This PDF file contains the front matter associated with SPIE Proceedings Volume 8705, including the Title Page, Copyright information, Table of Contents, Introduction, and the Conference Committee listing.

The frames of windows are typically made of wood in Italy, even though aluminum, PVC and other materials are more and more utilized in the building manufacture. On the other hand, the growing attention on the problem of energy saving makes more stringent the attention to the insulation properties of any component of the building envelope. Therefore, it is paramount to evaluate the thermal properties of wood that will be utilized in the windows frame manufacture.

Wood is a material characterized by a high anisotropy due to its characteristic growing. Mechanical properties, and thermal as well, are very different if considered along the direction of grain or perpendicular to it.

In manufacturing the frame for windows, the fiber or grain direction must be selected in such a way to maximize the thermal resistance along the inside to outside direction, that means the inside/outside direction of frame (i.e. inside/outside direction of window) must be perpendicular to the grain direction. Indeed the grain direction is the one with the maximum thermal conductivity while the perpendicular one (crossing the fiber direction) owns a lower conductivity value.

The anisotropic characteristics of wood made it a challenging material for the measurement of thermal conductivity. Three types of wood have been measured: oak, larch and spruce. Two instruments have been utilized: a) the hot disk apparatus; b) the IR thermography equipment in transmission (a variant of the Parker’s method) and reflection scheme complemented by density and specific heat measurements. In particular, IR thermography gives the possibility to evaluate by images the preferential direction of heat propagation by looking at the deformation of a localized heat source released on the surface (i.e. a circular shape can become an ellipse as heat diffuses on the surface). Results coming from different kind of measurements are compared and critically considered.

Applications of Infrared Thermography in buildings surveys are not limited to the identification of the temperature distribution and heat losses on building envelopes. As it is well known from NDT testing in industrial applications, active IR thermographic methods such as heating-up/cooling-down or lock-in thermography improve the results in many investigations. In civil engineering these techniques have not been used widely. Mostly, thermography is used in a quasistatic manner. This paper illustrates a new approach to achieve, by the lock-in technique, an in depth view of the structure of the wall evidencing the presence of buried elements, interfaces and cracks. The idea is to take advantage of the periodic heating and cooling of earth surface due to the alternating of day and night. The corresponding thermal wave has a period equal to 24 hours that can probe the walls of buildings with a penetration depth of the order of some centimeters. The periodic temperature signal is analysed to extract amplitude and phase. It is expected that the phase image gives the indication of inhomogeneity buried in the wall structure. As a case study, the exterior surface of Palazzo Ducale in Venice is analysed and illustrated. In addition to IR images, visible electromagnetic band is considered to evaluate the strength of the solar radiation and the geometrical distortion. Indeed, the periodicity due to the Earth rotation is only approximately of 24 hours. The passing clouds or the possibility of rainy days can superimpose other heating or cooling frequencies to the main one. The Fourier analysis of the impinging radiation on the wall is performed. The façade of Palazzo Ducale is tiled with stone of two different colours and types. A final attempt to automatically classify the stone tiles in the visible and infrared images is conducted.

Beginning about twenty-five years ago, there was a marked increase in the number of single-ply membrane roof designs used to cover and waterproof flat and low-sloped building roofs. Over the past ten years, there has been a substantial increase in the number of installations of white and more reflective single-ply roof systems, mostly using high density cellular foam insulation in the substrate for insulation. A major factor in the increase in the popularity of these highly insulated and more reflective roof systems is the fact that many governments began offering incentives for building owners to use reflective coverings and better insulated roofs. Now, owing to the energy efficient requirements for the design and construction of new buildings put forth in ASHRAE Standard 90.1, “Energy Standard for Buildings Except Low-Rise Residential Buildings” and the world’s apparent desire to be “green” (or at least appear to be), more and more roof designs will include these reflective single-ply membranes, which use the cellular foam insulation boards to meet these requirements. Using a lower density traditional insulation will mean that the roof will have to be very thick to comply, increasing the costs of installation. High density cellular foams do not absorb water until time, vapor pressure drive, UV and thermal shock break down the foam and it becomes more absorbent. This could be 5-7 years or longer, depending on the roof construction and other factors. This means that any water that enters the roof through a breach (leak) in the membrane goes straight into the building. This is not a good consequence since the failure mode of any roof is water entering the building. Keeping the water out of the building is the purpose of the waterproofing layer. This paper reviews the techniques of moisture testing on building roofs and infrared (IR) thermography, and puts forth the idea and reasoning behind having a sacrificial layer of very absorbent insulation installed in every flat and low-sloped roof so that when a breach occurs, it can easily be found, documented and repaired during an annual infrared inspection; as IR is an effective predictive maintenance technique and condition monitoring best practice for roof maintenance.

There has been a significant increase in the number of in‐house Infrared Thermographic Predictive Maintenance programs for Electrical/Mechanical inspections as compared to out‐sourced programs using hired consultants. In addition, the number of infrared consulting services companies offering out‐sourced programs has also has grown exponentially. These market segments include: Building Envelope (commercial and residential), Refractory, Boiler Evaluations, etc... These surges are driven by two main factors: 1. The low cost of investment in the equipment (the cost of cameras and peripherals continues to decline). 2. Novel marketing campaigns by the camera manufacturers who are looking to sell more cameras into an otherwise saturated market. The key characteristics of these campaigns are to over simplify the applications and understate the significances of technical training, specific skills and experience that’s needed to obtain the risk‐lowering information that a facility manager needs. These camera selling campaigns focuses on the simplicity of taking a thermogram, but ignores the critical factors of what it takes to actually perform and manage a creditable, valid IR program, which in‐turn expose everyone to tremendous liability. As the In‐house vs. Out‐sourced consulting services compete for market share head to head with each other in a constricted market space, the price for out‐sourced/consulting services drops to try to compete on price for more market share. The consequences of this approach are, something must be compromised to be able to stay competitive from a price point, and that compromise is the knowledge, technical skills and experience of the thermographer. This also ends up being reflected back into the skill sets of the in‐house thermographer as well. This over simplification of the skill and experience is producing the “Perfect Storm” for Infrared Thermography, for both in‐house and out‐sourced programs.

Quantitative temperature measurements of large-scale fires are of key interest to FM Global’s researchers and engineers. In this study, the effectiveness of extending an uncooled fixed-integration-time IR camera’s temperature range via a reduced aperture was investigated. The corresponding calibration of the focal plane array (FPA) was performed, in situ, by investigating spatially resolved radiance levels with and without the aperture present. In-the-field calibration results were compared and validated using a blackbody (ε = 1) source. The effect of reduced radiant intensity on the noise equivalent temperature difference (NETD) was investigated over a wide temperature range. This study shows the effective temperature extension of a fixed-integration-time (microbolometer) IR camera from 1200ºF (650ºC) to 2192ºF (1200ºC), making this camera particularly suitable for studying fires. The temperature extension was accomplished at low cost without changing the integration time of the focal plane array (FPA), removing the camera’s lens, or by using a neutral density (ND) filter.

It is becoming increasingly evident that intelligent systems are very bene¯cial for society and that the further development of such systems is necessary to continue to improve society's quality of life. One area that has drawn the attention of recent research is the development of automatic surveillance systems. In our work we outline a system capable of monitoring an uncontrolled area (an outside parking lot) using infrared imagery and recognizing suspicious events in this area. The ¯rst step is to identify moving objects and segment them from the scene's background. Our approach is based on a dynamic background-subtraction technique which robustly adapts detection to illumination changes. It is analyzed only regions where movement is occurring, ignoring in°uence of pixels from regions where there is no movement, to segment moving objects. Regions where movement is occurring are identi¯ed using °ow detection via sparse frame analysis. During the tracking process the objects are classi¯ed into two categories: Persons and Vehicles, based on features such as size and velocity. The last step is to recognize suspicious events that may occur in the scene. Since the objects are correctly segmented and classi¯ed it is possible to identify those events using features such as velocity and time spent motionless in one spot. In this paper we recognize the suspicious event suspicion of object(s) theft from inside a parked vehicle at spot X by a person" and results show that the use of °ow detection increases the recognition of this suspicious event from 78:57% to 92:85%.

A laser mediated methodology for remote thermal excitation of analytes followed by standoff IR detection is proposed. The goal of this study was to determine the feasibility of using laser induced thermal emission (LITE) from vibrationally excited explosives residues deposited on surfaces to detect explosives remotely. Telescope based FT-IR spectral measurements were carried out to examine substrates containing trace amounts of threat compounds used in explosive devices. The highly energetic materials (HEM) used were PETN, TATP, RDX, TNT, DNT and ammonium nitrate with concentrations from 5 to 200 μg/cm². Target substrates of various thicknesses were remotely heated using a high power CO2 laser, and their mid-infrared (MIR) thermally stimulated emission spectra were recorded. The telescope was configured from reflective optical elements in order to minimize emission losses in the MIR frequencies and to provide optimum overall performance. Spectral replicas were acquired at a distance of 4 m with an FT-IR interferometer at 4 cm- 1 resolution and 10 scans. Laser power was varied from 4-36 W at radiation exposure times of 10, 20, 30 and 60 s. CO₂ laser powers were adjusted to improve the detection and identification of the HEM samples. The advantages of increasing the thermal emission were easily observed in the results. Signal intensities were proportional to the thickness of the coated surface (a function of the surface concentration), as well as the laser power and laser exposure time. For samples of RDX and PETN, varying the power and time of induction of the laser, the calculated low limit of detections were 2 and 1 μg/cm², respectively.

The level of protection offered by a given ballistic material is typically evaluated in terms of a set of projectiles and their associated velocity at which a certain percentage of the projectiles are expected to perforate. (i.e. FSP 17gr : V₅₀ = 500m/s, 9mm FMJ; V₀=500m/s). These metrics give little information about the physical phenomena by which energy is dispersed, spread or absorbed in a specific target material. Aside from post-test inspection of the impacted material, additional information on the target response is traditionally obtained during a test from the use of high speed imaging, whether it is from a single camera aimed at the impact surface or the backface, or from a set of camera allowing full 3-D reconstruction of a deformed surface. Again, this kind of data may be difficult to interpret if the interest is in the way energy is managed in the target in real time. Recent technological progress in scientific grade high-speed infrared (IR) camera demonstrated that these phenomena can straightforwardly be measured using IR thermal imaging. This paper presents promising results obtained from Telops FAST-IR 1500 infrared camera on an aramid-based ballistic composite during an impact from a small caliber fragment simulating projectile (FSP).

Dust cloud combustion is unfortunately at risk in many working environments, jeopardizing several workers. The heat and shock waves resulting from the flame propagation into the dust cloud are harmful and lead to major endangerment or casualties. More precisely, dust cloud (small particles) explosions are even more malicious since they often result from ordinary materials such as coal, flour or pollen. Also, many metal powdered (such as aluminum oxide and magnesium) can form dangerous dust cloud when they are in suspensions in air. The understanding of this particular type of combustion is critical for the preventive care of sites and workers afflicted to such conditions. This paper presents the results of a dynamic flow analysis of metal particles combustion in a dust cloud. The ignition points, the flow rate as well as the propagation direction of the flow have been characterized using fast infrared imagery.

This work introduces a new framework for active and passive infrared image fusion for face recognition applications. Two multispectral face recognition databases were used in our experiments: Equinox Database (Visible, SWIR, MWIR, LWIR) and m-Faces Database (Visible, NIR, MWIR, LWIR). The proposed framework uses a fusion scheme in texture space in order to increase the performance of face recognition. The proposed texture space is based on the use of binary and ternary patterns. A new adaptive ternary pattern is also introduced. Active (SWIR and NIR) and passive (MWIR, LWIR) infrared modalities are used in this fusion scheme. An intraspectral and inter-spectral fusion approaches are introduced. The obtained results are promising and show an increase in the recognition performance when texture channels are fused in a multi-scale fusion scheme.

Face recognition is an area of computer vision that has attracted a lot of interest from the research community. A growing demand for robust face recognition in security applications has driven interesting advancements in this field. In this work, we introduce a new multistep approach for face recognition in the infrared spectrum. The proposed approach works in texture space using binary and ternary pattern descriptors. The approach operates in two steps. In the first step, dimensionality reduction techniques are used to classify the preprocessed infrared face image. This operation permits the selection of the highest score candidates. In the second step, a small set of these candidates are then classified using a correlation based approach. This last step permits the selection of the best matching candidate. The obtained results show a high increase in the face recognition performance when a multistep approach is used compared to dimensionality reduction face recognition techniques alone.

Terrorists conceal highly energetic materials (HEM) as Improvised Explosive Devices (IED) in various types of materials such as PVC, wood, Teflon, aluminum, acrylic, carton and rubber to disguise them from detection equipment used by military and security agency personnel. Infrared emissions (IREs) of substrates, with and without HEM, were measured to generate models for detection and discrimination. Multivariable analysis techniques such as principal component analysis (PCA), soft independent modeling by class analogy (SIMCA), partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and neural networks (NN) were employed to generate models, in which the emission of IR light from heated samples was stimulated using a CO₂ laser giving rise to laser induced thermal emission (LITE) of HEMs. Traces of a specific target threat chemical explosive: PETN in surface concentrations of 10 to 300 ug/cm² were studied on the surfaces mentioned. Custom built experimental setup used a CO₂ laser as a heating source positioned with a telescope, where a minimal loss in reflective optics was reported, for the Mid-IR at a distance of 4 m and 32 scans at 10 s. SVM-DA resulted in the best statistical technique for a discrimination performance of 97%. PLS-DA accurately predicted over 94% and NN 88%.

Dfgfdg Due to international environmental regulations, aircraft turbojet manufacturers are required to analyze the gases exhausted during engine operation (CO, CO₂, NO_x, particles, unburned hydrocarbons (aka UHC), among others).Standard procedures, which involve sampling the gases from the exhaust plume and the analysis of the emissions, are usually complex and expensive, making a real need for techniques that allow a more frequent and reliable emissions measurements, and a desire to move from the traditional gas sampling-based methods to real time and non-intrusive gas exhaust analysis, usually spectroscopic. It is expected that the development of more precise and faster optical methods will provide better solutions in terms of performance/cost ratio. In this work the analysis of high-speed infrared emission spectroscopy measurements of plume exhaust are presented. The data was collected during the test trials of commercial engines carried out at Turbojet Testing Center-INTA. The results demonstrate the reliability of the technique for studying and monitoring the dynamics of the exhausted CO₂ by the observation of the infrared emission of hot gases. A compact (no moving parts), high-speed, uncooled MWIR spectrometer was used for the data collection. This device is capable to register more than 5000 spectra per second in the infrared band ranging between 3.0 and 4.6 microns. Each spectrum is comprised by 128 spectral subbands with aband width of 60 nm. The spectrometer operated in a passive stand-off mode and the results from the measurements provided information of both the dynamics and the concentration of the CO₂ during engine operation.

The Infrared Images and Other Data Acquisition Station enables a user, who is located inside a laboratory, to acquire visible and infrared images and distances in an outdoor environment with the help of an Internet connection. This station can acquire data using an infrared camera, a visible camera, and a rangefinder. The system can be used through a web page or through Python functions.

Infrared thermography techniques have been used for many years in the non-destructive testing and evaluation (NDT and E) of materials and structures. The main advantage of thermography over classical NDT techniques resides in the possibility of inspecting large areas in a fast and safe manner without needing to have access to both sides of the component. Nonetheless infrared thermography is limited to the detection of relatively shallow defects (a few millimetres under the surface), since it is affected by 3D heat diffusion. However, the most common types of anomalies found on composites, such as GRP wind turbine blades are delaminations, disbonds, water ingress, node failure and core crushing, and can be effectively detected and sometimes quantified using active thermographic techniques. This research work presents the use of infrared thermography on glass reinforced plastic (GRP) wind turbine blades assessment. Finally, the development of an autonomous, novel and lightweight multi-axis scanning system, as a concept, deploying in situ thermography NDT is also presented, with the intention of developing maximisation of the blade area coverage in a single run, at a known sensitivity, with the utilisation of the minimum number of system degrees of freedom and the maximum repeatability, as well as positional accuracy possible.

Development of a chicken embryo is conventionally assumed to follow a set growth pattern over the course of 21 days. However, despite identical incubation settings, many factors may contribute to an egg developing at a different rate from those around it. Being able to determine an embryo’s actual development instead of relying on chronological assumptions of normal growth should prove to be a useful tool in the poultry industry for responding early to abnormal development and improving hatch rates. Previous studies have used infrared imaging to enhance candling observation, but relatively little has been done to implement infrared imaging in problem-solving. The purpose of this research is to construct a quantitative model for predicting the development stage and early viability of a chicken embryo during incubation. It may be noted that a similar project was conducted previously using different input parameters. This study seeks to improve upon the results from the earlier project. In this project, infrared images of eggs were processed to calculate air cell volumes and cooling rates, and daily measurements of egg weight and ambient temperature were compiled. Artificial neural networks (ANNs) were “trained” using multiple input parameters to recognize patterns in the data. Various training functions and topologies were evaluated in order to optimize prediction rates and consistency. The prediction rates obtained for the ANNs were around 81% for development stage and around 92% for viability. It is recommended for future research to expand the potential combinations of input parameters used in order to increase this model’s versatility in the field.

Real-time, stand-off sensing of human subjects to detect emotional state would be valuable in many defense, security and medical scenarios. We are developing a multimodal sensor platform that incorporates high-resolution electro-optical and mid-wave infrared (MWIR) cameras and a millimeter-wave radar system to identify individuals who are psychologically stressed. Recent experiments have aimed to: 1) assess responses to physical versus psychological stressors; 2) examine the impact of topical skin products on thermal signatures; and 3) evaluate the fidelity of vital signs extracted from thermal imagery and radar signatures. Registered image and sensor data were collected as subjects (n=32) performed mental and physical tasks. In each image, the face was segmented into 29 non-overlapping segments based on fiducial points automatically output by our facial feature tracker. Image features were defined that facilitated discrimination between psychological and physical stress states. To test the ability to intentionally mask thermal responses indicative of anxiety or fear, subjects applied one of four topical skin products to one half of their face before performing tasks. Finally, we evaluated the performance of two non-contact techniques to detect respiration and heart rate: chest displacement extracted from the radar signal and temperature fluctuations at the nose tip and regions near superficial arteries to detect respiration and heart rates, respectively, extracted from the MWIR imagery. Our results are very satisfactory: classification of physical versus psychological stressors is repeatedly greater than 90%, thermal masking was almost always ineffective, and accurate heart and respiration rates are detectable in both thermal and radar signatures.

Oak Ridge National Laboratory (ORNL) has been utilizing the ARCAM electron beam melting technology to additively manufacture complex geometric structures directly from powder. Although the technology has demonstrated the ability to decrease costs, decrease manufacturing lead-time and fabricate complex structures that are impossible to fabricate through conventional processing techniques, certification of the component quality can be challenging. Because the process involves the continuous deposition of successive layers of material, each layer can be examined without destructively testing the component. However, in-situ process monitoring is difficult due to metallization on inside surfaces caused by evaporation and condensation of metal from the melt pool. This work describes a solution to one of the challenges to continuously imaging inside of the chamber during the EBM process. Here, the utilization of a continuously moving Mylar film canister is described. Results will be presented related to in-situ process monitoring and how this technique results in improved mechanical properties and reliability of the process.

An extended-range IR camera was used to make temperature measurements of samples as they are being manufactured. The objective is to quantify the temperature variation of the parts as they are being fabricated. The IR camera was also used to map the temperature within the build volume of the oven. The development of the temperature map of the oven provides insight into the global temperature variation within the oven that may lead to understanding variations in the properties of parts as a function of build location within the oven. The observation of the temperature variation of a part during construction provides insight into how the deposition process itself creates temperature distributions, which can lead to failure.

Additive manufacturing is a rapidly growing field where 3-dimensional parts can be produced layer by layer. NASA’s electron beam freeform fabrication (EBF³) technology is being evaluated to manufacture metallic parts in a space environment. The benefits of EBF³ technology are weight savings to support space missions, rapid prototyping in a zero gravity environment, and improved vehicle readiness. The EBF³ system is composed of 3 main components: electron beam gun, multi-axis position system, and metallic wire feeder. The electron beam is used to melt the wire and the multi-axis positioning system is used to build the part layer by layer. To insure a quality deposit, a near infrared (NIR) camera is used to image the melt pool and solidification areas. This paper describes the calibration and application of a NIR camera for temperature measurement. In addition, image processing techniques are presented for deposit assessment metrics.

Thermo-mechanical fatigue (TMF) tests and strain to crack (SC) tests at elevated temperature are important aspects to the total fatigue life for many engineering applications. During a TMF test, crack inspections are commonly done in a disruptive manner using an acetate replication method; and post-test crack evaluations are done using both optical and scanning electronic microscopy methods. Similarly, inspections during a typical SC test are also performed in a disruptive manner. This paper demonstrates that infrared imaging can be used as an in-situ inspection approach to detect crack during TMF and SC tests at high temperature. It is also demonstrated that this technique allows for the reduction or elimination of the need for downtime that is typically required for disruptive inspection. The results obtained by induction thermography are compared to those obtained via traditional methods and post-test evaluation. The induction thermography inspections were carried out at several temperatures and showed that the temperature used during the test does not influence the crack detection capability. It is demonstrated that induction thermography can detect cracks smaller than 500 μm and has potential for monitoring and generating a crack growth curve.

Many forms of damages in fiber reinforcement polymer (FRP) composites are difficult to detect because they occurs in subsurface layers of the composites. One challenging need for inspection capabilities is in adhesively bonded joints between composite components, a common location of premature failure in aerospace structures. This paper investigates pulsed phase thermography (PPT) imaging of fatigue damage in these adhesively bonded joints. Simulated defects were created to calibrate parameters for fatigue loading conditions, PPT imaging parameters, and a damage sizing algorithm for carbon fiber reinforced polymer (CFRP) single lap joints. Afterwards, lap joint specimens were fabricated with varying quality of manufacturing. PPT imaging of the pristine specimens revealed defects such as air bubbles, adhesive thickness variations, and weak bonding surface between the laminate and adhesive. Next, fatigue testing was performed and acquired PPT imaging data identified fatigue induced damage prior to final failure cycles. After failure of each sample, those images were confirmed by visual inspections of failure surface.

Defects in glass are a major problem in the manufacturing industry as these defects cause for recall and rejection of materials and products. Defects such as bubbles need to be detected early on in the manufacturing process in order to avoid loss of revenue for a company. Many of these defects are not immediately visible making them difficult to detect. In the past, methods have included embedding artificial defects within two layers of a different material, and then optical and visual methods have been used in order to classify and locate these defects. In this study an infrared camera was used in order to take images of non-visible artificial defect samples and understand the use of infrared technology for defect detection. This could then be used to locate and classify defects based on diameter and depth. Artificial Neural Networks were used to predict the sample classifications based on temperature variation, cooling rate, and the time at which the image was taken. Artificial Neural Networks showed to be a good prediction method for depth classification as it reached a 76% accuracy rate, whereas this method was not as effective for diameter classification. Results from this study produced a mathematical model for this range of data and showed that temperature variation amongst different depths of samples was higher compared to the temperature variation of diameter size. Artificial Neural Networks can therefore be used to classify a sample as defective or non-defective, and then the mathematical model presented can be used to estimate the depth of the defect.

Active thermography data for nondestructive testing has traditionally been evaluated by either visual or numerical identification of anomalous surface temperature contrast in the IR image sequence obtained as the target sample cools in response to thermal stimulation. However, in recent years, it has been demonstrated that considerably more information about the subsurface condition of a sample can be obtained by evaluating the time history of each pixel independently. In this paper, we evaluate the capabilities of two such analysis techniques, Pulse Phase Thermography (PPT) and Thermographic Signal Reconstruction (TSR) using induction and optical flash excitation. Data sequences from optical pulse and scanned induction heating are analyzed with both methods. Results are evaluated in terms of signal-tobackground ratio for a given subsurface feature. In addition to the experimental data, we present finite element simulation models with varying flaw diameter and depth, and discuss size measurement accuracy and the effect of noise on detection limits and sensitivity for both methods.

GRP-type composites (Glass-fibre Reinforced Plastics) have been continuously employed in the oil industry in recent years, often on platforms, especially in pipes for water or oil under moderate temperatures. In this case, the pipes are usually connected through adhesive joints and, consequently, the detection of defects in these joints, as areas without adhesive or adhesive failure (disbonding), gains great importance. One-sided inspection on the joint surface (front side) is a challenging task because the material thickness easily exceeds 10 mm that is far beyond the limits of the capacity of thermography applied to GRP inspection, as confirmed by the experience. Detection limits have been evaluated both theoretically and experimentally as a function of outer wall thickness and defect lateral size. The 3D modeling was accomplished by using the ThermoCalc-6L software. The experimental unit consisted of a FLIR SC640 and NEC TH- 9100 IR imagers and some home-made heaters with the power from 1,5 to 30 kW. The results obtained by applying pulsed heating have demonstrated that the inspection efficiency is strongly dependent on the outer wall thickness with a value of about 8 mm being a detection limit.

Natural fibers constitute an interesting alternative to synthetic fibers, e.g. glass and carbon, for the production of composites due to their environmental and economic advantages. The strength of natural fiber composites is on average lower compared to their synthetic counterparts. Nevertheless, natural fibers such as flax, among other bast fibers (jute, kenaf, ramie and hemp), are serious candidates for seismic retrofitting applications given that their mechanical properties are more suitable for dynamic loads. Strengthening of structures is performed by impregnating flax fiber reinforced polymers (FFRP) fabrics with epoxy resin and applying them to the component of interest, increasing in this way the load and deformation capacities of the building, while preserving its stiffness and dynamic properties. The reinforced areas are however prompt to debonding if the fabrics are not mounted properly. Nondestructive testing is therefore required to verify that the fabric is uniformly installed and that there are no air gaps or foreign materials that could instigate debonding. In this work, the use of active infrared thermography was investigated for the assessment of (1) a laboratory specimen reinforced with FFRP and containing several artificial defects; and (2) an actual FFRP retrofitted masonry wall in the Faculty of Engineering of the University of L’Aquila (Italy) that was seriously affected by the 2009 earthquake. Thermographic data was processed by advanced signal processing techniques, and post-processed by computing the watershed lines to locate suspected areas. Results coming from the academic specimen were compared to digital speckle photography and holographic interferometry images.

Impact damage in thin carbon fiber reinforced polymer composites often results in a relatively small region of damage at the front surface, with increasing damage near the back surface. Conventional methods for reducing the pulsed thermographic responses of the composite tend to underestimate the size of the back surface damage, since the smaller near surface damage gives the largest thermographic indication. A method is presented for reducing the thermographic data to produce an estimated size for the impact damage that is much closer to the size of the damage estimated from other NDE techniques such as microfocus x-ray computed tomography and pulse echo ultrasonics. Examples of the application of the technique to experimental data acquired on specimens with impact damage are presented. The method is also applied to the results of thermographic simulations to investigate the limitations of the technique.

Pulsed Thermography (PT) is one of the most widely used approaches for the inspection of composites materials, being its main attraction the deployment in transient regime. However, due to the physical phenomena involved during the inspection, the signals acquired by the infrared camera are nearly always affected by external reflections and local emissivity variations. Furthermore, non-uniform heating at the surface and thermal losses at the edges of the material also represent constraints in the detection capability. For this reason, the thermographics signals should be processed in order to improve – qualitatively and quantitatively – the quality of the thermal images. Signal processing constitutes an important step in the chain of thermal image analysis, especially when defects characterization is required. Several of the signals processing techniques employed nowadays are based on the one-dimensional solution of Fourier’s law of heat conduction. This investigation brings into discussion the three-most used techniques based on the 1D Fourier’s law: Thermographic Signal Reconstruction (TSR), Differential Absolute Contrast (DAC) and Pulsed Phase Thermography (PPT), applied on carbon fiber laminated composites. It is of special interest to determine the detection capabilities of each technique, allowing in this way more reliable results when performing an inspection by PT.

Detection of defects in Laser Powder Deposition (LPD) produced components has been achieved by laser thermography. An automatic in-process NDT defect detection software system has been developed for the analysis of laser thermography to automatically detect, reliably measure and then sentence defects in individual beads of LPD components. A deposition path profile definition has been introduced so all laser powder deposition beads can be modeled, and the inspection system has been developed to automatically generate an optimized inspection plan in which sampling images follow the deposition track, and automatically control and communicate with robot-arms, the source laser and cameras to implement image acquisition. Algorithms were developed so that the defect sizes can be correctly evaluated and these have been confirmed using test samples. Individual inspection images can also be stitched together for a single bead, a layer of beads or multiple layers of beads so that defects can be mapped through the additive process. A mathematical model was built up to analyze and evaluate the movement of heat throughout the inspection bead. Inspection processes were developed and positional and temporal gradient algorithms have been used to measure the flaw sizes. Defect analysis is then performed to determine if the defect(s) can be further classified (crack, lack of fusion, porosity) and the sentencing engine then compares the most significant defect or group of defects against the acceptance criteria – independent of human decisions. Testing on manufactured defects from the EC funded INTRAPID project has successful detected and correctly sentenced all samples.

InfraRed Thermography (IRT) is one of the promising technique for non-destructive testing method for characterization of materials. This technique relies on evaluation of the surface temperature variations to detect the presence of surface and subsurface anomalies within the material. Due to its whole field and remote testing capabilities, IRT has gained significant importance in testing of Glass Fiber Reinforced Plastic (GFRP) materials. A GFRP sample with defects of various sizes at a given depth was inspected using non-stationary thermographic techniques. In order to highlight the defect detection capabilities of the proposed non-stationary schemes, a comparison has been made using matched excitation energy in frequency domain by taking signal to noise ratio into consideration.

Active infrared thermography for nondestructive testing and evaluation is a rapidly developing technique for quick and remote inspection of subsurface details of test objects. Sinusoidal modulated thermal wave imaging such as Lock-in thermography (LT) significantly contributed to this field by allowing low power controlled modulated stimulations and phase based subsurface detail extraction capabilities. But demand of repetitive experimentation required for depth scanning of the test object, limits its applicability for realistic applications and demands multi frequency low power stimulations. Non-stationary thermal wave imaging methods such as frequency modulated thermal wave imaging (FMTWI), digitized FMTWI and coded thermal wave imaging methods permitting multi frequency stimulations to cater these needs and facilitate depth scanning of the test object in a single experimentation cycle. This contribution highlights theory, modeling and simulation for non-stationary modulated thermal wave imaging methods for non-destructive characterization of solid materials.

Thermal Wave Detection and Ranging (TWDAR) for non-destructive testing (TNDT) is a whole field, non-contact and non-destructive inspection method to reveal the surface or subsurface anomalies in the test sample, by recording the temperature distribution over it, for a given incident thermal excitation. Present work proposes recent trends in nonstationary thermal imaging methods which can be performed with less peak power heat sources than the widely used conventional pulsed thermographic methods (PT and PPT) and in very less time compared to sinusoidal modulated Lockin Thermography (LT). Furthermore, results obtained with various non-stationary thermal imaging techniques are compared with the phase based conventional thermographic techniques.

Fact: The U.S Government Centers for Disease Control and Prevention (CDC), Office of Public Health Preparedness and Response, rather remarkably has dedicated part of their web site to” Zombie Preparedness”. See: http://www.cdc.gov/phpr/zombies.htm for more information. This is a tongue‐incheek campaign with messages to engage audiences with the hazards of unpreparedness. The CDC director, U.S. Assistant Surgeon General Ali S. Khan (RET), MD, MPH notes, "If you are generally well equipped to deal with a zombie apocalypse you will be prepared for a hurricane, pandemic, earthquake, or terrorist attack. Make a plan, and be prepared!” (CDC Website, April 26th, 2013). Today we can make an easy comparison between the humor that the CDC is bringing to light, and what is actually happening in the Thermographic Industry. It must be acknowledge there are “Zombie Thermographers” out there. At times, it can be observed from the sidelines as a pandemic apocalypse attacking the credibility and legitimacy of the science and the industry that so many have been working to advance for over 30 years. This paper outlines and explores the trends currently taking place, the very real risks to facility plant, property, and human life as a result, and the strategies to overcome these problems.