对迭代重建工业X射线计算机断层扫描数据的缺陷可检测性进行评估

该项目旨在研究先进的优化与机器学习算法如何提升工业部件内部缺陷检测的X射线计算机断层扫描3D图像质量。研究过程中，将利用真实及模拟X射线数据，对不同物体几何形状下的图像重建技术进行评估。

About the project In this project, you will study the influence that different advanced optimization and machine learning algorithms have on the quality of 3D images generated from X-ray computed tomography measurements of industrial components. The focus is on optimizing the ability to detect internal object defects that would compromise component safety. Modern manufacturing processes, such as additive manufacturing (3D printing), offers unrivalled flexibility in the development of component shapes, leading to light-wight and efficient designs not achievable with more traditional manufacturing techniques. For safety critical applications such as aviation or energy production, these components need to be inspected before and during service. Traditional non-destructive inspection methods, such as ultrasound inspection, can however not be used for many objects of complex shapes. X-ray computed tomography is a promising alternative non-destructive testing methodology. Whilst it can be applied to the inspection of complex geometries, providing detailed images of internal structures, the inspection process in often slow. Furthermore, complex geometries with high aspect ratios or for components where the X-ray path length varies greatly during inspection, significant artefacts and noise can contaminate the images, making defect detection challenging. To overcome these challenges, the use of advanced iterative or machine learning based image reconstruction is often proposed. Whilst these methods have shown promising performance in simplified and well controlled settings, for real applications, a range of questions however remain. For iterative optimization, there are a range of algorithm dependent parameters, such as the number of iterations or the optimal regularisation parameter, that need to be chosen. Unfortunately, it remains unclear how to do this choice or how to efficiently evaluate the resultant images in terms of the visibility of expected defects. In this project, you will thus use real and simulated X-ray tomography data to evaluate the performance of advanced reconstruction algorithms. Defect visibility is typically a function of both image resolution and image noise. By measuring anisotropic resolution and noise for a wide range of object geometries, parametric studies of key advanced reconstruction approaches can then be explored. You will be working alongside the team at the University of Southampton’s µ-VIS facility , a dedicated X-ray Computed Tomography (XCT) centre. The centre is part of the UK’s National Facility for lab-based XCT and houses some of the UK’s largest micro-focus CT scanning systems, capable of unveiling sub-surface information from materials, components and structures. With strong links between both research and industry, the centre itself is used for aircraft crash investigations, Formula 1, space technology and much more. Potential funding to support this position will be available to the strongest candidates through the Faculty of Engineering and Physical Sciences graduate school studentship programme. This funding will be awarded on a highly competitive basis. The School of Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.

Entry requirements A UK 2:1 honours degree, or its international equivalent . A very good undergraduate degree; at least a UK 1st class honours degree, or its international equivalent.

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