Dr Sofia Catalucci
Quality in measurement refers to multiple aspects, including fast inspection rates, expanded range of covered scales, high density in point-based sampling, increased degree of coverage, high level of accuracy, and more. In the context of advanced manufacturing, performance monitoring and dedicated indicators of quality assessment are indispensable tools that stakeholders should implement to increase the efficiency of the production floor. This talk will present intelligent metrological solutions for guiding machines in decisional tasks and corrective actions to run in real-time. Knowledge-driven solutions pave the way to a new integrated smart way of manufacturing, characterised by high levels of adaptability and rapid design changes, novel information sharing and digital technologies.
Sofia Catalucci is a Research Associate in In-Process Optical Sensing at the University of Nottingham, as part of the Manufacturing Metrology Team and The Midlands Centre for Data-Driven Metrology. Her Ph.D. in Manufacturing Engineering focused on the development of knowledge-driven algorithmic point cloud smart processing solutions for measurement optimisation, implemented into optical systems in form of performance indicators. The indicators were developed to guide instruments towards automated measurement planning, corrections of tasks in real-time, and assessment of measurement quality and uncertainty for application in flexible manufacturing environments. Sofia is currently conducting industrial research into optical sensing technologies and, as a continuation of her Ph.D. project, she is developing methods for fast data analysis, particularly for in-process measurement applications. Sofia’s expertise is in point cloud processing and analysis, including filtering, segmentation, registration, and uncertainty evaluation.
Room B38 Advanced Manufacturing Building
Nottingham, NG8 1BB
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