Mechanical Engineering


Improving Additive Manufacturing Productivity Using Correlative Chemical and X-ray Imaging

A fully funded PhD studentship (UK, EU or international) in the Department of Mechanical Engineering

Key information

Lead supervisor: Dr Chu Lun Alex Leung
Application deadline: ongoing
Project start date: flexible
Project duration: 3.5 years
Studentship funding: full tuition fees and maintenance stipend (currently £19,668/year)
Project location: UCL Harwell campus and UCL Bloomsbury campus

PhD project description

Additive manufacturing (AM) or 3D printing is an emerging digital manufacturing technology that produces components with complex shapes. Laser powder bed fusion (LPBF) technology fuses powder particles into components, layer-by-layer, directly from a digital file. The process and product qualification of LPBF is governed by the complex interactions between laser, powder, and the processing environment which take place in milliseconds and are difficult to study. Our research group aims to develop novel process monitoring technologies to improve machine intelligence that enables machines/users to observe, understand, evaluate, and deploy optimum process parameters.

This project is integrated with the UK’s hub for Manufacturing with Advanced Powder Processes, Materials Made Smarter Research Centre, and Manufacturing by Design consortium. The PhD research will be based at the Harwell Campus. You will have opportunities to use advanced characterisation facilities at the Harwell Campus, UCL Bloomsbury Campus, Henry Royce Institute, European Synchrotron Radiation Facilities, and other large facilities worldwide.

The successful PhD candidate will develop next-generation chemical imaging technique to study the metal vaporisation during AM; combining this new technology with our flagship X-ray imaging experiments, you will provide a detailed understanding on the fluid flow behaviour and defect formation during AM. You will have an opportunity to extend our machine-learning data analytics to extract new insight from these experiments. The extracted information could also be used to develop next-generation multiphase process simulations to accelerate the process and product qualification.

Person specification

Applicants should ideally have a first-class, or equivalent, undergraduate degree in Chemistry, Physics, Materials Science and Engineering, or a related discipline.

Excellent organisational, interpersonal, and communication skills, along with a stated interest in interdisciplinary research, are essential.

Ideally, you will have experience in one, or more, of the following:

  • Metallurgy
  • Additive manufacturing
  • Image analysis
  • Signal processing
  • Matlab and Python programming

How to apply

Eligible applicants should first contact Dr Chu Lun Alex Leung (alex.leung@ucl.ac.uk). Please enclose the following documents:

  • A one-page statement outlining suitability for the project
  • A two pages CV (including contact details of two referees)

After discussing the project with Dr Leung, eligible applicants should also submit a formal PhD application via the UCL website.

The supervisory team will arrange interviews for short-listed candidates.