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

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.

Automated summary

This study improved the cutting conditions for Inconel-800 super alloy by employing sustainable methods with suspended nanoparticle-enhanced minimum quantity lubrication. A relationship model between process parameters and machining responses was established using orthogonal array design and response surface methodology, with multi-objective optimization solved by non-dominated sorting genetic algorithm. The combination of response surface methodology and genetic algorithm proved effective in reducing specific cutting energy and energy consumption.