A novel normalized fatigue progressive damage model for complete stress levels based on artificial neural network
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OpenAlex topics: Fatigue and fracture mechanics · Engineering Applied Research · Ultrasonics and Acoustic Wave Propagation
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References (54)
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1. Recent progress of reinforcement materials: a comprehensive overview of composite materials10.1016/j.jmrt.2019.09.068 ↗
2. Review: Fatigue of Fiber-Reinforced Composites, Damage and Failure10.1007/s41745-021-00280-y ↗
3. Fatigue damage mechanisms in unidirectional carbon-fibre-reinforced plastics10.1023/A:1004684228765 ↗
4. Fatigue of Composites—Fatigue Modulus Concept and Life Prediction10.1177/002199838602000203 ↗
5. A new approach to fatigue analysis in composites based on residual strength degradation10.1016/S0263-8223(99)00093-8 ↗
6. A physical model and constitutive equations for complete characterization of S-N fatigue behavior of metals10.1016/j.actamat.2016.09.001 ↗
7. Probabilistic S-N fields based on statistical distributions applied to metallic and composite materials: State of the art10.1177/1687814019870395 ↗
8. From S-N to the Paris law with a new mixed-mode cohesive fatigue model for delamination in composites10.1016/j.tafmec.2020.102499 ↗
9. A cumulative damage model to predict the fatigue life of composite laminates including the effect of a fibre-matrix interphase10.1016/0142-1123(95)99735-S ↗
10. A study of factors affecting life prediction of composites under spectrum loading10.1016/j.ijfatigue.2008.07.010 ↗
11. Predicting Fatigue Damage of Composites Using Strength Degradation and Cumulative Damage Model10.3390/jcs2010009 ↗
12. A reliable progressive fatigue damage model for life prediction of composite laminates incorporating an adaptive cyclic jump algorithm10.1016/j.compscitech.2022.109587 ↗
13. An experimentally validated 3D progressive fatigue damage model for fatigue life prediction of Flax-epoxy laminates10.1016/j.compositesa.2022.107054 ↗
14. Progressive Fatigue Damage Modeling of Composite Materials, Part I: Modeling10.1177/002199830003401301 ↗
15. Progressive Fatigue Damage Modeling of Composite Materials, Part II: Material Characterization and Model Verification10.1177/002199830003401302 ↗
16. Modeling the variable amplitude fatigue of composite materials: A review and evaluation of the state of the art for spectrum loading10.1016/j.ijfatigue.2008.07.002 ↗
17. A fatigue damage model of composite materials10.1016/j.ijfatigue.2009.02.027 ↗
18. Influence of Stress Level and Fibre Volume Fraction on Fatigue Performance of Glass Fibre-Reinforced Polyester Composites10.3390/polym14132662 ↗
19. Progressive fatigue damage modeling of laminated composites using a novel combined fatigue life model10.1177/00219983241236943 ↗
20. Multiscale fatigue damage model for CFRP laminates considering the effect of progressive interface debonding
21. Benchmark test for mode I fatigue-driven delamination in GFRP composite laminates: Experimental results and simulation with the inter-laminar damage model implemented in SAMCEF10.1016/j.compositesb.2023.110529 ↗
22. Progressive Fatigue Modelling of Open-Hole Glass-Fibre Epoxy Laminates10.3390/jcs7120516 ↗
23. Modelling fatigue behaviours and lifetimes of novel GLARE laminates under random loading spectrum10.1016/j.compstruct.2023.116799 ↗
24. Experimental and numerical investigation on tension–tension fatigue behavior of three-dimensional five-directional hybrid braided composites10.1016/j.ijfatigue.2023.107975 ↗
25. A novel creep-fatigue life evaluation method for ceramic-composites components10.1016/j.ijmecsci.2023.108259 ↗
26. Analysis of fatigue damage in composite laminates10.1016/0142-1123(80)90022-5 ↗
27. Fracture of fatigue-loaded composite laminates10.1016/0142-1123(82)90001-9 ↗
28. Residual Stresses in a Composite with Continuously Varying Young’s Modulus in the Fiber/Matrix Interphase10.1177/002199839202600601 ↗
29. Multiaxial fatigue behaviour of unidirectional plies based on uniaxial fatigue experiments m I10.1016/S0142-1123(96)00074-6 ↗
30. Multiaxial fatigue behaviour of unidirectional plies based on uniaxial fatigue experiments—II. Experimental evaluation10.1016/S0142-1123(96)00068-0 ↗
31. Fatigue damage model for fibre-reinforced polymers at different temperatures considering stress ratio effects10.1177/0021998318773466 ↗
32. Predicting the Degradation of the Residual Strength in Cyclic Loading of Layered Composites10.1007/s11029-022-10047-w ↗
33. A numerical investigation of the interaction between interlaminar and intralaminar damages in a fatigued composite panel10.1111/ffe.13956 ↗
34. Artificial neural networks applied to polymer composites: a review10.1016/S0266-3538(03)00106-4 ↗
35. Fatigue modeling using neural networks: A comprehensive review10.1111/ffe.13640 ↗
36. The use of neural networks for the prediction of fatigue lives of composite materials10.1016/S1359-835X(99)00027-5 ↗
37. Fatigue life prediction of unidirectional glass fiber/epoxy composite laminae using neural networks
38. Prediction of the fatigue life of unidirectional glass fiber/epoxy composite laminae using different neural network paradigms
39. Energy-based fatigue life prediction of fiberglass/epoxy composites using modular neural networks10.1016/S0263-8223(02)00071-5 ↗
40. Prediction of fatigue damage growth in notched composite laminates using an artificial neural network10.1016/S0266-3538(02)00261-0 ↗
41. Fatigue life prediction of glass fiber reinforced epoxy composites using artificial neural networks10.1016/j.coco.2021.100812 ↗
42. Predicting shear strength of FRP-reinforced concrete beams using novel synthetic data driven deep learning10.1016/j.engstruct.2022.114083 ↗
43. Integrating machine learning and response surface methodology for analyzing anisotropic mechanical properties of biocomposites10.1080/09276440.2023.2260239 ↗
44. Optimized machine learning with hyperparameter tuning and response surface methodology for predicting tribological performance in bio-composite materials10.1002/pc.28418 ↗
45. Integrating response surface methodology and machine learning for analyzing the unconventional machining properties of hybrid fiber-reinforced composites10.1002/pc.28180 ↗
46. ASTM Standard D3039. Standard Test Method for Tensile Properties of Polymer Matrix Composite Materials. American Society for Testing and Materials, West Conshohocken, PA. 2014.
47. ASTM Standard D3518. Standard Test Method for In-Plane Shear Response of Polymer Matrix Composite Materials by Tensile Test of a ±45° Laminate. American Society for Testing and Materials, West Conshohocken, PA. 2013.
48. ASTM Standard D6641. Standard Test Method for Determining the Compressive Properties of Polymer Matrix Composite Laminates Using a Combined Loading Compression (CLC) Test Fixture. American Society for Testing and Materials, West Conshohocken, PA. 2009.
49. ASTM Standard D3479. Standard Test Method for Tension-Tension Fatigue of Polymer Matrix Composite Materials. American Society for Testing and Materials, West Conshohocken, PA. 2019.
50. An alternative three-node triangular composite shell element in terms of Reddy-type higher-order theory10.1016/j.tws.2021.108568 ↗
51. Life–prediction for constant–stress fatigue in carbon–fibre composites10.1098/rsta.1997.0055 ↗
52. Life prediction for fatigue of T800/5245 carbon-fibre composites: I10.1016/0142-1123(94)90478-2 ↗
53. Strength prediction and progressive damage analysis of carbon fiber reinforced polymer-laminate with circular holes by an efficient Artificial Neural Network10.1016/j.compstruct.2022.115835 ↗
54. Shape-preserving curve interpolation10.1080/00207160.2011.627434 ↗