Laser powder bed fusion is an emerging technique with enormous potential for additive manufacturing, while its future development is bottlenecked by identifying and mitigating the detrimental defects. High-fidelity numerical modeling is indispensable in eliminating the constraints imposed by traditional observational technologies and trial and error approaches. This dissertation makes four key contributions: (1) the development and validation of a high-fidelity, physics-based computational framework; (2) the quantification of keyhole evolutions and powder-liquid-vapor interactions; (3) the identification of inherent mechanisms of keyhole pore formation; and (4) the proposal of a mechanism-based optimization strategy to reduce keyhole porosity.

First, a semi-coupled resolved Computationa...[ Read more ]

Laser powder bed fusion is an emerging technique with enormous potential for additive manufacturing, while its future development is bottlenecked by identifying and mitigating the detrimental defects. High-fidelity numerical modeling is indispensable in eliminating the constraints imposed by traditional observational technologies and trial and error approaches. This dissertation makes four key contributions: (1) the development and validation of a high-fidelity, physics-based computational framework; (2) the quantification of keyhole evolutions and powder-liquid-vapor interactions; (3) the identification of inherent mechanisms of keyhole pore formation; and (4) the proposal of a mechanism-based optimization strategy to reduce keyhole porosity.

First, a semi-coupled resolved Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) is proposed to simulate a class of granular media problems that involve thermal-induced phase changes and particle-fluid interactions. The proposed method is validated by simulations of a typical powder-based selective laser melting process.

Second, two innovative features are further implemented into the proposed semi-coupled resolved CFD-DEM framework, including an evaporation model and a ray tracing model compatible with the volume of fluid method. We demonstrate the proposed method can capture interdependent physics involving melt pool evolutions, keyhole dynamics and powder motions.

Third, the proposed computational tool is employed to identify the critical physics underlying keyhole pore instability. We show that vapor condensation is the major mechanism that may result in pore collapse and splitting. We further propose an optimization strategy based on a parametric study of the condensation rate to potentially eliminate keyhole pores during laser melting.

Fourth, an optimization strategy using adaptive laser power is proposed to reduce keyhole porosity based on the keyhole fluctuation mechanisms. Adaptive indices are proposed to quantify keyhole fluctuations, enabling the adaptive laser power. Simulations results demonstrate that the proposed optimization strategy can stabilize the keyhole and reduce the occurrence of keyhole porosity.