Adaptive Neuro-Fuzzy Inference System for Prediction of Surface Roughness Under Biodegradable Nano-lubricant

Author:

Okokpujie Imhade P.ORCID,Tartibu Lagouge K.ORCID

Publisher

Springer Nature Switzerland

Reference24 articles.

1. Okokpujie, I.P., Bolu, C.A., Ohunakin, O.S., Akinlabi, E.T., Adelekan, D.S.: A review of recent application of machining techniques, based on the phenomena of CNC machining operations. Procedia Manuf. 35, 1054–1060 (2019)

2. Li, M., Yu, T., Yang, L., Li, H., Zhang, R., Wang, W.: Parameter optimization during minimum quantity lubrication milling of TC4 alloy with graphene-dispersed vegetable-oil-based cutting fluid. J. Clean. Prod. 209, 1508–1522 (2019)

3. Pervaiz, S., Kannan, S., Kishawy, H.A.: An extensive review of the water consumption and cutting fluid based sustainability concerns in the metal cutting sector. J. Clean. Prod. 197, 134–153 (2018)

4. Okokpujie, I.P., Ajayi, O.O., Afolalu, S.A., Abioye, A.A., Salawu, E.Y., Udo, M., Okonkwo, U.C., Orodu, K.B., Ikumapayi, O.M.: Modeling and optimization of surface roughness in end milling of aluminium using least square approximation method and response surface methodology. Int. J. Mech. Eng. Technol. (IJMET) 9(1), 587–600 (2018)

5. Prajina, N.V.: Multi response optimization of CNC end milling using response surface methodology and desirability function. Int. J. Eng. Res. Technol. 6(6), 739–746 (2013)

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