Manipulating Nanoparticle-Modified Melt Pool Dynamics in Additive Manufacturing

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Metal additive manufacturing (AM), the layer-by-layer printing of 3D shapes, is an emerging and potentially disruptive technology that allows the efficient fabrication of intricate parts that cannot be made using other manufacturing methods. However, because of the high temperature and repeated melting and solidification to which the metal is exposed as it is printed, structural flaws such as pores and cracks are common in AM parts, and only a small number of metals have been successfully used in the process. This research will study the benefits of adding nano-sized ceramic particles to the material during the AM process. Early experiments have shown that the presence of nanoparticles can favorably affect the flow of the molten metal and the formation of microscale structures, eliminating cracking and improving part quality. In many cases, however, the causes of these experimental results are not well understood. Through the development of predictive theory and models for these phenomena, the current work aims to provide scientific understanding of nanoparticle effects, and yield new strategies for controlling and optimizing AM part quality. Broadening the use of AM will advance the competitiveness of U.S. manufacturing and significantly impact U.S. industry by enabling the rapid production of highly customizable parts. The main technical objective of the researched work is to cultivate a physical theory and computational simulations for the dynamics of a nanoparticle-modified melt pool in metallic AM. The research will be undertaken through three interrelated tasks. First, thermophysical behavior of liquid metal with dilute and dense nanoparticles will be investigated by combining spatially inhomogeneous population balance equations (PBEs) and computational thermal fluid dynamics (CTFD). The effects of nanoparticles on thermophysical properties in liquid metal will be characterized and validated. Second, transport and aggregation of nanoparticles will be predicted by building a mechanistic model of aggregation and breakage processes; the model will be validated against measurements of particle distributions in solidified material. Finally, to unravel mechanisms of nanoparticle-induced grain refinement and hot cracking reduction, the thermal and particle information obtained from previous thrust areas will be coupled with a detailed solidification model, in which a cellular automaton (CA) grain growth simulation and an intergranular flow model will predict cracking susceptibility. Effects of nanoparticles will be included via a physics-based nucleation model to elucidate nanoparticle-affected hot cracking mechanics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.