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Hardness Optimization for Al6061-MWCNT Nanocomposite Prepared by Mechanical Alloying Using Artificial Neural Networks and Genetic Algorithm | ||
| Journal of Ultrafine Grained and Nanostructured Materials | ||
| مقاله 4، دوره 50، شماره 1، شهریور 2017، صفحه 23-32 اصل مقاله (566.41 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.7508/jufgnsm.2017.01.04 | ||
| نویسندگان | ||
| Mehrdad Mahdavi Jafari؛ Soheil Soroushian؛ Gholam Reza Khayati* | ||
| Department of Materials Science and Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. | ||
| چکیده | ||
| Among artificial intelligence approaches, artificial neural networks (ANNs) and genetic algorithm (GA) are widely applied for modification of materials property in engineering science in large scale modeling. In this work artificial neural network (ANN) and genetic algorithm (GA) were applied to find the optimal conditions for achieving the maximum hardness of Al6061 reinforced by multiwall carbon nanotubes (MWCNTs) through modeling of nanocomposite characteristics. After examination the different ANN architectures an optimal structure of the model, i.e. 6-18-1, is obtained with 1.52% mean absolute error and R2 = 0.987. The proposed structure was used as fitting function for genetic algorithm. The results of GA simulation predicted that the combination sintering temperature 346 °C, sintering time 0.33 h, compact pressure 284.82 MPa, milling time 19.66 h and vial speed 310.5 rpm give the optimum hardness, (i.e., 87.5 micro Vickers) in the composite with 0.53 wt% CNT. Also, sensitivity analysis shows that the sintering time, milling time, compact pressure, vial speed and amount of MWCNT are the significant parameter and sintering time is the most important parameter. Comparison of the predicted values with the experimental data revealed that the GA–ANN model is a powerful method to find the optimal conditions for preparing of Al6061-MWCNT. | ||
| کلیدواژهها | ||
| Carbon nanotubes؛ Metal–matrix composites؛ Genetic Algorithm؛ Artificial Neural Network | ||
| مراجع | ||
|
1. Iijima S. Helical microtubules of graphitic carbon. Nature. 1991;354(6348):56.
2. Bernier P, Maser W, Journet C, Loiseau A, de la Chapelle ML, Lefrant S, Lee R, Fischer JE. Carbon single wall nanotubes elaboration and properties. Carbon. 1998;36(5):675-80.
3. Ajayan PM, Schadler LS, Giannaris C, Rubio A. Single-walled carbon nanotube–polymer composites: strength and weakness. Advanced materials. 2000;12(10):750-3.
4. Kilbride BE, Coleman JN, Fraysse J, Fournet P, Cadek M, Drury A, Hutzler S, Roth S, Blau WJ. Experimental observation of scaling laws for alternating current and direct current conductivity in polymer-carbon nanotube composite thin films. Journal of Applied Physics. 2002;92(7):4024-30.
5. Biercuk MJ, Llaguno MC, Radosavljevic M, Hyun JK, Johnson AT, Fischer JE. Carbon nanotube composites for thermal management. Applied physics letters. 2002;80(15):2767-9.
6. Peigney A, Laurent C, Flahaut E, Rousset A. Carbon nanotubes in novel ceramic matrix nanocomposites. Ceramics International. 2000;26(6):677-83.
7. Van Lier G, Van Alsenoy C, Van Doren V, Geerlings P. Ab initio study of the elastic properties of single-walled carbon nanotubes and graphene. Chemical Physics Letters. 2000;326(1):181-5.
8. Treacy MJ, Ebbesen TW, Gibson JM. Exceptionally high Young's modulus observed for individual carbon nanotubes. Nature. 1996;381(6584):678.
9. Yu MF, Lourie O, Dyer MJ, Moloni K, Kelly TF, Ruoff RS. Strength and breaking mechanism of multiwalled carbon nanotubes under tensile load. Science. 2000;287(5453):637-40.
10. Baughman RH, Zakhidov AA, De Heer WA. Carbon nanotubes--the route toward applications. Science. 2002;297(5582):787-92.
11. Mamedov AA, Kotov NA, Prato M, Guldi DM, Wicksted JP, Hirsch A. Molecular design of strong single-wall carbon nanotube/polyelectrolyte multilayer composites. Nature materials. 2002;1(3):190-4.
12. Coleman JN, Khan U, Gun'ko YK. Mechanical reinforcement of polymers using carbon nanotubes. Advanced materials. 2006;18(6):689-706.
13. Ahir SV, Terentjev EM. Photomechanical actuation in polymer–nanotube composites. Nature materials. 2005;4(6):491-5.
14. George R, Kashyap KT, Rahul R, Yamdagni S. Strengthening in carbon nanotube/aluminium (CNT/Al) composites. Scripta Materialia. 2005;53(10):1159-63.
15. Kuzumaki T, Miyazawa K, Ichinose H, Ito K. Processing of carbon nanotube reinforced aluminum composite. Journal of Materials Research. 1998;13(09):2445-9.
16. Suryanarayana C. Mechanical alloying and milling. Progress in materials science. 2001;46(1):1-84.
17. Son HT, Kim TS, Suryanarayana C, Chun BS. Homogeneous dispersion of graphite in a 6061 aluminum alloy by ball milling. Materials Science and Engineering: A. 2003;348(1):163-9.
18. Lahiri D, Bakshi SR, Keshri AK, Liu Y, Agarwal A. Dual strengthening mechanisms induced by carbon nanotubes in roll bonded aluminum composites. Materials Science and Engineering: A. 2009;523(1):263-70.
19. Laha T, Chen Y, Lahiri D, Agarwal A. Tensile properties of carbon nanotube reinforced aluminum nanocomposite fabricated by plasma spray forming. Composites Part A: Applied Science and Manufacturing. 2009;40(5):589-94.
20. Zhou SM, Zhang XB, Ding ZP, Min CY, Xu GL, Zhu WM. Fabrication and tribological properties of carbon nanotubes reinforced Al composites prepared by pressureless infiltration technique. Composites Part A: Applied Science and Manufacturing. 2007;38(2):301-6.
21. Tokunaga T, Kaneko K, Horita Z. Production of aluminum-matrix carbon nanotube composite using high pressure torsion. Materials Science and Engineering: A. 2008;490(1):300-4.
22. Morsi K, Esawi AM, Lanka S, Sayed A, Taher M. Spark plasma extrusion (SPE) of ball-milled aluminum and carbon nanotube reinforced aluminum composite powders. Composites Part A: Applied Science and Manufacturing. 2010;41(2):322-6.
23. Wang L, Choi H, Myoung JM, Lee W. Mechanical alloying of multi-walled carbon nanotubes and aluminium powders for the preparation of carbon/metal composites. Carbon. 2009;47(15):3427-33.
24. Perez-Bustamante R, Estrada-Guel I, Antúnez-Flores W, Miki-Yoshida M, Ferreira PJ, Martínez-Sánchez R. Novel Al-matrix nanocomposites reinforced with multi-walled carbon nanotubes. Journal of Alloys and compounds. 2008;450(1):323-6.
25. Datta S, Chattopadhyay PP. Soft computing techniques in advancement of structural metals. International Materials Reviews. 2013;58(8):475-504.
26. Wong BK, Lai VS, Lam J. A bibliography of neural network business applications research: 1994–1998. Computers & Operations Research. 2000;27(11):1045-76.
27. Zhang GP. Neural networks for classification: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2000;30(4):451-62.
28. Rashidi AM, Hayati M, Rezaei A. Application of artificial neural network for prediction of the oxidation behavior of aluminized nano-crystalline nickel. Materials & Design. 2012;42:308-16.
29. Varol T, Canakci A, Ozsahin S. Artificial neural network modeling to effect of reinforcement properties on the physical and mechanical properties of Al2024–B 4 C composites produced by powder metallurgy. Composites Part B: Engineering. 2013;54:224-33.
30. Vettivel SC, Selvakumar N, Leema N. Experimental and prediction of sintered Cu–W composite by using artificial neural networks. Materials & Design. 2013;45:323-35.
31. Hornik K, Stinchcombe M, White H. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural networks. 1990;3(5):551-60.
32. Mirzadeh H, Najafizadeh A. Aging kinetics of 17-4 PH stainless steel. Materials chemistry and physics. 2009;116(1):119-24.
33. Guo Z, Sha W. Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network. Computational Materials Science. 2004;29(1):12-28.
34. Jeyasimman D, Sivaprasad K, Sivasankaran S, Narayanasamy R. Fabrication and consolidation behavior of Al 6061 nanocomposite powders reinforced by multi-walled carbon nanotubes. Powder Technology. 2014;258:189-97.
35. Wu Y, Kim GY. Carbon nanotube reinforced aluminum composite fabricated by semi-solid powder processing. Journal of Materials Processing Technology. 2011;211(8):1341-7.
36. Nikpour N. Production and Characterization of Natural Fiber-polymer Composites Using Ground Tire Rubber as Impact Modifier. 2016, PhD dissertation, Université Laval, Canada.
37. Wu Y, Kim GY, Russell AM. Effects of mechanical alloying on an Al6061–CNT composite fabricated by semi-solid powder processing. Materials Science and Engineering: A. 2012;538:164-72.
38. Song RG, Zhang QZ. Heat treatment technique optimization for 7175 aluminum alloy by an artificial neural network and a genetic algorithm. Journal of materials processing technology. 2001;117(1):84-8.
39. Muc A, Gurba W. Genetic algorithms and finite element analysis in optimization of composite structures. Composite Structures. 2001;54(2):275-81.
40. Holland JH. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press; 1992.
41. Anijdan SM, Bahrami A, Hosseini HM, Shafyei A. Using genetic algorithm and artificial neural network analyses to design an Al–Si casting alloy of minimum porosity. Materials & design. 2006;27(7):605-9.
42. Wong KP, Wong YW. Genetic and genetic/simulated-annealing approaches to economic dispatch. IEE Proceedings-Generation, Transmission and Distribution. 1994;141(5):507-13.
43. Aijun L, Hejun L, Kezhi L, Zhengbing G. Applications of neural networks and genetic algorithms to CVI processes in carbon/carbon composites. Acta Materialia. 2004;52(2):299-305.
44. Liu W, Liu Q, Ruan F, Liang Z, Qiu H. Springback prediction for sheet metal forming based on GA-ANN technology. Journal of Materials Processing Technology. 2007;187:227-31.
45. Fu Z, Mo J, Chen L, Chen W. Using genetic algorithm-back propagation neural network prediction and finite-element model simulation to optimize the process of multiple-step incremental air-bending forming of sheet metal. Materials & design. 2010;31(1):267-77.
46. Anijdan SM, Madaah-Hosseini HR, Bahrami A. Flow stress optimization for 304 stainless steel under cold and warm compression by artificial neural network and genetic algorithm. Materials & design. 2007;28(2):609-15.
47. Ci L, Ryu Z, Jin-Phillipp NY, Rühle M. Investigation of the interfacial reaction between multi-walled carbon nanotubes and aluminum. Acta Materialia. 2006;54(20):5367-75.
48. Liu ZY, Xu SJ, Xiao BL, Xue P, Wang WG, Ma ZY. Effect of ball-milling time on mechanical properties of carbon nanotubes reinforced aluminum matrix composites. Composites Part A: Applied Science and Manufacturing. 2012;43(12):2161-8.
49. Poirier D, Gauvin R, Drew RA. Structural characterization of a mechanically milled carbon nanotube/aluminum mixture. Composites Part A: Applied Science and Manufacturing. 2009;40(9):1482-9. | ||
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