Machine Learning-Enabled Competitive Grain Growth Behavior Study in Directed Energy Deposition Fabricated Ti6Al4V

Directed energy deposition (DED) of titanium alloys is a rapidly developing technology due to its flexibility in freeform fabrication and remanufacturing. However, the uncertainties of solidification microstructure during deposition process are limiting its development. This paper presents an artificial neural network (ANN) to investigate the relation between grain boundary tilt angle and three causative factors, namely thermal gradient, crystal orientation and Marangoni effect. A series of wire feedstock DED, optical microscope (OM) and electron backscatter diffraction (EBSD) experiments were carried out under Taguchi experimental design to gather the training and testing data for ANN. Compared to the conventional microstructure simulation methods, the strategy and ANN model developed in this work were demonstrated to be a valid way to describe the competitive grain growth behavior in DED fabricated Ti6Al4V. They can used to achieve a quantitatively microstructure simulation and extended to other polycrystal material solidification process.