Citation: | Zhang J M, Yang W D, Li Y. Application of artificial intelligence in composite materials. Advances in Mechanics, 2021, 51(4): 865-900 doi: 10.6052/1000-0992-21-019 |
[1] |
杜善义, 关志东. 2008. 我国大型客机先进复合材料技术应对策略思考. 复合材料学报, 1: 1-10 (Du S Y, Guan Z D. 2008. Strategic considerations for development of advanced composite technology for large commercial aircraft in China. Acta Materiae Compositae Sinica, 1: 1-10). doi: 10.3321/j.issn:1000-3851.2008.01.001
|
[2] |
宁莉, 杨绍昌, 冷悦, 任学明, 苏霞, 闫超. 2020. 先进复合材料在飞机上的应用及其制造技术发展概述. 复合材料科学与工程, 5: 123-128 (Ning L, Yang S C, Leng Y, Ren X M, Su X, Yan C. 2020. Overview of the application of advanced composite materials on aircraft and the development of its manufacturing technology. Composites Science and Engineering, 5: 123-128). doi: 10.3969/j.issn.1003-0999.2020.09.020
|
[3] |
亓欣波, 陈国锋, 李勇, 程宣, 李长鹏. 2019. 将基于神经网络的机器学习方法应用于增材制造——应用现状、当前挑战和未来前景. 工程, 5: 275-294 (Qi X B, Chen G F, Li Y, Cheng X, Li C P. 2019. Applying neural-network-based machine learning to additive manufacturing: current applications, challenges, and future perspectives. Engineering, 5: 275-294).
|
[4] |
吴陈铭, 戴澄恺, 王昌凌, 刘永进. 2019. 多自由度3D打印技术研究进展综述. 计算机学报, 42: 1918-1938 (Wu C M, Dai C K, Wang C L, Liu Y J. 2019. Recent Progress on Multi-DOF 3D Printing: A Survey. Chinese Journal of Computers, 42: 1918-1938). doi: 10.11897/SP.J.1016.2019.01918
|
[5] |
杨乃宾. 2008. 新一代大型客机复合材料结构. 航空学报, 3: 596-604 (Yang N B. 2008. Composite structures for new generation large commercial jet. Acta Aeronautica ET Astronautica Sinica, 3: 596-604). doi: 10.3321/j.issn:1000-6893.2008.03.010
|
[6] |
Addona D D, Teti R, Caprino G. 2012. Residual strength prediction of artificially damaged composite laminates based on neural networks. Journal of Intelligent & Fuzzy Systems, 23: 217-223.
|
[7] |
Al-Assadi M, El Kadi H, Deiab I M. 2009. Predicting the fatigue life of different composite materials using artificial neural networks. Applied Composite Materials, 17: 1-14.
|
[8] |
Al-Assadi M, El Kadi H A, Deiab I M. 2010. Using artificial neural networks to predict the fatigue life of different composite materials including the stress ratio effect. Applied Composite Materials, 18: 297-309.
|
[9] |
Aleksendrić D, Carlone P, Ćirović V. 2016. Optimization of the temperature-time curve for the curing process of thermoset matrix composites. Applied Composite Materials, 23: 1047-1063. doi: 10.1007/s10443-016-9499-y
|
[10] |
Alvarez-Montoya J, Carvajal-Castrillón A, Sierra-Pérez J. 2020. In-flight and wireless damage detection in a UAV composite wing using fiber optic sensors and strain field pattern recognition. Mechanical Systems and Signal Processing, 136: 106526. doi: 10.1016/j.ymssp.2019.106526
|
[11] |
Antil S K, Antil P, Singh S, Kumar A, Pruncu C I. 2020. Artificial neural network and response surface methodology based analysis on solid particle erosion behavior of polymer matrix composites. Materials (Basel)
|
[12] |
António C C. 2014. A memetic algorithm based on multiple learning procedures for global optimal design of composite structures. Memetic Computing, 6: 113-131. doi: 10.1007/s12293-014-0132-z
|
[13] |
António C C, Davim J P, Lapa V. 2007. Artificial neural network based on genetic learning for machining of polyetheretherketone composite materials. The International Journal of Advanced Manufacturing Technology, 39: 1101-1110.
|
[14] |
António C C, Hoffbauer L N. 2010. Uncertainty propagation in inverse reliability-based design of composite structures. International Journal of Mechanics and Materials in Design, 6: 89-102. doi: 10.1007/s10999-010-9123-5
|
[15] |
António C C, Hoffbauer L N. 2013. Uncertainty assessment approach for composite structures based on global sensitivity indices. Composite Structures, 99: 202-212. doi: 10.1016/j.compstruct.2012.12.001
|
[16] |
Apalak M K, Karaboga D, Akay B. 2013. The artificial bee colony algorithm in layer optimization for the maximum fundamental frequency of symmetrical laminated composite plates. Engineering Optimization, 46: 420-437.
|
[17] |
Artero-Guerrero J A, Pernas-Sánchez J, Martín-Montal J, Varas D, López-Puente J. 2018. The influence of laminate stacking sequence on ballistic limit using a combined Experimental/FEM/Artificial Neural Networks (ANN) methodology. Composite Structures, 183: 299-308. doi: 10.1016/j.compstruct.2017.03.068
|
[18] |
Aymerich F, Serra M. 1997. Prediction of fatigue strength of composite laminates by means of neural networks. Key Engineering Materials, 144: 231-242. doi: 10.4028/www.scientific.net/KEM.144.231
|
[19] |
Babu U H, Sai N V, Sahu R K. 2020. Artificial intelligence system approach for optimization of drilling parameters of glass-carbon fiber/polymer composites. Silicon. https://doi.org/10.1007/s12633-020-00637-5
|
[20] |
Bai G H, Meng S H, Zhang B M, Liu Y. 2008. Prediction on carbon/carbon composites ablative performance by artificial neutral net. Journal of Materials Science & Technology, 24: 945-952.
|
[21] |
Ball N R, Sargent P M, Ige D O. 1993. Genetic algorithm representations for laminate layups. Artificial Intelligence in Engineering, 8: 99-108. doi: 10.1016/0954-1810(93)90020-G
|
[22] |
Barbosa A, Upadhyaya P, Iype E. 2020. Neural network for mechanical property estimation of multilayered laminate composite. Materials Today: Proceedings, 28: 982-985. doi: 10.1016/j.matpr.2019.12.336
|
[23] |
Barry T J, Kesharaju M, Nagarajah C R, Palanisamy S. 2015. Defect characterisation in laminar composite structures using ultrasonic techniques and artificial neural networks. Journal of Composite Materials, 50: 861-871.
|
[24] |
Bezerra E M, Ancelotti A C, Pardini L C, Rocco J A F F, Iha K, Ribeiro C H C. 2007. Artificial neural networks applied to epoxy composites reinforced with carbon and E-glass fibers: Analysis of the shear mechanical properties. Materials Science and Engineering: A, 464: 177-185. doi: 10.1016/j.msea.2007.01.131
|
[25] |
Bhoopal R S, Luyt A S, Sharma P K, Singh R. 2015. Prediction of the mechanical properties of copper powder-filled low-density polyethylene composites. a comparison between the ann and theoretical models. Composites: Mechanics, Computations, Applications: An International Journal, 6: 53-73. doi: 10.1615/CompMechComputApplIntJ.v6.i1.30
|
[26] |
Bisagni C, Lanzi L. 2002. Post-buckling optimisation of composite stiffened panels using neural networks. Composite Structures, 58: 237-247. doi: 10.1016/S0263-8223(02)00053-3
|
[27] |
Bobbili R, Madhu V. 2020. A machine learning approach in drilling of E-glass woven composites. Mechanics Based Design of Structures and Machines, 1-9.
|
[28] |
Cacciola M, Lay-Ekuakille A, Megali G. 2013. Incremental Bayesian learning for in-service analysis of aeronautic composites. IET Science, Measurement & Technology, 7: 334-342.
|
[29] |
Califano A, Chandarana N, Grassia L, D’Amore A, Soutis C. 2020. Damage detection in composites by artificial neural networks trained by using in situ distributed strains. Applied Composite Materials, 27: 657-671. doi: 10.1007/s10443-020-09829-z
|
[30] |
Chakraborty D. 2005. Artificial neural network based delamination prediction in laminated composites. Materials & Design, 26: 1-7.
|
[31] |
Chandrashekhara K, Okafor A C, Jiang Y P. 1998. Estimation of contact force on composite plates using impact-induced strain and neural networks. Composites Part B-Engineering, 29: 363-370.
|
[32] |
Chen C T, Gu G X. 2020. Generative deep neural networks for inverse materials design using backpropagation and active learning. Adv Sci (Weinh)
|
[33] |
Chen R T Q, Rubanova Y, Bettencourt J, Duvenaud D. 2018. Neural ordinary differential equations. advances in neural information processing systems 31, La Jolla
|
[34] |
Choi J H, Lee D G. 1995. Expert cure system for the carbon-fiber epoxy composite-materials. Journal of Composite Materials, 29: 1181-1200. doi: 10.1177/002199839502900903
|
[35] |
Cupertino L F, Vilela Neto O P, Pacheco M A C, Vellasco M B R, d'Almeida J R M. 2011. Modeling the Young modulus of nanocomposites: a neural network approach// 2011 International Joint Conference on Neural Networks, San Jose, CA
|
[36] |
Daghigh V, Lacy T E, Daghigh H, Gu G, Baghaei K T, Horstemeyer M F, Pittman C U. 2020. Machine learning predictions on fracture toughness of multiscale bio-nano-composites. Journal of Reinforced Plastics and Composites, 39: 587-598. doi: 10.1177/0731684420915984
|
[37] |
De Fenza A, Sorrentino A, Vitiello P. 2015. Application of Artificial Neural Networks and Probability Ellipse methods for damage detection using Lamb waves. Composite Structures, 133: 390-403. doi: 10.1016/j.compstruct.2015.07.089
|
[38] |
Eder M A, Chen X. 2020. Fastigue: A computationally efficient approach for simulating discrete fatigue crack growth in large-scale structures. Engineering Fracture Mechanics, 233: 107075. doi: 10.1016/j.engfracmech.2020.107075
|
[39] |
Ehsani A, Dalir H. 2019. Multi-objective optimization of composite angle grid plates for maximum buckling load and minimum weight using genetic algorithms and neural networks. Composite Structures, 229: 111450. doi: 10.1016/j.compstruct.2019.111450
|
[40] |
El Kadi H. 2008. Predicting the crushing behavior of axially loaded elliptical composite tubes using artificial neural networks. Applied Composite Materials, 15: 273-285. doi: 10.1007/s10443-008-9074-2
|
[41] |
Erdal O, Sonmez F O. 2005. Optimum design of composite laminates for maximum buckling load capacity using simulated annealing. Composite Structures, 71: 45-52. doi: 10.1016/j.compstruct.2004.09.008
|
[42] |
Fernández-Fdz D, López-Puente J, Zaera R. 2008. Prediction of the behaviour of CFRPs against high-velocity impact of solids employing an artificial neural network methodology. Composites Part A: Applied Science and Manufacturing, 39: 989-996. doi: 10.1016/j.compositesa.2008.03.002
|
[43] |
Freirejr R, Neto A, Deaquino E. 2007. Use of modular networks in the building of constant life diagrams. International Journal of Fatigue, 29: 389-396. doi: 10.1016/j.ijfatigue.2006.06.005
|
[44] |
Fu T, Zhang Z, Liu Y, Leng J. 2015. Development of an artificial neural network for source localization using a fiber optic acoustic emission sensor array. Structural Health Monitoring: An International Journal, 14: 168-177. doi: 10.1177/1475921714568406
|
[45] |
Fu X, Ricci S, Bisagni C. 2015. Minimum-weight design for three dimensional woven composite stiffened panels using neural networks and genetic algorithms. Composite Structures, 134: 708-715. doi: 10.1016/j.compstruct.2015.08.077
|
[46] |
Gautam G D, Mishra D R. 2019. Firefly algorithm based optimization of kerf quality characteristics in pulsed Nd:YAG laser cutting of basalt fiber reinforced composite. Composites Part B: Engineering, 176: 107340. doi: 10.1016/j.compositesb.2019.107340
|
[47] |
Geng X, Lu S, Jiang M, Sui Q, Lv S, Xiao H, Jia Y, Jia L. 2018. Research on FBG-based cfrp structural damage identification using bp neural network. Photonic Sensors, 8: 168-175. doi: 10.1007/s13320-018-0466-0
|
[48] |
Gerrard D D, Fullwood D T, Halverson D M. 2014. Correlating structure topological metrics with bulk composite properties via neural network analysis. Computational Materials Science, 91: 20-27. doi: 10.1016/j.commatsci.2014.04.014
|
[49] |
Ghaboussi J, Pecknold D A, Zhang M F, Haj-Ali R M. 1998. Autoprogressive training of neural network constitutive models. International Journal for Numerical Methods in Engineering, 42: 105-126. doi: 10.1002/(SICI)1097-0207(19980515)42:1<105::AID-NME356>3.0.CO;2-V
|
[50] |
Ghanem R, Soize C, Mehrez L, Aitharaju V. 2020. Probabilistic learning and updating of a digital twin for composite material systems. International Journal for Numerical Methods in Engineering, doi:10.1002/nme.6430" target="_blank">10.1002/nme.6430">doi:10.1002/nme.6430
|
[51] |
Graham D, Maas P, Donaldson G B, Carr C. 2004. Impact damage detection in carbon fibre composites using HTS SQUIDs and neural networks. NDT & E International, 37: 565-570.
|
[52] |
Gu G X, Chen C-T, Buehler M J. 2018a. De novo composite design based on machine learning algorithm. Extreme Mechanics Letters, 18: 19-28. doi: 10.1016/j.eml.2017.10.001
|
[53] |
Gu G X, Chen C-T, Richmond D J, Buehler M J. 2018b. Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment. Materials Horizons, 5: 939-945. doi: 10.1039/C8MH00653A
|
[54] |
Hanafi I, Khamlichi A, Cabrera F M, Nuñez López P J, Jabbouri A. 2012. Fuzzy rule based predictive model for cutting force in turning of reinforced PEEK composite. Measurement, 45: 1424-1435. doi: 10.1016/j.measurement.2012.03.022
|
[55] |
Herencia J E, Weaver P M, Friswell M I. 2007. Optimization of long anisotropic laminated fiber composite panels with T-shaped stiffeners. Aiaa Journal, 45: 2497-2509. doi: 10.2514/1.26321
|
[56] |
Hinton G E, Salakhutdinov R R. 2006. Reducing the dimensionality of data with neural networks. Science, 313: 504-507. doi: 10.1126/science.1127647
|
[57] |
Hornik K. 1991. Approximation capabilities of multilayer feedforward networks. Neural Networks, 4: 251-257. doi: 10.1016/0893-6080(91)90009-T
|
[58] |
Jac Fredo A R, Abilash R S, Femi R, Mythili A, Kumar C S. 2019. Classification of damages in composite images using Zernike moments and support vector machines. Composites Part B: Engineering, 168: 77-86. doi: 10.1016/j.compositesb.2018.12.064
|
[59] |
Jayatheertha C, Webber J P H, Morton S K. 1996. Application of artificial neural networks for the optimum design of a laminated plate. Computers & Structures, 59: 831-845.
|
[60] |
Jeon W S, Song J H. 2002. An expert system for estimation of fatigue properties of metallic materials. International Journal of Fatigue, 24: 685-698. doi: 10.1016/S0142-1123(01)00184-0
|
[61] |
Jiang Z, Gyurova L A, Schlarb A K, Friedrich K, Zhang Z. 2008. Study on friction and wear behavior of polyphenylene sulfide composites reinforced by short carbon fibers and sub-micro TiO2 particles. Composites Science and Technology, 68: 734-742. doi: 10.1016/j.compscitech.2007.09.022
|
[62] |
Jin Z, Zhang Z, Gu G X. 2019. Automated real-time detection and prediction of interlayer imperfections in additive manufacturing processes using artificial intelligence. Advanced Intelligent Systems, 2: 1900130.
|
[63] |
Just-Agosto F, Serrano D, Shafiq B, Cecchini A. 2008. Neural network based nondestructive evaluation of sandwich composites. Composites Part B: Engineering, 39: 217-225. doi: 10.1016/j.compositesb.2007.02.023
|
[64] |
Kalantari M, Dong C, Davies I J. 2017. Effect of matrix voids, fibre misalignment and thickness variation on multi-objective robust optimization of carbon/glass fibre-reinforced hybrid composites under flexural loading. Composites Part B: Engineering, 123: 136-147. doi: 10.1016/j.compositesb.2017.05.022
|
[65] |
Kalita K, Mukhopadhyay T, Dey P, Haldar S. 2019. Genetic programming-assisted multi-scale optimization for multi-objective dynamic performance of laminated composites: The advantage of more elementary-level analyses. Neural Computing and Applications, 32: 7969-7993.
|
[66] |
Kamarian S, Shakeri M, Yas M H. 2018. Natural frequency analysis and optimal design of CNT/fiber/polymer hybrid composites plates using mori-tanaka approach, GDQ technique, and firefly algorithm. Polymer Composites, 39: 1433-1446. doi: 10.1002/pc.24083
|
[67] |
Kazi M K, Eljack F, Mahdi E. 2020. Optimal filler content for cotton fiber/PP composite based on mechanical properties using artificial neural network. Composite Structures, 251: 112654. doi: 10.1016/j.compstruct.2020.112654
|
[68] |
Khan A, Kim H S. 2018. Assessment of delaminated smart composite laminates via system identification and supervised learning. Composite Structures, 206: 354-362. doi: 10.1016/j.com |