@article{SchmittGottwaltWinkleretal.2021, author = {Schmitt, Matthias and Gottwalt, Albin and Winkler, Jakob and Tobie, Thomas and Schlick, Georg and Stahl, Karsten and Tetzlaff, Ulrich and Schlip, Johannes and Reinhart, Gunther}, title = {Carbon Particle In-Situ Alloying of the Case-Hardening Steel 16MnCr5 in Laser Powder Bed Fusion}, volume = {11}, pages = {11060896}, journal = {Metals}, number = {6}, publisher = {MDPI}, address = {Basel}, issn = {2075-4701}, doi = {https://doi.org/10.3390/met11060896}, year = {2021}, abstract = {The carbon content of steel affects many of its essential properties, e.g., hardness and mechanical strength. In the powder bed fusion process of metals using a laser beam (PBF-LB/M), usually, pre-alloyed metal powder is solidified layer-by-layer using a laser beam to create parts. A reduction of the carbon content in steels is observed during this process. This study examines adding carbon particles to the metal powder and in situ alloying in the PBF-LB/M process as a countermeasure. Suitable carbon particles are selected and their effect on the particle size distribution and homogeneity of the mixtures is analysed. The workability in PBF-LB is then shown. This is followed by an evaluation of the resulting mechanical properties (hardness and mechanical strength) and microstructure in the as-built state and the state after heat treatment. Furthermore, potential use cases like multi-material or functionally graded parts are discussed.}, language = {en} } @thesis{Winkler2023, author = {Winkler, Jakob Matthias}, title = {Entwicklung eines modellgest{\"u}tzten Deep Learning Ansatzes zur Rekonstruktion multiparametrischer Magnetresonanz-Korrelationsspektren}, publisher = {Technische Hochschule Ingolstadt}, address = {Ingolstadt}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:573-36441}, pages = {80}, year = {2023}, abstract = {In der Arbeit werden Neuronale Netze verwendet, um T1/T2-Spektren aus MRT-Messdaten zu rekonstruieren. Anschließend werden die Neuronalen Netze mit einem in-vivo-Datensatz evaluiert.}, language = {de} }