Multi response optimization of dual jet CO2+SQL in milling Inconel 718

In this modern production era, the requirement for safe and long-lasting machining is essential. As an outcome, a study is being conducted to address the gap in milling of Inconel 718 between sustainability (ideal condition) and machining features (surface roughness, tool wear, and chip characteristics). In this work, a dual jet cooling and lubrication medium was jetted to the rake face; a combination of small quantity lubrication (SQL) and cryogenic carbon dioxide (CO2) was presented to improve the efficacy. The outcomes attained have highlighted the outpaced hybrid cooling followed by cryogenic and SQL regarding sustainability measures and machining characteristics. In a nutshell, it is reasonable to assert that a hybrid strategy is accountable for positive outcomes. Results of this study exhibited that the hybrid condition is capable of generating lower flank wear of 27% and 49% in comparison with CO2 and SQL. Additionally, the chip thickness was considerably reduced under CO2+SQL condition. In this approach, Grey Relational Analysis (GRA) was performed to figure out the optimal working parameters and Artificial Neural Network (ANN) technique is employed to predict GRG values that lead to better machining performance. ANOVA produced a maximum contribution of 98.13% by the cutting environment.