Investigation of uni- and bi-directional soft pneumatic actuators and its implication using rapid prototyping
Delan Winston Dsilva, Kevin Amith Mathias, Shivashankar Hiremath
- Year
- 2025
- Citations
- 3
- Access
- Open access
Abstract
Abstract In the age of automation and robotics, pneumatic grippers play a crucial role in material handling across diverse industrial sectors. This study explores the design and performance of uni- and bi-directional pneumatic grippers, which provide enhanced versatility and multi-directional gripping capabilities, using a rapid prototyping approach to address modern manufacturing needs. Through finite element analysis and experimental testing, the functionality of the gripper is evaluated, with a focus on hyperelastic materials like thermoplastic polyurethane. Three mesh techniques—automatic, tetrahedral, and hex-dominant—are analyzed, with the hex-dominant mesh proving the most effective for all models. Pressures up to 100 kPa, applied in 25 kPa increments to simulate real-world conditions, assessing deformation in both the uni- and bi-directional gripper mechanisms. Key design optimizations based on FE analysis include: a 1 mm gap between the chambers, which has shown an optimal deformation improvement of 109%, and a wall thickness ranging from 0.5 to 1 mm, which balances flexibility while minimizing deformation. In the bi-directional configuration, a chamber height of 4 mm achieves faster deformation with a maximum of 387.86%. Additionally, a 1 mm mid-layer deforms more rapidly, indicating that a thinner mid-layer enhances deformation and increases flexibility. The optimized Model-5, with refined geometry, successfully resolved issues of air leakage and printing defects, demonstrating effective bi-directional gripping capabilities and improved flexibility. The results validate the effectiveness of the design and analysis approach used in this study. These findings underscore the potential of bi-directional pneumatic grippers to transform material handling into industrial applications, offering significant improvements in adaptability and efficiency.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002