Modeling and Assessment of Some Factors that Influence Surface Roughness for the Machining of Particle Reinforced Metal Matrix Composites

Abstract

In this work the influence of cutting speed, size and particle weight fraction on the surface roughness during the machining of 2024 Al alloy composites reinforced with Al 2 O 3 particles was investigated. Machining tests were performed by the turning process, using the coated carbide tools K10 and TP30 at different cutting speeds, and the experiments were based on the Taguchi technique. A surface roughness model was developed in terms of these selected factors by multiple linear regression. An analysis of variance was also used to determine the validity of the proposed parameters and their contributions. The results of these tests showed that the optimum surface roughness was obtained at a cutting speed of 160 m/min for the machining of the 30 wt% particles reinforced composite with a particle size of 66 µm for both types of cutting tools. For the average surface roughness the cutting speed was found to be the most effective factor for the TP30 tool, while for the K10 tool the most effective factor was the weight fraction of the particles. A good agreement between the predicted and experimental surface roughness values of the cutting tools was obtained at a 95% confidence level.