-bonding inspection for carbon nanotube reinforced nanocomposites. [ABSTRACT FROM AUTHOR]

• Reports on the use of LSTM-RNN model to automatically classify four kinds of common defects and non-defective areas in honeycomb materials. • The proposed LSTM-RNN model had a greater than 90% sensitivity in classifying water and hydraulic oil ingress. • The trained LSTM-RNN model identified the non-artificial designed adhesive pooling defects which were produced in the manufacturing process. Honeycomb-structured materials are widely used in commercial and military aircraft. Manufacturing defects and damage during operation have become primary safety threats. This has increased the demand for non-destructive testing (NDT) for damage and flaws during aircraft operation and maintenance. Characterizing, or classifying defects, in addition to detecting them, is important. Classifying the liquids trapped in aircraft honeycomb cells is an example. A small amount of ingressed water is often tolerable, whereas a small amount of hydraulic oil may be an early warning of hydraulic system malfunction. This paper proposes an infrared thermography-based NDT technique and a long short term memory recurrent neural network (LSTM-RNN) model which automatically classifies common defects occurring in honeycomb materials. These including debonding, adhesive pooling, and liquid ingress. This LSTM-based algorithm has a greater than 90% sensitivity in classifying water, and hydraulic oil ingress. It has a greater than 70% sensitivity in classifying debonding and adhesive pooling. [ABSTRACT FROM AUTHOR]

This paper reports on the use of a neural network in infrared thermography to classify defects, such as air, oil, and water, which can degrade material performance. A finite element method and experiment were adopted to simulate air, water, and oil ingress. Raw data, and thermographic signal reconstruction coefficients were used to train, and test the two multilayer, feed-forward NN models. Quantitative comparisons showed that the model using coefficients as features performed better than the one using raw data. It was more precise and had better test repeatability. This indicates the model is more generalizable. [ABSTRACT FROM AUTHOR]

In contrast to conventional non-destructive testing (NDT) and non-destructive evaluation (NDE) methodologies, including radiography, ultrasound, and eddy current analysis, coplanar capacitive sensing technique emerges as a novel and promising avenue within the field. This paper endeavors to elucidate the efficacy of coplanar capacitive sensing, also referred to as capacitive imaging (CI), within the realm of NDT. Leveraging extant scholarly discourse, this review offers a comprehensive and methodical examination of the coplanar capacitive technique, encompassing its fundamental principles, factors influencing sensor efficacy, and diverse applications for defect identification across various NDT domains. Furthermore, this review deliberates on extant challenges and anticipates future trajectories for the technique. The manifold advantages inherent to coplanar capacitive sensing vis-à-vis traditional NDT methodologies not only afford its versatility in application but also underscore its potential for pioneering advancements in forthcoming applications.

Efficient multi-modal image fusion plays an important role in the non-destructive evaluation (NDE) of infrastructures, where an essential challenge is the precise visualizing of defects. While automatic defect detection represents a significant advancement, the determination of the precise location of both surface and subsurface defects simultaneously is crucial. Hence, visible and infrared data fusion strategies are essential for acquiring comprehensive and complementary information to detect defects across vast structures. This paper proposes an infrared and visible image registration method based on Euclidean evaluation together with a trade-off between key-point threshold and non-maximum suppression. Moreover, we employ a multi-modal fusion strategy to investigate the robustness of our image registration results.

Efficient image stitching plays a vital role in the Non-Destructive Evaluation (NDE) of infrastructures. An essential challenge in the NDE of infrastructures is precisely visualizing defects within large structures. The existing literature predominantly relies on high-resolution close-distance images to detect surface or subsurface defects. While the automatic detection of all defect types represents a significant advancement, understanding the location and continuity of defects is imperative. It is worth noting that some defects may be too small to capture from a considerable distance. Consequently, multiple image sequences are captured and processed using image stitching techniques. Additionally, visible and infrared data fusion strategies prove essential for acquiring comprehensive information to detect defects across vast structures. Hence, there is a need for an effective image stitching method appropriate for infrared and visible images of structures and industrial assets, facilitating enhanced visualization and automated inspection for structural maintenance. This paper proposes an advanced image stitching method appropriate for dual-sensor inspections. The proposed image stitching technique employs self-supervised feature detection to enhance the quality and quantity of feature detection. Subsequently, a graph neural network is employed for robust feature matching. Ultimately, the proposed method results in image stitching that effectively eliminates perspective distortion in both infrared and visible images, a prerequisite for subsequent multi-modal fusion strategies. Our results substantially enhance the visualization capabilities for infrastructure inspection. Comparative analysis with popular state-of-the-art methods confirms the effectiveness of the proposed approach.

Infrared thermography (IRT), is one of the most interesting techniques to identify different kinds of defects, such as delamination and damage existing for quality management of material. Objective detection and segmentation algorithms in deep learning have been widely applied in image processing, although very rarely in the IRT field. In this paper, spatial deep-learning image processing methods for defect detection and identification were discussed and investigated. The aim in this work is to integrate such deep-learning (DL) models to enable interpretations of thermal images automatically for quality management (QM). That requires achieving a high enough accuracy for each deep-learning method so that they can be used to assist human inspectors based on the training. There are several alternatives of deep Convolutional Neural Networks for detecting the images that were employed in this work. These included: 1. The instance segmentation methods Mask-RCNN (Mask Region-based Convolutional Neural Networks) and Center-Mask; 2. The independent semantic segmentation methods: U-net and Resnet-U-net; 3. The objective localization methods: You Only Look Once (YOLO-v3) and Faster Region-based Convolutional Neural Networks (Fast-er-RCNN). In addition, a regular infrared image segmentation processing combination method (Absolute thermal contrast (ATC) and global threshold) was introduced for comparison. A series of academic samples composed of different materials and containing artificial defects of different shapes and nature (flat-bottom holes, Teflon inserts) were evaluated, and all results were studied to evaluate the efficacy and performance of the proposed algorithms.

In the present study, a relatively novel non-destructive testing (NDT) method called the coplanar capacitive sensing technique was applied in order to detect different sizes of rebars in a reinforced concrete (RC) structure. This technique effectively detects changes in the dielectric properties during scanning in various sections of concrete with and without rebars. Numerical simulations were carried out by three-dimensional (3D) finite element modelling (FEM) in COMSOL Multiphysics software to analyse the impact of the presence of rebars on the electric field generated by the coplanar capacitive probe. In addition, the effect of the presence of a surface defect on the rebar embedded in the concrete slab was demonstrated by the same software for the first time. Experiments were performed on a concrete slab containing rebars, and were compared with FEM results. The results showed that there is a good qualitative agreement between the numerical simulations and experimental results.

This paper presents a thermal imaging dataset from composite material samples (carbon and glass fiber reinforced plastic) that were inspected by pulsed thermography with the goal of detecting and characterizing subsurface defective zones (Teflon inserts representing delaminations between plies). The pulsed thermography experiment was applied to 6 academic plates (inspected from both sides) all having the dimensions of 300 mm x 300 mm x 2 mm and same distribution of defects but made of different materials: three plates on carbon fiber-reinforced plastic (CFRP) and three plates made on glass fiber reinforced plastic (GFRP) specimens with three different geometries: planar, curved and trapezoidal. Each plate contains 25 inserts having length/depth ratios between 1.7 and 75. Two FX60 BALCAR photographic flashes (6.2 kJ per flash) were used to generate the heat pulse (2 ms duration), an X6900 FLIR infrared camera using ResearchIR software to record the thermal images and a custom-built software/control unit to synchronize data recording with pulse generation. Finally, the dataset proposed consists of 12 sequences of approximately 2000 images of 512 × 512 pixels each., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article., (© 2020 The Author(s).)