Prediction of Surface Roughness of Ti-6Al-4V In Electrical Discharge Machining: A Regression Model

This paper develops a single order mathematical model for correlating the various electrical discharge machining (EDM) parameters and performance characteristics utilizing relevant experimental data as obtained through experimentation. Besides the effect of the peak ampere, pulse on time and pulse off time on surface roughness has been investigated. Experiments have been conducted on titanium alloy Ti-6Al-4V with copper electrode retaining negative polarity as per Design of experiments (DOE). Response surface methodology (RSM) techniques are utilized to develop the mathematical model as well as to optimize the EDM parameters. Analysis of Variance (ANOVA) has been performed for the validity test of fit and adequacy of the proposed models. It can be seen that increasing pulse on time causes the fine surface until a certain value and afterward deteriorates in the surface finish. The excellent surface finish is investigated in this study in the case of the pulse on time below 80 >s. This result guides to pick the required process outputs and economic industrial machining conditions optimizing the input factors.

[1]  P. V. Rao,et al.  The effect of process parameters on machining of magnesium nano alumina composites through EDM , 2010 .

[2]  Rupinder Singh,et al.  Comparison of Statistically Controlled Rapid Casting Solutions of Zinc Alloys using Three Dimensional Printing , 2011 .

[3]  R. Singh,et al.  Comparison of Statistically Controlled Machining Solutions of Titanium Alloys using USM , 2010 .

[4]  U. Çaydas,et al.  Electrical discharge machining of titanium alloy (Ti–6Al–4V) , 2007 .

[5]  B. Yan,et al.  The effect in EDM of a dielectric of a urea solution in water on modifying the surface of titanium , 2005 .

[6]  B. Yan,et al.  Improvement of surface finish on SKD steel using electro-discharge machining with aluminum and surfactant added dielectric , 2005 .

[7]  Z. G. Wang,et al.  A Review on High-Speed Machining of Titanium Alloys ∗ , 2006 .

[8]  Surjya K. Pal,et al.  Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II , 2007 .

[9]  Andrew Kusiak,et al.  Handbook of design, manufacturing and automation , 1994 .

[10]  M. Kiyak,et al.  Examination of machining parameters on surface roughness in EDM of tool steel , 2007 .

[11]  S. H. Lee,et al.  Study of the effect of machining parameters on the machining characteristics in electrical discharge machining of tungsten carbide , 2001 .

[12]  Rosli Abu Bakar,et al.  Experimental Investigation into Electrical Discharge Machining of Stainless Steel 304 , 2011 .

[13]  Kazuo Yamazaki,et al.  A fundamental study on Ti–6Al–4V's thermal and electrical properties and their relation to EDM productivity , 2008 .

[14]  I. Puertas,et al.  A study on the machining parameters optimisation of electrical discharge machining , 2003 .

[15]  Sameh Habib,et al.  Study of the parameters in electrical discharge machining through response surface methodology approach , 2009 .

[16]  C. L. Lin,et al.  Optimisation of the EDM Process Based on the Orthogonal Array with Fuzzy Logic and Grey Relational Analysis Method , 2002 .

[17]  Pei-Jen Wang,et al.  Semi-empirical Model on Work Removal and Tool Wear in Electrical Discharge Machining , 2001 .