Applications are invited for a PhD studentship supported by the EPSRC I-CASE and ESI Group to conduct research in manufacturing of fibre-reinforced polymer composites using resin transfer moulding (RTM). The work will be closely aligned with the ESI Group’s programme to develop a new paradigm for modelling of manufacturing processes under uncertainty by combining physics-based and data-driven models.
A student working on this project will develop and test novel Bayesian Inversion algorithms (BIA) which will estimate local material properties using in-process information. These estimations will be used for a novel non-destructive evaluation method. The student will also create a robust active control systems (ACS) for RTM processes using advanced BIAs. To implement the algorithms, the student will use state-of-the-art commercial software, PAM-COMPOSITES, developed by the industrial partner, ESI. For successful industrial exploitation of BIA and ACS, the use of Model Order Reduction (MOR) techniques is critical for speeding-up the complex calculations. Implementing MOR methods, such as Proper Generalized Decomposition and Machine Learning, will be key to the project.
The project will be also aligned with one of the EPSRC Future Composites Manufacturing Research Hub Core projects “Resin injection into reinforcement with uncertain heterogeneous properties” and include the opportunity to perform experimental validations of the developed modelling approaches and to work closely with the industrial and academic partners.