## 2 Literature Review
used an \(L_{Z7}\) orthogonal array to train an artificial neural network (ANN) for modeling and predicting surface roughness in the wire electrical discharge machining (WEDM) of Inconel 718. They demonstrated the effectiveness of the ANN in predicting output values. The ANN model utilized a multilayer perceptron with back-propagation (BPNN), and the dataset was divided into distinct groups for training, testing, and validation purposes. [PERSON] et al. [19] investigated the impact of wire electrical discharge machining parameters on surface roughness and material removal rate for aluminum alloys AA 6061 and AA 5083 using a single-objective optimization approach. The primary variables studied were pulse on-time, pulse off-time, and peak current. The results showed that for AA 6061, surface roughness was mainly affected by pulse on-time and pulse off-time, while for AA 5083, pulse off-time and peak current were significant factors influencing surface quality. In the research conducted by [PERSON] et al. [20], the study explored the effects of different machining parameters on surface roughness in the Wire Electrical Discharge Texturing (WEDT) of INCONEL 718 super alloy. The investigated parameters included rotational speed, pulse on-time, pulse off-time, servo voltage, wire feed rate, and flushing pressure. It was found that pulse on-time had a significant impact on the surface roughness of INCONEL 718 alloy during WEDT, where higher pulse on-times were associated with increased surface roughness. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{l}{**Author**} & \multicolumn{2}{c}{**Method**} & \multicolumn{5}{c}{**Features**} & \multicolumn{5}{c}{**Experiments**} \\ \hline  & **Teguchi** & **BSM** & **AI** & **Other** & **Pulse** & **Pulse** & **Wave** & **Vulse** & **Pulse** & **Wave** & **Wave** & **Serve** & **Surface** & **Surface** & **Other** & **Rapple** & **Material Removal Rate** & **Energy** & **Other** \\ \hline This week & \(L_{  
[PERSON] et al. [21] focused on optimizing process parameters for D2 steel machining using WEDM. Wire speed, gap voltage, flushing pressure, and current were analyzed for their impact on surface roughness and material removal rate. Results indicated that MRR was predominantly influenced by current, with flushing pressure having the least impact, while surface roughness was most affected by wire speed and least affected by gap voltage during the WEDM process on D2 steel workpieces. In the study by [PERSON] et al. [22], the focus was on a multi-objective optimization of the wire electrical discharge machining of Stellite to enhance surface roughness, material removal rate, and overcut. The Taguchi-Grey approach and single-objective optimization techniques were employed to analyze the effects of pulse on-time, pulse off-time, and wire tension at different levels. The authors highlighted that pulse on-time was the key variable influencing changes in all three responses (surface roughness, material removal rate, and overcut) in the WEDM of Stellite. In the study by [PERSON] et al. [23], the wire electrical discharge machining of Inconel 625 was investigated with a focus on optimizing process parameters including pulse on-time, pulse off-time, and peak current. The desired performance measures considered were material removal rate and overcut. The analysis showed that increasing all three parameters (pulse on-time, pulse off-time, and peak current) led to improved material removal rate. For minimizing the overcut, the optimal combination involved a high value for pulse on-time and low values for pulse off-time and peak current. The aim of the study by [PERSON] et al. [24] was to determine the optimal values of current, pulse on-time, pulse off-time, wire tension, and wire feed to achieve specific goals: minimizing surface roughness and wire consumption rate while maximizing material removal rate in wire electrical discharge machining.