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Data-Driven Prediction of Mechanical Performance in Cast Irons Based On Machine-Learning Techniques (SAM21)

Project Description

Loughborough University is a top-ten rated university in England for research intensity (REF2014). In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Doctoral College, including tailored careers advice, to help you succeed in your research and future career.

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Project detail

Metal alloys used in automotive and aerospace industry are nowadays subjected to extreme loading and environmental conditions. Large strains developed during manufacturing or under operational conditions often lead to localisations and fractures across scales. Hence, the accurate description and modelling of mechanical performance in these settings is a challenge for the understanding of materials’ failure and the design of structural components that operate under extreme conditions.

The proposed research project aims at the systematic quantification and prediction of the long-term performance of cast irons and the effects of microstructure on it. The project workplan offers a unique opportunity for skills development as it comprises experimentation and advanced numerical simulations. Specifically, novel numerical codes capable of predicting localisation and fracture will be developed. Besides, microstructural features that drive deformation at the microscale will be quantified using state-of-the art monitoring and characterisation techniques. In turn, the obtained experimental/computational datasets will be fused in machine learning schemes to correlate performance indicators with material microstructure. These tools are expected to have real world impact and affect the future design of such engineering alloys.

Start date of studentship: 01 October 2020.
Entry requirements:
Applicants who apply for this project will be considered on a competitive basis in March 2020 against candidates shortlisted for this and other projects with the advert reference beginning ‘SAM’. Early submission is advised, and a complete application must be received before the advert’s closing date.

How to apply

All applications should be made online at Under programme name, select Mechanical, Electrical & Manufacturing Engineering.

Please quote reference number: SAM21.

Funding Notes

If successful, candidates will be awarded a 3-year school studentship providing a tax-free stipend and tuition fees at the UK/EU rate (currently £15,009 and £4,327, respectively, in 2019-20 which are likely to rise by 2020/21). Non-EU-nationals may apply but the studentship will cover the cost of the international tuition fee only.

Successful candidates will be notified by 26 March 2020.


K.P. Baxevanakis et al. (2018), An integrated approach to model strain localization bands in magnesium alloys, Computational Mechanics 61, 119-135,

K.P. Baxevanakis et al. (2018), Data-driven damage model based on nondestructive evaluation, Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems 1, 031007-1-12,

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