LAUSR

Abstract Minimum Quantity Lubrication has been widely used in the titanium alloy milling process as an advanced and clean means of cooling and lubrication. The Minimum Quantity Lubrication parameters have a significant influence on the milling characteristics and so, determining an optimal Minimum Quantity Lubrication parameter combination is vital to obtaining the best milling characteristics. In this study, Minimum Quantity Lubrication with graphene-dispersed vegetable-oil-based cutting fluids was adopted in the milling of TC4 alloy, where the cutting fluids were prepared by dispersing graphene nanoparticles into the vegetable-oil-based cutting fluid to improve the milling characteristics of the TC4 alloy. The integrated Taguchi-Principal component analysis-Gray relational analysis optimization method was used to evaluate the effects of the Minimum Quantity Lubrication parameters on the milling characteristics and obtain the optimal Minimum Quantity Lubrication parameter combination. The milling characteristics of TC4 alloy, namely, the milling force, milling temperature, surface micro-hardness, and surface roughness were evaluated and analyzed, and the optimal Minimum Quantity Lubrication parameter combination was obtained. A verification experiment was conducted and the results indicated that all the four milling characteristics were significantly improved after the optimization process. The improvement rates of the milling force, milling temperature, surface micro-hardness, and surface roughness are 18.13%, 13.59%, 8.36%, and 24.82%, respectively. In summary, appropriately chosen Minimum Quantity Lubrication parameters can enhance the lubrication and cooling properties of the oil film and improve the milling characteristics. The results of this study attest to the feasibility of the integrated Taguchi-Principal component analysis-Gray relational analysis optimization method and provide an experimental basis for the application of graphene additive in Minimum Quantity Lubrication milling.