Investigation of machinability indicators during sustainable milling of 17-4PH stainless steel under dry and MQL environments

17-4PH steel, which has the perfect combination of corrosion resistance and high mechanical properties, is especially preferred in defense and aerospace applications, but its machinability is poor. Thus, an extensive research has been conducted on its milling under sustainable cutting regimes (dry and minimum quantity lubrication_MQL) to contribute to both more efficient use and sustainable machining. First, the changes in resultant cutting force ( Fr ), the average surface roughness ( Ra ), the mean roughness depth ( Rz ) and total energy consumption ( P c T ) were investigated after the experiments performed by applying the L 18 orthogonal array. Subsequently, machining conditions were optimized for the minimization of machinability indicators with the Taguchi-based grey relational analysis technique. Finally, the predictive models for these indicators were developed by regression analysis. The order of importance for Fr and P c T was the depth of cut and feed, while for Ra and Rz this ordering was found to be feed rate and cutting regime. Short curved chips formed in MQL cutting regime contributed positively to the minimization of the considered machinability indicators. Although the energy consumption due to spindle speed increased with increasing cutting speed in dry cutting environment, the decrease in material strength resulted in a decrease in P c T . Since the cooling effect of MQL reduces the cutting temperature, material softening and thus the expected decrease in cutting resistance could not be achieved, so the decrease in P c T was not as much as dry cutting. Optimum machining conditions were determined as MQL cutting regime, the cutting speed of 120 m/min, the cutting depth of 0.5 mm and feed rate of 0.05 mm/rev. The determination coefficients of the predictive models developed by regression analysis showed that these models can be used safely in up milling.