To reduce the uncertainty of build performance in metal additive manufacturing, robust process monitoring systems that can detect imperfections and improve repeatability are desired. One of the most promising methods for in situ monitoring is thermographic imaging. However, there is a challenge in using this technology due to the difference in surface emittance between the metal powder and solidified part being observed that affects the accuracy of the temperature data collected. The purpose of the present study was to develop a method for properly calibrating temperature profiles from thermographic data to account for this emittance change and to determine important characteristics of the build through additional processing. The thermographic data was analyzed to identify the transition of material from metal powder to a solid as-printed part. A corrected temperature profile was then assembled for each point using calibrations for these surface conditions. Using this data, the thermal gradient and solid-liquid interface velocity were approximated and correlated to experimentally observed microstructural variation within the part. This work shows that by using a method of process monitoring, repeatability of a build could be monitored specifically in relation to microstructure control.

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There is a synergy between welding and additive manufacturing with reference to spatial and temporal variations of heat transfer. In this research, in-situ measurements of heat transfer conditions are considered as a viable qualification methodology for additive manufacturing (AM). Infrared imaging (IR) was performed within a laser powder bed fusion (L-PBF) AM machine equipped with an IR camera. Infrared thermal signatures as a function of space and time, while processing Ti6Al4V and 316L stainless steel powders, were extracted and analysed. The analyses correlated the defect evolution at low-and high-heat input conditions to thermal decay and integrated intensities. The IR based results were validated by processing a 316L cylinder with engineered porosities and detecting the same with ground truth data from computed tomography.