Journal of Mechanical Science and Technology

, Volume 28, Issue 7, pp 2831–2844 | Cite as

A hybrid Taguchi-artificial neural network approach to predict surface roughness during electric discharge machining of titanium alloys

  • Sanjeev Kumar
  • Ajay Batish
  • Rupinder Singh
  • T. P. Singh
Article

Abstract

In the present study, electric discharge machining process was used for machining of titanium alloys. Eight process parameters were varied during the process. Experimental results showed that current and pulse-on-time significantly affected the performance characteristics. Artificial neural network coupled with Taguchi approach was applied for optimization and prediction of surface roughness. The experimental results and the predicted results showed good agreement. SEM was used to investigate the surface integrity. Analysis for migration of different chemical elements and formation of compounds on the surface was performed using EDS and XRD pattern. The results showed that high discharge energy caused surface defects such as cracks, craters, thick recast layer, micro pores, pin holes, residual stresses and debris. Also, migration of chemical elements both from electrode and dielectric media were observed during EDS analysis. Presence of carbon was seen on the machined surface. XRD results showed formation of titanium carbide compound which precipitated on the machined surface.

Keywords

Electric discharge machining (EDM) Titanium; Machining Taguchi ANOVA Surface roughness (SR) Artificial neural network (ANN) 

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Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Sanjeev Kumar
    • 1
  • Ajay Batish
    • 1
  • Rupinder Singh
    • 2
  • T. P. Singh
    • 3
  1. 1.Department of Mechanical EngineeringThapar UniversityPatialaIndia
  2. 2.Department of Production EngineeringGNDECLudhianaIndia
  3. 3.Symbiosis Institute of TechnologyPuneIndia

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