LEAP-HI: AI-Optimized 3D Printing of Super-Soft Materials for Personalized Sensing

A common need in the medical community is an ability to monitor local tissue continuously, but current sensing technology lacks the personalization necessary to contour sensors for the unique anatomy of different individuals. This Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) research seeks to develop low-cost personalized sensors which can be fabricated on demand to enhance the health and well-being of Americans from all walks of life.

The approach leverages fundamental research into new materials in tandem with advanced machine learning and artificial intelligence to ‘3D print’ personalized sensors with applications in health care—from prosthetics to diagnostics and therapeutics—that could impact millions of people. These advances spanning materials science, engineering, and computation will improve the economic competitiveness of the United States’ innovation and help train the next generation of scientists and engineers through a tight synergy between experimental and computational research.

A series of initiatives spanning diversity, education, and outreach to further will advance key aspects of the work across age groups. This includes (a) modules on cybermanufacturing in a new cyber physical systems minor at Iowa State University, (b) working with the local chapters of Society for Advancement of Chicanos/Hispanics, Native Americans in Science, and the National Society of Black Engineers to draw students into research, (c) K-12 engagement through activities at the Wolf Museum of Exploration and Innovation (MOXI) for children, (d) working with Center for Industrial Research and Service (CIRAS) to disseminate best practices and training modules in cyber manufacturing, and (e) working with the communications offices of both the University of California at Santa Barbara and Iowa State University to disseminate the research to the public to promote the need for Engineering Leadership in the United States.

This research will overcome the limitations of conventional sensor technologies for personalized health monitoring by developing new materials and processing techniques to 3D print polymers with mechanical properties that are matched to human tissue. Our strategy will yield fundamental insights into the translation of advanced materials to manufacturing by: (1) Designing and synthesizing materials that inherently provide processability for 3D printing while maintaining biocompatibility and mechanical function; (2) Advanced multi-scale simulations of the printing process through modeling and predictive dynamics at the device scale to understand macroscopic progression during the build process, and at the microstructure scale to tailor the material properties as a function of material deposition and curing; (3) Data fusion from sensor response and physics-aware machine learning models to enable real-time prediction and control of the 3D printing process; and (4) Creating ultra-soft structured sensors that enable unique sensing modalities for societal impact in personalized human health.

These research aims to harness the data revolution by exploiting computational and machine-learning models to design more effective polymers and sensors in a convergent fashion. This research will yield experimental and computational methodologies that can also be applied to other engineering disciplines.

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.