Optimization of Surface Roughness in EDM for D2 Steel by RSM-GA Approach

Abstract

Modeling and optimization of machining parameters are very important in any machining processes. The current study provides predictive models for the functional relationship between various factors and responses of electrical discharge machined AISI D2 steel component. Surface Roughness (Ra) is important as it influences the quality and performance of the products, hence the minimization of surface roughness in manufacturing sectors is of maximum importance. It is also realistic and desirable if the finished parts do not need further any operations to meet the required optimum level of surface quality. For achieving the required optimum levels of surface quality, the proper selection of machining parameters in EDM is essential. Four significant machining parameters, Ip (Pulse Current), Ton (Pulse on Time), Toff (Off Time) and V (Gap Voltage) in the EDM process have been selected and with the various combination experiments were conducted. A mathematical regression model was developed to predict the average Surface Roughness in electrical discharge machined surface. The developed model was validated with new experimental data. The model was coupled with genetic algorithm to predict the minimum possible surface roughness. It is found that the predicted and experimental values were close to a certain extent, which specifies that the established model can be successfully used to predict the surface roughness. Also, the developed model could be used for the selection of the levels in the EDM process for saving in machining time and product cost can be achieved by utilizing the model.

Experimental and Statistical Investigation of Machinability of AISI D2 Steel Using Electroerosion Machining Method in Different Machining Parameters

“…In their study, Anitha et al reported the results obtained by processing AISI D2 die steel on an EDM machine with processing parameters determined as discharge current, pulse time, waiting time, and voltage, with three different values for each factor. As a result of the study, in order to find the most suitable conditions for the regression model developed with response surface methodology (RSM) for minimum surface roughness, the results were analyzed again with genetic algorithm (GA) and the formula to minimize the processing time was determined [18]. In his study, Ali investigated the machining of AISI D2 cold work tool steel on an EDM machine with different processing parameters (amperage and time-on) using a copper electrode.…”

Mathematical Modeling Using ANN Based on k-fold Cross Validation Approach and MOAHA Multi-Objective Optimization Algorithm During Turning of Polyoxymethylene POM-C

“…It is a sophisticated mathematical and computer science method that establishes a mathematical relationship between a response variable (𝑌) and one or more independent variables (𝑋 𝑖 , where 𝑖 can be 1, 2, ..., 𝑛). In recent years, researchers in various fields, such as prediction of friction stir welding [40], hard turning [41], turning process [42], machine condition monitoring [43], drilling process [44], electrical discharge machining process [45], extensively use ANN modeling, which underlines the importance of this mathematical technique in material removal machining. ANNs consist of three interconnected layers: the input layer, the hidden layer, and the output layer (see figure2) [46].…”

Optimization of Threadfin Bream (Nemipterus sp.) Protein Hydrolysate Using Response Surface Methodology Analysis

“…Quadratic vs 2FI model is the recommended model because it has a p-value of 0.0002. The p value < 0.05 which means it is real [21]. The p value <0.05 means that the probability of error in the model is less than 5% or the quadratic model has a significant effect on the response.…”