journal article·2015-09-28

Effect of bladder wall thickness on miniature pneumatic artificial muscle performance

Thomas E Pillsbury, Curt S Kothera, Norman M Wereley

Bioinspiration & Biomimetics· Volume 10· Issue 5
Publisher page

Abstract

Pneumatic artificial muscles (PAMs) are actuators known for their high power to weight ratio, natural compliance and light weight. Due to these advantages, PAMs have been used for orthotic devices and robotic limbs. Small scale PAMs have the same advantages, as well as requiring greatly reduced volumes with potential application to prostheses and small scale robotics. The bladder of a PAM affects common actuator performance metrics, specifically: blocked force, free contraction, hysteresis, and dead-band pressure. This paper investigates the effect that bladder thickness has on static actuation performance of small scale PAMs. Miniature PAMs were fabricated with a range of bladder thicknesses to quantify the change in common actuator performance metrics specifically: blocked force, free contraction, and dead-band pressure. These PAMs were then experimentally characterized in quasi-static conditions, where results showed that increasing bladder wall thickness decreases blocked force and free contraction, while dead-band pressure increases. A nonlinear model was then applied to determine the structure of the stress-strain relationship that enables accurate modeling and the minimum number of terms. Two nonlinear models are compared and the identified parameters are analyzed to study the effect of the bladder thickness on the model.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Prosthetics and Rehabilitation Robotics · Muscle activation and electromyography studies · Advanced Sensor and Energy Harvesting Materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (31)

Show source-provided references
1. 10.1177/1045389X1143349510.1177/1045389X11433495 ↗
2. 10.1177/1045389X1143543510.1177/1045389X11435435 ↗
3. 10.1177/1045389X1142421610.1177/1045389X11424216 ↗
4. 10.2514/6.2012-150910.2514/6.2012-1509 ↗
5. Doepke E B · Proc. SPIE 9061, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace 2014 · 2014
6. 10.1177/1045389X1141765310.1177/1045389X11417653 ↗
7. 10.1007/BF0258444010.1007/BF02584440 ↗
8. 10.1177/027836490505243710.1177/0278364905052437 ↗
9. 10.1177/027836490606934310.1177/0278364906069343 ↗
10. 10.1109/ROBOT.1999.77242310.1109/ROBOT.1999.772423 ↗
11. Boblan I · Embodied Artificial Intelligence · 2003 · 160
12. 10.1109/IROS.2007.439896310.1109/IROS.2007.4398963 ↗
13. Park Y-L · Bioinspiration Biomimetics · 2014 · 9
14. Azizi E · Bioinspiration Biomimetics · 2013 · 810.1088/1748-3182/8/2/026002 ↗
15. 10.1115/1.315898210.1115/1.3158982 ↗
16. 10.1177/1045389X1453343010.1177/1045389X14533430 ↗
17. 10.1109/37.34186310.1109/37.341863 ↗
18. 10.3390/act302004110.3390/act3020041 ↗
19. 10.1109/ROBOT.2010.550939310.1109/ROBOT.2010.5509393 ↗
20. Vashisth A · ASME 2013 Conf. on Smart Material Adaptive Structure Intelligence System (Snowbird, UT) · 2013 · 2 · 1
21. Hocking E G · Smart Mater. Struct. · 2013 · 2210.1088/0964-1726/22/1/014016 ↗
22. 10.1177/1045389X1141886010.1177/1045389X11418860 ↗
23. 10.1109/IROS.2001.97627410.1109/IROS.2001.976274 ↗
24. 10.1177/1045389X1243862710.1177/1045389X12438627 ↗
25. 10.1016/j.sna.2011.01.00210.1016/j.sna.2011.01.002 ↗
26. 10.1177/1045389X1143174310.1177/1045389X11431743 ↗
27. Chen Y · Smart Mater. Struct. · 2011 · 20
28. 10.1177/1045389X1454987210.1177/1045389X14549872 ↗
29. 10.1117/12.200953310.1117/12.2009533 ↗
30. 10.1109/70.48175310.1109/70.481753 ↗
31. 10.1115/1.48247810.1115/1.482478 ↗