Modeling the mechanical behavior of fiber-reinforced polymeric composite materials using artificial neural networks—A review
Source abstract
Abstract
Abstract Artificial neural networks (ANN) have emerged as one of the useful artificial intelligence concepts used in the various engineering applications. Due to their massively parallel structure and ability to learn by example, ANN can deal with non-linear modeling for which an accurate analytical solution is difficult to obtain. ANN have already been used in medical applications, image and speech recognition, classification and control of dynamic systems, among others; but only recently have they been used in modeling the mechanical behavior of fiber-reinforced composite materials. This work is an attempt to reflect on the work done in the mechanical modeling of fiber-reinforced composite materials using ANN during the last decade.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Structural Health Monitoring Techniques · Ultrasonics and Acoustic Wave Propagation · Smart Materials for Construction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (64)
Show source-provided references
1. Kartalopoulos · 1996
2. Skapura · 1996
3. Schalkoff · 1997
4. Jang · 1997
5. Haykin · 1998
6. Aleksander · 1990
7. Fausett · 1994
8. Bishop · 1995
9. Swingler · 1996
10. A logic calculus of the ideas imminent in nervous activity10.1007/BF02478259 ↗
11. The perceptron: a probabilistic model for information storage and organization in the brain10.1037/h0042519 ↗
12. Rosenblatt · 1962
13. Kohonen · 1984
14. Powell MJD. Radial basis function for multi-variable interpolation—a review. In: Proceedings of IMA conference on algorithm for the approximation of function and data. RMCS, Shrivenham, 1985.
15. Artificial neural networks applied to polymer composites: a review10.1016/S0266-3538(03)00106-4 ↗
16. Intelligent model-based control of preform permeation in liquid composite molding processes, with online optimization10.1016/S1359-835X(01)00013-6 ↗
17. Closed-loop flow control in resin transfer molding using real-time numerical process simulations10.1016/S0266-3538(01)00213-5 ↗
18. Rapid cure simulation using artificial neural networks10.1016/S1359-835X(97)00046-8 ↗
19. Monitoring the process of curing of epoxy/graphite fiber composites with a recurrent neural network as a soft sensor
20. Soft computing in multidisciplinary aerospace design—new directions for research10.1016/S0376-0421(01)00015-X ↗
21. Material model for composites using neural networks. Technical notes10.2514/3.11810 ↗
22. Grimaldi D, Olivito RS, Surace L. Strain measurements in FRP composite materials by mean of neural networks. In: Proceeding of the 11th international conference on experimental mechanics, Oxford, UK, 1998. p. 1411–5.
23. Grimaldi D, Olivito RS, Surace L. Damage evaluation in FRP composites by means of neural networks. In: Fifth international conference on composite engineering, Las Vegas, Nevada, 1998. p. 683–4.
24. A neural diagnostic system for measuring strain in FRP composite materials10.1016/S0958-9465(02)00103-8 ↗
25. Failure prediction of fibre-reinforced materials with neural networks10.1177/073168449301201202 ↗
26. Failure of carbon/epoxy composite tubes under combined axial and torsional loading 1. Experimental results and predictions of biaxial strength by the use of neural networks10.1016/S0266-3538(99)00038-X ↗
27. Fatigue life prediction of unidirectional glass fiber/epoxy composite laminae using neural networks10.1016/S0263-8223(00)00179-3 ↗
28. Prediction of the fatigue life of unidirectional glass fiber/epoxy composite laminae using different neural network paradigms10.1016/S0263-8223(01)00152-0 ↗
29. Energy-based fatigue life prediction of fiberglass/epoxy composites using modular neural networks10.1016/S0263-8223(02)00071-5 ↗
30. NeuroSolutions software version 4.14, 2001. Available from: www.nd.com.
31. El Kadi H, Al-Assaf Y. The use of modular neural networks in the prediction of the fatigue life of fiberglass/epoxy composites, in preparation.
32. Prediction of fatigue strength of composite laminates by means of neural network10.4028/www.scientific.net/KEM.144.231 ↗
33. The use of neural networks for the prediction of fatigue lives of composite materials10.1016/S1359-835X(99)00027-5 ↗
34. NEUDESK. Neural Computer Sciences. Available from: www.neusciences.com.
35. Prediction of fatigue damage growth in notched composite laminates using an artificial neural network10.1016/S0266-3538(02)00261-0 ↗
36. MATLAB Neural Networks Toolbox of MathWorks. Available from: www.mathworks.com.
37. Dynamic mechanical properties of PTFE based short carbon fibre reinforced composites: experiments and artificial neural network prediction10.1016/S0266-3538(02)00036-2 ↗
38. Explicit and implicit viscoplastic models for polymeric composite10.1016/j.ijplas.2003.11.017 ↗
39. Al-Haik MS. Durability of a polymer matrix composite: a neural network approach. Ph.D. thesis, Mechanical Engineering, Florida State University, Tallahassee, FL, 2002.
40. A neural network thrust force controller to minimize delamination during drilling of graphite–epoxy laminates10.1016/0890-6955(96)00013-2 ↗
41. Delamination prediction in composite beams with built-in piezoelectric devices using modal analysis and neural network10.1088/0964-1726/5/3/012 ↗
42. Neuralworks Professional II. Available from: www.neuralware.com.
43. A thick composite-beam model for delamination prediction by the use of neural networks10.1016/S0266-3538(00)00063-4 ↗
44. Dynamic learning rate neural network training and composite structural damage detection10.2514/2.7480 ↗
45. Damage detection of CFRP laminates using electrical resistance measurement and neural network10.1016/S0263-8223(00)00016-7 ↗
46. The effect of number of electrodes and diagnostic tool for monitoring the delamination of CFRP laminates by changes in electrical resistance10.1016/S0266-3538(01)00088-4 ↗
47. Optimal ultrasonic pulse repetition rate for damage detection in plates using neural networks10.1016/S0963-8695(00)00078-5 ↗
48. Adaptive multilayer perceptron networks for detection of cracks in anisotropic laminated plates10.1016/S0020-7683(00)00368-1 ↗
49. Ultrasonic scanning and acoustic emission of composite tubes subjected to multiaxial loading10.1016/S0041-624X(97)00097-8 ↗
50. Identification of failure modes in GFRP using PVDF sensors: ANN approach10.1016/j.compstruct.2003.10.019 ↗
51. Estimation of contact force on composite plates using impact-induced strain and neural networks10.1016/S1359-8368(98)00003-1 ↗
52. Wear volume prediction with artificial neural networks10.1016/S0301-679X(00)00115-8 ↗
53. Prediction on tribological properties of short fibre composites using artificial neural networks10.1016/S0043-1648(02)00023-6 ↗
54. System identification and control of smart structures using neural networks10.1016/0094-5765(96)00053-7 ↗
55. Robust vibration control of composite beams using piezoelectric devices and neural networks10.1088/0964-1726/6/2/007 ↗
56. Self-adaptive vibration control of smart composite beams using recurrent neural architecture10.1016/S0020-7683(01)00125-1 ↗
57. Ross · 1995
58. A fuzzy-logic based approach to qualitative modeling10.1109/TFUZZ.1993.390281 ↗
59. Damage assessment of composite structures—a fuzzy logic integrated neural network approach10.1016/0045-7949(94)00624-C ↗
60. NISA. Engineering Mechanics Research Corporation. Available from: www.emrc.com.
61. Neural fuzzy analysis of delaminated composites from shearography imaging10.1016/S0263-8223(01)00103-9 ↗
62. A fuzzy set analysis for a fracture and fatigue damage response of composite materials10.1016/S0263-8223(01)00099-X ↗
63. Neuro-fuzzy modeling of fatigue life prediction of unidirectional glass fibre/epoxy composite laminates10.1177/0021998302036006176 ↗
64. ANFIS: adaptive network based fuzzy inference systems10.1109/21.256541 ↗