Multi-Objective Optimization in WEDM of Al 7075 Alloy Using TOPSIS and GRA Method

Introduction

Now a day, high strength to weight ratio material like Al 7075 alloy become extremely useful in high temperature and wear resistant application. These extremely useful materials are difficult to machine by conventional process (Ezugwu, 2005 & Selvakumar et. al. 2013; 2014; 2016). Non-conventional machining technique like WEDM is the potential solution to successfully machine of this alloy. (Abbas et. al. 2007) WEDM is a non-traditional machining process, where machining takes place due to the potential difference between tool electrode and work piece. The principle criterion of this machining process is that the work piece must be electrically conductive (Ho et. al. 2003 & Sundaram et. al 2008). Melting and evaporation of material in WEDM takes place due to thermal heating within the machining zone. Heat is generated by the spark in each pulse. Any kind of complex 3D shape and contour can be manufactured with the aid of WEDM (Mandal et. al. 2019). The main aim of WEDM process is to improving the efficiency of the machining as well as quality of the product after manufacturing. Thereby, contribution of WEDM in manufacturing field plays a pivotal role (Sarkar et. al. 2005; 2006; 2008; 2011). The performance characteristics of the WEDM process are strongly influenced by input process parameters. However, the main problem is to selection of appropriate process parameters, which completely depends on machining material. An appropriate selection of process parameters in WEDM process relies deeply on the machining operator and their experiences (Sundaram et. al. 2008). In general, process parameters table supplied by machine tool manufacturer cannot meet the requirement of the manufacturing operator. Since, for a particular requirement of job; builder cannot provide the optimal parameter settings (Garg et. al. 2010 & Jawahir et. al. 2011). Hence, the selection of optimal process parameters is important to attain the desire quality characteristics and productive efficiency in WEDM.

Multi attribute optimization (MAO) is the most prominent approach to choose the optimum variable process parameters in WEDM. The sophisticated MAO methods such as analytical hierarchy process (AHP), technique for order of preference by similarity to ideal solution (TOPSIS), multi objective optimization on the basis of ratio analysis (MOORA), quality function deployment (QFD) and grey relational analysis (GRA) are generally used to solve the engineering problems (Yuan et. al. 2008; Saaty, 1980; Roy, 1990; Kumar et al. 2014 & Tripathy et. al. 2016). Tosun et al. 2004 assessed the significance of process variables and proposed mathematical model on kerf width and surface roughness. Buckingham pi theorem also has been employed in this study to find out the correlation between process variables and responses. Chiang et. al. 2006 employed the grey relational analysis to optimize the process parameters in WEDM with multiple outputs measure such as material removal rate and surface roughness and confirmatory experiments also carried out to validate the optimum results. Somasekhar et. al. 2010 established the artificial neural network model and optimizes the process parameters in micro-WEDM using genetic algorithm. On the other hand, so many research works have been carried out over the past few years on parametric optimization in WEDM using different kind of MAO technique (Pradhan et. al. 2013). A combined approach of RSM based GRA is implemented for reckoning the influence of input factors on surface integrity of WEDM for tool steel. The collective approach of GRA has been decoratively clarified for multi-attribute optimizations in WEDM of Inconel 825 alloy (Rajyalakshmi et. al 2013). A combine approach of Taguchi and Fuzzy-TOPSIS has been developed to solve the multi response optimization problem in different manufacturing field (Sivapirakasam et. al. 2011). An extensive research work is required in WEDM of Al 7075 alloy for the analysis of machining speed and corner inaccuracy. Therefore, it is demanding to optimize the process parameters using combine approach in WEDM for this alloy.