OPTIMIZATION OF FACE MILLING PROCESS OF TI-6AL-4V TITANIUM ALLOY UNDER MQL CONDITIONS USING A COMBINATION OF MOPSO AND TOPSIS METHODS

This paper presents the optimization of the face milling process of the Ti-6Al-4V titanium alloy using the minimum quantity lubrication (MQL) technique. The main objectives are to improve machining efficiency, enhance surface quality, and minimize environmental impact. To achieve these goals, a combined approach using multiobjective particle swarm optimization (MOPSO) and a technique for order preference by similarity to ideal solution (TOPSIS) is proposed. This study focuses on five process parameters: air pressure, P (bar); oil flow rate, Q (ml/h); cutting speed, Vc(m/min); feed rate, fz (mm/tooth); and depth of cut, ap (mm). The objective is to optimize three key responses: surface roughness, Ra(mu m); cutting power, Pc(W); and material removal rate (MRR) (mm3/min). By applying the MOPSO-TOPSIS approach, the optimal cutting parameters were determined as follows: air pressure, P: 3.968 bar; oil flow rate, Q: 50ml/h; cutting speed, Vc: 126.8843m/min; feed rate, fz: 0.0697mm/tooth; and depth of cut, ap: 0.8730mm. These optimized parameters resulted in a surface roughness, Ra, value of 0.3627 mu m; cutting power, Pc, of 1772.74W; and MRR, of 1772.74mm3/min. This study significantly contributes to the field of machining optimization by presenting a comprehensive methodology for face milling of titanium alloys using the MQL technique. The combination of the MOPSO and TOPSIS methods offers a robust optimization framework that can be applied to similar machining scenarios, making it a valuable tool for manufacturers and researchers. Furthermore, compared with existing methods, the proposed methodology demonstrates superior performance in optimizing the face milling process of the Ti-6Al-4V titanium alloy. The MOPSO-TOPSIS approach provides a more efficient and accurate solution by simultaneously considering multiple objectives. The optimized parameters obtained in this study outperformed those in previous studies in terms of surface roughness, cutting power, and MRR. This study sets a new benchmark for future studies and opens new possibilities for further advancements in the field of machining optimization, particularly for titanium alloys.