PhD Studentship: Control-Oriented Modelling and Predictive Quality Regulation for Large-Scale Additive Manufacturing

This 3.5-year PhD project is fully funded; students who are eligible to pay tuition fees at the Home rate are eligible to apply. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£21,805 for 2026/27) and tuition fees will be paid. We expect the stipend to increase each year. The start date is October 2026.

The project is expected to start in September 2026, but applications will be accepted throughout the 2026/27 academic year, subject to availability.

Large-scale additive manufacturing (AM) is a key enabler of sustainable, decentralised production for aerospace, renewable energy and infrastructure components. Autonomous and mobile AM platforms further extend this capability by replacing oversized gantries with coordinated motion. However, scaling AM to large volumes introduces severe challenges in process stability and quality regulation: geometric errors accumulate layer by layer due to thermal effects and deformation, while intra-layer deposition dynamics remain highly nonlinear and sensitive to force, material and environmental variability. Conventional feedback controllers and offline-calibrated parameters become ineffective under such varying operating conditions.

This PhD will develop a multiscale, control-oriented learning framework that delivers stable, robust and physically interpretable quality regulation for large-scale AM. Two challenges will be addressed: (1) How to establish control-oriented data-driven model for nonlinear deposition dynamics; and (2) How to develop multiscale predictive quality regulation under uncertainty. The developed techniques will be validated through simulation and experimental studies on representative large-scale AM platforms available at UoM.

Applicants should have or expect to achieve at least a UK 2.1 honours degree in Mechanical and Mechatronic Engineering, Manufacturing Engineering, Computer Science or related disciplines. Experience in CAD/CAM, autonomous system and robotics development will be an advantage.