Two-Step Calibration Method for Inverse Finite Element with Small Sample Features
When the inverse finite element method (inverse FEM) is used to reconstruct the deformation field of a multi-element structure with strain measurements, strain measurement errors can lower the reconstruction accuracy of the deformation field. Furthermore, the calibration ability of a self-structurin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/16/4602 |
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| author | Libo Xu Feifei Zhao Jingli Du Hong Bao |
| author_facet | Libo Xu Feifei Zhao Jingli Du Hong Bao |
| author_sort | Libo Xu |
| collection | DOAJ |
| description | When the inverse finite element method (inverse FEM) is used to reconstruct the deformation field of a multi-element structure with strain measurements, strain measurement errors can lower the reconstruction accuracy of the deformation field. Furthermore, the calibration ability of a self-structuring fuzzy network (SSFN) is weak when few strain samples are used to train the SSFN. To solve this problem, a novel two-step calibration method for improving the reconstruction accuracy of the inverse FEM method is proposed in this paper. Initially, the errors derived from measured displacements and reconstructed displacements are distributed to the degrees of freedom (DOFs) of nodes. Then, the DOFs of nodes are used as knots, in order to produce non-uniform rational B-spline (NURBS) curves, such that the sample size employed to train the SSFN can be enriched. Next, the SSFN model is used to determine the relationship between the measured strain and the DOFs of the end nodes. A loading deformation experiment using a three-element structure demonstrates that the proposed algorithm can significantly improve the accuracy of reconstruction displacement. |
| first_indexed | 2024-03-10T17:21:14Z |
| format | Article |
| id | doaj.art-40b24fbaaaca4232ba17aed12cbdda16 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T17:21:14Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-40b24fbaaaca4232ba17aed12cbdda162023-11-20T10:19:35ZengMDPI AGSensors1424-82202020-08-012016460210.3390/s20164602Two-Step Calibration Method for Inverse Finite Element with Small Sample FeaturesLibo Xu0Feifei Zhao1Jingli Du2Hong Bao3Key Laboratory of Electronic Equipment Structure Design of Ministry of Education, Xidian University, Xi’an 710071, ChinaKey Laboratory of Electronic Equipment Structure Design of Ministry of Education, Xidian University, Xi’an 710071, ChinaKey Laboratory of Electronic Equipment Structure Design of Ministry of Education, Xidian University, Xi’an 710071, ChinaKey Laboratory of Electronic Equipment Structure Design of Ministry of Education, Xidian University, Xi’an 710071, ChinaWhen the inverse finite element method (inverse FEM) is used to reconstruct the deformation field of a multi-element structure with strain measurements, strain measurement errors can lower the reconstruction accuracy of the deformation field. Furthermore, the calibration ability of a self-structuring fuzzy network (SSFN) is weak when few strain samples are used to train the SSFN. To solve this problem, a novel two-step calibration method for improving the reconstruction accuracy of the inverse FEM method is proposed in this paper. Initially, the errors derived from measured displacements and reconstructed displacements are distributed to the degrees of freedom (DOFs) of nodes. Then, the DOFs of nodes are used as knots, in order to produce non-uniform rational B-spline (NURBS) curves, such that the sample size employed to train the SSFN can be enriched. Next, the SSFN model is used to determine the relationship between the measured strain and the DOFs of the end nodes. A loading deformation experiment using a three-element structure demonstrates that the proposed algorithm can significantly improve the accuracy of reconstruction displacement.https://www.mdpi.com/1424-8220/20/16/4602non-uniform rational B-splineinverse finite elementdeformation reconstructionfuzzy network |
| spellingShingle | Libo Xu Feifei Zhao Jingli Du Hong Bao Two-Step Calibration Method for Inverse Finite Element with Small Sample Features Sensors non-uniform rational B-spline inverse finite element deformation reconstruction fuzzy network |
| title | Two-Step Calibration Method for Inverse Finite Element with Small Sample Features |
| title_full | Two-Step Calibration Method for Inverse Finite Element with Small Sample Features |
| title_fullStr | Two-Step Calibration Method for Inverse Finite Element with Small Sample Features |
| title_full_unstemmed | Two-Step Calibration Method for Inverse Finite Element with Small Sample Features |
| title_short | Two-Step Calibration Method for Inverse Finite Element with Small Sample Features |
| title_sort | two step calibration method for inverse finite element with small sample features |
| topic | non-uniform rational B-spline inverse finite element deformation reconstruction fuzzy network |
| url | https://www.mdpi.com/1424-8220/20/16/4602 |
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