Session: Rising Stars of Mechanical Engineering Celebration & Showcase

Paper Number: 148572

148572 - The Self-Sensing Inverse Problem: Deducing Full-Field Material Condition From Electrical Data 

Many materials, both naturally occurring and engineered, exhibit intrinsic coupling between their electrical state and their underlying condition (e.g., stress/strain, damage, temperature, chemical, etc.). Existent methods such as electrical impedance tomography (EIT) are able to detect and even spatially map changes in electrical properties. This is limiting, however, because engineers and scientists are often not interested in the electrical response of the material; they would rather know the underlying condition that gives rise to an observed electrical response. Deducing full-field material condition is referred to as the self-sensing inverse problem. Preliminary work has shown that this inverse problem is ill-posed and challenging to solve. Despite this challenge, there exists much incentive to solve the self-sensing inverse problem because it can have game-changing impacts in diverse and far-reaching fields. Examples include next-generation national security assets having total material state awareness to reduce inspection and maintenance costs while also continuously assessing mission suitability; creating new paradigms for extreme materials testing in which traditional sensors are inadequate; enabling full-field visualization of post-landslide residual stresses in geospatial applications; proprioception in soft robotics made of self-sensing materials; and, among other examples, biomedical tissue stiffness mapping for disease detection and diagnosis. Importantly, electrical methods have been explored in each of these examples, which greatly facilitates the practical feasibility of the self-sensing inverse problem. These early-career grants support work to deeply understand, stabilize, and uniquely solve the self-sensing inverse problem through sensor data fusion concepts and the incorporation of physics-based constraints. That is, sensor data fusion will incorporate limited known data points (e.g., acquired through strain gauges, accelerometers, etc.) into the self-sensing inverse problem while also optimizing the number and location of additional sensors. Additionally, physics-based constraints seek to reduce the potential solution space of the self-sensing inverse problem by requiring that the underlying condition satisfy both the electrical observations and any other known physics of the material (e.g., in elastic imaging, the displacement field must give rise to the observed electrical state and satisfy the Navier equations of elasticity). These concepts are being explored through a combination of computational and experimental work. Our preliminary results have shown that it is indeed possible to fully map quantitative pressure distributions in pressure sensors and predict the onset of failure in materials with stress concentrations by solving the self-sensing inverse problem. Future experimental validation is planned for a range of material systems and shapes of representative interest to the US Air Force and the National Science Foundation.

Presenting Author: Tyler Tallman Purdue University

Presenting Author Biography: Dr. Tyler N. Tallman is an Associate Professor in the School of Aeronautics and Astronautics at Purdue University. He earned BS degrees in physics and engineering mechanics from the University of Wisconsin-Eau Claire and the University of Wisconsin, respectively, in 2010. He earned his MS in 2012 and PhD in 2015, both in mechanical engineering and from the University of Michigan. Dr. Tallman's research interests exist at the intersection of multifunctional materials, embedded sensing, and inverse problems, areas in which he has authored or co-authored over 100 combined journal papers, conference proceedings, abstracts, and patents. His research contributions have been recognized by winning the AFOSR YIP Award, the NSF CAREER Award, and several best paper awards from ASME in addition to serving as an associate editor for the Journal of Intelligent Material Systems and Structures. Dr. Tallman is also a passionate educator. He has won the Elmer F. Bruhn and W. A. Gustafson teaching awards from the Purdue University School of Aeronautics and Astronautics and the Faculty Excellence Award in Early Career Teaching from the Purdue University College of Engineering.

Authors:

Tyler Tallman Purdue University