Litcius/Paper detail

Influence of Process Parameters on the Resistivity of 3D Printed Electrically Conductive Structures

Kacper Dembek, Bartłomiej Podsiadły, Marcin Słoma

2022Micromachines19 citationsDOIOpen Access PDF

Abstract

With recent developments in conductive composites, new possibilities emerged for 3D printed conductive structures. Complementary to a vast number of publications on materials properties, here we investigate the influence of printing parameters on the resistance of 3D printed structures. The influence of printing temperature on the resistance is significant, with too low value (210 °C) leading to nozzle clogging, while increasing the temperature by 20 °C above the recommended printing settings decreases resistivity by 15%, but causing degradation of the polymer matrix. The limitations of the FDM technique, related to the dimension accuracy emerging from the layer-by-layer printing approach, greatly influence the samples' cross-section, causing irregular resistivity values for different layer heights. For samples with layer thickness lower than 0.2 mm, regardless of the nozzle diameter (0.5-1 mm), high resistance is attributed to the quality of samples. But for a 1 mm nozzle, we observe stabilized values or resistance for 0.3 to 1 mm layer height. Comparing resistance values and layer height generated from the slicer software, we observe a direct correlation-for a larger height of the sample resistance value decrease. Presented modifications in printing parameters can affect the final resistance by 50%. Controlling several parameters simultaneously poses a great challenge for designing high-efficiency structural electronics.

Topics & Concepts

NozzleMaterials scienceElectrical conductorElectrical resistivity and conductivityComposite materialLayer (electronics)3D printingElectrical resistance and conductanceSheet resistanceMechanical engineeringElectrical engineeringEngineeringAdditive Manufacturing and 3D Printing TechnologiesAdvanced Sensor and Energy Harvesting MaterialsAdvanced Memory and Neural Computing