Euan Duernberger
Papers
1
Total Citations
25
H-Index
1
About
Euan Duernberger is a researcher at the forefront of non-destructive evaluation (NDE), specialising in the application of machine learning to advanced materials inspection. His key research areas include automated defect detection in carbon fibre reinforced plastics (CFRPs) and the integration of artificial intelligence with ultrasonic testing. Duernberger’s most notable contribution is a 2024 study on machine learning object detection performance for phased array ultrasonic testing of CFRPs, which has already garnered 25 citations. This work addresses a critical challenge in aerospace quality assurance: the efficient interpretation of vast datasets from robotic ultrasonic inspections. By demonstrating how deep learning models can reliably identify and localise defects in complex composite structures, his research paves the way for faster, more accurate, and less subjective NDE processes. Duernberger’s work is particularly impactful given the aerospace industry’s increasing reliance on CFRPs for lightweight, high-strength components. His findings not only advance the field of automated inspection but also promise to reduce human error and inspection times, directly supporting safer and more cost-effective manufacturing. With his innovative fusion of computer vision and materials science, Duernberger is establishing himself as a key voice in the next generation of intelligent NDE systems.
Research Focus
Key Achievements
Top Papers
- 1