Titanium alloys are essential to aerospace applications but are 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 for downstream processing; however, cogging schedule design remains highly empirical and reliant on expert trial-and-error.

The project will focus on generating high-fidelity simulation datasets, training surrogate machine learning models to predict process outcomes (such as temperature, strain, and microstructure evolution), embedding these models within a digital twin framework, and validating predictions against experimental titanium forging trials. The outcome will be an intelligent, AI-enabled digital twin capable of accelerating process design, reducing development costs, and supporting the digital transformation of advanced metal forging.

This project is part of Cohort 3 of the EPSRC CDT in Materials 4.0 and is a collaboration between the University of Strathclyde, the Advanced Forming Research Centre (AFRC), the National Manufacturing Institute Scotland (NMIS), and the Department of Design, Manufacturing & Engineering Management (DMEM). The research will be embedded within the FutureForge digital twin platform, one of the world’s most advanced hot-forging research and innovation facilities.