Modeling of Electrical Discharge Machining Process Using Conventional Regression Analysis and Genetic Algorithms

Abstract

An attempt was made to model input-output relationships of an electrical discharge machining process based on the experimental data (collected according to a central composite design) using multiple regression analysis. Three input parameters, such as peak current, pulse-on-time and pulse-duty-factor, and two outputs, namely, material removal rate (MRR) and surface roughness (SR) had been considered for the said modeling. The value of regression coefficient was determined for each model. The performances of the developed models were tested with the help of some test cases collected through the real experiments and were found to be satisfactory. It had been posed as an optimization problem and solved using a genetic algorithm to determine the set(s) of optimal parameters for ensuring the maximum MRR and minimum SR. It was also formulated as a multi-objective optimization problem and a Pareto-optimal front of solutions had been obtained.

Optimization of machining processes using pattern search algorithm

“…Considering that the previous researchers have adjusted the main parameters of GA and ABC algorithm (Yusup et al, 2013;Bhushan et al, 2012;Kilickap et al, 2011;Maji & Pratihar, 2011), while in this paper the PS optimization solutions have been obtained without any adjustments of the main PS algorithm parameters such as mesh size, expansion and contraction factor values, one can conclude that deterministic direct search methods, such as the PS algorithm, have good competitive potential in solving machining optimization problems against stochastic direct search methods such as metaheuristic algorithms. The main scope of future work will be the analysis of the PS algorithm parameters and selection of initial solutions by the use of Taguchi's experimental design technique and the application with comparative analysis of other direct search methods for solving machining optimization problems.…”

“…The obtained optimization solution obtained using the PS algorithm and the solution obtained by Maji and Pratihar (2011) by using the binary coded GA are compared in Table 5. …”

“…In the past decade, the new trend in the optimization of the machining processes has been based on the use of meta-heuristic algorithms (Zain et al, 2011;Yusup et al, 2013;Bhushan et al, 2012;Savas & Ozay, 2008;Kilickap et al, 2011;Maji & Pratihar, 2011;Rao & Pawar, 2009;Rao & Pawar, 2010;Rao, 2011;Rao & Kalyankar, 2013;Goswami & Chakraborty, 2014). It has been widely reported that these algorithms have the possibility to deal with discontinuous, non-differentiable, and multidimensional machining process models.…”

Performance Assessment and Mathematical Modeling of Process Parameters in Electrical Discharge Machining of EN-31 Tool Steel Material Using Taguchi DOE

“…During machining on EDM, input current & pulse-on time can be able to increase the depth of crater more because of high intensity of sparks generated. This leads to an increase in TWR, whereas with increase in Pulse-off time MRR decreases [18]. To check the variation of input variables on responses, in this study, regression equations are developed for the response variables like Material removal rate (MRR), Electrode wear rate (TWR) and Surface roughness (SR) in terms of the dependent variables like Pulse-on time, Pulse-off time, Input current and gap voltage.…”

“…To check the variation of input variables on responses, in this study, regression equations are developed for the response variables like Material removal rate (MRR), Electrode wear rate (TWR) and Surface roughness (SR) in terms of the dependent variables like Pulse-on time, Pulse-off time, Input current and gap voltage. Equations (3)- (5) shown below are the fitted regression models [18,19] …”