Summary. - This investigative study was conducted for the parametric optimization of EN-31 material by using a non-

conventional machining known as Electric discharge machining (EDM). EN-31 is a steel alloy that is generally used in

aerospace industry, automotive parts manufacturing, die making etc. because it possesses high degree of rigidity with

extremely good compressive strength and resistance to abrasion. The primary objective of this study was to analyze the

significant factors with optimized values of 50 A and 6.5 µs, respectively, resulting in a Tm of 654.29 seconds. For

material removal rate (MRR), Ton was the significant factor with an optimized value of 6.5 µs, achieving a maximum

MRR of 0.0157 g/min. For electrode wear rate (EWR), LV and Ton were significant with optimized values of 30 A

and 4 µs, respectively, resulting in a minimum EWR of 0.07 g/min. Ra optimization revealed that the combination of

HV, LV, Ton and Toff were significant, with optimized settings of 50 A, 0.7 V, 4.0 µs and 6.5 µs, respectively, yielding

a minimum Ra of 0.018 mm. For base radius (R), the significant factors were HV, LV, Ton and Toff, with optimized

values of 0.6152 V, 50 A, 6.5 µs and 6.5 µs, respectively, resulting in a base radius of 1.5 mm. This parametric

optimization is extremely significant because EN-31 is a material used in critical applications requiring high strength,

hardness and abrasion resistance such as automobile engine components, aerospace rocket parts and dies subjected to

extreme temperatures and pressures throughout their lifecycle. Optimizing EDM parameters facilitates the use of this

1. Introduction. - Machining processes have progressed significantly over time [1]. The reason of this progression is

actually driven by the growing demand of industry to use parts and components that have excellent precision and

accuracy as per the designed component, must be produced by using efficiency manufacturing techniques because of

mass manufacturing demands of industry and cost reduction and manufacturing techniques’ capacity to work with a

wide range of materials while maintaining the excellent surface finish [2] [3]. Traditional machining techniques such

as turning, milling and drilling have been used in manufacturing industry from many centuries [4] [5]. These

conventional manufacturing techniques are the foundation of manufacturing sectors. These techniques have limitations

especially when dealing with exceptionally hard or brittle materials, during manufacturing of complicated geometries

and where tight tolerances are required especially in manufacturing of automotive parts or during die making [6] [7].

In order to address these issues researchers started working on non-traditional machining technologies and amongst

sparks raise the temperature of workpiece and the tool’s surface above their melting or boiling temperatures [12]. Thus,

material is scraped off in the form liquid and vapor phases and the surface that are formed consist of debris melted or

vaporized during machining [13] [14]. During this whole process tool does not physically contact the workpiece and

because of these mechanical properties of material are unaffected during this whole non-conventional machining

and 1.2%), (0.30% to 0.75%), chromium (1.0% to 1.6%) and silicon (0.10% to 0.35%) and all of these contribute to its

excellent performance qualities in high wear environment [19]. EN-31 is extremely hard material with excellent

strength and addition of chromium increases the corrosion resistance making it a preferred material to be used in harsh

conditions specially in the components which are subjected to severe abrasion, wear and high surface loading on

periodic basis. These properties like its ability to retain high tensile strength and durability following heat treatment

makes it a preferred material to be used in the parts made for automotive, aerospace and manufacturing industries

because component reliability and longevity are of extreme importance in these industries and single components

failure can cause unbearable amount of damage [20]. Understanding the qualities and applications of EN-31 can provide

substantial insights into its role in enhancing engineering processes and boosting performance and longevity [21].

but in EDM it is a trade off in which optimizing one parameter impacts the other parameter negatively [25]. For

example, increasing the current leads to high rate of material removal from workpiece usually but it results in a rougher

surface finish (Ra) and higher electrode wear rate (EWR) and this results in reduced dimensional accuracy and base

radius of the part that is being machined. Similarly, in order to increase surface quality by varying Ton time and Toff

time frequently result in longer machining times and lower MRR leading to loss in productivity. There is a no data

related to EDM optimization of EN-31 material is available in which all output responses mentioned previously were

optimized at the same time. Addressing this gap is of extreme importance in order to use EDM for mass manufacturing

of parts and for industries like automotive, aerospace, die making etc. in which parts quality and properties are of

extreme importance.

qualities [26] . Precision machining of EN-31 is critical in aerospace applications for making highly stressed

components like landing gears and turbine shafts. In the automobile industry, EN-31 is utilized for transmission gear,

engine parts and in bearings where dimensional accuracy is of extreme importance along with excellent surface finish

as these are critical for performance and longevity. By developing an optimized EDM processing strategy, this study

enables manufacturers to achieve higher productivity, lower electrode wear, improved surface quality and lower

manufacturing costs, making EDM a more viable and efficient method for machining EN-31 steel than traditional

techniques.

The main target of this research study is to improve the process parameters of electric discharge machining (EDM) for

the outputs. This research study will help industry practitioners in optimizing efficiency and productivity of their

manufacturing setup by increasing the product quality, reducing production time and increasing the productivity of the

machinery reducing the manufacturing cost of the product and increasing the profit margins. This research and

experimental work were performed in a very controlled environment where settings of all input parameters were

industrial usage on such a large scale and the challenges manufacturers face during the production of the parts made up

of EN-31. It is well-known that EN-31 exhibit excellent properties specifically harness and has very high degree of

wear resistance and tensile strength. It is also widely being used in the production of key components such as bearings,

gears, automotive engine parts, bio medical equipment manufacturing and die making where durability and precision

are of extreme importance. These qualities make EN-31 extremely beneficial but on the other hand make it extremely

difficult for manufacturers to process it using conventional machining methods thus resulting in swift tool wear, poor

surface finishes, high machining times and low material removal rates. Optimizing parameters of electric discharge

machining (EDM) for EN-31 can increase efficiency of machining, tool life and surface quality (by reducing surface

roughness) thus resolving the constraints of EN-31 machining via conventional machining processes. Composition EN-

31 [27] is mentioned in Table 1.

For this parametric optimization study of EN-31, copper (Cu) was chosen as the electrode material. Cu was chosen as

electrode material because of excellent electrical and thermal conductive properties and these are basic requirement for

used higher tool wear was observed along with poor surface finishing of the part being produced [29]. Copper has better

wear resistance and electrode wear rate is comparatively less as compared to other materials such as graphite. As a

result of this frequency of electrode replacements during manufacturing of part produced via EDM reduces. There are

other materials available as well like tungsten, copper alloys etc. with properties that are suitable for EDM

manufacturing process but they are quite expensive and economically not viable. Because of these reasons copper was

chosen for this optimization study of EN-31.

In this study, two EN-31 blocks, each measuring 100 × 10 × 20 mm, were used and secured together. The electrodes

were produced with a step-turned geometry, combining a flat circular tip of 3 mm diameter.

microscope/image analysis system. The radius was defined as the curvature at the machined cavity base and geometric

fitting determined from captured images. Multiple measurements were taken to ensure repeatability, and the average

value was reported to minimize measurement uncertainty.

moisture or surface deposits, proper handling procedures were followed.

Surface roughness was determined using a calibrated surface roughness tester with a predetermined cutoff length (e.g.,

0.8 mm) and evaluation length (e.g., 4 mm). Multiple traces (at least three) were collected at various sites, with the

average value presented. Measurements were taken perpendicular to the machining direction to capture the actual

surface imperfections. Prior to testing, the instrument was calibrated using a standard reference specimen.

EWR was calculated using the equation [30] stated below in Equation 1:

To confirm the effect of input factors on output responses, the experiments were designed using a DOE technique. This

method involves investigating combinations of the input parameters at various levels (low and high). In this study four

input parameters i.e., Ton, Toff, LV and HV were evaluated at two distinct levels. This binary factor level arrangement

provides for a thorough examination of the key effects and interactions between the input and output parameters.

The total number of experimental runs needed is 24 = 16 runs with four input parameters, each of which has two levels.

Table 2 lists the level of input parameters, and Table 3 Table lists the fundamental experimental runs.

components assigned to various sources, revealing the relative significance of each input parameter and their

interactions.

The model is then adjusted by eliminating the insignificant factors and a revised ANOVA table is created to highlight

the most significant ones. The standardized effects normal plot and residual plots for the variable's response are

generated again. Additionally, Main effect and interaction plots are generated and analyzed. Significant interactions

are shown by non-parallel lines between levels in the interaction plot, whereas a steep slope in the main effect plot

indicates the significance of the effect.

The Response Optimizer program generates practical solutions. A specified aim (Minimize, Equate, or Maximize) is

parameters but also the combined effect of different input parameters on output responses. By using ANOVA, this

investigative study ensures that study is accurate.

4.1 Machining time (Tm) ANOVA results. - Minitab software was used to construct the ANOVA table for Tm in

table in Table 7 and the Normal Probability Plot in Figure 4. After that, the model was rebuilt using only LV and Ton

as input variables and excluding non-significant input parameters.

Main effect plots (Figure 5) and interaction plots (Figure 6) were created following the identification of relevant factors.

The main effect plot's steep mean slopes support the idea that LV and Ton have a major influence on Tm optimization

in EN-31. Calculating the optimal values of the important input factors was the following stage. The upper number was

set to 427 seconds, which indicated the quickest machining time, and the target value was set to 0 seconds in order to

optimize machining time. After evaluating the desirability functions (Figure 7), a desirability value (d) of 0 indicates

that the answer is far from the target, indicating that the target value is less significant. A desirability value of 1 might

have been attained if the goal had been set closer to 600 with a value greater than 2000.

runs as mentioned in table 4 in Appendix 1 also shows an inverse relationship between LV and Ton in with Tm. As we

know that greater the LV and Ton time values result in large depressions/craters on the workpiece and these large

craters results in poor surface finish (Ra) and poor surface finish leads to the final product which is not fit for use

especially in the fields of bio implant manufacturing, automotive parts manufacturing etc. where good surface finish is

of extreme importance. That why parametric optimization of all the output responses of EN-31 in EDM is necessary to

have a product that meets the customer and manufacturer demands.

4.2 MRR ANOVA results. - The ANOVA (Table 11) for MRR was created in Minitab. All four input variables were

taken in to account while performing the analysis. Ton is a significant factor in MRR in EN-31, according to the

ANOVA table (Table 11) and the Normal Probability Plot (Figure 9). Following that, non-significant components were

Main effect Plot (Figure 13) and Interaction Plot (Figure 14) were prepared for MRR after the determination of

significant factors. The Main effect Plot shows that Ton is a significant factor whereas the Interaction Plot shows that

Ton and LV are significant factors as LV p-value is 0.055 which makes it quite close to be a significant factor. Now

the optimized value of significant factors is calculated. MRR was optimized with a goal value of 1 g/min and a lower

value of 0.0267 g/min, indicating the highest observed value of MRR. The desirability functions (Figure 15) were then

assessed. A desirability value (d) of 0 shows that the response is distant from the target, meaning that the target value

is not as important. If the objective had been set closer to 0.015 with a lower value less than 0.014, a desirability value

near to 1 may have been obtained.

The optimized value for Ton was found to be 6.5 µs, as detailed in Appendix 1. For this optimized setting, the maximum

possible final product, the parametric optimization of the EDM process aimed to balance all output reactions.

4.3 EWR ANOVA results. - Minitab was used to create the EWR ANOVA table (Table 12). Ton, Toff, LV, and HV

are all considered as input elements. LV and Ton are important factors affecting EWR in EN-31, according to the

Following the identification of important components, EWR's main effect plot (Figure 19) and interaction plot (Figure

20) were created. Steep mean slopes in the Main Effect Plot show how LV and Ton have a major influence on EWR in

values for the pertinent input factors was the following stage. EWR was optimized by setting the upper value to 0.00523

g/min, the lowest measured EWR, and the target value to 0 g/min. The desirability functions (Figure 20) were then

assessed. A desirability value (d) of 0 shows that the response is far from the target, meaning that the target value is not

as important. If the objective had been set closer to 0.015 with a lower value less than 0.014, a desirability value near

to 1 may have been obtained. The optimized values were found to be 30 A for LV and 4 µs for Ton, as detailed in

Appendix 1. For these optimized settings, the minimum EWR was calculated to be 0.07 g/min.

optimization of the EDM process aimed to balance all output reactions.

4.4 Surface finish (Ra) ANOVA results. - The ANOVA table (Table 13) for Ra was created in Minitab. All input

factors (Ton, Toff, LV, HV) were considered. The ANOVA table (Table 13) and the Normal Probability Plot (Figure

23showed that there are no significant factors influencing Ra in EN-31. As a result, there was no need to retrofit the

model. However, the Main effect Plot (Figure 25) and Interaction Plot (Figure 26were still generated for Ra. The

Interaction Plot shows non-parallel lines, showing that the parameters interact significantly.

The optimal values for the significant factors were then calculated. Ra was optimized with a goal value of 0 mm and

an upper value of 0.006 mm, which represented the minimum observed surface roughness. The desirability functions

that the target value is not as important. Had the target been set closer to 0.009 with an upper value greater than 0.015,

a desirability value close to 1 could have been achieved.

The optimized values of Ra for EN-31 were found to be LV = 50 A & HV = 0.7 V, Ton = 4.0 µs and Toff = 6.5 µs as

shown in Appendix 1. For these optimized settings, the minimum Ra was calculated to be 0.018 mm.

4.5 Base radius (R) ANOVA results. - The ANOVA table (Table 14) for ‘R’ was created in Minitab, considering all

input variables. The ANOVA table (Table 14) and the Normal Probability Plot (Figure 28) showed that the interactions

HV*LV*Ton and HV*LV*Ton*Toff are significant factors for R in EN-31. As a result, the model was refined (Table

15) by eliminating non-significant in, leaving only HV*LV*Ton and HV*LV*Ton*Toff as input variables. The p-

values from the updated model's ANOVA table (Table 15) supported the significance of these interactions and the

set to 1.5 mm, with maximum and lower boundaries of 1.55 mm and 1.45 mm, respectively. The desirability functions

(Figure 34) were then assessed. A desirability value (d) near to one shows a strong preference for the target value,

implying that the response closely matches the desired target. The optimized values were found to be HV = 0.6152 V,

LV = 50 A, Ton = 6.5 µs and Toff = 6.5 µs as shown in detail in Appendix 1. For these optimized settings, the base

a desirable overall machining performance. Therefore, the parametric optimization of the EDM process aimed to

balance all output responses to achieve an optimal final product.

5. Conclusion. - This study focused on optimizing processing parameters in Electric Discharge Machining (EDM) for

Tm, maximize MRR, reduce EWR, enhance Ra and control ‘R’. A full factorial experimental approach, combined with

regression analysis and ANOVA, was used to identify and optimize the most significant parameters, namely Ton, Toff,

LV and HV.

The observed results are governed by electro-thermal interactions occurring in EDM. The primary mechanism involves

accelerating material removal. However, increasing these parameters beyond optimal levels resulted in excessive

energy input, leading to large craters, poor surface finish (Ra) and undesired tool wear. This highlights the need for a

balanced parameter selection, as excessively high current can degrade dimensional accuracy due to excessive thermal

damage.

For EWR, a lower LV (30 A) and shorter Ton (4 µs) minimized electrode erosion (0.07 g/min), which is crucial for

extending tool life. This occurs because lower discharge energy reduces thermal stress on the electrode, thereby slowing

down the material erosion rate.

Ra is influenced by a complex interplay of spark intensity and pulse durations. The study found that an optimal