Recent advances in 3D printed stainless steel have garnered great interest, but many questions remain about its material properties, due to its inherent variability, and how to guarantee standards for its safety and manufacture. This project is using statistical techniques in conjunction with material science to address these challenges.
Explaining the science
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 is 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.
Bringing together experts in material testing, engineering, statistics, and mathematics so address questions about the properties and manufacturability of 3D printed steel.
Detailed experiments are being performed to probe the material properties of 3D printed stainless steel and this data will be analysed with novel statistical methods, to be developed, in order to provide important insight into the advanced material.
This project is a joint venture between the Turing, Imperial College London, Newcastle University, the University of Bath, the University of Exeter, King’s College London, and 3D printing company MX3D.
This work is being done in conjunction with the Turing’s other project with MX3D, which is producing and monitoring the world’s first 3D printed steel bridge. Researchers are measuring, monitoring, and analysing the performance of the 12 metre-long bridge. Data from a sensor network installed on the bridge is being inputted into a ‘digital twin’ of the bridge which acts as a living computer model that imitates the physical bridge with growing accuracy in real time as the data comes in.
With the improved knowledge from the statistical techniques from this project, the engineers and designers working on the bridge and future 3D printed steel structures, will have a richer understanding of how such structures can and will function.
Collaboration with Science Foundation Ireland
The programme on data-centric engineering are delighted to partner with Science Foundation Ireland in the award of a joint Ireland-UK PhD studentship to work under Prof. Nial Friel, Dr. Chris Oates and Prof. Mark Girolami on STEAM. The funding was awarded to investigate “Approximate Bayesian computation with application to 3D printing” and will commence in September 2018.