Effect of process parameters on tensile properties of SS 316 prepared by directional energy deposition

Abstract

Directed energy deposition (DED) recently have become the most used and popular metal additive manufacturing (AM) process and has extensive applications in repair, overhauling and rapid prototyping. Most of the AM processes are likely to have pre-programmed and fixed optimized process parameters for materials whereas DED machines comes with flexibility. The process parameters, such as laser power, scanning speed, energy density and layer height, play a great deal in controlling and affecting the properties of DED fabricated parts. Therefore, it is required to identify the beneficial process parameters and their effects on the print quality for a particular output and material in DED process. In this study, the effects of the process parameters on the tensile behaviours of the 316 stainless steel parts fabricated by DED process is exhibited using a machine learning algorithm called XGBoost based on F score.