Author:
Sikulskyi Stanislav,Ren Zefu,Mekonnen Danayit T.,Holyoak Aleiya,Srinivasaraghavan Govindarajan Rishikesh,Kim Daewon
Abstract
Dielectric elastomer actuator (DEA) is a smart material that holds promise for soft robotics due to the material’s intrinsic softness, high energy density, fast response, and reversible electromechanical characteristics. Like for most soft robotics materials, additive manufacturing (AM) can significantly benefit DEAs and is mainly applied to the unimorph DEA (UDEA) configuration. While major aspects of UDEA modeling are known, 3D printed UDEAs are subject to specific material and geometrical limitations due to the AM process and require a more thorough analysis of their design and performance. Furthermore, a figure of merit (FOM) is an analytical tool that is frequently used for planar DEA design optimization and material selection but is not yet derived for UDEA. Thus, the objective of the paper is modeling of 3D printed UDEAs, analyzing the effects of their design features on the actuation performance, and deriving FOMs for UDEAs. As a result, the derived analytical model demonstrates dependence of actuation performance on various design parameters typical for 3D printed DEAs, provides a new optimum thickness to Young’s modulus ratio of UDEA layers when designing a 3D printed DEA with fixed dielectric elastomer layer thickness, and serves as a base for UDEAs’ FOMs. The FOMs have various degrees of complexity depending on considered UDEA design features. The model was numerically verified and experimentally validated through the actuation of a 3D printed UDEA. The fabricated and tested UDEA design was optimized geometrically by controlling the thickness of each layer and from the material perspective by mixing commercially available silicones in non-standard ratios for the passive and dielectric layers. Finally, the prepared non-standard mix ratios of the silicones were characterized for their viscosity dynamics during curing at various conditions to investigate the silicones’ manufacturability through AM.
Subject
Artificial Intelligence,Computer Science Applications
Cited by
4 articles.
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