DOI: 10.5937/jaes0-30580
This is an open access article distributed under the CC BY 4.0
Volume 19 article 856 pages: 813-820
This experimental research aimed to build the regression model of grinding S50C carbon steel based on a Regression Optimizer. The workpiece specimens were JIS S50C carbon steel that was hardened at 52HRC. Taguchi L27 orthogonal array was performed with 5 3-levels-factors. The studied factors were combining cutting parameters, such as cutting speed, feed rate, depth of cut, and lubricant parameters, including air coolant flow rate Q and air pressure P. The results show that cutting parameters includes workpiece velocity Vw, feed rate f, and depth of cut ap, influence the most on surface roughness Ra, Root Mean Square Roughness Rq, and Mean Roughness Depth Rz,. By contrast, the influence of lubrication parameters is fuzzy. Therefore, this present work focused on predicting and optimizing Ra, Rz, Rq in surface grinding of JSI S50C carbon steel using MQL of peanut oil.
In this work, combining of grinding parameters and lubrication parameters were considered as input factors. The regression models of Ra, Rz, and Rq were obtained using Minitab 19 by Regression Optimizer tool, and then the multi-objective¬¬ optimization problem was solved.
The present findings have shown that Vietnamese vegetable peanut oil could be considered as the lubricant in the grinding process. The optimum grinding and lubricant parameters as following: the workpiece velocity Vw of 5 m/min, feed rate f of 3mm/stroke, depth of cut of 0.005mm and oil flow rate, air pressure of 91.94 ml/h, 1 MPa, respectively. Corresponding to the surface roughness Ra, Root Mean Square Roughness Rq, and Mean Roughness Depth Rz of 0.6512m, 4.592m, 0.8570m, respectively.
The authors highly appreciate the support from Hanoi University of Industry (HaUI-https://haui.edu.vn) for this present research.
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