Obtaining strength and ductility synergy for directed energy deposited Ti17 alloys by machine learning

Abstract

Recently, obtaining a balance of tensile strength and ductility in direct energy deposited (DED) titanium alloy components has been a major concern, which obstructs their further application. Herein, machine learning (ML) methods were applied to find the optimal process window of DEDed titanium alloy parts from a wide range of possible depositing process variables. Four algorithms were used for ML model training and tensile property prediction of DEDed titanium alloy. The results showed that the prediction ability of the XGBoost model was the best. The optimized ‘laser power’-‘scanning rate’ process window produced DEDed titanium alloy specimens with an ultimate tensile strength (UTS) of 1050 MPa and an elongation (E) of 12.5 %. It was demonstrated that ML methods could promote the discovery of the optimal process window, leading to an outstanding synergy of tensile strength and ductility.