R. Fuentes

University of Sheffield, University of Strathclyde

Papers

Total Citations

116

H-Index

About

R. Fuentes is a leading researcher at the intersection of robotics, machine learning, and non-destructive evaluation (NDE), with a focus on advancing structural health monitoring (SHM) for Industry 4.0. Their work bridges the gap between traditional NDE and autonomous inspection systems, enabling smarter, more efficient damage detection in critical infrastructure. Fuentes’s most-cited paper (58 citations) provides a foundational framework for integrating machine learning with ultrasonic and guided-wave inspection, clarifying the synergies between NDE and SHM. They have also pioneered robotic adaptive behaviour for automated quality inspection (34 citations), demonstrating how industrial robotic arms, paired with sensors and actuators, can revolutionize non-destructive testing (NDT) workflows. A standout contribution is their development of autonomous ultrasonic inspection using Bayesian optimisation and robust outlier analysis (24 citations), which addresses the challenge of processing large datasets from robotic NDT by intelligently guiding data collection and anomaly detection. This work reduces human intervention while improving inspection reliability. Fuentes’s research is pivotal for realizing fully automated, data-driven inspection paradigms, with significant implications for aerospace, manufacturing, and civil engineering. Their achievements position them as a key innovator in the future of intelligent infrastructure monitoring.