MODELING THE DRILLING PROCESS OF SOME AL-MG-CU ALLOYS AND AL-MG-CU/SIC COMPOSITES USING ARTIFICIAL NEURAL NETWORK

Abstract

Machining of metal matrix composites (MMC's) is very important process and has been a major problem that attracts many researchers to study of characteristics of MMC's during machining process like turning, milling and drilling. This paper concerns with the potential of using feed forward backpropagation neural network in prediction of torque and thrust force during dry drilling of aluminum-copper/silicon carbide composites produced by stir casting method. The effect of the addition of copper as alloying element and silicon carbide as reinforcement particles to Al-4wt.% Mg metal matrix has been investigated by using artificial neural networks. The mean absolute relative errors between experimental and predicted values from network were 2.03% for torque, and 3.46% for thrust force. Therefore, it is suggested that by using ANN outputs, it is possible to predict the results of cutting parameters in drilling process which will be in a good agreement with the experimental ones

Keywords

• Aluminum, Artificial Neural Network (ANN), Casting, Drilling, Metal matrix Composites (MMCs)

References

1. Abdelhay. A.M. (2002) . Application of artificial neural networks to predict the carbon content and the grain size for carbon steel. Egyptian Journal of Sol. 25(2), 229-243.
2. Akhlagi.F.&Maghanaki. H.M.(2004). Effect of casting temperature on the microstructure and wear resistance of Compocast A356/SiCp composites: a comparison between SS and SL routes. Journal of Materials Processing Technology 155-156 (2004) 1874-1880.
3. Altinkok.N.& Koker.R.(2005) Use of artificial neural network for prediction of physical properties and tensile strengths in particle reinforced aluminum matrix composites. Journal of Materials Science 40 1767 – 1770.
4. Altinkok.N.& Koker.R.(2006). Modeling of the prediction of tensile and density properties in particle reinforced metal matrix composites by using neural networks. Materials & Design 27 625-631.
5. Candan.S.& Bilgic M(2004). Corrosion behavior of Al-60vol.%SiCP composites in NaCl. Materials letters 58 2787-2790.
6. Durmus. H.K., Ozkaya.E.& Meric.C.(2006) The use of neural networks for the prediction of wear loss and surface roughness of AA 6351 aluminum alloy. Materials & Design 27 156–159.
7. Frouzan.S.& Akbarzadeh.A.(2006). Prediction of effect of thermo-mechanical parameters on mechanical properties and anisotropy of aluminum alloy AA3004 using artificial neural network. Materials & Design.
8. Gansen.G., Raghukandan.K., Kathikeyan.R.& Pai. B.C. (2005). Development of processing map for 6061 Al/15%SiCP through neural networks.Journal of materials processing technology 166 423-429.

9. Genel.K., Kurnaz. S.C.& Durman.M.(2003). Modeling of tribological properties of alumina fiber reinforced zinc–aluminum composites using artificial neural network. Materials Science and Engineering A363203–210.
10. Hassan. A.M., Tashtoush.G.M.& Alkhalil. J.A.(2007). The effect of graphite and/or silicon carbide addition on the hardness and surface roughness of Al- 4 wt.%Mg alloy. Composite materials vol.41 No. 4 (2007) 453-465.
11. J.E. Wyatt and G.J. Trmal. Machinability: Employing a drilling experiment as a teaching tool. Journal of industrial technology. 1/22 (2006) 1-12.
12. Kelly. J.F.& Cotterell. M.G.Minimal lubrication machining of aluminum alloys. Journal of Materials Processing Technology 120 (2002) 327–334.
13. Kilickap. E., Akır.O.C., Aksoy.M.&Inan.A. (2005). Study of tool wear and surface roughness in machining of homogenized SiC-p reinforced aluminium metal matrix composite. Journal of Materials Processing Technology 164–165 862–867
14. Lee. J.A., Almond. D.P. & Harris. (1999).The use of neural networks for the prediction of fatigue lives of composite materials. Composites: Part A 30 1159–1169.
15. Lin. J.T., Bharracharyya.D.&Kecman.V.(2003).Multiple regression and neural networks analysis in composite machining. Composite science and technology 63 539-548.
16. Monaghan.J.&Reily.O.P. Machinability of an alloy/silicon carbide metal matrix composite. Journal of Processing of Advanced Materials 2 (1992) 37–46.
17. Negnevitsky.M. (2003). Artificial Intelligence. Second Edition. Addison-Wesley,185-189.
18. Ramulu.M. Rao. P.N.&Kao.H.(2002). Drilling of (Al2O3)p/6061 metal matrix composites. Journal of Materials Processing Technology 124 244–254.
19. Rogier. J.R.& Geatz M.W. (2003). Data mining: A tutorial-based primer, Addison-Wesely,
20. Tash.M., Samuel. F.H., Mucciardi. F., Doty. H.W. & Valtierra., S. (2006).Effect of metallurgical parameters on the machinability of heat-treated 356 and 319 aluminum alloys. Materials Science and Engineering A 434: 207– 217.
21. Tekman.C., Ozdemir.I., Cocen.U. & Onel. K.(2003). The mechanical response of Al-Si-Mg/SiCP composites: influence of porosity. Materials Science and Engineering A 360 365-371.
22. Tosun.G.& Muratoglu.M(2004). The drilling of Al/SiCp metal–matrix composites. Part II: workpiece surface integrity. Composites Science and Technology 64 1413-1418.
23. Tosun.G.& Muratoglu.M(2004).The drilling of an Al/SiCP metal-matrix composites. Part I: microstructure. Composites Science and Technology 64 299-308
24. Wain.N., Thomas. N.R., Hickman.S., Wallbank.J.&Teer. (2005). D.G. Performance of low-friction coatings in the dry drilling of automotive Al–Si alloys. Surface & Coatings Technology 200 1885 – 1892.