Fully Funded PhD - DATAAM-F1 Design Additive Test Adapt: A digital twin framework for data driven additive manufacturing in formula 1

In collaboration with the Aston Martin Aramco Formula One Team, we are offering a unique PhD opportunity to investigate how additive manufacturing parameters influence the aerodynamic performance of wind tunnel components in elite motorsport.

This interdisciplinary project will develop a data-driven approach to understand and predict how process settings, build orientation, machine variability, and material properties affect dimensional accuracy and aerodynamic behaviour, ultimately improving the reliability of aerodynamic testing. The project combines additive manufacturing, data analytics and fluid dynamics to bridge the gap between manufacturing and aerodynamic testing, supporting the next generation of high-performance engineering. 

What you’ll do:

• Investigate the relationship between manufacturing parameters (build orientation, resin choice, post-processing) and aerodynamic performance in wind tunnel tests.
• Develop an integrated performance model linking process data, 3D metrology, and wind tunnel results, allowing for predictive insights into the impact of manufacturing deviations on aerodynamic performance.
• Gain hands-on experience with state-of-the-art technologies, including stereolithography (SLA) printing and wind tunnel testing at Aston Martin Aramco Formula One and Manchester Met’s PrintCity.
• Collaborate with experts in additive manufacturing, fluid dynamics, and data science, producing impactful research publications while advancing knowledge at the intersection of engineering and motorsport.

This project is highly relevant for candidates with a passion for advanced manufacturing, motorsport, and engineering innovation. If you are up for the challenge and want to join our team, apply now!

Project aims and objectives

The main aim of this project is to develop a data-driven, predictive framework for additive manufacturing that links process parameters, dimensional accuracy, and aerodynamic performance for high-precision components used in wind tunnel testing. The specific objectives are:

1. Investigate the impact of SLA process settings (build orientation, resin type, post-processing) on geometric fidelity and aerodynamic behaviour.
2. Develop an integrated performance model combining SLA process data, metrology, and wind tunnel results to predict manufacturing-induced aerodynamic deviations.
3. Explore machine-to-machine variability and evaluate how local and systemic errors affect part quality.
4. Enhance CAD-to-print workflows to capture and mitigate sources of manufacturing variation.
5. Provide recommendations for process optimization and compensation strategies to improve repeatability and accuracy in high-performance applications.