About the Project
Supervisory team: Professor Thomas Blumensath, Dr Richard Boardman and Dr Joseph Lifton
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.
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.
Fees and funding
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.
We also offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.
Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.
Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs, for top-ranked applicants.
For more information, please visit our postgraduate research funding pages.
How to apply
You need to:
- choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences
- select Full time or Part time
- search for programme PhD Engineering & the Environment (7175)
- add name of the supervisor in section 2 of the application
Applications should include:
- your CV (resumé)
- 2 academic references
- degree transcripts and certificates to date
- English language qualification (if applicable)
Contact us
Faculty of Engineering and Physical Sciences
If you have a general question, doctoralcollege-admissions@soton.ac.uk.
Project leader
For an initial conversation, email Thomas.Blumensath@soton.ac.uk.
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.