New Optical Imaging and Analysis Techniques for the Early Detection and Quantification of Corrosion on Special Nuclear Materials Packages

用于早期检测和量化特殊核材料包腐蚀的新光学成像和分析技术

• 批准号: 2607841
• 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-2607841
• 关键词: New; Optical; Imaging; Analysis; Techniques

This PhD research will focus on designing novel signal and image processing techniques using a combination of state-of-the art Deep Neural Networks and Traditional methods to segment, analyse and quantify corrosion products on materials used in special nuclear materials packages including stainless steel and aluminium extrusions. The work will be conducted in collaboration with University of Manchester (funded by Sellafield and FIND CDT) who will corrode a series of stainless steel and aluminium coupons over time with chlorides of interest for which the chloride index will be known in each experiment. Corrosion coupons will be imaged at regular intervals throughout their lifetime using a range of optical sensor technologies including Stereomicroscopy, Optical Microscopy, Laser Confocal Microscopy, LIBS and RAMAN (University of Manchester) through to RGB and Hyperspectral Imaging (University of Strathclyde). Novel algorithms will be designed at the University of Strathclyde (UoS) to analyse images of all modalities which will result in the creation of a set of multiscale image analysis techniques to detect and quantify corrosion from the microscopic to macroscopic scale using spatial and spectral information. The acquisition of the described dataset, combined with the proposed analysis routines, will be a first-of-their-kind suite of tools to detect - with confidence - the earliest point at which corrosion products can be robustly identified in all imaging technologies. It is anticipated that the detection ability and sensitivity of the sensors will be enhanced by the design of our proposed image analysis routines which will also fuse data from an optimal set of sensors to be identified in this study. Fundamentally, this research will be made possible through the analysis of microscopic imaging (to provide reliable ground truth of the earliest presence of corrosion) and spectroscopic analysis techniques combined with full knowledge of all chlorides used to induce corrosion and sophisticated monitoring and analysis of the entire corrosion lifecycle on a range of metals of interest. From an academic perspective, the proposed research aims to make the following key novel contributions:-Introduction of new techniques for processing and analyzing corrosion products at the macroscopic scale for inspection purposes using data from a diverse set of advanced optical sensor technology.-Design, implementation and testing of new corrosion detection tools by advancing state-of-the-art Machine Learning techniques for data fusion and classification of a range of complementary, multimodal, optical sensors.-Creation of novel corrosion chronology analysis algorithms capable of modelling the progression of corrosion over time. Once developed using data gathered for training, this will be applied to unseen inspection footage and used to predict corrosion progression and help identify any necessary intervention to prevent adverse outcomes.-Robust validation of the proposed array of optical sensor technology when combined with our proposed techniques using expert microscopy and materials science (UoM).-Creation of a unique, information-rich dataset gathered using a suite of state-of-the-art optical sensor technology. To the best of the authors' knowledge, this will be the first dataset of its kind to allow the time-lapse analysis of corrosion product lifecycle from the microscopic to macroscopic scale alongside the underlying chemical properties of the materials understudy.

这项博士研究将专注于设计新的信号和图像处理技术，使用最先进的深度神经网络和传统方法相结合来分割、分析和量化特殊核材料包装中使用的材料(包括不锈钢和铝挤压件)上的腐蚀产物。这项工作将与曼彻斯特大学(由Sellafield和Find CDT资助)合作进行，该大学将随着时间的推移用感兴趣的氯化物腐蚀一系列不锈钢和铝试片，每个实验中都会知道氯化物指数。腐蚀试片将在其整个生命周期内使用一系列光学传感器技术进行定期成像，包括体视显微镜、光学显微镜、激光共聚焦显微镜、LIBS和拉曼(曼彻斯特大学)，直至RGB和高光谱成像(斯特拉斯克莱德大学)。斯特拉斯克莱德大学(UoS)将设计新的算法来分析所有模式的图像，这将导致创建一套多尺度图像分析技术，以利用空间和光谱信息从微观到宏观尺度检测和量化腐蚀。所述数据集的获取与拟议的分析程序相结合，将是第一套能够在所有成像技术中可靠地检测腐蚀产物的最早点的工具。预计传感器的检测能力和灵敏度将通过我们建议的图像分析例程的设计而得到增强，该例程还将融合本研究中要识别的一组最佳传感器的数据。从根本上说，这项研究将通过显微成像分析(提供最早存在腐蚀的可靠地面真相)和光谱分析技术，结合用于诱发腐蚀的所有氯化物的全面知识，以及对一系列感兴趣的金属的整个腐蚀生命周期的复杂监测和分析来实现。从学术角度来看，这项拟议的研究旨在做出以下重要的新贡献：-引入新技术，利用来自各种先进光学传感器技术的数据，在宏观尺度上处理和分析腐蚀产品，以进行检查。-通过发展最先进的机器学习技术，对一系列互补的、多模式的光学传感器进行数据融合和分类，设计、实施和测试新的腐蚀检测工具。-创建新的腐蚀年表分析算法，能够模拟腐蚀随时间的发展。一旦使用为培训收集的数据进行开发，它将应用于不可见的检查胶片，并用于预测腐蚀进程，并帮助确定任何必要的干预措施，以防止不利结果。-与我们使用专家显微镜和材料科学(UOM)建议的技术相结合时，对拟议的光学传感器技术阵列进行强有力的验证。-创建使用一套最先进的光学传感器技术收集的独特的、信息丰富的数据集。据作者所知，这将是此类数据集中的第一个，允许对腐蚀产品生命周期进行从微观到宏观的延时分析，以及正在研究的材料的基本化学性质。