Author details

Umesh K. Vates<sup>1</sup> \*, N.K. Singh<sup>2</sup> , B.P. Sharma<sup>1</sup> and S. Sivarao<sup>3</sup>

1 Department of Mechanical Engineering, ASET, Amity University, Uttar Pradesh, India

2 Indian Institute of Technology (ISM) Dhanbad, India

3 UTeM, Melaka, Malaysia

\*Address all correspondence to: u.k.vates@gmail.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

using WEDM. From best modeled training data, optimum parametric combinations (Vg, Fr, Ton, Toff, Wf and Wt) observed as 90 V, 8 Lit./min, 1.05 μS, 190 μS, 2 m/min and 900 g respectively and found the values of Ra = 0.9638 μm at MRR = 105 mg/min,

whereas the average Ra = 1.3654 μm at MRR = 114.8 mg/min. It has been concluded that ANN modeling technique is best fitted for surface roughness prediction and able to successfully minimize (SR) is 29.41% with 8.53% decreases the MRR from its average values on D2 steel using BPANN under WEDM. Such combinations may be applied for industrial application, where it is needed.

(a–f) 3D scattered plot between Ra vs. MRR vs. individual independent parameter.

Figure 6.

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