Rapid assessment of out-of-plane nonlinear shear stress–strain response for thick-section composites using artificial neural networks and DIC. (15th April 2023)
- Record Type:
- Journal Article
- Title:
- Rapid assessment of out-of-plane nonlinear shear stress–strain response for thick-section composites using artificial neural networks and DIC. (15th April 2023)
- Main Title:
- Rapid assessment of out-of-plane nonlinear shear stress–strain response for thick-section composites using artificial neural networks and DIC
- Authors:
- Wei, Gaojian
Hao, Ziqing
Chen, Guangchang
Ke, Hongjun
Deng, Linlin
Liu, Liu - Abstract:
- Abstract: Thick-section fiber-reinforced polymer matrix composites (TSCs) have been increasingly used in primary and secondary load-bearing structures. Due to complex deformation mechanisms, accurate out-of-plane constitutive relations, including nonlinear shear stress–strain response, are required for structural analysis and failure prediction of TSC structures. A plate torsion test is proposed to assess the nonlinear out-of-plane shear stress–strain response using an artificial neural network (ANN) and digital image correlation (DIC). The ANN model was developed using three-dimensional finite element numerical simulations, and the out-of-plane shear stress was obtained with good accuracy. The efficiency of the shear stress calculation has been improved. The usability of the test method has been refined substantially. A comparison of the shear stress with the FEM-calculated result showed that the average error of the ANN-predicted shear stress was less than 0.5%. Accordingly, the constitutive parameters of a 15-ply thick S6C10/AC318 glass/epoxy unidirectional tape panel have been extracted from the minimization of the normalized error between ANN-calculated shear stress and DIC-measured shear strain iteratively. The efficiency of stress updating for the plate-torsion specimen has been improved by 10 5 times to extract the material constitutive parameters. Not limited to plate-torsion specimens, the proposed ANN-DIC method can be extended further for rapid assessment ofAbstract: Thick-section fiber-reinforced polymer matrix composites (TSCs) have been increasingly used in primary and secondary load-bearing structures. Due to complex deformation mechanisms, accurate out-of-plane constitutive relations, including nonlinear shear stress–strain response, are required for structural analysis and failure prediction of TSC structures. A plate torsion test is proposed to assess the nonlinear out-of-plane shear stress–strain response using an artificial neural network (ANN) and digital image correlation (DIC). The ANN model was developed using three-dimensional finite element numerical simulations, and the out-of-plane shear stress was obtained with good accuracy. The efficiency of the shear stress calculation has been improved. The usability of the test method has been refined substantially. A comparison of the shear stress with the FEM-calculated result showed that the average error of the ANN-predicted shear stress was less than 0.5%. Accordingly, the constitutive parameters of a 15-ply thick S6C10/AC318 glass/epoxy unidirectional tape panel have been extracted from the minimization of the normalized error between ANN-calculated shear stress and DIC-measured shear strain iteratively. The efficiency of stress updating for the plate-torsion specimen has been improved by 10 5 times to extract the material constitutive parameters. Not limited to plate-torsion specimens, the proposed ANN-DIC method can be extended further for rapid assessment of material constitutive parameters for other anisotropic polymer-based composite materials using complex, statically indeterminate tests. Highlights: The out-of-plane shear stress-strain response was extracted using the ANN-DIC method. Identification efficiency for the shear response has been increased by 1 0 5 times. The effect of the interaction between the image number and strain noise on identification is studied. The in-plane shear response is essential for extracting the out-of-plane shear behavior. … (more)
- Is Part Of:
- Composite structures. Volume 310(2023)
- Journal:
- Composite structures
- Issue:
- Volume 310(2023)
- Issue Display:
- Volume 310, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 310
- Issue:
- 2023
- Issue Sort Value:
- 2023-0310-2023-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-04-15
- Subjects:
- Plate torsion -- Artificial neural network -- Digital image correlation -- Out-of-plane -- Nonlinear shear behavior
Composite construction -- Periodicals
Composites -- Périodiques
624.18 - Journal URLs:
- http://www.sciencedirect.com/science/journal/02638223 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.compstruct.2023.116770 ↗
- Languages:
- English
- ISSNs:
- 0263-8223
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3364.970000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 26085.xml