CC BY 4.0Heinrich, FerdinandDorn, ChristianHagelauer, AmelieTarar, RaedRaedTarar2025-08-112025-08-112025-08-02https://publica.fraunhofer.de/handle/publica/490379https://doi.org/10.24406/publica-503910.24406/publica-5039This thesis was conducted in cooperation with Vectoflow GmbH. It addresses the development of an automated uncertainty analysis tool for pressure sensors, based on the DKD-R 6-1 guideline and ISO/IEC 17025. The goal was to extend an existing LabVIEW-based calibration routine by integrating a Python-based post-processing module, which is capable of calculating all uncertainty contributions in compliance with DKD-R 6-1 requirements. The tool processes raw calibration data and performs standardized uncertainty calculations for both analog and digital sensors. It then outputs data and plots intended for certificate generation. A modular software architecture was implemented, tested extensively with unit tests, and made callable from LabVIEW through a Python wrapper module. The resulting package replaces manual post-processing with an automated and traceable workflow. This work forms the foundation for DAkkS-accredited calibrations at Vectoflow and opens the possibility for future extensions, including fully automated certificate generation.enDrucksensor KalibirerungAutomatisierungPython Modulbachelor thesis