Mr Oliver Reed

As a Postdoctoral Research Associate in the Structural Materials Group, my current research focuses on the mechanisms of linear friction welding in Ti–6Al–4V blisks. This work aims to develop a fundamental understanding of the thermomechanical processes occurring at the weld interface, including microstructural evolution, deformation mechanisms, and the development of residual stress, with the goal of supporting the design and qualification of welded aeroengine components.

My doctoral research centred on NiTi-based shape memory alloys, which undergo a reversible martensitic transformation on heating and cooling, enabling the material to recover a defined shape after deformation. The focus of this work was to understand how alloy composition and thermomechanical processing influence the transformation temperatures on heating and cooling — which are not necessarily equivalent — so that the functional response can be precisely tailored for a target application. This included investigation of the role of ternary alloying additions and processing-induced microstructural changes on transformation behaviour and thermal hysteresis.

Beyond NiTi, my research interests extend to metastable beta-titanium and beta-zirconium alloys for superelastic applications, where the focus is on understanding how composition and microstructure govern the stress-induced transformation response and functional fatigue behaviour. I also have an interest in the development of novel titanium alloys more broadly, including computational approaches to alloy design.

A wide range of experimental and computational techniques underpin this work, including metallographic preparation (cutting, mounting, polishing, spark erosion), hot rolling, differential scanning calorimetry (DSC), scanning electron microscopy (SEM: BSE, EDX, EBSD), transmission electron microscopy (TEM: bright-field imaging, diffraction, STEM and EDX), mechanical testing across the range −150°C to 700°C including digital image correlation (DIC), and X-ray diffraction at both laboratory and synchrotron sources. This experimental work is complemented by machine learning and data analysis approaches, applied to the extraction of insights from large materials datasets.