PhD Studentship: Development of a Digital Twin for Smart Sustainable Steel Section Production

Overview

The Advanced Steel Research Centre at WMG, University of Warwick, is looking for a PhD student to join its Digital Twins team and build a digital twin for steel section production. The team is developing a suite of models for through-process prediction of microstructure and mechanical properties in steel. Integrated into a digital twin, they would let producers predict and respond quickly to live production conditions and optimise process parameters, product performance and efficiency.

The steel industry faces rising energy prices, volatile raw material costs and demanding property specifications. The UK industry is moving toward more scrap steel feedstock, with potentially greater variability, so digital twins are needed to understand effects on processing and properties and to choose control strategies. The project combines empirical modelling, finite element analysis and artificial intelligence in a real-time framework, aiming for reduced reject and downgrade rates and better energy efficiency and sustainability.

The student will combine several existing models into a through-process framework, working with industry partners and real production data. The scientific challenge is to use the models and machine learning with live mill data such as temperature and rolling loads to infer the current microstructural state of the material and give feedback on optimised next-stage processing. Because finite element models are computationally heavy, their outputs will be used to train a machine learning tool for fast feedback. The student will define the architecture, robustness and limitations of the framework and its industrial implementation, including a placement with the industrial partner whose timing and length are agreed among the parties.

What you get

The stipend is 21,805 pounds per year, and the studentship is funded through the DigitalMetals CDT.

Key dates

The closing date is 28 February 2027.