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A New Hybrid Approach to Optimize the End Milling Process for Al/SiC Composites using RSM and GA
The centre of research is to optimize the cutting Forces (FR), surface Roughness (Ra) and Material Removal Rate (MRR) of end milling for Aluminium compositeusing Response Surface Methodology (RSM) and Genetic Algorithm (GA). The RSM L31 empirical model is conducted with Al/SiC composites of various compositions. The cutting forces and the surface roughness are measured using 3-axis milling tool dynamometer and MarTalk Profilometer respectively. The second order mathematical models in terms of machining parameters are developed for predicting responses with adequacy above 85%. The optimal configuration of end milling are 5 wt. % of reinforcement, 0.3 mm depth of cut, feed rate of 49.3 mm/min and cutting speed of 474.3 rpm to acquire minimum FR, Ra with maximum MRR is done by Genetic Algorithm (GA). From the estimated model, the responses are with the experimental deviation of 11% MRR, 13% Ra and 17% FR for the desirability of 98.7%. The optimization of three machining parameters with a advance hybrid approach brought a new scope for the researchers and manufactures to improve the standard of automated machining.
ANOVA, Aluminium, Composites, Milling, Optimization.
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