Automated Ultrasound Data Processing for Defect Detection and Characterization Through Machine Learning

通过机器学习进行自动超声数据处理，用于缺陷检测和表征

• 批准号: 2603322
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2603322
• 关键词: Automated; Ultrasound; Data; Processing; Defect

This project will explore the implementation of automated PAUT data interpretation for CFRPs through two approaches: I) developing a low latency Deep Neural Network (DNN) to analyze the A-scan data on the fly, while the scan is being performed, for geometrical feature recognition, automated gating of time series, and defect detection, and II) developing a Multitask Network (MN) [2] for image analysis, detection of geometrical features/defects on each B-scan, D-scan, and C-scan projection, and cross-validation of findings at the combination stage. The real-time DNN applied to the A-scan data will serve to provide warnings for defects flagged during the inspection while the MN, with a potentially improved learning through different related tasks empowered by the multi-view analysis of the data, should be able to detect the defects with higher confidence.

该项目将通过两种方法探索 CFRP 的自动 PAUT 数据解释的实施：I) 开发低延迟深度神经网络 (DNN)，在扫描执行时动态分析 A 扫描数据，以实现几何特征识别、时间序列自动门控和缺陷检测；II) 开发多任务网络 (MN) [2]，用于图像分析、几何检测 每个 B 扫描、D 扫描和 C 扫描投影的特征/缺陷，以及组合阶段结果的交叉验证。应用于 A 扫描数据的实时 DNN 将为检查期间标记的缺陷提供警告，而 MN 通过数据多视图分析所支持的不同相关任务可能会改进学习，应该能够以更高的置信度检测缺陷。