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

Author(s)
Ahmad Mayyas^{*},
Awni Qasaimeh,
Khalid Alzoubi,
Susan Lu,
Mohammed T. Hayajneh,
Adel M. Hassan

Affiliation(s)

Department of Automotive Engineering, Clemson University International Center for Automotive Research (CU-ICAR),.

System Science and Industrial Engineering, Binghamton University, State University of New York, New York, USA.

Industrial Engineering Department, Jordan University of Science and Technology, Irbid, Jordan.

Department of Automotive Engineering, Clemson University International Center for Automotive Research (CU-ICAR),.

System Science and Industrial Engineering, Binghamton University, State University of New York, New York, USA.

Industrial Engineering Department, Jordan University of Science and Technology, Irbid, Jordan.

ABSTRACT

In recent years, aluminum-matrix composites (AMCs) have been widely used to replace cast iron in aerospace and automotive industries. Machining of these composite materials requires better understanding of cutting processes re- garding accuracy and efficiency. This study addresses the modeling of the machinability of self-lubricated aluminum /alumina/graphite hybrid composites synthesized by the powder metallurgy method. In this study, multiple regression analysis (MRA) and artificial neural networks (ANN) were used to investigate the influence of some parameters on the thrust force and torque in the drilling processes of self-lubricated hybrid composite materials. The models were identi- fied by using cutting speed, feed, and volume fraction of the reinforcement particles as input data and the thrust force and torque as the output data. A comparison between two prediction methods was developed to compare the prediction accuracy. ANNs showed better predictability results compared to MRA due to the nonlinearity nature of ANNs. The statistical analysis accompanied with artificial neural network results showed that Al_{2}O_{3}, Gr and cutting feed (f) were the most significant parameters on the drilling process, while spindle speed seemed insignificant. Since the spindle speed was insignificant, it directed us to set it either at the highest spindle speed to obtain high material removal rate or at the lowest spindle speed to prolong the tool life depending on the need for the application.

In recent years, aluminum-matrix composites (AMCs) have been widely used to replace cast iron in aerospace and automotive industries. Machining of these composite materials requires better understanding of cutting processes re- garding accuracy and efficiency. This study addresses the modeling of the machinability of self-lubricated aluminum /alumina/graphite hybrid composites synthesized by the powder metallurgy method. In this study, multiple regression analysis (MRA) and artificial neural networks (ANN) were used to investigate the influence of some parameters on the thrust force and torque in the drilling processes of self-lubricated hybrid composite materials. The models were identi- fied by using cutting speed, feed, and volume fraction of the reinforcement particles as input data and the thrust force and torque as the output data. A comparison between two prediction methods was developed to compare the prediction accuracy. ANNs showed better predictability results compared to MRA due to the nonlinearity nature of ANNs. The statistical analysis accompanied with artificial neural network results showed that Al

Cite this paper

A. Mayyas, A. Qasaimeh, K. Alzoubi, S. Lu, M. Hayajneh and A. Hassan, "Modeling the Drilling Process of Aluminum Composites Using Multiple Regression Analysis and Artificial Neural Networks,"*Journal of Minerals and Materials Characterization and Engineering*, Vol. 11 No. 10, 2012, pp. 1039-1049. doi: 10.4236/jmmce.2012.1110108.

A. Mayyas, A. Qasaimeh, K. Alzoubi, S. Lu, M. Hayajneh and A. Hassan, "Modeling the Drilling Process of Aluminum Composites Using Multiple Regression Analysis and Artificial Neural Networks,"

References

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[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

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[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.

[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.