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https://doi.org/10.1109/roboso...
Article . 2019 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
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A Multi-Material Self-Healing Soft Gripper

Authors: Ellen Roels; Seppe Terryn; Joost Brancart; Guy Van Assche; Bram Vanderborght;

A Multi-Material Self-Healing Soft Gripper

Abstract

In the field of soft robotics, the material selection plays an important role and markedly influences the properties of the actuators. More complex actuators can be manufactured by combining the strengths of multiple materials in a single design. To allow this, a good connection between the different materials is indispensable. Making a physical connection between flexible materials, having different properties, is difficult and leads to failure and damage due to stress concentrations at the interface. This is why in soft robots, most of the time single-material actuators are used. In this work, re-mendable elastomeric polymers are used to construct multi-material soft actuators. These Diels-Alder polymers consist of a thermore-versible covalent network that allows chemical bonding at the interface between two parts. Two Diels-Alder polymers were synthesised with contrasting mechanical properties. Although, having dissimilar Young's moduli, these different materials can chemically bind at the interface, resulting in a very strong connection. This principle was elaborated in a dual-material tendon-driven soft gripper. Additionally, the reversible network allows to heal damages using mild heating. This healing ability was demonstrated by subsequently damaging and completely healing the dual-material soft actuator multiple times.

Country
Belgium
Related Organizations
Keywords

smart materials, Self-healing materials, Diels-Alder polymers, Soft Robot Materials and Design

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    23
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
23
Top 10%
Top 10%
Top 10%
Green