D.B. prakash , G.R. Balaji , A.G. chand, V.A. kumar, D.V.N.Prabhaker, “Optimization of machining parameters for aluminium alloy 6082 in cnc end milling,” International Journal of Eng. Research and Applications (IJERA), ISSN: 2248-9622, Vol. 3, Issue 1, pp.505-510, 2013.
 F. Puh, Z. Jurkovic, M. Perinic, M. Brezocnik and S. Buljan, “Optimization of machining parameters for turning operation with multiple quality characteristics using Grey Relational Analysis,” Tehnicki vjesnik, vol.23, no.2, pp.377-382, 2016.
 M.S. Mahendra and B Sibin, “Optimization of milling parameters for minimum surface roughness using Taguchi method,” IOSR Journal of Mechanical and Civil Eng. (IOSR-JMCE), e-ISSN: 2278-1684, p-ISSN: 2320-334X, pp.01-05, 2016.
 S. Sakthivelu, M. Meignanamoorthy, M. Ravichandran and M. Kumar, “Effect of machining parameters on surface roughness and material removal rate in CNC end milling,” International Journal of Scientific
 ,” ICONNECT Research and Eng. Studies (IJSRES), vol.2, Issue 4, ISSN: 2349-8862, 2015.
 P. Pothys, R. Selvam, and A. S. Kumar, “Evaluation of optimal machining parameters for turning by using genetic algorithm - 2k18, ” Conference Proceedings, International Journal of Eng. Research & Technology (IJERT), ISSN: 2278-0181, Volume 6, Issue 07, 2018.
 A. Zerti , M.A. Yallese, O. Zerti , M. Nouioua and R. Khettabi, “Prediction of machining performance using RSM and ANN models in hard turning of martensitic stainless steel AISI 420,” Journal of Mechanical Eng. Science, Vol. 0, No. 0, pp. 1–24, 2019.
 Z. Amarta, B. O Pramoedyo, S. Sutikno, and R. Norcahyo, “Multi response optimization in vulcanization process using backpropagation neural network-genetic algorithm method for reducing quality loss cost,” AIP Conference Proceedings 2114, 2019.
 P. A. L. Laot, and S. Sampurno , “Multi-response optimization of cutting force and surface roughness in carbon fiber reinforced polymer end milling using back propagation neural network and genetic algorithm,” AIP Conference Proceedings 2114, 2019.
 J. Mumtaz, Z. Li, M. Imran, Lei Yue, M. Jahanzaib, S. Sarfraz, E. Shehab, S. Oluwarotimi and K. Afzal, “Multi-objective optimization for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithm,” Advances in Mechanical Eng., Vol. 11, No. 4, pp.1–13, 2019.
 A. Batwara and P. Verma, “Influence of process parameters on surface roughness and material removal rate during turning in CNC lathe – an Artificial Neural Network and surface response methodology,” International Journal of Recent Advances in Mechanical Eng. (IJMECH), vol.5, No.1, 2016.
 M. J. Madic and M. R. Radovanovic, “Optimal selection of ANN training and architectural parameters using Taguchi method: A case study,” FME Transations, vol.39, No.2, pp.79-86, 2011.
 S. Cetin and T. Kivak, “Optimization of the machining parameters for the turning of 15-5 PH stainless steels using the Taguchi method,” Materiali in Technologiji, vol.51, No.1, pp.133-140, 2017.
 S. Karabulut, “Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural netwotks and Taguchi method,” Measurement, vol.66, pp.139-149, 2015.
 M. M.H. AL-Khafaji, H. L. Alwan and B. M.H. Albaghdadi, “Roughness assessment for machined surfaces in turning operation using neural Network,” Eng. & Tech. Journal, Vol.32, Part (A), No.5, 2014.
 S. Dave , Jay J. Vora , N. Thakkar , A. Singh, S. Srivastava, B. Gadhvi, V. Patel, A. Kumar, “Optimization of EDM drilling parameters for Aluminum 2024 alloy using Response Surface Methodology and Genetic Algorithm,” Key Eng. Materials, ISSN: 1662-9795, Vol. 706, pp. 3-8, 2016.