Sofia Catalucci is a Research Fellow at the University of Nottingham as part of the Manufacturing Metrology Team. She obtained a long-cycle BSc + MSc degree in Architecture and Building Engineering at the University of Perugia in Italy, producing her final Thesis in collaboration with the Mechanical Engineering department, specifically the metrology and measurement team, based on optical technologies applied for the measurement and restoration of Cultural Heritages. She recently completed a PhD in metrology at the University of Nottingham.
Sofia's research focuses on the development at an algorithmic level of smart, adaptive measurement systems, capable to autonomously plan and assess quality in measurements. Currently, she is focusing… read more
CATALUCCI S, SENIN N, SIMS-WATERHOUSE D, ZIEGELMEIER S, PIANO S and LEACH R K, 2020. Measurement of complex freeform additively manufactured parts by structured light and photogrammetry Measurement. 164, 108081
Sofia's research focuses on the development at an algorithmic level of smart, adaptive measurement systems, capable to autonomously plan and assess quality in measurements. Currently, she is focusing on knowledge-driven algorithmic point cloud processing solutions, quality metrics, and uncertainty point cloud indicators for application in flexible manufacturing environments, in particular applied to complex free-form hollow components typical in additive manufacturing.
Her PhD project addressed the issue of quality in measurement, proposing algorithmic solutions to compute indicators of measurement performance directly from the measured point clouds and in a fully automated way. A first set of indicators investigated the relationships between the measured point cloud and the reference geometry to assess coverage and sampling density in relation to the individual surfaces of the measured part. A second set investigated local dispersion of the point cloud, as well as local bias, by using a statistical point cloud models fitted to repeated measurement data. The solutions developed represented novel measurement performance assessment tools, which could be integrated into smart measurement systems, assessing metrological performance in repeatability/reproducibility conditions. In the future, intelligent instruments will be capable of self-assessing their own performance in-process, planning the most suitable corrective actions, and will be integrated with manufacturing machines, leading to the realisation of more flexible and more autonomous production systems.