CAREER: Inverse Mechanics in Self-Sensing Materials: Basic Knowledge, Education, and Service

Purdue University, West Lafayette IN

Investigators

Abstract

This Faculty Early Career Development (CAREER) supports research on inverting electro-mechanical coupling in self-sensing materials. Mechanical self-sensing concepts have been widely studied in diverse applications including robotics, structural sensing, healthcare and rehabilitation. Even though electro-mechanical coupling in these materials is well known, potential consumers of self-sensing materials are generally not interested in electrical properties. They would much rather know the mechanical deformation and damage that gives rise to an observed electrical change. By inverting the relationship between electrical changes and mechanical loading in these materials, it is possible to know the full-field mechanics from only a small number of electrical measurements. This research will discover the basic nature of this inverse problem, which will lead to more accurate, more robust, and faster solutions. A self-sensing mechanics education and outreach ecosystem will also be created at Purdue University. The work will leverage connections with the Society of Women Engineers and the Women in Engineering Program to integrate self-sensing mechanics into outreach activities serving the greater Lafayette/West Lafayette area. A Vertically Integrated Projects program will also be created based on the technical work. This program will positively impact a diverse cohort of undergraduate students by providing research opportunities and a mentoring network. Participants in this program will receive service-learning experience by contributing to the planned outreach activities. To date, methods for inverting electro-mechanical coupling have been largely unstudied. Prior work has shown that recovering full-field mechanics from electrical data is an ill-posed inverse problem, but basic mechanics have not yet been incorporated into the inverse problem. This work seeks to make the mechanical self-sensing inverse problem stable and well-posed through the inclusion of novel mechanics-based constraints, advanced regularization and structural priors, and sensor data fusion concepts. If successful, this research will create the first intellectual pathway to in-situ quantitative full-field mechanics imaging via simple, benign, and easily multiplexed electrical measurements. Because many materials, both naturally occurring and engineered, exhibit self-sensing properties, this basic knowledge can positively affect broad areas of human health, societal prosperity, and national security. Examples include shape awareness in soft robots and morphing structures, visualization of ultra-fast loading in energetic materials, damage mapping in aging infrastructure, and, among others, tissue stiffness mapping for disease detection. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.