Executive Summary : | Additive Manufacturing (AM) of metallic alloys, particularly laser-based methods, has become increasingly popular for producing complex parts. However, material development and qualification for AM have not kept up with the rapid advances in mechanical design. The main bottleneck is controlling the as-built structure, leading to cracks and the need for custom post-treatment routines. This work aims to investigate composition segregation, a key aspect of the as-built structure, as laser-based AM processes experience high thermal gradients and high cooling rates, resulting in far-from-equilibrium solidification. This segregation affects cracking and post-treatment.
Previous models to predict segregation include analytical models like Scheil-Gulliver approximation, cellular automata, and computationally expensive phase-field models. However, these models do not include all key aspects of non-equilibrium solidification, such as solute trapping, solid morphology, and multicomponent interactions. The project aims to develop an analytical model to predict segregation, including all rapid solidification effects for multicomponent alloys, and integrate it into a materials design framework for AM. The project will involve developing a consistent dataset, simulating the temperature profile, developing a model to predict segregation, and performing sensitivity analysis of key input parameters. The results will be disseminated through national conferences and peer-reviewed international journal publications. The long-term goal is to accelerate new material design and qualification for AM through the development of process-structure-property linkage models. |