Optimization of the EDM parameters on machining Ti–6Al–4V with multiple quality characteristics

In this paper, parameter optimization of the electrical discharge machining process to Ti–6Al–4V alloy considering multiple performance characteristics using the Taguchi method and grey relational analysis is reported. Performance characteristics including the electrode wear ratio, material removal rate and surface roughness are chosen to evaluate the machining effects. The process parameters selected in this study are discharge current, open voltage, pulse duration and duty factor. Experiments based on the appropriate orthogonal array are conducted first. The normalised experimental results of the performance characteristics are then introduced to calculate the coefficient and grades according to grey relational analysis. The optimised process parameters simultaneously leading to a lower electrode wear ratio, higher material removal rate and better surface roughness are then verified through a confirmation experiment. The validation experiments show an improved electrode wear ratio of 15%, material removal rate of 12% and surface roughness of 19% when the Taguchi method and grey relational analysis are used.

[1]  B. Yan,et al.  Influence of kerosene and distilled water as dielectrics on the electric discharge machining characteristics of Ti–6A1–4V , 1999 .

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

[3]  J. L Lin,et al.  The use of the orthogonal array with grey relational analysis to optimize the electrical discharge machining process with multiple performance characteristics , 2002 .

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

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

[6]  Fu-Chen Chen,et al.  Multi-objective optimisation of high-speed electrical discharge machining process using a Taguchi fuzzy-based approach , 2007 .

[7]  Jiahn-Piring Yur,et al.  Characteristic Analysis of EDMed Surfaces Using the Taguchi Approach , 2000 .

[8]  G. Krishna Mohana Rao,et al.  Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm , 2009 .

[9]  C. Fung.  Manufacturing process optimization for wear property of fiber-reinforced polybutylene terephthalate composites with grey relational analysis , 2003 .

[10]  Biing-Hwa Yan,et al.  Machining characteristics of titanium alloy (Ti–6Al–4V) using a combination process of EDM with USM , 2000 .

[11]  Der-An Wang,et al.  Optimization of machining parameters in magnetic force assisted EDM based on Taguchi method , 2009 .

[12]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[13]  Ji-Liang Doong,et al.  Optimization of the injection molding process of short glass fiber reinforced polycarbonate composites using grey relational analysis , 2000 .

[14]  K. Chiang,et al.  Optimization of the WEDM process of particle-reinforced material with multiple performance characteristics using grey relational analysis , 2006 .

[15]  Biing-Hwa Yan,et al.  Micro-hole machining of carbide by electric discharge machining , 1999 .

[16]  C. K. Huang,et al.  Milling of MAR-M247 nickel-based superalloy with high temperature and ultrasonic aiding , 2007 .

[17]  V. N. Gaitonde,et al.  Methodology of Taguchi optimization for multi-objective drilling problem to minimize burr size , 2007 .

[18]  Y. Liu,et al.  Effect of technological parameter on the process performance for electric discharge milling of insulating Al2O3 ceramic , 2008 .