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

使用机器学习和人工智能对分支应力腐蚀裂纹进行超声波检测建模

• 批准号: 2889063
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889063
• 关键词: Modelling; Ultrasonic; Inspection; Branched; Stress

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.

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，以改进对高度复杂和具有挑战性的缺陷物种的超声波检测建模，这些缺陷物种对工业工厂构成严重的安全风险。它将补充RCNDE核心项目MUSICA（用于自动化分析的挑战性缺陷建模超声检查，从2023年3月开始），将热疲劳和氢裂纹的工作扩展到分支应力腐蚀裂纹。该项目将考虑超越传统单晶扫描的广泛超声波检测技术，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，包括核能和发电部门复杂粗糙缺陷的表征和大小，模拟和建模技术的发展，以及人工智能和数字孪生自主决策的实施的发展。