I am Associate Professor in Composites Digital Manufacturing at the University of Bristol, jointly appointed between the Bristol Composites Institute and the NCC. My research sits at the intersection of manufacturing, materials science, advanced numerical modelling and, increasingly, artificial intelligence, with a focus on the simulation of the manufacturing of composite materials.

The aim of my work is to make process development faster and cheaper, and to support the introduction of new feedstock formats such as more recyclable materials. I pursue this through four connected strands. The first is the development of constitutive models that capture the behaviour of composite feedstock during manufacturing. The second uses these models to simulate manufacturing processes, building towards full system descriptions, or digital shadows, of how a part is made. The third develops the workflows needed to industrialise these models, including digital twins, robust design and process optimisation. Underpinning all of these is a substantial experimental effort to characterise material behaviour and validate models against experiments across multiple scales. This is so that the simulations can be relied on in an industrial setting.

Some of the team simulation tools are released as open-source software, which has supported collaborations and industrial partnerships. Translating academic research into industrial practice is central to my work, and my tools are used across the aerospace, energy, automotive and defence sectors. Beyond my own work, I chair the NAFEMS Composites Manufacturing Process Simulation Focus Group, where I work to broaden the use of process simulation across the industry by raising awareness of what these tools can do and leading the community effort to define good practice.

Research interests and expertise:

• Constitutive modelling of composite feedstock during manufacturing
• Process simulation and full system description (digital shadows)
• Industrialisation workflows (digital twins, robust design, process optimisation etc)
• Experimental characterisation and multi-scale model validation
• Physics-guided AI and machine learning for manufacturing
• Forming and consolidation modelling