Application of Taguchi-grey multi responses optimization on process parameters in electro erosion

Abstract Convention Taguchi method deals with only single response optimization problems. Since the electrical discharge machining process involved with many response parameters, Taguchi method alone cannot help to obtain optimal process parameters in such process. In the present work, an endeavor has been made to derive optimal combination of electrical process parameters in electro erosion process using grey relational analysis with Taguchi method. This multi response optimization of the electrical discharge machining process has been conducted with AISI 202 stainless steel with different tool electrodes such as copper, brass and tungsten carbide. Gap voltage, discharge current and duty factor have been used as electrical excitation parameters with different process levels. Taguchi L 27 orthogonal table has been assigned for conducting experiments with the consideration of interactions among the input electrical process parameters. Material removal rate, electrode wear rate and surface roughness have been selected as response parameters. From the experimental results, it has been found that the electrical conductivity of the tool electrode has the most influencing nature on the machining characteristics in EDM process. The optimal combination of the input process parameters has been obtained using Taguchi-grey relational analysis.

[1]  A. Haq,et al.  Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method , 2008 .

[2]  Shankar Chakraborty,et al.  Optimization of Correlated Responses of EDM Process , 2012 .

[3]  T. Muthuramalingam,et al.  Monitoring and Fuzzy Control Approach for Efficient Electrical Discharge Machining Process , 2014 .

[4]  P. V. Rao,et al.  Determination of an Optimum Parametric Combination Using a Surface Roughness Prediction Model for EDM of Al2O3/SiCw/TiC Ceramic Composite , 2009 .

[5]  A. Senthil Kumar,et al.  Multi-response optimisation of sintering parameters of Al–Si alloy/fly ash composite using Taguchi method and grey relational analysis , 2009 .

[6]  Stephen T. Newman,et al.  State of the art electrical discharge machining (EDM) , 2003 .

[7]  Won Tae Kwon,et al.  Optimization of EDM process for multiple performance characteristics using Taguchi method and Grey relational analysis , 2010 .

[8]  Deepak Kumar Panda,et al.  Modelling and Optimization of Multiple Process Attributes of Electrodischarge Machining Process by Using a New Hybrid Approach of Neuro–Grey Modeling , 2010 .

[9]  Mohan Charan Panda,et al.  Intelligent Modeling and Multiobjective Optimization of Die Sinking Electrochemical Spark Machining Process , 2012 .

[10]  T. Muthuramalingam,et al.  Influence of Tool Electrode Properties on Machinability in Spark Erosion Machining , 2013 .

[11]  T. Muthuramalingam,et al.  Influence of Discharge Current Pulse on Machinability in Electrical Discharge Machining , 2013 .

[12]  L. Hwang,et al.  Machining Characteristics and Optimization of Machining Parameters of SKH 57 High-Speed Steel Using Electrical-Discharge Machining Based on Taguchi Method , 2006 .

[13]  Yan-cherng Lin,et al.  Machining Performance and Optimizing Machining Parameters of Al2O3–TiC Ceramics Using EDM Based on the Taguchi Method , 2009 .

[14]  Man Singh Azad,et al.  Grey Relational Analysis of Micro-EDM Machining of Ti-6Al-4V Alloy , 2012 .

[15]  S. Chakraborty,et al.  Selection of EDM Process Parameters Using Biogeography-Based Optimization Algorithm , 2012 .

[16]  Jose Mathew,et al.  Optimization of Material Removal Rate in Micro-EDM Using Artificial Neural Network and Genetic Algorithms , 2010 .