AI Surrogate Modelling to Enhance the Digital Twin of Titanium Cogging with FutureForge

This project is part of cohort 3 of the EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce Institute. It will be a collaboration among the University of Strathclyde, the Advanced Forming Research Centre (AFRC), the National Manufacturing Institute Scotland (NMIS) and the Design, Manufacturing & Engineering Management (DMEM).

Titanium alloys are essential to aerospace applications but remain extremely challenging to forge due to their narrow processing windows, strong sensitivity to temperature gradients, and tendency to develop defects. Cogging is a critical hot-working process used to refine ingot microstructures and prepare billets of different sizes and shapes for downstream processing. The design of cogging schedules is currently highly empirical, relying on expert know-how and time-consuming trial-and-error. With the sector accelerating toward digital transformation, forging processes must evolve beyond traditional empirical practice. Digital twin technologies—integrating physics-based simulation, in-process sensing, and AI models—provide a transformative platform for understanding and optimising titanium cogging, enabling faster development cycles, improved quality, and data-driven manufacturing.

This PhD will tackle the challenge of digital transformation in forging by developing AI-driven surrogate models for titanium cogging and embedding them into the FutureForge digital twin at the National Manufacturing Institute Scotland (NMIS). FutureForge is one of the world’s most advanced and largest hot-forging research and innovation facilities, powered by digital data science. It offers a data-rich environment where the forging industry can de-risk the development of new processes, products, and technologies for faster industrial adoption.

The research will focus on:

• Developing and validating physics-based models for titanium cogging, generating datasets to understand how key process parameters and schedule designs influence deformation and microstructure.
• Training surrogate machine learning models to rapidly predict process outcomes such as temperature distribution, strain evolution and microstructural changes.
• Integrating these surrogate models into a digital twin framework to enable high-speed prediction, quick exploration of forging schedules, and optimisation under physical and operational constraints.
• Validating AI predictions against experimental titanium forging trials to ensure accuracy, robustness, and industrial relevance.
• Creating an automated decision-support and optimisation system that enables engineers to design and refine cogging schedules more efficiently and with greater confidence.

The vision is to establish an intelligent digital twin system through which engineers can evaluate, optimise, and adapt cogging schedules to accelerate process design, reduce development costs, and support the digital transformation of advanced metals processing. By creating an AI-empowered digital twin framework for cogging, this PhD will deliver both new scientific insights and transformative digital manufacturing tools for the aerospace metals industry.

Funding Notes

This is a fully funded project, part of cohort 3 of the EPSRC CDT in Developing National Capabilities in Materials 4.0. The studentship covers home fees, a tax-free stipend of at least £20,780 plus London allowance if applicable, and a research training support grant.

Enquiries

For general enquiries, please contact doctoral-training@royce.ac.uk.

For application-related queries, please contact Dr Dorothy Evans (dorothy.evans@strath.ac.uk).

If you have specific technical or scientific queries about this PhD, we encourage you to contact the lead supervisor, Dr Jianglin Huang (jianglin.huang@strath.ac.uk)

Application Process

Please note that each partner of the CDT in Materials 4.0 will have its own application process.

For Strathclyde, please email Dr Jianglin Huang, with Dr Dorothy Evans in cc (both email addresses above), using the subject line ‘Application to the Materials 4.0 CDT’.

The Materials 4.0 CDT is committed to Equality, Diversity and Inclusion. We strongly encourage applications from underrepresented groups.