Machining of the nickel-based alloy is very demanding due to its extreme mechanical properties, for example, higher fatigue strength, better corrosion and creep resistance feature, substantial work hardening capability, and appreciable tensile and shear strength. Owing to these properties, the selection of machining parameters is a major challenge for modern machining industries. Therefore, the present experimental work is carried out to select the best parametric combination of the wire electrical discharge machining (WEDM) machine for reducing machining cost and human effort. The Trapezoidal Interval Type-2 fuzzy number (T2FS) integrated Analytical Hierarchy Process (AHP)-based Additive Ratio Assessment (ARAS) method is used for selecting the best WEDM process parameters of Inconel-800 superalloy. Finally, the results were compared with some existing multi-criteria decision-making methods to confirm the validity of the adopted method. The comparison shows that Type-2 Fuzzy AHP-ARAS synergy can help to formulate the problem and facilitate the assessment and ranking of WEDM process parameters when multiple criteria are jointly considered.

With the development of ideas such as green and sustainable processing, recently evolved lubrication methods are commonly used to resolve the disadvantages of the flood lubrication approach. In the minimum quantity lubrication (MQL) technology, a small lubricant mist is inserted into the tool-workpiece interface to achieve better lubrication. The present study, therefore, explored the viability of alumina-reinforced palm oil as a lubricant in the MQL environment. A diverse volume fraction of aluminium (0-1.4%) was mixed with palm oil, and the optimal concentration of nanoparticles (0.8%) was chosen through spectroscopic analysis. Subsequently, twenty-seven milling operations were carried out on Inconel 690 material under the best lubricating medium. Statistical analysis of the machining values was conducted using the main effect plot (MEP), empiric cumulative distribution (ECD), and analysis of variance (ANOVA). Besides, Response surface methodology (RSM) was used to create a mathematical equation between input and machining responses. Finally, the Particle Swarm Optimization (PSO) approach was applied to achieve an optimal machining environment: cutting speed = 88.348 m/min, feed rate = 0.108 mm/tooth, and depth of cut = 1 mm. The optimal machining conditions were confirmed by functional experimentation, which has shown that the mean error between the experimental and the predictive outputs is minimal (less than 2%).