Parametric Optimization for Material Removal Rate During Face Milling: Using Experimental and Mathematical Modelling Approach

Machining is emerging with a scorching potential to produce the tiniest to most complicated geometrical shapes. This study examines how cutting velocity (CV), depth of cut (DOC), feed per tooth (FPT), coolant flow rate (CFR), nozzle tool distance (NTD), and nozzle elevation angle (NEA) affect MQL face milling of the substrate with titanium nitride-coated carbide inserts. To establish a mathematical model and recognize the significant process factors of these process parameters, several machining experiments based on 3-factor and 3-level factorial experiment designs were completed (L27) with ANOVA tool. The models revealed a correlation between cutting parameters and material removal rate (MRR). The insert geometry, which incorporates a scraping edge, allows for a higher material removal rate even at the critical depth of cut, followed by coolant flow. Optimal process parameters increase MRR by 236%. MVLR second-order empirical model with R2 adjusted of 0.935. Additionally, a mathematical model grounded in empirical data is built to verify material removal rate estimates that would significantly enhance the application of aluminium alloy substrate.