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.

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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
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article

Enhancing 3-D-Printed Piezoresistive Sensors: An Investigation Into Process Parameters, Sensor Geometries and Materials Selection

S M Al Islam Ovy, Gianluca Percoco, Gianni Stano, Abhishek Singh et al.
10 citations
IEEE Sensors Journal
Additive Manufacturing and 3D Printing Technologies
6.83
article

Enhancing 3-D-Printed Piezoresistive Sensors: An Investigation Into Process Parameters, Sensor Geometries and Materials Selection

S M Al Islam Ovy, Gianluca Percoco, Gianni Stano, Abhishek Singh, Yonas Tadesse, Syed Imranuddin
article en
10 citations

Abstract

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.

IEEE Sensors JournalVol. 25(8)
The University of Texas at Dallas (US), Polytechnic University of Bari (IT)
Openalex Percentile: Top 3%
Additive Manufacturing and 3D Printing Technologies
6.83
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