THESIS
2023
1 online resource (8 unnumbered pages, xiv, 241 pages) : illustrations (some color)
Abstract
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...[
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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.
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