Development of Flexible 3D Printed Strain Sensors

Abstract

This thesis investigates the development of fully 3D-printed capacitive strain sensors fabricated from flexible, multi-walled carbon nanotube (MWCNT) reinforced hot-melt adhesive composites. These copolyester- and copolyamide-based filaments (5 wt.% and 10 wt.% MWCNT) enable the creation of compliant, conductive architectures not achievable with conventional carbon-filled polylactide materials. The research first examines how key parameters of fused deposition modeling (FDM), including layer height, raster orientation, printing speed, and nozzle temperature, influence electrical resistivity by altering interlayer fusion, filler alignment, and the continuity of percolated CNT networks. Results show that thicker layers and higher extrusion temperatures promote more stable conductive pathways, while orientation strongly dictates anisotropy in both copolyester and copolyamide systems.

Building on these findings, the dielectric response of the composites is characterized under dynamic shear and temperature-dependent conditions using broadband dielectric spectroscopy. Across 10²-10⁵ Hz, both formulations exhibit conduction-dominated behaviour, with loss permittivity exceeding storage permittivity by two to three orders of magnitude. Mechanical deformation reduces permittivity by disrupting MWCNT-MWCNT contacts, and temperature elevation from 125 °C to 200 °C produces a pronounced increase in permittivity attributable to thermally activated charge transport. Classical dipolar relaxation models do not accurately reproduce the measured spectra, indicating that conduction and interfacial processes dominate relaxation behaviour.

Finally, a single step, multi-material capacitive strain sensor was designed and fabricated using co-printed MWCNT electrodes embedded within a flexible non-conductive sensor body. The hexagonal geometry provides stable deformation paths while simplifying electrode separation. A resonance-based readout system built around a commercially available evaluation module enables high-resolution capacitance tracking, supported by a Python acquisition framework for real-time filtering, drift correction, and visualization.