Tailoring Vacuum Artificial Muscles: A Multi-Parametric FEA-Driven Optimization and Monolithic Fabrication

Vacuum-actuated muscle-inspired pneumatic structures (VAMPs) are a promising alternative to traditional pneumatic artificial muscles, offering uniform force distribution, reduced material fatigue, and improved reliability through the use of negative pressure; however, their design-performance relati...

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Vydané v:IEEE robotics and automation letters Ročník 11; číslo 1; s. 210 - 217
Hlavní autori: Galassi, Laura, Lorenzon, Lucrezia, Pagliarani, Niccolo, Sarti, Alberto, Cianchetti, Matteo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.01.2026
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Actuators
Artificial muscles
Automation
Axial strain
Biological system modeling
Biomedical materials
Complexity
Fabrication
Finite element analysis
Finite element method
Force
Force distribution
Geometry
Hydraulic/pneumatic actuators
methods and tools for robot system design
Optimization
Prototypes
Robotics
soft robot applications
soft robot materials and design
Soft robotics
Strain
Strain measurement
ISSN:2377-3766, 2377-3766
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Popis
Shrnutí:Vacuum-actuated muscle-inspired pneumatic structures (VAMPs) are a promising alternative to traditional pneumatic artificial muscles, offering uniform force distribution, reduced material fatigue, and improved reliability through the use of negative pressure; however, their design-performance relationship remains poorly understood, and current multi-step fabrication methods limit precision and complexity. To overcome these challenges, we developed a multi-parametric finite element analysis (FEA) framework exploring 100 parameter combinations to optimize axial strain, enabling application-specific actuator designs based on geometry, size, and contraction capacity. We also propose a cost-effective monolithic fabrication process that eliminates multi-step casting and allows for complex 3D structures. Validated by pressure-strain experiments with only 4% error, our approach achieves a 21% strain improvement over state-of-the-art VAMPs, broadening their potential in wearable robotics and biomedical applications.
Bibliografia:ObjectType-Article-1
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2025.3632110