04 Fakultät Energie-, Verfahrens- und Biotechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/5

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    ItemOpen Access
    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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    ItemOpen Access
    Fatigue improvement of AlSi10Mg fabricated by laser-based powder bed fusion through heat treatment
    (2021) Sajadi, Felix; Tiemann, Jan-Marc; Bandari, Nooshin; Cheloee Darabi, Ali; Mola, Javad; Schmauder, Siegfried
    This study aimed to identify an optimal heat-treatment parameter set for an additively manufactured AlSi10Mg alloy in terms of increasing the hardness and eliminating the anisotropic microstructural characteristics of the alloy in as-built condition. Furthermore, the influence of these optimized parameters on the fatigue properties of the alloy was investigated. In this respect, microstructural characteristics of an AlSi10Mg alloy manufactured by laser-based powder bed fusion in non-heat-treated and heat-treated conditions were investigated. Their static and dynamic mechanical properties were evaluated, and fatigue behavior was explained by a detailed examination of fracture surfaces. The majority of the microstructure in the non-heat-treated condition was composed of columnar grains oriented parallel to the build direction. Further analysis revealed a high fraction of pro-eutectic α-Al. Through heat treatment, the alloy was successfully brought to its peak-hardened condition, while eliminating the anisotropic microstructural features. Yield strength and ductility increased simultaneously after heat treatment, which is due to the relief of residual stresses, preservation of refined grains, and introduction of precipitation strengthening. The fatigue strength, calculated at 107 cycles, improved as well after heat treatment, and finally, detailed fractography revealed that a more ductile fracture mechanism occurred in the heat-treated condition compared to the non-heat-treated condition.
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    ItemOpen Access
    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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    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.