Prediction Micro-Hardness of Al-based Composites by Using Artificial Neural Network in Mechanical Alloying

Document Type : Original Article


Department of Materials Science and Engineering, Shahid Bahonar University of Kerman,Kerman, Iran


Aluminum composites are one of the most important alloys with a wide range of properties and applications. In this paper, we predict the micro-hardness of aluminum-based alloys by artificial neural method (ANN). First, the effective parameters in mechanical alloying include weight percentage and micro hardness of reinforcement materials, milling time, the ball to powder weight ratio, vial speed, the pressure of presses, sintering time and temperature, selected for inputs and micro-hardness of Al composite considered as the output. A feed-forward back propagation artificial neural network designed with 16 and 10 neurons in the first and second hidden layers, respectively. The created network with the mean percentage error of 5.6% was able to predict micro hardness of the Al composites. Finally, the effect of each parameter was determined by sensitivity analysis which volume fraction of alloying elements, milling speed and sintering time had the highest impact on the micro hardness of Al-based composites.


[1] H. Kala, K. Mer, and S. Kumar, Procedia Mater. Sci., (2014) 6, 1951.
[2] W. S. Miller, L. Zhuang, J. Bottema, A. J. Wittebrood, P. De. Smet, A. Hazler and A. Vieregge, Mater. Sci. Eng., A, (2000), 280(1), 37.
[3] P. Rambabu, N. Eswara Prasad, V. V. Kutumbarao and R. J. H. Wanhill, Aerospace Mater. Technol. Springer, (2017), 29.
[4] H. M. Yehia, A. O. Elkady, Y. Reda, K. E. Ashraf, Mater. Res., (2019), 72(1), 85.
[5] L. Soler, J. Macanas, M. Munoz and J. Casado, J. Power Sources, (2007), 169(1), 144.
[6] Md. T. Alam, A. H. Ansari, Md. Tanwir Alam, S. Arif and Md. Naushad Alam, Adv. Mater. Process. Technol., (2017) 3(4), 600.
[7] S. Kandemir, A. Yalamanchili, and H. V. Atkinson., Key Eng. Mater., (2012), 504-506, 339.
[8] H. J. Roven, H. Nesboe, J. C. Werenskiold, T. Seibert, Mater. Sci. Eng., A, (2005), 410, 426.
[9] J. B. Fogagnolo, F. Velasco, M. H. Robert and J. M. Torralba, Mater. Sci. Eng., A, (2003), 342(1-2), 131.
[10] A. Canakci, F. Erdemir, T. Varol and A. Patir, Powder Technol., (2012) 228, 26.
[11] A. Canakci, F. Erdemir, T. Varol and A. Patir, Anal. Meas., (2013), 46(9), 3532.
[12] A. Sinha, S. Sikdar (Dey), P. P. Chattopadhyay and S. Datta, Mater. Des., (2013), 46, 227.
[13] R. P. Lippmann, Anintroduction to computing with neural nets. IEEE Assp magazine,(1987),4(2), 4.
[14] T. Varol, A. Canakci and S. Ozsahin, Part B: Eng., (2013) 54, 224.
[15] R. Esmaeili and M. Dashtbayazi, Expert Syst. Appl., (2014), 41(13), 5817.
[16] H. Arik, Technique. Mater. Des., (2004) 25(1), 31.
[17] M. Kubota, J. Kaneko and M. Sugamata, Mater. Sci. Eng.: A, (2008), 475(1-2), 96.
[18] S. S. Nayak, M. Wollgarten, J. Banhart, S. K. Pabi and B. S. Murty, Mater. Sci. Eng.: A, (2010), 527(9), 2370.
[19] H. Abdoli, H. Asgharzadeh and E. Salahi, J. alloys compd., (2009), 473(1-2), 116.
[20] S. Nayak, S. Pabi and B. Murty, J. Alloys Compd., (2010), 492(1-2), 128.
[21] M. Kubota and P. Cizek, J. alloys compd., (2008), 457(1-2), 209.
[22] M. S. El-Eskandarany, J. Alloys Compd., (1998), 279(2), 263.
[23] L. Kollo, M. Leparoux, C. R. Bradbury, C. Jäggi, E. Carreno-Morelli and M. Rodríguez-Arbaizar, J. Alloys Compd., (2010), 489(2), 394.
[24] R. Perez-Bustamante, I. Estrada-Guel, W. Antúnez-Flores, M. Miki-Yoshida, P. J. Ferreira and R. Martínez-Sánchez, J. Alloys compd., (2008) 450(1-2), 323.
[25] Z. Sadeghian, B. Lotfi, M. H. Enayati and P. Beiss, J.Alloys Compd., (2011), 509(29), 7758.
[26] S. Rajasekaran and G. V. Pai, Synth. app. (with cd), (2003): PHI Learning Pvt. Ltd.
[27] S. Haykin, Neural networks: a comprehensive foundation, (1994), Prentice Hall PTR.
[28] S. Vettivel, N. Selvakumar and N. Leema, Mater. Des., (2013), 45, 323.
[29] Z. Jiang, L. Gyurova, Z. Zhang, K. Friedrich and A. K. Schlarb, Mater. Des., (2008) 29(3), 628.
[30] M. Yazdanmehr, S. H. MousaviAnijdan, A. Samadi and A. Bahrami, Comput. Mater. Sci., (2009), 44(4), 1231.
[31] S. O. Mirabootalebi and R. M. Babaheyari, Prediction length of carbon nanotubes in CVD method by artificial neural network, Iran JOC, 11, 4, (2019), 2731.