Multi Response Optimization by Using the Hybrid Technique in Electro Discharge Machining of AISI 304

Abstract

Electro discharge machining (EDM) is most popular non-conventional electro-thermal machining process where electrical energy is used to generate a spark and thermal energy used to remove material from the workpiece. The primary goal of EDM is getting more material removal rate (MRR) with lower tool wear rate (TWR). For this investigation, machining parameters like peak current, pulse on time, gap voltage and duty cycle are considered as process parameter, and material removal rate (MRR) and tool wear rate (TWR) are considered as response. AISI 304 stainless steel and tungsten carbide are used as work material and tool material respectively. Taguchi L27 orthogonal array has been applied for designing the experiment. A hybrid optimization technique like desirability in combination with grey relational analysis (GRA) has been performed to get the optimum level of the control parameter for getting higher MRR and lower TWR. Analysis of variance (ANOVA) is performed for the statistical analysis. These results show that peak current is the most significant parameter for MRR and TWR. The optimal parameter setting for maximum MRR and minimum TWR has obtained by desirability coupled with Grey relational analysis.

Multi-response optimization of EDM parameters using grey relational analysis (GRA) for Ti-5Al-2.5Sn titanium alloy

“…The steps comprises in the optimization method are detailed below (Bhaumik and Maity, 2016): Normalizing of MRR, ROC, R a and TWR for all experiments For calculating the normalizing value “Lower the better (LTB)” is selected for TWR, ROC and R a and “Higher the better (HTB)” is selected for MRR. For LTB, normalizing equation is: For HTB, normalizing equation is: where, y i (k) is i th normalized response value and x i (k) is the observed value for the i th run of the k th response. Calculation of the grey relational coefficient (GRC) The GRC is determined to demonstrate the correlation amid the optimal and actual normalized experimental results. GRC {ζ i ( k )} is determined using the following equation: where Δ min and Δ max are the global minimum and maximum values in different data series, respectively, for the k th response. The Δ i (k) is the k th value in Δ i different data series.…”

A Hybrid Design of Experiment Approach in Analyzing the Electrical Discharge Machining Influence on Stir Cast Al7075/B4C Metal Matrix Composites

“…This calculation is performed to determine the GRC value. Multi-objective problems are converted into a single objective problem in GRA process [75,76]. The calculation of GRC is determined through the utilisation of Equation (11) provided in references [58,59].…”

Multi-objective optimization of some correlated process parameters in EDM of Inconel 800 using a hybrid approach

“…The projected quadratic model to foresee the hybrid MOORA-PCA over the experimental region can be expressed using Eqs. (8) and (9).…”

“…Chakravorty et al [8] used PCA-based different optimizations to optimize past experimental data and showed the capability of PCA to optimize multiple responses in EDM. Bhaumik et al [9] utilized a hybrid optimization technique in EDM where desirability was coupled with grey relational analysis (GRA), to ascertain optimum setting of the input parameters for higher MRR and lower tool wear rate (TWR). Besides the use of MOORA technique in non-traditional machines, it has been effectively executed for the parametric optimization of numerous other manufacturing processes like milling [10], turning [11], welding [12], etc.…”