Prediction of Mechanical Properties of Extruded Aluminium Profiles Using Machine Learning and Microstructural Insights

Applications are invited for one full-time EPSRC Industrial CASE (ICASE) PhD studentship for the project “Prediction of Mechanical Properties of Extruded Aluminium Profiles Using Machine Learning and Microstructural Insights”

BCAST is a specialist research centre in metallurgy with a focus on the processing of metallic materials for lightweighting applications. See www.brunel.ac.uk/bcast for more information. The project is sponsored by Constellium, a leading global manufacturer of high-quality, technically advanced aluminium products and systems.

Successful applicants will receive an annual stipend (bursary) starting from approximately £23,000 plus payment of their full-time home tuition fees for a period of up to 48 months (4 years).

Machine Learning (ML) has emerged as a powerful tool for predicting mechanical properties of aluminium alloys, offering the potential to accelerate materials design and optimisation. By leveraging large datasets and complex algorithms, ML models can uncover intricate relationships between composition, processing parameters, and resulting properties. However, two significant challenges persist in this domain. First, the extrapolation of ML predictions beyond the range of existing data remains problematic, as models often struggle to accurately forecast properties for new alloy compositions or processing conditions. Second, capturing and incorporating microstructural features into ML models presents another hurdle. Microstructure plays a crucial role in determining mechanical properties, yet integrating this information into predictive models is complex.

This project will focus on developing a combination of advanced machine learning and deep learning methods to enhance the predictions beyond existing data. By incorporating microstructural features into predictive models, the aim is to create a reliable data-driven modelling framework that accurately predicts mechanical properties based on the alloy composition, processing parameters and microstructural information of extruded aluminium profiles for automotive applications.

The project will be part of the activities of the Constellium University Technology Centre (UTC) established with BCAST. The successful candidate will have the opportunity to interact with researchers in BCAST and with Constellium’s industrial research engineers. An industrial supervisor of the project will be appointed by Constellium. This close collaboration provides a strong foundation for a future career, whether in industry or academia.

Please contact Prof. Isaac Chang at Isaac.Chang@brunel.ac.uk for an informal discussion about the project.

Eligibility

You should have or expect to receive a first degree at 2:1 or above in a suitable engineering and physical science discipline, e.g. metallurgy, materials science, mechanical engineering, chemical engineering or physics. A Master’s level qualification is desirable but not essential. A strong background in materials science and/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable.

In addition, applicants should be highly motivated, able to work independently, as well as in a team and have effective communication skills.

Applicants must be eligible for home tuition fees through either nationality, residency (living in the UK for at least three years and not wholly for educational purposes) or other connection to the UK.

How to apply

Please submit your application documents (see list below) in one PDF file by noon on 24 April 2025 to cedps-pgr-office@brunel.ac.uk. Interviews will take place in May 2025.

  • Your up-to-date CV;
  • Your personal statement (300 to 500 words) summarising your background, skills and experience;
  • Your Undergraduate/Postgraduate degree certificate(s) and transcript(s);
  • Evidence of your English language skills to IELTS 6.5 (or equivalent, 6.0 in all sections), if appropriate;
  • Contact details for two referees, one of whom can be an academic member of staff in the College.

Remember to state the title of the project at the top of your personal statement.

Meet the Supervisor(s)

Isaac Chang - Prof. Isaac Chang is the appointed Professor of Metallurgy & Materials and Head of LiME Training Centre. Prior to this, he was a Reader and Head of Education at School of Metallurgy and Materials, University of Birmingham. He received his DPhil in Materials Science from University of Oxford (1991) and BSc(Eng) in Materials & Metallurgy from Imperial College, London University. He specializes in the field of physical and powder metallurgy, as well as nanotechnology and ceramic science. His research is focussed on the understanding of the relationship between processing, microstructure and properties of materials for industrial applications in transport, energy, healthcare, defence and electronic sectors. He was the first to discover the solid solution with a face centred cubic (FCC) crystal structure in an equiatomic FeCrCoNiMn alloy (the so-called Cantor alloy) in 2004 together with Prof. Brian Cantor, which has contributed to a brand-new field of materials science known as ‘High Entropy Alloys’ or ‘Multiple Principle Element Alloys’. He holds 7 patents and has published over 121 research papers in scientific journals, book chapters and conference proceedings. He is a Fellow of Institute of Materials, Minerals and Mining (IOM3) and a member of the editorial board for Journal of Materials, Chemistry and Physics. His current research interest includes muticomponent lightweight alloys, high entropy alloys, metallic glasses, nanocomposites, graphene, high throughput material processing for rapid alloy discovery and synthetic biology for advanced materials development.