Skip to content
aiqbench takeMachine Learning

aiqbench analysis

This article describes the development of a robotic system specifically designed to automate the fabrication of dielectric elastomer actuators, focusing on optimizing spin coating and carbon nanotube electrode application processes. While robotics is involved, the core advancement lies in applying automation techniques directly to the manufacturing workflow for soft capacitors used as sensors and actuators within AI-driven systems or advanced material applications.
Source coveringarXiv
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

A Robotic System for Automated Manufacturing of Dielectric Elastomer Actuators

arXiv1 min read

arXiv:2608.00369v1 Announce Type: new Abstract: This letter presents an automated robotic manufacturing system for soft capacitors which operate as actuators and sensors. Emphasis is placed on the two processes that most directly govern device quality, dielectric layer formation by spin coating and carbon nanotube (CNT) electrode application by stamping. Twenty multilayer DEAs, each comprising 12 dielectric layers with a mean thickness of 55.37 +- 2.04 um and 11 alternating CNT electrodes, were fabricated reducing total process time by 14.2% and removing the operator from 56.1% of it.

Abstract:This letter presents an automated robotic manufacturing system for soft capacitors which operate as actuators and sensors. Emphasis is placed on the two processes that most directly govern device quality, dielectric layer formation by spin coating and carbon nanotube (CNT) electrode application by stamping. Twenty multilayer DEAs, each comprising 12 dielectric layers with a mean thickness of 55.37 +- 2.04 um and 11 alternating CNT electrodes, were fabricated reducing total process time by 14.2% and removing the operator from 56.1% of it.

Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2608.00369 [cs.RO]

(or arXiv:2608.00369v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.00369 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Full text under the publisher's open licence · 179 words

Related coverage