## 2 Literature Review  
[PERSON] et al. [12] provides a comprehensive review on wire electrical discharge machining. Their work examines the wide range of investigations that have been conducted, from WEDM through the EDM process' spin-offs. It describes a WEDM investigation that requires variables' optimization and an assessment of the many influences on machining efficiency and accuracy. Many researchers have employed designed experiments to analyze and optimize process characteristics in wire electrical discharge machining (WEDM). Previous works have utilized full factorial, fractional factorial, response surface methods, and Taguchi designs to optimize responses like surface roughness or material removal rate. Some authors have also utilized artificial neural networks or genetic algorithms for similar optimization tasks. Table 1 presents a compilation of prior studies related to this topic, highlighting the optimization methods used, the control factors examined, and the responses targeted in each study. The table clearly shows that Taguchi methods are widely used for identifying optimal parameter sets to optimize a desired response. The primary control variables chosen by researchers typically involve pulse on-time, pulse off-time, and wire speed. Surface roughness and material removal rate are the main responses of interest in most studies; however, the energy consumption aspect of the process is often overlooked or insufficiently explored. In a recent work, [PERSON] et al. [14] used a fractional factorial \(2^{6-1}\) experiment with pulse on-time, pulse off-time, spark voltage, peak current, wire speed, and workpiece thickness as control factors to study WEDM. The authors selected as responses the cutting rate, surface roughness, wire electrode temperature, and material removal rate to assess the best process values to machine armor steel. The modified crow search algorithm (MCSA) was used in that study for single- and multi-objective optimizations. Their research revealed that the pulse on-time and workpiece thickness had the most significant contributions to the cutting rate and surface roughness, and that pulse off-time and spark voltage were the major contributors to the wire electrode temperature. [PERSON] et al. [15] investigated the impact of machining parameters on material removal rate in wire electrical discharge machining of MONEL 400 K. They identified wire feed, pulse on-time, and pulse off-time as key influential factors affecting MRR. The study revealed that increasing pulse off-time enhanced material removal rate while simultaneously reducing surface roughness, indicating effective material flushing and achieving high-quality machining outcomes. Additionally, the research emphasized the novelty of the parametric evaluation in the WEDM of MONEL 400 K and highlighted the effectiveness of the response surface methodology for optimizing material removal rate. In the study conducted by [PERSON] et al. [16], the focus was on optimizing wire electrical discharge machining (WEDM) parameters for machining AISI H13 tool steel. The study examined gap voltage, pulse on-time, pulse off-time, and wire feed as key factors, aiming to maximize material removal rate and minimize surface roughness. The research identified the gap voltage as the most influential factor impacting both material removal rate and surface roughness in the WEDM of AISI H13 tool steel. In their investigation [17], [PERSON] et al. explored optimization techniques for the wire electrical discharge machining (WEDM) of dual-phase steel (DP980) using a brass wire. The study focused on improving machining performance by varying process parameters such as pulse on-time, pulse off-time, and voltage. The authors identified that the interaction between voltage and pulse off-time significantly affected surface roughness, while pulse on-time emerges as the most influential factor impacting material removal rate in the WEDM of DP980 steel. In their study [18], [PERSON] et al.