International Journal of
Physical Sciences

  • Abbreviation: Int. J. Phys. Sci.
  • Language: English
  • ISSN: 1992-1950
  • DOI: 10.5897/IJPS
  • Start Year: 2006
  • Published Articles: 2568

Full Length Research Paper

Modeling the machining parameters of AISI D2 tool steel material with multi wall carbon nano tube in electrical discharge machining process using response surface methodology

S. Prabhu1* and B. K. Vinayagam2      
1School of Mechanical Engineering, S.R.M. University, Chennai 603 203, Tamil Nadu, India. 2Department of Mechatronics, S.R.M. University, Chennai 603 203, Tamil Nadu, India.  
Email: [email protected]

  •  Accepted: 15 December 2011
  •  Published: 09 January 2012


This work investigates the machining characteristics of American Iron and Steel Institute (AISI) D2 tool steel with copper as a tool electrode during electrical discharge machining (EDM) process. The multi wall carbon nano tube (MWCNT) is mixed with dielectric fluids in EDM process to analyze the surface characteristics of surface roughness. Regression model were developed to predict the surface roughness (SR) in EDM process. In the development of predictive models, machining parameters of pulse current, pulse duration and pulse voltage were considered as model variables. The collection of experimental data adopted Box-Behnken central composite design (CCD). Analysis of variance (ANOVA) and F-test were used to check the validity of regression model and to determine the significant parameter affecting the surface roughness. Later, the AISI D2 tool steel was analyzed and the parameters are optimized using MINITAB software, and regression equation are compared with and without MWCNT used in EDM process. The average 34% of surface finish was improved by using carbon nano tube (CNT) mixed as dielectric fluid. The maximum test errors for regression model using copper electrode are 8.18% for without CNTs and 5.44% for with CNTs. The Rvalue of developed empirical model for SR with MWCNTs is 69.45% compared without CNT is 55.4%. The high Rvalue indicates that the better the model fits the data.


Key words: Multiwall carbon nanotube, electrical discharge machining (EDM), roughness, Box-Behnken central composite design, analysis of variance (ANOVA).