Evaluating defect detectability for iteratively reconstructed industrial X-ray computed tomography data博士研究员

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Postgraduate research project

Evaluating defect detectability for iteratively reconstructed industrial X-ray computed tomography data

Funding

Competition funded

Competition funded

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Type of degree

Doctor of Philosophy

Entry requirements

2:1 honours degree

2:1 honours degree

View full entry requirements

Faculty graduate school

Faculty of Engineering and Physical Sciences

Closing date

29 Jan 2027

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About the project

Potential supervisors

Entry requirements

Fees and funding

How to apply

Contact us

About the project

Potential supervisors

Entry requirements

Fees and funding

How to apply

Contact us

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.

Potential supervisors

Lead supervisor

Professor Thomas Blumensath

Professor

Research interests

• I develop and study advanced algorithms that can solve challenging inverse problems by efficiently exploiting complex prior information. Using techniques from mathematics, statistics and machine learning, my work concentrates primarily on problems in x-ray tomographic image reconstruction and modelling.

• I work closely with state-of-the-art imaging facilities (µ-VIS, the National Research Facility in Lab-based XCT, the UK’s synchrotron facility at the Diamond Light Source, and ISIS neutron imaging beamline) to find practical solutions to a range of important scientific problems from plant science to manufacturing.

• My research interests cover areas such as: Theoretical and computational methods for Signal and Image Processing (Machine Learning, Compressed Sensing, Statistical Signal and Image Processing, Quantum Computing, Inverse Problems, Optimisation, X-ray Tomographic Imaging); Advanced tomographic imaging strategies: (limited angle tomography and laminography, Spectral X-ray imaging, Stereo and extreme limited view tomography); Efficient computational methods for tomographic reconstruction, including GPU acceleration, distributed computation and advanced optimisation strategies, Constrained optimisation for ill-conditioned and underdetermined tomographic inverse problems, Applications of X-ray tomography to the inspection of manufactured components, Multimodal tomographic imaging

Supervisors

Dr Joseph Lifton

Lecturer in Mechatronics

RB

Dr Richard Boardman

PhD, MInstP

Principal Enterprise Fellow

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

Apply now

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, email our Doctoral College (doctoralcollege-admissions@soton.ac.uk).

Project leader

For an initial conversation, email Professor Thomas Blumensath (Thomas.Blumensath@soton.ac.uk).