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.

Effect of Process Parameters in Additively Manufactured Sensors prepared via Material Extrusion Processes: Correlation among Electrical, Mechanical and Microstructure Properties / Stano, Gianni; Sayah, Neshat; Smith, Douglas E.; Fleck, Trevor J.. - In: ADDITIVE MANUFACTURING. - ISSN 2214-8604. - STAMPA. - 9:(2024). [10.1016/j.addlet.2024.100194]

Effect of Process Parameters in Additively Manufactured Sensors prepared via Material Extrusion Processes: Correlation among Electrical, Mechanical and Microstructure Properties

Stano, Gianni;
2024-01-01

Abstract

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.
2024
Effect of Process Parameters in Additively Manufactured Sensors prepared via Material Extrusion Processes: Correlation among Electrical, Mechanical and Microstructure Properties / Stano, Gianni; Sayah, Neshat; Smith, Douglas E.; Fleck, Trevor J.. - In: ADDITIVE MANUFACTURING. - ISSN 2214-8604. - STAMPA. - 9:(2024). [10.1016/j.addlet.2024.100194]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/265880
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