Application of Taguchi coupled with genetic algorithm (GA) for optimizing surface quality in drilling of duplex stainless steel 2205

Abstract

This paper aims to explore the drilling performance of duplex stainless steel (DSS) 2205 under sustainable machining, that is dry, MQL, and LCO₂. Furthermore, for the correct choice of process parameters, the Taguchi statistical method is used to create a relationship between input parameters, that is cooling environment, spindle speed (N), feed rate (V_f), and responses, that is Ra, and hole deviation. From the ANOVA analysis, it was discovered that for measured Ra and hole deviation, the most effective parameter was the cooling environment. The model for Ra and hole deviation was obtained with a precision of 99.32% and 99.70%, respectively. In addition, a genetic algorithm (GA) was implemented for an optimum combination of process parameters and an optimized value of responses. From GA, it was obtained that the optimum process parameters were an LCO₂ cooling environment with a high spindle speed (1120 RPM) and a low feed rate (0.02, 0.04 mm/rev), respectively. Also, the optimized value for Ra and hole deviation is 0.375271 and 0.0207519, respectively.

Keywords

Dry machining MQL machining cryogenic machining Taguchi method genetic algorithm machinability optimization surface engineering

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