Recent advances in hyper-elastic materials and self-sensing sensor designs have enabled dense compliant sensor networks for the cost-effective monitoring of large-scale structures. These sensors are often engineered to mimic biological skin, where local anomalies can be monitored over a global area. The authors have proposed sensing skin based on soft polymer composites by developing soft elastomeric capacitor (SEC) technology that transduces any geometric variation into a measurable change in capacitance. The technology has been characterized and demonstrated in dense network configurations to diagnose, locate, and quantify damage. Prior work has shown that a limitation of the technology is in its low gauge factor and lack of sensing directionality. In this paper, we propose a corrugated SEC through surface texture, which provides improvements in its performance by significantly decreasing its transverse Poisson’s ratio, and thus improving its sensing directionality and gauge factor. In particular, we investigate five patterns inspired by auxetic structures for enhanced unidirectional strain monitoring. These include the re-entrant hexagonal honeycomb, re-entrant triangular-shaped honeycomb, re-entrant star system, chiral truss, and zigzag triangular network patterns. A numerical model is constructed and validated to numerically evaluate the performance of each pattern. Overall, all auxetic patterns showed improvement in sensing directionality, stability, and linearity, when compared against a non-textured SEC and also against a diagrid-like pattern that has shown promise in prior work. Among all patterns, the re-entrant hexagonal honeycomb pattern yielded the best results in terms of reducing the transverse Poisson's ratio, resulting in an increase of 22\\% in the gauge factor compared to a non-textured SEC.


Numerical Investigation of Auxetic Textured Sensing Skin for Structural Health Monitoring

No proceedings papers in 2020. Video only.

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