Open Access Open Access  Restricted Access Subscription or Fee Access

Experimental Evaluation and Neural Network Prediction of Fatigue Life of E-Glass Fiber Reinforced Hybrid Composite Material


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/ireme.v16i8.20995

Abstract


The present study focuses on investigating the impact of hybrid composite content and loading parameters on the fatigue life of E-glass fiber-reinforced composite. A mathematical model of the fatigue life was developed based on experimentation and expressed as a function of the composite content (Epoxy content (80%, 60%) wt., Novolac content (20%, 40%) wt., E-Fiber Glass content (10%, 20%, and 30%) wt.), and different loads using Adaptive Neural Fuzzy Inference Systems (ANFIS). In order to validate the ANFIS model, 30 fatigue cycle experiments were conducted. Using the experimental data sets, the ANFIS was trained with a momentum algorithm and an average absolute percentage error of 6.045% was obtained and compared to the output of the conventional Artificial Neural Network (ANN) Model. The testing accuracy was then verified with 6 extra experimental data sets and the average predicting error was 5.18%. the predicted ANFIS output data were then used to discuss the effect of the composite content and loading parameters on the fatigue life. The number and shapes of cracks observed on the failed specimens were analyzed under an optical microscope. The analysis revealed that the loading impact showed the highest influence on fatigue life for all types of enforcement and the sequence of strength was E- glass fiber, epoxy, and Novolac contents, respectively.
Copyright © 2022 Praise Worthy Prize - All rights reserved.

Keywords


Fatigue Life; Epoxy; Glass Fiber; Novolac; Stress; ANFIS

Full Text:

PDF


References


Jinlian, H., L. Yi, and S. Xueming, Study on void formation in multi-layer woven fabrics. Composites Part A: Applied Science and Manufacturing, 2004. 35(5): p. 595-603.
https://doi.org/10.1016/j.compositesa.2003.11.007

Abo-Elkhier, M., A. Hamada, and A.B. El-Deen, Prediction of fatigue life of glass fiber reinforced polyester composites using modal testing. International Journal of Fatigue, 2014. 69: p. 28-35.
https://doi.org/10.1016/j.ijfatigue.2012.10.002

Launay, A., et al., Cyclic behavior of short glass fiber reinforced polyamide for fatigue life prediction of automotive components. Procedia Engineering, 2010. 2(1): p. 901-910.
https://doi.org/10.1016/j.proeng.2010.03.097

Elanchezhian, C., B.V. Ramnath, and J. Hemalatha, Mechanical behaviour of glass and carbon fiber reinforced composites at varying strain rates and temperatures. Procedia Materials Science, 2014. 6: p. 1405-1418.
https://doi.org/10.1016/j.mspro.2014.07.120

Landesmann, A., C.A. Seruti, and E.d.M. Batista, Mechanical properties of glass fiber reinforced polymers members for structural applications. Materials Research, 2015. 18(6): p. 1372-1383.
https://doi.org/10.1590/1516-1439.044615

Juárez-Alvarado, C., et al., Fibre-reinforced cementing compounds with low environmental impact mechanical behavior. Revista ALCONPAT, 2017. 7(2): p. 135-147.
https://doi.org/10.21041/ra.v7i2.189

Allah, S.A.A., A.I. Al-Mosawi, and A.A. Al-Jeebory, Effect of Percentage of Fibers Reinforcement on Thermal and Mechanical Properties for Polymeric Composite Material.

Zuo, P., et al., Multi-scale analysis of the effect of loading conditions on monotonic and fatigue behavior of a glass fiber reinforced polyphenylene sulfide (PPS) composite. Composites Part B: Engineering, 2018. 145: p. 173-181.
https://doi.org/10.1016/j.compositesb.2018.03.031

Ferdous, W., et al., Testing and modeling the fatigue behaviour of GFRP composites-Effect of stress level, stress concentration, and frequency. Engineering Science and Technology, an International Journal, 2020.
https://doi.org/10.1016/j.jestch.2020.01.001

Mesogitis, T., A.A. Skordos, and A. Long, Uncertainty in the manufacturing of fibrous thermosetting composites: A review. Composites Part A: Applied Science and Manufacturing, 2014. 57: p. 67-75.
https://doi.org/10.1016/j.compositesa.2013.11.004

Kazi, M.-K., F. Eljack, and E. Mahdi, Optimal filler content for cotton fiber/PP composite based on mechanical properties using artificial neural network. Composite Structures, 2020. 251: p. 112654.
https://doi.org/10.1016/j.compstruct.2020.112654

Bezerra, E., et al., Artificial neural network (ANN) prediction of kinetic parameters of (CRFC) composites. Computational Materials Science, 2008. 44(2): p. 656-663.
https://doi.org/10.1016/j.commatsci.2008.05.002

Kazi, M.-K., F. Eljack, and E. Mahdi, Predictive ANN models for varying filler content for cotton fiber/PVC composites based on experimental load-displacement curves. Composite Structures, 2020. 254: p. 112885.
https://doi.org/10.1016/j.compstruct.2020.112885

Nirmal, U., Prediction of friction coefficient of treated betelnut fiber reinforced polyester (T-BFRP) composite using artificial neural networks. Tribology International, 2010. 43(8): p. 1417-1429.
https://doi.org/10.1016/j.triboint.2010.01.013

Barbosa, L.C.M., G. Gomes, and A.C.A. Junior, Prediction of temperature-frequency-dependent mechanical properties of composites based on thermoplastic liquid resin reinforced with carbon fibers using artificial neural networks. The International Journal of Advanced Manufacturing Technology, 2019. 105(5-6): p. 2543-2556.
https://doi.org/10.1007/s00170-019-04486-4

Suiffi, H., et al., The effect of using polypropylene fibers on the durability and fire resistance of concrete. Frattura ed Integrità Strutturale, 2021. 15(58): p. 296-307.
https://doi.org/10.3221/IGF-ESIS.58.22

Mohammed, T., Nasser, I., Saeed, I., Influence of Expanded Clay Aggregate on Various Properties of Lightweight Concrete Tiles, (2021) International Review of Civil Engineering (IRECE), 12 (2), pp. 85-92.
https://doi.org/10.15866/irece.v12i2.18505

Hasan, Z., Nasr, M., Abed, M., Combined Effect of Silica Fume, and Glass and Ceramic Waste on Properties of High Strength Mortar Reinforced with Hybrid Fibers, (2019) International Review of Civil Engineering (IRECE), 10 (5), pp. 267-273.
https://doi.org/10.15866/irece.v10i5.16960

Ahmed, A., Ali, M., Abbas, A., Recycling of Disposal Syringe Needles to Produce Fiber Reinforced Concrete, (2018) International Review of Civil Engineering (IRECE), 9 (4), pp. 148-153.
https://doi.org/10.15866/irece.v9i4.15275

Sultan, H., Mohammad, A., Qasim, O., Maula, B., Aziz, H., Ductility Factor Evaluation of Concrete Moment Frame Retrofitted by FRP Subjected to Seismic Loads, (2020) International Review of Civil Engineering (IRECE), 11 (6), pp. 275-282.
https://doi.org/10.15866/irece.v11i6.18670

Behnam, B., Alfraihat, A., Properties of Fiber-Reinforced Structural and Non-Structural Ultra Lightweight Aggregate Concrete, (2019) International Review of Civil Engineering (IRECE), 10 (5), pp. 227-234.
https://doi.org/10.15866/irece.v10i5.16971

Ali, Y.A., et al., Use of expanded polystyrene wastes in developing hollow block masonry units. Construction and Building Materials, 2020. 241: p. 118149.
https://doi.org/10.1016/j.conbuildmat.2020.118149

Salih, Y., Sabeeh, N., Yass, M., Ahmed, A., Abdulla, A., Behavior of Ferrocement Slabs Strengthening with Jute Fibers under Impact Load, (2020) International Review of Civil Engineering (IRECE), 11 (2), pp. 66-72.
https://doi.org/10.15866/irece.v11i2.17322

Fazilat, H., et al., Predicting the mechanical properties of glass fiber reinforced polymers via artificial neural network and adaptive neuro-fuzzy inference system. Computational Materials Science, 2012. 58: p. 31-37.
https://doi.org/10.1016/j.commatsci.2012.01.012

Suiffi, H., El Maliki, A., Cherkaoui, O., Dalal, M., Mechanical Properties and Ductility of a Concrete Matrix Reinforced with Polypropylene and Sisal Fibers, (2021) International Review of Civil Engineering (IRECE), 12 (3), pp. 199-208.
https://doi.org/10.15866/irece.v12i3.19963

Alkhafaji, B., Alwash, M., Effect of Adding Various Fibers on Some Properties of Gypsum, (2020) International Review of Civil Engineering (IRECE), 11 (4), pp. 173-179.
https://doi.org/10.15866/irece.v11i4.17712

Mohammed, H., Ibrahim, A., Ali, H., Saab, S., Concrete Bricks Manufactured Using EPS Beads with Steel Fibers, (2021) International Review of Civil Engineering (IRECE), 12 (4), pp. 218-227.
https://doi.org/10.15866/irece.v12i4.18782

Meliani, M., Echaabi, J., Mallil, E., Maziri, A., Insulating Bricks Filled with Cellulose Fibers, Packed in Recycled Plastic and Covered with Mortar Coating, (2020) International Review of Civil Engineering (IRECE), 11 (6), pp. 294-303.
https://doi.org/10.15866/irece.v11i6.19161

Kambekar, A. and M. Deo, Estimation of pile group scour using neural networks. Applied Ocean Research, 2003. 25(4): p. 225-234.
https://doi.org/10.1016/j.apor.2003.06.001

Kurnaz, S., O. Cetin, and O. Kaynak, Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles. Expert systems with Applications, 2010. 37(2): p. 1229-1234.
https://doi.org/10.1016/j.eswa.2009.06.009

Roushangar, K., R. Ghasempour, and R. Valizadeh, Effect of channel boundary conditions in predicting hydraulic jump characteristics using an anfis-based Approach. Journal of Applied Fluid Mechanics, 2018. 11(3): p. 555-565.
https://doi.org/10.29252/jafm.11.03.27933


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize