Additive Manufacturing of Complexly Shaped SiC with High Density Via Extrusion-Based Technique - Effects of Slurry Thixotropic Behavior and 3D Printing Parameters


Additive manufacturing of dense SiC parts was achieved via an extrusion-based process followed by electrical-field assisted pressure-less sintering. The aim of this research was to study the effect of the rheological behavior of SiC slurry on the printing process and quality, as well as the influence of 3D printing parameters on the dimensions of the extruded filament, which are directly related to the printing precision and quality. Different solid contents and dispersant- Darvan 821A concentrations were studied to optimize the viscosity, thixotropy and sedimentation rate of the slurry. The optimal slurry was composed of 77.5 wt% SiC, Y2O3 and Al2O3 powders, 0.25 wt% dispersant and 0.01 wt% defoamer. The printing parameters studied included extrusion pressure, nozzle size, layer height and printing speed; the one that had the most prominent effect on filament width and height was indicated as layer height. The nozzle inner diameter of 1.04 mm, speed of 350 mm/min, layer height of 0.7 mm and extrusion air pressure of 0.31 MPa were the optimal printing parameters. Furthermore, the relationship between the printing parameters and the filament dimensions was successfully predicted by using machine learning and grey system theory. Finally, the relative density of the printed SiC parts sintered at 1900 °C reached 94.7±1.5%.


Mechanical and Aerospace Engineering

Second Department

Materials Science and Engineering


This study was supported by a seed grant from the Advanced Manufacturing Signature Area of Missouri University of Science and Technology. J. Rittenhouse thanks the Office of Nuclear Energy of U.S. Department of Energy for an Integrated University Program graduate fellowship. H.M. Wen also acknowledges the U.S. Nuclear Regulatory Commission Faculty Development Program (award number NRC 31310018M0044).

Keywords and Phrases

Additive Manufacturing; Machine Learning; Printing Parameters; Rheological Behavior; Silicon Carbide

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Article - Journal

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Publication Date

01 Oct 2022