Intelligence in Soft Mechanical Material Systems
Restricted (Penn State Only)
- Author:
- Hyatt, Lance
- Graduate Program:
- Mechanical Engineering (PHD)
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- February 19, 2024
- Committee Members:
- Robert Kunz, Professor in Charge/Director of Graduate Studies
Zoubeida Ounaies, Major Field Member
Daniel Cortes Correales, Major Field Member
Yun Jing, Outside Unit & Field Member
Jared Butler, Chair & Dissertation Advisor - Keywords:
- Intelligent Materials
Mechanical Metamaterials
Mechanical Computing
Soft Mechanical Materials - Abstract:
- Recent attention on soft engineered matter has accelerated the development of versatile compliant elastomeric mechanical systems that may emulate functional features of natural or biological systems. While current soft material systems can exhibit complex responses to environmental stimulus, there is increasing interest in integrating the adaptive capabilities of natural systems to design autonomous engineered matter. The goal of this research is to develop a foundation for soft intelligent material systems capable of sensing, processing, adapting to, and responding to external stimuli independent of external controls. To achieve this goal, the first objective is to develop methods to exploit elastic instabilities in soft materials to facilitate programmable shape reconfiguration in a mechanical computing platform. The second objective is to create a framework for adaptive information processing with signal feedback facilitated by multiphysics material interactions. The final objective is to develop design strategies to integrate individual system components into a functional intelligent material system in soft matter. Analytical models are developed to characterize the salient mechanical behavior of multimodal material systems. Experimental and computational methods are used throughout to validate analytical findings. A design framework is outlined to program complex transition sequences between metastable states to enable the conversion of mechanical signals to digital material configurations to enable mechanical computing. Methods are established to integrate electroactive field-responsive liquid crystal elastomer materials with digital mechanical systems to create self-adaptive materials capable of complex sequential logic operation. The culmination of the design methodologies and principles established is an experimental embodiment of a soft robotic system with integrated sensing of optical signals, information processing, and locomotive capabilities.