Research
Self-Sensing Additive Manufacturing
Conductive nanofillers can be used to impart electrical conductivity to traditionally insulating polymers. These materials are said to be self-sensing because their conductivity is deformation-dependent. In other words, we can monitor the strain of these materials via simple electrical measurements. This work seeks to develop methods of producing high-quality self-sensing additive manufacturing materials and to characterize the electro-mechanical performance of these materials.
Collaborators: Dr. Brittany Newell and Dr. Jose Garcia-Bravo
Sponsor: Naval Engineering Education Consortium
Synergistic Multifunctionality in Aerospace Composites
Multifunctionalization of composites via nanofiller modification has received much attention. To date, however, work has focused overwhelmingly on modifying composites with just a single nanofiller type or multiple nanofiller types that contribute to a single multifunctional property. For example, using carbon nanotubes (CNTs) and graphene nanoplatelets to achieve higher electrical conductivity. This work seeks to discover synergistic multifunctionality properties in composites modified with several nanofiller phases that arise due to interactions between the different nanofiller phases, such as CNTs and carbon-coated iron nanoparticles (CCFeNPs). Below, it can be seen that certain combinations of CNTs and CCFeNPs have higher conductivity and magnetic saturation than either phase alone.
Sponsor: Air Force Office of Scientific Research
Material State Awareness in Polymer Matrix Composites
Continuously knowing the state of a composite (e.g., stress, strains, and damages) will allow next-generation aerostructures to be pushed harder with reduced maintenance and inspection costs. Self-sensing principles have potential to achieve this capability; however, current methods only report on conductivity changes rather than the underlying mechanical state. This work seeks to understand the role of sensor data fusion (SDF) and define suitable optimality metrics for considerations such as sensor type and placement to solve the self-sensing inverse problem (SSIP). This conceptual framework will be extended to additively manufactured polymer matrix composites.
Sponsor: Air Force Office of Scientific Research
Inverse Mechanics in Self-Sensing Materials
Many materials—composites, cements, geological materials, physiological tissues, etc.—exhibit some extent of deformation-dependent electrical conductivity. Monitoring their electrical properties via modalities such as electrical impedance tomography (EIT) therefore provides insight into their mechanical state. However, people working in these areas generally are not interested in electrical properties; they would rather know the underlying stresses, strains, and displacements that give rise to an observed electrical response. This work seeks to invert the conductivity-strain relationship through the inclusion of mechanics-based constraints and regularization in order to achieve full-field mechanics imaging. Because of the diverse range of materials that exhibit self-sensing properties, basic knowledge created through this work can have far-reaching positive impacts in areas such as biomedical imaging, geospatial imaging, robotics, and, among others, structural assessment. Below, this principle is demonstrated on a soft piezoresistive carbon nanofiber (CNF)-modified polyurethane for tactile sensing.
Sponsor: National Science Foundation