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Abstract In recent years, the preservation and conservation of ancient cultural heritage necessitate the advancement of sophisticated non-destructive testing methodologies to minimize potential damage to artworks. Therefore, this study aims to develop an advanced method for detecting defects in ancient polyptychs using infrared thermography. The test subjects are two polyptych samples replicating a 14th-century artwork by Pietro Lorenzetti (1280/85–1348) with varied pigments and artificially induced defects. To address these challenges, an automatic defect detection model is proposed, integrating numerical simulation and image processing within the Faster R-CNN architecture, utilizing VGG16 as the backbone network for feature extraction. Meanwhile, the model innovatively incorporates the efficient channel attention mechanism after the feature extraction stage, which significantly improves the feature characterization performance of the model in identifying small defects in ancient polyptychs. During training, numerical simulation is utilized to augment the infrared thermal image dataset, ensuring the accuracy of subsequent experimental sample testing. Empirical results demonstrate a substantial improvement in detection performance, compared with the original Faster R-CNN model, with the average precision at the intersection over union = 0.5 increasing to 87.3% and the average precision for small objects improving to 54.8%. These results highlight the practicality and effectiveness of the model, marking a significant progress in defect detection capability, providing a strong technical guarantee for the continuous conservation of cultural heritage, and offering directions for future studies.

Abstract In recent years, the conservation and protection of ancient cultural heritage have received increasing attention, and non-destructive testing (NDT), which can minimize the damage done to the test subject, plays an integral role therein. For instance, NDT through active infrared thermal imaging can be applied to ancient polyptychs, which can realize accurate detection of damage and defects existing on the surface and interior of the polyptychs. In this study, infrared thermography is used for non-invasive investigation and evaluation of two polyptych samples with different pigments and artificial defects, but both reproduced based on a painting by Pietro Lorenzetti (1280/85–1348) using the typical tempera technique of the century. It is noted that, to avoid as far as possible secondary damages done to the ancient cultural heritages, repeated damage-detection experiments are rarely carried out on the test subjects. To that end, numerical simulation is used to reveal the heat transfer properties and temperature distributions, as to perform procedural verification and reduce the number of experiments that need to be conducted on actual samples. Technique-wise, to improve the observability of the experimental results, a total variation regularized low-rank tensor decomposition algorithm is implemented to reduce the background noise and improve the contrast of the images. Furthermore, the efficacy of image processing is quantified through the structural-similarity evaluation.

The present review aims to analyze the application of infrared thermal imaging, aided by bio-heat models, as a tool for the diagnosis of skin and breast cancers. The state of the art of the related technical procedures, bio-heat transfer modeling, and thermogram post-processing methods is comprehensively reviewed. Once the thermal signatures of different malignant diseases are described, the updated thermographic techniques (steady-state and dynamic) used for cancer diagnosis are discussed in detail, along with the recommended best practices to ensure the most significant thermal contrast observable between the cancerous and healthy tissues. Regarding the dynamic techniques, particular emphasis is placed on innovative methods, such as lock-in thermography, thermal wave imaging, and rotational breast thermography. Forward and inverse modeling techniques for the bio-heat transfer in skin and breast tissues, supporting the thermographic examination and providing accurate data for training artificial intelligence (AI) algorithms, are reported with a special focus on real breast geometry-based 3D models. In terms of inverse techniques, different data processing algorithms to retrieve thermophysical parameters and growth features of tumor lesions are mentioned. Post-processing of infrared images is also described, citing both conventional processing procedures and applications of AI algorithms for tumor detection.

The study focuses on reducing noise and nonstationary backgrounds in data collected through active infrared thermography (AIRT) for defect detection in materials. The authors employ adaptive fixed-rank kriging to analyze a sequence of thermograms obtained in the AIRT experiment. Using basis functions derived from thin-plate splines, the data features are represented at various resolution levels, resulting in a concise spatial covariance function representation. Eigenfunctions are then derived from the estimated covariance function to capture spatial structures at different scales. Visualizing these eigenfunctions highlights defect information. The authors validate their approach through a pulsed thermography experiment on a carbon-fiber-reinforced plastic (CFRP) sample, demonstrating its effectiveness in detecting defects.

Residual stress significantly affects the mechanical properties, physical properties, and durability of materials. It is meaningful to evaluate the residual stress using non-destructive methods. However, existing techniques are expensive and have low accuracy and narrow applicability. In this paper, both infrared and terahertz techniques were used to quantitatively evaluate the residual stress of high-density polyethylene (HDPE) under tensile loading. For the terahertz technique, a new theory based on stress-optics law and Poisson’s effect is proposed. For the infrared technique, line-scan thermographic (LST) was used to extract the thermal diffusivity of HDPE. The residual stress caused by plastic deformation is quantitatively related to thermal diffusivity. Conclusively, THz-TDS shows an obvious improvement in residual stress detectability.

As commonly used materials in building and interior furniture decoration, wood materials should be processed via the pyrolysis technique in order to improve their corrosion resistance, flame retardancy, and dimensional stability. However, during the process of pyrolysis, temperature and time are significant factors for avoiding internal damage. The main reason comes from the decrease in internal structural water. Existing non-destructive evaluation methods have difficulty detecting moisture content and delamination within wood due to its complex internal structure. In this work, a novel image processing method is proposed to detect the delamination of wood materials. The results show that, compared to spruce and oak, meranti is more resistant to pyrolysis and has a higher structural stability.

Infrared thermography is a widely utilized nondestructive testing technique in the field of artwork inspection. However, raw thermograms often suffer from problems, such as limited quantity and high background noise, due to limitations inherent in the acquisition equipment and experimental environment. To overcome these challenges, there is a growing interest in developing thermographic data enhancement methods. In this study, a defect inspection method for artwork based on principal component analysis is proposed, incorporating two distinct deep learning approaches for thermographic data enhancement: spectral normalized generative adversarial network (SNGAN) and convolutional autoencoder (CAE). The SNGAN strategy focuses on augmenting the thermal images, while the CAE strategy emphasizes enhancing their quality. Subsequently, principal component thermography (PCT) is employed to analyze the processed data and improve the detectability of defects. Comparing the results to using PCT alone, the integration of the SNGAN strategy led to a 1.08% enhancement in the signal-to-noise ratio, while the utilization of the CAE strategy resulted in an 8.73% improvement.

Prior to restoration work, the frescoes created at the end of the XV century by the painter Saturnino Gatti (1463–1518) in the apse of the Church of St. Panfilo in Villagrande di Tornimparte (L’Aquila) were the subject of a thorough diagnostic study involving several tests, from in situ non-destructive analysis to laboratory micro-destructive analysis on the collected samples. In this paper, we report the application of the sonic pulse velocity test, hygrometric tests, and infrared thermography to assess the state of conservation of the frescoes, i.e., the combined system of plaster and wall support. The complete analysis of the frescoes’ state of conservation revealed significant insights. The integrity of the plaster was evaluated through sonic pulse velocity tests, which highlighted several areas of detachment or degradation phenomena. Hygrometric analysis described humidity variations, particularly near the boundary between the conch area and the church naves. Passive infrared thermography detected temperature inhomogeneities, emphasizing differences in the wall texture and the masonry structure. Moreover, by comparing sonic pulse velocity and passive thermography images, a certain degree of correlation between hot areas and slow areas in the presence of possible detachments was noticed. In addition, pulse-compression active thermography was applied in a few spots, and for the first time, to the best of our knowledge, the virtual wave concept was applied to the cultural heritage field. This strategy helps in better associating anomalies with depth. The measurement campaign was part of a research project conducted by members of the Italian Association of Archaeometry (AIAr), and the results were compared and integrated with those of other non-destructive and analytical methods.

Photovoltaic (PV) cells are a major part of solar power stations, and the inevitable faults of a cell affect its work efficiency and the safety of the power station. During manufacturing and service, it is necessary to carry out fault detection and classification. A convolutional-neural-network (CNN)-architecture-based PV cell fault classification method is proposed and trained on an infrared image data set. In order to overcome the problem of the original dataset’s scarcity, an offline data augmentation method is adopted to improve the generalization ability of the network. During the experiment, the effectiveness of the proposed model is evaluated by quantifying the obtained results with four deep learning models through evaluation indicators. The fault classification accuracy of the CNN model proposed here has been drawn by the experiment and reaches 97.42%, and it is superior to that of the models of AlexNet, VGG 16, ResNet 18 and existing models. In addition, the proposed model has faster calculation, prediction speed and the highest accuracy. This method can well-identify and classify PV cell faults and has high application potential in automatic fault identification and classification.

Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of artworks while avoiding the loss of any precious materials that make them up. The use of Infrared Thermography is an interesting concept since surface and subsurface faults can be discovered by utilizing the 3D diffusion inside the object caused by external heat. The primary goal of this research is to detect defects in artworks, which is one of the most important tasks in the restoration of mural paintings. To this end, machine learning and deep learning techniques are effective tools that should be employed properly in accordance with the experiment’s nature and the collected data. Considering both the temporal and spatial perspectives of step-heating thermography, a spatiotemporal deep neural network is developed for defect identification in a mock-up reproducing an artwork. The results are then compared with those of other conventional algorithms, demonstrating that the proposed approach outperforms the others.

The present review describes the up-to-date state of the evaluation of thermophysical properties (TP) of materials with three different procedures: modeling (also including inverse problems), measurements and analytical methods (e.g., through computing from other properties). Methods to measure specific heat and thermal conductivity are described in detail. Thermal diffusivity and thermal effusivity are a combination of the previously cited properties, but also for these properties, specific measurement and calculation methods are reported. Experiments can be carried out in steady-state, transient, and pulse regimes. For modeling, special focus is given to the inverse methods and parameter estimation procedures, because through them it is possible to evaluate the thermophysical property, assuring the best practices and supplying the measurement uncertainty. It is also cited when the most common data processing algorithms are used, e.g., the Gauss–Newton and Levenberg–Marquardt least squares minimization algorithms, and how it is possible to retrieve values of TP from other data. Optimization criteria for designing the experiments are also mentioned.

Over the recent period, there has been an increasing interest in the use of pulsed infrared thermography (PT) for the non-destructive evaluation of Cultural Heritage (CH). Unlike other techniques that are commonly employed in the same field, PT enables the depth-resolved detection of different kinds of subsurface features, thus providing helpful information for both scholars and restorers. Due to this reason, several research activities are currently underway to further improve the PT effectiveness. In this manuscript, the specific use of PT for the analysis of three different types of CH, namely documentary materials, panel paintings–marquetery, and mosaics, will be reviewed. In the latter case, i.e., mosaics, passive thermography combined with ground penetrating radar (GPR) and digital microscopy (DM) have also been deepened, considering their suitability in the open field. Such items have been selected because they are characterized by quite distinct physical and structural properties and, therefore, different PT (and, in some cases, verification) approaches have been employed for their investigations.

In building construction, it is very important to reduce energy consumption and provide thermal comfort. In this regard, defects in insulating panels can compromise the capability of these panels of reducing the heat flow by conduction with the surroundings. In recent years, both experimental techniques and numerical methods have been used for investigating the effect of defects on the thermal behavior of building panels. The main novelty of this work regards the application of both numerical and experimental approaches based on infrared thermography techniques for studying the effects of defects such as debonding on the insulation properties of cork panels. In particular, the effects of defects were investigated by using the Long Pulse Thermography technique and then by analyzing the thermal behavior of the panel during the cooling phase. Results show the capability of the proposed approaches in describing the effects of defects in cork panels such as detachments and the benefit effect of a shield coating in improving the insulation properties of the panel.

Natural fibres present ozone-friendly solutions in the field of construction. The attenuation of the sound and heat losses is an important feature in such type of materials above all, when used in non-woven fabrics and fibre-reinforced composites. Hemp fibres show robust insulation performance; this research work should be considered beneficial to the development of a non-destructive thermographic methodology, which can address the thermal barrier (typically applied on multi-layer panel) effects. The intent is to assess the integrity of the sub-surface reinforcing glass fibres; such integrity state will help confer the rigidity and the resistance to mechanical stresses. The testing proposed in this study can be further developed in a laboratory right after the manufacturing process of similar type of components. The testing needs preliminary numerical simulations to help guide the selection of the appropriate pre- and post-processing algorithms combined with or without segmentation operators. A set of numerical and experimental tests were performed through controlled thermal stimulation while recording the thermal responses. The study also highlights the advantages, disadvantages, and future development of the presented technique and methodologies.

Fibre metal laminates are widely implemented in the aerospace industry owing to the merits of fatigue resistance and plastic properties. An effective defect assessment technique needs to be investigated for this type of composite materials. In order to achieve accurate impact-induced damage evaluation, a multi-excitation infrared fusion method is introduced in this study. Optical excitation thermography with high performance on revealing surface and subsurface defects is combined with vibro-thermography to improve the capability of detection on defects. Quantitative analysis is carried out on the temperature curve to assess the impact-induced deformation. A new image fusion framework including feature extraction, feature selection and fusion steps is proposed to fully utilize the information from two excitation modalities. Six fibre metal laminates which contain aluminium-basalt fibre reinforced plastic and aluminium-glass fibre reinforced plastic are investigated. Features from different perspectives are compared and selected via intensity contrast on deformation area for fusion imaging. Both types of defects (i.e., surface and sub-surface) and the internal deformation situation of these six samples are characterized clearly and intuitively.

Non-destructive testing of objects and structures is a valuable tool, especially in cultural heritage where the preservation of the inspected sample is of vital importance. In this paper, a decorative marquetry sample is inspected with three non-destructive testing (NDT) techniques: air-coupled ultrasound, X-ray micro-tomography, and infrared thermography. Results from the three techniques were compared and discussed. X-ray micro-tomography presented the most detailed results. On the other hand, infrared thermography provided interesting results with the advantage of being cheap and easy in the deployment of the NDT method.

Infrared thermography has been widely adopted in many applications for material structure inspection, where data analysis methods are often implemented to elaborate raw thermal data and to characterize material structural properties. Herein, a multiscale thermographic data analysis framework is proposed and applied to building structure inspection. In detail, thermograms are first collected by conducting solar loading thermography, which are then decomposed into several intrinsic mode functions under different spatial scales by multidimensional ensemble empirical mode decomposition. At each scale, principal component analysis (PCA) is implemented for feature extraction. By visualizing the loading vectors of PCA, the important building structures are highlighted. Compared with principal component thermography that applies PCA directly to raw thermal data, the proposed multiscale analysis method is able to zoom in on different types of structural features.

Non-destructive testing (NDT) describes techniques that measure properties of the body without disturbing their state [...]

The monitoring of heritage objects is necessary due to their continuous deterioration over time. Therefore, the joint use of the most up-to-date inspection techniques with the most innovative data processing algorithms plays an important role to apply the required prevention and conservation tasks in each case study. InfraRed Thermography (IRT) is one of the most used Non-Destructive Testing (NDT) techniques in the cultural heritage field due to its advantages in the analysis of delicate objects (i.e., undisturbed, non-contact and fast inspection of large surfaces) and its continuous evolution in both the acquisition and the processing of the data acquired. Despite the good qualitative and quantitative results obtained so far, the lack of automation in the IRT data interpretation predominates, with few automatic analyses that are limited to specific conditions and the technology of the thermographic camera. Deep Learning (DL) is a data processor with a versatile solution for highly automated analysis. Then, this paper introduces the latest state-of-the-art DL model for instance segmentation, Mask Region-Convolution Neural Network (Mask R-CNN), for the automatic detection and segmentation of the position and area of different surface and subsurface defects, respectively, in two different artistic objects belonging to the same family: Marquetry. For that, active IRT experiments are applied to each marquetry. The thermal image sequences acquired are used as input dataset in the Mask R-CNN learning process. Previously, two automatic thermal image pre-processing algorithms based on thermal fundamentals are applied to the acquired data in order to improve the contrast between defective and sound areas. Good detection and segmentation results are obtained regarding state-of-the-art IRT data processing algorithms, which experience difficulty in identifying the deepest defects in the tests. In addition, the performance of the Mask R-CNN is improved by the prior application of the proposed pre-processing algorithms.

Nowadays, infrared thermography, as a widely used non-destructive testing method, is increasingly studied for impact evaluation of composite structures. Sparse pattern extraction is attracting increasing attention as an advanced post-processing method. In this paper, an enhanced sparse pattern extraction framework is presented for thermographic sequence processing and defect detection. This framework adapts cropping operator and typical component extraction as a preprocessing step to reduce the dimensions of raw data and applies sparse pattern extraction algorithms to enhance the contrast on the defect area. Different cases are studied involving several defects in four basalt-carbon hybrid fiber-reinforced polymer composite laminates. Finally, comparative analysis with intensity distribution is carried out to verify the effectiveness of contrast enhancement using this framework.

Infrared thermography (IRT) is a competitive method for nondestructive testing; yet it is susceptible to errors when testing objects with complex geometries. This work investigates the effects of regulating different thermographic testing parameters to optimize the IRT outcomes when testing complex shaped geometries, particularly cylindrical coupons. These parameters include the scanning routine, feed-rate, and heat intensity. Fine-tuning these parameters will be performed with respect to three different variables consisting of workpiece density, defect size, and defect depth. The experimental work is designed around 3D-printed cylindrical coupons, then the obtained thermal images are stitched via image processing tool to expose defects from different scans. The analysis employs a Signal-to-Noise Ratio (SNR) metric in an orthogonal tabulation following a Taguchi Design of Experiment. Moreover, test sensitivity and the best combination of factor levels are determined using Analysis of Means (ANOM) and Analysis of Variance (ANOVA). The outcomes show that the heating intensity factor is the most dominant in exposing flaws with close to 40% mean shift and up to 47% variance fluctuation. The paper introduces the tools employed in the study, and then explains the methodology followed to test one sample quadrant. The results for running the testing on all the scenarios are presented, interpreted, and their implications are recommended.

An inlay sample with artificial defects was inspected via the pulse-compression thermography (PuCT) technique. The sample belongs to the cultural heritage field, and it was realized by a professional restorer based on his long-time experience, imitating historical art crafting styles. The tesserae composing the inlay were not treated by any protective paints, so that external thermal stimuli may cause physical/mechanical alterations of the cell walls, with consequent colour changes, cracks, and eventually damage to its surface. To avoid any alteration of the sample, the PuCT technique was used for inspecting the inlay sample as it allows the heating power to be very low, while assuring enough thermal contrast for the defects to be detected after the exploitation of the pulse-compression algorithm. Even if a maximum ΔT slightly exceeding 1 °C was detected during the PuCT test of the inlay sample, it is shown that this is enough for detecting several defects. Further, image processing based on the Hilbert transform increases defect detection and characterization. In addition, a novel normalization technique, i.e., a pixel-by-pixel data normalization with respect to the absorbance estimated by considering a characteristic value of the compression peak, is introduced here for the first time. The proposed normalization enhances the defect detection capability with respect to the standard pixel-by-pixel amplitude visualization. This has been demonstrated for two experimental setups, both exploiting the same LED chips system as heating source but different thermal camera sensors, i.e., one in the mid-infrared spectrum, the other in the far infrared one. Thus, the present work is also the first small-scale test of a future portable system that will include low-power LED chip feed in DC by metal-oxide-semiconductor field-effect transistor (MOSFET) devices, and a handy far-infrared camera.

The continuous deterioration of elements, with high patrimonial value over time, can only be mitigated or annulled through the application of techniques that facilitate the preventative detection of the possible agents of deterioration. InfraRed Thermography (IRT) is one of the most used techniques for this task. However, there are few IRT methodologies, which can automatically monitor the cultural heritage field, and are vitally important in eliminating the subjectivity in interpreting and accelerating the analysis process. In this work, a study is performed on a tessellatum layer of a mosaic to automatically: (i) Detect the first appearance of the thermal footprint of internal water, (ii) delimit the contours of the thermal footprint of internal water from its first appearance, and (iii) classify between harmful and non-harmful internal water. The study is based on the analysis of the temperature distribution of each thermal image. Five thermal images sequences are acquired during the simulation of different real situations, obtaining a set of promising results for the optimization of the thermographic inspection process, while discussing the following recommended steps to be taken in the study for future researches.

The relationship between wood and its degree of humidity is one of the most important aspects of its use in construction and restoration. The wood presents a behavior similar to a sponge, therefore, moisture is related to its expansion and contraction. The nondestructive evaluation (NDE) of the amount of moisture in wood materials allows to define, e.g., the restoration procedures of buildings or artworks. In this work, an integrated study of two non-contact techniques is presented. Infrared thermography (IRT) was able to retrieve thermal parameters of the wood related to the amount of water added to the samples, while the interference pattern generated by speckles was used to quantify the expansion and contraction of wood that can be related to the amount of water. In twenty-seven wooded samples, a known quantity of water was added in a controlled manner. By applying advanced image processing to thermograms and specklegrams, it was possible to determine fundamental values controlling both the absorption of water and the main thermophysical parameters that link the samples. On the one hand, results here shown should be considered preliminary because the experimental values obtained by IRT need to be optimized for low water contents introduced into the samples. On the other hand, speckle interferometry by applying an innovative procedure provided robust results for both high and low water contents.

The use of different spectral bands in the inspection of artworks is highly recommended to identify the maximum number of defects/anomalies (i.e., the targets), whose presence ought to be known before any possible restoration action. Although an artwork cannot be considered as a composite material in which the zero-defect theory is usually followed by scientists, it is possible to state that the preservation of a multi-layered structure fabricated by the artist’s hands is based on a methodological analysis, where the use of non-destructive testing methods is highly desirable. In this paper, the infrared thermography and hyperspectral imaging methods were applied to identify both fabricated and non-fabricated targets in a canvas painting mocking up the famous character “Venus” by Botticelli. The pulse-compression thermography technique was used to retrieve info about the inner structure of the sample and low power light-emitting diode (LED) chips, whose emission was modulated via a pseudo-noise sequence, were exploited as the heat source for minimizing the heat radiated on the sample surface. Hyper-spectral imaging was employed to detect surface and subsurface features such as pentimenti and facial contours. The results demonstrate how the application of statistical algorithms (i.e., principal component and independent component analyses) maximized the number of targets retrieved during the post-acquisition steps for both the employed techniques. Finally, the best results obtained by both techniques and post-processing methods were fused together, resulting in a clear targets map, in which both the surface, subsurface and deeper information are all shown at a glance.

This work is focused on the use of line-scan thermography (LST) method for the inspection of an ancient book cover. Three widely used image post-processing techniques (i.e., pulsed phase thermography, partial least square thermography and principal component thermography) were applied to the acquired thermal sequences. Flash thermography (FT) anticipated the LST results in order to have a comparison of the results. It was concluded that LST is an effective technique for paper-based materials, and it can additionally provide a higher image contrast if compared to classical FT technique.

InfraRed Thermography, IRT, is one of the most recent techniques to monitor heritage elements, finding existing pathologies in order to implement the required maintenance tasks. However, most IRT results on cultural heritage are interpreted subjectively. Then, this work presents a methodology for the automatic detection and delimitation of moisture inside a heritage element, a mosaic in this case, from a thermographic sequence, based on the analysis of the temperature distribution of the thermal images acquired. Comparing the experimental test results with typical thermographic algorithms, the detection performance of the algorithm proposed was similar, but the new method is able to delimit and automate the whole process.

Nondestructive inspection (NDI) has immensely contributed to the restoration of historic and artistic works. As one of the most common used NDI methods, active thermography is an easy-to-operate and efficient technique. Principal component thermography (PCT) has been widely used to deal with thermographic data for enhancing the visibility of subsurface defects. Unlike PCT, edge-group sparse PCT introduced herein enforces sparsity of principal component (PC) loadings by considering the spatial connectivity of thermographic image pixels. The feasibility and effectiveness of this method is illustrated by the experimental results of the defect characterization in an ancient marquetry sample with a fir wood support.

Defects related to degradation were observed in an ancient book paperboard cover through nuclear magnetic resonance relaxometry and infrared thermography. Data collected with this combined method allowed identifying areas with moisture content and thermal diffusivity anomalies within the front board, corresponding to the different conservation status of the cellulose-based material. Non-destructive testing analytical procedures provide comprehensive knowledge for preserving precious library archives.

Ceramic-coated materials used in different engineering sectors are the focus of world-wide interest and have generated a need for inspection techniques that detect very small structural anomalies. Non-destructive testing is increasingly being used to evaluate coating thickness and to test for coating flaws. The main pros of non-destructive testing is that the tested object remains intact and available for continued use afterward. This paper reports on an integrated, non-destructive testing approach that combines infrared thermography and acousto-ultrasonics to evaluate advanced aerospace sandwich structure materials with the aim of exploring any potential for detecting defects of more than one type. Combined, these two techniques successfully detected fabrication defects, including inclusions and material loss.

Natural fibers, including mineral and plant fibers, are increasingly used for polymer composite materials due to their low environmental impact. In this paper, thermographic non-destructive inspection techniques were used to evaluate and characterize basalt, jute/hemp and bagasse fibers composite panels. Different defects were analyzed in terms of impact damage, delaminations and resin abnormalities. Of particular interest, homogeneous particleboards of sugarcane bagasse, a new plant fiber material, were studied. Pulsed phase thermography and principal component thermography were used as the post-processing methods. In addition, ultrasonic C-scan and continuous wave terahertz imaging were also carried out on the mineral fiber laminates for comparative purposes. Finally, an analytical comparison of different methods was given.

The use of fiber reinforced materials such as randomly-oriented strands has grown in recent years, especially for manufacturing of aerospace composite structures. This growth is mainly due to their advantageous properties: they are lighter and more resistant to corrosion when compared to metals and are more easily shaped than continuous fiber composites. The resistance and stiffness of these materials are directly related to their fiber orientation. Thus, efficient approaches to assess their fiber orientation are in demand. In this paper, a non-destructive evaluation method is applied to assess the fiber orientation on laminates reinforced with randomly-oriented strands. More specifically, a method called pulsed thermal ellipsometry combined with an artificial neural network, a machine learning technique, is used in order to estimate the fiber orientation on the surface of inspected parts. Results showed that the method can be potentially used to inspect large areas with good accuracy and speed.

In this paper, an infrared pre-processing modality is presented. Different from a signal smoothing modality which only uses a polynomial fitting as the pre-processing method, the presented modality instead takes into account the low-order derivatives to pre-process the raw thermal data prior to applying the advanced post-processing techniques such as principal component thermography and pulsed phase thermography. Different cases were studied involving several defects in CFRPs and GFRPs for pulsed thermography and vibrothermography. Ultrasonic testing and signal-to-noise ratio analysis are used for the validation of the thermographic results. Finally, a verification that the presented modality can enhance the thermal image performance effectively is provided.

The reduction of building energy consumption requires appropriate planning and design of the building’s envelope. In the last years, new innovative materials and construction technologies used in new or refurbished buildings have been developed in order to achieve this objective, which are also needed for reducing greenhouse gases emissions and building maintenance costs. In this work, the thermal conductance of a brick, made of wood and cement, used in a low-rise building, was investigated with a heat flow meter (HFM) and with numerical simulation using the Ansys® software package (Canonsburg, PA, USA). Due to their influence on the buildings’ thermal efficiency, it is important to choose an appropriate design for the building blocks. Results obtained by the finite element modeling of the construction material and by in-situ analysis conducted on a real building are compared, and furthermore a thermal optimization of the shape of the material is suggested.