Strength and life in thermoplastic composite laminates under static and fatigue loads. Part I: Experimental

Jen, Ming Hwa R.; Lee, C.-H

doi:10.1016/s0142-1123(98)00029-2

Cited by 95 publications

(38 citation statements)

References 15 publications

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“…Thus plots of the ultimate axial tensile stress versus the fiber orientation angle, h, need not be symmetric about h = 45°; the same holds for the plot of the axial strain at failure versus the fiber orientation angle. Jen and Lee [42] Fig. 9a give a value of 129 MPa.…”

Section: Off-axis Loadsmentioning

confidence: 96%

“…For five configurations simulated, identically oriented fibers in each ply made an angle of 15°, 30°, 45°, 60°or 75°counterclockwise with the X 1 -axis. Weeks and Sun [41], and Jen and Lee [42] have experimentally tested these specimens. Note that results of these experiments are not used to find values of material parameters for the AS4/PEEK composite.…”

Section: Off-axis Loadsmentioning

confidence: 99%

“…Thus the comparison of computed results with experimental observations of Refs. [41,42] provides a good test for the proposed model.…”

Section: Off-axis Loadsmentioning

confidence: 99%

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Modeling damage in polymeric composites

Hassan

Batra

2008

Composites Part B: Engineering

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Section: Off-axis Loadsmentioning

confidence: 96%

Section: Off-axis Loadsmentioning

confidence: 99%

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Modeling damage in polymeric composites

Hassan

Batra

2008

Composites Part B: Engineering

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“…Experimental fatigue data collected from a variety of published works [19][20][21][22][23][24][25][26][27] was used to train and test the ANN. This data included results obtained for various types of composite materials with numerous fiber orientation angles subjected to several values of the stress ratio.…”

Section: Experimental Fatigue Datamentioning

confidence: 99%

Using Artificial Neural Networks to Predict the Fatigue Life of Different Composite Materials Including the Stress Ratio Effect

2010

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Artificial Neural Networks (ANN) have been successfully used in predicting the fatigue behavior of fiber-reinforced composite materials. In most cases, the predictions were obtained for the same material used in training subjected to different loading conditions. The method would be of greater value if one could predict the failure of materials other than those used for training the network. In a recent paper, ANN trained using the experimental fatigue data obtained for composites subjected to a constant stress ratio R ¼ s min =s max ð Þ was successfully used to predict the cyclic behavior of a composite made of a different material. In this work, this method is extended to include the stress ratio effect. The results show that ANN can provide accurate fatigue life prediction for different materials under different values of the stress ratio. These results can allow for the development of a materials smart database that can be used for various engineering applications.

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“…Martin and Murri (1990) performed fatigue delamination growth experiments for two different load ratios, R ¼ 0.1, 0.5, on unidirectional AS4/PEEK laminated composite material. The material properties for AS4/PEEK laminate are given in Table 4 (Jen and Lee, 1998).…”

Section: Finite Element Simulationsmentioning

confidence: 99%

High Cycle Fatigue Damage Model for Delamination Crack Growth in CF/Epoxy Composite Laminates

Gornet

Ijaz

2010

International Journal of Damage Mechanics

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This article presents the development of a fatigue damage model which helps to carry out simulation of the evolution of delamination in the laminated composite structures under cyclic loadings. A classical interface damage evolution law, which is commonly used to predict the static debonding process, is modified further to incorporate fatigue delamination effects due to high cycle loadings. An improved formulation is also presented to incorporate the ‘R’ ratio effects. The proposed fatigue damage model is identified using fracture mechanics tests like double cantilever beam, end-notched-flexure and mixed-mode bending. Then a non-monotonic behavior is used to predict the fatigue damage parameters able to carry out delamination simulations for different mode mixtures. Linear Paris plot behaviors of the above-mentioned fracture mechanics tests are successfully compared with available experimental data on HTA/6376C and AS4/PEEK unidirectional materials.

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Strength and life in thermoplastic composite laminates under static and fatigue loads. Part I: Experimental

Cited by 95 publications

References 15 publications

Modeling damage in polymeric composites

Modeling damage in polymeric composites

Using Artificial Neural Networks to Predict the Fatigue Life of Different Composite Materials Including the Stress Ratio Effect

High Cycle Fatigue Damage Model for Delamination Crack Growth in CF/Epoxy Composite Laminates

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