@inproceedings{UrbanoGolowinLoehnertetal., author = {Urbano, Jose and Golowin, Andrej and L{\"o}hnert, Stefan and Bestle, Dieter}, title = {Mechanical Integrity of Steel Discs with Corrosion Pits}, series = {12th International Fatigue Congress (FATIGUE 2018), Poitiers Futuroscope, France, May 27-June 1st, 2018}, booktitle = {12th International Fatigue Congress (FATIGUE 2018), Poitiers Futuroscope, France, May 27-June 1st, 2018}, publisher = {EDP Sciences}, address = {Les Ulis}, doi = {10.1051/matecconf/201816504012}, pages = {8}, abstract = {Currently, prediction of crack initiation by corrosion pits is only possible by assuming regular geometrical shapes, such as semi-spheres or semi-ellipsoids. Moreover, typical fatigue life diagrams associate the crack initiation life with geometrical features, such as pit depth or aspect ratio, often leading to unsatisfactory correlations due to high pit shape variability and data scatter. In the context of blade-disc fixation in aero engine turbines, this limitation translates into highly conservative life estimations. Therefore, a new crack initiation predictor is formulated based on experimental testing and numerical analysis of 28 artificial corrosion pits. A low-cycle fatigue test campaign is conducted using three-point bending test specimens to simulate maximum takeoff operation conditions of the aero engine and the associated loading of the blade root designed as firtree. An artificial pit is located at the critical point of each test specimen, respectively. The prediction criterion is based on finite element analysis and is formulated as the lowest plastic strain of a plastic region with a certain volume in the corrosion pit. This reference volume is varied until an optimum correlation with experimental crack initiation life is obtained. The criterion shows a superior correlation with crack initiation life compared to pure geometrical parameters such as pit depth.}, language = {en} } @phdthesis{UrbanoCasuasango, author = {Urbano Casuasango, Jos{\`e} Mauricio}, title = {Learning-Based Manufacturing Deviation Estimation for Robust Computer-Aided Design}, publisher = {Shaker}, address = {D{\"u}ren}, isbn = {978-3-8440-8904-2}, pages = {154}, language = {en} } @misc{UrbanoBestleGerstbergeretal., author = {Urbano, Jos{\`e} and Bestle, Dieter and Gerstberger, Ulf and Flassig, Peter Michael}, title = {Low-Order Representation of Manufacturing Variations Based on B-Spline Morphing}, series = {ASME 2019 International Mechanical Engineering Congress and Exposition, November 11-14, 2019 Salt Lake City, Utah, USA}, journal = {ASME 2019 International Mechanical Engineering Congress and Exposition, November 11-14, 2019 Salt Lake City, Utah, USA}, publisher = {American Society of Mechanical Engineers}, isbn = {978-0-7918-5938-4}, doi = {10.1115/IMECE2019-10294}, language = {en} }