This PDF file contains the front matter associated with SPIE Proceedings Volume 11743 including the Title Page, Copyright information, and Table of Contents.

Introduction to SPIE Defense and Commercial Sensing conference 11743: Thermosense: Thermal Infrared Applications XLIII

A plant’s root system absorbs water and necessary nutrients, and synthesizes organic matter, which is essential for plant growth and regeneration. Therefore, investigating root system architecture (RSA) can potentially provide deep understanding and useful information about plant growth. Current approaches involve soil-coring and use of mini-rhizotrons, which can damage the root or be time consuming. Groundpenetrating radar has been employed but is not suitable for small plants because of the resolution needed. Nuclear magnetic resonance could provide valuable information of tiny roots, but the equipment is costly. In this study, infrared imaging—a-non-destructive method—was used to reveal the shape and position of small root systems, such as sugar beet roots. The finite element analysis (FEA) methodology was implemented toA plant’s root system absorbs water and necessary nutrients, and synthesizes organic matter, which is essential for plant growth and regeneration. Therefore, investigating root system architecture (RSA) can potentially provide deep understanding and useful information about plant growth. Current approaches involve soil-coring and use of mini-rhizotrons, which can damage the root or be time consuming. Ground-penetrating radar has been employed but is not suitable for small plants because of the resolution needed. Nuclear magnetic resonance could provide valuable information of tiny roots, but the equipment is costly. In this study, infrared imaging, a-non-destructive method, was used to reveal the shape and position of small root systems, such as sugar beet roots. The finite element analysis (FEA) methodology was implemented to validate the practicality of applying infrared imaging to detect roots. Artificial neural network (ANN) methods were used to determine the existence of a root system. Support vector machine (SVM) and ANN were employed to predict root depth and statistical tests were used to compare the results. The results of these experiments suggest that infrared imaging can be used to predict the presence and depth of roots. validate the practicality of applying infrared imaging to detect roots. Artificial neural network (ANN) methods were used to determine the existence of a root system. Support vector machine (SVM) and ANN were employed to predict root depth and statistical tests were used to compare the results. The results of these experiments suggest that infrared imaging can be used to predict the presence and depth of roots.

An individual's face is a biometric feature that can be used in a computerized security system to identify or authenticate that particular person. The main challenge, while identifying a face through the use of a machine, is to match precisely the captured person's face with the image of the same individual's face already existing in the system's face database. Visual spectrum face images are affected by variations in lighting, head orientation, aging, and disguise resulting in poor visual face detection performance. Infrared imaging is used to help overcome some of these limitations. In this work, we propose a deep Deep Convolutional Neural Network architecture based on the FaceNet architecture and the MTCNN model to perform face recognition on a set of thermal data. Tests conducted on the USTC-NVIE dataset show promising results and the possibility of using deep learning in thermal face recognition.

Thermographic imaging has been shown an efficacious method for analysis of gait abnormality in canines. A total of 168 images were used from multiple views of canine’s legs. An algorithm was developed for the automatic mask creation of these thermographic canine leg images to locate important gait related areas. The algorithm is compared with other segmentation and enhancement combinations using the CVIPtools Algorithm Testing and Analysis Tool (ATAT). The ATAT software was able to identify an algorithm with an average 88.4% success rate using the Dice coefficient and Jaccard Index as the main error measurements.

Diagnosis and prognosis of failures for aircrafts’ integrity are some of the most important regular functionalities in complex and safety-critical aircraft structures. Further, development of failure diagnostic tools such as Non-Destructive Testing (NDT) techniques, in particular, for aircraft composite materials, has been seen as a subject of intensive research over the last decades. The need for diagnostic and prognostic tools for composite materials in aircraft applications rises and draws increasing attention. Yet, there is still an ongoing need for developing new failure diagnostic tools to respond to the rapid industrial development and complex machine design. Such tools will ease the early detection and isolation of developing defects and the prediction of damages propagation; thus allowing for early implementation of preventive maintenance and serve as a countermeasure to the potential of catastrophic failure. This paper provides a brief literature review of recent research on failure diagnosis of composite materials with an emphasis on the use of active thermography techniques in the aerospace industry. Furthermore, as the use of unmanned aerial vehicles (UAVs) for the remote inspection of large and/or difficult access areas has significantly grown in the last few years thanks to their flexibility of flight and to the possibility to carry one or several measuring sensors, the aim to use a UAV active thermography system for the inspection of large composite aeronautical structures in a continuous dynamic mode is proposed.

The quality control of structures and fuselages in both the wind-turbine and solar sectors is a fundamental part that allows a lifetime assessment of their elements, from its initial assembly to the recurring inspection cycles. Automating the active thermography on this scale, cannot be achieved with conventional industrial robots. Unmanned vehicles, such UAVs and UGVs, present distinctive advantages that should certainly be exploited, but, its inherent static motion is one of the main stumbling blocks towards its use in an active thermography inspection. In this paper, a two-step digital stabilization scheme has demonstrated its efficacy in real defects located in both a wind blade and solar panel. The combination of a featurebased registration algorithm and a dense parametric optical flow direct alignment has enabled the pseudo-static reconstruction of the thermograms. The adopted experimental methodology, employing a robot with both halogens and IR camera, subjected to random motions with varying speed and amplitudes, has allowed a direct repeatable comparison of static and stabilized phase images. The phase image contrast comparison of both static and dynamic tests, have been carried out on a flat bottom hole (FBH) wind blade GFRP sample, showing nearly identical phase contrast with marginal differences. Likewise, a real GFRP wind-blade impact delamination defect has also reached a close phase contrast regarding its counterpart, albeit with a decreased contrast. Additionally, the registration algorithm has been used to stitch the individual frames, derived from a dynamic recording of an electroluminescent solar panel, to allow for a unified detection and mapping of defects.

Industry 4.0 represents a significant change in manufacturing with the inclusion of such technologies as additive manufacturing and data analytics. In support of these changes, infrared thermal imaging (IR) and very-near infrared (VNIR) have proven invaluable in numerous applications related to additive manufacturing. In this review, these applications will be presented as they relate to polymer and metal-based additive manufacturing processes. Applications including in-situ characterization, post-build analysis, experimental setups, modeling, and machine learning will be covered. With a general overview complete, the review will conclude with a discussion of challenges identified throughout the literature and thoughts on future trends.

Additive manufacturing of metals offers the opportunity to build parts with a high degree of complexity without additional costs, opening a new space for design optimization. However, the processes are highly complex and due to the rapid thermal cycles involved, high internal stresses and peculiar microstructures arise, which influence the parts mechanical properties. To systematically examine the formation of internal stresses and the microstructure, in-process spatially resolved measurements of the part temperature are needed. Usually, thermography is used to measure temporally resolved thermal fields. The thermal cameras are calibrated at black body reference radiators (unity emissivity) for the conversion of the measured thermal radiation intensity to temperatures. If the emissivity of the inspected part is known, its thermodynamic temperature can be reconstructed by a suited radiometric model. However, in additive manufacturing of metals, the emissivity of the part surface is strongly inhomogeneous and rapidly changing due to variations of, e.g., the degree of oxidation, the material state and temperature. However, measuring the process thermal radiation at different wavelengths simultaneously enables one to separate temperature and emissivity spatially resolved to obtain further insight into the process. Here, we present results of a study using multispectral thermography to obtain real temperatures and emissivities in the laser metal deposition (LMD) process. For a better understanding of the basic processes, the measurements have been performed first without powder supply and by recording images at different wavelength in subsequent runs.

Laser powder bed fusion is used to create near net shape metal parts with a high degree of freedom in geometry design. When it comes to the production of safety critical components, a strict quality assurance is mandatory. An alternative to cost-intensive non-destructive testing of the produced parts is the utilization of in-situ process monitoring techniques. The formation of defects is linked to deviations of the local thermal history of the part from standard conditions. Therefore, one of the most promising monitoring techniques in additive manufacturing is thermography. In this study, features extracted from thermographic data are utilized to investigate the thermal history of cylindrical metal parts. The influence of process parameters, part geometry and scan strategy on the local heat distribution and on the resulting part porosity are presented. The suitability of the extracted features for in-situ process monitoring is discussed.

This paper describes the inline (coaxial to laser) near infrared (NIR) camera sensor on the Configurable Architecture Additive Testbed (CAAT). The CAAT is an instrument that provides the capability to investigate laser based additive manufacturing (AM) processes and is configured for the metal powder bed fusion process. A low cost NIR camera is radiometrically calibrated to obtain coaxial, inline, imagery of laser generated melt pools. The camera capabilities, system optical path, and the uncertainty in the temperature measurement from NIR surface area scans on a bare titanium alloy plate are presented and discussed. The surface radiance measurements are compared to optical microscopy images of the melt pool width and depth. A metallic additive manufacturing process thermal model is developed in order to predict thermal distributions during laser scanning. The predicted thermal distributions by the model for different configurations are compared to the coaxial NIR measurements and discussed.

Thermographic nondestructive techniques with focused laser excitation have proven as very efficient tools for the detection of narrow cracks. Moreover, it has been shown that in the ideal case of infinite cracks, the width of the crack can be assessed quantitatively using laser spot thermography, both in lock-in and pulsed regimes. In this ideal case, the surface temperature of the cracked material can be obtained analytically. However, real cracks feature finite penetration and length and, in these conditions, the calculation of the surface temperature needs to be performed numerically. In this work, we combine laser-spot lock-in thermography with finite elements modelling (FEM) to perform a full characterization of the local values of the width and depth of narrow cracks along the whole crack length in two Alalloys plates after fatigue test. First, in order to locate and image the crack, we combine the squares of the spatial derivatives of the amplitude thermograms along two perpendicular directions for different positions of the laser spot. Then, we place the laser close to the crack and we fit the numerical model to the amplitude data, so as to obtain the values of the width and depth of the crack at the current position of the laser. By displacing the laser spot at different positions along the crack length, we fully characterize the width and depth of the crack, whose resulting values are of the order of 1 µm and 0.5 mm, respectively.

Inductive thermography is a well-established NDT method to detect surface cracks in metallic materials. The induced eddy current density decays exponentially below the surface, penetrating up to the so-called skin-depth. This depth depends on the excitation frequency and on material parameters, as the magnetic permeability. As a surface crack is an obstacle for the eddy current and for the heat flow, it becomes visible in the infrared images. It is investigated whether cracks ending below the surface, can be detected by inductive thermography. It is stated, that when the crack end is lying closer the surface than the half skin-depth, then it can be detected. This statement is investigated for ferro-magnetic and non-magnetic samples and for different excitation frequencies. The inspection is usually done in reflection mode, but for thin wall work-pieces the transmission mode provides a good detection possibility. Experimental results are compared with finite element simulation results.

The Thermoelastic Stress Analysis (TSA) is a contactless technique based on the thermoelastic effect that consists of the generation of small temperature variations caused by the volume variations induced by stresses applied in the linearelastic range. Recent works demonstrated the capability of the TSA for the characterization of materials behaviour in presence of residual stresses. The use of a general TSA analytical expression allows the researchers to find a relationship between the amplitude of the thermal signal varying at the same frequency as the applied load and the characteristics of the residual stress tensor in terms of principal stresses and their direction. The just said relationship, under certain conditions, can be also affected by the uncertainty in the knowledge of the thermo-physical properties of the material which can enhance or blur the presence of residual stresses. In this work, the effect of the main variables, such as the material properties and the presence of residual stress on the TSA were investigated by applying a sensitivity analysis to the analytical general model. The analytical results were then verified and compared with TSA experimental measurements performed on AA2024 samples affected by biaxial residual stresses and the residual stresses measured with a standard test method.

Pulse-compression thermography is an emerging technique that has shown versatility by combination of pulsed and lock-in thermography. Accordingly, several aspects of this technique are still unexplored, and some others not fully developed yet. Barker codes were widely used in radar applications due to their simplicity and their optimum autocorrelation function. Nevertheless, applications were limited by the amplitude of the sidelobes present in the autocorrelation function and therefore, several filters have been developed which aim to reduce the sidelobes. However, the filters usually depend on empirical parameters which must be determined for each application. A better alternative would improve the applicability of the Barker codes. In this work, we further develop the pulse-compression thermography technique by introducing a 13-bit modified Barker code (mBC): This allows to drastically reduce the sidelobes characteristic of the 13-bit Barker code (BC). Consequently, the thermographic impulse response, obtained by cross-correlation, is almost free of such sidelobes. Deeper defects become easier to detect in comparison with using a 13-bit Barker code. Numerical simulations using the finite element method are used for comparison and experimental measurements are performed in a sample of steel grade St 37 with machined notches of three different depths: 2 mm, 4 mm and 6 mm.

A key composite delamination characteristic that determines thermographic detectability is the extent to which it blocks the flow of heat from one ply to an adjacent ply. A measure of this heat flow barrier is the contact resistance which is proportional to gap spacing between the two plies. From thermographic data acquired on a composite with delaminations and a three-dimensional simulation of the heat flow in a delaminated composite based on computed tomography characterization of the delaminations, a delamination contact resistance map is estimated. The contact resistance values are smaller than expected based on the gap spacing estimated from computed tomography data. A model is presented that assumes there are small variations in the gap spacing which are not captured by the computed tomography. This model indicates if there is sufficient variation in the gap spacing, the contact resistance is significantly smaller than a value obtained from the average gap spacing. The contact resistances calculated from different amplitudes of the variations are compared to the estimates of contact resistance from experimental data.

An interesting preliminary evaluation for a low-cost approach for thermographic Non-Destructive Evaluation of carbon fiber-reinforced polymer is presented. Lock-in active thermography, by halogen lamps, has been used on a CFRP specimen with several simulated defects and two different IR-Cameras were used. Both cameras have microbolometric detectors but belong to different price range (A65 and A655), with a difference of cost that changes of a factor of 3. Phase maps for three principal harmonics were analyzed and the limits of low-cost IR camera were investigated by means of a quantitative analysis in terms of the number of detected defects and normalized phase contrast (NPC), changing the acquisition frame rate

In this study, we investigated the assessment of the damaged area on composites ballistic plates subjected to high velocity impact. The active pulsed thermography technique was used for performing post-mortem analysis of the impacted specimens. Quantitative analysis of the damaged areas shows consistent results with the size of the projectile suggesting high precision of the quantification done in this work. This quantitative defect analysis combined with knowledge of projectile velocity allows for characterization of absorbed energy and differentiation of generated defect types. This allow for the evaluation of material efficiency in spreading absorbed energy over large areas. Our observations indicate that high velocity shots tend to induce smaller impact damage areas characterized primarily by fiber breakage, while low velocity shots tend to induce larger impact damage areas featuring predominantly delamination and matrix cracking damage mechanisms.

In the last decades, composite materials, particularly thermosetting carbon fiber reinforced polymers, have become the main structural material for the aerospace industry. Recently, interest has grown in thermoplastic composites, since they are chemically more stable, faster to process, fatigue-resistant and recyclable. Nevertheless, when submitted to high temperatures these materials may degrade in ways not presently well known. Therefore, the study of the thermo-mechanical properties of thermoplastic composites when exposed to fire or high-temperature events is of primary interest. In particular, a good knowledge of its behavior could improve physical modeling to the point of reducing the number of prescribed fire tests by virtualizing some of them. The first step is to measure the thermal parameters of real samples in a practical way. We have established a methodology that extends the classical flash method to obtain the effective thermal parameters (diffusivity, specific heat, heat conductivity, and Biot number) of thermoplastic composite materials by a non-contact method based on IR imaging. Values obtained have been used to simulate thermal behavior with a FEM-based solver, from room temperature up to 900°C, with an agreement with experimental data better than 1% in temperature (K) for temperatures below ∼ 260°C and better than 3% up to ∼ 850°C.

Infrared non-destructive testing and evaluation is one of the most promising inspection methods for evaluating variety of materials due to its merits such as remote based, whole field, safe and quantitative inspection capabilities. Among the various infrared non-destructive evaluation methods, pulse based thermography and mono frequency excited modulated lock-in thermography gained importance due to their simple experimentation procedure and data processing approaches involved. However, recently proposed matched filter based non-periodic infrared thermographic approaches gained importance due to their superior sub-surface defect identification capabilities in terms of detection resolution and sensitivity. The present work demonstrates the merits of pulse compression favorable thermal wave imaging approach for identification of flat bottom holes in a carbon fiber reinforced polymer material.

Racing tires, both for cars and for motorbikes, become effective by reaching the sufficient grip at a temperature that ranges between 120 and 180°C. That is the reason why the tires are preheated before the race by enveloping them within an electric blanket. During the race, the vehicle is continuously submitted to two kinds of acceleration: the one that is needed to rise the speed of the vehicle that is given by the engine, or to decrease it when the brakes are activated, and the other that works when turning the vehicle to follow the motor racing track. In both cases, either rectilinear motion and acceleration, or rotatory motion and centripetal acceleration, the tire must guarantee the necessary grip to avoid the slipping. Modeling the behaviour of the tire at the race conditions is interesting both to optimise the grip with the asphalt of the road and to optimise its wear resistance. For doing that, the assessment of the thermophysical properties of the tires is mandatory.

Do-it-yourself-for-you fablabs, autonomous mini factories responding to customer specific needs, require advanced cost efficient measurement systems for validating quality of end product. An example of end product is laundry detergent powder. The customer chooses raw materials with preferences related to for example odor, health and environmental aspects. Thus, the raw materials vary in each mixture. Operational risks include customized laundry detergent powder product missing a desired compound. To mitigate this risk, verification of each raw material existence in the mixture is essential. According to previous studies, optical measurement such as spectroscopy in short-wavelength infrared range is one promising way to identify raw materials in laundry detergent powders. For proving feasibility, we examined shortwavelength infrared hyperspectral imaging of laundry detergent powder samples - final products and raw materials. Additionally, we tested liquid soap samples with same method and experimental setup as in the detergent powder samples’ case. This study shows, that existence of desired raw materials can be verified from detergent powder with short-wavelength infrared imaging. Final testing of customized laundry detergent powder product avoiding desired compound absence failure is enabled by spectroscopic measurement and analysis. Liquid soap is more challenging test subject because of strong water absorption in the short-wavelength infrared range. Further studies should cover testing and comparing more optical measurement and analysis methods for finding accurate and affordable do-it-yourself-foryou fablab solutions.

Thermographic super-resolution techniques allow the resolution of defects/inhomogeneities beyond the classical limit, which is governed by the diffusion properties of thermal wave propagation. Photothermal super-resolution is based on a combination of an experimental scanning strategy and a numerical optimization which has been proven to be superior to standard thermographic methods in the case of 1D linear defects. In this contribution, we report on the extension of this approach towards a full frame 2D photothermal super-resolution technique. The experimental approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample. In our work we extend the possibilities of the method to efficiently detect and resolve defect cross sections with a fully 2D-structured blind illumination.

Compression algorithms have been implemented for k-means and k-means++ clustering and applied to thermographic images. The overall algorithm has four stages and are the same for the two algorithms except for the initialization of the centroids. The compression ratio and quality are primarily dependent on the number of clusters used for the algorithm. A MATLAB GUI was developed to run the algorithms and a comparison has been performed with subjective evaluations and objective RMS error, peak SNR and compression ratio metrics. The average compression ratio was 1.3 and 1.6 for the k-means and k-means++ clustering respectively. The k-means++ clustering provides subjectively better visual results than the standard k-means clustering.

Temperature measurement in flames is a challenging problem. Recently, hyperspectral imaging has demonstrated to be able to provide accurate temperature maps in a standard flame. However, hyperspectral imagers are expensive instruments, and the data analysis is laborious. Thus, a more simple approach to temperature imaging would be advisable. Since important and systematic differences exist in the low-resolution spectra of flames as a function of their temperature and chemical composition, it is in principle possible to retrieve these parameters by means of multispectral imaging. In this work, a standard flame, whose temperature and CO₂ concentration are known, is studied with an infrared camera in the MIR band (3 to 5 μm), provided with a six interference filter wheel. High- resolution emission spectra are calculated, using the HITEMP2010 database, as a function of flame temperature (T) and CO₂ column density (Q_CO2 , measured in ppm·m), and integrated over the spectral transmittance profile of the selected interference filters. Measured radiances in each channel are compared to these simulated values and the absolute error is minimized at each pixel to retrieve values of T and Q, obtaining temperature and column density maps for the flame. Results are compared to the known values of the standard flame. First estimations of errors are found to be ΔT< 100 K and ΔQ_CO2 < 400 ppm·m for flames with T∼2200 K and Q_CO2 ∼3500 ppm·m. The possibility of reducing the number of filters and their effect on accuracy is studied.

The objective of the Infrared Laboratory-LIR has been the design and development of simple and low-cost IR systems for the resolution of specific problems in real time. The study of clouds in the IR region is a problem studied since the first on-board measurement systems. Nowadays imaging systems are multispectral instruments that provide accurate information on clouds. These systems are generally large in size, weight and cost. However, in many situations, the goal is simply to get auxiliary information about the clouds. Then, the low-cost approach of LIR is ideal for designing simple cloud characterization systems. In this work, a methodology is presented to determine the emissivity and temperature of the clouds based on the brightness temperature measured from space by a bi-spectral camera in the 10 and 12 microns bands. In addition, we provide quantitative information on the capabilities of the methodology based on real data provided by MODIS instrument.

The here discussed infrared camera-based monitoring system observes the temperatures of concentrated solar power plants to avoid damages. Besides, it allows to control the temperature-relevant parameters of the plant like salt flux and heliostat positioning and therefore needs to be designed with a high-level fallback strategy. In this presentation possible influences by environment, plant-specific behaviors, geographical situation and failure scenarios will be discussed and the solutions described. These include the infrared-optical measurement adaptation, the mechanical installation, the electrical and data network design and finally the software-based algorithms to ensure a 24/7 reliability under all possible conditions defined by the mentioned influences.

Infrared non-destructive testing and evaluation is one of the promising inspection methods for characterization of wide verity of materials due to its merits and applicability to test materials irrespective of their electrical, mechanical, acoustical and magnetic properties. Among the various infrared non-destructive evaluation modalities such as pulse based thermography and mono frequency excited modulated lock-in thermography, recently proposed matched filter based non-periodic infrared thermographic approaches gained their importance due to their superior sub-surface defect detection in terms of resolution and sensitivity. The present work demonstrates the merits of frequency modulated thermal wave imaging for identification of concrete in concrete structure. Obtained results shows the matched filter based post-processing schemes exhibits better depth scanning capabilities compared with the conventional frequency domain phase approach to detect corrosion in the concrete structures.

InfraRed Thermography (IRT) is one of the widely used Non-destructive Testing and Evaluation (NDT and E) method for characterization of fiber reinforced polymers. Among various testing methodologies and associated post processing schemes, recently proposed pulse compression favorable thermal wave imaging methodologies gained importance due to their enhanced test sensitivity and resolution for identifying the sub-surface defects. The present paper highlights a highly depth resolved pulse compression favorable thermal wave imaging methodology for identification of subsurface defects in a Glass Fiber Reinforced Polymer (GFRP) test specimen.