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In this study, a naturally sourced cutting oil mixture using for the magnetorheological finishing (MRF) as an environmentally friendly carrier liquid. In addition, fuzzy grey relation analysis has been developed to predict and give optimal cutting parameters, the main factors affecting surface quality and material removal rate (MRR) identified. Experimental polishing procedures Ti-6Al-4V alloy were performed to confirm the availability of MRF models of the surface quality and MRR proposed. The fuzzy grey levels of elements to the polishing surface quality, namely the workpiece speed (nw), working distances (K), MRF carrier speed (nMRF) and feed rate (F), were 0.6983, 0.8057, 0.7818, and 0.7817, respectively. The analysis showed that the working distances (K) showed the most remarkable influence on the polishing effect, while the effect of workpiece speed (nw) was the least important. Microscopic observations significantly minimize scratches on the surface. This observation provides an excellent reference value for high surface quality and material removal rate when polishing Ti-6Al-4V alloys.

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This study aims to build a regression model when surveying the milling process on S50C steel using Minimum Quantity Lubrication (MQL) of Vietnamese peanut oil-based on Response Surface Methodology. The paper analyses and evaluates the effect of cutting parameters, flow rates, and pressures in minimum quantity lubrication system on cutting force and surface roughness in the milling process of S50C carbon steel materials after heat treatment (reaching a hardness of 52 HRC). The Taguchi method, one of the most effective experimental planning methods nowadays, is used in this study. The statistical analysis software, namely Minitab 19, is utilized to build a regression model between parameters of the cutting process, flow rates and pressures of the minimum quantity lubrication system and the cutting force, surface roughness of the part when machining on a 5-axis CNC milling machine. Thereby analyzing and predicting the effect of cutting parameters and minimum quantity lubrication conditions on the surface roughness and cutting force during machining to determine the influence level them. In this work, the regression models of Ra and F were achieved by using the optimizer tool in Minitab 19. Moreover, the multi-response optimization problem was solved. The optimum cutting parameters and lubricating conditions are as follows: Cutting velocity Vc=190.909 m/min, feed rate fz=0.02 mm/tooth, axial depth of cut ap=0.1 and nozzle pressure P=5.596 MPa, flow rate Q=108.887 ml/h. The output parameters obtained from the above parameters are Ra=0.0586  and F=162.035 N, respectively. This result not only provides the foundation for future research but also contributes reference data for the machining process

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