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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.
Etalon effects in THz transmittance spectra, either taken in time- domain or frequency-domain, often hamper or even hinder the interpretation of film properties. In this paper we discuss the transferability and applicability of spectroscopic methods typically employed in the near and mid infrared spectral range to eliminate the fringes in the transmittance spectra in the THz spectral range.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
Microplastic detection in water samples becomes important for tracing microplastic sources. Microplastic may harm desalination facilities by blocking filters and disturbing the marine food chain. Thermo analytical methods such as pyrolysis gas chromatography mass spectroscopy, and spectroscopic methods like (micro) Raman spectroscopy or (micro) Fouriertransform infrared spectroscopy in combination with appropriate filters and sample preparation are suitable for analyzing microplastics on a scale from 1 µm to 1000 µm fast and unambiguous. While the thermo analytical methods are suitable for larger sample volumes, Raman spectroscopy and Fouriertransform infrared spectroscopy are able to detect and analyze single microplastic particles for instance in bottled water. Machine learning algorithms ensure a reliable classification of different plastic materials.
The majority of ultrasonic devices used for non-destructive testing in civil engineering require contact with the surface of the concrete (specimen), which significantly increases the time required for the measurement. This makes it impractical for extensive investigation of large-scale structures such as bridge decks, foundations, or tunnels.
In a pioneering approach, fluidic oscillators are used as contact free ultrasonic sources to overcome the aforementioned limitations. These robust and cost-effective actuators require only pressurised air and are ideally suited for harsh environments. At a constant supply pressure, they generate a continuous mono-frequent actuation signal. Further, varying the supply pressure via a fast pressure regulator was found to generate a frequency modulated signal which enabled time-of-flight measurement with an added advantage of increased signal to noise ratio. To demonstrate the feasibility of this novel idea of non-contact ultrasound, the results of the initial tests are presented.
Reliability assessment of Structural Health Monitoring (SHM) systems
poses new challenges pushing the research community to address many questions which are still open. For guided wave-based SHM it is not possible to evaluate the system performance without taking into account the target structure and applied system parameters. This range of variables would result in countless measurements.
Factors like environmental conditions, structural dependencies and wave characteristics demand novel solutions for performance analysis of SHM systems compared to those relying on classical non-destructive evaluation. Such novel approaches typically require model-assisted investigations which may not only help to explain and understand performance assessment results but also enable complete studies without costly experiments. Within this contribution, a multi input multi output approach using a sparse transducer array permanently installed on a composite structure to excite and sense guided waves is considered. Firstly, the method and the analysis of path-based performance assessment are presented considering an open-access dataset from the Open Guided Wave platform. Then, a performance analysis of a guided wave-based SHM system using Probability of Detection is presented. To explain some unexpected results, the model-assisted investigations are used to understand the physical phenomena of wave propagation in the test specimen including the interaction with damage. Finally, issues and future steps in SHM systems’ performance assessment and their development are discussed.
Performance assessment for GuidedWave (GW)-based Structural Health Monitoring (SHM) systems is of major importance for industrial deployment.
With conventional feature extraction methods like damage indices, pathbased probability of detection (POD) analysis can be realized. To achieve reliability quantification enough data needs to be available, which is rarely the case.
Alternatives like methods for performance assessment on system level are still in development and in a discussion phase. In this contribution, POD results using an Artificial Intelligence (AI)-based data analysis are compared with those delivered by conventional data analysis. Using an open-access dataset from Open Guided Wave platform, the possibility of performance assessment for GW-based SHM systems using AI-based data analysis is shown in detail. An artificial neural network (ANN) classifier is trained to detect artificial damage in a stiffened CFRP plate. As input for the ANN, classical damage indicators are used. The ANN is tested to detect damage at another position, whose inspection data were not previously used in training. The findings show very high detection capabilities without sorting any specific path but only having a global view of current damage metrics. The systematic evaluation of the ANN predictions with respect to specific damage sizes allows to compute a probability of correct identification versus flaw dimension, somehow equivalent to and compared with the results achieved through classic path-based POD analysis. Also, sensitive paths are detected by ANN predictions allowing for evaluation of maximal distances between path and damage position. Finally, it is shown that the prediction performance of the ANN can be improved significantly by combining different damage indicators as inputs.
Temperature Compensation Strategies for Lamb Wave Inspection using Distributed Sensor Networks
(2022)
The application of temperature compensation strategies is crucial in structural health monitoring approaches based on guided waves. Actually, the varying temperature influences the performance of the inspection system inducing false alarms or missed detection, with a consequent reduction of reliability. This paper quantitatively describes a method to compensate the temperature effect, namely the optimal baseline selection (OBS), extending its application to the case of distributed sensor networks (DSN). The effect of temperature separation between baseline time-traces in OBS are investigated considering multiple couples of sensors employed in the DSN. A combined strategy that uses both OBS and frequent value warning is considered. Theoretical results are compared, using data from two several experiments, which use different frequency analysis with either predominantly A0 mode or S0 mode data or both. The focus is given on the fact that different paths are available in a sensor network and several possible combination of results are available. Nonetheless, introducing a frequent value warning it is possible to increase the efficiency of the OBS approach making use of fewer signal processing algorithms. These confirm that the performance of OBS quantitatively agrees with predictions and also demonstrate that the use of compensation strategies improve detectability of damage.
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.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
In this work we introduce an algorithm based on the well-known Canny approach for effectual crack detection in thermographic films obtained using flying spot thermography (FST) or flying line thermography (FLT). The proposed algorithm performs faster than another algorithm, for crack detection, based on the application of two Sobel filters (one in x and another one in y directions). For FLT it is shown that processing 10-25 % of the thermograms of a thermographic film required to scan a whole sample is enough to obtain good results. In contrast, using the Sobel filter approach requires the processing of twice the thermographic film length. Experimental measurements are performed on a metallic component of complex shape which contains real defects, that is, surface breaking cracks due to industrial use. The specimen is tested using flying line thermography. Three different scanning speeds are tested: 10, 30 and 60 mms-1 with laser powers of 50, 60 and 120 W respectively. The sample and an infrared camera are aligned and fixed on a motorized linear stage. The diode laser LDM500 (500 W max power) is fixed on an optical bench separately from the linear stage. The results obtained
with the proposed algorithm are additionally compared with a previously established algorithm for flying spot thermography based on the Sobel filter. It is shown that the proposed algorithm based on the Canny approach, can be used in automated systems for thermographic non-destructive testing.
The duraBASt area at the highway intersection “Köln-Ost” is a large test field for road testing.
It includes the duraBASt test bridge which is approximately 66 m long and 14.25 m broad. This bridge is part of the “road in the 21st Century” project and is equipped with several sensors for structural health monitoring. This feasibility study is carried out to investigate the prerequisites necessary for the constraction of intelligent bridges in the foture. Embedded sensors are able to measure quantities such as temperature, strain, moisture, corrosion, etc. In the presented work, the focus is set on passive RFID-based sensors. The communication and energy supply are realised by a mobile transmitter through the concrete cover. Hence, no cables or batteries are required. The sensors are designed to withstand the alkaline environment in concrete. Thus, moisture and corrosion values are measurable for several decades.
Active thermography as a nondestructive testing modality suffers greatly from the limitations imposed by the diffusive nature of heat conduction in solids. As a rule of thumb, the detection and resolution of internal defects/inhomogeneities is limited to a defect depth to defect size ratio greater than or equal to one. Earlier, we demonstrated that this classical limit can be overcome for 1D and 2D defect geometries by using photothermal laser-scanning super resolution. In this work we report a new experimental approach using 2D spatially structured illumination patterns in conjunction with compressed sensing and computational imaging methods to significantly decrease the experimental complexity and make the method viable for investigating larger regions of interest.
Pulse-compression thermography is an emerging technique that has shown versatility by combination of pulsed and lock-in thermography. Accordingly, several aspects of this technique are still unexplored, and some others not fully developed yet. Barker codes were widely used in radar applications due to their simplicity and their optimum autocorrelation function. Nevertheless, applications were limited by the amplitude of the sidelobes present in the autocorrelation function and therefore, several filters have been developed which aim to reduce the sidelobes. However, the filters usually depend on empirical parameters which must be determined for each application. A better alternative would improve the applicability of the Barker codes. In this work, we further develop the pulse-compression thermography technique by introducing a 13-bit modified Barker code (mBC): This allows to drastically reduce the sidelobes characteristic of the 13-bit Barker code (BC). Consequently, the thermographic impulse response, obtained by cross-correlation, is almost free of such sidelobes. Deeper defects become easier to detect in comparison with using a 13-bit Barker code. Numerical simulations using the finite element method are used for comparison and experimental measurements are performed in a sample of steel grade St 37 with machined notches of three different depths: 2 mm, 4 mm and 6 mm.
Novel Technique for On-Line Monitoring of the Curing Process of Fiber Reinforced Polymer Composites
(2021)
A specially developed experimental set up has been integrated into a commercially available FT IR Spectrometer to study the curing of Epoxy Systems. MIR and FIR spectra measured during the curing process at different temperatures of an epoxy system will be presented in dependency of the curing time. The experiments and the selected set up are discussed to demonstrate its potential for future monitoring and ageing control applications during a manufacturing process of polymer composites.
Thermographic super-resolution techniques allow the resolution of defects/inhomogeneities beyond the classical limit, which is governed by the diffusion properties of thermal wave propagation. Photothermal super-resolution is based on a combination of an experimental scanning strategy and a numerical optimization which has been proven to be superior to standard thermographic methods in the case of 1D linear defects. In this contribution, we report on the extension of this approach towards a full frame 2D photothermal super-resolution technique. The experimental approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample. In our work we extend the possibilities of the method to efficiently detect and resolve defect cross sections with a fully 2D-structured blind illumination.
Structural health monitoring (SHM) is a term that groups together techniques adopted to evaluate in a continued fashion the structural integrity and degradation of technical appliances. SHM is particularly attractive for components that are difficult to access or expensive to take off-line. Among many other techniques, SHM can be performed using ultrasonic guided waves (UGW) which have an advantage of traveling over Long distances. Various guided wave modes exist along with many methods for their generation and sensing, e.g by means of interdigital transducers (IDT). This contribution is dedicated to the design and characterization of a flexible piezopolymerbased IDT which allows for the selective excitation of UGW, resulting in more straightforward data analysis. The designed IDT was characterized using a 3D Laser Doppler Vibrometer (3D LDV) in the air to identify and analyze the IDT’s Vibration modes. Then the transducer was mounted on an aluminum plate, and the generated wavefield was measured with the 3D LDV.
According to this investigation, we demonstrate that it is possible to selectively excite desired guided wave mode, namely the A0 mode, suppressing the excitation of the S0 mode. Moreover, the measured wavefield allows for analysis of the directivity of the designed IDT. All in all the results show good correlation between theoretical predictions and measured values, thus allowing to use the current design in terms of selective excitation as it is.
Monitoring chemical reactions is the key to chemical process control. Today, mainly optical online methods are applied, which require excessive calibration effort. NMR spectroscopy has a high potential for direct loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and harsh environ¬ments for advanced process monitoring and control, as demonstrated within the European Union’s Horizon 2020 project CONSENS.
We present a range of approaches for the automated spectra analysis moving from conventional multivariate statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations). By using the benefits of traditional qNMR experiments data analysis models can meet the demands of the PAT community (Process Analytical Technology) regarding low calibration effort/calibration free methods, fast adaptions for new reactants or derivatives and robust automation schemes.
Historians and librarians are interested in watermarks and mould surface patterns in historic papers, because they represent the “fingerprints” of antique papers. However, these features are usually covered or hidden by printing, writing or other media. Different techniques have been developed to extract the watermarks in the paper while avoiding interference from media on the paper. Beta radiography provides good results, but this method cannot be widely used because of radiation safety regulations and the long exposure times required due to weak isotope sources employed. In this work, two promising methods are compared which can be used to extract digital high-resolution images for paper watermarks and these are electron radiography and low energy X-ray radiography. For electron radiography a “sandwich” of a lead sheet, the paper object, and a film in a dark cassette, is formed and it is exposed at higher X-ray potentials (> 300 kV). The photoelectrons escaping from the lead sheet penetrate the paper and expose the film. After development, the film captures the watermark and mould surface pattern Images for the paper being investigated. These images are then digitized using an X-ray film digitizer. The film employed could potentially be replaced by a special type of imaging plate with a very thin protection layer to directly generate digital Images using computed radiography (CR). For the second method, a low energy X-ray source is used with the specimen paper placed on a digital detector array (DDA). This method directly generates a low energy digital radiography (DR) image. Both methods provide high quality images without interference from the printing media, and provide the potential to generate a “fingerprint”
database for historical papers. There were nevertheless found to be differences in the images obtained using the two methods.
The second method, using a low energy X-ray source, has the potential to be integrated in a portable device with a small footprint incorporating user safety requirements. Differences obtained using the two methods are shown and discussed.