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Polymer Bulletin

09-01-2025 | REVIEW PAPER

Advancements of machine learning techniques in fiber-filled polymer composites: a review

Authors: R. Alagulakshmi, R. Ramalakshmi, Arumugaprabu Veerasimman, Geetha Palani, Manickam Selvaraj, Sanjay Basumatary

Published in: Polymer Bulletin

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Abstract

The integration of machine learning (ML) techniques in the characterization and optimization of fiber-filled polymer composites is a topic of increasing importance in industries such as aerospace, automotive, and construction. Traditional experimental methods for characterizing these composites can be time-consuming and limited in scope, driving the adoption of ML approaches. This review article explores various ML paradigms and their applications in polymer composite manufacturing and process simulation. The objective of the study is to investigate ML-based methods for predicting mechanical properties, optimizing fabrication processes, conducting microstructure analysis, and predictive modeling of composite performance. Furthermore, the review addresses challenges and identifies future research opportunities in leveraging ML for advancing composite material design and optimization. By synthesizing current research findings and highlighting potential areas for development, this review contributes to the ongoing exploration of ML’s role in revolutionizing the field of fiber-filled polymer composites.

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Shahzad A (2018) Investigation into fatigue strength of natural/synthetic fiber-based composite materials. Elsevier, Amsterdam

Khalid MY, Al Rashid A, Arif ZU, Ahmed W, Arshad H, Zaidi AA (2021) Natural fiber reinforced composites: sustainable materials for emerging applications. Res Eng 11:100263. https://doi.org/10.1016/j.rineng.2021.100263CrossRef

Mittal V, Saini R, Sinha S (2016) Natural fiber-mediated epoxy composites—a review. Compos B Eng 99:425–435. https://doi.org/10.1016/j.compositesb.2016.06.051CrossRef

Väisänen T, Das O, Tomppo L (2017) A review on new bio-based constituents for natural fiber-polymer composites. Elsevier Ltd., AmsterdamCrossRef

Pedro HTC, Coimbra CFM, David M, Lauret P (2018) Assessment of machine learning techniques for deterministic and probabilistic intra-hour solar forecasts. Renew Energy 123:191–203. https://doi.org/10.1016/j.renene.2018.02.006CrossRef

Sarkar S, Vinay S, Raj R, Maiti J, Mitra P (2019) Application of optimized machine learning techniques for prediction of occupational accidents. Comput Oper Res 106:210–224. https://doi.org/10.1016/j.cor.2018.02.021CrossRef

Sreenivasulu R (2013) Optimization of surface roughness and delamination damage of GFRP composite material in end milling using taguchi design method and artificial neural network. Procedia Eng 64:785–794. https://doi.org/10.1016/j.proeng.2013.09.154CrossRef

Sharma A, Mukhopadhyay T, Rangappa SM, Siengchin S, Kushvaha V (2022) Advances in computational intelligence of polymer composite materials: machine learning assisted modeling, analysis and design. Arch Comput Methods Eng 29(5):3341–3385. https://doi.org/10.1007/S11831-021-09700-9CrossRef

Xu H, Liu R, Choudhary A, Chen W (2015) A machine learning-based design representation method for designing heterogeneous microstructures. J Mech Des. https://doi.org/10.1115/1.4029768CrossRef

10.

Qi Z, Zhang N, Liu Y, Chen W (2019) Prediction of mechanical properties of carbon fiber based on cross-scale FEM and machine learning. Compos Struct 212:199–206. https://doi.org/10.1016/j.compstruct.2019.01.042CrossRef

11.

Kalita K, Mukhopadhyay T, Dey P, Haldar S (2020) Genetic programming-assisted multi-scale optimization for multi-objective dynamic performance of laminated composites: the advantage of more elementary-level analyses. Neural Comput Appl 32(12):7969–7993. https://doi.org/10.1007/s00521-019-04280-zCrossRef

12.

Msekh MA, Cuong NH, Zi G, Areias P, Zhuang X, Rabczuk T (2018) Fracture properties prediction of clay/epoxy nanocomposites with interphase zones using a phase field model. Eng Fract Mech 188:287–299. https://doi.org/10.1016/j.engfracmech.2017.08.002CrossRef

13.

Cassola S, Duhovic M, Schmidt T, May D (2022) Machine learning for polymer composites process simulation – a review. Compos B Eng 246:110208. https://doi.org/10.1016/J.COMPOSITESB.2022.110208CrossRef

14.

Francisco M, Revollar S, Vega P, Lamanna R (2005) A comparative study of deterministic and stochastic optimization methods for integrated design of processes. IFAC Proc 38:335–340. https://doi.org/10.3182/20050703-6-cz-1902.00917CrossRef

15.

Reifsnider KL (1994) Modelling of the interphase in polymer-matrix composite material systems. Composites 25(7):461–469. https://doi.org/10.1016/0010-4361(94)90170-8CrossRef

16.

Maithil P, Gupta P, Chandravanshi ML (2023) Study of mechanical properties of the natural-synthetic fiber reinforced polymer matrix composite. Mater Today Proc. https://doi.org/10.1016/J.MATPR.2023.01.245CrossRef

17.

Shirke MB, Shelke SN (2020) Significance of chemical treatment on the performance of natural fiber: a review by. i-Manager’s J Mater Sci 8(2):23

18.

Milosevi DS, Pickering KL (2017) Characterizing the mechanical properties of fused deposition modelling natural fiber recycled polypropylene composites. J Compos Sci 1(1):7. https://doi.org/10.3390/jcs1010007CrossRef

19.

Sgriccia N, Hawley MC, Misra M (2008) Characterization of natural fiber surfaces and natural fiber composites. Compos Part A Appl Sci Manuf 39:1632–1637. https://doi.org/10.1016/j.compositesa.2008.07.007CrossRef

20.

Characterization of silane-treated and untreated natural fibers from stem of Leucas Aspera: Journal of Natural Fibers: vol 18(12). Accessed: Jan. 04, 2024. [Online]. Available: https://www.tandfonline.com/doi/abs/https://doi.org/10.1080/15440478.2019.1710651

21.

Gholampour A, Ozbakkaloglu T (2019) A review of natural fiber composites: properties, modification and processing techniques, characterization, applications. J Mater Sci 55(3):829–892. https://doi.org/10.1007/s10853-019-03990-yCrossRef

22.

Sanjay MR, Siengchin S, Parameswaranpillai J, Jawaid M, Pruncu CI, Khan A (2019) A comprehensive review of techniques for natural fibers as reinforcement in composites: preparation, processing and characterization. Elsevier Ltd., Amsterdam

23.

Sanjay MR, Madhu P, Jawaid M, Senthamaraikannan P, Senthil S, Pradeep S (2018) Characterization and properties of natural fiber polymer composites: a comprehensive review. Elsevier Ltd., Amsterdam

24.

Senthamaraikannan P, Kathiresan M (2018) Characterization of raw and alkali treated new natural cellulosic fiber from Coccinia grandis.L. Carbohydr Polym 186:332–343. https://doi.org/10.1016/j.carbpol.2018.01.072CrossRefPubMed

25.

Breister AM et al (2020) Soil microbiomes mediate degradation of vinyl ester-based polymer composites. Commun Mater 1(1):101. https://doi.org/10.1038/s43246-020-00102-1CrossRef

26.

Alagumalai V, Shanmugam V, Balasubramanian NK, Krishnamoorthy Y, Ganesan V, Försth M, Sas G, Berto F, Chanda A, Das O (2021) Impact response and damage tolerance of hybrid glass/kevlar-fibre epoxy structural composites. Polymers 13(16):2591. https://doi.org/10.3390/POLYM13162591CrossRefPubMedPubMedCentral

27.

Mouritz AP, Mathys Z (2000) Mechanical properties of fire-damaged glass-reinforced phenolic composites. Fire Mater 24(2):67–75. https://doi.org/10.1002/1099-1018(200003/04)24:2%3c67::AID-FAM720%3e3.0.CO;2-0CrossRef

28.

Hussain F, Roy S, Narasimhan K, Vengadassalam K, Lu H (2007) E-glass/polypropylene pultruded nanocomposite: Manufacture, characterisation, thermal and mechanical properties. Polym Polym Compos 15(4):91–102. https://doi.org/10.1177/0892705707079604CrossRef

29.

Courgneau C, Rusu D, Henneuse C, Ducruet V, Lacrampe MF, Krawczak P (2013) Characterisation of low-odour emissive polylactide/cellulose fibre biocomposites for car interior. Express Polym Lett 7(9):787–804. https://doi.org/10.3144/expresspolymlett.2013.76CrossRef

30.

Carlone P, Aleksendrić D, Rubino F, Ćirović V (2018) Artificial neural networks in advanced thermoset matrix composite manufacturing, Lecture Notes in Mechanical Engineering, 0(9783319895628), pp 78–88, https://doi.org/10.1007/978-3-319-89563-5_5.

31.

Sacco C, Baz Radwan A, Anderson A, Harik R, Gregory E (2020) Machine learning in composites manufacturing: a case study of automated fiber placement inspection. Compos Struct. https://doi.org/10.1016/J.COMPSTRUCT.2020.112514CrossRef

32.

Selvaraj SK, Raj A, Rishikesh Mahadevan R, Chadha U, Paramasivam V (2022) A review on machine learning models in injection molding machines. Adv Mater Sci Eng. https://doi.org/10.1155/2022/1949061CrossRef

33.

Humfeld KD, Dawei G, Butler GA, Nelson K, Zobeiry N (2021) A machine learning framework for real-time inverse modeling and multi-objective process optimization of composites for active manufacturing control. Compos Part B: Eng 223:109150. https://doi.org/10.1016/j.compositesb.2021.109150CrossRef

34.

Brasington A, Sacco C, Halbritter J, Wehbe R, Harik R (2021) Automated fiber placement: a review of history, current technologies, and future paths forward. Compos Part C: Open Access. https://doi.org/10.1016/J.JCOMC.2021.100182CrossRef

35.

Oromiehie E, Prusty BG, Compston P, Rajan G (2019) Automated fibre placement based composite structures: review on the defects, impacts and inspections techniques. Compos Struct. https://doi.org/10.1016/J.COMPSTRUCT.2019.110987CrossRef

36.

Bleiziffer P, Hofmann J, Zboray R, Wiege T, Herger R (2021) Predicting the fiber orientation in glass fiber reinforced polymers using the moment of inertia and convolutional neural networks. Eng Appl Artif Intell. https://doi.org/10.1016/J.ENGAPPAI.2021.104351CrossRef

37.

Yarlagadda PKDV (2001) Prediction of processing parameters for injection moulding by using a hybrid neural network. Proc Inst Mech Eng B J Eng Manuf 215(10):1465–1470. https://doi.org/10.1243/0954405011519097CrossRef

38.

Hürkamp A et al (2020) Combining simulation and machine learning as digital twin for the manufacturing of overmolded thermoplastic composites. J Manuf Mater Process 4(3):92. https://doi.org/10.3390/JMMP4030092CrossRef

39.

Hürkamp A, Gellrich S, Dér A, Herrmann C, Dröder K, Thiede S (2021) Machine learning and simulation-based surrogate modeling for improved process chain operation. Int J Adv Manuf Technol 117(7–8):2297–2307. https://doi.org/10.1007/S00170-021-07084-5CrossRef

40.

Sabiston T, Inal K, Lee-Sullivan P (2020) Application of artificial neural networks to predict fibre orientation in long fibre compression moulded composite materials. Compos Sci Technol. https://doi.org/10.1016/J.COMPSCITECH.2020.108034CrossRef

41.

Zimmerling C, Poppe C, Stein O, Kärger L (2022) Optimisation of manufacturing process parameters for variable component geometries using reinforcement learning. Mater Des. https://doi.org/10.1016/J.MATDES.2022.110423CrossRef

42.

Mulenga TK, Ude AU, Vivekanandhan C (2021) Techniques for modelling and optimizing the mechanical properties of natural fiber composites: a review. Fibers 9(1):6. https://doi.org/10.3390/fib9010006CrossRef

43.

Zimmerling C, Trippe D, Fengler B, Kärger L (2019) An approach for rapid prediction of textile draping results for variable composite component geometries using deep neural networks. AIP Conf Proc. https://doi.org/10.1063/1.5112512CrossRef

44.

Zimmerling C, Dörr D, Henning F, Kärger L (2019) A machine learning assisted approach for textile formability assessment and design improvement of composite components. Compos Part A Appl Sci Manuf. https://doi.org/10.1016/J.COMPOSITESA.2019.05.027CrossRef

45.

Khalid MY, Al Rashid A, Arif ZU, Sheikh MF, Arshad H, Nasir MA (2021) Tensile strength evaluation of glass/jute fibers reinforced composites: An experimental and numerical approach. Res Eng 10:100232. https://doi.org/10.1016/j.rineng.2021.100232CrossRef

46.

Goh GD, Sing SL, Yeong WY (2021) A review on machine learning in 3D printing: applications, potential, and challenges. Artif Intell Rev 54(1):63–94. https://doi.org/10.1007/S10462-020-09876-9CrossRef

47.

Nardi D, Sinke J (2021) Design analysis for thermoforming of thermoplastic composites: prediction and machine learning-based optimization. Compos Part C: Open Access. https://doi.org/10.1016/J.JCOMC.2021.100126CrossRef

48.

Matos MAS, Pinho ST, Tagarielli VL (2019) Application of machine learning to predict the multiaxial strain-sensing response of CNT-polymer composites. Carbon N Y 146:265–275. https://doi.org/10.1016/J.CARBON.2019.02.001CrossRef

49.

Xue T, Wallin TJ, Menguc Y, Adriaenssens S, Chiaramonte M (2020) Machine learning generative models for automatic design of multi-material 3D printed composite solids. Extrem Mech Lett. https://doi.org/10.1016/J.EML.2020.100992CrossRef

50.

Zimmerling C, Dörr D, Henning F, Kärger L (2018) A meta-model based approach for rapid formability estimation of continuous fibre reinforced components. AIP Conf Proc. https://doi.org/10.1063/1.5034843CrossRef

51.

Reiner J, Vaziri R, Zobeiry N (2021) Machine learning assisted characterisation and simulation of compressive damage in composite laminates. Compos Struct. https://doi.org/10.1016/j.compstruct.2021.114290CrossRef

52.

Pattnaik P, Sharma A, Choudhary M, Singh V, Agarwal P, Kukshal V (2021) Role of machine learning in the field of fiber reinforced polymer composites: a preliminary discussion. Mater Today Proc 44:4703–4708. https://doi.org/10.1016/J.MATPR.2020.11.026CrossRef

53.

Khalid MY, Arif ZU, Al Rashid A (2022) Investigation of tensile and flexural behavior of green composites along with their impact response at different energies. Int J Precis Eng Manuf-Green Technol 9(5):1399–1410. https://doi.org/10.1007/s40684-021-00385-wCrossRef

54.

Phunpeng V, Saensuriwong K, Kerdphol T, Uangpairoj P (2023) The flexural strength prediction of carbon fiber/epoxy composite using artificial neural network approach. Materials 16(15):5301. https://doi.org/10.3390/ma16155301CrossRefPubMedPubMedCentral

55.

Machello C, Bazli M, Rajabipour A, Rad HM, Arashpour M, Hadigheh A (2023) Using machine learning to predict the long-term performance of fibre-reinforced polymer structures: a state-of-the-art review. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2023.133692CrossRef

56.

Axinte A, Taranu N, Bejan L, Hudisteanu I (2017) Optimisation of fabric reinforced polymer composites using a variant of genetic algorithm. Appl Compos Mater 24(6):1479–1491. https://doi.org/10.1007/S10443-017-9594-8CrossRef

57.

Golkarnarenji G, Naebe M, Badii K, Milani AS, Jazar RN, Khayyam H (2019) A machine learning case study with limited data for prediction of carbon fiber mechanical properties. Comput Ind 105:123–132. https://doi.org/10.1016/j.compind.2018.11.004CrossRef

58.

Yaghoobi H, Fereidoon A (2018) Modeling and optimization of tensile strength and modulus of polypropylene/kenaf fiber biocomposites using Box-Behnken response surface method. Polym Compos 39:E463–E479. https://doi.org/10.1002/PC.24596CrossRef

59.

Deng B, Shi Y (2019) Modeling and optimizing the composite prepreg tape winding process based on grey relational analysis coupled with BP neural network and bat algorithm. Nanoscale Res Lett 14(1):1. https://doi.org/10.1186/S11671-019-3118-4CrossRef

60.

Pfrommer J, Zimmerling C, Liu J, Kärger L, Henning F, Beyerer J (2018) Optimisation of manufacturing process parameters using deep neural networks as surrogate models. Procedia CIRP 72:426–431. https://doi.org/10.1016/J.PROCIR.2018.03.046CrossRef

61.

Velumani S, Navaneetha Krishnan P, Jayabal S (2014) Mathematical modeling and optimization of mechanical properties of short coir fiber-reinforced vinyl ester composite using genetic algorithm method. Mech Adv Mater Struct 21(7):559–565. https://doi.org/10.1080/15376494.2012.699599CrossRef

62.

Jiang Z, Gyurova L, Zhang Z, Friedrich K, Schlarb AK (2008) Neural network based prediction on mechanical and wear properties of short fibers reinforced polyamide composites. Mater Des 29(3):628–637. https://doi.org/10.1016/J.MATDES.2007.02.008CrossRef

63.

Li J, Salim RD, Aldlemy MS, Abdullah JM, Yaseen ZM (2019) Fiberglass-reinforced polyester composites fatigue prediction using novel data-intelligence model. Arab J Sci Eng 44(4):3343–3356. https://doi.org/10.1007/S13369-018-3508-4CrossRef

64.

Seyhan AT, Tayfur G, Karakurt M, Tanoǧlu M (2005) Artificial neural network (ANN) prediction of compressive strength of VARTM processed polymer composites. Comput Mater Sci 34(1):99–105. https://doi.org/10.1016/J.COMMATSCI.2004.11.001CrossRef

65.

Mrówka M, Woźniak A, Prężyna S, Sławski S (2021) The influence of zinc waste filler on the tribological and mechanical properties of silicone-based composites. Polymers (Basel) 13(4):1–15. https://doi.org/10.3390/POLYM13040585CrossRef

66.

Khalid MY, Arif ZU, Sheikh MF, Nasir MA (2021) Mechanical characterization of glass and jute fiber-based hybrid composites fabricated through compression molding technique. IntJ Mater Form 14(5):1085–1095. https://doi.org/10.1007/s12289-021-01624-wCrossRef

67.

Xiong J, Zhang TY, Shi SQ (2020) Machine learning of mechanical properties of steels. Sci China Technol Sci 63(7):1247–1255. https://doi.org/10.1007/S11431-020-1599-5/METRICSCrossRef

68.

Shi B, Lookman T, Xue D (2023) Multi-objective optimization and its application in materials science. Mater Genome Eng Adv 1(2):e14. https://doi.org/10.1002/MGEA.14CrossRef

69.

Chibani S, Coudert F-X (2020) Machine learning approaches for the prediction of materials properties. APL Mater. https://doi.org/10.1063/5.0018384CrossRef

70.

Kibrete F, Trzepieciński T, Gebremedhen HS, Woldemichael DE (2023) Artificial intelligence in predicting mechanical properties of composite materials. J Compos Sci 7(9):364. https://doi.org/10.3390/jcs7090364CrossRef

71.

Stoll A, Benner P (2021) Machine learning for material characterization with an application for predicting mechanical properties. GAMM-Mitteilungen 44(1):e202100003. https://doi.org/10.1002/gamm.202100003CrossRef

72.

Wang H, Zhao H, Zhan Z, Chen H, Li M (2024) Prediction model of material dynamic mechanical properties embedded with physical mechanism neural network. JOM. https://doi.org/10.1007/s11837-024-06719-8CrossRefPubMedPubMedCentral

73.

Thomas AJ, Barocio E, Pipes RB (2022) A machine learning approach to determine the elastic properties of printed fiber-reinforced polymers. Compos Sci Technol 220:109293. https://doi.org/10.1016/j.compscitech.2022.109293CrossRef

74.

Maffi JM, Sosa J, Estenoz DA (2024) Development of hybrid (AI/first principle) models for the optimization of styrene polymerization processes. Chem Eng Sci. https://doi.org/10.1016/j.ces.2024.119875CrossRef

75.

Baseer AA, Ravi Shankar DV, Hussain MM (2020) Interfacial and tensile properties of hybrid FRP composites using dnn structure with optimization model. Surf Rev Lett 27(2):1950099. https://doi.org/10.1142/S0218625X19500999CrossRef

76.

Sourabh K, Singh K, Kumar S, Singh KK (2022) Computational data-driven based optimization of tribological performance of graphene filled glass fiber reinforced polymer composite using machine learning approach. Mater Today Proc 66:3838–3846. https://doi.org/10.1016/j.matpr.2022.06.253CrossRef

77.

Chen CT, Gu GX (2019) Machine learning for composite materials. MRS Commun 9(2):556–566. https://doi.org/10.1557/MRC.2019.32CrossRef

78.

Ul Qadir N, Jack DA (2009) Modeling fibre orientation in short fibre suspensions using the neural network-based orthotropic closure. Compos Part A Appl Sci Manuf 40(10):1524–1533. https://doi.org/10.1016/J.COMPOSITESA.2009.06.010CrossRef

79.

Stepashkin AA et al (2023) ANN-based structure peciliaties evaluation of polymer composite reinforced with unidirectional carbon fiber. Alex Eng J 82:218–239. https://doi.org/10.1016/J.AEJ.2023.09.062CrossRef

80.

Pilania G, Wang C, Jiang X, Rajasekaran S, Ramprasad R (2013) Accelerating materials property predictions using machine learning. Sci Rep 3:2810. https://doi.org/10.1038/srep02810CrossRefPubMedPubMedCentral

81.

Baturynska I, Semeniuta O, Martinsen K (2018) Optimization of process parameters for powder bed fusion additive manufacturing by combination of machine learning and finite element method: a conceptual framework. Procedia CIRP 67:227–232. https://doi.org/10.1016/j.procir.2017.12.204CrossRef

82.

Salah LS, Chouai M, Danlée Y, Huynen I, Ouslimani N (2020) Simulation and optimization of electromagnetic absorption of polycarbonate/CNT composites using machine learning. Micromachines (Basel) 11(8):778. https://doi.org/10.3390/MI11080778CrossRef

83.

Khalid MY, Al Rashid A, Arif ZU, Akram N, Arshad H, García Márquez FP (2021) Characterization of failure strain in fiber reinforced composites: under on-axis and off-axis loading. Crystals (Basel) 11(2):216. https://doi.org/10.3390/cryst11020216CrossRef

84.

Zhang Q, Li K, Fang Y, Guo Z, Wei Y, Sheng K (2022) Conversion from bamboo waste derived biochar to cleaner composites: synergistic effects of aramid fiber and silica. J Clean Prod 347:131336. https://doi.org/10.1016/j.jclepro.2022.131336CrossRef

85.

Mujahid Y, Sallih N, Mustapha M, Abdullah MZ, Mustapha F (2020) Effects of processing parameters for vacuum-bagging-only method on shape conformation of laminated composites. Processes 8(9):1147. https://doi.org/10.3390/pr8091147CrossRef

86.

Pattnaik P, Sharma A, Choudhary M, Singh V, Agarwal P, Kukshal V (2020) Role of machine learning in the field of fiber reinforced polymer composites: a preliminary discussion. Mater Today Proceed 44:4703–4708. https://doi.org/10.1016/j.matpr.2020.11.026CrossRef

87.

Liu Y, Steven Greene M, Chen W, Dikin DA, Liu WK (2013) Computational microstructure characterization and reconstruction for stochastic multiscale material design. CAD Comput Aided Des 45(1):65–76. https://doi.org/10.1016/J.CAD.2012.03.007CrossRef

88.

Xu H, Liu R, Choudhary A, Chen W (2015) A machine learning-based design representation method for designing heterogeneous microstructures. J Mech Des 137(5):051403. https://doi.org/10.1115/1.4029768/383805CrossRef

89.

Okafor CE et al (2023) Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems. Hybrid Adv 2:100026. https://doi.org/10.1016/j.hybadv.2023.100026CrossRef

90.

Mashayekhi F, Bardon J, Berthé V, Perrin H, Westermann S, Addiego F (2021) Fused filament fabrication of polymers and continuous fiber-reinforced polymer composites: advances in structure optimization and health monitoring. Polymers 13(5):789. https://doi.org/10.3390/POLYM13050789CrossRefPubMedPubMedCentral

91.

Liu R, Kumar A, Chen Z, Agrawal A, Sundararaghavan V, Choudhary A (2015) A predictive machine learning approach for microstructure optimization and materials design. Sci Rep 5(1):1–12. https://doi.org/10.1038/srep11551CrossRef

92.

Sundararaghavan V, Zabaras N (2005) Classification and reconstruction of three-dimensional microstructures using support vector machines. Comput Mater Sci 32(2):223–239. https://doi.org/10.1016/J.COMMATSCI.2004.07.004CrossRef

93.

Li M, Zhang H, Li S, Zhu W, Ke Y (2022) Machine learning and materials informatics approaches for predicting transverse mechanical properties of unidirectional CFRP composites with microvoids. Mater Des 224:111340. https://doi.org/10.1016/j.matdes.2022.111340CrossRef

94.

Ammasai Sengodan G (2021) Prediction of two-phase composite microstructure properties through deep learning of reduced dimensional structure-response data. Compos B Eng 225:109282. https://doi.org/10.1016/j.compositesb.2021.109282CrossRef

95.

Yoon S et al (2023) Defect detection in composites by deep learning using solitary waves. Int J Mech Sci 239:107882. https://doi.org/10.1016/j.ijmecsci.2022.107882CrossRef

96.

Pütz F, Henrich M, Fehlemann N, Roth A, Münstermann S (2020) Generating input data for microstructure modelling: a deep learning approach using generative adversarial networks. Materials 13(19):4236. https://doi.org/10.3390/ma13194236CrossRefPubMedPubMedCentral

97.

Pathan MV et al (2019) Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning. Sci Rep 9(1):13964. https://doi.org/10.1038/s41598-019-50144-wCrossRefPubMedPubMedCentral

98.

Lu L, Hou J, Yuan S, Yao X, Li Y, Zhu J (2023) Deep learning-assisted real-time defect detection and closed-loop adjustment for additive manufacturing of continuous fiber-reinforced polymer composites. Robot Comput Integr Manuf 79:102431. https://doi.org/10.1016/j.rcim.2022.102431CrossRef

99.

Kumari R, Saini K, Anand A (2022) Predictive analytics to improve the quality of polymer component manufacturing. Meas: Sens 24:100428. https://doi.org/10.1016/j.measen.2022.100428CrossRef

100.

Fu J, Xiao D, Li D, Thomas HR, Li C (2022) Stochastic reconstruction of 3D microstructures from 2D cross-sectional images using machine learning-based characterization. Comput Methods Appl Mech Eng 390:114532. https://doi.org/10.1016/j.cma.2021.114532CrossRef

101.

Wan Z, Chen S, Feng X, Sun Z-Y (2024) From processing to properties: enhancing machine learning models with microstructural information in polymer nanocomposites. Compos Commun 51:102072. https://doi.org/10.1016/j.coco.2024.102072CrossRef

102.

Wan L, Ullah Z, Yang D, Falzon BG (2023) Probability embedded failure prediction of unidirectional composites under biaxial loadings combining machine learning and micromechanical modelling. Compos Struct 312:116837. https://doi.org/10.1016/j.compstruct.2023.116837CrossRef

103.

Balcıoğlu HE, Seçkin AÇ (2021) Comparison of machine learning methods and finite element analysis on the fracture behavior of polymer composites. Arch Appl Mech 91(1):223–239. https://doi.org/10.1007/s00419-020-01765-5CrossRef

104.

Hasan M, Acar P (2021) Uncertainty Quantification of Metallic Microstructures with Analytical and Machine Learning Based Approaches. AIAA J. https://doi.org/10.2514/1.J060372CrossRef

105.

Nefs K, Menkovski V, Bos FP, Suiker ASJ, Salet TAM (2023) Automated image segmentation of 3D printed fibrous composite micro-structures using a neural network. Constr Build Mater 365:130099. https://doi.org/10.1016/j.conbuildmat.2022.130099CrossRef

106.

Su M, Zhong Q, Peng H, Li S (2021) Selected machine learning approaches for predicting the interfacial bond strength between FRPs and concrete. Constr Build Mater 270:121456. https://doi.org/10.1016/j.conbuildmat.2020.121456CrossRef

107.

Mansouri I, Ozbakkaloglu T, Kisi O, Xie T (2016) Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and M5 model tree techniques. Mater Struct 49(10):4319–4334. https://doi.org/10.1617/s11527-015-0790-4CrossRef

108.

Zhang Z, Shi J, Yu T, Santomauro A, Gordon A, Gou J, Wu D (2020) Predicting flexural strength of additively manufactured continuous carbon fiber-reinforced polymer composites using machine learning. J Comput Inf Sci Eng 20(6):061015. https://doi.org/10.1115/1.4047477CrossRef

109.

Omer B et al (2024) Exploring the potential of soft computing for predicting compressive strength and slump flow diameter in fly ash-modified self-compacting concrete. Arch Civ Mech Eng 24(2):95. https://doi.org/10.1007/S43452-024-00910-ZCrossRef

110.

Jalal M, Ramezanianpour AA (2012) Strength enhancement modeling of concrete cylinders confined with CFRP composites using artificial neural networks. Compos B Eng 43(8):2990–3000. https://doi.org/10.1016/j.compositesb.2012.05.044CrossRef

111.

Datta SD et al (2024) Analysis of the characteristics and environmental benefits of rice husk ash as a supplementary cementitious material through experimental and machine learning approaches. Innov Infrastruct Solut 9(4):121. https://doi.org/10.1007/S41062-024-01423-7CrossRef

112.

Di Boon Y, Joshi SC, Bhudolia SK, Gohel G (2020) Recent advances on the design automation for performance-optimized fiber reinforced polymer composite components. J Compos Sci 4(2):61. https://doi.org/10.3390/jcs4020061CrossRef

113.

Mortazavian S, Fatemi A (2017) Tensile behavior and modeling of short fiber-reinforced polymer composites including temperature and strain rate effects. J Thermoplast Compos Mater 30(10):1414–1437. https://doi.org/10.1177/0892705716632863CrossRef

114.

Lim JC, Karakus M, Ozbakkaloglu T (2016) Evaluation of ultimate conditions of FRP-confined concrete columns using genetic programming. Comput Struct 162:28–37. https://doi.org/10.1016/j.compstruc.2015.09.005CrossRef

115.

Elsanadedy HM, Al-Salloum YA, Abbas H, Alsayed SH (2012) Prediction of strength parameters of FRP-confined concrete. Compos B Eng 43(2):228–239. https://doi.org/10.1016/j.compositesb.2011.08.043CrossRef

116.

Yaseen ZM, Tran MT, Kim S, Bakhshpoori T, Deo RC (2018) Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: a new approach. EngStruct 177:244–255. https://doi.org/10.1016/j.engstruct.2018.09.074CrossRef

117.

Al-Musawi AA, Alwanas AAH, Salih SQ, Ali ZH, Tran MT, Yaseen ZM (2020) Shear strength of SFRCB without stirrups simulation: implementation of hybrid artificial intelligence model. Eng Comput 36(1):1–11. https://doi.org/10.1007/s00366-018-0681-8CrossRef

118.

Lee S, Lee C (2014) Prediction of shear strength of FRP-reinforced concrete flexural members without stirrups using artificial neural networks. EngStruct 61:99–112. https://doi.org/10.1016/j.engstruct.2014.01.001CrossRef

119.

Tapeh ATG, Naser MZ (2023) Artificial intelligence, machine learning, and deep learning in structural engineering: a scientometrics review of trends and best practices. Arch Comput Methods Eng 30(1):115–159. https://doi.org/10.1007/S11831-022-09793-WCrossRef

120.

Qs Khan MSMH (2019) Predicting strength and strain enhancement ratios of circular fiber-reinforced polymer tube confined concrete under axial compression using artificial neural networks. AdvStructEng 22:1426–1443

121.

Stamopoulos AG, Tserpes KI, Dentsoras AJ (2018) Quality assessment of porous CFRP specimens using X-ray computed tomography data and artificial neural networks. Compos Struct 192:327–335. https://doi.org/10.1016/j.compstruct.2018.02.096CrossRef

122.

Fan HT, Wang H (2014) Predicting the open-hole tensile strength of composite plates based on probabilistic neural network. Appl Compos Mater 21(6):827–840. https://doi.org/10.1007/S10443-014-9387-2CrossRef

123.

Fontes A, Shadmehri F (2023) Data-driven failure prediction of fiber-reinforced polymer composite materials. Eng Appl Artif Intell 120:105834. https://doi.org/10.1016/J.ENGAPPAI.2023.105834CrossRef

124.

Wanigasekara C, Oromiehie E, Swain A, Prusty BG, Nguang SK (2021) Machine learning-based inverse predictive model for AFP based thermoplastic composites. J Ind Inf Integr. https://doi.org/10.1016/j.jii.2020.100197CrossRef

125.

Yang K, Wu S, Guan J, Shao Z, Ritchie RO (2017) Enhancing the mechanical toughness of epoxy-resin composites using natural silk reinforcements. Sci Rep 7(1):11939. https://doi.org/10.1038/s41598-017-11919-1CrossRefPubMedPubMedCentral

126.

Cuntze RG (2004) The predictive capability of failure mode concept-based strength criteria for multi-directional laminates-part B. Fail Criteria Fibre-Reinf-Polym Compos. https://doi.org/10.1016/B978-008044475-8/50035-4CrossRef

127.

Islam F, Wanigasekara C, Rajan G, Swain A, Prusty BG (2022) An approach for process optimisation of the automated fibre placement (AFP) based thermoplastic composites manufacturing using machine learning, photonic sensing and thermo-mechanics modelling. Manuf Lett 32:10–14. https://doi.org/10.1016/j.mfglet.2022.01.002CrossRef

128.

Lee CS, Hwang W, Park HC, Han KS (1999) Failure of carbon/epoxy composite tubes under combined axial and torsional loading 1. Experimental results and prediction of biaxial strength by the use of neural networks. Compos Sci Technol 59(12):1779–1788. https://doi.org/10.1016/S0266-3538(99)00038-XCrossRef

129.

Garg A, Mukhopadhyay T, Belarbi MO, Li L (2023) Random forest-based surrogates for transforming the behavioral predictions of laminated composite plates and shells from FSDT to Elasticity solutions. Compos Struct. https://doi.org/10.1016/j.compstruct.2023.116756CrossRef

130.

Wanigasekara C, Oromiehie E, Swain A, Prusty BG, Nguang SK (2020) Machine learning based predictive model for AFP-based unidirectional composite laminates. IEEE Trans Industr Inform 16(4):2315–2324. https://doi.org/10.1109/TII.2019.2932398CrossRef

131.

Chen J, Wan L, Ismail Y, Ye J, Yang D (2021) A micromechanics and machine learning coupled approach for failure prediction of unidirectional CFRP composites under triaxial loading: a preliminary study. Compos Struct. https://doi.org/10.1016/j.compstruct.2021.113876CrossRef

132.

Milad A, Hussein SH, Khekan AR, Rashid M, Al-Msari H, Tran TH (2022) Development of ensemble machine learning approaches for designing fiber-reinforced polymer composite strain prediction model. Eng Comput 38(4):3625–3637. https://doi.org/10.1007/S00366-021-01398-4/METRICSCrossRef

133.

Bhaduri A, Gupta A, Graham-Brady L (2022) Stress field prediction in fiber-reinforced composite materials using a deep learning approach. Compos B Eng. https://doi.org/10.1016/j.compositesb.2022.109879CrossRef

134.

Qian C, Tan RK, Ye W (2022) Design of architectured composite materials with an efficient, adaptive artificial neural network-based generative design method. Acta Mater 225:117548. https://doi.org/10.1016/J.ACTAMAT.2021.117548CrossRef

135.

Yang Z, Yu CH, Guo K, Buehler MJ (2021) End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures. J Mech Phys Solids. https://doi.org/10.1016/j.jmps.2021.104506CrossRefPubMedPubMedCentral

136.

Syduzzaman M, Al Faruque MA, Bilisik K, Naebe M (2020) Plant-based natural fibre reinforced composites: a review on fabrication, properties and applications. Coatings 10(10):973. https://doi.org/10.3390/coatings10100973CrossRef

Title: Advancements of machine learning techniques in fiber-filled polymer composites: a review
Authors: R. Alagulakshmi
R. Ramalakshmi
Arumugaprabu Veerasimman
Geetha Palani
Manickam Selvaraj
Sanjay Basumatary
Publication date: 09-01-2025
Publisher: Springer Berlin Heidelberg
Published in: Polymer Bulletin
Print ISSN: 0170-0839
Electronic ISSN: 1436-2449
DOI: https://doi.org/10.1007/s00289-025-05638-1