Three cross cutting themes underpin our knowledge‑base and enable the research programme. The themes focus on developing novel in-situ observation, characterisation, modeling and control, to gain deeper understanding of the interaction between complex processes and materials.

In-situ Process and Performance Characterisation (X1)

Our focus is on novel in-situ experimental cells for in-process measurements, allowing time-resolved synchrotron and neutron observation of structure, chemistry and phase evolution across the length scales from nano to mm during powder-based manufacturing. This will enable a new understanding of how powder compositions and processing routes first affect the phases formed in the powder, and then how these can be tailored to optimise their processing. We aim to develop in-process production monitoring and metrology techniques to provide real-time feedback on the new powder manufacturing methods being developed and innovated within MAPP.

Advanced Characterisation (X2)

A full understanding of the nature of our starting materials, and the changes they experience at critical stages in processing, is key to developing a deeper understanding of the underpinning material-process interaction phenomena. Our focus here is on the development of innovative characterisation technologies and techniques to examine process-related material change at higher resolutions than currently possible using in-situ methods. These range from automated high-resolution optical microscopy and analysis, high-resolution transmission and scanning through to tomography of powders and resultant products.

Modelling, Optimisation and Control (X3)

The abundance of process data that can be captured from our processes via real-time process monitoring, historical data, or in-situ monitoring provides enormous systems based modeling opportunities. The fast track development of emerging powder-based process technologies will be enabled through merging knowledge capture from intelligent experimental design and the novel approaches developed in the X1 and X2 theme with computational intelligence (CI) modelling / machine learning (ML). Our aim is to turn the information and data from advanced processing and monitoring technologies into process understanding and control.