Thomas Schromm, M.Sc.
| E-Mail: | thomas.schromm(at)tum.de |
| Zur Person: | Studium: Applied and Engineering Physics / Technische Universität München Abschluss: Master of Science (2017) Aktuell: Doktorand der TU München in Kollaboration mit der BMW AG |
Laufende Dissertation
Automatisierte Auswertung von auf Grauwert basierten CT Daten von Fügeverbindungen
Forschungsschwerpunkte
- Machine Learning
- Bildverarbeitung
- Röntgen-Physik
Veröffentlichungen
Schromm T, Beckmann F, Moosmann J, Berthe D, Pfeiffer F, Grosse CU (2024). Challenges in non-destructive X-ray CT testing of riveted joints in the automotive industry. Discov Appl Sci 6, 333. https://doi.org/10.1007/s42452-024-05954-7
Schromm T, Grosse CU. (2022). From 2D projections to the 3D rotation matrix: an attempt for finding a machine learning approach for the efficient evaluation of mechanical joining elements in X-ray computed tomography volume data, SN Applied Sciences, (2023) 5:18, online: 12 Dezember, 2022, DOI: 10.1007/s42452-022-05220-8
Bauer F, Forndran D, Schromm T, Grosse CU. (2022). Practical Part-Specific Trajectory Optimization for Robot-Guided Inspection via Computed Tomography, Journal of Nondestructive Evaluation, 41:55, Published online 31 Jul 2022. (Link: https://doi.org/10.1007/s10921-022-00888-9)
Tagscherer N, Schromm T, Drechsler K. (2022). Foundational Investigation on the Characterization of Porosity and Fiber Orientation Using XCT in Large-Scale Extrusion Additive Manufacturing, Materials, 2022, 15(6):2290. https://doi.org/10.3390/ma15062290.
Schromm T, Grosse CU. (2021). Automatic generation of cross sections from computed tomography data of mechanical joining elements for quality analysis, SN Applied Sciences, (2021) 3:832, online: 09 Oktober, 2021, DOI: 10.1007/s42452-021-04806-y
Schromm T. (2017). Optimization Simulations for Propagation-Based Phase-Contrast Imaging with a Liquid Metal Jet Source, Masterarbeit, Technische Universität München.