Abstract
An artificial-neural-network (ANN) model was developed to estimate the crystalline size of ZnO nanopowder as a function on the milling parameters such as milling times and balls to powder ratio. This nanopowder was synthesized by high energy mechanical milling and the required data for training were collected from the experimental results. The synthesized ZnO nanoparticles are characterized by X-ray diffraction (XRD) and scanning electron microcopy (SEM). It was found that artificial neural network was very effective providing a perfect agreement between the outcomes of ANN modeling and experimental results with an error by far better than multiple linear regressions. An optimization model and this experimental validation of the ball milling process for producing the nanopowder ZnO are carried out.
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References
G. R. Gattorno, P. S. Jacinto and L. R. Vaszquez, Novel synthesis pathway of ZnO nanoparticles, J. Phys. Chem. B, 107 (2003) 12597.
E. Meulenkamp, Synthesis and growth of ZnO nanoparticles, J. Phys. Chem. B, 102 (1998) 5566.
K. J. Klabunde, Nanostructured materials, John Wiley and Sons, New York (2001) 88.
Y. H. Ni., X. W. Wei, J. M. Hong and Y. Ye, Hydrothermal and optical properties of ZnO nanorods, Mater. Sci. Eng. B, 121 (2005) 42–47.
C. Wang, E. Shen, E. Wang, L. Gao, Z. Kang, C. Tian, Y. Lan and C. Zhang, Mater. Lett., 59 (2005) 2867–2871.
M. Ristic, S. Music, M. Ivanda and S. Popovic, Sol-gel synthesis and characterization of nanocrystalline ZnO powders, J. Alloy Compd., 397 (2005) L1–L4.
C. Wu, X. Qiao, J. Chen, H. Wang, F. Tan and S. Li, A novel chemical route to prepare ZnO nanoparticles, Mater. Lett., 6 (2006) 1828–1832.
L. C. Damonte, L. A. Mendoza Zelis, B. MarıSoucase and M. A. Hernandez Fenollos, Nanoparticles of ZnO obtained by mechanical milling, Powder Technology, 148 (2004) 15–19.
M. Abdellaoui and E. Gaffet, physics of mechanical alloying in a planetary ball mill: mathematical treatment, Acta Metall Mater, 43 (1995) 1087–1098.
D’Incau M, Leoni M, Scardi P, J Mater Res., 22 (2007) 1744–1753.
P. P. Chattopadhyay, I. Manna and S. Talapatra, mathematical analysis of milling mechanics in a planetary ball mill, Mater Chem Phys., 68 (2001) 85–94.
D. Das, P. P. Chatterjee, I. Manna, A measure of enhanced diffusion kinetics in mechanical, Scripta Mater, 41 (1999) 861–966.
Abdellaoui M, Gaffet E., J Phys IV (4C) (1994) 291–296.
A. Y. Badmos and H. K. D. H. Bhadeshia, The evolution of solutions: a thermodynamic analysis of mechanical alloying, Metall Mater Trans., 28A (1997) 2189–2194.
L. Lu, M. O. Lai and S. Zhang, Diffusion in mechanical alloying, J Mater Process Technol., 67 (1997) 100–104.
C. Suryanarayana, Mechanical alloying and milling, Prog Mater Sci., 46 (2001) 1–184.
W. Sha and K. L. Edwards, The use of artificial neural networks in materials science based research, Mater Design, 28 (2007) 1747–1752.
M. R. Dashtbayazi, A. Shokuhfar and A. Simchi, Artificial neural network modeling of mechanical alloying process for synthesizing of metal matrix nanocomposite powders, Materials Science and Engineering A, 466 (2007) 274–283.
J. Maa et al, Materials and Design, 30 (2009) 2867–2874.
Truong-Thinh Nguyen., Prediction of deformations of steel plate by artificial neural network in forming process with induction heating, Journal of Mechanical Science and Technology, 23(4) (2009) 1211–1221.
Ramkumar Ramakrishnan and Ragupathy Arumugam, Optimization of operating parameters and performance evaluation of forced draft cooling tower using response surface methodology (RSM) and artificial neural network (ANN), Journal of Mechanical Science and Technology, 26(5) (2012) 1643–1650.
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Recommended by Associate Editor Dong Geun Lee
O. M. Lemine is is currently an Associate Professor at the physics department, College of Science, Al-imam University (Riaydh-Saudi Arabia). He received MSc (1995) and PhD (1999) in Materials Science from Henri Poicarre University (Nancy, France). His researches interests are in nd their applications including nanopowders and thin film. Before joining Al imam University, Dr Lemine was occupying several positions in Nancy University (France), Picardie University (France) and King Khaled University (Saudi Arabia).
M. A. Louly is currently a Professor at the Industrial Engineering Department, College of Engineering, King Saud University. He holds a Ph.D. in Operations Research and Management Science (UTT, Troyes, France), a Master in Computer Science (UHP, Nancy, France), and a Master in Mathematical Engineering (UHP, Nancy, France). His research focuses on Operations Research, Industrial Engineering, Production Planning, Transportations Problems, and Supply Chain Management. His main results are based on the exact mathematical programming methods and their intelligent coupling with heuristics algorithms. Before joining KSU, Prof Louly was occupying an associate professor position in the Industrial Engineering Department of University of technology of Troyes (France).
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Lemine, O.M., Louly, M.A. Application of neural network technique to high energy milling process for synthesizing ZnO nanopowders. J Mech Sci Technol 28, 273–278 (2014). https://doi.org/10.1007/s12206-013-0960-7
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DOI: https://doi.org/10.1007/s12206-013-0960-7