This study aims at determining the optimal material and process parameters that will maximize the quality characteristics of miscanthus fiber-reinforced polypropylene (MFRPP) composite. Taguchi L16 was utilized in the design of experiment and larger-the-better signal-to-noise ratio was used in the analysis of the impact strength of MFRPP. Optimal combination of material and process parameters and the main effects were determined, and the significant variables were identified using analysis of variance. Artificial neural network (ANN) and extreme learning machine (ELM) were utilized on the Taguchi experimental data and used in the prediction of the impact strength of MFRPP. The results showed the optimum impact strength occurred at 25 wt% miscanthus fiber loading. The results of the predictions made revealed that both ANN and ELM are very efficient in predicting the impact strength of MFRPP as shown by the relative errors when compared with the experimental data. Both the performance metrics assessments and the quality of prediction show that ELM is a better machine learning technique than ANN. The high impact strength of MFRPP composite is a confirmation that MFRPP is a very useful material for industrial applications.
ChandramohanDBharanichandarJ. Natural fibre reinforced polymer composites for automobile accessories. Am J Environ Sci2013; 9: 494–504.
2.
ThakurVKThakurMK. Processing and characterization of natural cellulose fibers/thermoset polymer composites. Carbohydr Polym2014; 109: 102–117.
3.
SanjayMRArpithaiGRNaikLL,et al.Applications of natural fibers and its composites: an overview. Nat Resour2016; 7: 108–114.
4.
IhuezeCCObioraJOOkaforCE. Characterization of plantain fiber reinforced high density polyethylene composite for application in design of auto body fenders. J Innov Res Eng Sci2016; 4: 574–587.
5.
IhuezeCCOluleyeAEOkaforCE,et al.Development of plantain fibres for application in design of oil and gas product systems. PTDJ2017; 7: 1.
6.
IhuezeCCOluleyeAEOkaforCE,et al.Plantain fibre particle reinforced HDPE(PFPRHDPE) for gas line piping design. Int J Plast Technol2017; 21: 370–396.
7.
IhuezeCCObiafudoOJOkaforCE. Biofibre in polymer matrixes: an application for auto body fender. Int J Plast Technol2017; 21: 171–193.
8.
IhuezeCCOkaforECObukaSN,et al.Integrity testing and cost evaluation of natural fiber/HDPE composite tailored for piping applications. J Thermoplast Compos Mater2021; 6: 49–61.
9.
GohilPPShaikhAA. Experimental investigation and micro mechanics assessement for longitudinal elastic modulus in undirectional cotton-polyester composites. Int J Eng Technol2010; 2: 111–118.
10.
RabbiMSIslamTIslamGMS. Injection-molded natural fiber-reinforced polymer composites—a review. Int J Mech Mater Eng2021; 16: 1–21. Article number: 15.
11.
HossainSIHasanMHasanMN,et al.Effect of chemical treatment on physical, mechanical and thermal properties of ladies finger natural fiber. Adv Mater Sci Eng2013; 2013: 1–6.
12.
SabistonTInalKLee-SullivanP. Application of artificial neural networks to predict fibre orientation in long fibre compression moulded composite materials. Compos Sci Technol2020; 190: 108034.
13.
IhuezeCCOkaforECUjamAJ. Optimization of tensile strengths response of plantain fibres reinforced polyesters composites (PFRP) applying Taguchi robust design. Innov Syst Des Eng2012; 3: 62–76.
14.
OkaforECIhuezeCCNwigboSC. Optimization of hardness strengths response of plantain fibers reinforced polyester matrix composites (PFRP) applying Taguchi robust design. Int J Eng Trans A: Basics2013; 26: 1–11.
15.
OkaforCEKebodiLCIhuezeCC,et al.Development of Dioscorea alata stem fibres as eco-friendly reinforcement for composite materials. J King Saud Univ Eng Sci2022. DOI: 10.1016/j.jksues.2022.02.003
16.
GirishaCSanjivmurthy and SrinivasG. Effect of alkali treatment, fiber loading and hybridization on tensile properties of sisal fiber, banana empty fruit bunch fiber and bamboo fiber reinforced thermoset composite. Int J Eng Sci Adv Technol2012; 2: 706–711.
17.
DasSCPaulDFahadMM,et al.Effect of fiber loading on the mechanical properties of jute fiber reinforced polypropylene composites. Adv Chem Eng Sci2018; 8: 215–224.
18.
RakeshKGMohdZ. Study of properties of polypropylene-natural fiber composite. Int J Eng Technol2017; 4: 3507–3511.
19.
MiliketTAAgezeMBTigabuMT,et al.Experimental characterizations of hybrid natural fiber-reinforced composite for wind turbine blades. Heliyon2022; 8: e09092.
20.
PereiraALBaneaMDNetoJSS,et al.Mechanical and thermal characterization of natural intralaminar hybrid composites based on sisal. Polymers (Basel)2020; 12: 866.
ChungTJParkJWLeeHJ,et al.The improvement of mechanical properties, thermal stability, and water absorption resistance of an eco-friendly PLA/kenaf biocomposite using acetylation. Appl Sci2018; 8: 376.
23.
AnbupalaniMSVenkatachalamCDRathanasamyR. Influence of coupling agent on altering the reinforcing efficiency of natural fiber-incorporated polymers—a review. J Reinf Plast Compos2020; 39: 520–544.
24.
Norul IzaniMAParidahMTAnwarUMK,et al.Effects of fiber treatment on morphology, tensile and thermogravimetric analysis of oil palm empty fruit bunches fibers. Compos Part B Eng2013; 45: 1251–1257.
25.
CaiMTakagiHNakagaitoAN,et al.Influence of alkali treatment on internal microstructure and tensile properties of abaca fibers. Ind Crops Prod2015; 65: 27–35.
26.
VenkateshwaranNElaya PerumalAArunsundaranayagamD. Fiber surface treatment and its effect on mechanical and visco-elastic behaviour of banana/epoxy composite. Mater Des2013; 47: 151–159.
27.
OkaforCEOnovoACAniOI,et al.Mathematical study of bio-fibre comminution process as first step towards valorization of post-harvest waste materials. Clean Mater2022; 4: 100067.
28.
NavaranjanNNeitzertTZhuM. Impact strength of natural fibre composites measured by different test methods: a review. MATEC Web Conf2017; 109: 01003.
29.
JosephPVMathewGJosephK, et al.Dynamic mechanical properties of short sisal fiber reinforced polypropylene composites. Compos Part A2003; 34: 275–290.
30.
ZamanHUBegMDH. Preparation, structure, and properties of the coir fiber/polypropylene composites. J Compos Mater2014; 48: 3293–3301.
31.
MalalliCSRamjiBR. Mechanical characterization of natural fiber reinforced polymer composites and their application in prosthesis: a review. Mater Today: Proc2022; 62: 3435–3443.
32.
IbrahimIDJamiruTSadikuR,et al.Impact of surface modification and nanoparticle on sisal fiber reinforced polypropylene nanocomposites. J Nanotechnol2016; 2016: Article ID 4235975.
ParkSH. Robust design and analysis for quality engineering. New York, NY, USA: Springer, 1996.
35.
MahfouzAHassanSAArishaA. Practical simulation application: evaluation of process control parameters in twisted-pair cables manufacturing system. Simul Model Pract Theory2010; 18: 471–482.
36.
ShimHJKimJK. Cause of failure and optimization of a V-belt pulley considering fatigue life uncertainty in automotive applications. Eng Fail Anal2009; 16: 1955–1963.
37.
MehatNMKamaruddinS. Optimization of mechanical properties of recycled plastic products via optimal processing parameters using the taguchi method. J Mater Process Technol2011; 211: 1989–1994.
38.
TaguchiGChowdhurySWuY. Taguchi’s quality engineering handbook. New Jersey, United States: John Wiley & Sons, 2004.
39.
PhadkeMS. Quality engineering using robust design. New Jersey: Prentice Hall, 1989.
40.
KrishnaPRVeenaNRPreetiS,et al.Optimization of processing parameters for a polymer blend using Taguchi method. Yambu J Eng Sci2010; 1: 59–67.
41.
KaziMKEljakFMahdiF. Predictive ANN models for varying filler content for cotton fiber/PVC composites based on experimental load displacement curves. Compos Struct2020; 254: 112885.
42.
GuoPMengWXuM,et al.Predicting mechanical properties of high-performance fiber-reinforced cementitious composites by integrating micromechanics and machine learning. Materials (Basel)2021; 14: 31–43.
43.
CerenKKazimTEsmaA,et al.Comparison of extreme learning machine and deep learning model in the estimation of the fresh properties of hybrid fiber-reinforced SCC. Neural Comput Appl2021; 33: 11641–11659.
44.
ZhangYXuX. Machine learning tensile strength and impact toughness of wheat straw reinforced composites. Mach Learn Appl2021; 6: 1–8.
45.
AmorNNomanMTPetruM,et al.Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites. Sci Rep2021; 11: 13649.
46.
WangJHuJ. A robust combination approach for short-term wind speed forecasting and analysis—combination of the ARIMA (autoregressive integrated moving average), ELM (extreme learning machine), SVM (support vector machine) and LSSVM (least square SVM) forecasts. Energy2015; 93: 41–56.
47.
YaseenZMDeoRCHilalA,et al.Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Softw2018; 115: 112–125.
48.
HuangGBZhuQYSiewCK. Extreme learning machine: theory and applications. Neurocomputing2006; 70: 489–501.
49.
IhuezeCCOkoliNCOnwurahUO,et al.The effect of chemical treatments on the mechanical properties of miscanthus X gigantheus fibre. J Eng Appl Sci2022; 21: 868–878.
50.
MarugánAPMárquezFPGPerezJMP,et al.A survey of artificial neural network in wind energy systems. Appl Energy2018; 228: 1822–1836.
51.
MohandesSRZhangXMahdiyarA. A comprehensive review on the application of artificial neural networks in building energy analysis. Neurocomputing2019; 340: 55–75.
52.
SharifzadehMSikinioti-LockAShahN. Machine-learning methods for integrated renewable power generation: a comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression. Renew Sustain Energy Rev2019; 108: 513–538.
53.
MohammedLAnsariMNMPuaG,et al.A review on natural fiber reinforced polymer composite and its applications. Int J Polym Sci2015; 2015: 1–15.
54.
PintoGMSilvaGCFechineGJM. Effect of exfoliation medium on the morphology of multi-layer graphene oxide and its importance for poly (ethylene terephthalate) based nanoparticles. Polym Test2020; 90: 106742.
55.
SilvérioHANetoWPFDantasNO,et al.Extraction and characterization of cellulose nanocrystals from corncob for application as reinforcing agent in nanocomposites. Ind Crops Prod2013; 44: 427–436.
56.
GanesanSHalliahGP. Synergetic performance of graphene oxide and chitosan on the removal of direct red 7. Orient J Chem2019; 35: 1789–1798.
57.
JouWTLinYTLeeWC,et al.Integrating the Taguchi method and response surface methodology for process parameter optimization of injection molding. Appl Math Inf Sci2014; 8: 1277–1285.
58.
OkaforCESundayIAniOI, et al.Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems. Hybrid Adv2023; 2: 100026.
59.
MishraAMishraN. Design and mechanical analysis of fiber reinforced polymer composite containing bamboo. Int J Innov Sci Eng Technol2016; 3: 336–345.
60.
Easwara-PrasadGLKeerthi GowdaBSVelmuruganR. Comparative study of impact strength characteristics of treated and untreated sisal polyester composites. Procedia Eng2017; 173: 778–785.
61.
SuhartyNSIsmailHDiharjoK,et al.Effect of kenaf fiber as a reinforcement on the tensile, flexural strength and impact toughness properties of recycled polypropylene/halloysite composites. Procedia Chem2016; 19: 253–258.
62.
YanZZhangJZhangH,et al.Improvement of mechanical properties of noil hemp fiber reinforced polypropylene composites by resin modification and fiber treatment. Adv Mater Sci Eng2013; 2013: Article ID 941617, 1–7.
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