Topic: PhD, In situ Visual Monitoring of Laser Metal Deposition for Additive Manufacturing
Additive manufacturing (AM) has been widely used in aerospace, medical implant and other industry sectors in recent years due to its extraordinary capability of net-shape building of parts with complex geometries.
As a member of the AM category, laser metal deposition (LMD) is a superior technology for repair, coating and refurbishment. Following its name, the mechanism of LMD is that metal powders are heated by a laser beam and become molten, after which they are blown from a nozzle. The molten metal drops are then directly deposited and solidified onto the surface of the substrate along the laser scanning path, which builds the part layer by layer. However, this technology still faces many challenges despite its inherent advantages. System inputs such as heat input rate and scanning speed are usually set empirically. If they are set inappropriately, defects (e.g. porosity and cracking) will occur and cause the failure of the part and material wastage. In recent years, many researchers are focusing on in situ monitoring of the process signatures (e.g. melt pool temperature and size) to characterise the impacts of the chosen values for the system inputs. But after measuring these signatures, what do they mean for final product qualities? Are there enough signatures to be monitored? This unclear relationship between the process signatures and the final product qualities is the most challenging problem in AM. Therefore, the aims of my research are to (i) determine if there is other process information that relates to product quality, and (ii) build the bridge between the family of process signatures and the final product qualities using a data-driven method.