DOCTORAL CANDIDATE 09
Automated Online Monitoring of Manufacturing Defects on Smart CFRP Structures
VANIA CATALINA GONZÁLEZ CARTES
EDUCATION
- BSc. in Mechanical Engineering - University of Chile, Chile
PhD in Industrial Engineering - University of Naples Federico II, Italy
ABOUT
Vania González is a Mechanical Engineer from the University of Chile and currently a PhD candidate within the Horizon Europe doctoral network ASSESS. She is enrolled in the PhD programme in Industrial Engineering at the University of Naples Federico II and conducts her doctoral research in collaboration with Novotech Aerospace Advanced Technology srl. Her research focuses on advanced monitoring technologies for aerospace composite structures, combining ultrasonic non-destructive testing, guided waves, acoustic emission, and machine learning to enable automated detection of manufacturing defects in smart CFRP structures produced using AFP and LRI processes.
Prior to starting her PhD, she worked as a Project Engineer at AED Ingeniería in Chile, where she contributed to mining engineering projects by analyzing and preparing technical documentation such as equipment data sheets, technical requisitions, and calculation reports. She also completed a research internship at the Université de Sherbrooke, Canada, where she worked on vibration control in periodic structures. This research resulted in the publication “Band Gap Generation in Cantilever Beams through Periodic Material Removal” in the journal Transactions of the Canadian Society for Mechanical Engineering, demonstrating how periodic structural modifications can generate frequency band gaps that reduce vibration propagation while maintaining lightweight structural designs.
Outside of research, she enjoys traveling, reading, and baking pastries.
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Objectives
- Develop an automated real-time diagnosis system for defects such as porosity, debonding, wrinkling, and voids.
- Reduce noise and increase accuracy by processing acoustic and emission signals using transformation metrics (e.g., PCA) prior to ML analysis.
- Validate the system with benchmark defects intentionally introduced during manufacturing (debonding, ply gaps, porosity zones).
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Expected results:
- Online quality assurance for an early detection of defects.
- ML-based damage assessment (size and location) of manufacturing defects.
- Enhanced damage diagnosis capability for online monitoring during AFP and LRI processes.
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Planned secondments:
POLITO, A. Pagani, months 28-33 (4-6 months): modelling support to damage diagnosis on the reinforced panel.
Leonardo Elicotteri, E. Fosco. months 37-42 (4-6 months)

