It is critical to choose machining parameters in WEDM to achieve optimum machining accomplishment. The appropriate machining parameters are usually established based on experience. This, however, does not guarantee that the chosen machining settings provide optimum or near-optimal machining performance for that specific electrical discharge machine and environment. Different ideas have been put forward in the literature about improving the different machining response parameters.

Nitin et al.** **studied the effects of wire-EDM machining input parameters on surface roughness and cutting speed of titanium (Ti-6Al-4V) alloy. Input parameters included servo voltage, wire feed speed, and wire tension. Response surface methodology (RSM) and analysis of variance (ANOVA) are used in this study [29]. The effects of WEDM machining input parameters on material removal rate, surface roughness, gap voltage, gap current, and cutting rate for AISI D2 steel were analyzed by Vikram. et al. Input parameters are pulse on time, pulse off time, peak current, servo voltage, and wire feed rate. The Taguchi L27 orthogonal array was used along with surface response methodology and analysis of variance [30]**.** The effects of WEDM input parameters on material removal rate for hot die steel AISI H-11 are explained by H.singh et al. Input parameters are pulse on time, pulse off time, gap voltage, peak current, wire feed, and wire tension. This paper uses a one variable at a time approach, and the result shows that the pulse on time is directly proportional to the MRR in this study [31]. G. selvakumar et al.** **studied the effects of WEDM input parameters on cutting speed, surface roughness, and the taper error for AISI D3 tool steel, which are pulse on time, peak current, wire tension, and taper angles. Taguchi-based grey relational analysis has been used along with the Taguchi L9 orthogonal array [32]. In another work,** **the effects of pulse on-time, pulse off-time, wire feed, and wire tension on the material removal rate and surface roughness for tungsten carbide were analyzed by P.Haja Syeddu Masooth et al .through the Taguchi L9 orthogonal array along with analysis of variance (ANOVA) [33]**.**

The effects of WEDM input parameters on the surface roughness and material removal rate for high-strength armor steel are explained by Ravindrannadh Bobbili et al. Input parameters are pulse-on time, pulse-off time, wire feed, flushing pressure, spark voltage, and wire tension. The Taguchi’s DOE with an L27 orthogonal array was used along with ANOVA [34]**.** M. Fakkir Mohamed et al.** **studied the effects of WEDM input parameters on the machining time for aluminium 6082 T6 alloy. Input parameters are pulse on-time, pulse off time, and current. Taguchi’s DOE with an L9 orthogonal array was used [35]. K. Satyanarayana et al**.** examined the effects of WEDM input parameters on the surface roughness and material removal rate for Inconel 600. Input parameters are current, pulse on, and pulse off time. Taguchi’s DOE with L9 orthogonal array was used along with ANOVA [36]. Swarup S. Deshmukh et al.** **explained the effects of WEDM input parameters on the surface roughness and kerf width of AISI 4140. Input parameters are pulse on time, pulse off time, servo voltage, and wire feed. Taguchi’s DOE with L9 orthogonal array was used along with grey relation analysis, ANOVA, and regression analysis [37]**.** Yasir Nawaz et al.** **examined the effects of WEDM input parameters on material removal rate, kerf width, and surface roughness of DC53 Die Steel. Input parameters are pulse on time, current, pulse off time, and wire speed. Taguchi’s DOE with L27 orthogonal array was used along with ANOVA [38]**.** V.K. SAINI et al. studied the effects of WEDM input parameters on the surface roughness of composite material (AL6061/SICP). Input parameters are pulse on-time, pulse off time, and current. Taguchi’s DOE with L9 orthogonal array was used along with ANOVA [39]**.** The effects of WEDM input parameters on the surface roughness of VANADIS 4e (powder metallurgical cold worked tool steel) were analyzed by D.Sudhakara et al**. **Input parameters are pulse on time, pulse off time, servo voltage, peak current, wire tension, and water pressure.** **Taguchi’s DOE with L27 orthogonal array was used along with ANOVA [40]. Zahid A. Khan et al**.** examined the effects of WEDM input parameters on surface roughness and kerf width of the stainless steel (SS 304). Input parameters are pulse on time, pulse off time, and current. Taguchi’s DOE with L9orthogonal array was used, along with grey relational analysis and analysis of variance (ANOVA) [41]**.** G. Rajyalakshmi et al. explained the effects of WEDM input parameters on material removal rate, surface roughness, and spark gap of Inconel 825. Input parameters are pulse on time, pulse off time, voltage, flushing pressure, wire feed rate, wire tension, spark gap, and servo feed. Taguchi’s DOE with L36 orthogonal array was used, along with grey relational analysis and analysis of variance (ANOVA) [42]**.**** **K. Lingadurai et al**.** examined the effects of WEDM input parameters on metal removal rate, kerf width, and surface roughness of stainless steel AISI grade-304. Input parameters are voltage, pulse on time, pulse off time, and wire feed rate. Taguchi’s DOE with L18 orthogonal array was used along with analysis of variance (ANOVA) [43]**. **

In light of the literature review, it is clear that optimizing the process parameters of WEDM is one of the most important design objectives for achieving a greater rate of material removal. Taguchi DOE has been shown to be the most effective technique for determining the optimal amounts of process variables and their corresponding interaction effects for a given target. In comparison to other DOE approaches, it is not only straightforward, efficient, and trustworthy for decreasing costs and enhancing quality, but it also dramatically decreases the number of trials. Then analysis of variance. The goal of this study is to find the best input machine settings for AISI 1045 medium carbon steel in order to get a higher rate of material removal.