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Pulsed thermography is a technique of significant interest in non-destructive testing, particularly in defect detection and depth characterisation of composite materials. This study presents an innovative methodology for simultaneously detecting defects and estimating depth using a combination of sequenced thermal signal encoding and a two-dimensional convolution neural network (CNN) model. We compare the results of the proposed method with those obtained from the feed-forward neural network (FFNN), a one-dimensional CNN, and a long short-term memory recurrent neural network (LSTM-RNN). The findings demonstrate that the proposed approach exhibits superior accuracy and robustness compared to the benchmarks. [ABSTRACT FROM AUTHOR]

Modern techniques such as laser-based additive manufacturing (LBAM) technologies are being used nowadays in many industries for the fabrication of components of complicated geometries in smaller lead times. To compete with the conventional fabrication route, additive manufacturing (AM) technology must progress with appropriate NDE methods to ensure defect-free components. The common defects noticed in the AM components are gas porosities, clusters of porosities, micro-cracks, balling, lack of fusion and layer delamination among others. This paper aims to inspect subsurface defects in AM products which are one of the more challenging issues in AM. The surface finish of the AM components in as-printed condition is not amenable for the detection of subsurface defects for NDE using contact methods, e.g. ultrasonic techniques. Laser shearography and pulsed thermography by thermal excitation are extensively used as noncontact NDT techniques for the detection of subsurface defects in composite materials. Noncontacting NDT techniques may play a significant role in the in-process inspection of AM components, especially subsurface defects. This paper presents the capability, advantages and limitations of pulsed thermography and laser shearography techniques for the assessment of engineered defects in aluminum alloy coupon made through the laser powder bed fusion (LPBF) AM process. [ABSTRACT FROM AUTHOR]

Combinative methodologies have the potential to address the drawbacks of unimodal non-destructive testing and evaluation (NDT & E) when inspecting multilayer structures. The aim of this study is to investigate the integration of information gathered via phased-array ultrasonic testing (PAUT) and pulsed thermography (PT), addressing the challenges posed by surface-level anomalies in PAUT and the limited deep penetration in PT. A center-of-mass-based registration method was proposed to align shapeless inspection results in consecutive insertions. Subsequently, the aligned inspection images were merged using complementary techniques, including maximum, weighted-averaging, depth-driven combination (DDC), and wavelet decomposition. The results indicated that although individual inspections may have lower mean absolute error (MAE) ratings than fused images, the use of complementary fusion improved defect identification in the total number of detections across numerous layers of the structure. Detection errors are analyzed, and a tendency to overestimate defect sizes is revealed with individual inspection methods. This study concludes that complementary fusion provides a more comprehensive understanding of overall defect detection throughout the thickness, highlighting the importance of leveraging multiple modalities for improved inspection outcomes in structural analysis. [ABSTRACT FROM AUTHOR]

Through-transmission pulsed thermography (PT) is an effective non-destructive testing (NDT) technique for assessing material thermal diffusivity. However, the current literature indicates that the technique has lagged behind the reflection mode in terms of technique development despite it offering better defect resolution and the detection of deeper subsurface defects. Existing thermal diffusivity measurement systems require costly setups, including temperature-controlled chambers, multiple calibrations, and strict sample size requirements. This study presents a simple and repeatable methodology for determining thermal diffusivity in a laboratory setting using the through-transmission approach by incorporating both finite element analysis (FEA) and laboratory experiments. A full-factorial design of experiments (DOE) was implemented to determine the optimum flash energy and sample thickness for a reliable estimation of thermal diffusivity. The thermal diffusivity is estimated using the already established Parker’s half-rise equation and the recently developed new least squares fitting (NLSF) algorithm. The latter not only estimates thermal diffusivity but also provides estimates for the input flash energy, reflection coefficient, and the time delay in data capture following the flash event. The results show that the NLSF is less susceptible to noise and offers more repeatable values for thermal diffusivity measurements compared to Parker, thereby establishing it as a more efficient and reliable technique.

Pulsed thermography is widely used for non-destructive testing of various materials. The temperature profile obtained after pulse heating is used to characterize the underlying defects in an object. In this paper, the automation of the process of defect visualization and depth quantification in pulsed thermography through various deep learning algorithms is reported. Stainless steel plate with artificial defects is considered for analysis. The raw temperature data is smoothed using moving average, Savitzky-Golay and quadratic regression filters to reduce noise. Thermal signal reconstruction, the conventional method to eliminate noise, is also used for generating filtered datasets. Defect visualization refers to identifying and locating the defects in an image sample and Mask region convolutional neural network (Mask R-CNN) is considered for not just detecting the defects but also locating them on the image. The located defects are utilized for depth estimation using the following networks-multi-layer perceptron (MLP), long short-term memory (LSTM) and gated recurrent units (GRU). The input to the networks is the temperature contrast characteristics which symbolizes the difference in temperature over defective and non-defective areas measured over 250 time points and output of the networks is the estimated depth. The study shows that LSTM based approach provides the least percentage error of 5.5% and is a very suitable approach for automation of defect characterization in pulsed thermography. [ABSTRACT FROM AUTHOR]

Pulsed thermography is an established nondestructive evaluation technology that excels at detecting and characterizing subsurface defects within specimens. A critical challenge in this domain is the accurate estimation of defect depth. In this paper, a new publicly accessible pulsed infrared dataset for PVC specimens is introduced. It was enriched with 3D positional information to advance research in this area. To ensure the labeling quality, a comparative analysis of two distinct data labeling methods was conducted. The first method is based on human domain expertise, while the second method relies on 3D CAD images. The analysis showed that the CAD-based labeling method noticeably enhanced the precision of defect dimension quantification. Additionally, a sophisticated deep learning model was employed on the data, which were preprocessed by different methods to predict both the two-dimensional coordinates and the depth of the identified defects. [ABSTRACT FROM AUTHOR]

Pulsed thermography has been applied to the quantitative characterization of the insertions of two ancient bronzes, the Boxer at Rest and the Hellenistic Prince. The analysis of the thermographic signal time dependence performed by a specifically developed model enabled the evaluation of the insertions' thickness and of elements which could provide indications about the procedure followed for their insertion. This could be achieved by exploiting a semi‐empirical relation establishing the thermal diffusivity dependence on the total effective weighted concentration of Sn and Pb atoms obtained from the analysis of the values determined on samples containing different concentrations of Sn and Pb. [ABSTRACT FROM AUTHOR]

The detection of defects within a material is a vital research area. The popular and effective quantitative prediction technique widely used is pulsed thermography (PT). Therefore, based on the relationship between depth and particular time, namely specific characteristic time (SCT), many methods have been reported in the literature. In this paper, a comparison between the use of five methods, namely: peak contrast time (PCT), peak slope time (PST), absolute peak slope time (APST), logarithmic peak second-derivative (LPSD), and early detection (ED) for the detection of defects in the composite material is presented. First, a brief theoretical modeling of the thermographic process associated with an infrared technique of each method is described. Then, numerical and experimental cases are performed in which the results are analyzed and compared. Regardless of the processing time, the PST, APST, and LPSD methods appear to be accurate in determining depth. Nevertheless, the TSR method coupled with ED approach is preferable and particularly interesting provided that the studied area is properly identified. [ABSTRACT FROM AUTHOR]

Featured Application: It is demonstrated that a line scan thermography (LST) system using a microbolometer camera can be used for the detection of porosity and impact damage in carbon fiber composites. This is interesting for the implementation of inline LST inspections during the production of aerospace components. In addition, pulsed thermography (PT) is used for the determination of the thermal diffusivity and the estimation of the defect depths using pulsed phase thermography. Nondestructive testing (NDT) of composite materials is of paramount importance to the aerospace industry. Several NDT methods have been adopted for the inspection of components during production and all through the aircraft service life, with infrared thermography (IRT) techniques, such as line scan thermography (LST) and pulsed thermography (PT), gaining popularity thanks to their rapidity and versatility. On one hand, LST is an attractive solution for the fast inspection of large and complex geometry composite parts during production. On the other hand, PT can be employed for the characterization of composite materials, e.g., the determination of thermal diffusivity and defect depth estimation. In this study, the use of LST with an uncooled microbolometer camera is explored for the identification of artificially produced porosity and barely visible impact damage (BVID) on academic samples. The performance of LST is quantitatively assessed with respect to PT (considered the gold standard in this case) using a high-definition cooled camera through the contrast-to-noise ratio (CNR) criterium. It is concluded that, although in most cases the measured CNR values were higher for PT than for LST (as expected since a high-definition camera and longer acquisition times were used), the majority of the defects were clearly detected (CNR ≥ 2.5) by LST without the need of advanced signal processing, proving the suitability of LST for the inspection of aerospace composite components. Furthermore, the deepest defect investigated herein (z ≈ 3 mm) was detected solely by LST combined with signal processing and spatial filtering (CNR = 3.6) and not by PT (since pulse heating was not long enough for this depth). In addition, PT was used for the determination of the thermal diffusivity of all samples and the subsequent depth estimation of porosity and damaged areas by pulsed phase thermography (PPT). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Corrosion of metals can occur under coatings making early visual detection almost impossible which enables corrosion to progress undetected. Nondestructive evaluation can be used to inspect parts during their life. Thermography uses the difference in thermal properties between defective and sound areas to locate defects by applying a short pulse of heat and monitoring the surface temperature of the sample with an infrared camera. However, currently, inspection is reliant on highly trained operators to analyze images for defects and in the case of corrosion, judge its growth. The aim of this research was to develop and test a novel algorithm to identify and quantify the onset of corrosion under coatings and monitor the growth. Using this algorithm, the corrosion was autonomously detected in a variety of mild steel samples exposed to three different corrosive environments. The method successfully detected, quantified, and monitored the growth of corrosion under coatings accurately and robustly using thermal images gathered by a microbolometer infrared camera. The extent of corrosion was reported with an accuracy of 3% absolute error when compared to the true extent. [ABSTRACT FROM AUTHOR]

Pulsed thermography is a nondestructive method commonly used to explore anomalies in composite materials. This paper presents a procedure for the automated detection of defects in thermal images of composite materials obtained with pulsed thermography experiments. The proposed methodology is simple and novel as it is reliable in low-contrast and nonuniform heating conditions and does not require data preprocessing. Nonuniform heating correction and the gradient direction information combined with a local and global segmentation phase are used to analyze carbon fiber-reinforced plastic (CFRP) thermal images with Teflon inserts with different length/depth ratios. Additionally, a comparison between the actual depths and estimated depths of detected defects is performed. The performance of the nonuniform heating correction proposed method is superior to that obtained on the same CFRP sample analyzed with a deep learning algorithm and the background thermal compensation by filtering strategy. [ABSTRACT FROM AUTHOR]

Among the different forms of art, the puppet theatre constitutes a long-standing and often little-known tradition. The use of puppets as support for acting dates back to the Greek age, and it was mainly developed during the modern period. The reason for such a large diffusion was due to the possibility of using affordable materials, such as papier-mâché, for the puppets’ manufacture. In this paper, a method based on the combined use of pulsed thermography (PT) and mid-wave infrared reflectography (MIR) is, for the first time, proposed for the characterization of papier-mâché artworks. In particular, some puppets belonging to the collection of the Museo delle Civiltà in Rome and made by Olga Lampe Minelli, a 20th-century puppet master, were investigated in order to detect damaged areas, such as those affected by insect attacks, and, consequently, to specifically plan suitable restoration works. Finally, the investigations were also carried out after the restoration to evaluate the effectiveness of the adopted treatments.

Owing to its advantages of low-cost and fast detection, pulsed thermography has become a promising technique to detect subsurface defects in materials of carbon fiber reinforced polymer (CFRP). Since defect signals in the detected results always suffer from low contrast due to the instability of detecting environment, feature extraction methods are required to enhance the visualization of defects. However, many state-of-the-art feature extraction methods have difficulties in overcoming the interference from noise and background, so that their effects are limited in highlighting the defects. To solve this problem, a novel methodology of combining signal filtering with feature extraction is proposed in this paper. In this approach, thermal images are first smoothed by a difference of Gaussian convolutional (DoGC) filters, which is designed to eliminate noise and uneven background based on their frequencies. Furthermore, the method of sparse principal component thermography (SPCT) is adopted to extract the features of defects. Two experiments on sample laminates have suggested that, DoGC-SPCT is superior to other feature extraction methods in the following aspects. Firstly, the DoGC filter can effectively eliminate most of the interference, thus facilitating defect identification during the process of feature extraction. Secondly, the computational outcomes show that DoGC-SPCT leads to higher values in the index of signal to noise ratios for the defects. Finally, DoGC-SPCT leads to higher interpretability, which has smoother background in the obtained results.

Pulsed thermography is an established nondestructive evaluation technology that excels at detecting and characterizing subsurface defects within specimens. A critical challenge in this domain is the accurate estimation of defect depth. In this paper, a new publicly accessible pulsed infrared dataset for PVC specimens is introduced. It was enriched with 3D positional information to advance research in this area. To ensure the labeling quality, a comparative analysis of two distinct data labeling methods was conducted. The first method is based on human domain expertise, while the second method relies on 3D CAD images. The analysis showed that the CAD-based labeling method noticeably enhanced the precision of defect dimension quantification. Additionally, a sophisticated deep learning model was employed on the data, which were preprocessed by different methods to predict both the two-dimensional coordinates and the depth of the identified defects.

Quantitative defect and damage reconstruction play a critical role in industrial quality management. Accurate defect characterisation in Infrared Thermography (IRT), as one of the widely used Non-Destructive Testing (NDT) techniques, always demands adequate pre-knowledge which poses a challenge to automatic decision-making in maintenance. This paper presents an automatic and accurate defect profile reconstruction method, taking advantage of deep learning Neural Networks (NN). Initially, a fast Finite Element Modelling (FEM) simulation of IRT is introduced for defective specimen simulation. Mask Region-based Convolution NN (Mask-RCNN) is proposed to detect and segment the defect using a single thermal frame. A dataset with a single-type-shape defect is tested to validate the feasibility. Then, a dataset with three mixed shapes of defect is inspected to evaluate the method's capability on the defect profile reconstruction, where an accuracy over 90% on Intersection over Union (IoU) is achieved. The results are compared with several state-of-the-art of post-processing methods in IRT to demonstrate the superiority at detailed defect corners and edges. This research lays solid evidence that AI deep learning algorithms can be utilised to provide accurate defect profile reconstruction in thermography NDT, which will contribute to the research community in material degradation analysis and structural health monitoring. [ABSTRACT FROM AUTHOR]

Thermal imaging plays a vital role in structural health monitoring of various materials and provides insight into the defect present due to aging, deterioration, and fault during construction. This study investigated the effectiveness of spatial filters during pre-processing of thermal images and a correlation technique in post-processing, as well as exploited its application in non-destructive testing and evaluation of defects in steel structures. Two linear filters (i.e., Gaussian and Window Averaging) and a non-linear filter (i.e., Median) were implemented during pre-processing of a pulsed thermography image sequence. The effectiveness of implemented filters was then assessed using signal to noise ratio as a quality metric. The result of pre-processing revealed that each implemented filter is capable of reducing impulse noise and producing high-quality images; additionally, when comparing the signal to noise ratio, the Gaussian filter dominated both Window Averaging and Median filters. Defect size was determined using a correlation technique on a sequence of pulsed thermography images that had been pre-processed with a Gaussian filter. Finally, it is concluded that the correlation technique could be applied to the fast measurement of defect size, even though the accuracy may depend on the detection limit of thermography and defect size to depth ratio. [ABSTRACT FROM AUTHOR]

Among the different forms of art, the puppet theatre constitutes a long-standing and often little-known tradition. The use of puppets as support for acting dates back to the Greek age, and it was mainly developed during the modern period. The reason for such a large diffusion was due to the possibility of using affordable materials, such as papier-mâché, for the puppets' manufacture. In this paper, a method based on the combined use of pulsed thermography (PT) and mid-wave infrared reflectography (MIR) is, for the first time, proposed for the characterization of papier-mâché artworks. In particular, some puppets belonging to the collection of the Museo delle Civiltà in Rome and made by Olga Lampe Minelli, a 20th-century puppet master, were investigated in order to detect damaged areas, such as those affected by insect attacks, and, consequently, to specifically plan suitable restoration works. Finally, the investigations were also carried out after the restoration to evaluate the effectiveness of the adopted treatments. [ABSTRACT FROM AUTHOR]

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Subsurface defects can be detected by the pulsed thermography (PT) technique analysing the raw thermal data with the application of different post-processing algorithms. In this regard, different methods, based on one-dimensional models, are used in the literature to estimate the depth and size of defects. Two of the most established methods are the thermal signal reconstruction (TSR) and the pulsed phase thermography (PPT) algorithms. These latter require a careful set up of the testing parameters such as the frame rate, the truncation window size, and the energy density to obtain an accurate estimation of both depths and sizes of defects. Even if some works have already investigated the issue of defect characterization, there are few works in which the correct procedures to obtain both the size and depth were deeply explained, above all for real components with real defects. The aim of this work is to propose a new empirical procedure to obtain depth and size estimation of the defects using the pulsed thermography technique and in particular the principal component thermography (PCT) algorithm. The proposed procedure is based on the experimental observation that exists a linear correlation between the defect contrasts and the relative aspect ratios. In this way, by means of a master specimen, a calibration curve can be obtained considering a suitable truncation window of the analysed data. Then, the size and depth of defects have been retrieved imposing threshold criteria. The procedure is quite general, and it can be also tested with other algorithms. Different experimental tests have been carried out on two materials, aluminium and glass fiber reinforced polymer (GFRP) and then the procedure has been applied and validated both on simulated (flat bottom holes) and real defects. [ABSTRACT FROM AUTHOR]

Nondestructive testing (NDT) of composite materials is of paramount importance to the aerospace industry. Several NDT methods have been adopted for the inspection of components during production and all through the aircraft service life, with infrared thermography (IRT) techniques, such as line scan thermography (LST) and pulsed thermography (PT), gaining popularity thanks to their rapidity and versatility. On one hand, LST is an attractive solution for the fast inspection of large and complex geometry composite parts during production. On the other hand, PT can be employed for the characterization of composite materials, e.g., the determination of thermal diffusivity and defect depth estimation. In this study, the use of LST with an uncooled microbolometer camera is explored for the identification of artificially produced porosity and barely visible impact damage (BVID) on academic samples. The performance of LST is quantitatively assessed with respect to PT (considered the gold standard in this case) using a high-definition cooled camera through the contrast-to-noise ratio (CNR) criterium. It is concluded that, although in most cases the measured CNR values were higher for PT than for LST (as expected since a high-definition camera and longer acquisition times were used), the majority of the defects were clearly detected (CNR ≥ 2.5) by LST without the need of advanced signal processing, proving the suitability of LST for the inspection of aerospace composite components. Furthermore, the deepest defect investigated herein (z ≈ 3 mm) was detected solely by LST combined with signal processing and spatial filtering (CNR = 3.6) and not by PT (since pulse heating was not long enough for this depth). In addition, PT was used for the determination of the thermal diffusivity of all samples and the subsequent depth estimation of porosity and damaged areas by pulsed phase thermography (PPT).

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]

Statistical methods, such as Principal component thermography (PCT) and Sparse Principal component thermography (SPCT) have been widely used for signal enhancement of subsurface defects in pulsed thermographic (PT) detection of composite materials. However, PCT and SPCT mainly focus on the temporal variation of thermographic data while leaving the structural variation un-modeled. In this paper, a method of sparse structural principal component thermography (S2PCT) is proposed. In S2PCT, the operation of shift-sampling is first conducted to augment the original thermographic matrix and capture the structural relationships inside the original thermal images. After that, the sparse trick is applied to extract features for defects and reduce signals of noise and non-uniform background. In the case study, two carbon fiber reinforced polymer (CFRP) specimens are detected with PT and the proposed S2PCT is evaluated for visualization enhancing purpose. The results of the experiments have revealed the proposed method helps to highlight the defect signals during the augmentation process, thus showing higher flexibility in reducing interference from background signals. As a conclusion, compared to the original statistical methods, S2PCT has better performance in visualization enhancing of defects. [ABSTRACT FROM AUTHOR]

Non-destructive testing (NDT) is a broad group of testing and analysis techniques used in science and industry to evaluate the properties of a material, structure, or system for characteristic defects and discontinuities without causing damage. Recently, infrared thermography is one of the most promising technologies as it can inspect a large area quickly using a non-contact and non-destructive method. Moreover, thermography testing has proved to be a valuable approach for non-destructive testing and evaluation of structural stability of materials. Pulsed thermography is one of the active thermography technologies that utilizes external energy heating. However, due to the non-uniform heating, lateral heat diffusion, environmental noise, and limited parameters of the thermal imaging system, there are some difficulties in detecting and characterizing defects. In order to improve this limitation, various signal processing techniques have been developed through many previous studies. This review presents the latest advances and exhaustive summary of representative signal processing techniques used in pulsed thermography according to physical principles and thermal excitation sources. First, the basic concept of infrared thermography non-destructive testing is introduced. Next, the principle of conventional pulsed thermography and signal processing technologies for non-destructive testing are reviewed. Then, we review advances and recent advances in each signal processing. Finally, the latest research trends are reviewed. [ABSTRACT FROM AUTHOR]

To ensure the quality of materials, pulsed thermography (PT) is an effective technique to detect the presence of internal defects in carbon fiber reinforced polymers (CFRP). However, non-uniform thermal excitation during the testing often leads to uneven heat distribution within the original thermal images, and the limited heat absorption by the surface of specimen may cause low-contrast defect signals that further impair the identification of defects. In response to these challenges, this study introduces an enhancement framework, termed Reflectance-Correction Retinex (RCR). In the RCR framework, the single-scale Retinex method is first applied to equalize the non-uniform background within a thermal image, and a sigmoid modification is proposed to adaptively amplify the contrast between defects and their background. The RCR framework is tested on PT images of man-made CFRP specimens. The experimental results show that our proposed method substantially enhances the visual quality of PT images, which facilitates precision and reliability in defect detection applications. [Display omitted] • Pulsed thermography is applied to detect internal defects inside CFRP. • An RCR framework is proposed to enhance visual quality of PT images. • SSR is applied to equalize non-uniform background of PT images. • An adaptive sigmoid modification is proposed for signal enhancement. • The RCR framework facilitates the accuracy of defect detection. [ABSTRACT FROM AUTHOR]

This paper presents two techniques of active thermography i.e. the pulsed thermography technique and the step heating technique. The aim of this article is to compare these two techniques and present the possibilities, advantages and limitations of their use in the context of non-destructive testing of composite materials. The experimental section presents the results of tests carried out on samples of the polymer composites reinforced with glass fiber.

Pulsed thermography is an active non-destructive technique which uses optical excitation source to stimulate heating of the object under investigation. This work is devoted to the simulation of the pulsed thermography method in a steel plate with a ceramic coating containing artificial defects of various depths and sizes. The simulation has been carried out on the base of a model which takes into account complex heat exchange of the sample with the surrounding by convection, conduction and radiation. Comparison of the temperature contrast with the experimental data has shown that the results are in a good qualitative and quantitative agreement in all stages of the cooling process. Due to the fact that the temperature contrast is often susceptible to surface noise of various nature the Kalman-based signal processing technique was developed. The comparative analysis has shown that the proposed filtration technique provides a better signal-to-noise ratio in comparison to the considered well-known techniques of signal reconstruction when proper calibration of the filtration parameters is carried out

The present work introduces a different data processing strategy, proposed in order to improve sub-surface defect detection on industrial composites; in addition, a resume of thermal data processing with most common algorithms in literature is presented and applied with new data. A deep comparison between the common absolute contrast, DAC, PCT, TSR and derivative methods and a new proposed contrast mapping procedure is implemented. Thermographic inspection was done in reflection mode on a Glass Fiber Reinforced Plastic plate, with flat bottom hole defects. Thermal data computation method is found to be critical for simultaneous defect detection and automatic mapping, optimized to identify defect boundaries at specific depth, with help of accurate image processing, implemented in a Matlab GUI for a reliable and rapid characterization of internal damage. The new processing approach, the Local Boundary Contrast method, elaborates different contrast maps and facilitates recognition of damage extension. Tanimoto criterion and the signal-to-noise ratio method were applied as a criterion to assess defect detectability of various processing methods. [ABSTRACT FROM AUTHOR]

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.

Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity, and low cost. However, the thorniest issue for the wider application of IRT is quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRUs) in composite material samples via pulsed thermography (PT). Finite Element Method (FEM) modeling provides the economic examination of the response pulsed thermography. In this work, Carbon Fiber Reinforced Polymer (CFRP) specimens embedded with flat bottom holes are stimulated by a FEM modeling (COMSOL) with precisely controlled depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the stimulated CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions. [ABSTRACT FROM AUTHOR]

The present work resumes thermal data processing with most common algorithms in literature and introduces in addition a different data processing strategy, proposed to improve subsurface defect detection on industrial composites. These materials are successfully controlled with infrared Non-Destructive Investigations, since defects are easily detected by temperature response under thermal pulses with reliable results. To reduce application limits for non-destructive inspections, the proposed research shows possibility to combine pulsed thermographic technique with accurate image-processing methods implemented in Matlab environment for a reliable and rapid characterization of subsurface and internal damage. Thermal processing methods are evaluated for the proposed case of study, as the well-established DAC, PCT, TSR procedures. In addition, the authors proposed a better defect characterization that is achieved with refined data processing and accurate experimental procedures, providing detailed contrast maps where defects are easily distinguished. This improved algorithm automates the defect mapping and enhances the accuracy of defects inspection, optimized to identify defect boundaries according to spatial variations in neighboring of each calculation point of the whole thermal frame. Thermal data are evaluated with standard methods and the local boundary method is for carbon-fiber composite specimens with artificial defects, evaluating processed images obtained by different methods employing the Tanimoto criterion. Proposed thermal computation method is found suitable for automatic mapping of defect distribution and optimized for simultaneous defect boundaries' detection in terms of Tanimoto criterion, in the inspected structure. In addition, ultrasonic controls are carried out for detection comparison between different control procedures. [ABSTRACT FROM AUTHOR]

In this work, the pulsed thermography process applied to inspect and analyze the internal corrosion defect in a pipe. The study carried out in two stages, the first one characterizes the control of the pipe at a standstill by considering the air inside the pipe, and the second one characterizes the control during operation by introducing a turbulent flow of seawater into the simulation. For this purpose, a 3D model of a fluid pipe containing three forms of internal rust defects performed using finite element software. The influence of parameters such as the size and penetration of rust into the tube evaluated by analyzing the contrast of the corresponding thermal images and the temporal and spatial variation of the temperature. The tube thickness introduced as a parameter influencing detection. [ABSTRACT FROM AUTHOR]

Material defects in fiber-reinforced polymers such as delaminations can rapidly degrade the material properties or can lead to the failure of a component. Pulse thermography (PT) has proven to be a valuable tool to identify and quantify such defects in opaque materials. However, quantification of delaminations within semitransparent materials is extremely challenging. We present an approach to quantify delaminations within materials being semitransparent within the wavelength ranges of the optical excitation sources as well as of the infrared (IR) camera. PT experimental data of a glass fiber-reinforced polymer with a real delamination within the material were reconstructed by one-dimensional (1D) mathematical models. These models describe the heat diffusion within the material and consider semitransparency to the excitation source as well to the IR camera, thermal losses at the samples surfaces and a thermal contact resistance between the two layers describing the delamination. By fitting the models to the PT data, we were able to determine the depth of the delamination very accurately. Additionally, we analyzed synthetic PT data from a 2D simulation with our 1D-models to show how the thermal contact resistance is influenced by lateral heat flow within the material. [ABSTRACT FROM AUTHOR]

Among widely used non-destructive testing (NDT) methods, infrared thermography (IRT) has gained importance due to its fast, whole-field, remote and quantitative inspection capabilities for the evaluation of various materials. Being fast and easy to implement, pulsed thermography (PT) plays a vital role in the infrared thermographic community. This paper provides a physical insight into the selection of empirical orthogonal functions obtained from principal component pulsed thermography for the detection of subsurface defects located inside a mild steel specimen. [ABSTRACT FROM AUTHOR]

In this study, a dynamic compression reconstruction (DCR) method for pulsed thermography sequence was proposed, which could effectively compress the original data length of pulsed thermography and significantly enhance the defect signal characteristics. The DCR method modulated the length of the pulsed thermography sequence to dynamically compress the image sequence with a newly defined F-function, thus reconstructing the DCR thermal image sequence. The length of the DCR thermal image sequence was compressed to 1/4 the length of the raw thermal image sequence. We assessed the DCR method using the pulsed simulation data of a homogeneous flat plate with circular defects in the subsurface, and found that the proposed DCR method could suppress temporal noise, reduce the edge blur of defects, and enhance defect recognition. To further verify the validity and practicability of this method, the pulsed thermography experimental data of the glass fiber reinforced plastic (GFRP) specimen was processed using the DCR method, and the experimental results were consistent with the numerical analysis results of the simulated data. The DCR thermal image of the GFRP specimen had lower spatial noise and higher signal-to-noise ratio (SNR) of the defects than the raw thermal image. Finally, the DCR method was compared with other pulse thermal signal processing methods, including TSR, PPT, and PCT, indicating that the pulsed thermal images processed by the DCR method could achieve the highest SNR of the defects. • Automatic update of pulse thermography sequence length for dynamic compression. • Effective suppression of the temporal noise in thermographic signal. • Significant peak and inversion in the reconstructed thermal signal corresponding to the defect region. • Remarkable improvement of the signal-to-noise ratio of defects in thermal images. • The DCR method can achieve the highest defect's SNR compared with TSR, PPT and PCT method. [ABSTRACT FROM AUTHOR]

Non-destructive testing (NDT) is a broad group of testing and analysis techniques used in science and industry to evaluate the properties of a material, structure, or system for characteristic defects and discontinuities without causing damage. Recently, infrared thermography is one of the most promising technologies as it can inspect a large area quickly using a non-contact and non-destructive method. Moreover, thermography testing has proved to be a valuable approach for non-destructive testing and evaluation of structural stability of materials. Pulsed thermography is one of the active thermography technologies that utilizes external energy heating. However, due to the non-uniform heating, lateral heat diffusion, environmental noise, and limited parameters of the thermal imaging system, there are some difficulties in detecting and characterizing defects. In order to improve this limitation, various signal processing techniques have been developed through many previous studies. This review presents the latest advances and exhaustive summary of representative signal processing techniques used in pulsed thermography according to physical principles and thermal excitation sources. First, the basic concept of infrared thermography non-destructive testing is introduced. Next, the principle of conventional pulsed thermography and signal processing technologies for non-destructive testing are reviewed. Then, we review advances and recent advances in each signal processing. Finally, the latest research trends are reviewed.

Cold Spray is an innovative technology to create coatings through the impact of metallic particles on substrates. Its application to composites’ surfaces is recently attracting the attention of the scientific community thanks to the possibility to functionalize and improve their thermal and wear properties. Within this context, the generation of the first metal-to-composite layer is fundamental. This work presented an experimental investigation of a composite panel, reinforced with glass fibers and coated with aluminum particles. The coating investigation was carried out through active pulsed thermography, analyzing the thermal response of single and double hatches. The thermal outputs were compared with a standard microscopic analysis, with a critical discussion supporting the identification of factors that influence the thermal response to the pulse: (1) layer’s thickness; (2) cold spray coverage; (3) layer compactness; (4) particle-substrate adhesion; (5) particle’s oxidation; and (6) surface roughness.

Pulsed thermography is a commonly used non-destructive testing method and is increasingly studied for the assessment of advanced materials such as carbon fibre-reinforced polymer (CFRP). Different processing approaches are proposed to detect and characterize anomalies that may be generated in structures during the manufacturing cycle or service period. In this study, matrix decomposition using Robust PCA via Inexact-ALM is investigated as a pre- and post-processing approach in combination with state-of-the-art approaches (i.e., PCT, PPT and PLST) on pulsed thermography thermal data. An academic sample with several artificial defects of different types, i.e., flat-bottom-holes (FBH), pull-outs (PO) and Teflon inserts (TEF), was employed to assess and compare defect detection and segmentation capabilities of different processing approaches. For this purpose, the contrast-to-noise ratio (CNR) and similarity coefficient were used as quantitative metrics. The results show a clear improvement in CNR when Robust PCA is applied as a pre-processing technique, CNR values for FBH, PO and TEF improve up to 164%, 237% and 80%, respectively, when compared to principal component thermography (PCT), whilst the CNR improvement with respect to pulsed phase thermography (PPT) was 77%, 101% and 289%, respectively. In the case of partial least squares thermography, Robust PCA results improved not only only when used as a pre-processing technique but also when used as a post-processing technique; however, this improvement is higher for FBHs and POs after pre-processing. Pre-processing increases CNR scores for FBHs and POs with a ratio from 0.43% to 115.88% and from 13.48% to 216.63%, respectively. Similarly, post-processing enhances the FBHs and POs results with a ratio between 9.62% and 296.9% and 16.98% to 92.6%, respectively. A low-rank matrix computed from Robust PCA as a pre-processing technique on raw data before using PCT and PPT can enhance the results of 67% of the defects. Using low-rank matrix decomposition from Robust PCA as a pre- and post-processing technique outperforms PLST results of 69% and 67% of the defects. These results clearly indicate that pre-processing pulsed thermography data by Robust PCA can elevate the defect detectability of advanced processing techniques, such as PCT, PPT and PLST, while post-processing using the same methods, in some cases, can deteriorate the results.

Pulsed thermography is an active non-destructive technique which uses optical excitation source to stimulate heating of the object under investigation. This work is devoted to the simulation of the pulsed thermography method in a steel plate with the ceramic coating containing artificial defects of various depths and sizes. The simulation has been carried out on the base of the model which takes into account complex heat exchange of the sample with the surrounding by convection, conduction and radiation. Comparison of the temperature contrast with the experimental data has shown that the results are in a good qualitative and quantitative agreement in all stages of the cooling process. Due to the fact that the temperature contrast is often susceptible to the surface noise of various nature the Kalman-based signal processing technique was developed. The comparative analysis has shown that the proposed filtration technique provides better value of signal-to-noise ratio in comparison to the considered well-known techniques of signal reconstruction when proper calibration of the filtration parameters is carried out

Pulsed thermography has been used significantly over the years to detect near and sub-surface damage in both metals and composites. Where most of the research has been in either improving the detectability and/or its applicability to specific parts and scenarios, efforts to analyse and establish the level of uncertainty in the measurements have been very limited. This paper presents the analysis of multiple uncertainties associated with thermographic measurements under multiple scenarios such as the choice of post-processing algorithms; multiple flash power settings; and repeat tests on four materials, i.e., aluminium, steel, carbon-fibre reinforced plastics (CFRP) and glass-fibre reinforced plastics (GFRP). Thermal diffusivity measurement has been used as the parameter to determine the uncertainty associated with all the above categories. The results have been computed and represented in the form of a relative standard deviation (RSD) ratio in all cases, where the RSD is the ratio of standard deviation to the mean. The results clearly indicate that the thermal diffusivity measurements show a large RSD due to the post-processing algorithms in the case of steel and a large variability when it comes to assessing the GFRP laminates.

Pulsed thermography is an active non-destructive technique which uses optical excitation source to stimulate heating of the object under investigation. This work is devoted to the simulation of the pulsed thermography method in a steel plate with a ceramic coating containing artificial defects of various depths and sizes. The simulation has been carried out on the base of a model which takes into account complex heat exchange of the sample with the surrounding by convection, conduction and radiation. Comparison of the temperature contrast with the experimental data has shown that the results are in a good qualitative and quantitative agreement in all stages of the cooling process. Due to the fact that the temperature contrast is often susceptible to surface noise of various nature the Kalman-based signal processing technique was developed. The comparative analysis has shown that the proposed filtration technique provides a better signal-to-noise ratio in comparison to the considered well-known techniques of signal reconstruction when proper calibration of the filtration parameters is carried out. [ABSTRACT FROM AUTHOR]

Abstract Active thermography is a well suited non-destructive testing method for the challenging inspection of wind rotor blades. Since the GFRP structures are up to some centimetres thick, long pulse heating is required to provide an appropriate energy input into the structure. So far, no best practice exists to guarantee a reliable detection of deep-lying flaws. In this work, a step wedge specimen having a maximum thickness of 34 mm is systematically investigated by experiment and well-matched simulations to assess the influence of the experimental parameters, like the absorbed energy, on thermal contrasts. Finally, a scheme to conduct full-scale test of a wind rotor blade in less than three hours is proposed. [ABSTRACT FROM AUTHOR]

• Neural network setup and processing of pulsed thermograms. • Depth estimation using DFF neural network. • FEM simulation of composite pulsed thermography. • Demonstration of depth estimation using NN for CFRP composites. Infrared thermography (IRT) proved to be a valuable technique in non-destructive testing due to its contactless nature, real-time application, and wide area coverage. However, the major hurdle to widespread application of IRT is quantification; more specifically quantifying the depth of defects from the acquired thermograms in low conductive host materials. In this paper, an artificial neural network (NN) is employed to detect defects depth in composite samples, coupled with a Pulsed Thermography PT setup, to complement prior work when NN algorithms were coupled to a line-scan thermography. Carbon Fiber Reinforced Polymer (CFRP) coupons with embedded and flat bottom holes defects were designed via 3D printing; to precisely control the defects morphology and depth. Firstly; the current study presents a proof of concept using a Multiphysics FEM simulation model of the inspection process, to generate the training sets of data, so that the developed NN is assessed in a deterministic (noise free) environment. Then, the proposed NN was further tested experimentally to validate its accuracy and performance. The accuracy of the developed NN for the synthetic data was more than 97% and for the experimental data was around 90%. [ABSTRACT FROM AUTHOR]

• Propose a new monitoring stress changes in composite structures using thermography. • FEM modeling to predict thermal emission variation vs. different tensile stresses. • Experimental ΔT of surface sample linearly rise with increasing tensile stress. In recent years, the increasing use of composite materials has resulted in an increased attention for calculating residual stress in the composite structure. Meanwhile, reliable measurement and prediction of residual stress remain a challenge for studying the composite behavior. Due to the fact, that destructive methods can cause additional stress in addition to existing residual stress, the use of non-destructive methods is more reliable and efficient in the industry. This paper presents a new non-destructive method for monitoring stress changes in Carbon Fiber Reinforced Plastic (CFRP) composites using pulsed thermography technique. By the analysis of different thermal emission, it is possible to measure the stress on the epoxy layer and on the carbon fiber. Numerical models using finite elements have been used to determine the behaviour of the thermal emission from a composite component subjected to different tensile stresses. The experimental measurements have been performed to verify the numerical results. In the experiment cases in which the tensile stress is constant, some differences are observed. Whereas, the thermography measurements show a linear increase of the temperature response with rising tensile stress, the accurate temperature measurements are quite limited due to the camera's low sensitivity. The experimental results revealed that the finite element simulations obtained in the present work are capable of detecting the stress (residual stress) in the carbon fiber/epoxy composites. [ABSTRACT FROM AUTHOR]