Artificial intelligence for ultrasonic characterisation of complex materials

We invite you to join the Dynamics Group within the University's Institute of Sound and Vibration Research. You will contribute to developing the exciting capability in AI-powered non-destructive evaluation (NDE), working alongside researchers using acoustics to interrogate and characterise structures and materials of different scales and complexities. We are a member of the UK's Research Centre for Non-Destructive Evaluation (RCNDE) and run projects in both ultrasonic and X-ray imaging (including EU-funded iWeld).

This PhD project tackles a long-standing challenge in ultrasonic non-destructive testing – characterisation and inspection of complex materials, such as castings and thick weld sections. These materials are safety-critical in various industries, e.g. nuclear energy. However, they are notoriously difficult to inspect because their complex microstructure deviates and scatters ultrasound. Up to now, we can only learn about the grain layout destructively which is holding the industry back in reliable asset management. This project aims to push the boundaries of characterisation, allowing for in-situ characterisation accounting for likely uncertainties in sensor positions, geometry and alike. To achieve this, we will explore various artificial intelligence tools combined with physics-based modelling and inversion.

The project will involve algorithm development and testing, numerical and analytical ultrasound simulation, and examination datasets processing. You will have access to IRIDIS - our powerful high-performance computing cluster- and muVIS advanced material characterisation facilities in the School of Engineering. You will also benefit from interactions with our industrial and academic partners in the UK and Europe, including ultrasonic measurements on industry-relevant samples.