Abstract
Metal three-dimensional (3D) printing includes a vast number of operation and material parameters with complex dependencies, which significantly complicates process optimization, materials development, and real-time monitoring and control. We leverage ultrahigh-speed synchrotron X-ray imaging and high-fidelity multiphysics modeling to identify simple yet universal scaling laws for keyhole stability and porosity in metal 3D printing. The laws apply broadly and remain accurate for different materials, processing conditions, and printing machines. We define a dimensionless number, the Keyhole number, to predict aspect ratio of a keyhole and the morphological transition from stable at low Keyhole number to chaotic at high Keyhole number. Furthermore, we discover inherent correlation between keyhole stability and porosity formation in metal 3D printing. By reducing the dimensions of the formulation of these challenging problems, the compact scaling laws will aid process optimization and defect elimination during metal 3D printing, and potentially lead to a quantitative predictive framework.
Highlights
Metal three-dimensional (3D) printing includes a vast number of operation and material parameters with complex dependencies, which significantly complicates process optimization, materials development, and real-time monitoring and control
Keyhole dynamics is inherently difficult to understand and predict because of its complex dependence upon many physical mechanisms but important to be able to quantify because it is highly related to energy absorption and defect formation in metal 3D printing
The keyhole depth is directly quantified from in situ X-ray images in this study, while most of literature measures the depth post facto from cross-sections of the fusion region[3,4,5]. We found that both the keyhole aspect ratio and its fluctuation exhibit universal scaling with only one dimensionless parameter (Fig. 1a), we define this dimensionless parameter as the Keyhole number Ke, Ke 1⁄4 À
Summary
Metal three-dimensional (3D) printing includes a vast number of operation and material parameters with complex dependencies, which significantly complicates process optimization, materials development, and real-time monitoring and control. Recent research capturing meso-nanosecond keyhole dynamics with high-fidelity simulations discovered keyhole-induced back-spattering and frozen depression defects[10] Such keyholes were studied for laser welding in 1970s11, high-quality in situ experimental data on keyhole dynamics only recently became available via high-speed X-ray imaging. Several mechanisms that lead to porosity formation have been identified, such as lack of fusion[21], instability of the depression zone[6], vaporization of volatile elements[22], and hydrogen precipitation[23] These efforts are still far from producing a predictive model for porosity—it is challenging to distinguish the quantitative impact of different mechanisms on the final, observed porosity. This adds simplicity to highly multivariate and/or multi-dimensional systems and helps guide process tailoring toward rapid scientific discovery and optimum engineering design[24]
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