Optimization of Cutting Parameters for Cutting Force in Shoulder Milling of Al7075-T6 Using Response Surface Methodology and Genetic Algorithm

This paper aims at developing a statistical model to predict cutting force in terms of machining parameters such as cutting speed, cutting feed rate and axial depth of cut. Response surface methodology experimental design was used for conducting experiments. The work piece material was Aluminum (Al 7075-T6) and the tool was a shoulder mill with two carbide insert. The cutting forces were measured using three axis milling tool dynamometer. The second order mathematical model in terms of machining parameters was developed for predicting cutting force. The adequacy of the predictive models was tested by analysis of variance and found to be adequate. The direct and interaction effect was graphically plotted which helps to study the significance of these parameters with cutting force. The optimization of shoulder mill machining parameters to acquire minimum cutting force was done by genetic algorithms (GA). A Matlab genetic algorithm solver was used to do the optimization.