Modelling Ultrasonic Inspection of Branched Stress Corrosion Cracks using Machine Learning and AI

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The project will develop a combination of theoretical mathematical techniques, numerical methods, and Machine Learning algorithms to improve the modelling of the ultrasonic inspection of highly complex and challenging defect species that pose a critical safety risk for industrial plant. It will complement the RCNDE Core Project MUSICA (Modelling UltraSonic Inspection of Challenging defects for Automated analysis, starting March 2023), extending the work there for thermal fatigue and hydrogen cracking to branched stress corrosion cracks. The project will consider a wide range of ultrasonic inspection techniques beyond conventional single crystal scanning, including phased array and full matrix capture. The project is aligned with several important topics identified in the NDEvR 5,10, 20-year RCNDE Vision, including characterisation and sizing of complex rough defects for both the Nuclear and Power Generation sectors, development of simulation and modelling techniques, and development towards the implementation of AI and digital twins for autonomous decision making.