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Fusion-based Material Extrusion (MEX) Additive Manufacturing (AM) processes have been extensively used for the fabrication of smart structures with embedded sensors, proving to have several benefits such as reduction in cost, manufacturing time, and assembly. A major issue negatively affecting 3D printed sensors is related to their poor electrical conductivity, as well as inconsistent electrical performance, which leads to electrical power losses amongst other issues. In the present paper, a set of process parameters (ironing, printing temperature, and infill overlap) has been analyzed by performing a Design of Experiment (DoE) factorial plan to minimize the electrical resistance. The best process parameters configuration involves a remarkable reduction of electrical resistance of 47.9%, as well as an improvement of mechanical properties of 31.9% (ultimate tensile strength), 25.8% (elongation at break) and 28.14% (flexural stress). The microstructure of the obtained results has also been analyzed by employing a high-resolution, X-ray Computed Tomography (X-Ray CT) system showing a reduction of intralayer voids of 19.5%. This work demonstrates a clear correlation between process parameters and the corresponding electrical properties, mechanical properties, and internal microstructure. In the present research, it has been shown that i) it is possible to significantly improve the overall 3D printed sensors performance by process parameter selection, and ii) small changes in the microstructure lead to remarkable improvements in electrical and mechanical performance.

Material extrusion (MEX) has become a highly desirable additive manufacturing technology for creating silicone-based structures in the biomedical and soft robotics fields due to its ease of fabrication of complex structure without molding or casting. However, the lack of models for the MEX process when extruding silicone has limited its application. This study seeks to bridge this gap by introducing semi-analytical and numerical models of the MEX process that can predict the extrusion force in practical scenarios, accounting the counterpressure force from the deposited silicone beads on a substrate. Using a custom-made MEX setup capable of extruding silicone, the two proposed models were validated through experimental tests. The models, semi analytical and numerical models, demonstrated maximum accuracy of 99.7% and 99.3% respectively in predicting the extrusion force based on the process parameters such as layer thickness, nozzle size and flow rate. The development of a tool capable of predicting the silicone printing force, such as the one proposed in this paper, can expand the role of MEX in the fabrication of silicone structures beyond the current limitations by improving the manufacturing process control, enabling the creation of thin-walled structures, and enhancing accuracy.