An Efficient Design Model for Parallel-Guided Layer Jamming Compliant Mechanisms

Abstract

Layer jamming (LJ) materials and structures have shown promise in designing variable stiffness compliant mechanisms for robotics. However, design challenges persist due to time-consuming prototyping, testing, and significant compu-tational resources needed for finite element (FE) simulations. The complexity stems from the intricate mechanics behavior between jamming materials and substrate structures. This article presents a hybrid model that combines machine learning (ML) with data generated from finite element (FE) analysis to predict the mechanical behavior of LJ -based compliant parallel-guided mechanisms, including force-deflection relationships, stiffness, and hysteresis. An experimentally validated FE model generates data by varying geometric and material parameters, capturing key mechanical performance metrics. This data serves as input for training a neural network model, which evaluates the impact of selected design parameters on performance metrics. The resulting ML model is highly efficient, with predictions taking seconds compared to hundreds of hours needed for FE simulations, and remarkably accurate, with less than a 5% error relative to FE simulations. This efficient computational model can be used for designing and analyzing LJ-based parallel-guided mechanisms, with the validated workflow process applicable to other LJ -compliant mechanisms and robotic systems.