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Subject: search.filters.subject.620Start date: 2020Institute: search.filters.UBSInstitut.Graduate School of Excellence for Advanced Manufacturing Engineering (GSaME)

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Now showing 1 - 10 of 24
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    Assessing fatigue life cycles of material X10CrMoVNb9-1 through a combination of experimental and finite element analysis
    (2023) Rahim, Mohammad Ridzwan Bin Abd; Schmauder, Siegfried; Manurung, Yupiter H. P.; Binkele, Peter; Dusza, Ján; Csanádi, Tamás; Ahmad, Meor Iqram Meor; Mat, Muhd Faiz; Dogahe, Kiarash Jamali
    This paper uses a two-scale material modeling approach to investigate fatigue crack initiation and propagation of the material X10CrMoVNb9-1 (P91) under cyclic loading at room temperature. The Voronoi tessellation method was implemented to generate an artificial microstructure model at the microstructure level, and then, the finite element (FE) method was applied to identify different stress distributions. The stress distributions for multiple artificial microstructures was analyzed by using the physically based Tanaka-Mura model to estimate the number of cycles for crack initiation. Considering the prediction of macro-scale and long-term crack formation, the Paris law was utilized in this research. Experimental work on fatigue life with this material was performed, and good agreement was found with the results obtained in FE modeling. The number of cycles for fatigue crack propagation attains up to a maximum of 40% of the final fatigue lifetime with a typical value of 15% in many cases. This physically based two-scale technique significantly advances fatigue research, particularly in power plants, and paves the way for rapid and low-cost virtual material analysis and fatigue resistance analysis in the context of environmental fatigue applications.
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    Simulation of the fatigue crack initiation in SAE 52100 martensitic hardened bearing steel during rolling contact
    (2022) Dogahe, Kiarash Jamali; Guski, Vinzenz; Mlikota, Marijo; Schmauder, Siegfried; Holweger, Walter; Spille, Joshua; Mayer, Joachim; Schwedt, Alexander; Görlach, Bernd; Wranik, Jürgen
    An investigation on the White Etching Crack (WEC) phenomenon as a severe damage mode in bearing applications led to the observation that in a latent pre-damage state period, visible alterations appear on the surface of the raceway. A detailed inspection of the microstructure underneath the alterations reveals the existence of plenty of nano-sized pores in a depth range of 80 µm to 200 µm. The depth of the maximum Hertzian stress is calculated to be at 127 µm subsurface. The present study investigates the effect of these nanopores on the fatigue crack initiation in SAE 52100 martensitic hardened bearing steel. In this sense, two micro-models by means of the Finite Element Method (FEM) are developed for both a sample with and a sample without pores. The number of cycles required for the crack initiation for both samples is calculated, using the physical-based Tanaka-Mura model. It is shown that pores reduce the number of cycles in bearing application to come to an earlier transition from microstructural short cracks (MSC) to long crack (LC) propagation significantly.
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    Many-scale investigations of the deformation behavior of polycrystalline composites: I - machine learning applied for image segmentation
    (2022) Schneider, Yanling; Prabhu, Vighnesh; Höss, Kai; Wasserbäch, Werner; Zhou, Zhangjian; Schmauder, Siegfried
    Our work investigates the polycrystalline composite deformation behavior through multiscale simulations with experimental data at hand. Since deformation mechanisms on the micro-level link the ones on the macro-level and the nanoscale, it is preferable to perform micromechanical finite element simulations based on real microstructures. The image segmentation is a necessary step for the meshing. Our 2D EBSD images contain at least a few hundred grains. Machine learning (ML) was adopted to automatically identify subregions, i.e., individual grains, to improve local feature extraction efficiency and accuracy. Denoising in preprocessing and postprocessing before and after ML, respectively, is beneficial in high quality feature identification. The ML algorithms used were self-developed with the usage of inherent code packages (Python). The performances of the three supervised ML models - decision tree, random forest, and support vector machine - are compared herein; the latter two achieved accuracies of up to 99.8%. Calculations took about 0.5 h from the original input dataset (EBSD image) to the final output (segmented image) running on a personal computer (CPU: 3.6 GHz). For a realizable manual pixel sortation, the original image was firstly scaled from the initial resolution 1080x1080 pixels down to 300x300. After ML, some manual work was necessary due to the remaining noises to achieve the final image status ready for meshing. The ML process, including this manual work time, improved efficiency by a factor of about 24 compared to a purely manual process. Simultaneously, ML minimized the geometrical deviation between the identified and original features, since it used the original resolution. For serial work, the time efficiency would be enhanced multiplicatively.
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    Mehrskalige Simulation von synthetischem Filtermaterial im rotativen Verarbeitungsprozess
    (2023) Höss, Kai; Schmauder, Siegfried (Prof. Dr. rer. nat. Dr. h.c.)
    Die vorliegende Arbeit erforscht simulativ das elastisch-plastische Verformungsverhalten von Polymerfaser-basierten Filtermedien im rotativen Verarbeitungsprozess. Die Berücksichtigung des anisotropen Materialverhaltens des Fasernetzwerks unter Zug, Druck und Schub ist hierbei essenziell. Eine 3D-Materialprüfungsreihe charakterisiert das Materialverhalten des porösen und faserigen Materials und dient dazu, erstmals das elastisch-plastische Verformungsverhalten von Filtermedien in allen drei Raumrichtungen und unter den Hauptlastfällen Zug, Druck und Schub umfassend zu untersuchen. Nach der Materialcharakterisierung wird ein robustes Mikrostruktursimulationsmodell entwickelt, das die Vorhersage des elastisch-plastischen Verhaltens von polymerbasierten Filtermedien ermöglicht. Besonderes Augenmerk liegt auf der Mikrostrukturanalyse und der Auswahl repräsentativer Volumenelemente. Das Modell wird anhand von Materialprüfungsergebnissen validiert. Die Mikrostruktursimulation liefert Erkenntnisse über die effektiven mechanischen Eigenschaften, die in einer Prozesssimulation des rotativen Verarbeitungsprozesses auf der Kontinuumsebene genutzt werden.
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    Feasibility assessment of parallelized helical drilling
    (2023) Brinkmeier, David; Onuseit, Volkher; Graf, Thomas
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    IDEA - towards an interactive tool that supports creativity sessions in automotive product development
    (2023) Kaschub, Verena Lisa; Wechner, Reto; Krautmacher, Lara; Diers, Daniel; Bues, Matthias; Lossack, Ralf; Kloos, Uwe; Riedel, Oliver
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    Beitrag zur Modellierung und Simulation des Strahlzerfalls bei der pneumatischen Lackzerstäubung
    (2020) Shen, Bo; Westkämper, Engelbert (Prof. Dr.-Ing. Prof. E.h. Dr.-Ing. E.h. Dr. h.c. mult.)
    Der Zerstäubungsprozess ist der zentrale Vorgang bei der Spritzlackierung. Bei pneumatischen Zerstäubern wird der Lack durch Zerstäubungsgase, die mit Hochgeschwindigkeit strömen, zerlegt. Der Einfluss der Eigenschaften der Zerstäubungsgase auf die Zerstäubung und den gesamten Spritzvorgang wird zuerst im Rahmen dieser Arbeit durch experimentelle und numerische Untersuchungen studiert. Hierbei ist festzustellen, dass Gase mit geringerer Dichte höhere Strömungsgeschwindigkeiten nahe am Zerstäuber erzielen und damit eine bessere Zerstäubung bewirken. Gleichzeitig fällt die Gasgeschwindigkeit schneller wieder ab, wodurch der Staudruck minimiert wird und ein hoher Lackauftragswirkungsgrad erzielt werden kann. Anschließend fokussiert diese Arbeit auf numerische Untersuchungen zum Primärzerfall von Flüssigkeitsstrahlen unter Verwendung der Volume-of-Fluid-Methode (VOF). In der Simulation sind unterschiedliche Zerfallserscheinungen zu beobachten. Die Länge des intakten Flüssigkeitsstrahls, welche häufig als Maßstab zur Bewertung der Zerstäubungsqualität verwendet wird, lässt sich ebenfalls bestimmen. Zum Herausfinden der Bedingungen für einen effizienten Primärzerfall werden zwei Zerfallsindizes eingeführt. Eine negative Korrelation zwischen den Zerfallsindizes und dem dynamischen Druckverhältnis ist festzustellen. Schließlich wird der Stahlzerfall separat mittels einer Hochgeschwindigkeitskamera und eines Laserbeugungssystems untersucht. Die erzielten Ergebnisse werden mit den Simulationsergebnissen verglichen.
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    Local vaporization at the cut front at high laser cutting speeds
    (2020) Bocksrocker, Oliver; Berger, Peter; Kessler, Steffen; Hesse, Tim; Rominger, Volker; Graf, Thomas
    High-speed videos of the cut front and spectrometric measurements were applied to detect local vaporization on the cut front at high cutting speeds to show that with increasing feed rate, temporally short and intense flashes are generated by vaporization phenomena on the upper part of the cut front. The latter are accompanied by the emergence of an interrupted striation pattern on the surface of the cutting edge. The findings support the assumption that local vaporization at the cut front might be the cause for reduced quality of the cutting process at elevated cutting speeds. The observation of vaporization serves as a diagnostic method to anticipate a fail cut and the interrupted striation pattern.
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    Data-driven prediction and uncertainty quantification of process parameters for directed energy deposition
    (2023) Hermann, Florian; Michalowski, Andreas; Brünnette, Tim; Reimann, Peter; Vogt, Sabrina; Graf, Thomas
    Laser-based directed energy deposition using metal powder (DED-LB/M) offers great potential for a flexible production mainly defined by software. To exploit this potential, knowledge of the process parameters required to achieve a specific track geometry is essential. Existing analytical, numerical, and machine-learning approaches, however, are not yet able to predict the process parameters in a satisfactory way. A trial-&-error approach is therefore usually applied to find the best process parameters. This paper presents a novel user-centric decision-making workflow, in which several combinations of process parameters that are most likely to yield the desired track geometry are proposed to the user. For this purpose, a Gaussian Process Regression (GPR) model, which has the advantage of including uncertainty quantification (UQ), was trained with experimental data to predict the geometry of single DED tracks based on the process parameters. The inherent UQ of the GPR together with the expert knowledge of the user can subsequently be leveraged for the inverse question of finding the best sets of process parameters by minimizing the expected squared deviation between target and actual track geometry. The GPR was trained and validated with a total of 379 cross sections of single tracks and the benefit of the workflow is demonstrated by two exemplary use cases.