Prediction of mechanical behaviors of L-DED fabricated SS 316L parts via machine learning

Laser-based directed energy deposition (L-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 paper, two data-driven machine learning algorithms, Extreme Gradient Boosting (XGBoost) and Random Forest (RF), were applied to predict the tensile behaviors including yield strength, ultimate tensile strength, and elongation (%) of the stainless steel 316L parts by DED. The results suggest that both models successfully predicted the tensile properties of the fabricated parts. The performance of the proposed methods was evaluated and compared with the Ridge Regression by the root mean squared error (RMSE), relative error (RE), and coefficient of determination (R2). XGBoost outperformed both Ridge Regression and Random Forest in terms of prediction accuracy.