Analysis, modelling and optimization during sustainable Dry and MQL turning of AISI 52100 steel using DF, GRA, EAMR, EDAS and FUCA methods

Nowadays, sustainable Minimum Quantity Lubrication (MQL) technology has significantly enhanced machinability, surface quality, and productivity comparing to dry machining. Furthermore, it is considered as a key factor that respects the ecological trends in machining and also contributes in reducing the consumption of cutting fluid and machining costs. The present paper brings a comparative research between sustainable dry machining and sustainable MQL machining while turning AISI 52100 steel with CVD coated (GC-2015) metal carbide inserts. First, parametric tests were carried out to evaluate the impact of machining parameters (CS, f, and Doc) on performance factors in terms of tool wear, cutting force, surface roughness, energy consumption, and cutting temperature. Then, a Taguchi L9 (3^3) experimental design was implemented in order to carry out a statistical analysis based on ANOVA and performance modelling (Ra, Fz, Pc, Kc, and MRR) by applying the (RSM) method. Finally, a comparative multi-criteria optimization was performed using the DF method and four other MCDM methods (GRA, EAMR, FUCA, and EDAS) combined with Taguchi's signal-to-noise-ratio (S/N). The results confirmed the effectiveness of MQL technique in enhancing machining performance over dry machining. Furthermore, the EAMR, EDAS, and GRA methods identified the same optimal combination (CS = 300 m/min, Doc = 0.45 mm, and f = 0.08 mm/rev), yielding the best results for (Ra, Kc, and MRR). The FUCA method promotes improvement of energy efficiency during machining by achieving minimal values for (Pc and Fz). Finally, the DF method stands out for its ability to produce balanced results across all performances.