Evidence map›Paper›PMID 37514748›Full record

ArticleSensors (Basel, Switzerland)2023

A 3D Anisotropic Thermomechanical Model for Thermally Induced Woven-Fabric-Reinforced Shape Memory Polymer Composites.

Yingyu Wang, Zhiyi Wang, Jia Ma, Chao Luo, Guangqiang Fang, Xiongqi Peng

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Yingyu WangInstitute of Aerospace System Engineering Shanghai, Shanghai 201108, China.ORCID 0000-0001-8023-6969
Zhiyi WangInstitute of Aerospace System Engineering Shanghai, Shanghai 201108, China.
Jia MaInstitute of Aerospace System Engineering Shanghai, Shanghai 201108, China.
Chao LuoSpace Structure and Mechanism Technology Laboratory of China Aerospace Science and Technology Group Co., Ltd., Shanghai 201108, China.
Guangqiang FangInstitute of Aerospace System Engineering Shanghai, Shanghai 201108, China.
Xiongqi PengSchool of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.

Funding

the National Natural Science Foundation, China 11972225
6 · The paper itself

Abstract

Soft robotic grippers offer great advantages over traditional rigid grippers with respect to grabbing objects with irregular or fragile shapes. Shape memory polymer composites are widely used as actuators and holding elements in soft robotic grippers owing to their finite strain, high specific strength, and high driving force. In this paper, a general 3D anisotropic thermomechanical model for woven fabric-reinforced shape memory polymer composites (SMPCs) is proposed based on Helmholtz free energy decomposition and the second law of thermodynamics. Furthermore, the rule of mixtures is modified to describe the stress distribution in the SMPCs, and stress concentration factors are introduced to account for the shearing interaction between the fabric and matrix and warp yarns and weft yarns. The developed model is implemented with a user material subroutine (UMAT) to simulate the shape memory behaivors of SMPCs. The good consistency between the simulation results and experimental validated the proposed model. Furthermore, a numerical investigation of the effects of yarn orientation on the shape memory behavior of the SMPC soft gripper was also performed.

Indexed as

anisotropicconstitutive modelshape memory polymer compositessoft robotic gripperwoven fabric reinforcement

Identifiers

PMID37514748
PMCPMC10383039

What OpenQuestion holds

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Registered trials

None linked

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.