Restricted accessResearch articleFirst published online 2022-2
Comparison of response surface methodology with artificial neural network for prediction of the tensile properties of friction stir-processed surface composites
In the present work, nanoboron carbide is integrated in the aluminum matrix using friction stir processing: by varying process parameters, that is, tool pin profile, tool rotational speed and tool traverse speed, based on Taguchi L16 design of experiment. A self-assembled monolayer is successfully developed on the substrate to homogeneously and uniformly distribute the reinforcement particles. Response surface methodology and artificial neural network models are developed using ultimate tensile strength and total elongation as responses. Percentage absolute error between the experimental and predicted values of ultimate tensile strength and total elongation for the response surface methodology model is 3.537 and 2.865, respectively, and for artificial neural network is 2.788 and 2.578, respectively. For both the developed models experimental and forecasted values are in close approximation. The artificial neural network model showed slightly better predictive capacity compared to the response surface methodology model. From the scanning electron microscopy micrograph, it is evident that throughout the matrix B4C reinforcement particles are well distributed also; with increasing tool rotational speed grain size decreases up to 1200 r/min; on further increasing the tool rotational speed particles starts clustering.
ButolaRTyagiLKemL,et al.Mechanical and wear properties of aluminium alloy composites: a review. In: SharmaVDixitUSørbyKBhardwajATrehanR, eds. Manufacturing engineering. Lecture notes on multidisciplinary industrial engineering. Singapore: Springer, 2020, pp.369–391.
2.
ReddyMShakoorRParandeG, et al.Enhanced performance of nano-sized SiC reinforced Al metal matrix nanocomposites synthesized through microwave sintering and hot extrusion techniques. Progress in Natural Science: Materials International2017; 27: 606–614.
3.
CoyalAYuvarajNButola R,et al.An experimental analysis of tensile, hardness and wear properties of aluminium metal matrix composite through stir casting process. SN Applied Sciences2020; 2.
4.
RajaAGuptaSVashista M,et al.Material characterization of friction stir welded IS-2062 steel plate by hysteresis loop analysis. Mater Res Express2019; 6: 116108.
5.
RajaAVashistaMKhan YusufzaiM. Estimation of material properties using hysteresis loop analysis in friction stir welded steel plate. J Alloys Compd2020; 814: 152265.
6.
TyagiLButolaRJhaA. Mechanical and tribological properties of AA7075-T6 metal matrix composite reinforced with ceramic particles and Aloe vera ash via friction stir processing. Mater Res Express2020; 7: 066526.
7.
PaidarMOjoOEzatpour H,et al.Influence of multi-pass FSP on the microstructure, mechanical properties and tribological characterization of Al/B4C composite fabricated by accumulative roll bonding (ARB). Surf Coat Technol2019; 361: 159–169.
8.
SathishkumarRMuruganNDinaharan I,et al.Role of friction stir processing parameters on microstructure and microhardness of boron carbide particulate reinforced copper surface composites. Sadhana2013; 38: 1433–1450.
9.
YusufzaiMRajaAGupta S,et al.Barkhausen noise analysis of friction stir processed steel plate. Indian Journal of Engineering & Materials Sciences2020; 27: 670–676.
10.
ButolaRTyagiLSingariR,et al.Mechanical and wear performance of Al/SiC surface composite prepared through friction stir processing. Mater Res Express2021; 8: 016520.
11.
ButolaRKanwarSTyagiL,et al.Optimizing the machining variables in CNC turning of aluminum based hybrid metal matrix composites. SN Applied Sciences2020; 2.
12.
ShaikhMRajaSAhmedM,et al.Rice husk ash reinforced aluminium matrix composites: fabrication, characterization, statistical analysis and artificial neural network modelling. Mater Res Express2019; 6: 056518.
13.
DinaharanIPalanivelRMurugan N,et al.Predicting the wear rate of AA6082 aluminum surface composites produced by friction stir processing via artificial neural network. Multidiscipline Modeling in Materials and Structures2019; 16: 409–423.
14.
VeličkovićSStojanovićBBabićM, et al.Parametric optimization of the aluminium nanocomposites wear rate. Journal of the Brazilian Society of Mechanical Sciences and Engineering2018; 41.
15.
ArifSAlamMAnsariA,et al.Analysis of tribological behaviour of zirconia reinforced Al-SiC hybrid composites using statistical and artificial neural network technique. Mater Res Express2018; 5: 056506.
16.
PramodRVeeresh KumarGGouda P,et al.A study on the Al2O3 reinforced Al7075 metal matrix composites wear behavior using artificial neural networks. Materials Today: Proceedings2018; 5: 11376–11385.
17.
AtrianAMajzoobiGNourbakhshS,et al.Evaluation of tensile strength of Al7075-SiC nanocomposite compacted by gas gun using spherical indentation test and neural networks. Adv Powder Technol2016; 27: 1821–1827.
18.
VishwakarmaDKumarNPadapA. Modelling and optimization of aging parameters for thermal properties of Al 6082 alloy using response surface methodology. Mater Res Express2017; 4: 046502.
19.
KumarNSinghSGautamG,et al.Synthesis and statistical modelling of dry sliding wear of Al 8011/6 vol.% AlB2in situ composite. Mater Res Express2017; 4: 106514.
20.
Abdel Ghaffar Abdel MaboudAEl-MahallawyNZoalfakarS. Process parameters optimization of friction stir processed Al 1050 aluminum alloy by response surface methodology (RSM). Mater Res Express2018; 6: 026527.
21.
DehghaniKNekahiAMirzaieM. Optimizing the bake hardening behavior of Al7075 using response surface methodology. Mater Des2010; 31: 1768–1775.
22.
SivasankaranSRamkumarKAl-Mufadi F,et al.Effect of TiB2/Gr hybrid reinforcements in Al 7075 matrix on sliding wear behavior analyzed by response surface methodology. Met Mater Int2019; 27: 1739–1755.
23.
TyagiLButolaRKem L,et al.Comparative analysis of response surface methodology and artificial neural network on the wear properties of surface composite fabricated by friction stir processing. Journal of Bio- and Tribo-Corrosion2021; 7.
24.
KarnikSGaitondeVDavimJ. A comparative study of the ANN and RSM modeling approaches for predicting burr size in drilling. The International Journal of Advanced Manufacturing Technology2007; 38: 868–883.
25.
PatelKBrahmbhattP. A comparative study of the RSM and ANN models for predicting surface roughness in roller burnishing. Procedia Technology2016; 23: 391–397.
26.
WangDNiYHuo Q,et al.Self-assembled monolayer and multilayer thin films on aluminum 2024-T3 substrates and their corrosion resistance study. Thin Solid Films2005; 471: 177–185.
27.
ButolaRMurtazaQSingariR. Formation of self-assembled monolayer and characterization of AA7075-T6/B4C nano-ceramic surface composite using friction stir processing. Surface Topography: Metrology and Properties2020; 8: 025030.
28.
ButolaRSingariRMurtazaQ. Mechanical and wear behaviour of friction stir processed surface composite through self-assembled monolayer technique. Surface Topography: Metrology and Properties2020; 8: 045007.
29.
OkewaleAOmoruwuoFAdesinaO. Comparative studies of response surface methodology (RSM) and predictive capacity of artificial neural network (ANN) on mild steel corrosion inhibition using water hyacinth as an inhibitor. J Phys Conf Ser2019; 1378: 022002.
30.
BeheraSMeenaHChakraborty S,et al.Application of response surface methodology (RSM) for optimization of leaching parameters for ash reduction from low-grade coal. International Journal of Mining Science and Technology2018; 28: 621–629.
31.
AbdelbaryAAbouelwafaMEl FahhamI. Evaluation and prediction of the effect of load frequency on the wear properties of pre-cracked nylon 66. Friction2014; 2: 240–254.
32.
JiangZZhangZFriedrichK. Prediction on wear properties of polymer composites with artificial neural networks. Compos Sci Technol2007; 67: 168–176.
33.
MahantaSChandrasekaranMSamanta S,et al.Multi-response ANN modelling and analysis on sliding wear behavior of Al7075/B4C/fly ash hybrid nanocomposites. Mater Res Express2019; 6: 0850h4.
34.
AlamMArifSAnsari A,et al.Optimization of wear behaviour using taguchi and ANN of fabricated aluminium matrix nanocomposites by two-step stir casting. Mater Res Express2019; 6: 065002.
35.
SharmaHTiwariSDeepmala K,et al.Investigation of mechanical and tribological properties of the Al7075-B4C composite fabricated by friction stir processing. International Journal of Applied Engineering Research2019; 14: 5.
36.
ButolaRSingariRMMurtazaQ. Fabrication and optimization of AA7075 matrix surface composites using taguchi technique via friction stir processing (FSP). Engineering Research Express2019; 1: 025015.
37.
ElangovanKBalasubramanianV. Influences of pin profile and rotational speed of the tool on the formation of friction stir processing zone in AA2219 aluminium alloy. Materials Science and Engineering: A2007; 459: 7–18.
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