Model-based process optimisation – a case study in High Speed Sintering
Dr George Panoutsos from the Department of Automatic Control and Systems Engineering has been awarded a £40k EPSRC Impact Acceleration Account to investigate the feasibility of model-based process optimisation in High Speed Sintering (HSS).
The aim is to develop a real-time process optimisation method, relying on previously EPSRC funded work, towards the scale-up of the process for advanced manufacturing. Dr Panoutsos will be working on this project alongside Dr Candice Majewski, from the Sheffield University Centre for Advanced Additive Manufacturing.
High speed sintering is a novel manufacturing process, which is classed as a form of 3D printing. Part manufacture starts with depositing a thin layer of powder on flat surface, and part building is achieved by ‘printing’ the required shape with an infrared-absorbing ink. An infrared lamp is then used, and the printed areas absorb higher levels of the lamp energy than un-printed areas. This leads to melting of the underlying particles, allowing very complex geometries to be achieved (Figure 1). The process is then repeated layer-by-layer, resulting in a 3D printed structure/part.
Dr. Panoutsos commented:
“The challenge in this project is to address the inherent process variability and capture uncertainty via machine learning data-driven process models. Subsequently, with the help of High Performance Computing, thousands of process simulations will be performed to learn about the underlying process-part amalgamated behaviour. This knowledge will be used to optimise a large-scale HSS machine.” (Figure 2)