Additive manufacturing: DED process parameters optimisation via simulation

Abstract

With the increasing popularity of Additive Manufacturing in recent years, there has been a growing focus on optimising its processes to leverage its numerous benefits over traditional subtractive manufacturing. Among the various additive manufacturing techniques, Directed Energy Deposition (DED) is the most prevalent, and extensive research is being carried out to optimise its operational parameters towards achieving multi-objective optimisation, thereby revolutionising the industry.

This Final Year Project (FYP) aims to investigate these input parameters, with a specific focus revolving around the convergence (focal point) of the powder flow. Python Programming was integrated to standardise manual processes and remove variabilities, enhancing accuracy. ANSYS simulation was employed to replicate a real life DED nozzle, with emphasis placed on improving meshing techniques to better visualize flow behaviour.

Results examination suggested that these new implementations are on the right path, as evidenced by the similarity of trends to experimental results. This offers promising new avenues for further refinement and improvements, which will help to narrow the gap in optimisation studies and advance the understanding of DED processes.