1. Introduction
Electrical discharge machining (EDM) is an unconventional machining process primarily utilized in the manufacturing of dies that uses thermal erosion as the mechanism of material removal. In EDM, stock removal from specimen is performed using continuous discharges between tool and workpiece. It leads to creation of a plasma channel, and the temperature reaches up to 8000–20,000 °C, thereby removing the material from the electrodes by melting and evaporation [
1,
2,
3,
4]. In EDM, removal of material from a conducting workpiece takes place by a string of repeated electrical discharges within the workpiece and tool electrode in the existence of a dielectric liquid. A servo-controlled feed mechanism is used to move the electrode towards the workpiece until the gap is small enough to ionize the dielectric by the applied voltage. A discharge of very short duration is produced in a liquid dielectric gap, thus removing the material from the electrodes by heating, melting and evaporation. The next spark will be developed at the closest location between the tool tip and workpiece. This sparking process keeps changing its location all across the surface of the electrode tool tip, thereby creating an exact replica of the electrode profile on the workpiece [
1]. Performance of the EDM process generally depends upon certain machine-dependent factors and user-specified factors. These factors include discharge current, breakdown voltage, gap voltage, pulse on/off duration, machining time, duty cycle, polarity, dielectric pressure, etc. Material removal rate (MRR), surface roughness (SR), tool wear rate (TWR), surface integrity and dimensional accuracy of the finished product are certain parameters for evaluating the efficiency of the EDM process. These responses have been optimized using different mathematical models and statistical techniques such as dimensional analysis, artificial neural network (ANN), genetic algorithm (GA), response surface methodology (RSM), Taguchi method, fuzzy theory, finite element method (FEM) and regression analysis (RA), which ensures better results of the outcomes and efficiency.
Fenggou and Dayong [
5] optimized ANN with GA and the node deletion algorithm to calculate process variables automatically. The use of genetic algorithms and back propagation (BP) algorithms enhanced the training speed of the model. Structure of the ANN was determined and optimized by node deletion algorithm. Rangajanardhaa and Rao [
6] optimized the response of current and voltage on SR during EDM of Ti6Al4V, HE15, 15CDV6 and M-250. With the use of a package, a neural network model was generated. The use of GA to optimize the model further reduced the error to less than 2% from 5%. Chattopadhyay et al. [
7] used the empirical model and predicted the SR of the surface machined with the EDM process. The range of maximum deviation for the prediction of response varied from 16.4% to −14.1%. The average prediction error for SR was found to be 0.05%. Torres et al. [
8] performed the EDM of hard to machine alloy (Inconel 718) using copper electrode and proposed models for MRR, EWR and SR. The R
2 values obtained for MRR, SR and electrode wear rate (EWR) models were found to be 99.03%, 85.97% and 93.32%, respectively. The R
2 value indicates the adequacy of the developed model to link the relationship between EDM variables and responses. Raja et al. [
9] applied firefly algorithm for the optimization of EDM process variables and to achieve the required surface roughness on hardened die steel workpiece in the least possible time. It was found that the influence of current on surface roughness and machining time was 45.53% and 37.53%, respectively, as compared to the influence of pulse duration on surface roughness and machining time being 1.37% and 1.04%, respectively. It was found that current influenced surface roughness and machining time more in comparison to pulse duration. Choudhuri et al. [
10] optimized the process parameters for SR during the wire EDM of stainless steel. Grey relational technique was used to determine the grey coefficient of every experiment. Fuzzy evaluated the performance characteristics index in accordance with the grey relational coefficient. RSM and analysis of variance were utilized for modelling and analysis of SR to predict and determine the effect of machining variables.
Payal et al. [
11] predicted MRR and SR during EDM of Inconel 825 with copper, copper–tungsten tools and graphite tools using ANN. ANN predicted the SR and MRR with an average percentage difference of 0.37 and 0.25, respectively. Nain et al. [
12] evaluated the performance of wire EDM of aeronautics super alloy using fuzzy logic and backpropagation neural network. To analyse the scattering around the agreement line, two more lines in the range of ±5% error were plotted. For the evaluation of SR and waviness of the machined surface BP-ANN proved to be a better method. Thankachan et al. [
13] used Taguchi and ANN to analyse and model MRR and SR during the wire EDM of aluminium-based metal matrix composite. Taguchi analysis revealed that the MRR increased with the percentage of tin and decreased with the rise in percentage of silicon carbide. Increase in SiC resulted in an increase in surface roughness, whereas the increase in tin resulted in a decrease in surface roughness. Sahu et al. [
14] investigated the influence of process variables on overcut and SR during the EDM of Al–SiC composite. Copper was used as a tool electrode. RSM and MOPSO were used for the mathematical modelling of responses and multiresponse optimization, respectively. Decrease in SR was recorded with the rising flushing pressure and pulse off time. To decrease the overcut, lower pulse durations and higher voltage settings were suggested. Rajneesh et al. [
15] reported the EDM of AISI 202 stainless steel with copper alloy tool. RSM was utilized to optimize MRR, EWR and SR. Current and pulse on/off were selected as process variables. Regression equations were formed, and interactive effects of process variables were analysed. R
2 was used to check the goodness of fit. The R
2 was calculated and found to be 95.09%, 95.05% and 96.68% for EWR, MRR and SR, respectively. Singh and Singh [
16] developed a semiempirical model for the prediction of SR during the EDM process. The model was successful in predicting the response with lesser than 5% error. Ulas et al. [
17] predicted SR using extreme learning machine and support vector regression-based models. Weighted extreme learning machine model was found as the best model with R
2 value of 0.9720. Bharti [
18] applied two-step optimization processes to find out the optimal parameters for EDM. Neural network-based multi-objective optimization technique was employed to generate the total possible combinations of input parameters. By applying the concept of dominance, 24 nondominated combinations of input parameters were obtained. TOPSIS was used to award rank to each nondominated solution.
Srivastava and Pandey [
19] studied the process performance of sintered copper (Cu)–titanium carbide (TiC) electrode tip in ultrasonic-assisted cryogenically cooled electrical discharge machining (UACEDM). The performance parameters evaluated were electrode wear ratio (EWR), material removal rate (MRR), surface roughness (SR), out-of-roundness and surface integrity. The process parameters considered in this study are discharge current, pulse on time, duty cycle and gap voltage. Cermet was fabricated, having a copper content of 75% and titanium carbide content of 25%, by mixing, pressing and sintering. It was observed that EWR and out-of-roundness decreased when the cermet electrode tip was used as compared to a conventional tool tip. It was also observed that MRR and SR increased when the cermet tool tip was used. The surface cracks’ density and crack width on a workpiece machined by a cermet tool tip have been found to be lesser as compared to the specimen machined by a conventional tool tip. Srivastava and Pandey [
20] studied the shape of the electrode and established the application of liquid nitrogen in reducing distortion of the electrode during electrical discharge machining of M2-grade high-speed steel using copper electrodes. A study of roundness was performed on the electrode to observe the shape of the electrode for both conventional EDM and EDM with a cryogenically cooled electrode. A scanning electron microscope (SEM) was used to study the shape of the electrode tip. The effect of various parameters such as discharge current and pulse on time were studied to understand the behaviour of distortion of the electrode. It was concluded that the shape retention was better in the case of a liquid nitrogen-cooled electrode. Srivastava and Pandey [
21] studied the cooling effect on copper electrodes while electrical discharge machining (EDM) an M2-grade high-speed steel workpiece. To evaluate the machinability, electrode wear ratio (EWR) and surface roughness (SR) were the two responses observed. Discharge current, pulse on time, duty cycle and gap voltage were the controllable process parameters. It was found that EWR reduced up to 20% by cryogenic cooling the electrode. With electrode cooling, SR was also found to have been reduced after machining. The effect of process parameters on EWR and SR were also analysed. The shape of the electrode was also measured, and it was found that the shape retention was better in cryogenic-assisted EDM as compared to conventional EDM.
It was found from the investigation of literature that a lot of research work is being done for the prediction of responses, whereas there is still a lack of literature available that highlights the use of change in out-of-roundness of the cermet tool tip and for predicting surface roughness of the machined workpiece, especially during the EDM of the hard workpiece. Surface roughness is an important criterion while evaluating the outcome of EDM, as it has a significant influence on the mating parts. The change in the shape of the tool during machining is very significant, as the final shape of the machined cavity is dependent on the shape of the tool during EDM. There are very few studies that analysed the change in the shape of the tool [
20,
21,
22]. Therefore, the present study is focused on investigating the surface roughness of the machined cavity and the change in the tool’s shape by observing the changes in out-of-roundness of the tool with changing process parameters. A novel approach to use machine learning techniques for the prediction of the mentioned responses has been attempted through this work.