Prediction of Mechanical Behaviors of Additively Manufactured SS 316L via Machine Learning

Abstract

Directed energy deposition (DED) is a rising field in the arena of metal additive manufacturing and has extensive applications in aerospace, medical and rapid prototyping. The process parameters, such as laser power, scanning speed, and layer thickness, play an important role in controlling and affecting the properties of DED fabricated parts. Nevertheless, both experimental and simulation methods have shown constraints and limited ability to generate accurate and efficient computational predictions on the correlations between the process parameters and the final part quality. In this work, data-driven machine learning models using XGBoost and Random Forest have been built and applied to predict the tensile behaviors of the stainless steel 316L parts by DED with variation in process parameters. The results suggest that both models can predict the tensile properties, e.g., yield strength, elongation, and ultimate tensile strength, of the fabricated parts and compute the significance of the factors affecting the quality of the part. The performance of the models was then compared with the Ridge Regression. XGBoost outperformed both Ridge Regression and Random Forest.