GA approach for optimization of parameters in machining Al alloy SiC particle composite for minimum cutting force

Abstract

Experiments were conducted to investigate the cutting forces during the machining of AA7075/15 wt% SiC (20–40 µm) composite. Process parameters such as cutting speed, feed, depth of cut and nose radius were varied to assess their effects on the cutting forces (tangential force, feed force and radial force). The dry turning of AA7075/15 wt% SiC was done on computerized numerical control (CNC) machine as per design of experiments. Mathematical models for the tangential force, feed force and radial force were determined by using the response surface methodology (RSM). Accuracy of the mathematical model is verified using experimental measurements. Desirability analysis was used to optimize the turning parameters to minimize the tangential, feed and radial force. Genetic algorithms (GA) was also used to optimize the turning parameters. The optimum values of the parameters obtained from the GA, experimental result, revalidated result and desirability analysis were compared. Calculated cutting forces were in line with the experimental results. A simple and practical method was devised to calculate the cutting forces. It was found that turning at optimum parameter values obtained from the GA decreased the tangential force, feed force and radial force values by a substantial amount, as compared to turning at optimum values of cutting speed, feed, depth of cut and nose radius obtained by other approaches. Research findings will act as recommendations for choice of optimal cutting conditions to minimize fluctuation of cutting forces.