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Computational Design of a Soft Robotic Myocardium for Biomimetic Motion and Function
Author(s) -
Park Clara,
Ozturk Caglar,
Roche Ellen T.
Publication year - 2022
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.202206734
Subject(s) - soft robotics , actuator , materials science , finite element method , computer science , mechanical engineering , material design , artificial muscle , process (computing) , biomedical engineering , nanotechnology , artificial intelligence , engineering , structural engineering , composite material , operating system
Abstract Soft robotic devices containing multiple actuating elements have successfully recapitulated complex biological motion, leading to their utility in biomedical applications. However, there are inherent nonlinear mechanics associated with soft composite materials where soft actuators are embedded in elastomeric matrices. Predicting their overall behavior prior to fabrication and subsequent experimental characterization can therefore present a hurdle in the design process and in efficiently satisfying functional requirements and specifications. In this work, a computational design framework for optimizing the motion and function of biomimetic soft robotic composites is demonstrated by conducting a design case study of soft robotic cardiac muscle (myocardium) with a particular focus on applications including replicating and assisting cardiac motion and function. A finite element model of a soft robotic myocardium is built, in which actuators are prescribed with anisotropic strain to simulate local deformation, and various design parameters are investigated by evaluating the performance of each configuration in terms of ventricular twist, volumetric output, and pressure generation. Then, an optimized design is proposed that recapitulates the physiological motion and hemodynamics of the heart, and its thrombogenicity is further explored using a fluid‐structure interaction model. This framework has broader utility in predicting the performance of other soft robotic embedded composites.