Thermographic Data Research Articles

Detection of gunshot residue by flash-pulse and long-pulse infrared thermography

Detection of gunshot residues (GSR) in a bullet hole area is one of the forensic investigations aiding in the reconstruction of crime scenes. Traditionally, chromogenic methods based on chemical exposure or microscopic/spectroscopic methods are used for this purpose. In this study, we explore the applicability of active excitation infrared thermography methods for GSR detection in the bullet hole area on fabric samples. A standard 9 mm full metal jacket ammunition with a nickel-plated shell and natural cotton fabric samples were used for experiments in this study. The applicability of active thermography methods based on two different light/heat excitation sources to detect the GSR was investigated. Flash-pulse and long-pulse thermography were compared through an experimental investigation. We evaluated the effectiveness of various thermographic data processing methods, including background subtraction, temperature derivative analysis, Fourier transform phase analysis, principal component analysis, and higher-order statistics for GSR evaluation. Our findings demonstrate that flash-pulse thermography and kurtosis analysis yield the highest contrast-to-noise ratio (CNR) and produce sharp, clear images of GSR, making it the optimal method for thermographic GSR detection. Our study indicates that even though the GSR particles are tiny, they can produce sufficient contrast to be detected by the thermographic methods if appropriate experimental and post-processing procedures are used. Thus, these methods could complement GSR detection as they are non-destructive and offer rapid inspection.

Thermography measurement for bridge displacement in the darkness using power-free target

Vision-based displacement measurement has become increasingly popular in bridge health assessment. However, this technology faces challenge that a complicated power-consuming supply system must be installed in the darkness, making it unable to meet the needs of low energy consumption and low cost. This paper proposes a bridge displacement measurement method using power-free target and thermography to address it. First, a thermography measurement system for bridge displacement in the darkness is developed. The power-free target with a hollowed-out circular feature is adopted to form the contrast to the background, and graphene is coated on the target surface to enhance the infrared emissivity and contrast further. Second, a sub-pixel elliptic center positioning algorithm based on thermography enhancement and data optimization is proposed to improve the accuracy and the ability to resist occlusion. The feasibility of the method is verified by laboratory tests and bridge field tests. When the measurement distance is within 15 m, the results meet the engineering error requirements of 5%, with the prospect of popularization in small and medium-sized bridges displacement measurement.

Dissipative and thermal aspects in cyclic loading of additive manufactured AISI 316L

This study presents a comprehensive understanding of the fatigue behavior of 316L stainless steel specimens produced using the laser powder bed fusion (PBF-LB/M) method. The investigation has been conducted through a multifaceted approach that includes surface roughness analysis, density measurements, microstructural examination, fatigue testing, strain measurements, and thermographic analysis. Thermographic data processing models, i.e. the bi-power law (TCM-modified) model and the TCM method, are applied to fatigue test results of standard samples to evaluate fatigue limit values. The fatigue limit of the samples was estimated using the Murakami Method (MM), Constant Amplitude Loading (CAL) and Step Loading (SL) tests. The proposed methodology allows exploration of the entire stress level range within a single test, which could allow evaluation of the fatigue limit of components within only one test. This study is the starting point for a rapid evaluation method for estimating the fatigue limit using thermography, offering a cost-effective, time-efficient, and non-destructive means of assessing the fatigue performance of materials produced using additive manufacturing processes.

Sizing horizontal metallic inclusions in insulators using lock-in inductive infrared thermography

We present a methodology to size the area and depth of horizontal metallic inclusions of unknown geometry, embedded in insulators, using lock-in inductive thermography. The method is based on averaging the amplitude and phase thermograms along circles concentric with the center of the heated region. We present an analytical calculation of the surface temperature distribution produced by a horizontal circular heat source, taking into account conductive coupling with the air, and we propose to fit the averaged data to this model. We present lock-in thermography data on resin samples containing embedded calibrated circular and rectangular Cu slabs that we excite inductively. The results indicate that it is possible to determine the area and depth of rectangular heat sources with aspect ratio below 1.5 with accuracy better than 10 %.

Impact of the thermal afterglow effect on infrared thermography data evaluation methods

In this work, the influence of the afterglow effects from a source impulse on commonly used data processing methods of infrared thermography (IRT), such as Pulsed Phase Thermography (PPT) and Thermographic Signal Reconstruction (TSR) was investigated. To resolve this issue, we propose the adoption of a modified mathematical model or an empirical function to approximate the flash lamp pulse, which we recorded during the test with a photodiode. Two models were evaluated: one consisting of a combination of exponential and power functions describing the entire pulse and a second, focusing only on the decreasing phase. The models were compared with well-established empirical functions, such as those of Degiovanni and Vageswar, using the widely known statistical scores R2adjusted and Akaike information criterion. The quantitative comparison revealed better fitting results for our proposed models. Additionally, we introduce an algorithm based on Hough transform-clustering for automated determination of the exact pulse length. Statistical analyses were performed to evaluate the reproducibility of the measured pulse shape. The results from applying the first model as pulse correction is compared to another widespread IR data treatment methodology, such as Modified Differential Absolute Contrast (MDAC). We also consider the impact of the afterglow effects on the Source Distribution Image (SDI). Two different specimens were used in the experiments: a homogeneous polymer sheet with blind holes as defects and an in-homogeneous plate made of carbon fiber reinforced polymer (CFRP) with artificial defects made from Tetrafluorethylen-Hexafluorpropylen-Copolymer (FEP) foil inserts. In both experiments a quantitative comparison was made using the Tanimoto criterion (TC), indicating an improved sensitivity. This work forms the basis for a faster and more reliable quantitative evaluation of experimental data, in particular algorithms for the automated detection and assessment of manufacturing defects or in-service damage will benefit from these results.

Fireball symmetry and its influence on perspective error from thermography data

Thermography uses high-speed color cameras to perform two-color pyrometry for measuring spatially resolved surface temperatures of condensed phases. One application is to investigate the thermal evolution of particles within fireballs, but data analysis is affected by emissivity and optical density. Fireball dynamics exhibit large variations in both properties across space and time, while diagnostics measure the line-of-sight radius of a maturing fireball, raising the question: does thermography accurately represent temperature distributions regardless of spatial perspective? Here, fireballs are observed at two 90° perspectives. Every frame of data is categorized based on symmetry, then compared using the median temperature difference. Symmetric flame profiles show higher congruity in global median temperature, whereas asymmetric flames produce varying optical density profiles leading to larger differences between perspectives. Methods to correct perspective errors are discussed.

THE SHORT-TERM EFFECTS OF KINESIO TAPE ON THE SKIN TEMPERATURE OF THE UPPER TRAPEZIUS MUSCLE IN HEALTHY INDIVIDUALS

The aim of the study is to verify the short-term effects of Kinesio Tape on the skin temperature of the upper trapezius muscle in asymptomatic subjects. Twenty-five university students were submitted to application of Y shaped Kinesio Tape over the upper trapezius muscle. Thermographic data was captured with and without KT and exported to FLIR Tools software, where the minimum, maximum and average of the skin temperature of the region between the strips was obtained. Paired t test were used to compare the outcomes of baseline with KT condition. There was no difference on the minimum (p = 0,283), maximum (p = 0,783) and average (p = 0,377) of the skin temperature of the upper trapezius muscle between the strips of KT. Therefore, there was no effect of Kinesio Tape over the skin temperature of the upper trapezius muscles in asymptomatic subjects. Future studies should try to evaluated areas below the strips or around involved areas and with different methodologies to evaluated KT effects on skin temperature.

Evaluation of Infrared Thermography Dataset for Delamination Detection in Reinforced Concrete Bridge Decks

Structural health monitoring and condition assessment of existing bridge decks is a growing challenge. Conventional manned inspections are costly, labor-intensive, and often risky to execute. Sub-surface delamination, a leading cause of deck replacement, can be autonomously and objectively detected using infrared thermography (IRT) data with developed deep learning AI models to address some of the limitations associated with manned inspection. As one of the most promising classifiers, deep convolutional neural networks (DCNNs) have not been utilized to their fullest potential for delamination detection, arguably due to the scarcity of realistic ground truth datasets. In this study, a common encoder–decoder semantic segmentation-based DCNN is adapted through domain adaptation. The model was tuned and trained on a publicly available dataset to detect subsurface delamination in IRT data collected from in-service bridge decks. The authors investigated the effect of dataset augmentation, class imbalance, the number of classes, and the effect of background removal in the training dataset, resulting in an overall number of seventy-five UNET models. Four out of five bridges were adopted for training and validation, and the fifth bridge was for testing. Most models averaged 80 iterations, and the training progress finally reached a training accuracy of 75% with a loss of about 0.6 without any overfitting. The result showed a substantial difference in the minimum and maximum values for the evaluated performance metrics (0.447 and 0.773 for global accuracy, 0.494 and 0.657 for mean accuracy, 0.239 and 0.716 for precision, 0.243 and 0.558 for true positive rate (TPR), 0.529 and 0.899 for true negative rate (TNR), 0.282 and 0.550 for F1-score. The results also indicated that the models trained on the raw annotated balanced dataset performed best for half of the metrics. In contrast, the models trained on raw data (with no dataset enhancement) performed better when only global accuracy was considered.

Link between Plant Phosphate and Drought Stress Responses.

The menace of drought has persistently loomed over global crop production, posing a serious threat to agricultural sustainability. Research on drought stress highlights the important role of the phytohormone abscisic acid (ABA) in orchestrating plant responses to drought conditions. ABA regulates various drought/dehydration-responsive genes, initiates stomatal closure, and influences cellular responses to drought stress. Additionally, plants employ a phosphate starvation response (PSR) mechanism to manage phosphate (Pi) deficiency, with ABA playing a role in its regulation. However, despite intensive research in these fields, the precise connection among PSRs, drought stress, and ABA signaling still needs to be determined. Recently, PSR-related gene induction has been reported to occur before the induction of ABA-responsive genes under progressive mild drought. Mild drought decreases Pi uptake and contents in plants, triggering PSRs, which play an important role in plant growth during mild drought. Both ABA-responsive and PSR-related gene expression could indicate plant perception of external moisture conditions. Thus, integrating the information regarding their associated gene expression with soil moisture contents and thermographic data can enable timely irrigation optimization to mitigate the effect of drought on crop productivity.

Usability Assessment of Technologies for Remote Monitoring of Knee Osteoarthritis.

Goal: To evaluate the usability of different technologies designed for a remote assessment of knee osteoarthritis. Methods: We recruited eleven patients affected by mild or moderate knee osteoarthritis, eleven caregivers, and eleven clinicians to assess the following technologies: a wristband for monitoring physical activity, an examination chair for measuring leg extension, a thermal camera for acquiring skin thermographic data, a force balance for measuring center of pressure, an ultrasound imaging system for remote echographic acquisition, a mobile app, and a clinical portal software. Specific questionnaires scoring usability were filled out by patients, caregivers and clinicians. Results: The questionnaires highlighted a good level of usability and user-friendliness for all the technologies, obtaining an average score of 8.7 provided by the patients, 8.8 by the caregivers, and 8.5 by the clinicians, on a scale ranging from 0 to 10. Such average scores were calculated by putting together the scores obtained for the single technologies under evaluation and averaging them. Conclusions: This study demonstrates a high level of acceptability for the tested portable technologies designed for a potentially remote and frequent assessment of knee osteoarthritis.

Influence of the scan pattern on the melt pool temperatures and the mechanical properties of thin-walled components in Laser-based Powder Bed Fusion of Polyamide 12

In laser-based powder bed fusion of plastics, the mechanical properties are geometry-dependent, which poses a challenge for producing lightweight, thin-walled aerospace and automotive components. To investigate this issue, this study utilized infrared thermography data to monitor the production of tensile test specimens with varying cross-sections and energy input. The thermographic measurements reveal accelerated cooling at the sample edges, even with contour exposure. This is because the cooling behavior is related to the sample’s geometry and scan pattern. Additionally, using x-y-alternating scan vectors results in layer-wise peak temperature variations, which correlate with the scan vector length and lead to higher porosity in corresponding layers. Consequently, reducing the energy input or the thermal overlap frequency in layers with short scan vectors and samples with small cross-sections leads to a porous boundary region, ultimately reducing their mechanical properties. This demonstrates that the scan pattern contributes significantly to the geometry-dependency of mechanical properties.

Steady melting in material extrusion additive manufacturing

PurposeA main cause of defects within material extrusion (MatEx) additive manufacturing is the nonisothermal condition in the hot end, which causes inconsistent extrusion and polymer welding. This paper aims to validate a custom hot end design intended to heat the thermoplastic to form a melt prior to the nozzle and to reduce variability in melt temperature. A full 3D temperature verification methodology for hot ends is also presented.Design/methodology/approachInfrared (IR) thermography of steady-state extrusion for varying volumetric flow rates, hot end temperature setpoints and nozzle orifice diameters provides data for model validation. A finite-element model is used to predict the temperature of the extrudate. Model tuning demonstrates the effects of different model assumptions on the simulated melt temperature.FindingsThe experimental results show that the measured temperature and variance are functions of volumetric flow rate, temperature setpoint and the nozzle orifice diameter. Convection to the surrounding air is a primary heat transfer mechanism. The custom hot end brings the melt to its setpoint temperature prior to entering the nozzle.Originality/valueThis work provides a full set of steady-state IR thermography data for various parameter settings. It also provides insight into the performance of a custom hot end designed to improve the robustness of melting in MatEx. Finally, it proposes a strategy for modeling such systems that incorporates the metal components and the air around the system.

Deep learning-based approach in surface thermography for inverse estimation of breast tumor size

Background and objectiveIn early breast cancer diagnosis, tumor size is key to improving the patient's survival chances. It helps doctors to determine the adequate treatment for each case. Surface thermography presents encouraging results to detect thermal patterns of breast tumor abnormality. However, the early and accurate estimation of tumor size based on temperature characteristics is quite challenging due to unavailability of labeled clinical data. This work proposes a Feed-Forward Deep Neural Network (FF-DNN) for an inverse estimation of breast tumor size using thermographic data. MethodsA 3D breast model was created by the COMSOL Multiphysics software incorporating tumors in the gland and covered by fat, muscle, and skin layers. Several tumor configurations are included to generate a large amount of training data. An initial thermal data analysis was performed to affirm the influence of breast tumor size on the skin surface temperature. Then, 1400 different cases were prepared, and the relevant features were extracted to train the deep learning model. The coefficient of determination (R2) and the Mean Square Error (MSE) were used as metrics for evaluating the predictive model. ResultsThe analysis of the normalized temperature variations demonstrated the influence of tumor size on the surface temperature of the breast. Thus, the comparison between the FF-DNN against the Convolutional Neural Network (CNN) showed the reliability of the proposed approach. As result, the prediction accuracy indicates the capacity of the proposed FF-DNN model to estimate tumor size from the provided relevant features with an MSE value of 0.194 and an R2 value of 0.998. ConclusionThe findings of this study indicate that surface thermography, when paired with deep learning, holds promise as a valuable diagnostic tool to improve the prognosis of breast cancer.

Mathematical Model of the Layer-by-Layer FFF/FGF Polymer Extrusion Process for Use in the Algorithm of Numerical Implementation of Real-Time Thermal Cycle Control.

An approach for improving and maintaining a consistent weld quality of the deposited material during the FFF printing process is proposed. The approach is based on the analysis of the printing process thermal cycle and the real-time nozzle temperature control. The mathematical model of the FFF printing process has been developed with the use of real-time control in the algorithm of numerical implementation. The successful solution of the thermal conductivity problem made it possible to determine segment-wise heating settings for use during the printing process, resulting in a high and stable quality of welding. Comparison of the results of modeling with other well-known mathematical models of the FFF printing process and experimental results showed the adequacy of the proposed model. A maximum deviation of 17.7% between the simulation results and the thermography data was observed. The proposed model was verified using rectangular 3D polylactide shapes printed with and without regulation of the power of the heat source according to the previously estimated settings. The overall quality of regulation, stability of the system, and the PI coefficients of the controller were evaluated using a simulated model of the control system. The results of the experiment fully correspond with the modeling results.

Potential use of an extended-distance thermal imaging camera for the assessment of thermal comfort in multi-occupant spaces

Thermographic cameras integrated with artificial intelligence (AI) models have recently become widespread to assess thermal comfort via extracted facial skin temperature. The study aimed to examine the potential use of camera-extracted facial skin temperature to predict thermal sensation for a group of people. To deal with the limitations of previous works, this study extended the camera-subject distance, enabling the detection of multiple faces facing different orientations. Thermal sensation and thermographic data were collected from 32 subjects under transient conditions, where the air temperature gradually changed between 21 °C and 29 °C. The relationship between their subjective votes and extracted skin temperatures across five facial regions and the whole face was analyzed. Using analysis of variance, results showed that the facial skin temperatures at each sensation scale were significant differences and provided a good prediction for the 3-point thermal sensation scale. The right cheek temperature was the most potential predictor of human thermal sensation, while the forehead temperature was less accurate due to noise interference. The proposed method is cost-effective and acceptably assesses thermal comfort for multi-occupant spaces.

Fabrication and characterization of graphene‐loaded recycled poly(ethylene terephthalate) electrospun composite nanofibrous mats with improved thermal conductivity

AbstractIn this study, graphene‐loaded electrospun recycled poly(ethylene terephthalate) (rPET) nanofibrous mats were produced and characterized morphologically, spectrally, mechanically and thermally. Particularly, the effects of graphene nanoplatelets (GNP) and multilayer graphene (GML) in improving the thermal conductivity and heat dissipation abilities of rPET‐based composite nanofibers were investigated. The morphological analyzes pointed out that 1% graphene loading led to smooth nanofibers while 5 and 10% of GNP‐loading resulted in coarser nanofibers with rougher surfaces and agglomerations. The differential scanning calorimetry results pointed out that the crystallization temperature increased with increasing graphene content as a result of the pronounced nucleation effect. The thermogravimetric analysis demonstrated an improvement in the thermal stability of the composite nanofibers. The thermal conductivity coefficients increased to 25.422 W/mK‐35.842 W/mK for rPET/GNP nanofibers and up to 62.669 W/mK for rPET/GML nanofibers, compared to that of 12.753 W/mK for neat rPET nanofibers which correspond to an increase between 99 and 391%. Heat dissipation capability of the graphene‐loaded composite nanofibers was illustrated with infrared thermography data, displaying an increase in the average surface temperature of the nanofibrous mats between 2 and 19°C at 30 s of heating. The results suggest the use of the graphene‐loaded rPET composite nanofibers as textile materials for thermoregulating applications.Highlights Recycled poly(ethylene terephthalate)/graphene composite nanofibers are electrospun. Thermal conductivity of graphene‐loaded nanofibers increases by up to 391%. Graphene loading in nanofibers leads to faster and more uniform heat dissipation. Mechanical properties of composite nanofibers improve. Value‐added recycled polyester materials for thermal management are foreseen.

Comparativestudy of experimental thermographic data and finite element analysis on temperature evolution of PET-G layer deposition during additive manufacturing process

Comparativestudy of experimental thermographic data and finite element analysis on temperature evolution of PET-G layer deposition during additive manufacturing process

The apparent effusivity method for normalized thermal contrast evaluation in infrared thermographic testing

Thermographic nondestructive testing is used for the detection of near-surface discontinuities in materials, where indications of potential defects or discontinuities are detected and evaluated by their thermal contrast. The key requirements for successful thermographic data processing include managing non-uniform heating and identifying nominal areas. Advanced methods often rely on numerical simulation, which can be challenging and time-consuming. This study suggests a new simple method of thermal contrast calculation for flash-pulse thermography. It is based on the analysis of the inverse apparent effusivity. It includes an automated indication of the sound (defect-free) area and the creation of an artificial reference sequence, which is based on a temperature profile of the sound area and the non-uniform heating map. Derivative analysis of the smoothed contrast data is applied to improve detectability and Contrast/Noise Ratio (CNR). An experimental comparison of the proposed method with other known methods is presented. In the presented example, it allowed detection of 23 defects with an average CNR of 6.13 dB, against the traditional differential absolute contrast method, which detected only 13 defects with an average CNR of 0.53 dB. Thus, high detectability and high CNR values provided by the proposed method are demonstrated.

Aerial urban observation to enhance energy assessment and planning towards climate-neutrality: A pilot application to the city of Turin

Based on the central role of cities in the decarbonisation process, this study aims at providing a novel methodology to assess and optimize the building energy demand and photovoltaic producibility at city scale. This is done by exploring the potential of aerial acquisition of local-scale thermographic data to integrate the existing database on building Energy Performance Certificates, on which the EU energy assessment and retrofit policies are based, on a dedicated GIS platform. Once the method is validated through testing the reliability of the resulting energy classification of buildings, the approach is used to esteem the energy demand and potential energy production in the current state and two alternative retrofit scenarios for a large district in Turin, Italy. For the analysed scenarios, potential savings are quantified up to 20 GWh and 5000 tCO2 yearly, decreasing by more than 70 % primary energy and CO2 emissions when photovoltaic panels and heat pumps are optimally used and integrated to the existing district heating system. Conclusions remark the potential of this tool to support urban policy making and governance, also with the future perspective of a thematic urban digital twin for planning energy retrofit intervention and large scale implementation of Renewable Energy Communities.

New Input Factors for Machine Learning Approaches to Predict the Weld Quality of Ultrasonically Welded Thermoplastic Composite Materials

Thermoplastic composites (TCs) enjoy high popularity in the field of engineering. Due to this popularity, there is a growing need to assemble this material with the help of fast and efficient joining processes. One joining process, which has seen increased use, is the process of ultrasonic welding. To make reliable statements about the quality of the joined material, some kind of quality assurance has to be made. In terms of ultrasonic spot welding, there are already some documented approaches for observing or predicting the joining quality, but some of these most promising parameters for quality assurance are difficult to measure in the process of continuous ultrasonic welding. This is why new parameters are investigated for their potential to improve the prediction of ultrasonic-welded TCs’ quality. Thermography and sound emission data have been found to have a correlation with the produced weld quality and are fed into different machine learning algorithms. Despite the relatively small dataset, trained algorithms reach binary classification rates of over 90%, indicating that the newly discovered parameters show the potential to improve the quality assurance of ultrasonic-welded TCs in the future. This improvement may enable the establishment of the ultrasonic welding of TCs in manufacturing.