UC Davis

Additive Manufacturing (AM) or 3D printing of metals has been expanding into a variety of different industrial sectors due to its many advantages which include but are not limited to fabrication of geometrically complex metallic components and minimal material waste. Laser Powder Bed Fusion (LPBF) processes are one of the most prominent metal AM technologies of the recent years. However, despite its unlimited potential, LPBF process has a chaotic nature with complex interactions and dependencies. Therefore, researchers have faced many challenges in accurately and efficiently capturing the complex micro-length and time scale phenomena in modeling this process through numerical approaches. Lack of control of the temperature field in the LPBF process would lead to microstructural, surface quality and structural defects during printing. After a comprehensive literature review, this research has identified two major categories of thermal modeling approaches for LPBF processes. The first group is based on thermo-fluid simulations, also called Computational Fluid Dynamics (CFD) simulations that couple fluid dynamics and heat transfer. Due to their high computational costs, these high-fidelity models are usually limited to a single or very few scanning tracks. The second group of thermal modeling techniques are based on efficient yet over-simplified conduction-only models that neglect melt-pool dynamics. Although the efficiency of these models makes them suitable for multi-layer modeling, their predictions are not accurate and hence would lead to subsequent poorly defined thermo-mechanical and microstructure modeling. Therefore, this dissertation develops a novel numerical approach that would efficiently account for major micro-scale phenomena in multi-layer simulations of the LPBF processes. In this dissertation, a Simplified Multi-scale Modeling (SMM) approach is presented to bridge the aforementioned numerical techniques accounting for lower length scale phenomena while allowing to conveniently scale up to larger domains for LPBF simulations. The thermal component of the SMM approach is the Comprehensive Thermal Model (CTM) which has multiple unique features that include numerical implementation of fluid flow effects (namely, evaporation, Marangoni convection, and process-induced micro-voids), process and material dependent absorptivity, latent heat, and phase transition effects, and temperature-dependent thermo-physical properties for bulk and powder material. The CTM is shown to be more accurate than a simplified conduction-only thermal model and more efficient than computationally expensive CFD simulations. The CTM developed is successfully verified through comparison with experimental temperature measurements from literature and then used as a computational tool to predict the thermal signature histories, surface cooling rates, and melt-pool dimensions for five of the most prominent AM alloys which are IN718, IN625, stainless steel 316L, Ti-6Al-4V, and AlSi10Mg. However, the proposed framework can also easily be extended to a wide range of alloys provided sufficient information is available. With its efficiency and accuracy for multi-layer modeling, the SMM can be used as a computational experiment for studying process variability and printability of different material systems. The presented model could also be used to extract datasets for developing data-driven and physics-informed Reduced-Order Models (ROMs) that have better computational performance compared to conventional numerical approaches and could serve as a building block of a digital twin of the AM process.