Search Results (117)

Composites have found applications in critical components and require a high degree of safety and reliability. To ensure this, structural health monitoring systems based on optical fibres embedded within structures are installed for continuous monitoring. Infrared thermography is a non-destructive method that can be applied to inspect the internal structure after manufacturing and during operation. This paper presents an application of pulsed thermography for observing and evaluating the internal structure of glass fibre-reinforced polymer samples with different arrangements of embedded optical fibres. The goal of the paper is to study the feasibility of using pulsed thermography to distinguish optical fibres from glass textile fibre bundles, as well as to track the arrangement of the optical fibres. Full article

This article presents the application of infrared thermography as a nondestructive testing method (NDT) for detecting osmotic damage in glass-fiber-reinforced polymer (GFRP) and glass-reinforced polymer (GRP) boat hull structures. The aim of the conducted experiments is to explore the possibilities of applying active infrared thermography to real structures and to establish a procedure capable of filtering out anomalies caused by various thermal influences, such as thermal reflections from surrounding objects, geometry effects, and heat flow variations on the observed object. The methods used for post-processing IR signals include lock-in thermography (LT), pulse thermography (PT), pulse phase thermography (PPT), and gradient pulse phase thermography (GT). The practical application and advantages and disadvantages of infrared thermography in identifying osmotic damage in GFRP and GRP boat hulls will be illustrated through three case studies. Each case study is based on specific conditions and characteristics of different types of osmotic damage, enabling a thorough analysis of the effectiveness of the method in detecting and assessing the severity of the damage. The post-processed thermal images enable a clearer distinction between damaged and undamaged zones, improving the robustness of detection under realistic field conditions. Full article

This study investigates the influence of cooling rates on the microstructure and phase transformations of the high-entropy alloy Cr₁₆Mn₁₆Fe₁₆Co₁₆Ni₁₆P₂₀. The alloy was synthesized via arc melting and subjected to three cooling conditions: slow cooling (52 K/s), accelerated cooling after a short electric arc pulse (3018 K/s), and rapid quenching (10⁵–10⁶ K/s) using the melt-spinning method. The microstructures were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Mössbauer spectroscopy. The thermal properties and phase transformations were analyzed using differential scanning calorimetry (DSC) and thermography. Slow cooling produced a complex crystalline microstructure, while accelerated cooling resulted in fewer phases. Rapid cooling yielded an amorphous structure, demonstrating that phosphorus and high mixing entropy enhance glass-forming ability. Phase transformations exhibited significant undercooling under accelerated cooling, with FCC phase crystallization shifting from 1706 K (slow cooling) to 1341 K, and eutectic crystallization from 1206 K to 960 K. These findings provide a foundation for optimizing cooling processes in high-entropy alloys for advanced structural and functional applications. Full article

Background/Objectives: Some vascular alterations such as peripheral arterial disease (PAD) or arterial stiffness can alter perfusion of the limbs, so we wondered if this is reflected in the temperature of the soles of the feet of diabetic patients who did not have a diagnosis of peripheral arterial disease. Foot sole temperature was correlated with the ankle–brachial index (ABI), carotid—femoral pulse wave velocity (cfPWV), brachial–ankle pulse wave velocity (baPWV), and arterial age. Methods: A total of 175 patients with type 2 diabetes mellitus, without a previous diagnosis of PAD, were recruited. Comorbidities, anthropometry, biochemical analysis results, temperature, ABI, cfPWV, baPWV, and arterial age were recorded. Forty-two temperature records were obtained from the sole of the foot with an FLIR T865 thermal imaging camera. ABI, cfPWV, baPWV, and arterial age were obtained using plethysmographic and oscillometric methods. Statistical analysis was performed with SPSS v.29.0 (correlations and multiple linear regression models). Results: All temperature points analyzed correlated negatively with ABI (p < 0.001) and rho values ranged from −0.168 to −0.210. Likewise, cfPWV, baPWV, and arterial age had similar results, since most temperature records showed low rho values and a negative correlation with these parameters. Four models were developed to explain the variables of interest. Temperature was involved in all of them. The temperature of the first toe was included in the prediction of cfPWV, baPWV, and arterial age. Conclusions: There is an inversely proportional relationship between temperature and ABI, cfPWV, baPWV, and arterial age in diabetic patients without a previous diagnosis of arterial disease. Temperature can be a predictor of these hemodynamic variables. Full article

In the field of thermal non-destructive testing and evaluation (TNDT&E), active thermography gained popularity due to its fast wide-area monitoring and remote inspection capability to assess materials without compromising their future usability. Among the various active thermographic methods, pulse compression-favorable frequency-modulated thermal wave imaging stands out for its enhanced detectability and depth resolution. In this study, an experimental investigation has been carried out on a hardened steel sample used in the ship building industry with a flat-bottom-hole-simulated defect using the frequency-modulated thermal wave imaging (FMTWI) technique. The defect detection capabilities of FMTWI have been investigated from various statistical post-processing approaches and compared by taking the signal-to-noise ratio (SNR) as a figure of merit. Among various adopted statistical post-processing techniques, pulse compression has been carried out using different methods, namely the offset removal with polynomial curve fitting and principal component analysis (PCA), which is an unsupervised learning approach for data reduction and offset removal with median centering for data standardization. The performance of these techniques was assessed through experimental investigations on hardened steel specimens used in ship building to provide valuable insights into their effectiveness in defect detection capabilities. Full article

Pulsed eddy current thermography can detect surface or subsurface defects in steel, but in the process of combining deep learning, it is expensive and inefficient to build a complete sample of defects due to the complexity of the actual industrial environment. Consequently, this study proposes a transfer learning method based on Twin-NMF and combines it with the SimAM attention mechanism to enhance the detection accuracy of the target domain task. First, to address the domain differences between the target domain task and the source domain samples, this study introduces a Twin-NMF transfer method. This approach reconstructs the feature space of both the source and target domains using twin non-negative matrix factorization and employs cosine similarity to measure the correlation between the features of these two domains. Secondly, this study integrates a parameter-free SimAM into the neck of the YOLOv8 model to enhance its capabilities in extracting and classifying steel surface defects, as well as to alleviate the precision collapse phenomenon associated with multi-scale defect recognition. The experimental results show that the proposed Twin-NMF model with SimAM improves the detection accuracy of steel surface defects. Taking NEU-DET and GC10-DET as source domains, respectively, in the ECTI dataset, [email protected] reaches 99.3% and 99.2%, and the detection accuracy reaches 98% and 98.5%. Full article

In the manufacture of ancient books, it was quite common to insert written scraps belonging to earlier library material into bookbindings. For scholars like codicologists and paleographers, it is extremely important to have the possibility of reading the text lying on such scraps without dismantling the book. In this regard, in this paper, we report on the detection of these texts by means of infrared (IR) pulsed thermography (PT), which, in recent years, has been specifically proven to be an effective tool for the investigation of Cultural Heritage. In particular, we present a quantitative analysis based, for the first time, on PT images obtained from books of historical relevance preserved at the Biblioteca Angelica in Rome. The analysis has been carried out by means of a theoretical model for the PT signal, which makes use of two image parameters, namely, the distortion and the contrast, related to the IR readability of the buried texts. As shown in this paper, the good agreement between the experimental data obtained in the historical books and the theoretical analysis proved that the capability of the adopted PT method could be fruitfully applied, in real case studies, to the detection of buried texts and to the quantitative characterization of the parameters affecting their thermal readability. Full article

Railways play a pivotal role in national economic development, freight transportation, national defense, and regional connectivity. The detection of rolling contact fatigue (RCF) defects in rail tracks is essential for railway safety and maintenance. Due to its efficiency and non-contact capability in detecting surface and near-surface defects, Eddy Current Pulsed Thermography (ECPT) has garnered significant attention from researchers. However, detecting multi-layer RCF defects remains a challenge. This paper introduces a sweeping Eddy Current Pulsed Thermal-Tomography system (ECPTT) to detect multi-layer RCF defects effectively. This system utilizes varying excitation frequencies to heat defects, altering skin depth and facilitating feature extraction to distinguish multi-layer RCF defects. Skewness and thermographic signal reconstruction (TSR) values are employed as features in the experiments. These features are qualitatively analyzed to differentiate the layers and depths of multi-layer RCF defects. Additionally, five different coils were compared and analyzed quantitatively. The results indicate that the ECPTT system can effectively detect and distinguish multi-layer RCF defects, thereby providing more detailed defect information and enhancing railway safety and maintenance efficiency. Full article

The detection of impact and depth defects in Glass Fiber Reinforced Polymer (GFRP) composites has been extensively studied to develop effective, reliable, and cost-efficient assessment methods through various Non-Destructive Testing (NDT) techniques. Challenges in detecting these defects arise from varying responses based on the geometrical shape, thickness, and defect types. Long Pulse Thermography (LPT), utilizing an uncooled microbolometer and a low-resolution infrared (IR) camera, presents a promising solution for detecting both depth and impact defects in GFRP materials with a single setup and minimal tools at an economical cost. Despite its potential, the application of LPT has been limited due to susceptibility to noise from environmental radiation and reflections, leading to blurry images. This study focuses on optimizing LPT parameters to achieve accurate defect detection. Specifically, we investigated 11 flat-bottom hole (FBH) depth defects and impact defects ranging from 8 J to 15 J in GFRP materials. The key parameters examined include the environmental temperature, background reflection, background color reflection, and surface emissivity. Additionally, we employed image processing techniques to classify composite defects and automatically highlight defective areas. The Tanimoto Criterion (TC) was used to evaluate the accuracy of LPT both for raw images and post-processed images. The results demonstrate that through parameter optimization, the depth defects in GFRP materials were successfully detected. The TC success rate reached 0.91 for detecting FBH depth defects in raw images, which improved significantly after post-processing using Canny edge detection and Hough circle detection algorithms. This study underscores the potential of optimized LPT as a cost-effective and reliable method for detecting defects in GFRP composites. Full article

Determining thermal material properties such as thermal diffusivity can provide valuable insights into a material’s thermal characteristics. A well-established method for this purpose is flash thermography using Parker’s half-rise equation. It assumes one-dimensional heat transfer for thermal diffusivity estimation through the thickness of the material. However, research evidence suggests that the technique has not developed as much as the reflection mode over the last decade. This systematic review explores the current state-of-the-art in through-transmission thermography. The methodology adopted for this review is the SALSA framework that seeks to Search, Appraise, Synthesise, and Analyse a selected list of papers. It covers the fundamental physics behind the technique, the advantages/limitations it has, and the current state-of-the-art. Additionally, based on the Population, Intervention, Comparison, Outcome, and Context (PICOC) framework, a specific set of inclusion and exclusion criteria was determined. This resulted in a final list of 81 journal/conference papers selected for this study. These papers were analysed both quantitatively and quantitatively to identify and address the current knowledge gap hindering the further development of through-transmission thermography. The findings from the review outline the current knowledge gap in through-transmission thermography and the challenges hindering the development of the technique, such as depth quantification in pulsed thermography and the lack of a standardised procedure for conducting measurements in the transmission mode. Overcoming some of these obstacles can pave the way for further development of this method to aid in material characterisation. Full article

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. Full article

The aim of this work is to verify the applicability of thermography as a non-destructive technique to quantify the wear performance of several high-entropy alloy coatings. Thermal profiles obtained from passive and active thermography were analyzed and the results were correlated with the classical tribological approaches defined in standards. HEA coatings made of several chemical compositions (Al_xCoCrCuFeNi and MnCoCrCuFeNi) and realized by using different cold spray temperatures (650 °C, 750 °C, and 850 °C) were tested in a pin-on-disk configuration, with a dedicated pin developed for the wear tests. Then, the wear performances of each sample were analyzed with the hardness and wear parameter results. The thermal profiles of passive and active thermography allowed a complete characterization of the wear resistance and performance analysis of the coatings analyzed. The results are also compared with those presented in the literature. Full article

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. Full article

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. Full article

A thermal diffusivity measurement approach is proposed in which the sample is thermally excited by a periodic train of pulsed, spatially random laser light. The temperature field on the surface is observed by IR camera and the diffusion is recorded by a sequence of IR images. The analysis of the diffusion in time and space is followed by suitable models. Fitting of the spatiotemporal evolution of the surface temperature allows to determine the thermal diffusivity of clay brick with an accuracy of 5%. Full article