journal article·2025-08

3D damage evolution and microstructural-based machine learning model for stiffness prediction in woven composite under cyclic loads

Mritunjay M. Hiremath, Nikhar Doshi, Timo Bernthaler, Pascal Anger, Sushil K. Mishra, Anirban Guha, Asim Tewari

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OpenAlex topics: Mechanical Behavior of Composites · Fiber-reinforced polymer composites · Epoxy Resin Curing Processes

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References (64)

Show source-provided references
1. Effect of transient overloads on the long term performance of glass fibre reinforced polymer (GFRP) pipes for power station cooling water systems
2. Creep-fatigue interactions in glass fibre/polyester composites10.1016/0266-3538(89)90020-1 ↗
3. Characterization and modeling of fatigue behavior of chopped glass fiber reinforced sheet molding compound (SMC) composite10.1016/j.ijfatigue.2021.106647 ↗
4. Durability of a graphite/epoxy woven composite under combined hygrothermal conditions10.1016/S0142-1123(00)00041-4 ↗
5. An engineering point of view about fatigue of polymer matrix composite materials10.1016/j.ijfatigue.2006.02.008 ↗
6. Micromechanisms in tension-compression fatigue of composite laminates containing transverse plies10.1016/S0266-3538(98)00061-X ↗
7. Constitutive modeling of woven composites considering asymmetric/anisotropic, rate dependent, and nonlinear behavior10.1016/j.compositesa.2007.08.003 ↗
8. Influence of the mesostructure on the fatigue behaviour of textile composites10.1016/j.compositesa.2024.108114 ↗
9. F. Kara, A. Çiçek, H. Demir Effect of Deep Cryogenic Treatment on Microstructure, Mechanical Properties, and Residual Stress of AISI 52100 Bearing Steel. Https://WwwEspublisherCom/ 2023;26:960-. https://doi.org/10.30919/ES960.10.30919/es960 ↗
10. Prediction of the cyclic durability of woven composite laminates10.1016/j.compscitech.2008.04.033 ↗
11. A multiaxial fatigue failure criterion based on the principal constant life diagrams for unidirectional carbon/epoxy laminates10.1016/j.compositesa.2012.03.003 ↗
12. A three-segment anisomorphic constant life diagram for the fatigue of symmetric angle-ply carbon/epoxy laminates at room temperature10.1016/j.compositesa.2010.06.012 ↗
13. J. Whitney A Residual Strength Degradation Model for Competing Failure Modes. Long-Term Behavior of Composites, ASTM International100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959; 1983, p. 225–45. https://doi.org/10.1520/STP31825S.10.1520/STP31825S ↗
14. Fatigue damage modeling of fibre-reinforced composite materials10.1115/1.1381395 ↗
15. Woven-fibre thermoset composites10.1533/9781855738577.3.296 ↗
16. R. Kumar, R. Talreja Fatigue damage evolution in woven fabric composites. Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, vol. 1, AIAA; 2000, p. 1841–9. https://doi.org/10.2514/6.2000-1685.10.2514/6.2000-1685 ↗
17. Non-destructive assessment of the fatigue strength and damage progression of satin woven fiber reinforced polymer matrix composites10.1016/j.compositesb.2014.11.005 ↗
18. A generalized damage model for woven ply laminates under static and fatigue loading conditions10.1016/j.ijfatigue.2009.02.016 ↗
19. Fatigue crack constraint in plain-woven CFRP using newly-developed spread tows10.1016/j.ijfatigue.2006.02.010 ↗
20. A VHCF life prediction method based on surface crack density for FRP10.1016/j.ijfatigue.2018.04.028 ↗
21. Fatigue damage evolution in woven composites with different architectures10.1016/j.ijfatigue.2022.107365 ↗
22. A multiscale strategy for assessing the micro-scale stress distribution in the matrix of textile composites10.1016/j.compositesa.2022.107026 ↗
23. A comparative study of twill weave reinforced composites under tension–tension fatigue loading: Experiments and meso-modelling10.1016/j.compstruct.2015.09.005 ↗
24. Fatigue and post-fatigue stress–strain analysis of a 5-harness satin weave carbon fibre reinforced composite10.1016/j.compscitech.2012.09.012 ↗
25. Nesting in textile laminates: geometrical modelling of the laminate10.1016/S0266-3538(02)00318-4 ↗
26. Elastic behavior of woven fabric composites: II—laminate analysis10.1177/002199839202601503 ↗
27. Local strain in a 5-harness satin weave composite under static tension: Part I - Experimental analysis10.1016/j.compscitech.2011.03.021 ↗
28. Local strain in a 5-harness satin weave composite under static tension: Part II - Meso-FE analysis10.1016/j.compscitech.2011.03.020 ↗
29. K. Reifsnider, E. Henneke, W. Stinchcomb, J. Duke Damage mechanics and NDE of composite laminates. In Mechanics of composite materials. Pergamon. Int J Fatigue 16;1:193–201.
30. Microstructural damage dependent stiffness prediction of unidirectional CFRP composite under cyclic loading10.1016/j.compositesa.2017.05.010 ↗
31. The accumulation of damage in a glass-reinforced plastic under tensile and fatigue loading
32. Observations on damage development in fibre reinforced polymer laminates under cyclic loading10.1016/S0142-1123(02)00118-4 ↗
33. Damage and fatigue in composites – A personal account10.1016/j.compscitech.2008.04.042 ↗
34. Russ · 2000 · vol. 2
35. Ervin · 1973
36. Influence of support vector regression (SVR) on cryogenic face milling10.1155/2021/9984369 ↗
37. Prediction of stiffness degradation based on machine learning: Axial elastic modulus of [0m /90n ]s composite laminates10.1016/j.compscitech.2021.109186 ↗
38. A novel method to predict the stiffness evolution of in-service wind turbine blades based on deep learning models10.1016/j.compstruct.2020.112702 ↗
39. Comparison of damage mechanisms in chopped strand mat and woven roving mat composites under cyclic tension10.1002/pc.28539 ↗
40. D3171-22 (2022). ASTM D3171 Standard Test Methods for Constituent Content of Composite Materials 2022. https://doi.org/10.1520/D3171-22.10.1520/D3171-22 ↗
41. Effects of fiber orientation and anisotropy on tensile strength and elastic modulus of short fiber reinforced polymer composites10.1016/j.compositesb.2014.11.041 ↗
42. Damage development in open-hole composite specimens in fatigue. Part 1: Experimental investigation10.1016/j.compstruct.2013.05.033 ↗
43. Water-induced degradations in MWCNT embedded glass fiber/epoxy composites: An emphasis on aging temperature10.1002/app.45987 ↗
44. W. Burger, M.J. Burge Principles of Digital Image Processing. London: Springer London; 2009. https://doi.org/10.1007/978-1-84800-191-6.10.1007/978-1-84800-195-4 ↗
45. Stiffness degradation of woven roving GFRP due to shear and compression fatigue damage10.1016/j.compstruct.2024.118725 ↗
46. Fatigue life and damage evolution in woven GFRP angle ply laminates10.1016/j.ijfatigue.2020.105964 ↗
47. Effects of void geometry on elastic properties of unidirectional fiber reinforced composites10.1016/j.compscitech.2005.02.019 ↗
48. Evolution of damage during the fatigue of 3D woven glass-fibre reinforced composites subjected to tension–tension loading observed by time-lapse X-ray tomography10.1016/j.compositesa.2015.09.001 ↗
49. Micro and meso modeling of woven composites: Transverse cracking kinetics and homogenization10.1016/j.compstruct.2014.06.035 ↗
50. Simulating in‐plane fatigue damage in woven glass fibre‐reinforced composites subject to fully reversed cyclic loading10.1111/j.1460-2695.2004.00851.x ↗
51. The appearance of fatigue striations in the SEM
52. Study on a cracked single-layer 2D woven composite plate: Fracture mode and damage analysis10.1007/s11223-018-0025-3 ↗
53. In-situ CT characterization of 2D woven SiCf/SiC composite loading under compression10.1515/secm-2022-0166 ↗
54. Fatigue damage modeling techniques for textile composites: Review and comparison with unidirectional composite modeling techniques10.1115/1.4029691 ↗
55. Recovering shear stiffness degradation curves from classification data with a neural network approach10.1007/s11440-023-01879-4 ↗
56. G. James, D. Witten, T. Hastie, R. Tibshirani, J. Taylor An Introduction to Statistical Learning. Cham: Springer International Publishing; 2023. https://doi.org/10.1007/978-3-031-38747-0.10.1007/978-3-031-38747-0 ↗
57. S.A. Glantz, B.K. Slinker, T.B. Neilands Primer of applied regression & analysis of variance 2016:1183.
58. Yield prediction of apricot using a hybrid particle swarm optimization-imperialist competitive algorithm- support vector regression (PSO-ICA-SVR) method10.1016/j.scienta.2019.108756 ↗
59. Support vector machines for classification
60. I. Goodfellow, Y. Bengio, A. Courville, J. Heaton Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning. Genetic Programming and Evolvable Machines 2017 19:1 2017;19:305–7. https://doi.org/10.1007/S10710-017-9314-Z.10.1007/s10710-017-9314-z ↗
61. Scikit-learn: Machine learning in python
62. Keras: The python deep learning library
63. Prediction of damage factor in end milling of glass fibre reinforced plastic composites using artificial neural network10.1007/s10443-012-9286-3 ↗
64. Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions10.1007/s10822-020-00314-0 ↗