Conventional manufacturing processes involve high cost and lead times because of design and manufacturing of product specific tooling for new materials and parts, hence making many production processes unsustainable. In some cases, macroscale models are used to perform virtual experiments, however such macroscale models are not based on realistic physical mechanisms and thus predictions aren’t accurate.
The proposed PhD work is a part of the bigger project whose aim is to develop a physics based multiscale computational framework, which accounts for real life physical mechanisms observed during experiments.
Recent experimental studies have shown that high strength aerospace alloys show extensive twinning and lattice rotation during deformation, while some of the alloys show deformation induced phase transformation causing a change in deformation behaviour. Things get more complicated once damage starts to nucleate and start to form microvoids, and cracks. The evolution of these defects and foreign particles changes the evolution of phase transformation and vice versa.
Therefore, it is necessary to investigate this behaviour through a realistic multiscale computational framework which can take into account the actual microstructural data from experiments and predict the microstructure evolution along with damage nucleation and propagation in such materials during sheet metal forming process.
This PhD work will be aimed at investing and then further extending recently developed computational framework to account for the effect of damage nucleation and evolution on phase transformation and transformation induced plasticity and vice versa during manufacturing processes.
Selection will be made on the basis of academic merit. The successful candidate should have, or expect to obtain, a UK Honours Degree at 2.1 or above in Mechanical engineering, or materials science or relevant.
Essential background: An Engineering or Applied physics background with knowledge of CAD and finite-element-based modelling, and computer programming preferably with machine learning.
Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php
• Apply for Degree of Doctor of Philosophy in Engineering
• State name of the lead supervisor as the Name of Proposed Supervisor
• State ‘Self-funded’ as Intended Source of Funding
• State the exact project title on the application form
When applying please ensure all required documents are attached:
• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)
• Detailed CV, Personal Statement/Motivation Letter and Intended source of funding