Optimization of Correlated Responses of EDM Process

Electrical discharge machining (EDM) process has several important performance measures (responses), some of which are correlated. For example, material removal rate (MRR) and electrode wear rate (EWR) are highly correlated. No reported research work on EDM process has taken into consideration the possible correlation between the response variables while determining the optimal process conditions. Thus, the results achieved by the past researchers are often suboptimal. In the recent past, a few multiresponse optimization methods have been proposed that make use of the principal component analysis (PCA) to take into account the possible correlation between the responses. So, ideally, these methods should be more effective for optimizing the EDM process. However, the relative optimization performances of these methods are unknown and therefore, the process engineers may face the difficulty in selecting the most appropriate method for optimizing an EDM process. In this article, two sets of past experimental data on EDM processes are analyzed using four PCA-based optimization methods. The optimization performances of these methods are compared with the results achieved by the past researchers, considering expected total signal-to-noise (S/N) ratio as the utility measure. It is found that the PCA-based approaches, in general, lead to better optimization performance and among the four PCA-based approaches, PCA-based proportion of quality loss reduction (PQLR) method results in the best optimization performance. So the PCA-based PQLR method can be applied for optimizing multiple responses of EDM process.

[1]  Li Tong,et al.  Multi-response optimization using principal component analysis and grey relational analysis , 2002 .

[2]  C. J. Luis,et al.  Methodology for developing technological tables used in EDM processes of conductive ceramics , 2007 .

[3]  G. Derringer,et al.  Simultaneous Optimization of Several Response Variables , 1980 .

[4]  Hung-Cheng Chen,et al.  Optimization of multiple responses using principal component analysis and technique for order preference by similarity to ideal solution , 2005 .

[5]  A. Khuri,et al.  Simultaneous Optimization of Multiple Responses Represented by Polynomial Regression Functions , 1981 .

[6]  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 .

[7]  Norliana Mohd Abbas,et al.  A review on current research trends in electrical discharge machining (EDM) , 2007 .

[8]  Madhan Shridhar Phadke,et al.  Quality Engineering Using Robust Design , 1989 .

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

[10]  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 .

[11]  Charles E. Heckler,et al.  Applied Multivariate Statistical Analysis , 2005, Technometrics.

[12]  Arunanshu S. Kuar,et al.  Electro Discharge Machining of Titanium Nitride-Aluminium Oxide Composite for Optimum Process Criterial Yield , 2009 .

[13]  N. Pellicer,et al.  Influence of Process Parameters and Electrode Geometry on Feature Micro-Accuracy in Electro Discharge Machining of Tool Steel , 2009 .

[14]  Y. S. Tarng,et al.  Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics , 2000 .

[15]  Hung-Chang Liao,et al.  Multi-response optimization using weighted principal component , 2006 .

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

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

[18]  C. L. Lin,et al.  The use of grey-fuzzy logic for the optimization of the manufacturing process , 2005 .

[19]  Murat Dilmec,et al.  Determination of optimal EDM machining parameters for machined pure titanium-porcelain adhesion , 2009 .

[20]  S. Abdulkareem,et al.  Cooling Effect on Electrode and Process Parameters in EDM , 2010 .

[21]  Kwok-Leung Tsui Robust design optimization for multiple characteristic problems , 1999 .

[22]  Vinod Yadava,et al.  Experimental study and parameter design of electro-discharge diamond grinding , 2008 .

[23]  Lee-Ing Tong,et al.  A NOVEL MEANS OF APPLYING NEURAL NETWORKS TO OPTIMIZE THE MULTIRESPONSE PROBLEM , 2001 .

[24]  T. A. El-Taweel Multi-response optimization of EDM with Al–Cu–Si–TiC P/M composite electrode , 2009 .

[25]  Kwang-Jae Kim,et al.  Simultaneous optimization of mechanical properties of steel by maximizing exponential desirability functions , 2000 .

[26]  R. Ramakrishnan,et al.  Multi response optimization of wire EDM operations using robust design of experiments , 2006 .

[27]  Muh-Cherng Wu,et al.  An enhanced Taguchi method for optimizing SMT processes , 1992 .

[28]  Lee-Ing Tong,et al.  Optimization of multi-response processes using the VIKOR method , 2007 .

[29]  P. Shahabudeen,et al.  Simultaneous optimization of multi-response problems in the Taguchi method using genetic algorithm , 2006 .

[30]  Chiuh-Cheng Chyu,et al.  Optimization of correlated multiple quality characteristics robust design using principal component analysis , 2004 .

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

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

[33]  C. Wang,et al.  Optimizing multiple quality characteristics via Taguchi method-based Grey analysis , 2007 .

[34]  J. Ciurana,et al.  Neural Network Modeling and Particle Swarm Optimization (PSO) of Process Parameters in Pulsed Laser Micromachining of Hardened AISI H13 Steel , 2009 .