Federica Graziano

Data-driven analysis and predictive maintenance of battery rack cooling using RNN and unsupervised machine learning for fault detection, Mean Time Between Failure (MTBF) analysis, and advance failure prediction to reduce system downtime - Advanced procedure for fault detection, 15-day failure prediction, and MTBF update, developed in 3 weeks - Achieving a 98% F1 score in failure prediction

Responsible for maintenance activities of the European rail freight, including fault monitoring, diagnostics, and predictive maintenance. Focal point for customers and workshops - Managing maintenance activities for a locomotive after a serious incident, ensuring timely operational restoration - Promoted to Maintenance Manager and Asset Manager after two years as a Fleet Planner, focusing on implementing predictive analytics for railway systems

Coordinator of European rail freight, overseeing maintenance planning, technical documentation, and spare parts management - Promoted to main customer interface and Team Leader after one year as a Fleet Planner, overseeing maintenance for a fleet of 30 locomotives

Designed aeronautical turbine blades for additive manufacturing using CAD/CAE, performed aeromechanics FEM and creep analyses on complex 3D shapes, managed printing jobs, and conducted cost analysis - Developed prototypes of lightweight aircraft engine blades using additive manufacturing, driving innovation in high-performance aerospace design.

Lessons and laboratory activities in: Mechanics and thermodynamics Materials processing and thermal processes Destructive tests and non-destructive tests Unconventional and conventional machining

Management and automation of spare parts lists

Aerodynamic optimization for automotive applications using POD, Artificial Intelligence (Kriging and Genetic Algorithms), steady and unsteady state analyses of turbulent flow - Contribution to a POD-based methodology reducing drag by 20%-50% and computational times by over 80%

Development and implementation of a procedure based on Artificial Neural Networks (ANN) for the aerodynamic optimization of aeronautical turbines

ML and AI for reliability, availability, diagnostics and prognostics

Specialization in aerodynamics, aeroacoustics and aeromechanics