Enhancing 3-D-Printed Piezoresistive Sensors: An Investigation Into Process Parameters, Sensor Geometries and Materials Selection
Material extrusion (MEX) additive manufacturing of electrically conductive polymers is an inexpensive method to fabricate piezoresistive-based sensors deployable in many fields such as biomedical, and soft robotics. Despite the clear benefits related to such a fabrication approach (i.e., zeroing assembly), 3-D printed sensors still suffer poor repeatability. This article introduces a systematic approach to studying structure-performance properties to enhance sensitivity and repeatability. We report a Gauge Factor (GF) of 6.98, which is threefold higher than traditional metal-based strain gauges, and a coefficient of variation (CV) of 1.84%, lower than any previously documented 3-D-printed sensors. The proposed approach is based on studying the impact of the main MEX variables, namely: 1) process parameters; 2) sensor geometry; and 3) substrate material. A direct correlation with layer height, number of extruded layers, and beads was found, proving that high performance in 3-D printed sensors is achieved when inter, and intra-layer voids are reduced. To prove the potentialities of the proposed high-performance sensor, the 3-D printed strain gauge was integrated within a silicone robotic gripper finger, and its performance was validated through a closed-loop proportional-integral-derivative (PID) control system that efficaciously modulated the finger’s bending angle. The present research demonstrates that the optimal tuning of MEX-related parameters abruptly improves the performance of 3-D printed strain gauges, bridging the gap between MEX and real-life applications.
Authors
- S M Al Islam Ovy (ORCID: https://orcid.org/0000-0002-6587-3844)
- Gianluca Percoco (ORCID: https://orcid.org/0000-0002-4438-0450)
- Gianni Stano (ORCID: https://orcid.org/0000-0002-6676-8472)
- Abhishek Singh (ORCID: https://orcid.org/0000-0003-0072-9494)
- Yonas Tadesse (ORCID: https://orcid.org/0000-0001-6606-7089)
- Syed Imranuddin
Institutions
- The University of Texas at Dallas (US)
- Polytechnic University of Bari (IT)
Publication Details
- Journal
- IEEE Sensors Journal
- Published
- 2025-03-06
- DOI
- https://doi.org/10.1109/jsen.2025.3546641
- Citations
- 10
- Primary Topic
- Additive Manufacturing and 3D Printing Technologies
- Type
- article
- Field-Weighted Citation Impact
- 6.83