Advanced Taguchi-Neural Network Prediction Model for Wire Electrical Discharge Machining Process

Sarojrani Pattnaik1, Mihir Kumar Sutar1
1Veer Surendra Sai University of Technology Burla, Burla, India

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Ghosh A, Mallik AK (2010) Manufacturing science, Unconventional machining processes. East-West Press Pvt Ltd

Gopal PM, Prakash KS (2019) Wire electric discharge machining of silica rich E-waste CRT and BN reinforced hybrid magnesium MMC. Silicon 11(3):1429–1440

Gopal PM, Prakash KS, Jayaraj S (2018) WEDM of Mg/CRT/BN composites: effect of materials and machining parameters. Mater Manuf Process 33(1):77–84

Habib S, Okada A (2016) Study on the movement of wire electrode during fine wire electrical discharge machining process. J Mater Process Technol 227:147–152

Hewidy MS, El-Taweel TA, El-Safty MF (2005) Modelling the machining parameters of wire electrical discharge machining of Inconel 601 using RSM. J Mater Process Technol 169:328–336

Ishfaq K, Mufti NA, Mughal MP (2018) Investigation of wire electric discharge machining of stainless-clad steel for optimization of cutting speed. Int J Adv Manuf Technol 96:1429–1443

Kavimani V, Prakash KS, Thankachan T (2019) Influence of machining parameters on wire electrical discharge machining performance of reduced graphene oxide/magnesium composite and its surface integrity characteristics. Compos Part B-Eng 167:621–630

Kavimani V, Prakash KS, Thankachan T, Nagaraja S, Jeevanantham AK, Jhon JP (2020) WEDM parameter optimization for silicon@ r-GO/magneisum composite using taguchi based GRA coupled PCA. Silicon 12(5):1161–1175

Magabe M, Sharma N, Gupta K, Davim JP (2019) Modeling and optimization of wire-EDM parameters for machining of Ni55.8Ti shape memory alloy using hybrid approach of Taguchi and NSGA-II. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-019-03287-z

Manjaiah M, Narendranath S, Basavarajappa S, Gaitonde VN (2014) Wire electric discharge machining characteristics of titanium nickel shape memory alloy. Trans Nonferrous Met Soc China 24:3201–3209

Nawaz Y, Maqsood S, Naeem K, Nawaz R, Omair M, Habib T (2019) Parametric optimization of material removal rate, surface roughness, and kerf width in high-speed wire electric discharge machining (HS-WEDM) of DC53 die steel. Int J Adv Manuf Technol 107:3231–3245. https://doi.org/10.1007/s00170-020-05175-3

Pattnaik S, Karunakar DB, Jha PK (2012a) Influence of injection process parameters on dimensional stability of wax patterns made by the lost wax process using Taguchi approach. P I Mech Eng L-J Mat 227(1):52–60

Pattnaik S, Karunakar DB, Jha PK (2012b) Optimization of multiple responses in the lost wax process using Taguchi method and grey relational analysis. P I Mech Eng L-J Mat 227(2):156–167

Pattnaik S, Karunakar DB, Jh PK (2013) A prediction model for the lost wax process through fuzzy-based artificial neural network. Proc Inst Mech Eng C J Mech Eng Sci 228:1259–1271. https://doi.org/10.1177/0954406213507701

Pramanik D, Kuar AS, Bose D (2018) Effects of wire EDM machining variables on material removal rate and surface roughness of Al 6061 alloy, Renewable energy and its innovative technologies Book chapter, Springer, 231–241

Rajmohan K, Senthil Kumar A (2015) Experimental investigation and prediction of optimum process parameters of micro-wire-cut EDM of 2205 DSS. Int J Adv Manuf Technol 93:187–201. https://doi.org/10.1007/s00170-016-8615-3

Rao PS, Ramji K, Satyanarayana B (2016) Effect of wire EDM conditions on generation of residual stresses in machining of aluminum 2014 T6 alloy. Alex Eng J 55:1077–1084

Rehman M, Khan SA, Naveed R (2020) Parametric optimization in wire electric discharge machining of DC53 steel using gamma phase coated wire. J Mech Sci Technol 34(7):2767–2773

Roy RK (1990) A primer on the Taguchi method. Competitive Manufacturing Series, Van Nostrand Reinhold, New York

Shivade AS, Shinde VD (2014) Multi-objective optimization in WEDM of D3 tool steel using integrated approach of Taguchi method & Grey relational analysis. J Ind Eng Int 10:149–162

Singh M, Bhandari R, Yadav VK (2017) An experimental investigation on machining parameters of AISI D2 steel using WEDM. Int J Adv Manuf Technol 93:203–214. https://doi.org/10.1007/s00170-016-8681-6

Thankachan T, Soorya Prakash K, Kavimani V, Silambarasan SR (2020) Machine learning and statistical approach to predict and analyze wear rates in copper surface composites. Met Mater Int. https://doi.org/10.1007/s12540-020-00809-3

Tosun N, Ozler L (2002) A study of tool life in hot machining using artificial neural networks and regression analysis method. J Mater Process Technol 124:99–104

Tsai KM, Wang PJ (2001) Predictions on surface finish in electrical discharge machining based upon neural network models. Int J Mach Tool Manu 41:1385–1403

Tsao CC, Hocheng H (2008) Evaluation of thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network. J Mater Process Technol 203:342–348