Additive Manufacturing (AM) has been identified as a disruptive technology which
must to be exploited by the UK’s key industrial sectors such as aerospace, medical
and creative technologies to remain internationally competitive. This project
aims to address several operational challenges facing the developers and users of
the technology using high-fidelity numerical simulations as an effective research
tool. However, to perform the simulations at operating conditions substantial
computational resources are required which can only be provided by state-of-the-
art facilities such as the ARCHER National Supercomputer. This project aims
1. to minimise the need for expensive trial and error procedures in laser sintering (a large scale AM technique), increase the production rate and repeatability of the process. This will be achieved by a detailed characterisation of the production process to identify the effects of device operating conditions on the integrity of product.
2. to design a new class of spreader devices for the Laser Sintering (LS) process. Two devices are commonly used for the particle spreading: rollers and blades. Rollers are generally more flexible and result in higher product quality; however, they are patented. The blades on the other hand, are available at the cost of a lower product quality or a more expensive calibration procedure. A new class of potentially patentable blade spreader will be developed by optimising the blade’s head profile.
3. to provide a predictive numerical tool to quantitatively link the bed quality to the integrity of manufactured parts. Although, the qualitative relation between the two is understood, currently a lack of mathematically sound predictive tools is contributing to low process yields. A high fidelity grain-scale sintering model will be integrated with a discrete element (DEM) code to provide a consistent framework to fill this gap.
The project was considered for allocation of resources on the National Supercomputer ARCHER by the Engineering and Physical Research Council Resource Allocation Panel (EPSRC RAP), on 13 July 2016 and was allocated 11843 kAu (equivalent to approximately 790,000 CPU hours).