GA Approach for Optimization of Surface Roughness Parameters in Machining of Al Alloy SiC Particle Composite

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Experiments were carried out using carbide turning inserts on AA7075/10 wt.% SiC (particle size 10-20 μm) composites to get actual input values to the optimization problem, so that the optimized results are realistic. By using experimental data, the regression model was developed. This model was used to formulate the fitness function of the genetic algorithm (GA). This investigation attempts to perform the application of GA for finding the optimal solution of the cutting conditions minimum value of surface roughness. The analysis of this investigation shows that the GA technique is capable of estimating the optimal cutting conditions that yield the minimum surface roughness value. With the highest speed, the lowest feed rate, the lowest depth of cut, and the highest nose radius of the cutting conditions' scale, the GA technique recommends 1.039 μm as the best minimum predicted surface roughness value. This means that the GA technique has decreased the minimum surface roughness value of the experimental sample data, regression modeling and desirability analysis by about 3%, 1%, and 2.8%, respectively.