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,"

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