This PDF file contains the front matter associated with SPIE Proceedings Volume 11409, including the Title Page, Copyright information, and Table of Contents.

Equivalent wave field (EWF) is a wave supporting media with corresponding geometry and boundary condition same as a thermal system. EWF transform (EWFT) is a transform of the solution from the thermal domain into the wave domain. EWFT enables simple inversion of time recording of thermographic data. It is based on similarities between wave and diffusion propagation. EWFT is simple to apply as it is performed by a matrix multiplication. EWFT was developed as a tomographic nondestructive evaluation method for testing of materials. EWFT shows its superior inversion capabilities both in active thermal NDE and dynamic breast thermal imaging. To perform EWFT one has to know the thermal excitation time profile. In this paper I show how using principal component thermography (PCT) enable us to obtained the thermal excitation profile from the measured data. While for the NDE we had total control on data collection, for the breast thermographic data at hand we use the Brazilian ‘Database for Mastology’ which we had no control over the data collection. The data was originally collected for use in artificial intelligence algorithm and not for time series analysis. Excitation was performed by operating a fan to cool the breast for a short period. Particular problems with this data were the recording starting only at the end of cooling with no synchronization and the spars sampling. We show how PCT enable us to extract the excitation time series and demonstrate high quality depth resolved images both for carbon composites and human breast tissue.

An algorithm is under development which can be used to detect bone cancer in canine thermograms for these body parts: elbow/knee, both anterior and lateral camera views, and wrist, lateral view only. Currently, veterinary clinical practice uses several imaging techniques including radiology, computed tomography (CT), and magnetic resonance imaging (MRI). But harmful radiation involved during imaging, expensive equipment setup, excessive time and the need for a cooperative patient during imaging, are major drawbacks of these techniques. In veterinary procedures, it is very difficult for animals to remain still for the time periods necessary for standard imaging without resorting to sedation – which creates another set of complexities. The algorithm has been optimized through thousands of experiments to identify bone cancer in thermographic images. Optimal histogram features, Laws texture features and gray level co-occurrence matrix (GLCM) texture features are extracted and the data is normalized using standard normal density and softmax normalization. Euclidean, Minkowski, and Tanimoto comparison metrics are used with nearest centroid for pattern classification. Classification success rates as high as 88% for elbow/knee anterior, 85% for wrist lateral and 86% elbow/knee lateral have been achieved.

Plant root systems absorb water and minerals and synthesize organic matter. The investigation and prediction of root system architecture (RSA) can provide significant information that is potentially beneficial for promoting plant growth and reproduction. Existing approaches use manual sampling, which involves digging up the plant and examining the root. This process is destructive and time-consuming. Ground-penetrating radar has been used for exploring root structures of large plants, such as trees, but not for small plants due to resolution limitations. For this study, a finite element analysis (FEA) model was built to investigate the feasibility of using infrared imaging to predict root depth given the amount of heat flux required to obtain an image, the image acquisition time, and the thickness of the plant container. Polynomial regression, support vector machine, and artificial neural network models were designed to predict root structure depth based on the thermal profile of the structure over time derived from FEA model. Analysis results suggest that infrared imaging can be used to provide depth information of root structures. However, the thickness and complexity of the root structure impact prediction accuracy. Future directions include (1) development of image enhancement algorithms to improve detection capability and accuracy, and (2) conducting experiments to confirm the findings from the simulation.

The relevance of understanding organic matter in agriculture includes its impact on soil texture, soil bulk density, water holding capacity, soil pH, cation exchange, and microbial biomass. . Currently organic matter is analyzed by the “Loss on Ignition” and “Walkley Black” methods. PDMI (Persistence Data Mining Inc.) is introducing the Soilytics^TM solution, our hyperspectral soil nutrient mapping technology. Hyperspectral technology allows for quick and easy soil organic matter testing at a cost-effective rate. This will dramatically improve the efficiency of soil labs, while reducing cost and response times. Not only will this be a huge breakthrough for the soil testing industry, it will serve the precision agricultural market by improving fertilizer application and efficiency. Our technology will help farmers increase crop yields, optimize input costs, and improve environmental protection. Organic matter hyperspectral results are well within the standard margin of error of loss on ignition organic matter analysis between different laboratories and is a cost-effective method to test and make prescriptions for fertilizers application in agriculture fields. Through the use of advanced algorithms Soilytics^TM is able to convert hyperspectral reflectance soil data into usable information to serve the agricultural industry. The Malvern Panalyticals ASD LabSpec is used to collect relevant data. Hyperspectral sensors allow us to visually see outside the range of human vision. Focusing on the SWIR (Short Wave Infrared), and VNIR (Visible-Near IR) spectrum have enabled a new advanced methodology to scan and collect data on nutrients and OM (organic matter) in soil. The data had to be analyzed for factors that could impact results based on texture, water content, and geology. The baselined data could then be processed into accurate information. Correlating these results to current lab methods resulted in the determination that the use of the full spectrum resulted in better results since limiting factors on confidence required additional spectral bands to properly baseline. LOI (Loss on Ignition) is the conventional method for organic matter analysis, cost constraints prevent more granular testing which impacts yields and costs. By remote sensing soil samples, we can take many more samples more quickly and efficiently. The sample data can then be uploaded directly to the laboratories, which eliminates the cost and delays of shipping samples.

Gray whales in the eastern North Pacific migrate annually between summer feeding areas in the Arctic to wintering areas off Baja California, Mexico. The abundance of this whale population has been documented by shore-based counts in central California conducted by human observers searching for and recording whale sightings during the southbound migration. Here, we describe a new semi-automated system for conducting gray whale counts, and compare such to the human observer based system. This new system consists of infrared cameras which continuously monitor a fixed field of view of the ocean, automated detection software for detecting whale blows, whale-blow verification software, and counting software which estimates the number of whales that have passed by the observation station. This technology is currently being considered to support naval, oil and gas, and merchant marine operations involving marine mammals.

Infrared thermography (IRT) has evolved as an important biomedical tool in recent years. One major application of IRT is the reliable monitoring of human respiration rate (RR) in a contactless manner. This method is especially useful in case of babies with delicate skin. The present work reports the human RR monitoring using passive IRT, by observing the variation in nasal temperature, during breathing. The observed breathing signal has a low signal to noise ratio (SNR), hence it is denoised using the Infinite Impulse Response (IIR) filters. The IIR filters are compared based on their SNR and Mean Square Error values. The Butterworth filter shows the best filtering performance amongst all the IIR filters, which further improves with increasing filter order. A novel “Breath detection algorithm" (BDA) is designed, that identifies the breaths in the acquired breathing signals as normal or abnormal, and yields the breaths per minute value, in an automated manner. The BDA is tested on 500 breathing signals under different scenarios like normal, slow and fast breathing, and with and without air conditioner and fan. The BDA performance is evaluated by calculating its sensitivity, precision, spurious cycle rate, and missed cycle rate values obtained as 98.4%, 99.19%, 0.80% and 1.6% respectively.

Infrared thermography has gained significant acceptance in agriculture practices for different applications ranging from crop yield estimation, fruit maturity evaluation, plants disease detection, nursery monitoring, bruise detection, irrigation scheduling, etc. The present study is an effort towards establishing a quantitative relationship between the thermal signature and the water stress developed in horticulture plants, through infrared thermography monitoring. The temperature measured is standardized by subtracting the ambient air temperature from mean canopy temperature. The change in the thermal signature of the leaves is a good indicator of the water needs of the plant. This observation helps us in developing a smart irrigation system.

Thermal infrared surveying is a great way to commission solar installations in-situ. After analyzing and commissioning over three gigawatts of solar panels in the past seven years, we have figured out what works and importantly, what does not work. All solar fields are not the same. Before planning a site visit, the thermographer must take into account the size and layout of the field, the types of panels, how the panels are wired, and then the best timing of the survey. First, the platform and methodology for scanning needs to be chosen, then scheduling can take place. Correct timing of the survey is the most important factor because the amount of solar insolation is absolutely critical to successful testing. To successfully accomplish the testing under good conditions, many factors need to be predicted with accuracy. These include the layout of the field, the ambient conditions (such as rain, sun, clouds, wind speed), and the angle of the Sun preceding and during the survey.

The use of drones is constantly increasing while prices of high-performance drones has fallen. Nowadays, drones can accommodate various complementary optical measuring instruments, such as thermal imagers and photographic imagers. Advanced data communication systems enable real-time wireless data transfer and data processing. .Drones have expanded capabilities for thermography and other optical measurements,. There is a need to develop interpretation keys and data processing techniques. Especially the use of drones is effective when scanning high-rise buildings, but the all the influencing factors must be known and taken into consideration. In this presentation,results and experiences of multi-story apartment houses and other buildings in Finland are presented, along with an evaluation of restrictions and conditions of UAV-based thermal scanning. Also some case studies from USA and the use of outdoor thermography in Russia have been discussed.

Two interconnector plates made out of ferritic steel are joined together by a thin layer of glass-ceramics and form an airtight assembly as a core part of a solid-oxide fuel cell (SOFC). The sealant has to withstand temperatures above 800°C and has to be gas-tight and mechanically stable. The solder layer must be free of larger voids and metallic inclusions. In particular, electrical shorts between the steel plates have to be detected and localized. Flash-light excited thermography in one-side access and in transmission was employed to detect artificial and natural local voids and inclusions in the glass-ceramics. Two flash-lights with each 6.4 kJ of energy were used. The recorded thermographic image sequences were pre-processed by pulsed phase thermography (PPT). Short- and long-time reproducibility tests were performed. Sets of samples with prepared test defects (air voids and metal platelets) of different size were measured after optimization of the excitation and detection parameters. One of the steel plates consists of two tightly joint separate sheets. This may cause false alarms in thermographic testing due to small air gaps between the sheets. The experimental findings were supported by numerical FEM simulations using COMSOL Multiphysics. A POD (probability of detection) analysis was performed showing that voids with a diameter of 2.3 mm can be detected reliably. Electrical shorts between the steel sheets could be localized by a lock-in technique using a modulated electrical current. Resistive losses at the internal contact points generate heat which becomes visible as a hot-spot in the thermal image.

For qualifying non-destructive inspection techniques, test blocks with reproducible cracks are necessary. Austenitic stainless steel samples with reference reflectors were created and compared with artificial fatigue cracks produced by a resonance testing machine. The samples were inspected by eddy current and ultrasonic testing to estimate their length and depth. Additionally, inductive thermography measurements were carried out. This method can be used to detect surface flaws in metallic materials. As the flaw depth influences the eddy current distribution and the heat diffusion, the temperature difference caused by a flaw correlates with its depth. The fatigue cracks become visible by evaluating the IR image sequence and by observing the typical ‘butterfly’ pattern. FEM simulations were used to model the thermography experiments. The signals of short and long cracks were compared to examine how the depth estimation derived for long cracks can also be applied for short fatigue cracks.

In this work Induction Thermography has been applied to inspect Inconel 718 EBW and TIGWelded components, focusing on the optimisation of both the induction tests and the algorithms needed for an automatic defect detection. The aim is 1) to ensure the inspectability of the component regardless of its geometry and 2) develop a robust automatic defect detection without false positives. For the first part, experiments have been carried out considering different inductor configurations (different windings, ferrite sections and geometries) and relative orientations between the inductor and the sample to be inspected to determine the importance of each magnitude. In the second part the work several thermal processing techniques have been tested: Fast Fourier Transform (FFT), Singular Value Decomposition (SVD) and Higher Order Statistical (HOS) analysis, to achieve images of higher quality (less noisy). This will improve the results of the previously developed detection algorithm (pDFT), diminishing the existing false positives. The second part of the work deals with the improvement of the automatic defect detection, based on the previously developed pDFT algorithm, which already provides an effective method of determining crack location, length and orientation. Hence, in this work the focus has been put on improving the processing in order to provide to the algorithm thermal processed images of higher quality (less noisy). In this way, the probability of detection failure will be diminished. Several processing algorithms have been tested: Fast Fourier Transform (FFT) and the Scaled Peak Amplitude, Singular Value Decomposition (SVD) and Higher Order Statistical (HOS) analysis. Then, to determine which is the best of them, a Signal to Noise Ratio (SNR) filter has been applied in the defective areas, looking always for the highest values.

The present research is focused on the use of different non-destructive techniques for detecting damage in CFRP composite structures obtained by an innovative technological process: Automated Fiber Placement. The component was a T-joint stringer adhesively bonded to a skin panel. The aim of the present work is to show the capability of these techniques to provide complementary information for detecting the damage in composites. Automated Fibre Placement consists in an automatic deposing of prepeg or dry plies on a specific mould. The innovation lies in the possibility to reduce the time of the manufacturing process of large and complex structures by using a robotic arm that contemporary deposes fibre tows and pre-polymerizes them. The resulting products present higher quality in terms of surface finish, internal flaws absent and higher mechanical properties. The T-joint component tested in the present research was addressed to both static and cyclic tests. After the damage was induced in the material it was performed a qualitative and quantitative study of the damage by using different nondestructive techniques: Thermoelastic stress analysis (TSA), Ultrasound tests (UT) and displacement/strain measurements provided by strain gages. Processing and post-processing procedures were developed to analyze the data from each tests and finally the comparison of the results allowed a complete characterization and an overview of damage in the component by observing specifically where and when it occurred and how many regions of the component were interested. Finally, dimension, shape and depth where assessed.

Adhesively bonded joints represent an interesting alternative to mechanical joints due to the advantages over conventional mechanical fasteners: continuity of the structure, high strength-to-weight ratio, design flexibility. The aim of this work is to assess and predict the quality of aeronautical adhesive bonded CFRP T-joints made by the automated fibre placement process by means of the Thermoelastic Stress analysis (TSA) technique used as non-destructive technique. The results provided by TSA technique, in terms of debonded area, were compared to the well-established lock-in thermography technique showing the capability of TSA to evaluate the quality of T-joints. The approach allows to perform a cost-efficient characterisation process by means of non-destructive evaluations.

Online process monitoring and quality assurance are highly favorable for composite manufacturing processes like automated tape layup (ATL), where the productivity is largely constrained by downtime consisting of quality inspection and error mitigation. The presented work details the use of thermography as an inspection tool for such a process, using thermoplastic based tape material. A new online monitoring system is developed containing Infrared camera integrated on a purpose build ATL test rig. Surface thermal history for the layup course over time is recorded, which is then extracted along the width of the tape. The end result is a single image containing sequence of images, detailing the temperature data over the length of a single tape. Temperature gradient throughout the layup is then used to recognize foreign bodies and defects. Variation in adhesion effects of ply to the tool, ply and ply and ply and foreign bodies can be detected and areas of weak bonding can be recognized.

In the last decades, advanced thermoplastics matrix composites (TPCs) were recognised as a valid alternative to thermosetting matrices for many advanced applications. One of the advantages in the use of TPCs is the capability to perform fusion bonding which avoids the use of external joints. Induction welding of carbon fiber reinforced TPCs has gained large interest thanks to the minimum surface preparation required, high efficiency and capability to localise heat at the welding surface. This study relies on a thermal wave technique for the in-situ and real-time evaluation of defects during electromagnetic induction welding of TPCs. The technique is based on a methodology which analyses thermal images acquired in real-time during the welding process to reveal discontinuities from variations in heat distribution. Furthermore, the proposed apparatus is used to conduct post-welding inspections on the damaged area for more detailed defects characterisation. An induction welding device is used to perform the bonding process and different kinds of defects were tested and evaluated. Real-time thermal images of the welding process of TCP samples were obtained by using Infrared (IR) cameras. The recorded data were elaborated and used to locate and evaluate the different kinds of damaged samples. A post-welding analysis of a detected damaged region was performed using heating parameters optimised for the thermography scan. Results show the reliability of the method in detecting and characterising the presence of defects during the welding process using the available heating source without altering the process parameters.

Infrared thermography can be used for many applications during the composites manufacturing process. The most common application is nondestructive inspection (NDI) of laminate and honeycomb core sandwich structures. Other applications involve adhesive bondline inspection as well as providing a visual aid during bondline rework.

Thermography has been shown to be a viable technique for inspection of composites. The quadrupole method is a valuable technique for rapidly simulating the thermal response of layered systems. Often, the effort has focused on a one-dimensional model, in particular for improved analysis of thermal data. For composites, the in-plane heat diffusion often significantly impacts the thermal response of defects of interest, therefore three-dimensional simulations are desirable. This paper discusses the extension of the quadrupole methodology to perform simulations of thermographic responses in three-dimensional configurations. This enables the more realistic simulations of the thermal response of delaminations in composites. The simulations are compared to finite element simulations of the same inspection configurations.

Non-destructive testing (NDT) of materials and structures is a very important industrial issue in the fields of transport, aeronautics and space as well as in the medical domain. Active infrared thermography is an NDT method that consists in providing an external excitation to cause an elevation of the temperature field in the material, consequently allowing evaluation of the resulting temperature field at the surface. However, thermal exciters that are used (flash lamps, halogen, lasers) act only on the surface of the sample. On the other hand, several energy conversion systems can lead to the generation of volumetric sources; the phenomena of thermoacoustics, thermo-induction, thermomechanics or thermochemistry can be cited. For instance, ultrasonic waves can generate volumetric heat sources if the material is viscoelastic or if there is a defect. The reconstruction of these sources is the initial process for the quantification of parameters responsible for the heating. Characterizing a heat source means reconstructing its geometry and the supplied power. Identification of volumetric heat sources from surface temperature fields is a mathematically ill-posed problem. The main cause of the issue is the diffusive nature of the temperature. In this work, 3D reconstruction of the volumetric heat sources from the resulting surface temperature field, measured by infrared thermography, is studied. An analysis of the physical problem enables specifying the limits of the reconstruction. In particular, a criterion on the achievable spatial resolution is defined, and a reconstruction limitation for in-depth sources is highlighted.

The identification of buried heat sources in a material from temperature data obtained at the surface is a problem that has attracted a great deal of attention in recent years. The reason for this interest lies on the fact that, under particular excitation types, some defects behave as heat sources. Such is the case of cracks excited with ultrasounds or metallic inclusions in electrical insulators excited electromagnetically. The possibility of identifying hidden heat sources from temperature data taken at the surface with an infrared camera opens the possibility of characterizing the defects. However, due to the diffusive nature of heat propagation, this inverse problem is severely ill-posed. In this contribution, we present a comprehensive description of the method we have developed to characterize vertical heat sources, based on regularized least square minimization. We put the method into context among other methodologies, emphasizing the need for a physical model that is able to predict the observed temperature distribution. We show the effect of different regularization fuctionals, illustrating how to make sensible use of the prior information available about the solution of the problem. We discuss on the effect of the regularization parameter and we present a methodology to determine the optimum value. We analyze to which extent the method enables identifying the shape and the quantitative intensity of the heat flux, as well as the capabilities to retrieve non uniform fluxes. We test the method with experimental vibrothermography data. Finally, we discuss on the strengths and weaknesses of this approach.

In active thermography, the structure below the surface can be reconstructed from measured surface temperature signals. The main drawback in active thermographic is the degradation of the spatial resolution with imaging depth. Recently, we used a mathematical compensation method to transform each measured surface temperature signal into a virtual acoustic wave, which is the solution of the wave equation and therefore ultrasound image reconstruction methods can be used. This allows a 3D thermo-tomography, which combines the advantages of thermographic and ultrasonic imaging, but the degradation of spatial resolution for deeper lying structures is still significant. A possibility to overcome this degradation is to incorporate prior information such as positivity and sparsity in the reconstruction process. Based on pulsed thermography data we show that the thermographic detection limit is extendible by a factor of four.

High strength and light weight, justify the frequent use of carbon fibre reinforced plastics for aeronautical applications. The manufacturing process of such material systems is a multi-stage process and susceptible to the formation of air-filled voids. This porosity weakens the epoxy matrix and causes noticeable degradation of mechanical properties. Active thermography with optical-excitation is an advantageous photothermal method because due to the infrared camera it is a non-contacting, fast testing method for the estimation of material properties or for defect detection. We use the Virtual Wave Concept, which allows ultrasonic testing methods for photothermal measurement data. Based on this ability, we apply the through-transmission method to determine the Time-of-Flight of virtual waves, which is directly related to the porosity dependent diffusion time. A signalto-noise ratio dependent approach is used for the temporal truncation of measurement data to get the optimum evaluation time. This ensures to evaluate only time-ranges which contain information of the heat diffusion inside the sample. In addition, undesired effects of heat losses due to convection and radiation are reduced. After the evaluation procedure is shown for simulated data, we demonstrate the experimental pixel-wise estimation of the porosity affected thermal diffusion times on a real aerospace part in transmission configuration. The results are validated by X-ray computed tomography reference measurements, where a good match can be achieved with active thermography results.

Infrared thermography is a well-known technique for the Nondestructive Testing (NDT) of industrial components. Typically, the raw results of a thermal inspection are processed with an algorithm to enhance the defect detectability and then analyzed by an expert. A challenging point of this workflow is the final step, as the assessment made by the operator could be biased or subjective. To tackle this issue, clustering algorithms could be used to define, in an unsupervised manner, whether a region under inspection is defective or sound. In this work, a steel sample with flat bottom-hole defect is investigated in a Flash Thermography setup. The recorded thermal sequence is then analyzed with a clustering algorithm (k-means). The algorithm is applied varying different parameters and assessing, for each scenario, the performance of the clustering in terms of defect detection, quantified through specificity and sensitivity.

Passive thermography is a large area inspection technique and is commonly used during fatigue loading of composite structures. Passive thermography allows for real time tracking of damage. This provides an accurate assessment of the damage progression as a function of load cycles and determines when the loading is stopped for ultrasonic or X-ray CT evaluations for detail damage assessment through-the-thickness. In addition, if new areas of damage growth are detected, with passive thermography, then those areas are additionally inspected with ultrasound or X-ray CT. A custom developed passive thermography inspection system for fatigue loading is presented. Passive thermography of damage progression is compared to ultrasound inspections as a function of the fatigue cycles. Discussion is made on processing of the thermal data for periodic and non-periodic thermal responses.

Non-destructive testing and evaluation methods demand various efficient post processing approaches to enhance their defect detection capabilities of the adopted technique. Among them, widely used statistical methods are Eigen domain based post processing approach such as principal component analysis and recently proposed correlation based pulse compression approach. In this work, experiments have been carried out to highlight the capabilities of these data processing schemes for detection of subsurface defects in fibre reinforced polymer test samples. Obtained results clearly show that the defect detection capability of the correlation (matched filter) based post processing approach is far superior than that of the principal component analysis based data processing approach. Further, the similarities and differences between these proposed methods have been highlighted.

Non-destructive Evaluation (NDE) is a field that is used to identify all kinds of structural damage in an object of interest without resulting in any permanent damage or modification to the object. This field has been intensively investigated for many years. Among several research topics in this field, the supervised defect detection methods are among the most innovative and challenging. In recent years, the deep learning field of artificial intelligence has made remarkable progress in image processing applications. Deep learning has shown its ability to overcome most of the disadvantages suffered by previous existing approaches in a great number of applications. In this paper, we propose a deep learning architecture based on infrared thermography inspection intended to automatically identify defects (including internal and invisible cracks, delamination, etc.) efficiently and accurately. We studied the proposed deep learning algorithms to achieve automatic defect detection and precise localization (subsurface defects case) from different thermal image sequences. To evaluate the efficiency and robustness of the proposed methodology, specimens containing artificial defects were selected for experimental configuration.

The conventional methods for inspection of industrial sites involve the revision of data by an experienced inspector during the acquisition process to avoid possible data missing and misinterpretation. Despite all the advantages of drone-based inspection, inspectors often do not easily have physical access to the site to check for any data ambiguity. Therefore, it is essential for autonomous or semi-autonomous systems to check for missing data or to highlight possible data ambiguity. Reflection in thermal imagery data is one of the main sources of misinterpretation, and it can be problematic when there is no physical access to the site for a secondary inspection. In this paper, we present a novel algorithm based on the analysis and stitching of consecutive aerial thermal images to detect areas with reflection effect and possibly reduce these effects. The conducted experiments have shown significant results in the detection of reflection in drone-based thermographic inspections.

Composite structures are subjected to internal defects and damages such as delamination and voids, rendering visual inspection techniques ineffective. Due to the benefits of non-contact and large area inspection1, active infrared thermography (AIT) is gaining popularity to identify, localize and evaluate sub-surface defects in composite structures. However, images of defects are not always obvious and interpretation of the data by human inspectors varies among individuals, and creates differences in the outcome. Therefore, it is highly desired to develop computerized methods so that consistent analysis of results can be automatically obtained. In this work, convolutional neural networks (CNN) and computer vision were employed to implement two CNN based models for detecting structural defects in samples made of composite materials. The aim is to integrate such deep learning (DL) models to enable interpretation of thermal images automatically. That requires achieving object detection with high enough accuracy so that they can be used to assist human inspectors. The recent success of DL in computer vision tasks such as face recognition among others motivates us to apply DL for boosting the performance of thermal imaging inspections. DL methods were recently evaluated for defect detection in AIT of carbon fiber reinforced plastic (CFRP) composites with handmade defects2. The input for that framework were thermal images acquired during the cooling down process. In our work, we will apply similar concepts to detect and classify void and delamination defects in composites so as to reduce reporting errors and improve consistency.

Unmanned aerial vehicles are a modern day solution for reducing the time of inspections. This work aims to address the difficulties of using a UAV to inspect aircraft structures. Challenges such as non-uniform heating, low spatial resolution, and environmental noise cause some difficulties for defect detection and characterisation. Contrary to this, mounting sensors onto a UAV’s will further increase the noise, due to the motion, vibrations and sequence mismatching. Methods to tackle stabilisation and indoor localisation are used by utilising a Vicon system, this aims to increase the accuracy of the captured data when inspecting without GPS i.e. inspecting indoors. Other than active thermography, various methods were trialled to locate defects, passive thermography, photogrammetry and RGB image processing.

In order to understand the risk posed to astronauts by electric arc-generated particles, high-speed, high-resolution, quantitative thermal imaging was needed. The measurement requirements appeared to be beyond the capabilities of commercial thermal imaging systems, but the particles were known to have a significant amount of emission in the visible spectrum. This challenge led to using commercially available, high-speed video cameras as imaging radiometers. Measured particle temperatures were consistent with predictions and other measurement data. The optical temperature measurement method, results, and conclusions are presented along with recommendations for further work. Keywords: thermal

Metal cutting by computer assisted laser is more and more utilized in advanced manufacturing. The increased accuracy, the non-contact processing, the higher productivity and the decrease of energy demand are some of the benefits that make competitive this technology in respect to the most traditional ones like the rotating tools or sewing. Typically, such systems are composed by a powerful diode sourcing light in continuous mode or periodically pulsed. The light is collected by an optical fiber and focused by the optical system on the manufact to be cutted. The optical system, on its turn, is mounted on a 2/3-axis head. Typical wavelength is around 1064 nm and average power ranges from 1 to several kW. Notwithstanding those appealing features, there is the concern on the effects of the high temperature developed during the melting of the metal on the cutting zone, and the surrounding as well, that can lead to the degradation of the mechanical characteristics of the final product. Several authors have proposed different kind of analysis on the Heat Affected Zone (HAZ): measure of the tensile strength, Vickers hardness, SEM and optical microscopy analysis. Correlation have been drawn with the speed of the laser head and the power of the source with the extension of the HAZ. The paper propose to utilise an IR camera to monitor the temperature in the surrounding of the cutting zone and a model to fit the data collected by the IR camera itself.

In this paper we explore the impact that interconnection devices could have or are having on the new emerging IoT / IIoT network as well as on AI-artificial intelligence. The new digital era (Fourth Industrial Revolution) presents challenges, risks, and opportunities for both industries and many social and commercial activities. Infrared Industry devices and sensors (+ Non-Contact Temperature Measurement) Involving Bands: NIR-SWIR-MWIR- LWIR and Hyperspectral / Multispectral Imaging are also part of this move. Infrared imaging and sensors could either stand alone in a system, be integrated with other sensors from various technologies, and/or unified in a robot, drone, and unmanned aerial vehicle, in ADAS (Advanced driver-assistance systems) and with other emerging technologies. On the other hand, the world is going through a hyper-connectivity network process with a noticeable improvement in speed data transfer and virtually instantaneous latency (LTE- 5G and Wi-Fi 6- IEEE 802.11ax) in which the potential autonomy of the systems with AI- artificial intelligence should also be included (Big data/ Data analytics / Machine learning / Deep learning).