This PDF file contains the front matter associated with SPIE Proceedings Volume 12109 including the Title Page, Copyright information, Table of Contents, Introduction, and Committee pages.

Additive manufacturing (AM) is a rapidly growing technology. An area of major importance is the integrity and repeatability of AM parts. The goal is to reduce obstacles to certify AM built parts to allow for use in critical aerospace applications. In-situ nondestructive evaluation sensors can be used for build assessment and can potentially play a key role in certifying AM parts. For example, melt pool features are understood to have a strong correlation to microstructural defects and the use of a near infrared (NIR) camera can be used to record the melt pool, cooling areas, and temperature gradients during the build. This work explores the use of a low cost NIR camera to obtain single line track imagery of the Ti-6Al-4V melt pools for various processing parameters. The NIR camera is radiometrically calibrated and configured in-line with the laser source to obtain high resolution imagery of the melt pool shape and dynamics. The challenge to measure melt pool shapes is to identify the transition points between the metal solid to liquid phase. Factors for melt pool measurements such as thermal camera pixel resolution, surface emissivity, and blurring are discussed. Lastly, the melt pool imagery are compared to optical microscopy measurements for validation.

Due to the diffusive nature of heat propagation in solids, the detection and resolution of internal defects with active thermography based non-destructive testing is commonly limited to a defect-depth-to-defect-size ratio greater than or equal to one. In the more recent past, we have already demonstrated that this limitation can be overcome by using a spatially modulated illumination source and photothermal super resolution-based reconstruction. Furthermore, by relying on compressed sensing and computational imaging methods we were able to significantly reduce the experimental complexity to make the method viable for investigating larger regions of interest. In this work we share our progress on improving the defect/inhomogeneity characterization using fully 2D spatially structured illumination patterns instead of scanning with a single laser spot. The experimental approach is based on the repeated blind pseudo-random illumination using modern projector technology and a high-power laser. In the subsequent post-processing, several measurements are then combined by taking advantage of the joint sparsity of the defects within the sample applying 2D-photothermal super resolution reconstruction. Here, enhanced nonlinear convex optimization techniques are utilized for solving the underlying ill-determined inverse problem for typical simple defect geometries. As a result, a higher resolution defect/inhomogeneity map can be obtained at a fraction of the measurement time previously needed.

Long wave infrared (LWIR) radiation (7-14 μm) allows illumination-less imaging, and spectroscopic chemical identification. Infrared imaging is ubiquitous in defense thermography, airborne and atmospheric sensing, fault detection, and medical testing. Visible speckle imaging can successfully image through complex scattering media. We describe a novel broadband LWIR speckle imaging-based wavefront sensor, utilizing a thin diffuser with an uncooled microbolometric camera. Due to the thin diffuser, local phase gradients produce speckle deformations which are estimated by a rapid image registration algorithm to generate a phase gradient map, whose 2-D integration yields the reconstructed wavefront. We demonstrate LWIR wavefront reconstruction using our setup in infrared optical samples, with future applications for LWIR imaging through visually non-transparent materials.

We propose a methodology to determine the thermal diffusivity of both isotropic and anisotropic samples when they move at constant speed, using laser spot thermography with continuous illumination. In the case of anisotropic samples, the method does not require knowledge of the thermal principal directions and is able to provide the orientation the principal axes with respect to the direction of motion. We show analytically that, once the steady state has been reached, the natural logarithm of the temperature along any temperature profile crossing the center of the laser spot features a linear dependence with the distance. The slopes of these straight lines are related with the speed of the sample, the principal thermal diffusivities and the orientation of the principal directions. We present experimental data taken on both, isotropic and anisotropic reference materials moving at constant speed. From the fitting of the slopes of the radial profiles in multiple directions, we are able to assess the values of the principal thermal diffusivities as well as the orientation of the principal axes with high accuracy. These results are promising regarding the quality control of industrial products in a production chain as, for instance, for fiber orientation monitoring of carbon fiber reinforced composites.

In the present work, a non-destructive thermographic method for evaluating the effectiveness of heat treatment in boron steel, has been developed. Considering that the thermal properties of steel are strictly connected with micro-structure, an active thermography technique has been used for measuring the thermal diffusivity, adopting a reflection mode set-up. For the choice of suitable experimental test parameters, a numerical approach through the Finite Element Analysis has been adopted. Two boron steel (Usibor® 1500) specimens with different structures (ferritic-paerlitic / 100% martensitic) have been experimentally investigated by two thermographic methods, in transmission and reflection mode, and results have been compared with semi-destructive hardness test to validate the method.

In this paper, a method of material category identification using the step heating thermography technique is proposed. The proposed method is simple, fast, implementable for in situ measurement and also reliable. The pertinency of the method has been demonstrated on different types of materials.

Monitoring and verification of structural changes in mining areas is necessary for mining operators - especially in terms of what events are taking place in the mining area and tailing ponds. Emptying or enriching a pond requires up-to-date information on the shape of the pond and amount of contained material. Increasing accurate measurements interests mining operators. Novel monitoring technologies include possibility of imaging spectroscopic optical measurements - such as hyperspectral imaging - with drone-based scanning feature. At Pyhäsalmi Mine in Finland, we established field trials with drone-based near-infrared multispectral imaging of mine tailing ponds and their dam structures. These field trials were part of the European Union’s Horizon 2020 project called ‘Goldeneye’, which aims to develop a data-acquisition and processing platform combining remote sensing and positioning technologies to produce actionable intelligence for mine operators.

Multi-sensor imaging option consisting of infrared (TIR), near infrared hyper-spectral (NIR HS) and red-green-blue (RGB) sensors was demonstrated to confirm the ability of the sensors to detect the floating debris and differentiate plastic from organic material in a realistic river environment. A drone and a fixed installation were utilised. The target objects were typical plastic products used in the households and the organic material consisted of pieces of wood and branches. The results suggest that multi-imaging with NIR HS, TIR and RGB sensors is a promising method for separating floating plastic waste from organic material. Further efforts will be targeted in possibility to distinguish different plastic types from each other and how this process could by applied by utilising machine learning methods.

We combine image correlation and thermography to measure the two-dimensional strain field along with the temperature distribution of a specimen in a tensile testing machine. Both methods have been combined before, yet they usually rely on different imaging devices, requiring the strain and temperature data to be aligned and parallax-corrected in post-processing. In our novel approach, we perform the image correlation directly on the thermography data – thus reducing the complexity of the measurement setup significantly. We demonstrate the method by quantifying the stress-strain-temperature relation of localized deformation bands in aluminum.

This paper proposes a new thermal contact resistance distribution measurement method using a lock-in thermography. To evaluate two-dimensional local thermal contact resistance distribution, a uniform intensity laser heating system was developed, which has top-hat intensity distribution with a diameter of 30 mm. By combining this heat source and the lockin thermography, a new measurement instrument was developed, which can evaluate local temperature behavior in the frequency domain affected by the contact interface in high spatial resolution of about 70 micrometers. Additionally, a new thermal contact resistance measurement principle was constructed based on one-dimensional heat transfer equation in consideration of the reflected and transmitted temperature wave at the boundary and contact interface. The thermal contact resistance was acquired as a solution of the inverse problem of the temperature response by fitting analyses. The validation of this method was evaluated with a sample made of bonded two isotropic graphite plates. The sample has intentional defect area which has slightly higher thermal resistance. As the results, the validity of the measurement method was confirmed in comparison with the other validated method. Also, the defect area was quantitatively detected clearly as high thermal resistance region. Furthermore, the measurement method was applied for two different contact interface roughness sample consisting of aluminum alloys and thermal grease as a practical example. Consequently, it was revealed and visualized that the contact interface with rough surface has high thermal resistance spot.

In this paper, thermal diffusivity measurements using a lock-in thermography based on the laser-periodic-heating method was used to quantify the micro-scale damage that occurred in the early-stage of fatigue. Measurement system using square-core optical fiber with a mode mixing was developed to ensure heating uniformity and enlarge the measurement area. A developed model was fitted to the frequency dependence of phase delay, which was corresponded to a time delay from the input periodical laser heating, to obtain the thermal diffusivity. Cross-ply CFRP laminates were prepared for fatigue tests and thermal diffusivity measurement. Two type of tensile fatigue load applied sinusoidally at maximum loads of 30% and 50% of static tensile strength, respectively. Fatigue load cycle reached to 105 times. Post-fatigue sample were confirmed by X-ray CT that there was no clack growth inside the sample. The results showed that the thermal diffusivity of the post-fatigue sample decreased by up to 3.1% compared to the unloaded sample. This result indicate that thermal diffusivity measurement has possibility to quantitate the early-stage fatigue damage. However, further experiment was needed to extract only the fatigue loading effect, since this result includes differences in the initial state before fatigue loading.

Composite materials unarguably represent the important structure parts in most modern transport applications such as the aerospace sector. One area that shows great potential in the battle against aircraft structural damage and the diagnosis of composite materials. Very often, detection and diagnosis tools offer a valuable and quick mechanism to the analysts and assist them in the monitoring of the health integrity of the composite materials. Although numerous initiatives to develop damage detection techniques and make operations more efficient were launched, there is still an on-going need to develop/improve upon the existing methods. In this work, Pulsed Thermography (PT) technique was used to acquire healthy and faulty datasets from specially designed composite samples of the same dimensions (300 mm x 300 mm x 2 mm) with three different geometries (planar, curved and trapezoidal). Three plates from carbon fibre-reinforced plastic (CFRP) were tested. The same defects distribution was first introduced to the different samples and the variation of surface temperature over time, and the flow of transient heat generated through an energy stimulus in the samples were then monitored. A machine learning (A Cubic Spine Support Vector Machine) based technique was applied to the resulting thermographic images in order to detect and classify damage on composite structures. The proposed classification model was evaluated for its performance using the common metrics such as the overall accuracy, sensitivity, precision, specificity, etc. It was concluded that the classification approach could provide a reliable estimate of composite material conditions and eventually could lead to 'go / no-go' decisions.

Up to now, the inspection of wind turbines with industrial climbers has been considered ”state of the art”. However, ever-larger wind turbines and advancing digitization make modern and automated inspection methods indispensable. Passive thermography can serve as such a digital and atomized method while it is well known for its applications in the inspection of buildings or electrical circuits. However, its application relies on thermal gradients in the inspected object such that a temperature contrast exists between damaged and sound areas. This also holds for unheated structures like rotor blades of wind turbines which show no intrinsic temperature gradient and can hardly be heated. Under certain weather conditions with sufficient solar loading and diurnal temperature variations, passive thermography is suitable for the in-service inspection of rotor blades. However, for a reliable use of passive thermography on ”thermal passive” components, the incorporation of these environmental conditions in the planning and evaluation of thermal inspections is crucial. Additionally, the complex inner structure of wind turbine blades in comparison to other objects and buildings require a specific method referencing the individual rotor blades to each other. This allows the distinction between the thermal response of design-specific structural features and damages or irregularities between the three blades. We show thermal signatures of damage in rotor blades and contrast them with structural characteristics by comparing the three blades. In addition to measurements in industrial environments, laboratory measurements are shown and compared to simulations. The long-term goal is to simulate the influence of different weather parameters and thus gain a better understanding of measurements in the field. The results shown here can be seen as one step towards industrial application.

Dynamic thermography has been widely used as a diagnostic tool in breast cancer screening before mammography and with clinical breast examination (CBE). Thermal imaging biomarkers, thermomics, are proven to highlight the heterogeneous thermal patterns and vasodilation indicating abnormalities in the area due to angiogenesis blood vessel formation. This study shows two sets of analyses. The first set is a feasibility study involving a combined multimodal imaging biomarker using mammographic and thermographic imaging for 11 cases of breast cancer screening. The second part of this paper shows the application of the t-distributed stochastic neighbor embedding (tSNE) method to provide a low dimensional representation of thermal sequence and tested for 55 breast cancer screening participants. We extracted high dimensional radiomics and thermomics and reduced the dimensionality of these features using spectral embedding technique, and trained a random forest model with tuned hyperparameters to perform diagnostic prediction. The results of tSNE combining clinical and demographics yield 77.4% (69.8%, 86.8%), while the highest accuracy belonged to Sparse PCT + Clinical with 79.3% (73.6%, 84.9%). The proposed method results indicated that the tSNE can preserve thermal patterns driven radiothermomics, which leads to significantly aid in CBE and early detection of breast cancer.

Recent trends in rail manufacturing show the application of composite materials for new rail carbodies. The paper investigates the theoretical application of infrared thermography for identifying defects in carbon fibre composite materials of monolithic or hybrid nature such as Carbon Fibre-PET sandwich materials. The study was performed using a finite difference thermal modeling software (i.e. ThermoCalc 3D), with the intention of identifying a series of subsurface defects in thick composite materials of various sizes and at different depths, leading to the use of thermography as a suitable technique in the inspection of rail composite materials.

Radiative transfer (RT) codes have many applications ranging from weather/climate predictions and atmospheric sciences to remote sensing and astrophysics. However, traditional RT codes are computationally very expensive and increasingly unable to process the large amounts of data resulting from modern global circulation models (GCM) or satellite feeds. One way to alleviate this problem is to use statistical emulators, i.e., fast and accurate approximate models based on statistical inference, to replace the deterministic RT codes. In his paper, we develop a statistical surrogate model which allows us to predict the radiances or brightness temperatures, i.e., the amount of electromagnetic energy measured by an electro-optical sensor, from the atmospheric state variables. The emulator is based on Gaussian Processes (GPs) which, for our purposes, are deemed to have several advantages over neural networks (NN). Unlike neural networks, GPs provide an analytical expression for the predictive error, the underlying model is interpretable and differentiable and the datasets required for training the model are considerably smaller than those for NNs.

Visible and thermal infrared are two imaging modalities which are used in a variety of applications. In deep learning we need large datasets to be able to train and optimize the algorithms. In thermal infrared imaging, there is a lack of large datasets. This work proposes a deep learning approach to transform visible light images into thermal infrared images using video sequences with moving objects. We propose to use and optimize a CycleGAN algorithm to transform frames from one spectrum to another by training two generators and two discriminators. The results are promising with impressive qualitative and quantitative results.

A semi-automatic thermographic procedure for the assessment of the welded area of resistance projection welded joints has been developed. Currently, to assess the quality of RPW joints destructive tests are used and the more commonly used non-destructive technique is the ultrasonic one. The possibility for a quantitative evaluation of the welded area by thermographic technique has been proved by means of an innovative procedure applied on steel RPW joints with ‘as it’ surface conditions. Measurements obtained by thermography and ultrasound have been compared, to verify the developed procedure.

We suggest a method for biomedical imaging with heat in the far infrared spectrum using principal and independent components analysis. This method produces novel results suggesting physiologic mechanisms of considerable importance for diagnostic imaging. When using thermal imaging to detect breast cancer the dominant heat signature is of indirect heat transported by the blood away from the tumor location into the skin. Interpretation is usually based on vascular angiogenesis and not by observing the direct cancerous heat. In this new method one uses sequence of thermal images of the patient breast following external temperature change. Data is recorded and analyze using independent component analysis (ICA) and principal component analysis (PCA). ICA separate the images sequence into new independent images having common characteristic time behavior. Using the Brazilian visual lab mastology data set we observed three type of images: Images corresponding to minimum change as function of applied temperature or time which are associate with the cancer generated heat, images which shows moderate temperature dependent and are associate with veins affected by vasomodulation and images that shows complex time behavior indicating heat absorption by high perfusion of the tumor. All components are clear and distinct.

Terahertz imaging systems require small, low cost and low power systems operating at room-temperature. Terahertz Seebeck nanoantennas are room temperature detectors which generate voltage due to incident electromagnetic radiation, they also provide polarization sensitivity, directivity, small footprint, tunability and the possibility of integration into electronic and photonic circuits. In this work a gold bowtie nanoantenna is designed and optimized to detect electromagnetic radiation at 2 THz. The resulting device is a gold bowtie antenna with asymmetric connection lines to optimize the Seebeck voltage. The connection lines are made of Sb2Te3 and Bi2Te3 to increase the generated voltage due to the incident electromagnetic radiation. Simulation results obtained using COMSOL Multiphysics are presented. The fabrication of the resulting optimized device was performed using photolithography and liftoff. The materials were deposited by sputtering. The fabricated device includes an external heater to measure the effective Seebeck coefficient. Experimental results of the effective Seebeck coefficient of the device as well as response measurements are presented and compared to Multiphysics simulations.

Complex mathematical models are often computationally expensive to run and involve many different input parameters. Therefore, the propagation of errors through these models is often difficult to evaluate. In this paper we will have a closer look at the error propagation through one such model, namely, Neon, an electro-optical Tactical Decision Aid (TDA) for predicting the apparent brightness temperature contrast between a target and its background, which is run operationally by the UK Met Office. Neon consists of four different parts, a land surface model (LSM) for predicting background temperatures, a land/maritime target model (TM), a radiative transfer model (RTM) and a detect and recognize model (D and R). Although, the accuracy of the individual Neon components has been studied before, no overall sensitivity analysis has ever been done for Neon. In this paper we utilize Morris’ Method to study the sensitivity of the Neon prediction system to uncertainties in its input parameters. The key message from this analysis is that Neon is particularly sensitive to uncertainties in the optical properties, in particular the albedo, of the target and background. Thus, this study allows us to focus further development of the system on the elements that contribute the greatest uncertainty.

Relation between evaporation rate and temperature change due to latent heat is investigated with thermal imaging in micro scale and a numerical simulation. Evaporation involves many complicated phenomena, in addition to vapor diffusion in the air. The surface of droplet is cooled during evaporation due to the latent heat, and the cooling has great influence on the evaporation rate. Therefore, heat and mass transfer phenomena are strong-coupled problem. Numerical simulation model to reproduce evaporation phenomenon is desired to investigate the complicated phenomenon, and heat and mass transfer equations are solved with CFD tool: openFOAM. The influences of evaporative cooling on the evaporation rate have been investigated with various droplet sizes and various contact radii. The temperature on the surface of evaporation of a water droplet was measured with a microscopic infrared imaging and a micro probe sensor with a thermoelectric hot junction.

Principal Component Thermography applies Singular Value Decomposition (SVD) to post-process data that are derived from active thermographic inspections. SVD provides useful compression of the data and allows for better understanding of substructure and indications of potential damage. In the standard approach, SVD is applied to a certain reshaping of a three-dimensional data stack into a two-dimensional array. This work applies the CANDECOMP-PARAFAC (CP) tensor rank decomposition directly to the three-dimensional data to avoid the initial reshaping step in order to begin to develop an inspection method that can more accurately detect defects in non-homogeneous and anisotropic materials. Tests against simulated data that compare the CP decomposition method with traditional Principal Component Thermography based on SVD are described. Finally, the method of Proper Generalized Decomposition (PGD) is used to derive the CP decomposition, and its performance against other algorithms is also discussed.

Most existing platform signature models use semi-empirical correlations to predict flow convection on internal and external surfaces, a key element in the prediction of accurate skin signature. Although these convection algorithms are capable of predicting bulk heat transfer coefficients between each surface and the designated flow area, they are not capable of capturing local effects such as flow stagnation, flow separation, and flow history. Most computational fluid dynamics (CFD) codes lack the ability to predict changes in background solar and thermal irradiation with variations in the environment and sun location, and do not include the thermal / optical properties of the surfaces and multi-bounce radiative surface exchanges with their solvers (by default). Existing interfaces between CFD and signature prediction tend to simply map the CFD predicted temperatures onto the signature model. This paper describes the latest efforts to develop a fully functional interface between the NATO-standard ship signature model (ShipIR) and the ANSYS Fluent CFD solver. Our previous work (Vaitekunas et al, 2011) has been updated to include a parallel version of the ShipIR User-Defined Function (UDF) library, which now transfers the net radiative and other non-conducting sources of heat flux to the Fluent solver, using either a wall heat flux for adiabatic walls or a heat generation rate for coupled and backside convection wall boundaries. The resultant wall temperatures and convective fluid heat fluxes are used to either iterate the coupled solution (coupled-T) or refine the local-area heat transfer coefficients and fluid temperatures in ShipIR (coupled-h). The updated interface is analysed using a detailed thermal/IR simulation of a commercial Bell 407 helicopter with a standard engine and tailpipe.

The need for greater efficiency in the field of shallow closed-loop geothermal systems has led to the proposal for groutless coaxial geothermal heat exchangers made of steel. In terms of heat transfer performance, they are superior to traditional grouted U-shaped or double-U plastic ones, but they are still not well accepted by the market because there are doubts about their safety in terms of reliability. This work aims to explore the detectability of defects that can lead to external pipe failures such as corrosion or leakages, using active infrared thermography, in order to contribute to the proposal of possible on-site inspection procedures. The experimental work was carried out in the laboratory on a pipe sample that was made of threaded-jointed sections of steel. Defects of various entities have been artificially introduced to simulate internal corrosion, generally related to the presence of chemicals in the heat transfer fluid. Different failures in threaded joints were also simulated and detected after the processing of thermal data.