Titanium and its alloys give immense specific strength, imparting properties such as corrosion and fracture resistance, making them the right candidate for medical and aerospace applications. There is a wide range of engineering applications that use titanium alloys in a variety of forms. The cost of these alloys is slightly higher in comparison to other variants due to the problematic extraction of the molten process. To reduce costs, titanium alloy products could be made by casting, isothermal forging, radial swaging, or powder metallurgy, although these techniques require some kind of finishing machining process. Titanium and its alloys are difficult to machine due to skinny chips leading to a small cutting tool-workpiece contact area. The thermal conductivity of titanium alloys is too low and the stress produced is too large due to the small contact area, which results in very high cutting temperatures. This paper deals with the experimental study of the influence of the Minimum Quantity Lubricant (MQL) environment in the milling of Ti-6Al-4V alloy considering the optimization of surface roughness and production rate. Taguchi-based TOPSIS and ANOVA were used to analyze the results. The experimental results show that MQL with vegetable oil is successfully applied in the milling of Ti-6Al-4V. The research confirms the suitability of TOPSIS in solving the Multiple Criteria Decision Making (MCDM) issue, by choosing the best alternative at Vc=120m/min, fz=0.065mm/tooth, and ap=0.2mm, where the surface roughness and material removal rate are 0.41µm and 44.1492cm3/min respectively. Besides, ANOVA can be used to predict the best parameters set in the milling process based on the regression model. The parameters predicted by ANOVA analysis do not coincide with any implemented parameters

The modern machining industry faces reducing manufacturing cost pressure and improve product quality expectations. Due to this competition a manufacturer must continually identify cost cutting opportunities in manufacturing process. The keys technology represents cost-saving opportunities associated with reducing cutting fluid consumption, cutting energy consumption, and improves the overall performance of machining operations at the same time. Hence, multiple response optimization techniques have recently become the focus of research to improve product quality by increasing surface quality and reduce costs by reducing cutting force, cutting fluids, and energy consumption of the cutting process recently. Among various optimization methods, few Taguchi-based likes as TOPSIS, COPRAS, MOORA, VIKOR... were chosen to solve complex multiple criteria problems. However, the limitation of these techniques is that it was helped to rank and select the best parameters set for the implemented experiments only. In this work, an attempt was made to streamline the milling and coolant condition parameters of S50C carbon steel under MQL condition. Experiments were performed based on Taguchi's L27 orthogonal array. Five input factors includes machining parameters including cutting speed (Vc), feed (fz), and depth of the cut (ap) combined with two coolant parameters such as coolant air pressure (P), flow rate of lubricant (Q) were considered the variants, while specific cutting energy (Ec), material removed rate (MRR) and surface roughness were response variables. Particle swarm optimization Support Vector Machines (SVM) was used to generate the regression model, then Non-dominated Sorting Genetic Algorithm (NSGA) was used to optimize surface roughness (Ra), specific cutting energy (Ec), and production rate (MRR).