A fast and efficient numerical prediction of compression after impact (CAI) strength of composite laminates and structures

Ouyang, Tian; Bao, Rui; Sun, Wei; Guan, Zhidong; Tan, Riming

doi:10.1016/j.tws.2019.106588

Cited by 39 publications

(14 citation statements)

References 47 publications

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“…Combined with Figure 8A, it can be seen that the significant increase of the dent depth after the knee point corresponds to the fiber failure on the impact side. This phenomenon has also been observed experimentally in Ref[35], as shown in Figure 12. Therefore, it can be concluded that the formation of the impact dent before the knee point is mainly related to matrix plasticity and damage caused by high stresses around the impactor, while the significant increase of the dent depth after the knee point is mainly attributed to fiber failure on the impact side.…”

Section: Resultssupporting

confidence: 86%

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Prediction of low‐velocity impact dent for composite laminates based on an anisotropic elastoplastic damage model

et al. 2021

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In this paper, a three-dimensional finite element (FE) model based on an anisotropic elastoplastic damage model is established to simulate the impact dent of composite laminates subjected to low velocity impact (LVI) by implementing a user-defined subroutine VUMAT in Abaqus/Explicit. To validate the FE model, numerical results are compared with the experimental data in terms of impact dent depth, contact force-time response, and projected delamination shape. The simulation shows that considering tensioncompression asymmetry in the elastoplastic constitutive relation has a significant influence on the prediction of dent depth. Besides, it is found that before the knee point, the formation of the impact dent is mainly related to matrix plasticity and damage; while after the knee point, the significant increase of dent depth is caused by fiber breakage on the impact side. The increase of bending stiffness for thicker laminates can decrease the overall deflection, which leads to less fiber breakage and less visible dent.

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Section: Resultssupporting

confidence: 86%

“…Impact dents before and at the knee point [35] F I G U R E 1 3 Comparison of the projected delamination area between experimental and numerical results…”

Section: Effect Of Dilatational Stress Terms On the Prediction Of Dent Depthmentioning

confidence: 99%

Prediction of low‐velocity impact dent for composite laminates based on an anisotropic elastoplastic damage model

et al. 2021

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“…Gaitanelis et al [ 35 ] discussed the influence of the property of the cohesive elements in the FEM on the prediction results of the residual compression strength of the I‐shaped stiffened CFRP laminate after LVI load, and thought that enlarging the number of the cohesive elements appropriately could ensure the accuracy of the simulation results of the residual compressive strength. Ouyang et al [ 36 ] and Sun et al [ 37 ] presented an equivalent damage FEM based on the virtual crack closure technique and softened inclusion in ABAQUS software to improve the prediction results of the final failure load of the composite stiffened panel with double L‐shaped and multiple T‐shaped stiffener subjected to the compression after LVI load respectively. Both of them confirmed that the fracture toughness of the matrix could affect the prediction results of the residual compression strength after LVI load memorably, and the fracture of the damaged stiffener would definitely contribute to the globe buckling and ultimate failure of the composite stiffened panel.…”

Section: Introductionmentioning

confidence: 99%

A novel methodology to research the residual compressive mechanical properties of composite stiffened panel after low velocity impact based on quantification damage simulation

Xiao

et al. 2022

Polymer Composites

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Composite stiffened panels have become one of the most usual components in the design process of the aircraft's structure due to their splendid mechanical properties in the last decades. The main purpose of this work is investigating and presenting a more precise method to forecast the residual compressive mechanical behavior of the composite stiffened panel. First of all, an axial compression after impact (CAI) test is performed to understand the failure mechanism of different type of L‐shaped stiffened carbon fiber reinforced polymer panels with barely visible impact damage, which is introduced by the low velocity impact test. Subsequently, a finite element model, which contains a quantification damage model, is established in ABAQUS software based on the CAI test results to predict the mechanical behavior of these composite stiffened panels during the CAI test process. Both of the experimental and predicted results demonstrate that the propagation of the delamination caused by LVI is going to exert a destructive influence on the total failure of the composite stiffened panel, and the prediction results of the residual compressive strength of these composite stiffened panels are well consistent to that measured in the CAI test.

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“…To alleviate the problems related to data, a variety of methods have been adopted by many scholars. Data augmentation [ 18 ], which artificially generates training data, helps expand the available data samples. Specialized learning algorithms [ 19 ] such as transfer learning, which transfer knowledge from the domain where training data are abundant to the target domain where data are scarce.…”

Section: Introductionmentioning

confidence: 99%

Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models

Liao

Qiu

Yang³

et al. 2022

Polymers

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Experimental and numerical investigations are presented for a theory-guided machine learning (ML) model that combines the Hashin failure theory (HFT) and the classical lamination theory (CLT) to optimize and accelerate the design of composite laminates. A finite element simulation with the incorporation of the HFT and CLT were used to generate the training dataset. Instead of directly mapping the relationship between the ply angles of the laminate and its strength and stiffness, a multi-layer interconnected neural network (NN) system was built following the logical sequence of composite theories. With the forward prediction by the NN system and the inverse optimization by genetic algorithm (GA), a benchmark case of designing a composite tube subjected to the combined loads of bending and torsion was studied. The ML models successfully provided the optimal layup sequences and the required fiber modulus according to the preset design targets. Additionally, it shows that the machine learning models, with the guidance of composite theories, realize a faster optimization process and requires less training data than models with direct simple NNs. Such results imply the importance of domain knowledge in helping improve the ML applications in engineering problems.

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A fast and efficient numerical prediction of compression after impact (CAI) strength of composite laminates and structures

Cited by 39 publications

References 47 publications

Prediction of low‐velocity impact dent for composite laminates based on an anisotropic elastoplastic damage model

Prediction of low‐velocity impact dent for composite laminates based on an anisotropic elastoplastic damage model

A novel methodology to research the residual compressive mechanical properties of composite stiffened panel after low velocity impact based on quantification damage simulation

Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models

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