Predictive Model and Optimisation of MQL Mist Flow Velocity Through CFD Analysis

Abstract

Minimum Quantity Lubricant (MQL) is a sustainable method offering better lubrication and cooling efficiency for high machining performances. To further enhance its sustainability, this study is conducted based on the computational fluid dynamic (CFD) analysis on a MQL delivery model using Treated Recycled Cooking Oil (TRCO) to simulate a high-speed cutting process. The main aim is to optimise the mist flow velocity which leads to optimum penetration of lubrication deep into the cutting zone. Through the design of experiment approach under the Box-Behnken Response Surface Methodology (RSM) method, 13 sets of parameters were simulated with controlled factors of oil flow rate (50-150 ml/hr), nozzle distance (20¬-60 mm), and nozzle diameter (1-2 mm). Then, Analysis of Variance (ANOVA) was applied to investigate how the controlled factors influence the response. The simulation works resulted in the MQL mist flow reaching velocity that varied from 15.43 to 115.52 m/s. The ANOVA revealed that the response is significantly influenced by nozzle distance, nozzle diameter, and the interaction between them. The highest velocity was generated at minimum nozzle distance and maximum nozzle diameter. Contrary, maximum nozzle distance and minimum nozzle diameter generated the lowest value. The flowback or rebound conditions of the mist flow at different flow velocities were also visualized and discussed with the aid of CFD contour images. Through optimization, the optimum MQL mist flow velocity at 115.34 m/s is predicted at; oil flow rate: 100 ml/hr, nozzle diameter: 2 mm, and nozzle distance: 20 mm from the tool edge. This optimum MQL mist flow is vital for high-speed cutting due to massive generation of friction and heat that require deep penetration of the lubricant into the cutting edge for maximum heat dissipation.