Session: SYMP 4-7: Multi-stable Smart Systems

Paper Number: 139978

139978 - Shape Reconstruction of Highly Multi-Stable Structures From In-Situ State Sensing 

Multi-stable structures have garnered significant interest due to their ability to morph between many stable states. These states correspond to strain energy minima and are maintained without continuous actuation. By creating multi-stable structures with a large number of stable states consisting of a periodic arrangement of multi-stable unit cells, a highly shape-tunable structure can be achieved. Highly multi-stable structures can be leveraged for applications such as morphing wings and tuning the RF properties of antennas. Consequently, efficient and accurate shape reconstruction of multi-stable structures is vital during operation. However, the state-of-the-art for shape reconstruction relies on computationally expensive techniques and metrology with prohibitive mass, low resolution, or environmental operating constraints. These challenges are further amplified in space applications, such as space-based phased arrays, where weight, power, and computing budgets are limited. Thus, there is a need for accurate shape reconstruction using low-cost computations and simple metrology by leveraging the multi-stability of the structure.

This study presents a novel shape reconstruction method for multi-stable structures. The structure used in this study, made from a carbon fiber-reinforced epoxy frame and pre-stretched springs, is created by combining multiple multi-stable unit cells to form a larger multi-stable structure. A single unit cell has 7 stable states. Shape reconstruction is achieved in two steps: stable state detection and state-to-shape conversion through shape approximation and correction.

Stable state detection leverages the characteristics of the multi-stable structure to reduce the number of sensors required to detect all possible stable states. In this study, state detection is realized using a sparse array of simple, low-cost strain sensors. For example, only 20 strain sensors are required to detect all 4864 possible stable states for a 5 x 5 unit cell arrangement, giving a stable-state-to-sensor ratio of over 240. State-to-shape conversion uses the detected state to create an approximate shape based on standardized unit cell building blocks that is corrected based on unit cell interactions and boundary conditions. The correction applied is determined by the influence function of factors such as interactions between unit cells and boundary conditions. In this study, the state-to-shape conversion will be demonstrated on a 5 x 5 unit cell structure. The accuracy of the reconstructed shape will be validated against high-fidelity finite element models to quantify the RMS shape error.

By leveraging the characteristics of multi-stable structures, we demonstrated and validated a novel approach to shape reconstruction through a simple metrology system for stable state detection and a computationally efficient state-to-shape conversion process to predict the shape of multi-stable structures.

Presenting Author: Enquan Chew Stanford University

Presenting Author Biography: Enquan is a Mechanical Engineering PhD candidate from Stanford University working under the supervision of Prof. Maria Sakovsky. He received his MSc in Advanced Mechanical Engineering from Imperial College London and his BEng (Hons) in Mechanical Engineering from the National University of Singapore. His current research focuses on enabling mechanical intelligence in highly multi-stable structures through shape reconfiguration and sensing.

Authors:

Enquan Chew
Maria Sakovsky