Session: 17-01-01: Research Posters

Paper Number: 149586

149586 - Direct Ink Write 3d Printing of Fully Dense and Functionally Graded Liquid Metal Elastomer Foams 

Liquid metal (LM) elastomer composites offer promising potential in soft robotics, wearable electronics, and human-machine interfaces. The unique combination of electrical, thermal, and mechanical properties enables the LM elastomer composite to excel in applications that demand mechanical compliance and metal-like thermal and electrical conductivity. Recently, we have shown that by systematically controlling the print processing conditions during direct ink write (DIW) 3D printing, LM microstructures can be generated on demand and locked in during printing. These structures range from spherical to needle-like droplets, curvilinear microstructures, geometrically complex embedded inclusion patterns, and connected LM networks. This approach is in contrast with typical DIW printing applications, where a single set of printing parameters is adopted to minimize the void content and rarely changed during the printing process. For DIW printing and programming of LM microstructures, the influence of the printing parameters directly influences the filament geometry and void content of the printed part. Here, a DIW strategy is introduced to control both LM microstructure (i.e. shape and orientation) and material architecture. We investigated three key process parameters–nozzle height, extrusion rate, and nondimensionalized nozzle velocity–and found that nozzle height and velocity predominantly influence filament geometry. By analyzing the filament geometry, we identified optimal parameters for fabricating dense multilayered structures and high aspect ratio features with improved surface quality and minimal internal defects. However, because of the die swelling and ink spreading post-deposition, which depends on printing conditions, the programmed layer height and the resulting printed filament height are often different. This discrepancy can result in increased surface roughness or the formation of voids. To address this, we adjust the programmed layer height based on the measured printed filament geometry to ensure the creation of high-quality multilayer structures while retaining control over the LM droplet microstructure. Under specific printing conditions, the printed filament can become discontinuous despite a continuous nozzle path. As nozzle speed and height increase, the extruded ink thins and eventually becomes unstable due to the Plateau-Rayleigh instability and poor adhesion to the substrate, resulting in discontinuous segments. We exploit this instability and fracture behavior of the viscoelastic emulsion ink to tailor the relative porosity of the structure and create functionally graded foam structures without modifying the ink formulation itself. The porous architectures with elongated LM droplets exhibit reduced density and enhanced thermal conductivity when compared to cast samples. As a dielectric in a soft capacitive sensor, these composites display high sensitivity (gauge factor = 9.0), with permittivity increasing with compressive strain. These results demonstrate the capability to simultaneously manipulate LM microstructure and geometric architecture in LM elastomer composites through precise control of print parameters, while maintaining geometric fidelity in the printed design. This advancement in additive manufacturing enables the creation of novel materials, structures, and devices that possess a unique combination of functionalities.

Presenting Author: Spencer Pak University of Nebraska-Lincoln

Presenting Author Biography: Spencer is a PhD student in the Department of Mechanical and Materials Engineering at the University of Nebraska-Lincoln. His research interests include additive manufacturing, medical diagnostics, and soft materials. He currently focuses on the development of soft multifunctional materials with applications in soft robotics, wearable electronics, and biomedical engineering.

Authors:

Spencer Pak University of Nebraska-Lincoln
Michael Bartlett Virgnia Tech
Eric Markvicka University of Nebraska-Lincoln