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PhD in Engineering: Machine learning techniques for the optimisation and simulation of metal additive layer manufacturing process chains

This funding opportunity has expired.

Key facts

Application deadline 1 October 2018
Start date 1 October 2018 (applications accepted all year round)
Duration 3 years
Level of study Postgraduate research
Award type PhD studentship
Number of studentships 1

The aim of this PhD is to develop new data analytic tools (eg machine learning, data mining) to support the understanding, the optimisation and the multi-scale and multi-physics simulation of metal additive layer machining (ALM) process chains.

These data analytics tools should meet the needs of the H2020 funded project MANUELA. In particular, to develop 'intelligent' feedback loops enabling 'online' manufacturing optimisation, design optimisation and tuning of multi-scale and multi-physics models used for simulations and for the implementation of accurate digital twins of the investigated pilot lines.  

Depending on the type of data available (eg temperature maps, machining parameters, localised acoustic information) and on the available controllable factors, various types of process modelling approaches could be used to extract knowledge and features.

State of the art modeling, data mining and machine learning tools will be reviewed (eg techniques for data regression/classification/clustering such as deep neural network, support vector machine, and dimension reduction learning models, as well as image processing algorithms) and the most relevant will be implemented and enhanced to meet the demands of real data collected at different stages of the pilot line.

Resources available

Among other standard computing and manufacturing equipment (manufacturing workshop, 3D printers), you will have access to the following resources specific to the project needs:

  • metal additive layer manufacturing machine
  • high-performance computing cluster
  • machine learning tool kit
  • indirect access to the H2020 project partners’ equipment (eg ALM machines, data analysis/control/simulation softwares).


Funding details

Tuition fee support Full UK/EU tuition fees
Maintenance stipend Doctoral stipend matching UK Research Council National Minimum

Eligibility criteria

Residency UK Research Council eligibility conditions apply
Academic criteria

The work will require the development of software based solutions in the context of ALM pilot lines (real manufacturing, simulation and optimisation), and therefore you should have a strong interest and knowledge in the following:

  • Object oriented programming (C++ or equivalent)
  • Data mining/machine learning
  • Additive Layer Manufacturing

Please note that in exceptional cases we may be able to make a full award to overseas candidates (full fees at the overseas rate and stipend).

The full value of any award (payment of fees and stipend) will be communicated to you prior to any offer being made.

Consideration is automatic upon application for admission to the Doctor of Philosophy in Engineering with an October 2018 start date.

In the 'Research proposal and Funding' section of your application, please specify the project title and supervisors of this project and copy the project description in the text box provided.

Please select 'No, I am not self-funding my research' when asked whether you are self-funding your research.

Please add 'PhD in Engineering: Machine learning techniques for the optimisation and simulation of Metal Additive Layer Manufacturing process chains' when asked 'Please provide the name of the funding you are applying for'.

We reserve the right to close applications early should sufficient applications be received.

Eligible research programmes