Tiziana Segreto
University of Naples Federico II, Fraunhofer Italia Research
Papers
4
Total Citations
117
H-Index
4
About
Tiziana Segreto is a researcher whose work sits at the intersection of advanced manufacturing, sensor-based process monitoring, and intelligent data analysis. Her research focuses primarily on robot-assisted polishing processes, an area where she has made significant contributions toward transforming traditionally manual finishing operations into reliable, automated systems. Segreto has developed and refined methodologies for extracting meaningful information from complex, multi-sensor environments, applying signal processing, pattern recognition, and machine learning techniques to assess surface roughness and detect process end-points in real time. Her 2016 study on signal processing and pattern recognition garnered 38 citations, while her 2019 work on machine learning-driven end-point detection attracted 36 citations, reflecting sustained community interest in her approaches. Earlier foundational contributions, including her 2015 investigations into acoustic emission monitoring and cognitive decision-making frameworks for robot-assisted polishing, further established her as a key voice in intelligent manufacturing research. Collectively, her body of work addresses a critical industrial need — ensuring consistent product quality in automated finishing — and demonstrates how data-driven strategies can meaningfully enhance process control, making her research particularly valuable for engineers and researchers advancing smart manufacturing technologies.
Research Focus
Key Achievements
Top Papers
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