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Detaillierte Kenntnisse der elastischen Materialeigenschaften sind in vielen ingenieurtechnischen Bereichen von grundlegender Bedeutung. Insbesondere für die Anwendung von Predictive Maintenance und Structural- Health-Monitoring Methoden mit Ultraschall ist die genaue Kenntnis der elastischen Materialkonstanten eine Grundvoraussetzung. Die von den Herstellern zur Verfügung gestellten Angaben zu den elastischen Materialkonstanten, insbesondere für Polymere und faserverstärkte Kunststoffe, sind jedoch oft unzureichend, da diese vom Produktionsprozess abhängig sind und sich zusätzlich aufgrund von Materialabbauprozessen oder Ermüdung ändern können. In der Praxis liegen polymere Werkstoffe, faserverstärkte Kunststoffe und Metalle oft als dünne, plattenförmige Strukturen vor, in welchen sich geführte Ultraschallwellen (UGWs) ausbreiten können. In der aktuellen Forschung sind bereits verschiedene Neuronale Modelle zur Bestimmung der elastischen Konstanten und der Materialcharakterisierung mittels UGWs bekannt. Ein einfaches neuronales Netz, mit aus Dispersionsbildern extrahierten Werten für Frequenz und Wellenzahl ausbeutungsfähiger Moden als Eingabe, zur Vorhersage der elastischen Konstanten wird in verwendet. Ein rekurrentes Neuronales Netz mit einem Zeit-Frequenz Vektor als Eingabe wird in angewandt, während in ein 1D- Convolutional-Neuronal-Networks (CNN) unter Verwendung der zeitlichen Auslenkung der Grundmoden und in ein 2D-CNN unter Verwendung einer polaren Gruppengeschwindigkeitsdarstellung zur Bestimmung der elastischen Konstanten verwendet wird. In diesem Vortrag wird ein Ansatz zur Bestimmung der isotropen elastischen Konstanten von dünnen Platten auf der Grundlage von UGWs unter Verwendung von Dispersionsbildern und 2D-CNNs vorgestellt. Dispersionsabbildungen aus numerischen Simulationen werden mithilfe verschiedener Methoden vorverarbeitet, um realistische Messdaten zu simulieren. Mit den modifizierten Daten wird das Modell trainiert und die Architektur optimiert. Anschließend wird die Genauigkeit des erzeugten Modells mit realen Messdaten validiert. Es wird gezeigt, dass 2D-CNNs in der Lage sind, die isotropen elastischen Konstanten anhand multimodaler Merkmale aus Dispersionsbildern vorherzusagen, ohne dass eine anfängliche Schätzung der Parameter oder manuelle Merkmalsextraktion erforderlich ist.
Only the nano-scaled structure of the nanocomposite and the dispersion of nanoparticles within the polymer matrix harbor multifunctional potential including superior fire retardancy. Thus, this chapter focuses on the dispersion of nanoplates, based mainly on studies of layered silicates and graphene/graphene-related nanoplates. The nanostructure and properties of the nanocomposites are dependent mainly on thermodynamic and kinetic factors during preparation. Improving nano-dispersion often directly improves flame retardancy. Therefore, the modification of the nanoplates as well as the preparation of nanocomposites becomes very important to control this dispersion. The dispersion of nanoplates functions as a prerequisite for the formation of an efficient protective layer, changing the melt flow and dripping behavior, or the improvement of the char properties.
Detailliertes Wissen über die mechanischen Eigenschaften verwendeter Materialien ist Grundvoraussetzung für viele ingenieurtechnische Aufgaben und Dienstleistungen. Zur Bestimmung der elastischen Materialparameter gibt es verschiedene klassische, zerstörende Prüfverfahren. Eine Möglichkeit der zerstörungsfreien Bestimmung liegt in der Auswertung von Messergebnissen, die auf Basis des Ausbreitungsverhaltens geführter Ultraschallwellen gewonnen wurden. Das Ausbreitungsverhalten geführter Ultraschallwellen kann mittels Dispersionsabbildungen dargestellt werden.
Um aus messtechnisch ermittelten Dispersionsabbildungen 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 Beitrag wird gezeigt, wie das Ausbreitungsverhalten von geführten Ultraschallwellen unter Verwendung von CNNs genutzt werden kann, um die isotropen elastischen Konstanten einer plattenförmigen Struktur zu bestimmen. Hierfür werden die verwendeten Daten analysiert, das Preprocessing erläutert und eine grundlegende CNN-Architektur gewählt. Zur Auswertung des generierten Modells werden verschiedene Verfahren wie Gradienten-Mapping und die Visualisierung der verschiedenen Schichten vorgestellt. Die Anwendbarkeit der Methode wird anhand synthetischer Daten demonstriert.
This presentation shows how the dispersive behavior of ultrasonic guided waves in isotropic materials can be used by means of Convolutional Neural Networks to determine the elastic parameters. For this purpose, the preprocessing, the training, the chosen architecture and the results are evaluated on the basis synthetic image data. This presentation was given at the SMSI 2021.
In the context of Industry 4.0 and especially in the field of Structural Health Monitoring, Condition Monitoring and Digital Twins, simulations are becoming more and more important. The exact determination of material parameters is required for realistic results of numerical simulations of the static and dynamic behavior of technical structures. There are many possibilities to determine
elastic material parameters. One possibility of non-destructive testing are ultrasonic guided waves. For the evaluation of the measurement results, mostly inverse methods are applied in order to be able to draw conclusions about the elastic material parameters from analysing the ultrasonic guided wave propagation. For the inverse determination of the elastic material Parameters with ultrasonic guided waves, several investigations were carried out, e.g. the determination of the isotropic material parameters through the point of zero-groupvelocity or anisotropic material parameters with a simplex algorithm. These investigations are based on the evaluation of dispersion images. Machine learning and in particular Convolutional Neural Networks (CNN) are one possibility of the automated evaluation from Image data, e.g. classification or object recognition problems. This article shows how the dispersive behavior of ultrasonic guided waves and CNNs can be used to determine the isotropic elastic constants of plate-like structures.
Elastic waves in inhomogeneous meshes avoiding numerical artifacts
Elastic waves in solids resulting from damage processes, e.g. microcracking are used to monitor the integrity of structures. The numerical modelling of these acoustic Emission processes is hindered by the different scales involved. Crack opening is a fast process and the size of the damaged zone is small, leading to small time steps and fine meshes in a numerical finite element simulation. On the other hand the relevant wave Propagation takes place on a much larger spatial scale, e.g covering the distance between Emission source and sensor.
To avoid numerical oscillations, the mesh size at the emission source has to be coupled to its time scale. Using higher order spectral elements can be beneficial with respect to the needed number of degrees of freedom. To make the computation of an acoustic emission process feasible one is lead to coarsening the mesh for larger distances to the source. Solution components with a higher frequency will be reflected at mesh density steps. The mesh coarsening has to be done in a way to avoid or minimize this kind of reflections.
To get more insight into the propagation characteristics of the numerical solution, dispersion curves are calculated for different element types assuming a structured mesh with constant element size. Coupling two meshes with different mesh densities will then lead to frequency dependent reflections at the boundary similar to the coupling of different materials.
The starting mesh density is dictated by the acoustic emission source time scale. The largest allowable mesh size needs to resolve the components of propagating signal with the highest frequency, smallest wavelength which is given by the bandwidth of the sensor.
Still coarser meshes may be used when high frequency components are propagated by a different method.
Pulse and flash thermography are experimental techniques which are widely used in the field of non-destructive testing for materials characterization and defect detection. We recently showed that it is possible to determine quantitatively the thickness of semitransparent polymeric solids by fitting of results of an analytical model to experimental flash thermography data, for both transmission and reflection configuration. However, depending on the chosen experimental configuration, different effective optical absorption coefficients had to be used in the model to properly fit the respective experimental data, although the material was always the same. Here, we show that this effect can be explained by the wavelength dependency of the absorption coefficient of the sample material if a polychromatic light source, such as a flash lamp, is used. We present an extension of the analytical model to describe the decay of the heating irradiance by two instead of only one effective absorption coefficient, greatly extending its applicability. We show that using this extended model, the experimental results from both measurement configurations and for different sample thicknesses can be fitted by a single set of parameters. Additionally, the deviations between experimental and modeled surface temperatures are reduced compared to a single optimized effective absorption coefficient.
Hydroformylation of short-chained olefins has been established as a standard industrial process for the production of C2 to C6 aldehydes. Using aqueous solutions of transition metal complexes these processes are carried out homogeneously catalyzed. A biphasic approach allows for highly efficient catalyst recovery. Regarding renewable feedstocks, the hydroformylation of long-chained alkenes (> C10) in a biphasic system, using highly selective rhodium catalysts has yet not been shown. Therefore, the Collaborative Research Center SFB/TR 63 InPROMPT develops new process concepts, involving innovative tuneable solvent systems to enable rather difficult or so far nonviable synthesis paths. One possible concept is the hydroformylation of long-chained alkenes in microemulsions. For this, a modular mixer-settler concept was proposed, combining high reaction rates and efficient catalyst recycling via the application of technical grade surfactants. The feasibility of such a concept is evaluated in a fully automated, modular mini-plant system within which the characteristics of such a multiphase system pose several obstacles for the operation. Maintaining a stable phase separation for efficient product separation and catalyst recycling is complicated by small and highly dynamic operation windows as well as poor measurability of component concentrations in the liquid phases. In this contribution, a model-based strategy is presented to enable concentration tracking and phase state control within dynamic mini-plant experiments. Raman spectroscopy is used as an advanced process analytical tool, which allows for online in-situ tracking of concentrations. Combined with optical and conductivity analysis optimal plant trajectories can be calculated via the solution of dynamic optimization problem under uncertainty. Applying these, a stable reaction yield of 40 % was achieved, combined with an oil phase purity of 99,8 % (total amount of oily components in the oil phase) and catalyst leaching below 0.1 ppm.
Multilayer graphene/chlorine-isobutene-isoprene rubber nanocomposites: the effect of dispersion
(2016)
Multilayer graphene (MLG) is composed of approximately 10 sheets of graphene. It is a promising nanofiller just starting to become commercially available. The Dispersion of the nanofiller is essential to exploit the properties of the nanocomposites and is dependent on the preparation method. In this study, direct incorporation of 3 parts per hundred of rubber (phr) MLG into chlorine-isobutene- isoprene rubber (CIIR) on a two-roll mill did not result in substantial enhancement of the material properties. In contrast, by pre-mixing the MLG (3 phr) with CIIR using an ultrasonically assisted solution mixing procedure followed by two-roll milling, the properties (rheological, curing, and mechanical) were improved substantially compared with the MLG/CIIR nanocomposites mixed only on the mill. The Young’s moduli of the nanocomposites mixed in solution increased by 38%. The CIIR/MLG nanocomposites produced via solution showed superior durability against weathering exposure.
In this paper, a method to determine the complex dispersion relations of axially symmetric guided waves in cylindrical structures is presented as an alternative to the currently established numerical procedures. The method is based on a spectral decomposition into eigenfunctions of the Laplace operator on the cross-section of the waveguide. This translates the calculation of real or complex wave numbers at a given frequency into solving an eigenvalue problem. Cylindrical rods and plates are treated as the asymptotic cases of cylindrical structures and used to generalize the method to the case of hollow cylinders. The presented method is superior to direct root-finding algorithms in the sense that no initial guess values are needed to determine the complex wave numbers and that neither starting at low frequencies nor subsequent mode tracking is required. The results obtained with this method are shown to be reasonably close to those calculated by other means and an estimate for the achievable accuracy is given.