Ti–6Al–4V alloy is a typical 3D printing metal, and its application has been expanded to various fields owing to its excellent characteristics such as high specific strength, high corrosion resistance, and biocompatibility. In particular, direct energy deposition (DED) has been actively explored in the fields of deposition and the repair of large titanium parts. However, owing to the complicated thermal history of the DED process, the microstructures of the fusion zone (FZ), heat-affected zone (HAZ), and base metal (BM) are different, which results in variations of their mechanical characteristics. Therefore, the process reliability needs to be optimized. In this study, the microstructure and hardness of each region were investigated with respect to various DED process parameters. An artificial neural network (ANN) model was used to correlate the measured characteristics of the FZ, HAZ, and BM of Ti–6Al–4V components with the process parameters. The variation in the mechanical characteristics between the FZ, HAZ, and BM was minimized through post-heat treatment. Heat treatment carried out at 950 °C for 1 h revealed that the microstructure and hardness values throughout the component were homogeneous.
A novel numerical method to predict the transient track geometry and thermomechanical effects through in-situ modification of the process parameters in Direct Energy Deposition
