Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network.

Junling Hou, Xuan Lu, Kaining Zhang, Yidong Jing, Zhenjie Zhang, Junfeng You, Qun Li

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing.Polymers · 2023
Article
Identification of Hyperelastic Material Parameters of Elastomers by Reverse Engineering Approach.Materials (Basel, Switzerland) · 2022
Article

4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Junling HouState Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Xuan LuState Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Kaining ZhangState Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Yidong JingXi'an Modern Chemistry Research Institute, Xi'an 710065, China.

Zhenjie ZhangState Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Junfeng YouThe 41st Institute of the Forth Academy of CASC, Xi'an 710025, China.

Qun LiState Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Funding

China Postdoctoral Science Foundation 2020M673374Fundamental Research Funds for the Central Universities in China xjh012020014National Natural Science Foundation of China 12002256Natural Science Basic Research Plan in Shaanxi Province of China 2020JQ-011Natural Science Foundation of Jiangsu BK20200246Zhejiang Provincial Natural Science Foundation LQ20A020010

6 · The paper itself

Abstract

In this study, we present a systematic scheme to identify the material parameters in constitutive model of hyperelastic materials such as rubber. This approach is proposed based on the combined use of general regression neural network, experimental data and finite element analysis. In detail, the finite element analysis is carried out to provide the learning samples of GRNN model, while the results observed from the uniaxial tensile test is set as the target value of GRNN model. A problem involving parameters identification of silicone rubber material is described for validation. The results show that the proposed GRNN-based approach has the characteristics of high universality and good precision, and can be extended to parameters identification of complex rubber-like hyperelastic material constitutive.

Indexed as

general regression neural network (GRNN)hyperelastic material modelparameters identification

Identifiers

PMID35683072

DOI10.3390/ma15113776

PMCPMC9181827

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