On the performance of additively manufactured CNF/PLA piezoresistive strain sensors

Robust and adaptable sensor technology is essential for achieving meaningful structural health monitoring (SHM) and integrated nondestructive evaluation (NDE). Unfortunately, prevailing sensor technologies are most often pre-packaged and therefore lack much adaptability. In other words, sensors are rarely structure-specific or application-specific. Rather, an existing pre-packaged sensor must be retrofit to the component or structure under inspection. Multifunctional additive manufacturing (AM) has immense potential to overcome this limitation by permitting stimulus-responsive materials to be printed onto or directly embedded within structures for application-specific sensing. Herein, we explore this concept for strain sensors fabricated via multifunctional AM. Specifically, pelletized polylactic acid (PLA) is modified by the addition of carbon nanofibers (CNFs) at 7.5% by weight. This modification is done through a dry-mix process which is followed by multiple reclaiming and re-extrusion cycles through a single-screw filament extruder. Through this process, the CNFs form an electrically conductive network within the PLA structure. Because the electrical conductivity of the CNFmodified PLA is deformation-dependent (i.e. the material is piezoresistive), the sensors printed from CNF/PLA filament can be leveraged for strain sensing. In this work, we utilize a commercially available fused deposition modeling (FDM) printer to print the CNF-modified PLA into small and thin dog-bone shapes. These sensors then are adhered to a comparatively stiff substrate such that resistance changes across the sensor can be monitored as a function of strain as the substrate is deformed within a load frame. Our preliminary results show that AM-produced CNF-modified PLA strain gauges can indeed be used to track strains consistently. These successful preliminary results show that multifunctional AM has considerable potential for the development of highly adaptive, application-specific, and on-demand sensing technology.

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