A study on die sinking EDM of Nimonic C-263 super alloy : an intelligent approach to predict the process parameters using ANN


  • Rama Bhadri Raju Chekuri
  • Ramakotaiah Kalluri
  • Rajesh Siriyala
  • Jamaleswara kumar Palakollu






Electrical discharge machining, Nimonic C-263, Metal Erosion Rate, Electrode Wear Rate, Surface Roughness, Dimensional Over Cut, ANN.


In current study, machining characteristics of Nimonic C-263 are analysed by TAGUCHI and modelled using Artificial Neural Networks (ANN). The response parameters under consideration are Material Erosion Rate (MER), Electrode Wear Rate (EWR), Surface Roughness (SR) and Dimensional Overcut (DOC). A regression mathematical model is also developed to verify the capabilities of ANN. The modelling of ANN includes identifying appropriate combination of hidden layers and number of neurons in each hidden layer. Study on machining characteristics revealed, peak current as the most influential process parameters affecting all the responses; followed by Pulse on-time. A contrary effect is observed for Pulse off-time. A rare process parameter named flushing pressure showed negligible influence on responses. Among various ANN architectures, 6-6 architecture is noted to possess phenomenal prediction accuracy of 99.71% compared to 93.55% of regression analysis.




[1] Amitava M, Amit Rai D, Chattopadhyaya S, Paramanik A, Sergej H & Grzegorz K. (2017), Improvement of surface integrity of Nimonic C-263 super alloy produced by WEDM through various post possessing techniques. Int. J. Adv. Manuf. Technol. 93(1-4), 433-443.

[2] Venkat Rao R & Kalyankar VD (2014), Optimization of modern machining processes using advanced optimization techniques. Int. J. Adv. Manuf. Technol. 73, 1159-1188.

[3] Mandeep K & Hari S (2016), Optimization of process parameters of wire EDM for material removal rate Taguchi technique. Indian Journal of Engineering and Material Science. 23, 223-230

[4] Torres A, Puertas I & Luis CJ (2016), EDM machinability and surface roughness analysis of INCONEL 600 using graphite electrodes. Int. J. Adv. Manuf. Technol. 84, 2671-2688.

[5] Patel KM, Pandey PM, Rao PV (2009), Determination of a optimum parametric combination using a surface roughness prediction model for EDM of Al2O3/SiCw/TiC ceramic composite. Mater. Manuf. Process. 24, 675-682.

[6] Rajendran S, Marimuthu K, Sakthivel M (2013), Study of crack formation and resolidified layer in EDM process on T90Mn2W50Cr45 Tool Steel. Materials and Manufacturing Processes. 28, 664-669.

[7] Wong YS, Lim LC, Lee LC (1995), Effects of flushing on electro discharge machined surfaces. J.Mater.Process.Technol. 48, 299-305.

[8] Belgassim O, Abusaada A (2011), Investigation of the influence of EDM parameters on the overcut for AISI D3 tool Steel, Proceedings of the institution of Mechanical Engineers, Part B : Journal of Engineering Manufacture 226(2), 365-370.

[9] Ming W, Zhang Z, Wang S, Huang H, Zhang Y, Yong Zhang, Shen D (2017), Investigating the energy distribution of workpiece and optimizing process parameters during the EDM of Al6061, Inconel718 and SKD11. Int. J. Adv. Manuf. Technol. 92(9-12), 4039-4056.

[10] Fenggou C, Dayong Y (2004), The study of high efficiency and intelligent optimization system in EDM sinking process. J.Mater. Process.Technol. 149(1-3), 83-87.

[11] Chiang ST, Liu DI, Lee AC, Chieng WH (1995), Adaptive control optimization in End milling using Neural Networks. Int.J.Mach.Tool Manuf. 35(4), 637-660.

[12] Habib SS (2009), Study of the parameters in electrical discharge machining through response surface methodology approach. Applied Mathematical modelling. 33, 4397-4407.

[13] Mohanty CP, Mahapatra SS & Singh MR (2017), An intelligent approach to optimize the EDM process parameters using utility concept and QPSO algorithm. Engineering Science and technology, an international journal. 20,552-562.

[14] Murahari K, Kumar A (2015), Effect of dielectric fluid with surfactant and graphite powder on electrical discharge machining of titanium alloy using Taguchi method. Engineering Science and technology, an international journal. 18, 524-535.

[15] Klocke F, Schwade M, Klink A & Veselovac D (2013), Analysis of material removal rate and electrode wear in sinking EDM roughing stratagies using different graphite grades. Procedia CIRP. 6, 163-167.

[16] Salman O, Kayacan MC (2008), Evolutionary programming method for modeling the EDM parameters for roughness. Journal of materials processing technology. 200, 347-355.

[17] Aveek Mohanty, Gangadharudu Talla, and Gangopadhyay S (2014), Experimental Investigation and Analysis of EDM Characteristics of Inconel 825. Materials and Manufacturing Processes. 29, 540–549.

[18] Che Haron CH, Ghani JA, Burhanuddin Y, Seong YK, Swee CY (2008), Copper and graphite electrodes performance in electrical-discharge machining of XW42 tool steel. Journal of materials processing technology. 201, 570–573.

[19] Ulaş Çaydaş & Ahmet Hasçalik (2008), Modeling and analysis of electrode wear and white layer thickness in die-sinking EDM process through response surface methodology. Int. J. Adv. Manuf. Technol. 38, 1148–1156.

[20] Torres A, Luis CJ & Puertas I (2015), Analysis of the influence of EDM parameters on surface finish, material removal rate, and electrode wear of an INCONEL 600 alloy. Int. J. Adv. Manuf. Technol. 80, 123–140.

[21] Amitesh Goswami, Jatinder Kumar (2017), Trim cut machining and surface integrity analysis of Nimonic 80A alloy using wire cut EDM. Engineering science and Technology; an International Journal. 20, 175-186.

[22] Dhar S, Purohit R, Saini N, Sharma A, Kumar GH (2007), Mathematical modeling of electric discharge machining of cast Al–4Cu–6Si alloy–10 wt.% SiCP composites. http://www.sciencedirect.com/science/journal/09240136">Journal of Materials Processing Technology. 194, 24-29.

[23] Pardhan MK, Das R, Biswas CK (2009), Comparisons of neural networks models on surface roughness in electrical discharge machining. Proceedings of the Institution of Mechanical Engineers, Part B, Journal of Engineering Manufacture. 223(7), 801-808.

[24] Asilturk I, Çunkas M (2011), Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Systems with Applications. 38, 5826-5832.

[25] Venkata Rao K, Murthy BSN, Rao NM (2014), prediction of cutting tool wear, Surface roughness and Vibration of work piece in boring of AISI 316 steel with artificial neural networks. J. Measurement. 51, 63-70.

[26] Neto FC, Geronimo TM, Cruz CED, Aguiar PR, Bianchi EEC (2013), Neural models for predicting hole diameters in drilling processes. Proceedings of 8th CIRP Conference on Intelligent Computation in Manufacturing Engineering. 12, 49-54.

[27] Ozkan G, Inal M (2014), Comparison of Neural Network application for fuzzy and ANFIS approaches for multi criteria decision making problems. Applied Soft Computing. 24, 232–238.

View Full Article: