Abstract
In this study, an artificial neural network (ANN) approach was used to predict the abrasive wear behavior of AA2014 aluminum alloy matrix composites reinforced with B4C particles. The abrasive wear properties of varying volume fraction of particles up to 12 % B4C particle reinforced AA2014MMCS produced by stir casting method were investigated using a block-on-disc wear tester. Wear tests were performed under 92 N against the abrasive suspension mixture with a novel three body abrasive. For wear behavior, the volume loss, specific wear rate and surface roughness of the composites were measured. The effect of sliding time and content of B4C particles on the abrasive wear behavior were analyzed in detail. As a result of this study, the ANN was found to be successful for predicting the volume loss, specific wear rate and surface roughness of AA2014/B4C composites. The mean absolute percentage error (MAPE) for the predicted values did not exceed 4.1 %. The results have shown that ANN is an effective technique in the prediction of the properties of MMCs, and quite useful instead of time-consuming experimental processes.
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Alizadeh A., Taheri N.E.: Wear Behavior of nanostructured Al and Al–B4C nanocomposites produced by mechanical milling and hot extrusion. Tribol. Lett. 44, 59–66 (2011)
Kimura Y.: Recent trends in tribology in Japan. J. Euro. Rib. 931, 1–9 (1993)
Raghumatham N., Ioannidis E.K., Sheppard T.: Fabrication, properties and structure of a high temperature light alloy composite. J. Mater. Sci. 26, 985–992 (1991)
Shibata K., Ushio H.: Tribological application of MMCs for reducing engine weight. Tribol. Int. 27, 39–41 (1994)
Chellman, D.J.; Langenbeck, S.L.: Aerospace applications of advanced aluminum alloys. J. Key. Eng. Mater. 77–78, 49–60 (1993)
Chawla N., Chawla K.K.: Metal matrix composites. Springer-Verlag, New York (2006)
Rohatgi, P.K.: Cast metal matrix composites, In: Metal Hand Book, vol. 15. 9th edn. ASM International, Metals Park (1988)
Sannino A.P., Rack H.J.: Tribological investigation of 2009Al-20 vol% SiCp/17-4 PH, Part I: composite performance. Wear 197, 151–159 (1996)
Sharma S.C.: The sliding wear behavior of Al6061–garnet particulate composites. Wear 249, 1036–1045 (2001)
Jiang J.Q., Tan R.S., Ma A.B.: Dry sliding wear behaviour of Al2O3–Al composites produced by centrifugal force infiltration. Mater. Sci. Technol. 12, 483–488 (1996)
How H.C., Baker T.N.: Characterisation of sliding frictioninduced subsurface deformation of Saffil-reinforced AA6061 composites. Wear 232, 106–115 (1999)
Canakci A.: Microstructure and abrasive wear behavior of B4C particle reinforced 2014 Al matrix composites. J. Mater. Sci. 46, 2805–2813 (2011)
Ramesh C.S., Keshavamurthy R., Channabasappa B.H., Pramod S.: Friction and wear behavior of Ni–P coated Si3N4 reinforced Al6061composites. Trib. Int. 43, 623–634 (2010)
Ipek, R.: Adhesive wear behaviour of B4C and SiC reinforced 4147 Al matrix composites (Al/B4C–Al/SiC). J. Mater Process. Technol. 162–163, 71–75 (2005)
Rosenberger M.R., Schvezov C.E., Forlerer E.: Wear of different aluminum matrix composites under conditions that generate a mechanically mixed layer. Wear 259, 590–601 (2005)
Hemanth J.: Tribological behavior of cryogenically treated B4Cp/Al–12 % Si composites. Wear 258, 1732–1744 (2005)
Kaczmar J.W., Pietrzak K., Wlosinski W.: The production and application of metal matrix composite materials. J. Mater. Process. Technol. 106, 58–67 (2000)
Mohanty R.M., Balasubramanian K., Seshadri S.K.: Boron carbide-reinforced aluminum 1100 matrix composites: fabrication and properties. Mater. Sci. Eng. A 498, 42–52 (2008)
Topcu I., Gulsoy H.O., Kadioglu N., Gulluoglu A.N.: Processing and mechanical properties of B4C reinforced Al matrix composites. J. Alloys. Comp. 482, 516–521 (2009)
Gyurova L.A., Friedrich K.: Artificial neural Networks for predicting sliding friction and wear properties of polyphenylene sulfide composites. Trib. Int. 44, 603–609 (2011)
Mahamood A.H., Alrashdan A., Hayajneh M.T., Mayyas A.T.: Prediction of density, porosity and hardness in aluminum–copper-based composite materials using artificial neural network. J. Mater. Process. Technol. 209, 894–899 (2009)
Ozerdem M.S., Kolukisa S.: Artificial neural network approach to predict the mechanical properties of Cu–Sn–Pb–Zn–Ni cast alloys. Mater. Des. 30, 764–769 (2009)
Muthukrishnana N., Davim J.P.: Optimization of machining parameters of Al/SiC-MMC with ANOVA and ANN analysis. J. Mater. Process. Technol. 209, 225–232 (2009)
Genel K., Kurnaz S.C., Durman M.: Modeling of tribological properties of alumina fiber reinforced zinc–aluminium composites using artificial neural network. Mater. Sci. Eng. A 363, 203–210 (2003)
Dashtbayazi M.R., Shokuhfar A., Simchi A.: Artificial neural network modeling of mechanical alloying process for synthesizing of metal matrix nanocomposite powders. Mater. Sci. Eng. A 466, 274–283 (2007)
Malinov S., Sha W.: Software products for modelling and simulation in materials science. Comput. Mater. Sci. 28, 179–198 (2003)
Canakci A., Arslan F., Yasar I.: Pre-treatment process of B4C particles to improve incorporation into molten AA2014 alloy. J. Mater. Sci. 42, 9536–9542 (2007)
Lee J.A., Almond D.P., Harris B.: The use of neural networks for the prediction of fatigue lives of composite materials. Compos. Part A 30, 1159–1169 (1999)
Karayel D.: Prediction and control of surface roughness in CNC lathe using artificial neural network. J. Mater. Process. Technol. 209, 3125–3137 (2009)
Senatore A., Agostino V.D., Giuda R.D., Petrone V.: Experimental investigation and neural network prediction of brakes and clutch material frictional behavior considering the sliding acceleration influence. Trib. Int. 44, 1199–1207 (2011)
Ma J., Zhu S.G., Wu C.X., Zhang M.L.: Application of back-propagation neural network technique to high-energy planetary ball milling process for synthesizing nanocomposite WC–MgO powders. Mater. Des. 30, 2867–2874 (2009)
Gyurova L.A., Justel P.M., Schlarb A.K.: Modeling the sliding wear and friction properties of polyphenylene sulfide composites using artificial neural networks. Wear 268, 708–714 (2010)
Sag\({\imath}\) roglu, S.; Besdok, E.; Erler, M.: Artificial intelligence applications in engineering-1: Artificial neural networks, Ufuk Books Stationery, Kayseri (2003)
Kök M.: Production and mechanical properties of Al2O3 particle-reinforced 2024 aluminum alloy composites. J. Mater. Process. Technol. 161, 381–387 (2005)
Kök M.: Abrasive wear of Al2O3 particle reinforced 2024 aluminum alloy composites fabricated by vortex method. Compos. A 37, 457–464 (2006)
Akhlaghi, F.; Lajervardi, A.; Maghanaki, H. M.: Effects of casting temperature on the microstructure and wear resistance of compocast A356/SiCp composites: a comparison between SS and SL routes. J. Mater. Process. Technol. 155-156, 1874–1880 (2004)
Suresh K.R., Niranjan H.B., Jebaraj P.M., Chowdiah M.P.: Tensile and wear properties of aluminum composites. Wear 255, 38–642 (2003)
Das S., Das S., Das K.: Abrasive wear of zircon sand and alumina reinforced Al–4.5 wt% Cu alloy matrix composites: a comparative study. Comp. Sci. Technol. 67, 746–751 (2007)
Acilar M., Gul F.: Effect of the applied load, sliding distance and oxidation on the dry sliding wear behaviour of Al–10Si/SiCp composites produced by vacuum infiltration technique. Mater. Des. 25, 209–217 (2004)
Axen N., Jacobson S.: Transitions in the abrasive wear resistance of fibre and particle reinforced aluminum. Wear 178, 1–7 (1994)
Lee H.L., Lu W.H., Chan S.L.: Abrasive wear of powder metallurgy Al alloy 6061-SiC particle composites. Wear 159, 223–231 (1992)
Yilmaz S., Bultoz O.: Abrasive wear of Al2O3-reinforced aluminum-based MMCs. Compos. Sci. Technol. 61, 2381–2392 (2001)
Prasad B.K., Jha A.K., Modi O.P., Das S., Yegneswaran A.H.: Abrasive wear characteristics of Zn-37.2Al-2.5Cu-0.2Mg alloy dispersed with silicon carbide particles. Mater. Trans. 36, 1048–1057 (1995)
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Canakci, A., Ozsahin, S. & Varol, T. Prediction of Effect of Reinforcement Size and Volume Fraction on the Abrasive Wear Behavior of AA2014/B4Cp MMCs Using Artificial Neural Network. Arab J Sci Eng 39, 6351–6361 (2014). https://doi.org/10.1007/s13369-014-1157-9
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DOI: https://doi.org/10.1007/s13369-014-1157-9