Modeling and optimization of machining parameters in milling of INCONEL-800 super alloy considering energy, productivity, and quality using nanoparticle suspended lubrication

Abstract

Inconel-800 super alloy is a newly difficult-to-cut material. To improve the cutting conditions for this metal, sustainable methods in which minimum quantity lubrication enhanced with suspended nanoparticle were employed. This work also aims to model the relationship between process parameters (cutting speed, feed per tooth, depth of cut, and corner radius of cutting tool) and machining responses (surface roughness, specific cutting energy, cutting power, and material removal rate) using orthogonal array design of experiment and response surface methodology. Non-dominated sorting genetic algorithm was used to solve the multi-objective optimization problems in terms of energy, productivity, and quality of the machining process. The results indicate that the application of the response surface methodology model in combination with non-dominated sorting genetic algorithm is appropriate for this study due to the goodness of fit of response surface methodology and the global optimum solution of genetic algorithm. Because multi-objective optimization gives multiple solutions, Pareto plot and data mining are employed to support the selection of process parameters that can save time and cost and increase energy efficiency, meanwhile, simultaneously improve productivity and surface quality. The research results show that the specific cutting energy and energy consumption can be reduced up to 20.2% and 6.4%, respectively.