Keywords

1 Introduction

Electrical discharge machining is a type of an unconventional machining technique where the electrical energy is directly used to remove or cut the metals. It is also called as spark erosion machining or electro-erosion machining [4]. The metal is removed by electrical spark discharge between electrode tool (cathode) and workpiece (anode). Electrical discharge machining is used in mould and die-making industries, automobile industries and also making of aerospace components. In die and mould making, a die-sinking EDM is especially used for machining intricate and unique patterns and shapes which otherwise are difficult to machine or time-consuming using conventional CNC milling machines. The paper describes an investigation of EDM process parameters and its optimization using Taguchi method and determining the major and minor parameters affecting MRR and EWR as well as finding optimized values based on the values considered for this experiment [1].

2 Literature Survey

Optimization of EDM process parameters using Taguchi method with copper electrode by Niraj Kumar Ohdar, Babuli Kumar Jena, Saumya Kanta Sethi discusses optimizing process parameters that are current, pulse-ON time, pulse-OFF time and flushing pressure using Taguchi method with copper electrode based on MRR and EWR with corresponding S/N ratio and then finding out mean of their S/N ratios with ‘larger the better’ for MRR and ‘smaller the better’ for EWR and then finding their residual plots and respectively the major and minor parameters were identified for both MRR and EWR.

The implementation of Taguchi method on EDM process of tungsten carbide by Mohd Amri Lajis, H. C. D. Mohd Radzi and A. K. M. Nurul Amin discusses using Taguchi method with graphite as an electrode and tungsten carbide as workpiece and accordingly finding optimized values for the parameters, i.e. current, pulse-ON time, pulse-OFF time and voltage, and similarly, the major and minor parameters based on its effects on MRR, EWR and surface roughness are determined with its optimized values from the graph plotted.

Optimization of EDM process parameters using Taguchi method with graphite electrode by Vishal J. Nadpara and Prof. Ashok Choudhary discusses optimizing process parameters that are current, pulse-ON time and pulse-OFF time by Taguchi method with graphite as electrode and AISI D3 Steel as workpiece. The MRR and EWR are respectively found based on machining time and weight loss, and the S/N ratio is founded for each values. The mean of S/N ratio graph is plotted based on ‘smaller the better’ for EWR and ‘larger the better’ for MRR for finding again the major and minor parameters affecting them and similarly finding the optimized values.

3 Experimental Set-Up

The experiments were conducted using a CNC die-sinking EDM machine manufactured by HCM Taiwan as shown in Fig. 1. The copper electrode is fed downwards into the workpiece under servo control in this EDM machine. The workpiece material used for this experiment was P20 tool steel. In this experiment, Taguchi design of experiments with L9 orthogonal array, the analysis had been carried out [2]. Using this, the proposed process parameters were arranged accordingly and corresponding MRR and EWR along with their signal-to-noise ratio based on ‘higher the better’ and ‘smaller the better’, respectively. With the help of Minitab software, the mean of S/N ratio graph is plotted, and accordingly, the values and the ranking of each parameter based on importance are found.

Fig. 1
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CNC die-sinking EDM machine setup

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4 Material Properties

The workpiece is P20 tool steel for this experiment with the following physical properties (Table 2) and Chemical Composition (Table 1) [5].

Table 1 Chemical composition of ‘P20’
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Table 2 Physical properties of ‘P20’
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5 Experiment Data

The parameters considered for this experiment are current, pulse-ON time and pulse-OFF time [3]. The duration of time (in μs) when the current is passed gives pulse-ON time (td) to flow per cycle. The duration of time (in μs) in between two consecutive sparks and current (I) is current flowing through the whole cycle (in AMP) is pulse-OFF time (to).

5.1 Experiment Procedure

During the experiment, 3 levels (Table 3) were taken for each parameter and then arranged in a L9 orthogonal array (Table 4) in Taguchi method. During the readings, all the weight loss and machining time are taken for calculating MRR and EWR for each of the 9 readings. These values are input in Minitab 19 software and then under DOE and analysis of Taguchi method, S/N ratio and mean table were calculated, and based on the values, graph was plotted accordingly for MRR and EWR, respectively, where the optimized values were then determined from the graph [2].

Table 3 Levels for process parameters
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Table 4 L9 orthogonal array for Taguchi method
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For calculating MRR, the formula:

$$\frac{{W_{\text{b}} - W_{\text{a}} }}{d * t}\;{\text{mm}}^{3} /{ \hbox{min} }$$

where

  • Wb—Workpiece weight before machining (g)

  • Wa—Workpiece weight after machining (g)

  • d—Density of P20 tool steel (g/cc)

  • t—Machining Time (in mins)

  • The density of P20 tool steel is 7.85 g/cc.

For calculating EWR, the formula:

$$\frac{{E_{\text{b}} - E_{\text{a}} }}{d * t}\;{\text{mm}}^{3} /{ \hbox{min} }$$

where

  • Eb—Electrode weight before machining (g)

  • Ea—Electrode weight after machining (g)

  • d—Density of electrode (g/cc)

  • t—Machining Time (in mins)

  • The density of copper electrode is 8.96 g/cc.

5.2 Results and Analysis

After putting the MRR values in Minitab software, for ‘larger the better’ option to get S/N ratio values, Table 5 represents the S/N ratio for MRR. Accordingly, the response table (Table 6) and graph (Figs. 2 and 3) for mean of S/N ratio along with a significance of each of the process parameters were ranked based on delta value from high to low (Table 7).

Table 5 S/N ratio
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Table 6 Response table for signal-to-noise ratios
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Fig. 2
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Signal to noise ratios for MRR

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Fig. 3
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Mean for MRR

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Table 7 Response table for means
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So accordingly, MRR is most affected by current followed by current, pulse-ON time and then pulse-OFF time. The parameters where MRR is said to be maximum are: current (6 A), pulse-ON time (100 µs) and pulse-OFF time (40 µs).

Similarly, for EWR for ‘smaller the better’ option is selected in Minitab software for obtaining S/N values, Table 8 represents the S/N ratio for EWR. Accordingly, the response table (Table 9) and graph (Figs. 4 and 5) for mean of S/N ratio along with significance of each of the process parameters were ranked based on delta value from high to low (Table 10).

Table 8 S/N ratio
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Table 9 Response table for signal-to-noise ratios
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Fig. 4
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Signal to noise ratios for EWR

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Fig. 5
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Mean for EWR

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Table 10 Response table for means
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So accordingly, the EWR is most affected by current followed by current, pulse-ON time and then pulse-OFF time. The parameters where EWR is said to be minimum are: current (4 A), pulse-ON time (100 µs) and pulse-OFF time (50 µs).

6 Conclusion

It was found that MRR is most affected by current followed by pulse-ON time and pulse-OFF time and is maximized at current (6 A); pulse-ON time (100 µs) and pulse-OFF time (40 µs).

As far as EWR is concerned, it is most affected again by current followed by pulse-ON time and pulse-OFF time, and further, EWR is minimized at current (4 A); pulse-ON time (100 µs) and pulse-OFF time (50 µs).