STEAM: Statistical Techniques for Engineering with Advanced Materials
The next decade will see step changes in data-driven technology, impacting all aspects of engineering and industry. The emergence of manufacturing protocols for 3D printed stainless steel promises a dramatic increase in the ambition and complexity of structures that can be designed and built. However, at the same time these techniques raise urgent statistical challenges which must first be addressed. On the microscale, the inherent variability of advanced printed materials is such that their basic material properties are in effect random, and this variation not yet well-characterised. On the macroscale, how manufacturing standards and safety guarantees can be provided in the context of an uncertain material is yet to be determined. Moreover, it is unclear how inspection and continued monitoring of these structures should be performed.
This project, a joint venture between the Lloyds-Turing programme on data-centric engineering at the Alan Turing Institute, Imperial College London, Newcastle University, the University of Bath, the University of Exeter and King's College London, brings together experts in material testing, engineering, statistics and mathematics so that these urgent questions can be addressed. Detailed experiments are being performed to probe the material properties of 3D printed stainless steel and these data will be analysed with novel statistical methods, to be developed, in order to provide important insight into novel advanced material.
- Craig Buchanan - Imperial College London, UK
- Gianluca Detommaso - University of Bath, UK
- Tim Dodwell - University of Exeter, UK
- Leroy Gardner - Imperial College London, UK
- Mark Girolami - Imperial College London, UK
- Peter Gosling - Newcastle University, UK
- Din-Houn Lau - Imperial College London, UK
- Alex Mijatovic - King's College London, UK
- Chris Oates - Newcastle University, UK (Project Lead)
- Rob Scheichel - University of Bath, UK
- Wing Wan - Imperial College London, UK
We are looking to recruit a PhD and PDRA to start in 2018. For informal enquiries, please contact Dr Chris Oates and Prof Mark Girolami. [CLICK TO APPLY]
For more information or to contact the project, email email@example.com.
This project is supported by the Lloyds-Turing programme on data-centric engineering. It ties in closely with its sister projects, designed to instrument and monitor a bridge that is being 3D printed from stainless steel: