Evidence map›Paper›PMID 36422459›Full record

ArticleMicromachines2022

An Optimization Design Method of Rigid-Flexible Soft Fingers Based on Dielectric Elastomer Actuators.

Fuhao Ouyang, Yuanlin Guan, Chunyu Yu, Xixin Yang, Qi Cheng, Jiawei Chen, Juan Zhao, Qinghai Zhang, Yang Guo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Micromachines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Fuhao OuyangSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China.
Yuanlin GuanSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China.ORCID 0000-0001-5035-9562
Chunyu YuSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China.
Xixin YangSchool of Automation, Qingdao University, Qingdao 266017, China.
Qi ChengSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China.
Jiawei ChenSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China.
Juan ZhaoSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China.
Qinghai ZhangSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China.
Yang GuoSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China.

Funding

Shandong Provincial Government, China 2019JZZY020101Shandong Provincial Government, China 2021TSGC1063Shandong Provincial Government, China ZR2019PEE018
6 · The paper itself

Abstract

The soft gripper has received extensive attention, due to its good adaptability and flexibility. The dielectric elastomer (DE) actuator as a flexible electroactive polymer that provides a new approach for soft grippers. However, they have the disadvantage of having a poor rigidity. Therefore, the optimization design method of a rigid-flexible soft finger is presented to improve the rigidity of the soft finger. We analyzed the interaction of the rigid and soft materials, using the finite element method (FEM), and researched the influence of the parameters (compression of the spring and pre-stretching ratio of the DE) on the bending angle. The optimal parameters were obtained using the FEM. We experimentally verified the accuracy of the proposed method. The maximum bending angle is 19.66°. Compared with the theoretical result, the maximum error is 3.84%. Simultaneously, the soft gripper with three fingers can grasp various objects and the maximum grasping quality is 11.21 g.

Indexed as

bending angledielectric elastomeroptimization design methodsoft finger

Identifiers

PMID36422459
PMCPMC9693624

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.