Akcije

Journal of Applied Engineering Science
kako citirati ovaj članak
podeli ovaj članak

Metrika

  • citati u SCIndeksu: 0
  • citati u CrossRef-u:0
  • citati u Google Scholaru:[]
  • posete u poslednjih 30 dana:5
  • preuzimanja u poslednjih 30 dana:5

Sadržaj

članak: 5 od 21  
Back povratak na rezultate
2021, vol. 19, br. 3, str. 814-821
Prediction and optimization of surface roughness in grinding of S50C carbon steel using minimum quantity lubrication of Vietnamese peanut oil
(naslov ne postoji na srpskom)
aHanoi University of Science and Technology, School of Mechanical Engineering, Hanoi, Vietnam
bHanoi University of Industry, Faculty of Mechanical Engineering, Hanoi, Vietnam

e-adresanguyenvancanh@haui.edu.vn
Sažetak
(ne postoji na srpskom)
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 0.6512mm, 4.592mm, 0.8570mm, respectively.
Reference
Anand, A., Vohra, K., Haq, M.I., Raina, A., Wani, M. (2016) Tribology in industry tribological considerations of cutting fluids in machining environment: A review corresponding author. Tribol. Ind, 463, 463-474
Awale, A.S., Vashista, M., Khan, Y.M.Z. (2020) Multi-objective optimization of MQL mist parameters for eco-friendly grinding. Journal of Manufacturing Processes, 56, 75-86
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, 14, 693
Cao, X., Chen, B., Yao, B., He, W. (2019) Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification. Computers in Industry, 106, 71-84
Cooper, C., et al. (2020) Convolutional neural network-based tool condition monitoring in vertical milling operations using acoustic signals. Procedia Manufacturing, 49, 105-111
Dung, H., Nguyen, N.T., Trung, D. (2020) Calculation of residual stress on the surface layer of workpiece when surface grinding the aisi 1018 steel. vol. 15, pp. 2229-2233
Ghosh, N. (2007) Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mech. Syst. Signal Process, 21(1), 466-479
Hadad, M.J., Tawakoli, T., Sadeghi, M.H., Sadeghi, B.H. (2012) Temperature and energy partition in minimum quantity lubrication-MQL grinding process. International Journal of Machine Tools and Manufacture, 54-55, 10-17
Hegab, H., Darras, B., Kishawy, H.A. (2018) Sustainability assessment of machining with nano-cutting fluids. Procedia Manufacturing, 26, 245-254
Hung-Chang, L., Yan-Kwang, C. (2002) Optimizing multi-response problem in the Taguchi method by DEA based ranking method. Int. J. Qual. Reliab. Manag, 19(7), 825-837
Mia, M., 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, 122, 380-391
Olympus Corporation Profile method (linear Roughness) parameters. https://www.olympus-ims.com/en/metrology/surface-roughness-measurement-portal/parameters
Senthilkumar, K.M., Thirumalai, R., Selvam, T.A., Natarajan, A., Ganesan, T. (2021) Multi objective optimization in machining of Inconel 718 using taguchi method. Materials Today: Proceedings, 37, 3466-3470
Yu, W., 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, 158, 364-369
 

O članku

jezik rada: engleski
vrsta rada: izvorni naučni članak
DOI: 10.5937/jaes0-30580
primljen: 28.01.2021.
prihvaćen: 05.03.2021.
objavljen u SCIndeksu: 25.09.2021.
metod recenzije: dvostruko anoniman
Creative Commons License 4.0

Povezani članci

Nema povezanih članaka