02 Fakultät Bau- und Umweltingenieurwissenschaften

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/3

Browse

Author: search.filters.author.Fritzen, FelixSubject: search.filters.subject.620

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Residual stresses in Cu matrix composite surface deposits after laser melt injection
    (2023) Zhang, Xingxing; Kornmeier, Joana R.; Hofmann, Michael; Langebeck, Anika; Alameddin, Shadi; Alessio, Renan Pereira; Fritzen, Felix; Bunn, Jeffrey R.; Cabeza, Sandra
    Tungsten carbide particles reinforced metal matrix composite (MMC) coatings can significantly improve surface wear resistance owing to their increased surface hardness. However, the presence of macro‐ and micro‐residual stresses in MMC coatings can have detrimental effects, such as reducing service life. In this study, neutron diffraction was used to determine the residual stresses in spherical fused tungsten carbide (sFTC) reinforced Cu matrix composite surface deposits after laser melt injection. We also developed a thermo‐mechanical coupled finite element model to predict residual stresses. Our findings reveal that sFTC/Cu composite deposits produced with a preheating temperature of 400°C have low residual stresses, with a maximum tensile residual stress of 98 MPa in the Cu matrix on the top surface. In contrast, the sFTC/bronze (CuAl10Ni5Fe4) composite deposit exhibits very high residual stresses, with a maximum tensile residual stress in the Cu matrix on the top surface reaching 651 MPa. These results provide a better understanding of the magnitudes and distributions of residual stresses in sFTC‐reinforced Cu matrix composite surface deposits manufactured via laser melt injection.
  • Thumbnail Image
    ItemOpen Access
    Reduced order homogenization of thermoelastic materials with strong temperature dependence and comparison to a machine-learned model
    (2023) Sharba, Shadi; Herb, Julius; Fritzen, Felix
    In this work, an approach for strongly temperature-dependent thermoelastic homogenization is presented. It is based on computational homogenization paired with reduced order models (ROMs) that allow for full temperature dependence of material parameters in all phases. In order to keep the model accurate and computationally efficient at the same time, we suggest the use of different ROMs at few discrete temperatures. Then, for intermediate temperatures, we derive an energy optimal basis emerging from the available ones. The resulting reduced homogenization problem can be solved in real time. Unlike classical homogenization where only the effective behavior, i.e., the effective stiffness and the effective thermal expansion, of the microscopic reference volume element are of interest, our ROM delivers also accurate full-field reconstructions of all mechanical fields within the microstructure. We show that the proposed method referred to as optimal field interpolation is computationally as efficient as simplistic linear interpolation. However, our method yields an accuracy that matches direct numerical simulation in many cases, i.e., very accurate real-time predictions are achieved. Additionally, we propose a greedy sampling procedure yielding a minimal number of direct numerical simulations as inputs (two to six discrete temperatures are used over a range of around 1000 K). Further, we pick up a black box machine-learned model as an alternative route and show its limitations in view of the limited amount of training data. Using our new method to generate an abundance of data, we demonstrate that a highly accurate tabular interpolator can be gained easily.