On the Development of Smart Framework for Printability Maps in Additive Manufacturing of AISI 316L Stainless Steel

In this work, we propose a methodology to develop printability maps for the laser powder bed fusion of AISI 316L stainless steel. Regions in the process space associated with different defect types, including lack of fusion, balling, and keyhole formation, have been considered as a melt pool geometry function, determined using a finite element method model containing temperature-dependent thermophysical properties. Experiments were performed to validate the printability maps, showing a reliable correlation between experiments and simulations. The validated simulation model was then applied to collect the data by varying laser scanning speed, laser power, powder layer thickness, and powder bed preheating temperature. Following this, the collected data were used to train and test the adaptive neuro-fuzzy interference system (ANFIS)-based machine learning model. The validated ANFIS model was used to develop printability maps by correlating the melt pool characteristics to the defect types. The smart printability maps produced by the proposed methodology can be used to identify the processing window to attain defects-free components, thus attaining dense parts.