Molten pool characteristics of a nickel-titanium shape memory alloy for directed energy deposition. (October 2021) - Gao, Shiming; Feng, Yuncong; Wang, Jianjian; Qin, Mian; Bodunde, Ojo Philip; Liao, Wei-Hsin; Guo, Ping
Graphical abstract: Highlights: Nickel-titanium molten pool characteristic is investigated by a multi-physics FE model. The laser-powder interaction is considered in the model through the effective coefficients. The influence of deposition parameters on the molten pool evolution is studied. The effect of the velocity field on the element distribution is investigated. The mechanism of microstructure evolution in the solidification process is revealed. Abstract: Fabrication of nickel-titanium shape memory alloy through additive manufacturing has attracted increasing interest due to its advantages of flexible manufacturing capability, low-cost customization, and minimal defects. The process parameters in directed energy deposition (DED) have a crucial impact on its molten pool characteristics (geometry, microstructure, etc.), thus influencing the final properties of shape memory effect and pseudoelasticity. In this paper, a three-dimensional numerical model considering heat transfer, phase change, and fluid flow has been developed to simulate the cladding geometry, melt pool depth, and deposition rate. The experimental and simulated results indicated that laser power plays a critical role in determining the melt pool width and deposition rate while scan speed and powder feed rate have less effect on cladding geometry and deposition rate. The fluid velocity has a huge influence on the distribution of elements in the molten pool. The temperature gradient G, solidification rate R, as well as shape factor G/R were calculated to illustrate the underlying mechanisms of grain structure evolution. The grain morphology distribution of cross-section from the experimental samples agreed well with the simulation results. The model reported in this paper is expected to shed light on the optimization of the deposition process and grain structure prediction.