The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the development of origami-inspired mechanical metamaterials. These techniques can generate tessellated patterns that augment and enhance common materials. With the capacity for simultaneously making conventional materials lighter, stronger, and multi-functional, this material design motif has the potential to impact a wide range of technologies in hardware, manufacturing, energy-efficiency, robotics, and aerospace. This potential can be tapped by embedding carefully designed geometric patterns into materials. The current unmet challenge for realizing this potential impact is the conspicuous absence of a standard library of metamaterial designs. This project addresses the challenge by exploring the feasibility of a complete kinematic set and to determine whether the resulting physical properties are suitable for broader engineering applications. If successful, the metamaterials designed and validated by this effort will lay the foundation for replacing, augmenting, or enhancing machines of all types with mechanical metamaterial-based technology.
This Small Business Innovation Research (SBIR) Phase I project utilizes an artificial intelligence-enhanced optimization scheme to automatically generate mechanical metamaterial designs meeting user-defined target properties. To generate the proposed set of kinematically-complete mechanical metamaterials the software-based approach to metamaterial design will be required to mesh with empirical validations. A combination of supervised and unsupervised machine learning techniques will be used to guarantee the metamaterial designer routinely generates useful, robust, and high-performing schematics that qualitatively and quantitatively improve as the pipeline for metamaterial design is repeatedly executed. The core algorithm improves the ability to produce high-impact metamaterial-based technology while taking less time to converge on optimized design schematics. As such, the barrier to market penetration of advanced material technology becomes lower, faster, and is driven by advances in machine learning.
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.
