Abstract

In this study, to develop soft pressure sensor applicable to wearable robots using stretchable polymers and conductive fillers, 3.25 wt% carbon nanotubes/thermoplastic polyurethane filament with shore 94 A were manufactured. Three infill densities (20%, 50%, and 80%) and patterns (zigzag (ZG), triangle (TR), honeycomb (HN)) were applied to print cubes via fused filament fabrication 3D printing. Most suitable infill conditions were confirmed based on the slicing images, morphologies, compressive properties, electrical properties, and electrical heating properties. For each infill pattern, ZG and TR divided the layers into lines and figures, and the layers were stacked by rotation. For HN, the same layers were stacked in a hexagonal pattern. Consequently, TR divided layer in various directions, showed the strongest compressive properties with toughness 1.99 J for of infill density 80%. Especially, the HN became tougher with increased infill density. Also, the HN laminated with the same layer showed excellent electrical properties, with results greater than 14.7 mA. The electrical heating properties confirmed that ZG and HN had the high layer density, which exhibited excellent heating characteristics. Therefore, it was confirmed that performance varies depending on the 3D printing direction, and it was confirmed that HN is suitable for manufacturing soft sensors.

Details

Title
Study on CNT/TPU cube under the 3D printing conditions of infill patterns and density
Author
Jung, Imjoo 1 ; Shin, Eun Joo 2 ; Lee, Sunhee 3 

 Dong-A University, Department of Fashion and Textiles, Busan, Republic of Korea (GRID:grid.255166.3) (ISNI:0000 0001 2218 7142) 
 Dong-A University, Department of Organic Materials and Polymer Engineering, Busan, Republic of Korea (GRID:grid.255166.3) (ISNI:0000 0001 2218 7142) 
 Dong-A University, Department of Fashion and Textiles, Busan, Republic of Korea (GRID:grid.255166.3) (ISNI:0000 0001 2218 7142); Dong-A University, Department of Fashion Design, Busan, Republic of Korea (GRID:grid.255166.3) (ISNI:0000 0001 2218 7142) 
Pages
17728
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2878560790
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.