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Advanced Intelligent Systems
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Soft Mechanosensing via 3D Printing: A review

Authors: Cafiso, Diana; Lantean, Simone; Pirri, Candido; Beccai, Lucia;

Soft Mechanosensing via 3D Printing: A review

Abstract

Recently, 3D printing, or additive manufacturing (AM), is emerging as a unique tool to fabricate soft mechanical sensors. Advanced performances can be obtained owing to the inherent 3D structures that enable enhanced and anisotropic deformations, to the multi‐material approach, and to the seamless fabrication procedure leading to higher reliability. Nevertheless, despite the remarkable advantages, the printing of soft and conductive materials shows consistent challenges. This review provides an extensive analysis of the current progress of 3D printing of soft mechanical sensors, which mainly rely on resistive and capacitive transduction. First, the most common materials used are described, like soft matrixes, conductive fillers, and polymers. Then, the 3D printers that are most widely adopted for the fabrication of soft sensors are identified, and the specific advantages and the difficulties of each technology are examined. Finally, by reporting exemplary case studies from the literature, an overview of the scientific progresses on this topic is provided. The unique advantages led by 3D printing are highlighted, in terms of multiple materials, the feasibility of achieving complex geometries, and the advanced and programmed sensors properties.

Country
Italy
Keywords

TK7885-7895, Computer engineering. Computer hardware, Control engineering systems. Automatic machinery (General), capacitive sensors, resistive sensors, soft mechanical sensors, TJ212-225, soft robots, 3D printing

  • BIP!
    Impact byBIP!
    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).
    19
    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).
    Average
    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!
19
Top 10%
Average
Top 10%
Green
gold