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.
1. A. Anand, K. Vohra, M. I. Ul Haq, A. Raina, and M. Wani (2016), "Tribology in Industry Tribological Considerations of Cutting Fluids in Machining Environment: A Review Corresponding author,"Tribol. Ind., vol. 463, pp. 463–474,
2. H. Dung, N.-T. Nguyen, and D. Trung (2020), "Calculation of Residual Stress on the Surface Layer of Workpiece When Surface Grinding the Aisi 1018 Steel," vol. 15, pp. 2229–2233, doi: 10.36478/jeasci.2020.2229.2233.
3. H. Hegab, B. Darras, and H. A. Kishawy (2018), "Sustainability Assessment of Machining with Nano-Cutting Fluids,"Procedia Manuf., vol. 26, pp. 245–254, doi: 10.1016/j.promfg.2018.07.033.
4. M. J. Hadad, T. Tawakoli, M. H. Sadeghi, and B. Sadeghi (2012), "Temperature and energy partition in minimum quantity lubrication-MQL grinding process,"Int. J. Mach. Tools Manuf., vol. 54–55, pp. 10–17, doi: https://doi.org/10.1016/j.ijmachtools.2011.11.010.
5. A. S. Awale, M. Vashista, and M. Z. Khan Yusufzai (2020), "Multi-objective optimization of MQL mist parameters for eco-friendly grinding,"J. Manuf. Process., vol. 56, pp. 75–86, doi: https://doi.org/10.1016/j.jmapro.2020.04.069.
6. L. Hung‐Chang and C. Yan‐Kwang (2002), "Optimizing multi‐response problem in the Taguchi method by DEA based ranking method,"Int. J. Qual. Reliab. Manag., vol. 19, no. 7, pp. 825–837, doi: 10.1108/02656710210434766.
7. M. Mia et al. (2018), "Taguchi S/N based optimization of machining parameters for surface roughness, tool wear and material removal rate in hard turning under MQL cutting condition,"Meas. J. Int. Meas. Confed., vol. 122, pp. 380–391, doi: 10.1016/j.measurement.2018.02.016.
8. K. M. Senthilkumar, R. Thirumalai, T. A. Selvam, A. Natarajan, and T. Ganesan (2020), "Multi objective optimization in machining of Inconel 718 using taguchi method,"Mater. Today Proc., doi: https://doi.org/10.1016/j.matpr.2020.09.333.
9. C. M., (2013), "A coupling method of response surfaces (CRSM) for cutting parameters optimization in machining titanium alloy under minimum quantity lubrication (MQL) condition,"Int. J. Precis. Eng. Manuf., vol. 14, p. 693.
10. X.-C. Cao, B.-Q. Chen, B. Yao, and W.-P. He (2018), "Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification,"Comput. Ind., vol. 106, pp. 71–84, doi: https://doi.org/10.1016/j.compind.2018.12.018.
11. C. Cooper et al. (2020), "Convolutional neural network-based tool condition monitoring in vertical milling operations using acoustic signals,"Procedia Manuf., vol. 49, pp. 105–111, doi: https://doi.org/10.1016/j.promfg.2020.07.004.
12. W. Yu et al. (2019), "Predictive control of CO2 emissions from a grate boiler based on fuel nature structures using intelligent neural network and Box-Behnken design,"Energy Procedia, vol. 158, pp. 364–369, doi: https://doi.org/10.1016/j.egypro.2019.01.116.
13. N. Ghosh et al. (2007), "Estimation of tool wear during CNC milling using neural network-based sensor fusion,"Mech. Syst. Signal Process., vol. 21, no. 1, pp. 466–479, doi: https://doi.org/10.1016/j.ymssp.2005.10.010.
14. OLYMPUS CORPORATION, Profile Method (Linear Roughness) Parameters, from https://www.olympus-ims.com/en/metrology/surface-roughness-measurement-portal/parameters/.