Daniel Dreelan is a PhD researcher based at UCD, focused on process modeling, particularly meso-scale modeling of alloy solidification during additive manufacturing processes. Daniel completed his bachelor’s and master’s degrees in mechanical engineering at UCD. His master’s thesis was on computational modeling of passive microfluidic mixers, which piqued his interest in numerical modeling and influenced his decision to pursue a PhD in a numerical modeling related topic.
Research Interests (Lay Summary)
Since starting his PhD in 2018, Daniel has begun development of meso-scale models, i.e. the prediction of grain size and shape, for metallic additive manufacturing (AM) processes. The texture or structure and arrangement of crystallographic grains in metallic parts is of utmost importance to the performance of a component. This microstructure is formed during solidification, and is of particular interest for AM processes, where extreme thermal conditions and multiple cycles of solidification and remelting can result in components with vastly different mechanical properties.
Numerical modeling of microstructure evolution in metallic components produced by additive manufacturing (AM) processes is paramount to improve understanding of not only general metal solidification, but also of how changes in processing conditions can impact the final mechanical properties of the component.
Daniel’s research is primarily concerned with meso-scopic modeling, that is, length scales of the order of grain size. Cellular automata modeling techniques offer efficient but powerful microstructure predictions and will form the basis of his models. Materials of primary focus include medical-grade titanium alloys such as Ti-6Al-4V, which is among the most commonly used AM materials, but models will be extensible to virtually any dendritic alloy.