Abstract




 
   

IJE TRANSACTIONS C: Aspects Vol. 29, No. 12 (December 2016) 1726-1733    Article in Press

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  ARTIFICIAL NEURAL NETWORK BASED PREDICTION HARDNESS OF AL2024-MULTIWALL CARBON NANOTUBE COMPOSITE PREPARED BY MECHANICAL ALLOYING
 
M. Mahdavi Jafari and G. R. Khayati
 
( Received: July 31, 2016 – Accepted in Revised Form: November 11, 2016 )
 
 

Abstract    In this study, artificial neural network was used to predict the microhardness of Al2024-multiwall carbon nanotube(MWCNT) composite prepared by mechanical alloying. Accordingly, the operational condition, i.e., the amount of reinforcement, ball to powder weight ratio, compaction pressure, milling time, time and temperature of sintering as well as vial speed were selected as independent input and the mean micro-hardness of composites was selected as model output. To train the model, a Multilayer perceptron neural network structure and feed-forward back propagation algorithm has been employed. After testing many different ANN architectures an optimal structure of the model i.e. 7-25-1 is obtained. The predicted results, with a correlation relation between 0.982 and 0.9952 and 3.26% mean absolute error, show a very good agreement with the experimental values. Furthermore, the ANN model was subjected to a sensitivity analysis and determined the significant inputs affecting hardness of the samples.

 

Keywords    Al2024 multiwall carbon nanotube composite; Artificial neural network; Microhardness; Mechanical milling

 

چکیده    در این پژوهش از شبکه عصبی مصنوعی به منظور پیش بینی میکرو سختی کامپوزیت Al2024 تقویت شده با نانو لوله‌های کربنی چند دیواره ساخته شده توسط آلیاژ‌سازی مکانیکی استفاده شده است. بنابراین، مقدار فاز تقویت کننده، نسبت گلوله به پودر، فشار پرس، زمان آسیاب کاری، دما، زمان تفت جوشی و سرعت اسیاکاری به عنوان پارامتر‌های مستقل ورودی و میکروسختی متوسط کامپوزیت به عنوان پارامتر خروجی انتخاب شده‌اند. برای آموزش مدل، از ساختار شبکه عصبی چند لایه پرسپترون و الگوریتم پس انتشار خطا استفاده شده است. بعد از امتحان معماری های ANN متفاوت، ساختار مدل بهینه بصورت 1-25-7 بدست آمد. نتایج پیش بینی شده با نسبت همبستگی بین 982 /0 و 9952/0 و 26/3 درصد خطای میانگین مطلق، سازگاری بسیار خوبی با مقادیر تجربی نشان میدهد. علاوه بر این، مدل شبکه عصبی به منظور پیدا کردن اهمیت پارامترهای ورودی موثر بر خواص مکانیکی نمونهها تحت آنالیز حساسیت قرار گرفت.

References   

1.      Baughman, R.H., Zakhidov, A.A. and de Heer, W.A., "Carbon nanotubes--the route toward applications", Science,  Vol. 297, No. 5582, (2002), 787-792.

2.      Yu, M.-F., Lourie, O., Dyer, M.J., Moloni, K., Kelly, T.F. and Ruoff, R.S., "Strength and breaking mechanism of multiwalled carbon nanotubes under tensile load", Science,  Vol. 287, No. 5453, (2000), 637-640.

3.      Peng, B., Locascio, M., Zapol, P., Li, S., Mielke, S.L., Schatz, G.C. and Espinosa, H.D., "Measurements of near-ultimate strength for multiwalled carbon nanotubes and irradiation-induced crosslinking improvements", Nature Nanotechnology,  Vol. 3, No. 10, (2008), 626-631.

4.      Liao, J.-z., Tan, M.-J. and Sridhar, I., "Spark plasma sintered multi-wall carbon nanotube reinforced aluminum matrix composites", Materials & Design,  Vol. 31, No., (2010), S96-S100.

5.      Curtin, W.A. and Sheldon, B.W., "Cnt-reinforced ceramics and metals", Materials Today,  Vol. 7, No. 11, (2004), 44-49.

6.      Kim, Y.A., Hayashi, T., Endo, M., Gotoh, Y., Wada, N. and Seiyama, J., "Fabrication of aligned carbon nanotube-filled rubber composite", Scripta Materialia,  Vol. 54, No. 1, (2006), 31-35.

7.      Ahmad, I., Unwin, M., Cao, H., Chen, H., Zhao, H., Kennedy, A. and Zhu, Y., "Multi-walled carbon nanotubes reinforced al 2 o 3 nanocomposites: Mechanical properties and interfacial investigations", Composites Science and Technology,  Vol. 70, No. 8, (2010), 1199-1206.

8.      Deng, C., Wang, D., Zhang, X. and Li, A., "Processing and properties of carbon nanotubes reinforced aluminum composites", Materials Science and Engineering: A,  Vol. 444, No. 1, (2007), 138-145.

9.      Zhong, R., Cong, H. and Hou, P., "Fabrication of nano-al based composites reinforced by single-walled carbon nanotubes", Carbon,  Vol. 41, No. 4, (2003), 848-851.

10.    Xu, C., Wei, B., Ma, R., Liang, J., Ma, X. and Wu, D., "Fabrication of aluminum–carbon nanotube composites and their electrical properties", Carbon,  Vol. 37, No. 5, (1999), 855-858.

11.    Laha, T., Chen, Y., Lahiri, D. and Agarwal, A., "Tensile properties of carbon nanotube reinforced aluminum nanocomposite fabricated by plasma spray forming", Composites Part A: Applied Science and Manufacturing,  Vol. 40, No. 5, (2009), 589-594.

12.    Bakshi, S.R., Singh, V., Balani, K., McCartney, D.G., Seal, S. and Agarwal, A., "Carbon nanotube reinforced aluminum composite coating via cold spraying", Surface and Coatings Technology,  Vol. 202, No. 21, (2008), 5162-5169.

13.    Kwon, H., Park, D.H., Silvain, J.F. and Kawasaki, A., "Investigation of carbon nanotube reinforced aluminum matrix composite materials", Composites Science and Technology,  Vol. 70, No. 3, (2010), 546-550.

14.    Morsi, K., Esawi, A., Lanka, S., Sayed, A. and Taher, M., "Spark plasma extrusion (SPE) of ball-milled aluminum and carbon nanotube reinforced aluminum composite powders", Composites Part A: Applied Science and Manufacturing,  Vol. 41, No. 2, (2010), 322-326.

15.    Uozumi, H., Kobayashi, K., Nakanishi, K., Matsunaga, T., Shinozaki, K., Sakamoto, H., Tsukada, T., Masuda, C. and Yoshida, M., "Fabrication process of carbon nanotube/light metal matrix composites by squeeze casting", Materials Science and Engineering: A,  Vol. 495, No. 1, (2008), 282-287.

16.    Lim, D., Shibayanagi, T. and Gerlich, A., "Synthesis of multi-walled cnt reinforced aluminium alloy composite via friction stir processing", Materials Science and Engineering: A,  Vol. 507, No. 1, (2009), 194-199.

17.    Morsi, K. and Esawi, A., "Effect of mechanical alloying time and carbon nanotube (cnt) content on the evolution of aluminum (al)–cnt composite powders", Journal of Materials Science,  Vol. 42, No. 13, (2007), 4954-4959.

18.    Sha, W. and Edwards, K., "The use of artificial neural networks in materials science based research", Materials & Design,  Vol. 28, No. 6, (2007), 1747-1752.

19.    Radnia, H., Ghoreyshi, A., Younesi, H., Masomi, M. and Pirzadeh, K., "Adsorption of Fe (II) from aqueous phase by chitosan: Application of physical models and artificial neural network for prediction of breakthrough", International Journal of Engineering-Transactions B: Applications,  Vol. 26, No. 8, (2013), 845.

20.    Kalantari, Z. and Razzaghi, M., "Predicting the buckling capacity of steel cylindrical shells with rectangular stringers under axial loading by using artificial neural networks", International Journal of Engineering-Transactions B: Applications,  Vol. 28, No. 8, (2015), 1154.

21.    Babaei, H., "Prediction of deformation of circular plates subjected to impulsive loading using gmdh-type neural network", International Journal of Engineering-Transactions A: Basics,  Vol. 27, No. 10, (2014), 1635-1644.

22.    Kalidass, S. and Ravikumar, T.M., "Cutting force prediction in end milling process of aisi 304 steel using solid carbide tools", International Journal of Engineering-Transactions A: Basics,  Vol. 28, No. 7, (2015), 1074-1081.

23.    Kahrobaee, S. and Kashefi, M., "Applications of impedance plane and magnetic differential permeability in microstructural characterization of aisi d2 tool steel", International Journal of Engineering-Transactions B: Applications,  Vol. 28, No. 2, (2014), 234-240.

24.    Yazdi, M.S., Bagheri, G.S. and Tahmasebi, M., "Finite volume analysis and neural network modeling of wear during hot forging of a steel splined hub", Arabian Journal for Science and Engineering,  Vol. 37, No. 3, (2012), 821-829.

25.    Perzyk, M. and Kochański, A.W., "Prediction of ductile cast iron

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

quality by artificial neural networks", Journal of Materials Processing Technology,  Vol. 109, No. 3, (2001), 305-307.

26.    Austin, N., Kumar, P.S. and Kanthavelkumaran, N., "Artificial neural network involved in the action of optimum mixed refrigerant (domestic refrigerator)(technical note)", International Journal of Engineering-Transactions A: Basics,  Vol. 26, No. 10, (2013), 1235-1242.

27.    Pradeep, J., Srinivasan, E. and Himavathi, S., "Neural network based recognition system integrating feature extraction and classification for english handwritten", International Journal of Engineering-Transactions B: Applications,  Vol. 25, No. 2, (2012), 99-107.

28.    Khanmohammadi, S., "Neural network sensitivity to inputs and weights and its application to functional identification of robotics manipulators", International Journal of Engineering,  Vol. 7, No. 17-23.

29.    Neshat, N., "An approach of artificial neural networks modeling based on fuzzy regression for forecasting purposes", International Journal of Engineering-Transactions B: Applications,  Vol. 28, No. 11, (2015), 1259-1265.

30.    Perez-Bustamante, R., Perez-Bustamante, F., Estrada-Guel, I., Licea-Jimenez, L., Miki-Yoshida, M. and Martinez-Sanchez, R., "Effect of milling time and CNT concentration on hardness of CNT/Al 2024 composites produced by mechanical alloying", Materials Characterization,  Vol. 75, (2013), 13-19.

31.    Perez-Bustamante, R., Perez-Bustamante, F., Barajas-Villaruel, J., Herrera-Ramirez, J.M., Estrada-Guel, I., Amezaga-Madrid, P., Miki-Yoshida, M. and Martinez-Sanchez, R., "Dispersion of CNTs in aluminum 2024 alloy by milling process", in Materials Science Forum, Trans Tech Publ. Vol. 691, (2011), 27-31.

32.    Jafari, M., Abbasi, M., Enayati, M. and Karimzadeh, F., "Mechanical properties of nanostructured Al2024–mwcnt composite prepared by optimized mechanical milling and hot pressing methods", Advanced Powder Technology,  Vol. 23, No. 2, (2012), 205-210.

33.    Deng, C., Zhang, X., Wang, D., Lin, Q. and Li, A., "Preparation and characterization of carbon nanotubes/aluminum matrix composites", Materials Letters,  Vol. 61, No. 8, (2007), 1725-1728.

34.    Chunfeng, D., ZHANG, X., Yanxia, M. and Dezun, W., "Fabrication of aluminum matrix composite reinforced with carbon nanotubes", Rare Metals,  Vol. 26, No. 5, (2007), 450-455.

35.    Itchhaporia, D., Snow, P.B., Almassy, R.J. and Oetgen, W.J., "Artificial neural networks: Current status in cardiovascular medicine", Journal of the American College of Cardiology,  Vol. 28, No. 2, (1996), 515-521.





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