Due to their lightweight, new classes of materials, including aluminumbased Metal Matrix Composites (MMCs), have been popular in recent years in various industries, including aircraft and automobiles. Because of its low cost and ease of availability, aluminum alloy (LM13) MMCs were developed using Rice Husk Ash (RHA) as reinforcement in this study rather than traditional reinforcement, and composites were prepared using the stir casting technique. LM13-15wt.%RHA composite was chosen for the present machining study. The Central Composite Design (CCD) with three input parameters at three levels based on the best outcomes was adopted for this experimental study. A mathematical model was developed to predict the machining responses of Material Removal Rate (MRR) and surface roughness. The most signi cant variables were evaluated using ANOVA. The main and interactive e ects of the input variables on the predicted responses are determined. The experimental and predicted values are nearly identical, indicating that the developed models can accurately predict responses. The optimal value of the turning parameters was obtained from desirability analysis. The obtained desirability value for turning parameters is 0.863, and for output response, the desirability value for surface roughness and MRR is 0.71663 and 0.747491, respectively, and the combined desirability is 0.731898.

This study investigates the optimization of CNC turning operation parameters for Al6061 nickel coated graphite (NCG) metal matrix composite using the Taguchi based grey relational analysis method. The turning operations are carried out with carbide cutting tool inserts. According to the Taguchi quality concept, 3-level orthogonal array was chosen for the experiments. The experiments are conducted at three different cutting speeds (125, 175, 225m/min) with feed rates (0.1, 0.15, 0.2mm/rev) and depth of cut (0.5, 1, 1.5mm) and different % of reinforcement (2.5%, 5%, 7.5%), signal to noise ratio and the analysis of variance are used to optimize cutting parameters. The effects of cutting speed, feed rate and depth of cut on surface roughness and MRR are analyzed. Mathematical models are developed by using the response surface method to formulate the cutting parameters experimental results shown that machining performance can be improved effectively by using this approach, the analysis of variance (ANOVA) is applied to identify the most significant factor for the turning operations according to the weighted sum grade of the GRG. The predict responses shows the models have more than 95% of confident level of R2 value, from the obtained confirmation experiment result, it is observed, there is a good agreement between the estimated value and the experimental value of the grey relational grade. This experimental study reveals that the grey-Taguchi and RSM can be applied successfully for multi response characteristic performances.