Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network
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 fi...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Materials |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1944/15/11/3776 |
| _version_ | 1797492841063120896 |
|---|---|
| author | Junling Hou Xuan Lu Kaining Zhang Yidong Jing Zhenjie Zhang Junfeng You Qun Li |
| author_facet | Junling Hou Xuan Lu Kaining Zhang Yidong Jing Zhenjie Zhang Junfeng You Qun Li |
| author_sort | Junling Hou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:09:28Z |
| format | Article |
| id | doaj.art-98a3cf7bc11a41eb8df6985e14b52ae7 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T01:09:28Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-98a3cf7bc11a41eb8df6985e14b52ae72023-11-23T14:20:00ZengMDPI AGMaterials1996-19442022-05-011511377610.3390/ma15113776Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural NetworkJunling Hou0Xuan Lu1Kaining Zhang2Yidong Jing3Zhenjie Zhang4Junfeng You5Qun Li6State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaState Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaState Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaXi’an Modern Chemistry Research Institute, Xi’an 710065, ChinaState Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaThe 41st Institute of the Forth Academy of CASC, Xi’an 710025, ChinaState Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaIn 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.https://www.mdpi.com/1996-1944/15/11/3776general regression neural network (GRNN)hyperelastic material modelparameters identification |
| spellingShingle | Junling Hou Xuan Lu Kaining Zhang Yidong Jing Zhenjie Zhang Junfeng You Qun Li Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network Materials general regression neural network (GRNN) hyperelastic material model parameters identification |
| title | Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network |
| title_full | Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network |
| title_fullStr | Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network |
| title_full_unstemmed | Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network |
| title_short | Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network |
| title_sort | parameters identification of rubber like hyperelastic material based on general regression neural network |
| topic | general regression neural network (GRNN) hyperelastic material model parameters identification |
| url | https://www.mdpi.com/1996-1944/15/11/3776 |
| work_keys_str_mv | AT junlinghou parametersidentificationofrubberlikehyperelasticmaterialbasedongeneralregressionneuralnetwork AT xuanlu parametersidentificationofrubberlikehyperelasticmaterialbasedongeneralregressionneuralnetwork AT kainingzhang parametersidentificationofrubberlikehyperelasticmaterialbasedongeneralregressionneuralnetwork AT yidongjing parametersidentificationofrubberlikehyperelasticmaterialbasedongeneralregressionneuralnetwork AT zhenjiezhang parametersidentificationofrubberlikehyperelasticmaterialbasedongeneralregressionneuralnetwork AT junfengyou parametersidentificationofrubberlikehyperelasticmaterialbasedongeneralregressionneuralnetwork AT qunli parametersidentificationofrubberlikehyperelasticmaterialbasedongeneralregressionneuralnetwork |