Search

In the present report, we focused on high reduction ratio hypoid gear with a three-tooth pinion and its meshing performance. First, we monitored the tooth surface temperature in meshing teeth, based on combined image from temperature images with a high-speed-camera thermography, and compared it with tooth contact image with a red paint. Moreover, we proposed a predicting method of tooth contact area from a combined temperature image on the tooth surface. Second, we estimated its real contact ratio based on measuring the tooth root stresses at the toe, middle and heel positions. Third, we measured the meshing transmission error (MTE) between the gear and the pinion, and the rotational acceleration at the gear outer in meshing teeth. As a result, it could be seen that the real contact ratio affected tooth surface temperature and MTE. Regarding a high reduction ratio hypoid gear, the deformation at the gear rim also affected the dynamic vibration in meshing gear and pinion teeth.

The type, surface texture, metamorphic degree, and developmental stage of coal and gangue significantly influence their emissivity. Accurate settings for emissivity parameters are essential for infrared temperature measurements and the identification of coal and gangue in infrared images. This study proposed a method for measuring the emissivity of coal and gangue based on the matching method. The approach integrated surface thermocouples with infrared thermography to assess emissivity. Samples were uniformly heated in a closed electric furnace, and once the temperature stabilized, a surface thermocouple measured the actual temperature of a selected area (denoted as t1). Concurrently, the infrared thermography system measured the temperature of the same area (denoted as t2). The emissivity of the infrared thermography system was calibrated until t2 equaled t1. At this point, the calculated emissivity reflected the true emissivity of the coal and gangue at that temperature. The experimental results indicated that: ① Under isothermal conditions, greater surface roughness of coal and gangue correlated with higher emissivity values, suggesting that surface roughness is a fundamental factor restricting the emissivity of these materials. ② The emissivity of four different types of coal and gangue decreased with increasing temperature, following a power function, with the fitting function's correlation coefficient (R2) exceeding 0.98, thereby confirming the feasibility of the matching method for measuring emissivity. ③ The inverse method revealed that the error rates between the measured and theoretical values under varying temperature conditions were all below 3%, validating the accuracy of the measured emissivity of coal and gangue.

Introduction Infrared thermography (IRT) has demonstrated high diagnostic accuracy for carpal tunnel syndrome (CTS) diagnosis in previous studies. However, the recovery of the autonomic function after treatment for CTS is rarely addressed in the literature, especially on the long-term.

ABSTRACT: Local adverse reactions following vaccination, often tied to the adjuvant type, can lead to pain, fever, reduced intake, and weight loss. Infrared thermography (IRT), a non-invasive human and veterinary medicine technique, can assess local inflammation. This study aimed to evaluate such reactions induced by reproductive vaccines with different adjuvants, using IRT alongside cardinal signs, rectal temperature, and haptoglobin concentration. Thirty-five Holstein heifers were grouped by vaccine type: Ah (aluminum hydroxide), Ow (oil-in-water), QAD (amphigen and quil A cholesterol and dimethyl-dioctadecyl ammonium bromide adjuvant), and a Control (saline solution). Assessments were made at 0, 6, 24-, 48-, 72-, and 168 hours post-vaccination for both doses, with an interval of 21 days. The local reactions were evaluated using the inflammatory cardinal signs and surface temperature measurement using IRT. The systemic reactions were identified by rectal temperature and the concentration of haptoglobin. A larger proportion of animals exhibiting local reactions based on scores assigned to the cardinal signs was found in the Vaccine QAD group, as well as the rectal temperature and the frequency of heifers with inflammation (Hp ≥2mg/dL). Nevertheless, Vaccine Ow demonstrated higher temperature at the site after the first vaccination dose for the IRT. Therefore, this approach is a valuable tool in classifying responses and local inflammation following vaccination in heifers with reproductive vaccines. Concurrently evaluating systemic manifestations, facilitates the surveillance of adverse reactions, thereby improving the discernment of the extent of systemic and local effects.

PurposeNon-contact measurement of facial temperature using infrared thermography has been used for mass screening of body temperature during a pandemic. We investigated the relative stability of temperature measurement in different facial regions of healthy individuals.MethodsTwenty healthy subjects underwent two experiments. In the first experiment, subjects washed their faces with a 20°C wet towel for 1 minute. Temperature changes compared to baseline in the forehead, cornea, inner canthus, and outer canthus were determined using an infrared camera for 10 minutes. In the second experiment, lubricating eye drops at 20°C were instilled over one eye. Temperature changes in the same regions of interest were monitored for 5 minutes.ResultsBaseline temperatures before face washing in the forehead and cornea, inner canthus, and outer canthus of the right eye were 33.4°C ± 0.8°C (mean ± SD), 33.3°C ± 0.8°C, 34.3°C ± 0.7°C, and 32.8°C ± 0.7°C, respectively. Reductions in temperature due to face washing were most significant for the forehead and least significant for the cornea. One minute after face washing, the corresponding changes were -2.8°C ± 0.6°C, -0.3°C ± 0.6°C, -0.6°C ± 0.7°C, and -0.9°C ± 0.7°C for the forehead, cornea, inner canthus, and outer canthus, respectively. After administering the eye drops, no significant temperature changes were observed.ConclusionsWhen facial temperature was exogenously cooled, the cornea had the most stable temperature readings.Translational relevanceWhen using infrared thermography to screen facial temperature, the measurement of corneal temperature is probably a better representative if the stability of temperature readings is critical.

Aim. To determine the effectiveness of infrared thermography to identify tissue debris of parenchymal organs and in the wound edges of hollow organs to be removed as well as for the surgical treatment of gunshot wounds of the anterior abdominal wall. Materials and methods. The results of the infrared thermography use in the provision of surgical care to 67 wounded at the second level of medical care in the conditions of the Forward Surgical Group of the Military Mobile Hospital were analyzed. Results. Clinical examples and results of the infrared thermography use for the diagnosis and treatment of gunshot wounds of the abdominal wall soft tissues, injuries to parenchymal and hollow abdominal organs are presented. Conclusions. Intraoperative infrared thermography allows to quickly assess the viability of the abdominal wall soft tissues, necrotic tissue margins of parenchymal organs, the state of blood supply to the walls of damaged hollow organs and their viability, thereby optimizing surgical tactics for abdominal gunshot wounds.

Infrared thermography has been widely applied in real industrial inspection of aerospace, energy management systems, engines, and electric systems. However, two-dimensional imaging modality limits its development. Here, a technique named frequency multiplexed photothermal correlation tomography (FM-PCT) was developed to enable non-destructive and contactless cross-sectional imaging for manufactured material evaluation and characterization. By combining advantages of photothermal tomography and pulsed thermography, FM-PCT facilitates the generation of three-dimensional thermal images through temporal superposition (stacking) of two-dimensional images from sequential subsurface depths. FM-PCT image processing involves pulsed excitation signals to which frequency delay and matched filtering techniques are applied. Major features of FM-PCT are high-resolution three-dimensional tomographic imaging under low camera frame-rate conditions with self-correcting capability for diffusion (blurring) correction of subsurface images due to cross-correlation processing of individual frequencies in the Fourier decomposition spectrum of the excitation pulse. Furthermore, FM-PCT extends truncated-correlation photothermal coherence tomography from chirp and pulsed signals to more general linear heating sources. Lock-in thermography and x-ray computed tomography validation demonstrate that 3D FM-PCT imaging accurately reveals subsurface discontinuities/defects in solids despite the diffusive nature of thermal-wave imaging.

Breast cancer remains one of the most prevalent and deadly cancers affecting women worldwide. Early detection is crucial, particularly for younger women, as traditional screening methods like mammography often struggle with accuracy in cases of dense breast tissue. Infrared thermography offers a non-invasive imaging alternative that enhances early detection by capturing subtle thermal variations indicative of breast abnormalities. This study investigates and compares the performance of various deep learning and machine learning models in analyzing thermographic data to classify breast tissue as healthy, benign, or malignant. To maximize detection accuracy, data preprocessing, feature extraction, and dimensionality reduction were implemented to isolate distinguishing characteristics across tissue types. Leveraging advanced feature extraction and visualization techniques inspired by geospatial data methodologies, we evaluated several deep learning architectures and classical classifiers using the DRM-IR and Breast Thermography Mendeley thermal datasets. Among the tested models, the ResNet152 architecture combined with a Support Vector Machine (SVM) classifier delivered the highest performance, achieving 97.62% accuracy, 95.79% precision, 98.53% recall, 94.52% specificity, an F1 score of 97.16%, an area under the curve (AUC) of 99%, a latency of 0.06 s, and CPU utilization of 88.66%. These findings underscore the potential of integrating infrared thermography with advanced deep learning and machine learning approaches to significantly improve the accuracy and efficiency of breast cancer detection, supporting its role as a valuable tool for early diagnosis.

Infrared thermography is a non-invasive measurement method that can accurately describe immediate temperature changes of an object. In the case of continuous in vivo hand measurements, extracting correct thermal data requires a first step of image segmentation to identify regions of interest. This step can be difficult due to parasitic hand movements. It is therefore necessary to regularly readjust the segmented areas throughout the recording. This process is time-consuming and presents a particular obstacle to studying a large number of areas of the hand and long duration sequences. In this work, we propose an automated segmentation methodology that can automatically detect these regions on the hand. This method differs from previous literature because it uses a secondary visual camera and a combination of computer vision and machine learning feature identification. The obtained segmentation models were compared to models segmented by two human operators via Dice and Intersection-over-Union coefficients. The results obtained are very positive: we were able to decompose the images acquired via IRT with our developed algorithms, regardless of the temperature variation, and this with processing times of less than a second. Thus, this technology can be used to study the long-term thermal kinetics of the human hand by automatic feature detection, even in situations where the hand temperature experiences a significant variation.

This study aimed to investigate the incidence of subclinical mastitis (SCM), the implicated pathogens, and their impact on milk quality in dairy sheep in Greece. Furthermore, we preliminarily evaluated infrared thermography and the application of AI tools for the early, non-invasive diagnosis of relevant cases. In total, 660 milk samples and over 2000 infrared thermography images were obtained from 330 phenotypically healthy ewes. Microbiological investigations, a somatic cell count (SCC), and milk chemical analyses were performed. Infrared images were analyzed using the FLIR Research Studio software (version 3.0.1). The You Only Look Once version 8 (YOLOv8) algorithm was employed for the automatic detection of the udder’s region of interest. A total of 157 mammary glands with SCM were identified in 122/330 ewes (37.0%). The most prevalent pathogen was staphylococci (136/160, 86.6%). Considerable resistance was detected to tetracycline (29.7%), ampicillin (28.6%), and sulfamethoxazole–trimethoprim (23.6%). SCM correlated with high total mesophilic count (TMC) values and decreased milk fat, lactose, and protein content. A statistically significant variation (p < 0.001) was identified in the unilateral SCM cases by evaluating the mean temperatures of the udder region between the teats in the thermal images. Finally, the YOLOv8 algorithm was employed for the automatic detection of the udder’s region of interest (ROI), achieving 84% accuracy in defining the ROI in this preliminary evaluation. This demonstrates the potential of infrared thermography combined with AI tools for the diagnosis of ovine SCM. Nonetheless, more extensive sampling is essential to optimize this diagnostic approach.

Defect detection plays a crucial role in industrial production, and the implementation of this technology has significant implications for improving both product quality and processing efficiency. However, the limited availability of defect samples for training deep-learning-based object detection models within industrial processes poses challenges for model training. In this paper, we propose a novel deep convolutional generative adversarial network with self-attention mechanism for the data augmentation of infrared thermal images for the application of aluminum foil sealing. To further expand its applicability, the proposed method is designed not only to address the specific needs of aluminum foil sealing but also to serve as a robust framework that can be adapted to a wide range of industrial defect detection tasks. To be specific, the proposed approach integrates a self-attention module into the generator, adopts spectral normalization in both the generator and discriminator, and introduces a two time-scale update rule to coordinate the training process of these components. The experimental results validated the superiority of the proposed approach in terms of the synthesized image quality and diversity. The results show that our approach can capture intricate details and distinctive features of defect images of aluminum foil sealing. Furthermore, ablation experiments demonstrated that the combination of self-attention, spectral normalization, and two time-scale update rules significantly enhanced the quality of image generation, while achieving a balance between stability and training efficiency. This innovative framework marks a notable technical breakthrough in the field of industrial defect detection and image synthesis, offering broad application prospects.

As a crucial component of the photovoltaic (PV) power generation system, effective defect detection of PV cells can help to ensure the power generation efficiency and the system safety. The photo-thermal-radiation infrared thermography experimental system has been built, and the infrared images of normal and defective PV cells were collected by an IR camera. An effective convolutional neural network (CNN) based model with the residual architecture is designed here to detect and classify the defects. An offline date augmentation method is performed to overcome the insufficient image dataset in the step of dataset establishment, so as to improve the defects classification capability of the designed CNN model. The defects classification accuracy of the proposed model is obtained as 96.96% with 3-class. In order to verify the defect classification ability of the designed CNN model, other four models were adopted for comparative analysis. The results indicate that the proposed CNN model has advantages in defect classification of PV cells. The completed work can provide meaningful exploration in the industrial application of defect detection of PV cells. [ABSTRACT FROM AUTHOR]

Objectives To evaluate whether in juvenile localized scleroderma (JLS), non-invasive imaging can differentiate affected from non-affected skin and whether imaging correlates with a validated skin score [Localised Scleroderma Cutaneous Assessment Tool (LoSCAT)]. Methods A total of 25 children with JLS were recruited into a prospective study and a single 'target' lesion was selected. High-frequency ultrasound (HFUS, measuring skin thickness), infrared thermography (IRT, skin temperature), laser Doppler imaging (LDI, skin blood flow) and multispectral imaging (MSI, oxygenation) were performed at four sites: two of affected skin (centre and inner edge of lesion) and two of non-affected skin (1 cm from the edge of the lesion 'outer' and contralateral non-affected side) at four visits at 3 month intervals. Results Differences between affected and non-affected skin were detected with all four techniques. Compared with non-affected skin, affected skin was thinner (P  < 0.001), with higher temperature (P  < 0.001–0.006), perfusion (P  < 0.001–0.039) and oxygenation (P  < 0.001–0.028). Lesion skin activity (LoSCAT) was positively correlated with centre HFUS [ r  = 0.32 (95% CI 0.02, 0.61), P  = 0.036] and negatively correlated with centre LDI [ r  = −0.26 (95% CI −0.49, −0.04), P  = 0.022]. Lesion skin damage was positively correlated with centre and inner IRT [ r  = 0.43 (95% CI 0.19, 0.67), P  < 0.001 and r  = 0.36 (95% CI 0.12, 0.59), P  = 0.003, respectively] and with centre and inner LDI [ r  = 0.37 (95% CI 0.05, 0.69), P  = 0.024 and r  = 0.41 (95% CI 0.08, 0.74), P  = 0.015, respectively]. Conclusion Non-invasive imaging can detect differences between affected and non-affected skin in JLS and may help to differentiate between activity (thicker, less well-perfused skin) and damage (thinner, highly perfused skin). [ABSTRACT FROM AUTHOR]

Simple Summary: The following review is based on the use of infrared thermography as a method to assess stress-related autonomic activity and its association with cardiovascular and heart rate variability in domestic animals. Stress-induced hyperthermia is one of the main physiological responses of animals to a stressor. The increase in body temperature activates compensatory peripheral mechanisms to increase heat dissipation and prevent further temperature increases. Changes in skin blood flow cause alterations in cutaneous temperature, alterations that can be assessed through infrared thermography and that will be discussed in the present review. Most of the responses present in animals when exposed to stressors are mediated by the autonomic nervous system. The sympathetic nervous system, known as the one responsible for the "fight or flight" reaction, triggers cardiovascular changes such as tachycardia or vasomotor alterations to restore homeostasis. Increase in body temperature in stressed animals also activates peripheral compensatory mechanisms such as cutaneous vasodilation to increase heat exchange. Since changes in skin blood flow influence the amount of heat dissipation, infrared thermography is suggested as a tool that can detect said changes. The present review aims to analyze the application of infrared thermography as a method to assess stress-related autonomic activity, and their association with the cardiovascular and heart rate variability in domestic animals. [ABSTRACT FROM AUTHOR]

Aim. To determine the effectiveness of infrared thermography to identify tissue debris of parenchymal organs and in the wound edges of hollow organs to be removed as well as for the surgical treatment of gunshot wounds of the anterior abdominal wall. Materials and methods. The results of the infrared thermography use in the provision of surgical care to 67 wounded at the second level of medical care in the conditions of the Forward Surgical Group of the Military Mobile Hospital were analyzed. Results. Clinical examples and results of the infrared thermography use for the diagnosis and treatment of gunshot wounds of the abdominal wall soft tissues, injuries to parenchymal and hollow abdominal organs are presented. Conclusions. Intraoperative infrared thermography allows to quickly assess the viability of the abdominal wall soft tissues, necrotic tissue margins of parenchymal organs, the state of blood supply to the walls of damaged hollow organs and their viability, thereby optimizing surgical tactics for abdominal gunshot wounds. [ABSTRACT FROM AUTHOR]

Background: the precision agriculture field optimizes resource use, enhancing performance. However, this leads to exposure to ergonomic risks for operators, in particular, tractor drivers, potentially causing musculoskeletal disorders (MSDs). This study investigates how the display position in a semi-automatic tractor system influences operator comfort and muscle activation during harrowing operations. Methods: the assessment of muscular strain involved the use of surface electromyographic devices, while posture was evaluated throughout the analysis of the distribution of pressure exerted by the operator's body on the seat, which was observed using two barometric pads, each positioned on the backrest and base of the seat. Finally, infrared thermography (IRT), a non-invasive tool to assess muscle activation, was used to measure the surface temperature of the driver's back. The results showed a significantly greater muscular activation display for the position of display in semi-automatic driving at 50° and 80°. Conclusions: this study showed how the position of the display on the vehicle negatively influences posture, exposing workers to the risk of developing fatigue and, therefore, discomfort, with the potential onset of MSDs. The combined use of sEMG and IRT allowed for a non-invasive, cheap, and repeatable mechanical and functionality analysis. [ABSTRACT FROM AUTHOR]

Rectal temperature is widely used as an indicator of animal health. However, rectal temperature is conventionally measured by an invasive method, which may reduce animal welfare. So, this study aimed to determine the relationships between the deep-body (core) temperature and body surface temperatures in goats and develop a linear regression equation to establish the core temperature based on body surface temperatures. Body surface temperatures (head, eye, muzzle, horn, back, scrotum and groin) of goats were measured by infrared thermography (IRT). Ambient temperatures were measured by digital thermometer. Core temperatures were measured by a digital vet thermometer. Pearson correlation analysis was used to analyze the relationship between body surface temperatures, ambient temperature, and core temperature. Simple linear regression analysis was used to develop core temperature assessment equations. Correlation analysis showed that groin temperature was highly correlated with core temperature, and low correlated with ambient temperature. The body surface temperature of other region was low correlated with core temperature, and highly correlated with ambient temperature. Regression analysis showed that the determination coefficient of core temperature assessment equation based on groin temperature was the highest (P < 0.0001, R2 = 0.55), and those based on surface temperature of other regions were low (P < 0.01, R2 ≤ 0.16). We concluded that body surface temperatures obtained by IRT could be used for the assessment of goat core temperature. The core temperature assessment equations developed by the temperature of the body surface, which is less affected by ambient temperature, was found to have a higher determination coefficient than the equations developed using body surface temperature that is more affected by ambient temperature. [ABSTRACT FROM AUTHOR]

Despite a continuous effort devoted by the scientific community, a large-scale employment of Pulsating Heat Pipes for thermal management applications is still nowadays undermined by the low reliability of such heat transfer systems. The main reason underlying this critical issue is linked to the strongly chaotic thermofluidic behavior of these devices, which prevents a robust prediction of their working behavior for different geometries and operating conditions, consequently hampering proper industrial design. The present work proposes to thoroughly compare data referring to previous infrared investigations on different Pulsating Heat Pipe layouts, which have focused on the estimation of heat fluxes locally exchanged at the wall–fluid interfaces. The aim is to understand the beneficial contribution of local heat transfer quantities in the prediction of the complex physics underlying such heat transfer systems. The results have highlighted that, regardless of the considered geometry and working conditions, wall-to-fluid heat fluxes are able to provide useful quantities to be employed, to some extent, to generalize Pulsating Heat Pipe operation and to improve their existing numerical models. [ABSTRACT FROM AUTHOR]

As photovoltaic (PV) panels are installed outdoors, they are exposed to harsh environments that can degrade their performance. PV cells can be coated with a protective material to protect them from the environment. However, the coated area has relatively small temperature differences, obtaining a sufficient database for training is difficult, and detection in low-resolution thermal images is complicated. This paper proposes a method for detecting the relative temperature difference on PV panels and a method for accumulating detection results within consecutive thermal images. To verify the performance of the proposed method, we installed PV panels coated with three different materials. Subsequently, 60 infrared (IR) thermal and visible images were acquired using an IR thermal imaging camera mounted on the drone. When more than 16 out of 60 results were accumulated, the highest performance was achieved with an F1 score of 0.7178. This case study demonstrated that even low-resolution thermal images can be acquired continuously to detect areas with small temperature differences without applying machine learning, which requires a large database. [ABSTRACT FROM AUTHOR]

Objective: Accurate assessment of burn depth and burn wound healing potential is essential to determine early treatments. Infrared thermography (IRT) is a non-invasive and objective tool to do this. This systematic review evaluated the accuracy of IRT to determine burn wound healing potential. Method: This systematic review and meta-analysis used MEDLINE, EMBASE, CINAHL, PEDro, DiTA and CENTRAL databases. IRT data were extracted from primary studies and categorised into four cells (i.e., true positives, false positives, true negatives and false negatives). Subgroup analysis was performed according to methods used to capture thermal images. Results: The search strategy identified 2727 publications; however, 15 articles were selected for review and 11 for meta-analysis. In our meta-analysis, the accuracy of IRT was 84.8% (63% sensitivity and 81.9% specificity). Conclusion: IRT is a moderately accurate tool to identify burn depth and healing potential. Thus, IRT should be used carefully for evaluating burn wounds. Declaration of interest: The authors have no conflicts of interest to declare. [ABSTRACT FROM AUTHOR]

A temperature gradient was induced in 316 stable austenitic stainless‐steel tension specimens, and the strain and temperature evolution during tensile deformation was monitored using optical and infrared cameras. The combination of global load with full‐field strain and temperature provided local information on the thermomechanical state of the investigated material. The deformation did not fully concentrate on the hotter portion of the specimen, but instead, the hottest portion strain hardened enough so that the colder portions of the specimen also experienced plastic deformation. Evidently, heat release occurred with plastic deformation and altered the initial temperature gradient as deformation progressed. The Taylor–Quinney coefficient was computed in integral and differential forms, and both are presented as a function of temperature and strain. The Johnson–Cook plasticity model was calibrated through an inverse method procedure in which only five tests were used, and the obtained temperature and strain rate dependencies of the model were comparable to those found in the literature for the same material. A local analysis was done to quantify the impact of adiabatic heating on the mechanical behaviour of the material. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of failures in low-voltage terminal connections within power transformers and proposes a fault classification methodology based on infrared thermography (IRT). Low-voltage terminal connections play a critical role in the reliable operation of power transformers, and their failures can lead to severe operational issues. In this study, we employ IRT as a noninvasive and efficient diagnostic tool to identify and classify various types of failures, including loose connections, overheating, and corrosion. The research involves the collection of infrared thermograms (IRT images) from the low-voltage terminals of power transformers under different operating conditions. The proposed methodology demonstrates its effectiveness in detecting and classifying low-voltage terminal connection failures, thereby enabling timely preventive maintenance and minimizing the risk of transformer malfunctions. This research contributes to enhancing the reliability and longevity of power transformers, reducing downtime, and optimizing maintenance practices in the power industry. [ABSTRACT FROM AUTHOR]

Infrared thermography is an imaging technique that can be used to inspect materials for flaws and various degradations in a non destructive way. In this work, we focused on the use of fast models to recover information about the material properties from experimental measurements recorded over time. Two different modelling approaches are compared to each other and to experimental data acquired on a composite plate. Then, the model based inverse problem consisting in estimating the plate properties is discussed. [ABSTRACT FROM AUTHOR]

The aim of this work is an experimental evaluation of a specific heat capacity as a function of plastic strain for thermo-mechanically rolled pipe material, with application of an infrared thermographic camera. The tensile load tests of samples prepared of L485ME (X70M) steel grade were performed with the use of a strength machine. Based on other known material thermophysical properties, the determination of heat source parameters was conducted with the use of an infrared thermography and with an optimization task solution. A linear regression equation describing the specific heat capacity as a function of plastic percentage elongation for L485ME steel grade was determined. The experimental results of the present study showed a linear increase in the specific heat capacity in the range of the analyzed tensile deformation up to 16%. The presented methodology is suitable for assessment of the material specific heat capacity as a function of strain up to the occurrence of the sample narrowing in a direction perpendicular to the tensile force. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A high-resolution infrared (IR) camera was used for temperature measurements in a pharmaceutical formulation (mannitol/sucrose solution, 4:1%, m/m) during a freeze-drying process. The temperature was measured simultaneously at the surface as well as vertically (e.g., in-depth) along the side of custom-made cuvettes equipped with a germanium (Ge) window. Direct imaging during 45 h from −40 °C to 40 °C took place every 60 s on the surface and on the side with 0.28 × 0.28 mm per IR-pixel providing 2700 thermograms. The spatial resolution per cuvette was approximately 4225 pixels for the surface view (without a probe) and 6825 IR-pixels for the side view. Temperature effects and gradients due to the presence of a Pt100 and a LyoRx-probe in the pharmaceutical formulation were clearly visible and were quantified during the freezing step as well as the primary and secondary drying stages. The temperature was about 3.5 K higher during primary drying as compared to the temperature measured in the same material in adjacent cuvettes without probes. During secondary drying, evaporative cooling of upper layers was clearly visible. [ABSTRACT FROM AUTHOR]

Inconel 718 is a challenging alloy to machine, commonly employed in the aeronautic and energy industries. There is a continual need to enhance our understanding of its cutting processes to improve its machining applications. This research presents an in situ analysis of the kinematic and thermal fields behaviour of a serrated chip, during orthogonal cutting in the primary shear zone. This study involves a specific self-designed optical system enabling the simultaneous acquisition of both a visible high-speed CCD camera and an infrared camera via a single × 25 magnification reflective objective. A particular attention is brought to evaluate the accuracy of the whole optical system to measure the thermomechanical fields in the unfavourable peculiar cutting conditions among such are high strain rates, texture evolution, strong thermal gradients, and very narrow observation window. The description of the digital image correlation technic within the visible range and the infrared images post-processing are both affronted, and their limitations exposed. To conclude, a deeper characterisation of the primary shear zone shape is done from the kinematic and the thermal point of views showing that, at least under the employed cutting conditions and for Inconel 718 alloy, the primary shear zone width exhibits no thickness despite the literature. [ABSTRACT FROM AUTHOR]

The transition characteristics of a modified RAE5243 airfoil were investigated using a wind tunnel test and numerical simulations. Transition detection is of great significance for the assessment of drag reduction. In wind tunnel tests, transition location can be detected by infrared thermography. However, in subsonic and transonic wind tunnel tests, the temperature difference between the laminar flow region and turbulent flow region is small. Moreover, the test models are usually made of metals, which make the transition location hard to identify. Combined with infrared thermography, a carbon nanotube heating coating powered by electricity was used to detect the transition location of a modified RAE5243 airfoil wing. The effects of heating power, angle of attack (AOA), and Mach number were studied. The results show that heating power has no impact on transition location. As the AOA increases, the transition location moves forward. With an increase in Mach number, the transition location moves forward first and then backward, and it reaches its most forward point at Ma = 0.75. The results of our numerical simulations indicate that, at Ma ≥ 0.75, a shock wave appears on the wing, and the transition is closely related to the shock wave rather than the adverse pressure gradient. [ABSTRACT FROM AUTHOR]

This study is devoted to the development of fusion techniques for data obtained by one or several nondestructive testing (NDT) methods. Experimental results were obtained by applying laser vibrometry and optical infrared thermography to evaluation of impact damage to carbon fiber composites. These NDT techniques are different by their physical nature and supply specific testing results. The proposed data fusion method allows increasing the reliability of inspection results and enables estimating defect parameters. It involves both averaging data of each single NDT technique and merging the results obtained by two methods. Vibrograms obtained by laser vibrometry are used to analyze acoustic response of the test sample to stimulation at various frequencies. In turn, infrared thermographic NDT supplies the sample response to thermal stimulation. It has been shown that the fusion of these two techniques supplies a comprehensive information on defect size and location. Also, the automation of the fusion procedure increases NDT productivity and reduces subjectivity of testing results. [ABSTRACT FROM AUTHOR]

Early and accurate assessment of peripheral arterial disease (PAD) is crucial given its fatal complications such as amputation. Infrared thermography (IRT) offers various advantages in studying PAD. This study aims to map the use of Infrared thermography in detecting Peripheral Arterial Disease in diabetic feet. This review applies the scoping review method to review the literature related to Infrared thermography in detecting vascular disorders in diabetic legs. Article searches are conducted on five databases, namely Google Scholar, PubMed, EBSCO, ScienceDirect, and ProQuest. The Boolean keywords and codes used are "Infrared Thermography" AND "Diabetic foot" AND "Peripheral Arterial Disease". Searches are limited to articles published between 2018 and 2024. The journal selection process follows PRISMA's guidelines. The search process yielded 1,969 article titles relevant to the keywords and Boolean operators used. After being selected based on the research topic, 8 articles were left for analysis. IRT can be a useful tool for detecting PAD in diabetic foot. IRT can help track the progression of PAD and classify the foot as PAD without or prone with high accuracy [ABSTRACT FROM AUTHOR]

The surface spectral absorptivity of surface-whitened mortars due to the occurrence of efflorescence (i.e., mortars whose surface was covered with calcium carbonate) was measured, and the relationship between the spectral absorptivity and inspection capability of active thermography inspection was investigated. The spectral absorptivity of mortars increased significantly at a wavelength of approximately 3000 nm regardless of the presence/absence of the discoloration. Experiments for mortar specimens using optical lights with wavelengths in the visible, short wavelength, and medium/long wavelength ranges showed that the heating efficiency and defect detection capability of active thermography inspection were correlated with the surface spectral absorptivity, and were higher when long wavelength light was used as a heater. Defects in the surface-whitened mortar specimen were detected more efficiently when the specimen was heated using a CO2 laser, whose wavelength is in the long wavelength range, than when using an optical light having a wavelength in the visible/short wavelength range. [ABSTRACT FROM AUTHOR]

The extreme temperatures in weld zone during friction stir welding (FSW) directly affect the welding quality of the joints. Therefore, characterization of the maximum and minimum temperatures in weld zone is essential to ensure welding quality. However, direct measurement or prediction based on simulation is difficult due to the occlusion of the tool shoulder and the severe plastic deformation of the weldment under thermo-mechanical effects during the welding process. To solve the problem, this paper presents a method for prediction of extreme temperatures in weld zone based on infrared thermography. Firstly, a 3D temperature field simulation model of FSW is established based on DEFORM, and the experiments are conducted to verify the validity of the simulation model. The data sets of temperature of surface feature points and the maximum and minimum temperatures in weld zone are obtained. Then, support vector regression (SVR) is used to establish a temperature prediction model, which represents the correlation between the temperature of surface feature point and the extreme temperatures in weld zone. In practical FSW process, an infrared thermal imager is used to measure the temperature of the surface feature point. Combined with the built temperature prediction model, the prediction of the extreme temperatures in the weld zone is achieved. The research provides references for temperature-based process control of FSW. [ABSTRACT FROM AUTHOR]

To detect the debonding defect of glass fiber reinforced polymer (GFRP) laminate, the lock-in infrared thermography non-destructive testing system is built and the systematic testing research are conducted, and the effect of different geometrical parameters of debonding defects on the testing results are analyzed. Algorithms such as inter frame differential-multi frame cumulative average method, polynomial fitting-correlation coefficient method, and time-difference contrast method are used to process the image sequence, and signal-to-noise ratio and information entropy are defined as the parameters for evaluating the performance of algorithms. [ABSTRACT FROM AUTHOR]

Mistletoe detection and sampling remain challenging for arborists, dendrologists, forest ecologists, and other specialists because of the limited access to host tree canopy. In this review, smart solutions for mistletoe detection based on airborne platforms are discussed. Airborne remote sensing (ARS) has the developing potential to provide rapid, accurate, and cost-efficient detection and research of mistletoe on tree level and large areas within the complex terrain. Herein, such mistletoe ARS research methods as image spectroscopy, infrared thermography, light detection and ranging, and structure from motion are overviewed. [ABSTRACT FROM AUTHOR]