A Methodology for Predicting Porosity From Thermal Imaging of Melt Pools in Additive Manufacturing Thin Wall Sections

Abstract

The microstructure and mechanical properties of Laser Based Additive Manufacturing (LBAM) are often inconsistent and unreliable for many industrial applications. One of the key technical challenges is the lack of understanding of the underlying process-structure-property relationship. The objective of the present research is to use the melt pool thermal profile to predict porosity within the LBAM process. Herein, we propose a novel porosity prediction method based on morphological features and the temperature distribution of the top surface of the melt pool as the LBAM part is being built. Self-organizing maps (SOM) are then used to further analyze the 2D melt pool dataset to identify similar and dissimilar melt pools. The performance of the proposed method of porosity prediction uses X-Ray tomography characterization, which identified porosity within the Ti-6Al-4V thin wall specimen. The experimentally identified porosity locations were compared to the porosity locations predicted based on the melt pool analysis. Results show that the proposed method is able to predict the location of porosity almost 85% of the time when the appropriate SOM model is selected. The significance of such a methodology is that this may lead the way towards in situ monitoring and on-the-fly modification of melt pool thermal profile to minimize or eliminate pores within LBAM parts.