Soft Mechanosensing via 3D Printing: A review

Autor: Diana Cafiso, Simone Lantean, Candido Fabrizio Pirri, Lucia Beccai
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Advanced Intelligent Systems, Vol 5, Iss 6, Pp n/a-n/a (2023)
Druh dokumentu: article
ISSN: 2640-4567
DOI: 10.1002/aisy.202200373
Popis: 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.
Databáze: Directory of Open Access Journals