Functional Mechano-Intelligence in Mechanical Metastructures
Abstract
The vision of this proposed research is to create a paradigm shift to develop the needed foundation in uncovering metastructures that can physically observe, learn, memorize, think-decide and act in the mechanical domain. The goal is to advance the state of the art by pioneering the framework of physical reservoir computing together with the elements of mechano-intelligence, in and through reconfigurable mechanical metastructures. For the first time, this research will harness physical reservoir computing and metastructure designs to achieve extraordinary mechano-intelligence and structural functionality. It will transform the field of engineered materials and structures and create new domains. The tasks are to (a) uncover mechano-intelligent platforms with observation and execution, (b) discover methodologies to embed physical reservoir computing for integrating observation and execution with learning, memory and decision-making in the mechanical domain, and (c) create functional intelligence in and through emerging metastructures. The project deliverables in terms of intellectual merit include the new knowledge on how to derive and coordinate the elements of intelligence and decision-making computing in the mechanical domain, insights of the correlations between the structures’ properties and their intelligence, understanding of how the mechano-intelligence should interface with electronics most efficiently and effectively, and novel concepts of embedding mechano-intelligence in metastructures for engineering functions, such as from noise and vibration isolation to wave guiding for information transferring, structural monitoring, and energy harvesting. This research will provide the basis for significant advances for national security needs, greatly surpassing current technologies with lower power consumption, faster reaction speed, and much better survivability in harsh environments. It will enable the addition of new functions and autonomy to Air Force structural systems without burdening the onboard computers, and without the concern of cybersecurity.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 06, 2024
- Source ID
- FA95502310466
Entities
People
- Kon-Well Wang
Organizations
- Air Force Office of Scientific Research
- Board of Regents of the University of Michigan
- United States Air Force