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International Journal on Interactive Design and Manufacturing (IJIDeM)

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15.12.2023 | Original Article

An ANN approach to predicting the impact parameters of GFRP composites under low-velocity impact

verfasst von: Vinayak S. Hiremath, Shreekant Patil, D. Mallikarjuna Reddy, Rajasekhara Reddy Mutra, Bhimgoud Patil, N. Poornima

Erschienen in: International Journal on Interactive Design and Manufacturing (IJIDeM) | Ausgabe 2/2024

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Abstract

Composite materials, whose excellent strength-to-weight ratio has made them popular in the marine and aviation sectors, are an excellent substitute for metallic materials. The current work investigates the influence of ply sequence, thickness, and impact energy on low-velocity impact (LVI) analysis. The basic objective of this study is to anticipate the peak force and absorbed energy in LVI using an artificial neural network (ANN). The ANN received the experiment data as input, and then the Python program was utilized to create, train, and test the ANN structure employing a back-propagating approach. According to research results, [45/0/− 45/90]_s orientation samples improved their energy absorption behaviour and peak force by 12.8%, 16.2%, and 18.6% compared to [0/0]_2s, [0/90]_2s, and [45/− 45/0/90]_s ply orientation samples. The ANN structure predicted the absorbed energy and peak force with satisfactory accuracy. The output from the network was R² = 0.999 for training, R² = 0.971 for testing, and R² = 0.977 for validation, respectively, and overall, R² = 0.992. Furthermore the failure analysis was demonstrated by using ultrasonic C-scan and field emission scanning electron microscopy (FESEM), and the results showed extreme changes in the behaviour of fibres and matrix after impact.

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Patil, S., Reddy, D.M., Reddy, M.: Low velocity impact analysis on composite structures—A review. In: AIP Conference Proceedings, American Institute of Physics Inc., 2018. https://doi.org/10.1063/1.5029585

Parveez, B., Kittur, M.I., Badruddin, I.A., Kamangar, S., Hussien, M., Umarfarooq, M.A.: Scientific advancements in composite materials for aircraft applications: a review. Polymers (2022). https://doi.org/10.3390/polym14225007CrossRefPubMedPubMedCentral

Hiremath, V.S., Reddy, D.M.: An experimental investigation on the adhesive, shear strength, and failure modes of glass fibre reinforced polymer flat-joggle-flat composite joints over different bonding techniques. Materwiss. Werksttech. 54(8), 1031–1037 (2023). https://doi.org/10.1002/mawe.202200268CrossRef

Tie, Y., Hou, Y., Li, C., Zhou, X., Sapanathan, T., Rachik, M.: An insight into the low-velocity impact behavior of patch-repaired CFRP laminates using numerical and experimental approaches. Compos. Struct. 190, 179–188 (2018). https://doi.org/10.1016/j.compstruct.2018.01.075CrossRef

Shi, Y., Swait, T., Soutis, C.: Modelling damage evolution in composite laminates subjected to low velocity impact. Compos. Struct. (2012). https://doi.org/10.1016/j.compstruct.2012.03.039CrossRef

Caminero, M.A., Rodríguez, G.P., Muñoz, V.: Effect of stacking sequence on Charpy impact and flexural damage behavior of composite laminates. Compos. Struct. 136, 345–357 (2016). https://doi.org/10.1016/j.compstruct.2015.10.019CrossRef

Caminero, M.A., García-Moreno, I., Rodríguez, G.P.: Damage resistance of carbon fibre reinforced epoxy laminates subjected to low velocity impact: effects of laminate thickness and ply-stacking sequence. Polym. Test. 63, 530–541 (2017). https://doi.org/10.1016/j.polymertesting.2017.09.016CrossRef

Cantwell, W.J., Morton, J.: Impact perforation of carbon fibre reinforced plastic. 1990

Tita, V., de Carvalho, J., Vandepitte, D.: Failure analysis of low velocity impact on thin composite laminates: experimental and numerical approaches. Compos. Struct. (2008). https://doi.org/10.1016/j.compstruct.2007.06.003CrossRef

10.

Meola, C., Carlomagno, G.M.: Non-destructive evaluation (NDE) of aerospace composites: detecting impact damage. In: Non-destructive evaluation (NDE) of polymer matrix composites: techniques and applications. (2013). https://doi.org/10.1533/9780857093554.3.367

11.

Panettieri, E., Fanteria, D., Montemurro, M., Froustey, C.: Low-velocity impact tests on carbon/epoxy composite laminates: a benchmark study. Compos. B Eng. 107, 9–21 (2016). https://doi.org/10.1016/j.compositesb.2016.09.057CrossRef

12.

Sarasini, F., et al.: Drop-weight impact behaviour of woven hybrid basalt-carbon/epoxy composites. Compos. B Eng. (2014). https://doi.org/10.1016/j.compositesb.2013.12.006CrossRef

13.

Haridas, A., Song, C., Chan, K., Murukeshan, V.M.: Nondestructive characterization of thermal damages and its interactions in carbon fibre composite panels. Fatigue Fract. Eng. Mater. Struct. (2017). https://doi.org/10.1111/ffe.12657CrossRef

14.

Garnier, C., Pastor, M.L., Eyma, F., Lorrain, B.: The detection of aeronautical defects in situ on composite structures using non destructive testing. Compos. Struct. 93(5), 1328–1336 (2011). https://doi.org/10.1016/j.compstruct.2010.10.017CrossRef

15.

Ibrahim, M.E., Smith, R.A., Wang, C.H.: Ultrasonic detection and sizing of compressed cracks in glass- and carbon-fibre reinforced plastic composites. NDT E Int (2017). https://doi.org/10.1016/j.ndteint.2017.08.004CrossRef

16.

Harizi, W., Chaki, S., Bourse, G., Ourak, M.: Mechanical damage characterization of glass fiber-reinforced polymer laminates by ultrasonic maps. Compos. B Eng. 70, 131–137 (2015). https://doi.org/10.1016/j.compositesb.2014.11.014CrossRef

17.

Amenabar, I., Mendikute, A., López-Arraiza, A., Lizaranzu, M., Aurrekoetxea, J.: Comparison and analysis of non-destructive testing techniques suitable for delamination inspection in wind turbine blades. Compos. B Eng. (2011). https://doi.org/10.1016/j.compositesb.2011.01.025CrossRef

18.

Dong, J., Kim, B., Locquet, A., McKeon, P., Declercq, N., Citrin, D.S.: Nondestructive evaluation of forced delamination in glass fiber-reinforced composites by terahertz and ultrasonic waves. Compos. B Eng. (2015). https://doi.org/10.1016/j.compositesb.2015.05.028CrossRef

19.

Meola, C., Boccardi, S., Carlomagno, G.M., Boffa, N.D., Monaco, E., Ricci, F.: Nondestructive evaluation of carbon fibre reinforced composites with infrared thermography and ultrasonics. Compos. Struct. 134, 845–853 (2015). https://doi.org/10.1016/j.compstruct.2015.08.119CrossRef

20.

Cantwell, W.J., Morton, J.: The impact resistance of composite materials—a review. Composites (1991). https://doi.org/10.1016/0010-4361(91)90549-VCrossRef

21.

Balasubramaniam, K., Whitney, S.C.: Ultrasonic through-transmission characterization of thick fibre-reinforced composites. (1996)

22.

Castellano, A., Foti, P., Fraddosio, A., Marzano, S., Piccioni, M.D.: Mechanical characterization of CFRP composites by ultrasonic immersion tests: experimental and numerical approaches. Compos. B Eng. 66, 299–310 (2014). https://doi.org/10.1016/j.compositesb.2014.04.024CrossRef

23.

De Luca, A., Caputo, F., Sharif Khodaei, Z., Aliabadi, M.H.: Damage characterization of composite plates under low velocity impact using ultrasonic guided waves. Compos. B Eng. (2018). https://doi.org/10.1016/j.compositesb.2017.11.042CrossRef

24.

Patil, S., Mallikarjuna Reddy, D.: Impact damage assessment in carbon fiber reinforced composite using vibration-based new damage index and ultrasonic C-scanning method. Structures 28, 638–650 (2020). https://doi.org/10.1016/j.istruc.2020.09.011CrossRef

25.

Liu, Y.J., Jiang, Z., Wen, H.M.: Predicting impact induced delamination of FRP laminates. Int. J. Impact Eng. (2020). https://doi.org/10.1016/j.ijimpeng.2019.103436CrossRef

26.

Hiremath, V.S., Reddy, D.M.: Effect of ply orientation and laminate thickness on carbon fibre reinforced polymers under low-velocity Einfluss der Gewebeorientierung und Laminatdicke auf. 2022. https://doi.org/10.1002/mawe.202200087.

27.

Jung, K.C., Chang, S.H.: Advanced deep learning model-based impact characterization method for composite laminates. Compos. Sci. Technol. (2021). https://doi.org/10.1016/j.compscitech.2021.108713CrossRef

28.

Hiremath, V.S., Mallikarjuna Reddy, D.: Prediction of failure load in flat-joggle-flat co-cured composite joints using artificial neural networks. Mater. Lett. 345, 134491 (2023). https://doi.org/10.1016/j.matlet.2023.134491CrossRef

29.

Naderpour, H., Kheyroddin, A., Amiri, G.G.: Prediction of FRP-confined compressive strength of concrete using artificial neural networks. Compos. Struct. (2010). https://doi.org/10.1016/j.compstruct.2010.04.008CrossRef

30.

Kalhor, R., Akbarshahi, H., Case, S.W.: Numerical modeling of the effects of FRP thickness and stacking sequence on energy absorption of metal-FRP square tubes. Compos. Struct. (2016). https://doi.org/10.1016/j.compstruct.2016.03.038CrossRef

31.

Artero-Guerrero, J.A., Pernas-Sánchez, J., Martín-Montal, J., Varas, D., López-Puente, J.: The influence of laminate stacking sequence on ballistic limit using a combined experimental/FEM/artificial neural networks (ANN) methodology. Compos. Struct. (2018). https://doi.org/10.1016/j.compstruct.2017.03.068CrossRef

32.

Malik, M.H., Arif, A.F.M.: ANN prediction model for composite plates against low velocity impact loads using finite element analysis. Compos. Struct. (2013). https://doi.org/10.1016/j.compstruct.2013.02.020CrossRef

33.

Gayatri Vineela, M., Dave, A., Kiran Chaganti, P.: Artificial neural network based prediction of tensile strength of hybrid composites. In: Materials Today: Proceedings, (2018). https://doi.org/10.1016/j.matpr.2018.06.356

34.

Ramasamy, P., Sampathkumar, S.: Prediction of impact damage tolerance of drop impacted WGFRP composite by artificial neural network using acoustic emission parameters. Compos. B Eng. (2014). https://doi.org/10.1016/j.compositesb.2013.12.028CrossRef

35.

Stephen, C., Thekkuden, D.T., Mourad, A.H.I., Shivamurthy, B., Selvam, R., Behara, S.R.: Prediction of impact performance of fiber reinforced polymer composites using finite element analysis and artificial neural network. J. Brazilian Soc. Mech. Sci. Eng. (2022). https://doi.org/10.1007/s40430-022-03711-8CrossRef

36.

Perera, R., Barchín, M., Arteaga, A., De Diego, A.: Prediction of the ultimate strength of reinforced concrete beams FRP-strengthened in shear using neural networks. Compos. B Eng. (2010). https://doi.org/10.1016/j.compositesb.2010.03.003CrossRef

37.

Choi, S.W., Song, E.J., Hahn, H.T.: Prediction of fatigue damage growth in notched composite laminates using an artificial neural network. Compos. Sci. Technol. (2003). https://doi.org/10.1016/S0266-3538(02)00261-0CrossRef

38.

Reda Taha, M.M., Lucero, J.: Damage identification for structural health monitoring using fuzzy pattern recognition. Eng. Struct. (2005). https://doi.org/10.1016/j.engstruct.2005.04.018CrossRef

39.

Birecikli, B., Karaman, Ö.A., Çelebi, S.B., Turgut, A.: Failure load prediction of adhesively bonded GFRP composite joints using artificial neural networks. J. Mech. Sci. Technol. 34(11), 4631–4640 (2020). https://doi.org/10.1007/s12206-020-1021-7CrossRef

Titel: An ANN approach to predicting the impact parameters of GFRP composites under low-velocity impact
verfasst von: Vinayak S. Hiremath
Shreekant Patil
D. Mallikarjuna Reddy
Rajasekhara Reddy Mutra
Bhimgoud Patil
N. Poornima
Publikationsdatum: 15.12.2023
Verlag: Springer Paris
Erschienen in: International Journal on Interactive Design and Manufacturing (IJIDeM) / Ausgabe 2/2024
Print ISSN: 1955-2513
Elektronische ISSN: 1955-2505
DOI: https://doi.org/10.1007/s12008-023-01668-z

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