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The BAMline at the 3rd generation synchrotron X-ray source BESSY II has been supporting researchers in a wide range of research areas for over 20 years. In addition to materials science, these fields also include biology, cultural heritage, and medicine. Being a non-destructive characterization method, synchrotron X-ray imaging, in particular tomography (SXCT), plays a particularly important role in structural characterization. A recent upgrade of key BAMline equipment expands the imaging capabilities: The upgraded dual multilayer monochromator offers flexibility by providing different energy spectra to optimize flux and energy resolution as desired. Different spectra (8 – 60 keV with ΔE/E 0.01%, 1.5%, 4% and pink beam) can be selected. The upgraded detector (in white beam configuration, equipped with an sCMOS camera) allows the higher flux to be exploited with reduced readout times. Shorter tomographic acquisition times in the range of seconds are now possible. Hence, in-situ and operando examinations are routinely available. An integrated slip ring allows continuous rotation of the sample stage for ease of wiring. The pink beam option allows tomographic observation of processes occurring in the time domain of a few seconds with a resolution down to ~ 1 µm. Different scan methods, optimized for quality and speed are available and discussed. Examples of energy related materials from fuel cell and battery research are shown.
An optional end station allows refraction enhanced imaging (synchrotron X-Ray refraction radiography (SXRR) and tomography (SXRCT)). That includes an analyzer Si-crystal in Bragg alignment between sample and detector. This technique obtains sensitivity to smaller structures (cracks, pores) down to a few wavelengths, while obtaining field of view sizes in the range of several mm. Besides medicine (e.g., teeth explants), several applications are found in material science, like studies on diesel particulate filters, ceramics, additively manufactured (AM) alloys and metal matrix composites (MMC). The in-situ capabilities include mechanical load (tension and compression) and heating up to 1100°C. A case study, the microstructural evolution during heat treatment of an AM AlSi10Mg, will be shown.
Responsible treatment of the environment and resources is a key element of sustainability. The building and construction industry is one of the largest consumers of natural resources. Consequently, there is a particular need for regulations and technologies that help to create closed material cycles. From the technological point of view, such efforts are complicated by the growing material diversity and the amount of composites contained in present and future construction and demolition waste (CDW). Nowadays, simple but proven techniques like manual sorting are mainly used. However, this practice not only poses health risks and dangers to the staff performing the work, but also relies on merely obvious, visually striking differences. Automated, sensor-based sorting of these building materials could complement or replace this practice to improve processing speed, recycling rates, sorting quality, and prevailing health conditions. The preliminary results for the identification of a wide variety of building materials with LIBS are presented.
Concrete structures often show severe damage during their lifetime. One such damage is pitting corrosion of the steel reinforcement caused by chloride ingress into the porous concrete structure. Laser-induced breakdown spectroscopy (LIBS) is a promising method in civil engineering, which is used for detection of chlorine in concrete structures in addition to conventional methods of wet chemistry. To assess LIBS as a trustful analytical technique, its accuracy and robustness is carefully tested. The presentation will outline the results of the interlaboratory comparison of chlorine quantification in cement paste samples, which was carried out by 12 laboratories in 10 countries. Two sets of samples with chloride content ranging from 0.06-1.95 wt.% in the training set and 0.23-1.51 wt.% in the test sample set (“unknowns”), with additional variations in the type of cement and chlorine source (salt type) were sent to the laboratories. The overall result demonstrates that LIBS is suitable for the quantification of the investigated sample compositions: average relative bias was mostly below 15 %. Considering that the laboratories did not receive instructions on how to perform the analysis or how to process the data, the results can be evaluated as a true status quo of the LIBS technique for this type of analysis.
The acquisition and appropriate processing of relevant information about the considered system remains a major challenge in assessment of existing structures. Both the values and the validity of computed results such as failure probabilities essentially depend on the quantity and quality of the incorporated knowledge. One source of information are onsite measurements of structural or material characteristics to be modeled as basic variables in reliability assessment. The explicit use of (quantitative) measurement results in assessment requires the quantification of the quality of the measured information, i.e., the uncertainty associated with the information acquisition and processing. This uncertainty can be referred to as measurement uncertainty. Another crucial aspect is to ensure the comparability of the measurement results.This contribution attempts to outline the necessity and the advantages of measurement uncertainty calculations in modeling of measurement data-based random variables to be included in reliability assessment. It is shown, how measured data representing time-invariant characteristics, in this case non-destructively measured inner geometrical dimensions, can be transferred into measurement results that are both comparable and quality-evaluated. The calculations are based on the rules provided in the guide to the expression of uncertainty in measurement (GUM). The GUM-framework is internationally accepted in metrology and can serve as starting point for the appropriate processing of measured data to be used in assessment. In conclusion, the effects of incorporating the non-destructively measured data into reliability analysis are presented using a prestressed concrete bridge as case-study.
Introduction of a monitoring system for Bingham fluids in additive manufacturing with concrete
(2022)
Freeform additive manufacturing of concrete structures is a rising technology in civil engineering with several fascinating advantages. Nonetheless, to ensure reliability and structural integrity, standards and quality control are required in the future to bring this technology into the market. As the concrete is manufactured continuously, continuous quality control of the printing process is also required, i.e. comprehensive process monitoring. At BAM, a test rig will be installed, enabling the printing of concrete structures with a maximum size of 2 m x 1 m x 1 m (l x w x h). Here, process monitoring is the focus of the test rig. In this study, we show the results of the first pump tests, including the measurement of several parameters such as temperature and pressure along the supply system, i.e. from the concrete pump to the printer head.
Introduction of a monitoring system for Bingham fluids in additive manufacturing with concrete
(2022)
Freeform additive manufacturing of concrete structures is a rising technology in civil engineering with several fascinating advantages. Nonetheless, to ensure reliability and structural integrity, standards and quality control are required in the future to bring this technology into the market. As the concrete is manufactured continuously, continuous quality control of the printing process is also required, i.e. comprehensive process monitoring. At BAM, a test rig will be installed, enabling the printing of concrete structures with a maximum size of 2 m x 1 m x 1 m (l x w x h). Here, process monitoring is the focus of the test rig. In this study, we show the results of the first pump tests, including the measurement of several parameters such as temperature and pressure along the supply system, i.e. from the concrete pump to the printer head.
Avoiding the formation of defects such as keyhole pores is a major challenge for the production of metal parts by Laser Powder Bed Fusion (LPBF). The use of in-situ monitoring by thermographic cameras is a promising approach to detect defects, however the data is hard to analyze by conventional algorithms. Therefore, we investigate the use of Machine Learning (ML) in this study, as it is a suitable tool to model complex processes with many influencing factors. A ML model for defect prediction is created based on features extracted from process thermograms. The porosity information calculated from an x-ray Micro Computed Tomography (µCT) scan is used as reference. Physical characteristics of the keyhole pore formation are incorporated into the model to increase the prediction accuracy. Based on the prediction result, the quality of the input data is inferred and future demands on in-situ monitoring of LPBF processes are derived.
Closed material cycles and unmixed material fractions are required to achieve high recovery and recycling rates in the building industry. The growing diversity of construction and demolition waste is leading to increasing difficulties in separating the individual materials. Manual sorting involves many risks and dangers for the executing staff and is merely based on obvious, visually detectable differences for separation. An automated, sensor-based sorting of these building materials could complement or replace this practice to improve processing speed, recycling rates, sorting quality, and prevailing health conditions.
A joint project of partners from industry and research institutions approaches this task by investigating and testing the combination of laser-induced breakdown spectroscopy (LIBS) and visual (VIS)/ near-infrared (NIR) spectroscopy. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3-containing building materials (gypsum, aerated concrete, etc.). Focusing on Berlin as an example, the entire value chain will be analyzed to minimize economic/technological barriers and obstacles at the cluster level and to sustainably increase recovery and recycling rates.
First LIBS measurements show promising results in distinguishing various material types. A meaningful validation shall be achieved with further practical samples. Future works will investigate the combination of LIBS and VIS/NIR spectroscopy in a fully automated measurement setup with conveyor belt speeds of 3 m/s.
In a joint project of partners from industry and research, the automated recycling of construction and demolition waste (CDW) is investigated and tested by combing laser-induced breakdown spectroscopy (LIBS) and near-infrared (NIR) spectroscopy. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3-cotaining building materials (gypsum, aerated concrete, etc.). The project focuses primarily on the Berlin site to analyze the entire value chain, minimize economic/technological barriers and obstacles at the cluster level, and sustainably increase recovery and recycling rates. First measurements with LIBS and NIR spectroscopy show promising results in distinguishing various material types and indicate the potential for a successful combination. In addition, X-ray fluorescence (XRF) spectroscopy is being performed to obtain more information about the quantitative elemental composition of the different building materials. Future work will apply the developed sorting methodology in a fully automated measurement setup with CDW on a conveyor belt.
This sequence of X-Ray images shows how one of the most common Italian moka pots actually work! The sequence starts with a completely prepared moka pot (water in the bottom part, coffee in the middle and hot plate on). During the process the water starts to boil and the steam pressure pushes the hot water through the coffee into the bassin at the top of the pot.
This video sequence and additional explanations can also be found on Wikipedia.
X-Ray computed tomography (XCT) scan of 11 individual metallic powder particles, made of (Mn,Fe)2(P,Si) alloy. The data set consists of 4 single XCT scans which have been stitched together [3] after reconstruction. The powder material is an (Mn,Fe)2(P,Si) alloy with an average density of 6.4 g/cm³. The particle size range is about 100 - 150 µm with equivalent pore diameters up to 75 µm. The powder and the metallic alloy are described in detail in [1, 2].
By means of laser thermography, surface defects, in particular surface breaking cracks, can be detected with high sensitivity. Basically, this requires a focused heat source (high-power laser), a thermographic camera and a relative movement between laser and test object, as well as a suitable evaluation algorithm to distinguish between surface defects and defect-free areas. In this paper we report on a method in which the relative motion is realised by a robot to fully inspect large and non-planar test objects such as rail sections, turbine blades, gears, etc. We show the influence of the excitation laser, which can be varied in terms of spot geometry, wavelength, and scan scheme, and we demonstrate our evaluation algorithms with the aim of automatically detecting surface defects.
In a joint project of partners from industry and research, the automated recycling of construction and demolition waste (CDW) is investigated and tested by combing laser-induced breakdown spectroscopy (LIBS) and near-infrared (NIR) spectroscopy. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3-cotaining building materials (gypsum, aerated concrete, etc.). The project focuses primarily on the Berlin site to analyze the entire value chain, minimize economic/technological barriers and obstacles at the cluster level, and sustainably increase recovery and recycling rates. First measurements with LIBS and NIR spectroscopy show promising results in distinguishing various material types and indicate the potential for a successful combination. In addition, X-ray fluorescence (XRF) spectroscopy is being performed to obtain more information about the quantitative elemental composition of the different building materials. Future work will apply the developed sorting methodology in a fully automated measurement setup with CDW on a conveyor belt.
Ultrasonic measurement technology has become indispensable in NDT-CE. Air-coupled ultrasonic (ACU) measurement techniques promise to reduce measurement time. However, the signal quality suffers from large specific impedance mismatch at the transducer-air and air-specimen interface. Additionally, large pressure amplitudes are necessary for the penetration depth required in NDT-CE applications. To address the specific requirements of ultrasonic testing in NDT-CE, a robust ACU transducer was developed, that generates ultrasound by quickly switching a pressurized air flow. The simple design of the fluidic transducer makes the device maintenance free and resilient against harsh environmental conditions. Since the signal is generated by aeroacoustics, there is no specific impedance mismatch between the transducer and the surrounding air. The ultrasonic signal exhibits frequencies in the 30-60 kHz range and is therefore well suited to penetrate heterogenous materials such as concrete. This contribution gives an introduction in the working principle and signal characteristics of the fluidic transducer. Its applicability to measurements in concrete is verified. A detailed outlook is given to discuss the future potential of fluidic ultrasonic actuators.
Several countries, which use or used nuclear power, started to destruct nuclear power plants. Thus, large amounts of radioactive waste have to be stored safely during the next decades. Large underground caverns will
be used a nuclear waste disposal. Once the cavern is full, a sealing construction is required. The sealing should avoid the contact to any fluids or substances of the storage barrels and reduce hazardous radiation.
Ultrasonic measurement technology has become indispensable in NDT-CE. Air-coupled ultrasonic (ACU) measurement techniques promise to reduce measurement time. However, the signal quality suffers from large specific impedance mismatch at the transducer-air and air-specimen interface. Additionally, large pressure amplitudes are necessary for the penetration depth required in NDT-CE applications.
To address the specific requirements of ultrasonic testing in NDT-CE, a robust ACU transducer was developed, that generates ultrasound by quickly switching a pressurized air flow. The simple design of the fluidic transducer makes the device maintenance free and resilient against harsh environmental conditions. Since the signal is generated by aeroacoustics, there is no specific impedance mismatch between the transducer and the surrounding air. The ultrasonic signal exhibits frequencies in the 30-60 kHz range and is therefore well suited to penetrate heterogenous materials such as concrete. This contribution gives an introduction in the working principle and signal characteristics of the fluidic transducer. A detailed outlook is given to discuss the future potential of fluidic ultrasonic actuators.
In non-destructive testing and structural health monitoring, ultrasonic guided waves (UGWs) are of great interest for finding and characterizing structural flaws. The interaction of the waves with the flaws can often not be described analytically due to waves’ complexity. This makes numerical simulations and experiments indispensable. However, mentioned field of research is still relatively young and most of the methods focus on flaw localization, using time of flight of the reflected signal. This leaves many possibilities for further research, especially when it comes to separate analysis of different propagating modes.
It is well-known that ultrasonic wave modes convert when interacting with flaws. When a fundamental mode (A0 or S0) encounters a notch, it interacts and converts to another fundamental mode. In this contribution, this effect is used to analyze the depth of a notch. Firstly, numerical simulations are presented, which show notch depth proportionality to amplitude ratio of fundamental modes. To validate these results, experiments were carried out on a metal plate with a shallow notch which was gradually deepened. UGWs were excited using a polymer-based interdigital transducer (IDT) and detected with a 3D laser Doppler vibrometer. The IDT is employed to ensure excitation of a single mode and thus to reduce the complexity of the analysis. Good agreement between numerical and experimental results has been found. The results show the potential of UGWs not only for defect reconstruction and localization, but also its precise sizing.
Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging
(2022)
Block-sparse regularization is already well known in active thermal imaging and is used for multiple-measurement-based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. We show the benefits of using a learned block iterative shrinkage thresholding algorithm (LBISTA) that is able to learn the choice of regularization parameters, without the need to manually select them. In addition, LBISTA enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present LBISTA and compare it with state-of-the-art block iterative shrinkage thresholding using synthetically generated and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations. Thus, this allows us to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super-resolution imaging.
The ability of industrial X-ray computed tomography (CT) to scan an object with several internal and external features at once causes increasing adoption in dimensional metrology. In order to evaluate the quality of a measurement value, the task-specific measurement uncertainty has to be determined. Currently, VDI/VDE 2630 part 2.1 gives a guideline to determine the uncertainty of CT measurements experimentally by conducting repeated measurements. This is costly and time-consuming. Thus, the aim is to determine the task-specific measurement uncertainty numerically by simulations (e. g. according to the guide to expression of uncertainty in measurement (GUM) Supplement 1). To achieve that, a digital twin is necessary. This contribution presents a simple first approach how a digital twin can be built. In order to evaluate this approach, a study comparing measurements and simulations of different real CT systems was carried out by determining the differences between the measurement results of the digital twin and of the measurement results of the real-world CT systems. The results have shown a moderate agreement between real and simulated data. To improve on this aspect, a standardized method to characterize CT systems and methods to implement CT parameters into the simulation with sufficient accuracy will be developed.
The residual stresses and load transfer in multiphase metal alloys and their composites (with both random planar-oriented short fibers and particles) will be shown, as studied by neutron diffraction, by X-ray computed tomography, and by a model based on the reformulation of classic Maxwell’s homogenization method.
Contrary to common understanding and state-of-the-art models, we experimentally observe that randomly oriented phases possess non-hydrostatic residual stress. Moreover, we disclose that the unreinforced matrix alloy stays under hydrostatic compression even under external uniaxial compression.
The recently developed modeling approach allows calculating the residual stress in all phases of the composites. It rationalizes the presence of deviatoric stresses accounting for the interaction of random oriented phases with fibers having preferential orientation. It also allows the explanation of the unconventional in-situ behavior of the unreinforced alloy and the prediction of the micromechanical behavior of other similar alloys.
The availability of non-destructive methods is particularly important for composites, e.g., carbon or glass fiber-reinforced polymers because their properties strongly depend on the manufacturing process and in-service conditions. Composites are mostly used in thin-walled components which are perfectly suited for the characterisation with ultrasonic guided waves (UGWs).
In this contribution, we demonstrate sensitivities of UGW to different elastic constants of a composite material. Then we present a characterisation procedure which can be used to in-fer elastic properties. Our approach is based on an inverse procedure in which the numeri-cally calculated dispersion curves are fitted to the measured curves. The acquisition is done by applying a broadband piezoelectric transducer (PZT) to excite various UGW modes and a 3D laser Doppler vibrometer (3D LDV) to record them. Compared to the approaches based on laser excitation, the PZT provides a better signal-to-noise ratio because more energy is brought into the structure. Whereas the 3D LDV compared to a 1D LDV or a PZT allows cap-turing in-plane and out-of-plane components giving more dispersion information for a better characterisation. Since the inverse procedure requires many iterations before elastic prop-erties are retrieved, an efficient tool for the calculation of the dispersion curves is necessary. For this, the Scaled Boundary Finite Element Method is used. All in all, a good agreement be-tween theoretical and experimental curves is demonstrated.
The residual stresses and load transfer in multiphase metal alloys and their composites (with both random planar-oriented short fibers and particles) will be shown, as studied by neutron diffraction, by X-ray computed tomography, and by a model based on the reformulation of classic Maxwell’s homogenization method.
Contrary to common understanding and state-of-the-art models, we experimentally observe that randomly oriented phases possess non-hydrostatic residual stress. Moreover, we disclose that the unreinforced matrix alloy stays under hydrostatic compression even under external uniaxial compression.
The recently developed modeling approach allows calculating the residual stress in all phases of the composites. It rationalizes the presence of deviatoric stresses accounting for the interaction of random oriented phases with fibers having preferential orientation. It also allows the explanation of the unconventional in-situ behavior of the unreinforced alloy and the prediction of the micromechanical behavior of other similar alloys.
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 and impact on probability of detection (POD).
This paper quantitatively describes two different methods to compensate the temperature effect, namely the optimal baseline selection (OBS) and the baseline signal stretch (BSS) extending their application to the case of a distributed sensor networks (DSN). This latter introduces a number of possible data to explore compensation strategies which do not necessarily returns univocal results. Hence, a decision framework is needed, which takes into consideration multiple ultrasonic time traces with different arrival times and amplitude. In detail, 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 a frequent value warning is introduced to find the more probable temperature with increasing reliability of the assessment. The same procedure is applied using the BSS algorithm. Finally, the use of both approaches is introduced, comparing the capability of the mixed algorithm to correctly sort temperature information from OBS and then apply the BSS combining the frequent value warning to have a unique correction all over the DSN.
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 combinations of results are available. Nonetheless, introducing a frequent value warning it is possible to increase the efficiency of the OBS and BSS approaches making use of fewer signal processing algorithms. These confirm that the performance of OBS quantitatively agrees with predictions. In addition, the possibility to combine BSS approached also demonstrates that the use of compensation strategies improves detectability and localization of damage even in a DSN. This result can be used to improve the SHM system reliability, with promising perspectives in increasing POD.
While there is a extensive literature on the micro-mechanical behavior of metal matrix composites (MMCs) under uniaxial applied stress, very little is available on multi-phase MMCs. In order to cast light on the reinforcement and damage processes in such multi-phase composites, an Al alloy with one and two ceramic reinforcements (planar-random oriented alumina fibers and SiC particles) were studied. In-situ compression tests during neutron diffraction experiments were used to track the load transfer among phases, while X-ray computed tomography was used to investigate pre-strained samples, in order to monitor and quantify damage. We found that damage progresses differently in composites with different orientations of the fiber mat. Because of the presence of the intermetallic network, it was observed that the second ceramic reinforcement changes the load transfer scenario only at very high applied load, when also intermetallic particles break. We rationalized the experimental results by means of a micromechanical model based on Maxwell’s homogenization scheme, and we could explain why no damage is observed in the ductile matrix under compression: the matrix finds itself in hydrostatic compression, and the Poisson’s tensile strain is totally carried by the reinforcement phases
Ultrasonic probes for high-temperature applications are provided with metallic wedges, which can withstand the contact with the high temperature of the inspected structure. The ultrasonic signal travels within the wedge and gets reflected from its boundaries, causing interference signals called “ghost echoes”. The current work presents an investigation of the additional damping effect provided by porous sintered metal plates applied onto the surface of the wedge. In particular, the study evaluates the effect of damping plate thickness on the interference signal level at different transmission frequencies. Damping plates made of sintered metal SIKA-R 15 AX were attached to a wedge prototype made of steel 1.4301. The study revealed, that the most effective thickness of damping plates in the selected frequency interval of 1 to 4 MHz is equal to 4 mm. The evaluation of the interference signal has shown that the application of such damping plates to the wedge surface contributes to an additional attenuation of an interference signal of 10 to 30 dB after 500 μs of signal propagation.
In this work, we report on a method for automatic inspection of components using laser scanning thermography, in which the relative motion is performed by a robot to fully inspect complex test objects such as turbine blades. We demonstrate our evaluation algorithms with the aim of automatically detecting surface defects on calibrated specimens. We show the influence of the excitation laser, which can be varied in terms of spot geometry, wavelength, and scan scheme. Additionally, we show some advantages, versatility, and current challenges of using a programmed robot for non-destructive evaluation in thermography.
Diffraction enhanced imaging (DEI) is an advanced digital radiographic imaging technique employing the refraction of X-rays to contrast internal interfaces. This study aims at qualitatively and quantitatively evaluating images acquired using this technique and to assess how different fitting functions to the typical rocking curves (RCs) influence the quality of images. RCs are obtained for every image pixel. This allows the separate determination of the absorption and the refraction properties of the material in a position-sensitive manner. Comparison of various types of fitting functions reveals that the Pseudo-Voigt (PsdV) function is best suited to fit typical RCs. A robust algorithm was developed in the Python programming language, which reliably extracts the physically meaningful information from each pixel of the image. We demonstrate the potential of the algorithm with two specimens: a silicone gel specimen that has well-defined interfaces, and an additively manufactured polycarbonate specimen.
Photothermal characterization of polyester composites loaded with parallelly arranged graphite rods
(2022)
One of the biggest challenges in the measurement of thermal properties is to measure inhomogeneous materials This work focuses on measuring the thermal properties of cylindrical polyester resin composite materials loaded with graphite rods oriented in the direction of the resin thickness, varying the graphite concentrations starting from zero to 8 75 of volumetric fraction and changing the distribution of graphite rods inside the polyester matrix
Fluid inclusion microthermometry is one of the analytical approaches that has been proposed for speleothem-based temperature reconstructions. The proxy of this paleothermometer is the density of drip water relicts that have been enclosed in microscopic fluid inclusions during speleothem growth. Prior to microthermometric analyses, the monophase liquid inclusions need to be transferred from a metastable liquid into a stable liquid-vapour two-phase state by stimulating vapour bubble nucleation by means of single ultra-short laser pulses. Subsequent microthermometric analyses determine the temperature at which the vapour bubble disappears again, the so-called liquid-vapour homogenisation temperature (Th(obs) ). The observed homogenisation temperature depends not only on the water density but also on the inclusion volume. In order to determine the water density, a thermodynamic model is used that takes account of the effect of surface tension on Th(obs) to calculate Th∞. This is the corresponding homogenisation temperature at saturation pressure that determines the water density and defines the formation temperature of the fluid inclusion. The analytical precision of the method ranges between 0.1 and 0.3 °C depending on the size of the inclusions.
Validation of a fast and traceable radiographic scale calibration of dimensional computed tomography
(2022)
A fast and highly precise method of determining the geometrical scale factor of computed tomography (CT) measurements has been validated successfully by Bundesanstalt für Materialforschung und -prüfung (BAM), the Federal Institute of Metrology (METAS) and Physikalisch-Technische Bundesanstalt (PTB) within the scope of AdvanCT (Advanced Computed Tomography for dimensional and surface measurements in industry), a project funded in the European Metrology Programme for Innovation and Research (EMPIR). The method has been developed by PTB and requires only two radiographic images of a calibrated thin 2D standard (hole grid standard) from two opposite directions. The mean grid distance is determined from both radiographs. From this and with the help of the calibration result, the radiographic scale and therefore the voxel size is determined. The procedure takes only a few minutes and avoids a time-consuming CT scan. To validate the method, the voxel sizes determined via this method were compared with voxel sizes determined from CT scans of calibrated objects. Relative deviations between the voxel sizes in the range of 10−5 were
achieved with minimal effort using cone-beam CT systems at moderate magnifications.
Methodologies for model parameterization of virtual CTs for measurement uncertainty estimation
(2022)
X-ray computed tomography (XCT) is a fast-growing technology for dimensional measurements in industrial applications. However, traceable and efficient methods to determine measurement uncertainties are not available. Guidelines like the VDI/VDE 2630 Part 2.1 suggest at least 20 repetitions of a specific measurement task, which is not feasible for industrial standards. Simulation-based approaches to determine task specific measurement uncertainties are promising, but require closely adjusted model parameters and an integration of error sources like geometrical deviations during a measurement. Unfortunately, the development of an automated process to parameterize and integrate geometrical deviations into XCT models is still an open issue. In this work, the whole processing chain of dimensional XCT measurements is taken into account with focus on the issues and requirements to determine suitable parameters of geometrical deviations. Starting off with baseline simulations of different XCT systems, two approaches are investigated to determine and integrate geometrical deviations of reference measurements. The first approach tries to iteratively estimate geometric deviation parameter values to match the characteristics of the missing error sources. The second approach estimates those values based on radiographs of a known calibrated reference object. In contrast to prior work both approaches only use a condensed set of parameters to map geometric deviations. In case of the iterative approach, some major issues regarding unhandled directional dependencies have been identified and discussed. Whereas the radiographic method resulted in task specific expanded measurements uncertainties below one micrometre even for bi-directional features, which is a step closer towards a true digital twin for uncertainty estimations in dimensional XCT.
This dataset contains raw data resulting from Impact-Echo measurements at the reference concrete block "IE Platte", located at BAM (German Federal Institute for Materials Research and Testing).
The specimen contains three polystyrene slabs and one polyethylene foil to act as reflectors. The specimen was produced in a three-step process. First, the base plate was cast. Second, the reflectors were taped to the base plate. Finally, the upper layer was cast on top of base plate and reflectors. A drawing is contained in the dataset.
The Impact-Echo method is based on the excitation of the zero-group-velocity frequency of the first symmetric Lamb mode of a plate-like structure, in order to assess its thickness. Numerous publications elaborate on Impact-Echo theory, examples are (Gibson and Popovics 2005, Schubert and Köhler 2008 , Abraham and Popovics 2010).
The measurements have been conducted using a setup that contains only commercially available components. The setup consists of an Olson CTG-2 concrete thickness gauge (Olsen Instruments, USA) for actuation and sensing and an 8-bit NI USB-5132 digital storage oscilloscope (National Instruments, USA) combined with the Echolyst software (Schweizerischer Verein für technische Inspektionen (SVTI), Switzerland) for data acquisition.
Measurements were conducted using a grid of 29x29 points with a spacing of 50 mm. At each point 8192 samples were recorded at a sampling rate of 1 MS/s.
The dataset contains the (X,Y) location in mm of the individual measurement points as well as the raw measurement data at those points.
The data is provided in the formats *.mir/*.mhdr (Echolyst), *.npy (Python) and *.mat (Matlab) and *.csv to ease the import in various post-processing tools.
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.
In this work, we report on our progress for investigating a new experimental approach for thermographic detection of internal defects by performing 2D photothermal super resolution reconstruction. We use modern high-power laser projector technology to repeatedly excite the sample surface photothermally with varying spatially structured 2D pixel patterns. In the subsequent (blind) numerical reconstruction, multiple measurements are combined by exploiting the joint-sparse nature of the defects within the specimen using nonlinear convex optimization methods. As a result, a 2D-sparse defect/inhomogeneity map can be obtained. Using such spatially structured heating combined with compressed sensing and computational imaging methods allows to significantly reduce the experimental complexity and to study larger test surfaces as compared to the one-dimensional approach reported earlier.
Acoustic methods are ideally suited for determining the mechanical properties of different materials non-destructively. The availability of such methods is particularly important for fiber-reinforced polymers (FRPs) because their properties strongly depend on the manufacturing process and in-service conditions. Since FRPs are mostly used in thin-walled components, properties can be derived from the dispersion curves of ultrasonic guided waves (UGWs).
Our approach is based on an inverse procedure in which the numerically calcu-lated dispersion curves are fitted to the measured curves. The acquisition is done by applying a broadband piezoelectric transducer (PZT) to excite and a 3D laser Doppler vibrometer (3D LDV) to record the waves. Compared to the ap-proaches based on laser excitation, the PZT provides a better signal-to-noise ra-tio because more energy is brought into the structure. Whereas the 3D LDV compared to a 1D LDV or a PZT allows capturing in-plane and out-of-plane components and thus providing more dispersion information. Since the inverse procedure requires many iterations before elastic properties are retrieved, an ef-ficient tool for the calculation of the dispersion curves is necessary. For this, the Scaled Boundary Finite Element Method is used. All in all, a good agreement between theoretical and experimental curves is demonstrated.
The overview of the activity of group 8.5 Micro-NDT (BAM, Belin, Germany) in the field of additively manufacturing material characterization will be presented. The challenges in the residual stress analysis of AM components are discussed on the basis on the show studies performed in BAM. Also, the synchrotron X-ray refraction technique, available in BAM, is presented, showing example of in-situ heating test of Al10SiMg AM material.
Additively manufactured (AM) metallic sheet-based Triply Periodic Minimal Surface Structures (TPMSS) meet several requirements in both bio-medical and engineering fields: Tunable mechanical properties, low sensitivity to manufacturing defects, mechanical stability, and high energy absorption. However, they also present some challenges related to quality control. In fact, the optimization of both the AM process and the properties of TPMSS is impossible without considering structural characteristics as manufacturing accuracy, internal defects, and as well as surface topography and roughness. In this study, the quantitative non-destructive analysis of TPMSS manufactured from Ti-6Al-4V alloy by electron beam melting was performed by means of laboratory X-ray computed tomography (XCT).
Thermographic non-destructive testing is based on the interaction of thermal waves with inhomogeneities. The propagation of thermal waves from the heat source to the inhomogeneity and to the detection surface according to the thermal diffusion equation leads to the fact that two closely spaced defects can be incorrectly detected as one defect in the measured thermogram. In order to break this spatial resolution limit (super resolution), the combination of spatially structured heating and numerical methods of compressed sensing can be used. The improvement of the spatial resolution for defect detection then depends in the classical sense directly on the number of measurements. Current practical implementations of this super resolution detection still suffer from long measurement times, since not only the achievable resolution depends on performing multiple measurements, but due to the use of single spot laser sources or laser arrays with low pixel count, also the scanning process itself is quite slow. With the application of most recent high-power digital micromirror device (DMD) based laser projector technology this issue can now be overcome.
Closed material cycles and unmixed material fractions are required to achieve high recovery and recycling rates in the building industry. The growing diversity of construction and demolition waste is leading to increasing difficulties in separating the individual materials. Manual sorting involves many risks and dangers for the executing staff and is merely based on obvious, visually detectable differences for separation. An automated, sensor-based sorting of these building materials could complement or replace this practice to improve processing speed, recycling rates, sorting quality, and prevailing health conditions.
A joint project of partners from industry and research institutions approaches this task by investigating and testing the combination of laser-induced breakdown spectroscopy (LIBS) and visual (VIS)/ near-infrared (NIR) spectroscopy. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3-containing building materials (gypsum, aerated concrete, etc.). Focusing on Berlin as an example, the entire value chain will be analyzed to minimize economic/technological barriers and obstacles at the cluster level and to sustainably increase recovery and recycling rates.
First LIBS measurements show promising results in distinguishing various material types. A meaningful validation shall be achieved with further practical samples. Future works will investigate the combination of LIBS and VIS/NIR spectroscopy in a fully automated measurement setup with conveyor belt speeds of 3 m/s.
The finite volume method (FVM), like the finite element method (FEM), is a numerical method for determining an approximate solution for partial differential equations. The derivation of the two methods is based on very different considerations, as they have historically evolved from two distinct engineering disciplines, namely solid mechanics and fluid mechanics. This makes FVM difficult to learn for someone familiar with FEM. In this paper we want to show that a slight modification of the FEM procedure leads to an alternative derivation of the FVM. Both numerical methods are starting from the same strong formulation of the problem represented by differential equations, which are only satisfied by their exact solution. For an approximation of the exact solution, the strong formulation must be converted to a so-called weak form. From here on, the two numerical methods differ. By appropriate choice of the trial function and the test function, we can obtain different numerical methods for solving the weak formulation of the problem. While typically in FEM the basis functions of the trial function and test function are identical, in FVM they are chosen differently. In this paper, we show which trial and test function must be chosen to derive the FVM alternatively: The trial function of the FVM is a “shifted” trial function of the FEM, where the nodal points are now located in the middle of an integration interval rather than at the ends. Moreover, the basis functions of the test function are no longer the same as those of the trial function as in the FEM, but are shown to be a constant equal to 1. This is demonstrated by the example of a 1D Poisson equation.
Sound production due to main-flow oriented vorticity-nozzle interaction in absence of a net swirl
(2022)
The downstream acoustic response due to the interaction of main-flow oriented vorticity with a choked nozzle in a swirl-free flow was experimentally demonstrated. The response was obtained by means of impulsive radial air injection in the pipe upstream from the nozzle. The resulting downstream acoustic data are shown to obey a scaling rule that differs, from the one for swirl-nozzle interaction, which according to the literature is proportional to the square of the swirl number. In contrast, here evidence is presented that points to the scaling of main-flow oriented vorticity noise with the cross-sectional average of the square of the transversal velocity at the throat divided by the square of the critical sound speed.
In this study, the concept of a Zero Mass Flow Liner is evaluated. The concept enables impedance control by the induction of periodic bias flow through the perforated facing sheet of the liner. The periodic bias flow is generated by a secondary high amplitude acoustic actuation. By means of the periodic bias flow, the liner can be tuned to different operating points in a given range of grazing flow velocities. The equivalent fluid impedance model for perforated plates is modified to account for the effects of periodic bias flow and grazing flow. An optimization routine, based on a genetic algorithm, is implemented. The method is applicable to any liner concept and uses the impedance of the lined surface as boundary condition in a numerical simulation. Thereby, a set of liner parameters is derived in order to obtain the desired damping characteristics. Based on the results of the optimization, a Zero Mass Flow Liner is manufactured and consequently evaluated experimentally. The damping characteristics are evaluated in form of the dissipated energy along the lined surface. Prediction and measurements show agreement. The Zero Mass
Flow Liner delivers broad band dissipation of high peak value over a range of grazing flow Mach numbers. Under grazing ow conditions, the effect of periodic bias flow is reduced. This poses high energy requirements in high Mach number flow regimes which might restrict the applicability of the Zero Mass Flow concept to grazing flows of low Mach numbers.
The residual stresses and load transfer in multiphase metal alloys and their composites (with both random planar-oriented short fibers and particles) will be shown, as studied by neutron diffraction, by X-ray computed tomography, and by a model based on the reformulation of classic Maxwell’s homogenization method.
Contrary to common understanding and state-of-the-art models, we experimentally observe that randomly oriented phases possess non-hydrostatic residual stress. Moreover, we disclose that the unreinforced matrix alloy stays under hydrostatic compression even under external uniaxial compression.
The recently developed modeling approach allows calculating the residual stress in all phases of the composites. It rationalizes the presence of deviatoric stresses accounting for the interaction of random oriented phases with fibers having preferential orientation. It also allows the explanation of the unconventional in-situ behavior of the unreinforced alloy and the prediction of the micromechanical behavior of other similar alloys.
A large and increasing number of scientific domains pushes for high neutron imaging resolution achieved in reasonable times. Here we present the principle, design and performance of a detector based on infinity corrected optics combined with a crystalline Gd3Ga5O12 : Eu scintillator, which provides an isotropic sub-4 μm true resolution. The exposure times are only of a few minutes per image. This is made possible also by the uniquely intense cold neutron flux available at the imaging beamline NeXT-Grenoble. These comparatively rapid acquisitions are compatible with multiple high quality tomographic acquisitions, opening new venues for in-operando testing, as briefly exemplified here.
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.
In this work, we present the recent in-situ imaging developments at the BAMline (of synchrotron BESSY II, HZB), focused on the in-situ characterization and understanding of microstructural evolution of additively manufactured materials subjected to different environments. Two show cases are presented. In the first, X-ray refraction radiography (SXRR) was combined with in-situ heat treatment to monitor the microstructural evolution as a function of temperature in a laser powder bed fusion (LPBF) manufactured AlSi10Mg alloy. We show that SXRR allows detecting the changes in the Si-phase morphology upon heating using statistically relevant volumes. SXRR also allows observing the growth of pores (i.e., thermally induced porosity), usually studied via X-ray computed tomography (XCT), but using much smaller fields-of-view. In the second case study, XCT was combined with in-situ tensile test to investigate the damage mechanism in a LPBF Aluminum Metal Matrix Composite (MMC). In-situ SXCT test disclosed the critical role of the defects in the failure mechanism along with pre-cracks in the reinforcement phase of MMC. We found that cracks were initiated from lack-of-fusion defects and propagated through coalescence with other defects.
Thermographic photothermal super resolution reconstruction enables the resolution of internal defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. Based on a combination of the application of special sampling strategies and a subsequent numerical optimization step in post-processing, thermographic super resolution has already proven to be superior to standard thermographic methods in the detection of one-dimensional defect/inhomogeneity structures. In our work, we report an extension of the capabilities of the method for efficient detection and resolution of defect cross sections with fully two-dimensional structured laser-based heating. The reconstruction is carried out using one of two different algorithms that are proposed within this work. Both algorithms utilize the combination of several coherent measurements using convex optimization and exploit the sparse nature of defects/inhomogeneities as is typical for most nondestructive testing scenarios. Finally, the performance of each algorithm is rated on reconstruction quality and algorithmic complexity. The presented experimental approach is based on repeated spatially structured heating by a high power laser. As a result, a two-dimensional sparse defect/inhomogeneity map can be obtained. In addition, the obtained results are compared with those of conventional thermographic inspection methods that make use of homogeneous illumination. Due to the sparse nature of the reconstructed defect/inhomogeneity map, this comparison is performed qualitatively.
An experimental cold-gas study of the response of a choked convergent–divergent nozzle to swirl perturbations is presented. The perturbations were obtained by means of upstream unsteady tangential injections into initially steady flows with different values of steady background swirl. The swirl perturbations induced changes in the axial mass-flow rate, due to either their ingestion or evacuation by the nozzle. This in turn caused a downstream acoustic response. For low-intensity background swirl the responses were found to be similar to those obtained without steady background swirl. Perturbations of a high-intensity background swirl led to different effects. For long injection times, the negative mass-flow rate modulation occurred in two stages. The first stage was similar to that of the background-swirl free case. The second stage occurred after a short time delay, and induced a much stronger negative acoustic response. This unexpected behavior suggests that a significant part of the tangentially injected fluid flows upstream inducing an accumulation of swirl, which is – after tangential injection is ceased – suddenly cleared out through the nozzle. A scaling rule for the amplitudes of these acoustic responses is reported. Furthermore, quasi-steady models, based on steady-state measurements are proposed. These models predict the downstream acoustic response amplitude within a factor two. Additionally, preliminary empirical evidence of the effect of swirl on the downstream acoustic response due to the interaction of entropy patches with a choked nozzle is reported. This was obtained by comparison of sound produced by abrupt radial or tangential sonic injection, upstream from the choked nozzle, of air from a reservoir at room temperature to that from a reservoir with a higher stagnation temperature. Because the mass flow through the nozzle does not increase instantaneously, the injected higher-enthalpy air accumulates upstream of the injection-port position in the main flow. This eventually induces a large downstream acoustic pulse when tangential injection is interrupted. The magnitude of the resulting sound pulse can reach that of a quasi-steady response of the nozzle to a large air patch with a uniform stagnation temperature equal to that of the upstream-injected heated air. This hypothesis is consistent with the fact that the initial indirect-sound pulse is identical to one obtained with unheated air injection. The authors posit that – given all of the insight gleaned from them in this case – acoustic measurements of indirect sound appear to be a potentially useful diagnostic tool.
Background Due to physical coupling between mechanical stress and magnetization in ferromagnetic materials, it is assumed in the literature that the distribution of the magnetic stray field corresponds to the internal (residual) stress of the specimen.
The correlation is, however, not trivial, since the magnetic stray field is also influenced by the microstructure and the geometry of component. The understanding of the correlation between residual stress and magnetic stray field could help to evaluate the integrity of welded components.
Objective This study aims at understanding the possible correlation of subsurface and bulk residual stress with magnetic stray field in a low carbon steel weld. Methods The residual stress was determined by synchrotron X-ray diffraction (SXRD, subsurface region) and by neutron diffraction (ND, bulk region). SXRD possesses a higher spatial resolution than ND. Magnetic stray fields were mapped by utilizing high-spatial-resolution giant magneto resistance (GMR) sensors.
Results The subsurface residual stress overall correlates better with the magnetic stray field distribution than the bulk stress.
This correlation is especially visible in the regions outside the heat affected zone, where the influence of the microstructural features is less pronounced but steep residual stress gradients are present.
Conclusions It was demonstrated that the localized stray field sources without any obvious microstructural variations are associated with steep stress gradients. The good correlation between subsurface residual stress and magnetic signal indicates that the source of the magnetic stray fields is to be found in the range of the penetration depth of the SXRD measurements.
The dataset presented contains ultrasonic data recorded in pulse echo mode. The investigated specimen is made of the isotropic homogeneous material polyamide and has a drill hole of constant diameter running parallel to the surface, which was scanned in a point grid using an automatic scanner system. At each measuring position, a pitch-catch measurement was performed using a sampling rate of 2 MHz. The probes used are arrays consisting of a spatially separated receiving and in-phase transmitting unit. The transmitting and receiving sides each consist of 12 point-shaped single probes. These dry-point contact (DPC) probes operate according to the piezoelectric principle at nominal frequencies of 55 kHz (shear waves) and 100 kHz (longitudinal waves), respectively, and do not require a coupling medium. The measurements are performed with longitudinal (100 kHz) and transverse (55 kHz) waves with different geometric orientations of the probe on the measurement surface. The data presented in the article provide a valid source for evaluating reconstruction algorithms for imaging in the low-frequency ultrasound range.
Atmospheric pressure plasmas interact in various physical ways with their surroundings. They release heat and generate charge carriers, which leads to two effects. The first effect is the generation of acoustic waves due to heat release, known as the thermoacoustic effect, and the second effect is the perturbation of the resting fluid provoked by the release of charge carriers, called “ionic wind”. The direct connection between the charge carrier production of the discharge arrangement and the surroundings also allows the detection of acoustic waves by tracking the electrical current of the arrangement.
This contribution introduces a multi-fluid model approach to describe the acoustic interaction of atmospheric plasmas. In addition, we present experimental results on commercially available and in-house fabricated discharge arrangements for either emitting or receiving acoustic waves.
This article presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super-resolution (SR) imaging. The photothermal SR approach is a well-known technique to overcome the spatial resolution limitation in photothermal imaging by extracting high-frequency spatial components based on the deconvolution with the thermal point spread function (PSF). However, stable deconvolution can only be achieved by using the sparse structure of defect patterns, which often requires tedious, handcrafted tuning of hyperparameters and results in computationally intensive algorithms. On this account, this article proposes Photothermal-SR-Net, which performs deconvolution by deep unfolding considering the underlying physics. Since defects appear sparsely in materials, our approach includes trained block-sparsity thresholding in each convolutional layer. This enables to super-resolve 2-D thermal images for nondestructive testing (NDT) with a substantially improved convergence rate compared to classic approaches. The performance of the proposed approach is evaluated on various deep unfolding and thresholding approaches. Furthermore, we explored how to increase the reconstruction quality and the computational performance. Thereby, it was found that the computing time for creating high-resolution images could be significantly reduced without decreasing the reconstruction quality by using pixel binning as a preprocessing step.
This paper describes a new ultrasonic measuring device called “W-Box”. It was developed based on the requirements of the DFG Forschergruppe (research unit) CoDA for a portable device for monitoring of concrete specimens, models and actual structures using embedded ultrasonic transducers as well as temperature and humidity sensors. The W-Box can send ultrasonic pulses with a variable frequency of 50–100 kHz to one selectable transducer and records signals from up to 75 multiplexed channels with a sample rate of 1 MHz and a resolution of 14 bits. In addition, it measures temperature and humidity with high accuracy, adjustable amplification, restarts automatically after a power failure and can be fully controlled remotely. The measured data are automatically stored locally on-site data quality checks and transferred to remote servers. The comparison of the W-Box with a laboratory setup using commercial devices proves that it is equally reliable and precise, at much lower cost. The W-Box also shows that their measurement capacities, with the used embedded ultrasonic transducers, can reach above 6 m in concrete.
The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples.
In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge.
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.
Additively manufactured (and in particular laser powder bed fused) materials represent a manyfold challenge for the materials scientist and engineer because of their distinctive microstructure. If laser powder bed fusion is used to produce components, the complexity level increases because meso-structures (e.g., overhanging features, surface and internal defects) gain importance. Furthermore, if the main advantage of additive manufacturing, i.e., the freedom of design, is to be fully exploited, and geometrically complex structures, such as lattices, are manufactured, then such structures become meta-materials. This means that the geometry and the materials properties become equally important.
This matryoshka-like (more literary than the dry “multi-scale”) complexity makes the characterization of the residual stress fields by means of diffraction methods so difficult with the current means, that new paradigms are necessary to tackle the challenge.
Indeed, classic open problems acquire an extra layer of difficulty, such that new solutions need to be found and the sometimes-dormant debate needs to be re-opened. Examples include the determination of: a- the unstrained reference: this reference can become location-dependent and needs to be carefully determined; b- the so-called diffraction elastic constants, which becomes immensely challenging since even the single-crystal elastic constants are not known for additively manufactured materials.
On top of this, other problems arise. The determination of the principal axes of stress becomes non-trivial because the hatching strategy sometimes dominates over the sample geometry. Even further, in complex structures, such as lattices, the textbook statement that the strain measurement in six independent directions uniquely identifies the strain tensor becomes simply invalid. The peculiar surface features of additively manufactured materials transform trivial tasks into formidable challenges: the precise alignment of a specimen in a beam or the determination of surface stresses with laboratory X-rays need to be thoroughly re-discussed and lay far from being routine tasks.
In this paper, we will show a few examples of the cases mentioned above. We will demonstrate that sometimes the classic approach works very well, but other times surprising conclusions can be drawn from in-depth studies of the residual stress in additively manufactured materials. In short, we predicate that classic methods cannot be used on additively manufactured materials and structures without a critical evaluation of their validity and application range.
Quality assessment of components produced by metal based additive manufacturing (AM) technologies such as laser powder bed fusion is rising in importance due to the increased use of AM in industrial production. Here, the presence of internal porosity was identified as a limiting factor for the final component quality. The utilization of thermography as an in-situ monitoring technique allows the determination of the part’s thermal history which was found to be connected to the porosity formation [1]. Combining the local thermal information derived from thermography with the porosity information obtained by x-ray micro computed tomography, machine learning algorithms can be utilized to predict the porosity distribution in the part. In this study, a first approach for the prediction of keyhole porosity in a cylindric specimen from AISI 316L stainless steel is presented. It is based on data augmentation using the “SmoteR” algorithm [2] to cure the dataset imbalance and a 1-dimensional convolutional neural network.
[1] C.S. Lough et al., Local prediction of Laser Powder Bed Fusion porosity by short-wave infrared thermal feature porosity probability maps. Journal of Materials Processing Technology, 302, p. 117473 (2022)
https://dx.doi.org/10.1016/j.imatprotec.2021.117473
[2] L. Torgo et al., SMOTE for Regression. Progress in Artificial Intelligence, Chapter 33, p. 378-289 (2013)
https://dx.doi.org/10.1007/978-3-642-40669-0_33
Atmospheric pressure plasmas interact in various physical ways with their surroundings. They release heat and generate charge carriers, which leads to two effects. The first effect is the generation of acoustic waves due to heat release, known as the thermoacoustic effect, and the second effect is the perturbation of the resting fluid provoked by the release of charge carriers, called “ionic wind”. The direct connection between the charge carrier production of the discharge arrangement and the surroundings also allows the detection of acoustic waves by tracking the electrical current of the arrangement.
This contribution introduces a multi-fluid model approach to describe the acoustic interaction of atmospheric plasmas. In addition, we present experimental results on commercially available and in-house fabricated discharge arrangements for either emitting or receiving acoustic waves.
This contribution presents a set of largely novel reference standards specially designed for testing different important physical effects and functionalities of radiography-based computed tomography (CT) simulation software. These standards were developed within the scope of the German cooperation project “CTSimU – Radiographic Computed Tomography Simulation for Measurement Uncertainty Evaluation” [1] and serve as tools for the basic qualification of the sufficient physical correctness and required features of simulation software of CT-based coordinate measurement systems (CMSs) via the analyses either of 2D projection images only or of full CT scans. The results serve as input to the German standardisation committee for the development of a new national VDI/VDE guideline in the series VDI/VDE 2630 dealing with the basic qualification aspect of CT simulation software and shall lay ground for the measurement uncertainty determination of dimensional measurements using CT.
An important focus of research in Industrial X-ray Computed Tomography (CT) is to determine the task-specific measurement uncertainty of CT measurements numerically by using simulations. For this, all relevant influence factors need to be identified and quantified. It is known, for example, that geometrical misalignments of the detector lead to measurement deviations if the reconstruction does not consider these misalignments. This contribution uses computer simulation of CT data to investigate the influence of geometrical misalignments of the detector on several measurands found in typical measurements tasks in the industry. A newly developed test specimen with a broad variety of features is used for this study. Angular and positional detector deviations are systematically introduced into the simulations and deliberately left uncompensated during the CT reconstruction. The resulting measurement deviations are shown and discussed.
The fourth dataset dedicated to the Open Guided Waves platform presented in this work aims at a carbon fiber composite plate with an additional omega stringer at constant temperature conditions. The dataset provides full ultrasonic guided wavefields. Two types of signals were used for guided wave excitation, namely chirp signal and tone-burst signal. The chirp signal had a frequency range of 20-500kHz. The tone-burst signals had a form of sine modulated by Hann window with 5 cycles and carrier frequencies 16.5kHz, 50kHz, 100kHz, 200kHz, 300kHz. The piezoceramic actuator used for this purpose was attached to the center of the stringer side surface of the core plate. Three scenarios are provided with this setup: (1) wavefield measurements without damage, (2) wavefield measurements with a local stringer debond and (3) wavefield measurements with a large stringer debond. The defects were caused by impacts performed from the backside of the plate. As result, the stringer feet debonds locally which was verified with conventional ultrasound measurements.
The fourth dataset dedicated to the Open Guided Waves platform presented in this work aims at a carbon fiber composite plate with an additional omega stringer at constant temperature conditions. The dataset provides full ultrasonic guided wavefields.
A chirp signal in the frequency range 20-500 kHz and Hann windowed tone-burst signal with 5 cycles and carrier frequencies of 16.5 kHz, 50 kHz, 100 kHz, 200 kHz and 300kHz are used to excite the wave. The piezoceramic actuator used for this purpose is attached to the center of the stringer side surface of the core plate.
Three scenarios are provided with this setup: (1) wavefield measurements without damage, (2) wavefield measurements with a local stringer debond and (3) wavefield measurements with a large stringer debond. The defects were caused by impacts performed from the backside of the plate. As result, the stringer feet debonds locally which was verified with conventional ultrasound measurements.
The dataset can be used for benchmarking purposes of various signal processing methods for damage imaging.
The detailed description of the dataset is published in Data in Brief Journal.
Traceable determination of non-static XCT machine geometry: New developments and case studies
(2022)
It is fundamental to determine the machine geometry accurately for dimensional X-ray computed tomography (XCT) measurements. When performing high-accuracy scans, compensation of a non-static geometry, e.g. due to rotary axis errors or drift, might become necessary. Here we provide an overview of methods to determine and account for such deviations on a per projection basis. They include characterisation of stage error motions, in situ geometry measurements, numerical simulations, and reconstruction-based optimization relying on image quality metrics and will be discussed in terms of their metrological performance. Since a radiographic calibration is always required to provide an initial absolute geometry, this method will be presented as well. The improvements of the XCT geometry correction methods are presented by means of case studies. The methods can be applied individually or in combination and are intended to provide a toolbox for XCT geometry compensation.
A software toolbox is introduced that addresses several needs common to computed tomography (CT). Built for the WIPANO CTSimU project to serve as the reference implementation for its image processing and evaluation tasks, it provides a Python 3 interface that is adaptable to many conceivable applications. Foremost, the toolbox features a pipeline architecture for sequential 2D image processing tasks, such as flat field corrections and image binning, and enables the user to create their own processing modules. Beyond that, it provides means to measure line profiles and image quality assessment algorithms to calculate modulation transfer functions (MTF) or to determine the interpolated basic spatial resolution (iSRb) using a duplex wire image. It can also be used to calculate projection matrices for the reconstruction of scans with arbitrary industrial CT geometries and trajectories. The CTSimU project defined a framework of projection- and volume-based test scenarios for the qualification of radiographic simulation software towards its use in dimensional metrology. The toolbox implements the necessary evaluation routines and generates reports for all projection-based tests.
Development of an Accurate and Robust Air-Coupled Ultrasonic Time-of-Flight Measurement Technique
(2022)
Ultrasonic time-of-flight (ToF) measurements enable the non-destructive characterization of material parameters as well as the reconstruction of scatterers inside a specimen. The time-consuming and potentially damaging procedure of applying a liquid couplant between specimen and transducer can be avoided by using air-coupled ultrasound. However, to obtain accurate ToF results, the waveform and travel time of the acoustic signal through the air, which are influenced by the ambient conditions, need to be considered. The placement of microphones as signal receivers is restricted to locations where they do not affect the sound field. This study presents a novel method for in-air ranging and ToF determination that is non-invasive and robust to changing ambient conditions or waveform variations. The in-air travel time was determined by utilizing the azimuthal directivity of a laser Doppler vibrometer operated in refracto-vibrometry (RV) mode. The time of entry of the acoustic signal was determined using the autocorrelation of the RV signal. The same signal was further used as a reference for determining the ToF through the specimen in transmission mode via cross-correlation. The derived signal processing procedure was verified in experiments on a polyamide specimen. Here, a ranging accuracy of <0.1 mm and a transmission ToF accuracy of 0.3μs were achieved. Thus, the proposed method enables fast and accurate non-invasive ToF measurements that do not require knowledge about transducer characteristics or ambient conditions.
Damage detection of reinforced concrete (RC) structures is becoming a more attractive domain due to the safety issues arising in the last few decades. The damage in concrete can be caused by excessive exploitation of the structure or environmental effects. The cracks in concrete can be detected by different nondestructive testing methods. However, the available methods used for this purpose have numerous limitations. The technologies available in the market nowadays have difficulties detecting slowly progressive, locally limited damage. In addition, some of These methods cannot be applied, especially in hard-to-reach areas in the superstructures. In order to avoid these deficiencies, an embedded ultrasonic methodology can be used to detect cracks in RC structures. In this study, the methodology of crack detection supported with the advanced Signal processing algorithm was proposed and verified on RC structures of various types, and cracks occurring between embedded sensors can be detected. Moreover, different pairs of ultrasonic sensors located in the considered structures are used for the analysis of the sensitivity of distance between them. It is shown that the ultrasonic sensors placed in the range of 1.5–2 m can detect cracks, even when the other methods failed to detect changes in the structure. The obtained results confirmed that diffuse ultrasonic sensor methodology is able to monitor real structures more effectively than traditional techniques.
Integration of fiber reinforcement in high-performance cementitious materials has become widely applied in many fields of construction. One of the most investigated advantages of steel Fiber reinforced concrete (SFRC) is the deceleration of crack growth and hence its improved sustainability.
Additional benefits are associated with its structural properties, as fibers can significantly increase the ductility and the tensile strength of concrete. In some applications it is even possible to entirely replace the conventional reinforcement, leading to significant logistical and environmental benefits.
Fiber reinforcement can, however, have critical disadvantages and even hinder the Performance of concrete, since it can induce an anisotropic material behavior of the mixture if the fibers are not appropriately oriented. For a safe use of SFRC in the future, reliable non-destructive testing (NDT) methods need to be identified to assess the fibers’ orientation in hardened concrete. In this study,
ultrasonic material testing, electrical impedance testing, and X-ray computed tomography have been investigated for this purpose using specially produced samples with biased or random Fiber orientations. We demonstrate the capabilities of each of these NDT techniques for fiber orientation measurements and draw conclusions based on these results about the most promising areas for future research and development.
Alternative to conventional transmission-based radiography and computed tomography, X-ray refraction techniques are being increasingly used to detect damage in light materials. In fact, their range of application has been recently extended even to metals. The big advantage of X-ray refraction techniques is that they are able to detect nanometric defects, whose size would lie below the resolution of even state-of-the-art synchrotron-based X-ray computed tomography (SXCT). The superiority of synchrotron X-ray refraction radiography and tomography (SXRR and SXRCT) has been shown in the case of light materials, in particular composites. X-ray refraction techniques also yield a quantitifaction of the amount of damage (the so-called relative internal specific surface) and can well be compared with damage models. At the same time, it is impossible for SXRR and SXRCT to image single defects. We show that the combination of refraction- and transmission-based imaging techniques yields an impressive amount of additional information about the type and amount of defects in microstructured materials such as additively manufactured metals or metal matrix composites. We also show that the use of data fusion techniques allows the classification of defects in statistically significant representative volume elements.
Turbine blades for gas turbines are exposed to extreme working conditions in a demanding environment. In-service inspection, maintenance and refurbishment of the heavily stressed parts is necessary to ensure both safety and efficiency, e.g. based on immersion ultrasound testing (UT).
In the course of NDE 4.0, the European project MRO 2.0 aims to innovate the maintenance, repair and overhaul of turbine blades by linking these with modern digital methods. For this, the goal of this project is to go beyond conventional automated and manual UT testing techniques.
The aim is to measure the actual geometry and wall thickness of the complex shaped parts by applying an adaptive TFM that takes into account the refraction of the ultrasonic waves at the transition from the coupling material (water) to the inspected part (steel). In this setup the phased array probe is held by a robotic arm that allows the part to be scanned while remaining mainly perpendicular to the inspected surface. In this way, even complex geometries can be inspected and a 3D model of the actual condition of the part can be created.
The laboratory setup is equipped with a Vantage 64 phased array instrument from Verasonics Inc. and an industrial robot from ABB. A 64 element linear array probe operating at 10 MHz is attached to the robot.
The focus is on optimizing resolution, reliability and inspection speed, as the reconstructed model will be fed to the digital twin at a later stage of the project and used for targeted repairs. In addition to enhancing the reconstruction algorithms, required probe geometry and the parameters needed to inspect turbine blades with partially thin walls and anisotropic materials will also be investigated.
This talk will describe the 3-year project and present the results of the first year. The main focus will be on the development of the reconstruction algorithms used and the experimental setup.
Alternative to conventional transmission-based radiography and computed tomography, X-ray refraction techniques are being increasingly used to detect damage in light materials. In fact, their range of application has been recently extended even to metals. The big advantage of X-ray refraction techniques is that they are able to detect nanometric defects, whose size would lie below the resolution of even state-of-the-art synchrotron-based X-ray computed tomography (SXCT). The superiority of synchrotron X-ray refraction radiography and tomography (SXRR and SXRCT) has been shown in the case of light materials, in particular composites. X-ray refraction techniques also yield a quantitifaction of the amount of damage (the so-called relative internal specific surface) and can well be compared with damage models. At the same time, it is impossible for SXRR and SXRCT to image single defects. We show that the combination of refraction- and transmission-based imaging techniques yields an impressive amount of additional information about the type and amount of defects in microstructured materials such as additively manufactured metals or metal matrix composites. We also show that the use of data fusion techniques allows the classification of defects in statistically significant representative volume elements.
While the problem of the identification of mechanisms of hydrogen assisted damage has and is being thoroughly studied, the quantitative analysis of such damage still lacks suitable tools. In fact, while, for instance, electron microscopy yields excellent characterization, the quantitative analysis of damage requires at the same time large field-of-views and high spatial resolution. Synchrotron X-ray refraction techniques do possess both features. In this work, we show how synchrotron X-ray refraction computed tomography (SXRCT) can quantify damage induced by hydrogen embrittlement in a lean duplex steel, yielding results that overperform even those achievable by synchrotron X-ray absorption computed tomography. As already reported in literature, but this time using a non-destructive technique, we show that the hydrogen charge does not penetrate to the center of tensile specimens. By the comparison between virgin and hydrogen-charged specimens, we deduce that cracks in the specimen bulk are due to the rolling process rather than hydrogen-assisted. We show that (micro)cracks propagate from the surface of tensile specimens to the interior with increasing applied strain, and we deduce that a significant crack propagation can only be observed short before rupture.
Um aus messtechnisch ermittelten Dispersionsabbildungen geführter Ultraschallwellen Rückschlüsse auf die Materialparameter zu ziehen, werden in der aktuellen Forschung verschiedene inverse Methoden diskutiert. Maschinelles Lernen und insbesondere Convolutional-Neural-Networks (CNNs) stellen eine Möglichkeit der automatisierten inversen Modellierung und Evaluierung von Bilddaten dar. In diesem Vortrag wird anhand synthetischer Daten gezeigt, wie das Ausbreitungsverhalten von geführten Ultraschallwellen unter Verwendung von CNNs genutzt werden kann, um die isotropen elastischen Konstanten einer plattenförmigen Struktur zu bestimmen.
The integral collection of information such as strains, cracks, or temperatures by ultrasound offers the best prerequisites to monitor structures during their lifetime. In this paper, a novel approach is proposed which uses the collected information in the coda of ultrasonic signals to infer the condition of a structure. This approach is derived from component tests on a reinforced concrete beam subjected to four-point bending in the lab at Ruhr University Bochum. In addition to ultrasonic measurements, strain of the reinforcement is measured with fiber optic sensors. Approached by the methods of moment-curvature relations, the steel strains serve as a reference for velocity changes of the coda waves. In particular, a correlation between the relative velocity change and the average steel strain in the reinforcement is derived that covers 90% of the total bearing capacity. The purely empirical model yields a linear function with a high level of accuracy (R 2 =0.99, R2=0.99, RMSE≈90μ
RMSE≈90μ strain).
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.
The benefits of including fibers in ultra-high performance concrete (UHPC) are attributed to their good bond with the matrix and, hence, an optimal utilization of their properties. At the same time, though, fiber reinforcement may contribute to anisotropy in the composite material and induce weak areas. The influence of the fibers’ orientation on the material properties is a matter of current scientific discourse and it is known to play a vital role in structural design. In the case studies presented herein, mechanical laboratory tests using pulsating load regimes on UHPC with a strength of more than 200 MPa were simulated by use of finite element models. The orientations of the fibers were measured for each test sample prior to failure using an X-ray computed tomography (CT) scanner, and these orientations are explicitly implemented into the model. The paper discusses the methodology of merging data retrieved by CT image processing and state-of-the-art FE simulation techniques Moreover, the CT scanning was carried out throughout the testing procedure, which further enables the comparison of the mechanical tests and the FE models in terms of damage propagation and failure patterns. The results indicate that the overall fiber configuration and behavior of the samples can be realistically modelled and validated by the proposed CT-FE coupling, which can enhance the structural analysis and design process of elements produced with steel fiber reinforced and UHPC materials.
Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.
Ultrasonic testing is a widely applied measurement method in materials research and medicine. Commonly, a transducer is coupled to the specimen directly or via a liquid coupling agent. While reducing acoustic transmission losses significantly, this procedure is time-consuming and cannot be used for sensitive specimens. Air-coupled ultrasound is a viable alternative in such cases, although suffering from very high acoustic transmission losses between transducer, air and specimen. The recently introduced fluidic transducer (FT) generates ultrasound by utilizing the instability of a supersonic air jet switched inside a fluidic amplifier. Since only air is used as the working medium and no vibrating surfaces are used for ultrasound generation, the transducer is able to efficiently generate large acoustic pressure amplitudes. The resulting acoustic field shares its directivity with the ejected high-velocity air jet. Thus, the acoustic energy needs to be redirected from the jet axis in order to make the fluidic transducer applicable to sensitive specimens. In this study, the effectivity of using sonic crystals (SCs) for this redirection is investigated using acoustic and flow measurements. SCs are air-permeable while being reflective to large acoustic frequency bands. It was shown that both a defect waveguide and a mirroring strategy successfully redirected the acoustic field from the air jet. Furthermore, the interaction of flow and SC showed strong acoustic quenching if the SC was placed too close to the FT outlet. Blockage of the jet entrainment due to the SC may result in slightly higher off-axis flow velocities locally, which should be considered in sensitive applications.
There are many different methods to characterize air-coupled ultrasonic transducers for non-destructive testing. Data sheets of various manufacturers contain information about some parameters important for the performance of transducers, but this information is not standardized, so that a comparison between probes of different manufacturers is difficult. Therefore, the German Society for Non-Destructive Testing (DGZfP) is working on a guideline to characterization of air-coupled probes.
One of the topics in this guideline is the application of thermoacoustic transducers for the characterization of receivers and another topic is the application of microphones for the characterization of transmitters. In this presentation we compare various characterization methods with the particular focus on the characterization of thermoacoustic transducers using an optical microphone. Both thermoacoustic transmitters and optical microphones have a very large bandwidth compared to conventional air-coupled transducers, but their spectrum is not entirely linear, which needs to be taken into account if they are applied as reference transducers.
Ultrasonic monitoring, making use of the sensitivity of the coda of repeated transmission meas-urements to changes in stress, temperature, moisture, as well as localized or distributed damage, has gotten at-tention in structural health monitoring (SHM) research recently. Analysis methods such as coda wave inter-ferometry (CWI), including its nonlinear extension, have been shown to be able to measure ultrasonic wave velocity changes with a 1∙10-5 resolution, while indicators such as cross-correlation or cross-coherence have been used to distinguish between reversible and irreversible changes. Several small- and large-scale laboratory experiments have demonstrated that stress changes in structures can be captured or damage detected in a very early stage. The use of this technique for pre-warning before failure are currently under investigation, as well as detailed research on the physical causes and the connection between ultrasonic wave properties and materi-al/structural behavior. Recently, several of large-scale laboratory and real structures have been instrumented with embedded ultrasonic transducers to gather experience and evidence on how to use this technology in re-al-world applications. Preliminary results from installations on a new bridge, an existing bridge, a tunnel, a la-boratory earthquake test as well as a historic stadium in Germany, Poland, and the United States, respectively, are presented. Environmental influences (mainly temperature) and validation by load tests are discussed.
Ultrasonic monitoring, making use of the sensitivity of the coda of repeated transmission meas-urements to changes in stress, temperature, moisture, as well as localized or distributed damage, has gotten at-tention in structural health monitoring (SHM) research recently. Analysis methods such as coda wave inter-ferometry (CWI), including its nonlinear extension, have been shown to be able to measure ultrasonic wave velocity changes with a 1∙10-5 resolution, while indicators such as cross-correlation or cross-coherence have been used to distinguish between reversible and irreversible changes. Several small- and large-scale laboratory experiments have demonstrated that stress changes in structures can be captured or damage detected in a very early stage. The use of this technique for pre-warning before failure are currently under investigation, as well as detailed research on the physical causes and the connection between ultrasonic wave properties and materi-al/structural behavior. Recently, several of large-scale laboratory and real structures have been instrumented with embedded ultrasonic transducers to gather experience and evidence on how to use this technology in re-al-world applications. Preliminary results from installations on a new bridge, an existing bridge, a tunnel, a la-boratory earthquake test as well as a historic stadium in Germany, Poland, and the United States, respectively, are presented. Environmental influences (mainly temperature) and validation by load tests are discussed.
NDE 4.0 in Civil Engineering
(2021)
Civil engineering industry is one of the most important industry sectors in the worldwide economy. It contributes significantly to the gross economic product and general employment. Even more important, it provides many of the basic needs of the society (e.g., housing, infrastructure, and protection from natural hazards). The concept of “Industry 4.0” or “Smart Production” has not yet made significant progress in the civil engineering industry. The designing, building, and operating processes are still widely dominated by the exchange of printed documents and drawings. Most objects (buildings and other constructions) are unique, and a large part of the production still requires a large amount of manual labor. As-built documentation and quality assurance are often neglected. Civil engineering is among the industry sectors with the lowest level of digitalization and the lowest gain in productivity. However, this is going to change. In the past decade, several drivers have challenged the ways clients, contractors, and authorities currently operate. These drivers include but are not limited to an increasing demand for serialization and automatization or the mandatory introduction of “Building Information Modeling” (BIM) in public procurement as well as the upcoming use of digital twins. NDE plays an increasing role in quality assurance, condition assessment, and monitoring of structures. However, with very few exceptions, applications are mostly nonstandardized and performed only at selected sites. To change this, the NDT-CE community including manufacturers, service providers, clients, and the scientific community must work consistently on open data formats, interfaces to BIM, standardization, and validated ways for a quantitative use of the results in the assessment of constructions.
Non-destructive testing methods are available in civil engineering for decades to estimate concrete properties or to detect flaws and features. But recently we have seen the dawn of next-generation tools, methods, and applications. Some of them will be discussed in the web talk: – Better tools: deeper and more detailed insight into concrete constructions – Better methods: Quantitative use in probabilistic structural assessment – Better rules: Towards standardization, qualification, and certification – Better application: Digitalization and Elimination of the boundaries between NDT, SHM, and BIM: NDT-CE 4.0 Not enough? I might show, how cosmic rays might become a game-changer in NDT-CE. This live webinar record was provided by https://eurostruct.org
The determination of the measurement uncertainty of quantitative and qualitative results is an important quality management tool, for example to describe measuring equipment, procedures, measurement results and the quality of products. The lecture presents the process description for the determination of the measurement uncertainty according to GUM, the determination of uncertainties in qualitative measurement results and the application of the measurement uncertainty for conformity assessment.
ML has been successfully applied to solve many NDT-CE tasks. This is usually demonstrated with performance metrics that evaluate the model as a whole based on a given set of data. However, since in most cases the creation of reference data is extremely expensive, the data used is generally much sparser than in other areas, such as e-commerce. As a result, performance indicators often do not reflect the practical applicability of the ML model. Estimates that quantify transferability from one case to another are necessary to meet this challenge and pave the way for real world applications.
In this contribution we invetigate the uncertainty of ML in new NDT-CE scenarios. For this purpose, we have extended an existing training data set for the classification of corrosion damage by a new case study. Our data set includes half-cell potential mapping and ground-penetrating radar measurements. The measurements were performed on large-area concrete samples with built-in chloride-induced corrosion of reinforcement. The experiment simulated the entire life cycle of chloride induced exposed concrete components in the laboratory. The unique ability to monitor deterioration and initiate targeted corrosion initiation allowed the data to be labelled - which is crucial to ML. To investigate transferability, we extend our data by including new design features of the test specimen and environmental conditions. This allows to express the change of these features in new scenarios as uncertainties using statistical methods. We compare different sampling and statistical distribution-based approaches and show how these methods can be used to close knowledge gaps of ML models in NDT.
The determination of the anisotropy of materials’ microstructure and morphology (pore space) in diesel particulate filter (DPF) materials is an important problem to solve, since such anisotropy determines the mechanical, thermal, and filtration properties of such materials. Through the use of a dedicated (and simple) segmentation algorithm, it is shown how to exploit the information yielded by 3D X-ray computed tomography data to quantify the morphological anisotropy. It is also correlated that such anisotropy of the pore space Such anisotropy of the pore space is also correlated with the microstructure and crystallographic anisotropy of the material in several showcases: a microstructurally isotropic material, such as SiC, and some morphologically and microstructurally anisotropic cordierite materials. In the later case, the finer the grain size, the more isotropic the microstructure.
Synchrotron X-Ray computed tomography at the BAMline is constantly evolving. During monochromatic tomographic scans a preview reconstruction is offered. Newly introduced scanning schemes suppress certain types of reconstruction artifacts. Additionally, the application of a pink beam enables for faster tomographic scans even within seconds.
Virtual CT with aRTist
(2021)
Simulation becomes more and more important in modern CT imaging. It is increasingly used to optimize techniques for complex applications, to support the preparation of written procedures, and for educational purposes. The radiographic simulator aRTist is a modelling tool which simulates X-ray imaging using a hybrid analytical and Monte Carlo method to efficiently model the radiation transport. In addition to the relevant physical effects such as absorption, scattering and fluorescence, simplified fast models are employed to describe the characteristics of the X-ray source and the detector. aRTist is well equipped to model realistic X-ray imaging setups due to the ability to load exported CAD object descriptions. A simple CT scan module is contained in aRTist which allows the simulation of standard (circular cone beam) scanning trajectories.
This training session starts with a general introduction to aRTist and will highlight its basic usage. Furthermore, the focus is on the configuration of scan trajectories and batch simulations for virtual CT. The aim is to enable the audience to use the aRTist software for their own experiments in virtual CT.
It is fundamental to determine the machine geometry accurately for dimensional X-ray computed tomography (XCT) measurements. When performing high-accuracy scans, compensation of a non-static geometry, e.g. due to rotary axis errors or drift, might become necessary. Here we provide an overview of methods to determine and account for such deviations on a per projection basis. They include characterisation of stage error motions, in situ geometry measurements, numerical simulations, and reconstruction-based optimization relying on image quality metrics and will be discussed in terms of their metrological performance. Since a radiographic calibration is always required to provide an initial absolute geometry, this method will be presented as well.
"The reassessment of bridges continues to take great importance both nationally and internationally. A major challenge is to find computation models reflecting the actual properties of the considered structures sufficiently accurate. Besides regular inspections, the conduction of advanced measurements is suitable to generate reliable information about a structure to be assessed. Prior to incorporating measurement results in reassessment, the relevance, the trueness, and the precision of the measured information needs to be stated. On the one hand, the use of information whose quality has not been assessed can lead to errors with serious consequences. On the other, the measurement of irrelevant information is inefficient. Although the use of measured data in assessment is currently mostly unregulated, their appreciation in reliability analyses is beneficial since the built environment can be assessed more realistically. Utilizing NDT in reassessment has the potential to extend remaining lifetimes of a structure, save resources, and improve infrastructural availabilities. The power of judgment regarding the decision on the reliability of an existing structure can be increased.
In this contribution, an approach is outlined to process non-destructively gathered measurement data in a comparableway in order to include themeasured information in probabilistic reliability assessments of existing structures. An essential part is the calculation of measurement uncertainties. The effect of incorporating evaluated NDT-results is demonstrated by means of a prestressed concrete bridge and GPR measurements conducted on this bridge as a case-study. The bridge is assessed regarding SLS Decompression using the NDT-results."
Corrosion of concrete reinforcement is one of the major damage mechanisms affecting both the load-bearing capacity and the serviceability of re-inforced concrete structures significantly. When externally discernible damages are observed during visual inspections on the structure, the extent of the damage inside the concrete is often already significant. Corrosion caused by carbonation often leads to severe discoloration of the surface or even large-area spalling of the concrete cover. In contrast, chloride-induced corrosion is usually difficult to observe visually but can cause much more serious damage in less time. The effect occurs locally and can lead to weakening of the cross-section of the reinforce-ment. This, in turn, can cause sudden structural collapses without prior notice.
In the meanwhile, various non-destructive and minimally invasive testing methods are available to evaluate the resistance to penetration of corrosion-pro-moting pollutants and to detect active corrosion. In this paper, a bridge crossing the river Regen is used as a case-study to demonstrate how the information ob-tained applying different testing methods can be combined and evaluated in the context of structural reassessments. Both the results of the permeability testing and the electrical resistance measurement are considered, as well as active corro-sion areas are localized using the half-cell potential mapping combined with the concrete cover measurement with the eddy current method and ground penetrat-ing radar. The results are evaluated using drill cores and in addition laser-induced breakdown spectroscopy was applied to obtain information about possible chlo-ride ion transport into the concrete.
Detection of surface breaking cracks using flying line laser thermography: A Canny-based algorithm
(2021)
In this work, we introduce a new algorithm for effectual crack detection using flying line laser thermography, based on the well-known Canny approach. The algorithm transforms the input thermographic sequence into an edge map. Experimental measurements are performed on a metallic component that contains surface breaking cracks due to industrial use. The specimen is tested using flying line thermography at different scanning speeds and laser input powers. Results obtained with the proposed algorithm are additionally compared with a previously established algorithm for flying spot thermography. The proposed Canny-based algorithm can be used in automated systems for thermographic non-destructive testing.
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.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
The Alpine Fault zone in New Zealand marks a major transpressional plate boundary that is late in its typical earthquake cycle. Understanding the subsurface structures is crucial to understand the tectonic processes taking place. A unique seismic survey including 2D lines, a 3D array, and borehole recordings, has been performed in the Whataroa Valley and provides new insights into the Alpine Fault zone down to ∼2 km depth at the location of the Deep Fault Drilling Project (DFDP)-2 drill site. Seismic images are obtained by focusing prestack depth migration approaches. Despite the challenging conditions for seismic imaging within a sediment filled glacial valley and steeply dipping valley flanks, several structures related to the valley itself as well as the tectonic fault system are imaged. A set of several reflectors dipping 40°–56° to the southeast are identified in a ∼600 m wide zone that is interpreted to be the minimum extent of the damage zone. Different approaches image one distinct reflector dipping at ∼40°, which is interpreted to be the main Alpine Fault reflector located only ∼100 m beneath the maximum drilled depth of the DFDP-2B borehole. At shallower depths (z < 0.5 km), additional reflectors are identified as fault segments with generally steeper dips up to 56°. Additionally, a glacially over-deepened trough with nearly horizontally layered sediments and a major fault (z < 0.5 km) are identified 0.5–1 km south of the DFDP-2B borehole. Thus, a complex structural environment is seismically imaged and shows the complexity of the Alpine Fault at Whataroa.