Analytische Chemie
Filtern
Erscheinungsjahr
- 2022 (188) (entfernen)
Dokumenttyp
- Vortrag (66)
- Zeitschriftenartikel (51)
- Beitrag zu einem Tagungsband (41)
- Posterpräsentation (21)
- Forschungsdatensatz (4)
- Sonstiges (2)
- Buchkapitel (1)
- Dissertation (1)
- Zeitschriftenheft (Herausgeberschaft für das komplette Heft) (1)
Sprache
- Englisch (188) (entfernen)
Schlagworte
- Non-destructive testing (17)
- Concrete (15)
- Ultrasound (14)
- NDT (11)
- Thermography (11)
- LIBS (9)
- Air-coupled ultrasound (8)
- Computed tomography (8)
- Additive manufacturing (7)
- Machine learning (7)
Organisationseinheit der BAM
- 8 Zerstörungsfreie Prüfung (188) (entfernen)
Eingeladener Vortrag
- nein (66)
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.
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 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.
Till now application of printed magnetoelectronics is hindered by lack of large area exchange coupled metallic multilayers required to produce printable magneto-sensory inks. Large-scale roll-to-roll (R2R) fabrication process is an attractive approach owing to its capabilities for high volume, high throughput, and large area manufacturing. Precise and high performance R2R sputtering technology is developed to fabricate large area giant magnetoresistive (GMR) thin-films stacks that contain 30 metallic bilayers prepared by continuous R2R sputtering of Co and Cu sequential on a hundred meters long polyethylene terephthalate (PET) web. The R2R sputtered Co/Cu multilayer on a 0.2 × 100 m2 PET web exhibits a GMR ratio of ≈40% achieving the largest area exchange coupled room temperature magneto-sensitive system demonstrated to date. The prepared GMR thin-film is converted to magnetosensitive ink that enables printing of magnetic sensors with high performance in a cost-efficient way, which promotes integration with printed electronics. An average GMR ratio of ≈18% is obtained for 370 printed magnetic sensors. The realized precise R2R sputtering approach can also be extended to a wide range of hybrid thin-film material systems opening up a path for new functional inks applied with printing technologies.
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.
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.
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.
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.
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.
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).
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.
Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques.
Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques.
We present our achievements in the development of distributed fiber optic sensing systems based on Brillouin optical frequency-domain analysis for structural health monitoring. The focus of the applications is on the gapless monitoring of geotechnical structures, large area infrastructures and electrical grids. The work includes the latest use of machine learning algorithms to reduce measurement time by coexistent increasing the measurement accuracy.
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.
Brillouin Optical Frequency Domain Analysis (BOFDA) is a powerful and well-established method for static distributed sensing of temperature and strain. Recently, we demonstrated a BOFDA system based on convolutional neural network which shortens the measurement time considerably. In this paper, we apply leave-one-out cross validation to evaluate the generalization performance and provide an unbiased and reliable machine learning model for a time-efficient BOFDA system.
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.
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.
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.