In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes

One major challenge of implementing Directed Energy Deposition (DED) Additive Manufacturing (AM) for production is the lack of understanding of its underlying process–structure–property relationship. Parts manufactured using the DED technologies may be too inconsistent and unreliable to meet the stringent requirements for many industrial applications. The objective of this research is to characterize the underlying thermo-physical dynamics of the DED process, captured by melt pool signals, and predict porosity during the build. Herein we propose a novel porosity prediction method based on the temperature distribution of the top surface of the melt pool as an AM part is being built. Self-Organizing Maps (SOMs) are then used to further analyze the two-dimensional melt pool image streams to identify similar and dissimilar melt pools. X-ray tomography is used to experimentally locate porosity within the Ti-6Al-4V thin-wall specimen, which is then compared with predicted porosity locations based on the melt pool analysis. Results show that the proposed method based on the temperature distribution of the melt pool is able to predict the location of porosity almost 96% of the time when the appropriate SOM model using a thermal profile is selected. Results are also compared with a previous study, that focuses only on the shape and size of the melt pool. We find that the incorporation of thermal distribution significantly improves the accuracy of porosity prediction. The significance of the proposed methodology based on the melt pool profiles is that this can lead the way toward in situ monitoring and minimize or even eliminate pores within the AM parts.