Molten pool characteristics of a nickel-titanium shape memory alloy for directed energy deposition

TL;DR

This study develops a 3D numerical model to analyze how process parameters in directed energy deposition affect the molten pool characteristics of nickel-titanium shape memory alloy, aiding process optimization.

Contribution

The paper introduces a comprehensive 3D simulation model that considers heat transfer, phase change, and fluid flow to predict molten pool features in DED of NiTi alloys.

Findings

Laser power significantly affects melt pool width and deposition rate.

Scan speed and powder feed rate have less influence on geometry.

Fluid velocity impacts element distribution in the molten pool.

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.