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

该项目将开发理论数学技术，数值方法和机器学习算法的组合，以改善对高度复杂和具有挑战性的缺陷种类的超声检测建模，这些缺陷种类对工业工厂构成严重的安全风险。它将补充RCNDE核心项目MUSICA（从2023年3月开始，对焊接缺陷进行建模超声检测以进行自动分析），将热疲劳和氢裂纹的工作扩展到分支应力腐蚀裂纹。该项目将考虑广泛的超声波检测技术，超越传统的单晶扫描，包括相控阵和全矩阵捕获。该项目与NDEvR 5，10，20年RCNDE愿景中确定的几个重要主题保持一致，包括核电和发电行业复杂粗糙缺陷的表征和尺寸，模拟和建模技术的开发，以及人工智能和数字孪生子的实施发展，以实现自主决策。

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.