References

Abhinandan, A.G. and A.B. Gaikwad, 2016. Predicting optimized EDM machining parameter through thermo mechanical analysis. J. Mechan. Civil Eng., 13: 71-85.

Chandramouli, S. and K. Eswaraiah, 2015. Modeling and optimization of electrical discharge machining process parameters using artificial neural networks. J. Mater. Sci. Mech. Eng., 2: 466-470.
Direct Link  |  

Chikalthankar, S.B., V.M. Nandedkar and S.V. Borde, 2013. Experimental investigations of EDM parameters. Int. J. Eng. Res. Dev., 7: 31-34.
Direct Link  |  

Harmanpreet, H.L. and G. Dhuria, 2015. Investigation and modelling of EDM process parameters in machining of incoloy-800. Intl. J. Res. Eng. Technol., 4: 112-117.
Direct Link  |  

Makino, 2017. Radical departures, advanced techniques in aerospace manufacturing from Makino. Meguro, Tokyo, Japan. https://www.radical-departures.net/

Moghaddam, A.M. and F. Kolahan, 2015. An empirical study on statistical analysis and optimization of EDM process parameters for inconel 718 super alloy using D-optimal approach and genetic algorithm. J. Comput. Appl. Mech., 46: 267-277.

Pradhan, M.K. and C.K. Biswas, 2008. Modelling of machining parameters for MRR in EDM using response surface methodology. Proceedings of National Conference on Mechanism Science and Technology: From Theory to Application, November 13-14, 2008, National Institute of Technology, Hamirpur, India, pp: 535-542.

Rao, P.S., K. Ramji and B. Satyanarayana, 2016. Effect of wire EDM conditions on generation of residual stresses in machining of aluminum 2014 T6 alloy. Alexandria Eng. J.,q 55: 1077-1084.
Direct Link  |  

Sahu, J., C.P. Mohanty and S.S. Mahapatra, 2013. A DEA approach for optimization of multiple responses in electrical discharge machining of AISI D2 steel. Procedia Eng., 51: 585-591.
Direct Link  |  

Salem, S.B., W. Tebni and E. Bayraktar, 2011. Prediction of surface roughness by experimental design methodology in Electrical Discharge Machining (EDM). J. Achiev. Mater. Manuf. Eng., 49: 150-157.
Direct Link  |  

Shandilya, P., P.K. Jain and N.K. Jain, 2012. Parametric optimization during wire electrical discharge machining using response surface methodology. Procedia Eng., 38: 2371-2377.
Direct Link  |  

Suryanita, R., H. Maizir and H. Jingga, 2017. Prediction of structural response based on ground acceleration using artificial neural networks. Intl. J. Technol. Eng. Stud., 3: 74-83.

Tawfeeq, A.N.Z. and O. Altun, 2016. Batch size for training convolutional neural networks for sentence classification. J. Adv. Technol. Eng. Stud., 2: 156-163.
CrossRef  |  Direct Link  |  

Thillaivanan, A., P. Asokan, K.N. Srinivasan and R. Saravanan, 2010. Optimization of operating parameters for EDM process based on the Taguchi method and artificial neural network. Int. J. Eng. Sci. Technol., 2: 6880-6888.
Direct Link  |  

Zainuddin, N.A., I. Norhuda, I.S. Adeib, A.N. Mustapa and S.H. Sarijo, 2015. Artificial neural network modeling ginger rhizome extracted using Rapid Expansion Super-Critical Solution (RESS) method. J. Adv. Technol. Eng. Res., 1: 1-14.
Direct Link  |