physics-informed unsupervised learning frameworks.
This project will build on the supervisors’ research in stochastic modelling, statistical learning, numerical analysis, and mathematically grounded anomaly detection. The research will focus on formulating flexible, label-free statistical machine learning for real-time anomaly detection and defect classification. Depending on candidate interest and project evolution, potential methodological avenues include combining spatial modelling with non-parametric hypothesis testing to separate systematic process variation from distributional shifts. To account for complex spatiotemporal dependencies, you will investigate robust calibration schemes (such as block-permutation or wild-bootstrap methods), while exploring techniques ranging from sparse matrix decomposition to geometric clustering and manifold learning to isolate and classify physical anomaly regimes like porosity, lack of fusion, or keyhole collapse.
Bridging spatial statistics, rough path theory, functional data analysis, and advanced process engineering, this project translates rigorous mathematical methodology into immediate industrial impact for metal additive manufacturing.
The project will be in collaboration with the LPBF team at the National Manufacturing Institute of Scotland (NMIS), and colleagues from the Department of Mathematics and Statistics at Strathclyde University.
Eligibility
Funding Notes
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.
Further information and other funding options.
Funding may become available on a competitive basis.
