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

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 regarding 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 identified 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.

Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies

“…cutting speed, feed rate) and deep cryogenic treatment on thrust force in the drilling of AISI 316 stainless steel. Using the same techniques Mayyas et al [19] investigated the influence of cutting speed, feed and volume fraction of the reinforcement particles used on the thrust force and torque in the drilling processes of self-lubricated hybrid composite materials. While Bajić et al [20] examined the influence of three cutting parameters (cutting speed, feed per tooth and depth of cut) on surface roughness, tool wear and cutting force components in a face milling as part of the off-line process control.…”

Assessment of the Surface Roughness and the Delamination in Drilling Hole of Al/SiCp MMCs

“…In Fig. (15), the same parameters used in the previous figure, but the volume fraction is changed from 15 to 25%. The average increase in (df) is between; 1.021 to 1.071 for the used feed rates and the used tools.…”

“…The results, when used the Analysis of Variance Method, indicated that feed rate has maximum influence (60.59%) followed by step angle (19.07%) and cutting speed (11.48%) in affecting the drilling of composites and the interaction of cutting speed and feed is found be significant (4.98%). Ahmad Mayyas, et al [15], present the modeling of the drilling process for aluminum composites using Multiple Regression Analysis and Artificial Neural Networks. The modeling of the machinability of selflubricated aluminum /alumina/graphite hybrid composites synthesized by the powder metallurgy method investigated in this work.…”

Parametric analysis on drilling of aluminium alloy hybrid composites reinforced with SIC/WC

“…Attempts have been made to fabricate mechanical components using these composite materials; however, some measure of finishing should be done to complete the assembly process [2]. The metal removal mechanism in oblique machining like drilling, the variation in cutting forces and their influence on the damages caused in the cutting tool and work piece is most significant [3]. The drilling forces and damages caused can be minimized through proper selection of tool geometries and machining parameters [4].…”