Simultaneous optimization of surface roughness and material removal rate in dry turning of super alloy Inconel 600

Nickel-based alloys are largely used in aerospace, power generation and petrochemical industries. Machining of these alloys is usually accompanied with high cutting force and temperature and low surface quality. In order to avoid these limitations, small values of cutting parameters are used in machining of nickel-based alloys that significantly decrease the productivity. In this paper, the effects of cutting speed, feed rate and depth of cut on the surface roughness in dry turning of nickel-based super alloy Inconel 600 by using carbide tool in finishing range have been experimentally investigated. Full factorial design of experiments was used and totally 48 tests were done. Results of the analysis of variance showed that feed rate 69%, depth of cut 6% and cutting speed 2% affects on surface roughness of Inconel 600. In addition to measuring the surface roughness, chip removal rate was calculated for each test and then, for achieving to minimum surface roughness and maximum chip removal rate at the same time, the optimal machining parameters have been achieved by using neural network and imperialist competitive optimization algorithm. By using this algorithm, several levels of optimum cutting parameters are set and a process planner according to the required surface roughness and material removal rate can use these cutting parameters.