Modeling the Drilling Process of Aluminum Composites Using Multiple Regression Analysis and Artificial Neural Networks

Show more

References

[1] J. P. Davim. “Study of Drilling Metal-Matrix Composites Based on the Taguchi Techniques,” Journal of materials processing technology, Vol. 132, No. 1-3, 2003, pp. 250- 254. doi:10.1016/S0924-0136(02)00935-4

[2] N. Altinkok and R. Koker. “Use of Artificial Neural Net- work for Prediction of Physical Properties and Tensile Strengths in Particle Reinforced Aluminum Matrix Com- posites,” Journal of Materials Science, Vol. 40, No. 7, 2005, pp. 1767-1770. doi:10.1007/s10853-005-0689-5

[3] N. Altinkok and R. Koker, “Modeling of the Prediction of Tensile and Density Properties in Particle Reinforced Metal Matrix Composites by Using Neural Networks,” Materials & Design, Vol. 27, No. 8, 2006, pp. 625-631.
doi:10.1016/j.matdes.2005.01.005

[4] A. M. Hassan, M. Hayajneh and M. Al-Omari, “The Ef- fect of the Increase in Graphite Volumetric Percentage on the Strength and Hardness of Al-4wt%Mg Graphite Composites,” Journal of Materials Engineering and Performance, Vol. 11, No. 3, 2002, pp. 250-255.
doi:10.1361/105994902770344024

[5] A. M. Hassan, A. Alrashdan, M. T. Hayajneh, A. T. May- yas, “Prediction of Density, Porosity and Hardness in Aluminum-Copper-Based Composite Materials Using Artificial Neural Network,” Journal of materials proc- essing technology, Vol. 209, No. 2, 2009, pp. 894-899.
doi:10.1016/j.jmatprotec.2008.02.066

[6] S. Kalpakjian and S. R. Schmid, “Manufacturing Engi- neering and Technology,” 4th Edition, Addi?son-Wesley, Boston, 2000.

[7] M. Ramulu, P. N. Rao and H. Kao, “Drilling of (Al2O3)p/6061 Metal Matrix Composites,” Journal of ma- terials processing technology, Vol. 124, No. 1-2, 2002, pp. 244-254. doi:10.1016/S0924-0136(02)00176-0

[8] J. F. Kelly and M. G. Cotterell, “Minimal Lubrication Machining of Aluminum Alloys,” Journal of Materials Processing Technology, Vol. 120, No. 1-3, 2002, pp. 327- 334. doi:10.1016/S0924-0136(01)01126-8

[9] M. Tash, F. H. Samuel, F. Mucciardi, H. W Doty and S. Valtierra, “Effect of Metallurgical Parameters on the Hard- ness and Microstructural Characterization of As-Cast and Heat-Treated 356 and 319 Aluminum Alloys,” Materials Science and Engineering: A, Vol. 443, No. 1-2, 2007, pp. 185-201. doi:10.1016/j.msea.2006.08.054

[10] M. Nouari, G. List, F. Girot and D. Coupard, “Experi- mental Analysis and Optimisation of Tool Wear in Dry Machining of Aluminium Alloys,” Wear, Vol. 255, No. 7-12, 2003, pp. 1359-1368.
doi:10.1016/S0043-1648(03)00105-4

[11] G. Tosun and M. Muratoglu, “The Drilling of Al/SiCp Metal-Matrix Composites. Part II: Workpiece Surface In- tegrity,” Composites Science and Technology, Vol. 64, No. 10-11, 2004, pp. 1413-1418.
doi:10.1016/j.compscitech.2003.07.007

[12] G. Tosun and M. Muratoglu, “The Drilling of an Al/SiCP Metal-Matrix Composites. Part I: Microstructure,” Composites Science and Technology, Vol. 64, No. 2, 2004, pp. 299-308. doi:10.1016/S0266-3538(03)00290-2

[13] J. T. Lin, D. Bharracharyya and V. Kecman, “Multiple Regression and Neural Networks Analysis in Composite Machining,” Composite Science and Technology, Vol. 63, No. 3-4, 2003, pp. 539-548.
doi:10.1016/S0266-3538(02)00232-4

[14] M. T. Hayajneh, A. M. Hassan, A. Alrashdan and A. T. Mayyas, “Prediction of Tribological Behavior of Aluminum-Copper Based Composite Using Artificial Neural Network,” Journal of Alloys and Compounds 2009, Vol. 470, No. 1-2, 2009, pp. 584-588.
doi:10.1016/j.jallcom.2008.03.035

[15] S. Frouzan and A. Akbarzadeh, “Prediction of Effect of Thermo-Mechanical Parameters on Mechanical Properties and Anisotropy of Aluminum Alloy AA3004 Using Artificial Neural Network,” Materials & Design, Vol. 28, No. 5, 2007, pp. 1678-1684.
doi:10.1016/j.jallcom.2008.03.035

[16] K.Genel, S. C. Kurnaz and M. Durman, “Modeling of Tribological Properties of Alumina Fiber Reinforced Zinc-Aluminum Composites Using Artificial Neural Net- work,” Materials Science and Engineering: A, Vol. 363, No. 1-2, 2003, pp. 203-210.
doi:10.1016/S0921-5093(03)00623-3

[17] D. Montgomery and G. C. Runger, “Applied Statistics and Probability for Engineers,” John Wiley and Sons, New York, 2003.

[18] M. Negnevitsky, “Artificial Intelligence,” 2nd Edition, Addison-Wesley, Boston, 2005.

[19] J. R. Rogier and M. W. Geatz, “Data Mining: A Tuto- rial-Based Primer,” Addison-Wesley, Boston, 2003.

[20] Z. Zhang, K. Friedrich and K. Velten, “Prediction on Tribological Properties of Short Fiber Composites Using Artificial Neural Networks,” Wear, Vol. 252, No. 7-8, 2002, pp. 668-675. doi:10.1016/S0043-1648(02)00023-6

[21] S. Kumanan, S. K. N. Saheb and C. P. Jesuthanam, “Pre- diction of Machining Forces Using Neural Networks Trained by a Genetic Algorithm,” Institution of Engineers Journal, Vol. 87, No. 3, 2006, pp. 11-15.

[22] M. M. Hamasha, A. T. Mayyas, A. M. Hassan and M. T. Hayajneh, “The Effect of Time, Percent of Copper and Nickel on Naturally Aged Al-CuNi Cast Alloys,” Journal of Minerals & Materials Characterization & Engineering, Vol. 11, No. 2, 2012, pp. 117-131.

[23] A. T. Mayyas, M. M. Hamasha, A. Alrashdan, A. M. Hassan and M. T. Hayajneh, “Effect of Copper and Sili- con Carbide Content on the Corrosion Resistance of Al-Mg Alloys in Acidic and Alkaline Solutions,” Journal of Minerals & Materials Characterization & Engineering, Vol. 11, No. 4, 2012, pp. 435-452.

[24] M. M. Hamasha, A. T. Mayyas, A. M. Hassan and M. T. Hayajneh, “The Effect of Time, Percent of Copper and Nickel on the Natural Precipitation Hardness of Al-Cu-Ni Powder Metallurgy Alloys Using Design of Experiments,” Journal of Minerals & Materials Characterization & Engineering, Vol. 10, No. 6, 2011, pp. 479-492.