Nature Inspired Metaheuristic Approach for Best Tool Work Combination for EDM Process

*Goutam Kumar Bose and Pritam Pain*

## **Abstract**

As the modern day, the technologies are approaching with high accuracy at the same time with low material costing, non-traditional machining is very much essential to sustain in this modern manufacturing system. In this present research work Electric Discharge Machining (EDM) is used with different types of tools like Copper, Aluminium, and Brass are used while machining High Carbon High Chromium (HCHCr), Hot Die Steel (HDS) and Oil Hardened Nitride Steel (OHNS) workpiece material. This research work is aimed to find out the most efficient tool material for different workpiece materials while satisfying the contradictory objectives of high material removal rate (MRR) and low Tool Wear Rate (TWR). The experimental data are trained and validated by using Artificial Neural Network (ANN). Finally, the results obtained through Genetic Algorithm are hybridized with a Fuzzy- Multi Criteria Decision Making (MCDM) technique to obtain a single parametric combination of the process control parameters which satisfies these two contradictory objectives function simultaneously.

**Keywords:** EDM, ANN, Genetic Algorithm, Fuzzy, MDCM

## **1. Introduction**

The progressive growth in the advanced manufacturing process is aimed to achieve a finished material with a complex shape having very high accuracy. This precise manufacturing leads the industries to modern non-traditional machining processes. Electric Discharge Machining (EDM) is one such wieldy used nontraditional machining, where material removal takes place by the control erosion through spark discharge from the cathode tool on the anode workpiece. The conductive tool and the workpiece are submerged in flowing dielectric fluid and are separated by a small gap, known as a spark gap. The temperature of the spark varies from 8000°C to 12000°C which melts and vaporizes the workpiece material instantly. This electric discharge process is used to manufacturing complex part of a metal mould, tool and die, industrial instruments, aerospace's instruments, etc. The main advantage of EDM is that this process can machine hard material accurately [1]. EDM is a complex machining process which depends upon several interrelated control parameters; hence it is important to find an optimal control parameter setting so that the machining can be optimized for a better output. In the modern era, the optimization techniques are mostly nature inspired metaheuristics process. Artificial Neural Network (ANN) is one such nature-inspired technique where the

results are trained, validated and finally tested within an artificial network. In 1940, D.O. Hebb first introduced neural plasticity-based learning and then finally backpropagation is developed by Werbos in 1975 [2].

Previous research works which have been carried out to optimize the process control parameters either analytically or by simulation is presented here. Bose and Pain [3] have studied the effect of EDM on different types of tool material used in plastic industries and they have concluded different process control parameters for different material. Ho and Newman [4] have experimented on different types of an electric spark in EDM and have designed a simplified electrode to increase the performance index of the process. Zhou et al. [5] studied on minimum variance and pole placement coupled controller along with two-step prediction controllers to stabilize the machining in EDM. Equbal and Sood [6] have discussed the various parameters of the EDM process. They have also elaborated the future scope of EDM in industries. Choudhary and Jadoun [7] has experimented various types of EDM fluid in order to optimize machining productivity. They have developed die-sinking EDM, dry EDM, powder mixed EDM and also water-based EDM process. Abulais [8] has researched on various types of EDM like ultrasonic vibration dry EDM, powder-based EDM, and also used water as the dielectric fluid. Lin et al. [9] used Grey Neural Network on EDM and verified that the data are very similar to the actual experimental result. Ni [10] has discussed the various type of application of the Artificial Neural Network (ANN) in various uneasy condition and achieved a key technology for this application.

The experimental results are optimized by Genetic Algorithm (GA). Contradictory responses during machining like high Material Removal Rate (MRR) and low Tool Wear Rate (TWR) can be optimized by applying Fuzzy- Multi-Criteria Decision Making (MCDM) techniques. The objective of this research work is to identify the best tool work combination which will satisfy the contradictory responses of high MRR and low TWR.

### **2. Experimental design**

The present research study is done on the Die Sinking EDM (Electronica make). In this research work the workpiece material, tool material, current and pulse on time (POT) are varied simultaneously. The tools considered here are Copper, Aluminum, and brass. While the workpiece material used are High Carbon High Chromium (HCHCr) steel, Hot Die Steel (HDS) and Oil Hardened Nitride Steel (OHNS). During experimentation, the current is varied in three levels as 10 amps, 15 amps and 20 amps and the Pulse on Time (POT) are varied in three levels as 800 μsec, 1600 μsec, and 2000 μsec respectively. Other parameters which have a significant effect on the process are kept constant. Here kerosene is used as the dieelectric fluid, voltage is kept at 85 Volts, pulse off time is set at 800 μsec, depth of cut considered is 2 mm and spark gap is 5 mm. During the experimental run, various parameters are varied simultaneously by following the L9 Orthogonal Array (OA) so that the experimental time and as well as experimental cost can be reduced to a great extent. To analyze the data statistically the tool materials and also workpiece materials are expressed by their respective density as in **Table 1**.

From the experimental run the regression equitation is obtained for the MRR, where a1, b1, c1 and d1 are constant terms:

$$\begin{array}{l} \text{MRR} = \mathbf{a1} + \mathbf{b1} \ast \text{Tool Density} + \mathbf{c1} \ast \mathbf{W/P Density} + \mathbf{d1} \ast \text{Current} + \mathbf{e1} \ast \text{POT} \\ \quad + \mathbf{f1} \ast \text{Machining Time} \end{array}$$

From the same experimental run Tool Wear Rate (TWR) is obtained where a2,

**Materials Symbols Density (g/cm3**

Al 2.78 Br 8.73

HDS 7.75 OHNS 8.67

Tool Materials Cu 8.96

*Nature Inspired Metaheuristic Approach for Best Tool Work Combination for EDM Process*

Work Materials HCHCr 7.7

TWR ¼ a2 þ b2 ∗ Tool Density þ c2 ∗W*=*P Density þ d2 ∗Current þ e2 ∗ POT

Here Artificial Neural Network (ANN) is utilized to test and validate the experimental data. Then the responses Material Removal Rate (MRR) and Tool Wear Rate (TWR) are optimized by Genetic Algorithm (GA) in 'MATLAB R2015a' environment. The global optimal solution is then calculated by the Multi-Criteria Deci-

sion Making (MCDM) technique by applying Fuzzy set theory. Finally, the calculated data are analyzed by actual experimentation to validate the final result.

the threshold value, then these responses transfer to the next neuron. The

<sup>I</sup>*Ii* <sup>¼</sup> <sup>X</sup>*<sup>n</sup> i*�**1**

*Oi* ¼ *f I*ð Þ¼ *<sup>i</sup>*

The working principle of an artificial neuron is shown in **Figure 1**.

The output responses are defined by sigmoid function as shown in Eq. (4).

There are generally three types of architectures in case of Neural Network.

**1 1** þ *e*�*Ii*

the input calculation for each hidden layer of the neuron.

Artificial Neural Network (ANN) is the replica of the actual neural network system and there working principle is quite similar to the biological neural network [11]. Outer nodes collect the input responses. The other nodes are inter-connected, and finally, nodes give the responses to the input responses. In a neuron, there are synapses, they multiply each input by the weighted value, only if this value receded

interconnected inner layer of the neuron is known as a hidden layer. Eq. (3) shows

*WiXi* (3)

(2)

**)**

(4)

b2, c2 and d2 are constant terms:

*Density of the tool and workpiece materials.*

*DOI: http://dx.doi.org/10.5772/intechopen.96725*

**Table 1.**

**3. Artificial Neural Network**

• Feedforward-neural networks

• Feedback-neural networks

**139**

þ f2 ∗ Machining Time


*Nature Inspired Metaheuristic Approach for Best Tool Work Combination for EDM Process DOI: http://dx.doi.org/10.5772/intechopen.96725*

**Table 1.**

results are trained, validated and finally tested within an artificial network. In 1940,

Previous research works which have been carried out to optimize the process control parameters either analytically or by simulation is presented here. Bose and Pain [3] have studied the effect of EDM on different types of tool material used in plastic industries and they have concluded different process control parameters for different material. Ho and Newman [4] have experimented on different types of an electric spark in EDM and have designed a simplified electrode to increase the performance index of the process. Zhou et al. [5] studied on minimum variance and pole placement coupled controller along with two-step prediction controllers to stabilize the machining in EDM. Equbal and Sood [6] have discussed the various parameters of the EDM process. They have also elaborated the future scope of EDM in industries. Choudhary and Jadoun [7] has experimented various types of EDM fluid in order to optimize machining productivity. They have developed die-sinking EDM, dry EDM, powder mixed EDM and also water-based EDM process. Abulais [8] has researched on various types of EDM like ultrasonic vibration dry EDM, powder-based EDM, and also used water as the dielectric fluid. Lin et al. [9] used Grey Neural Network on EDM and verified that the data are very similar to the actual experimental result. Ni [10] has discussed the various type of application of the Artificial Neural Network (ANN) in various uneasy condition and achieved a

The experimental results are optimized by Genetic Algorithm (GA). Contradictory responses during machining like high Material Removal Rate (MRR) and low Tool Wear Rate (TWR) can be optimized by applying Fuzzy- Multi-Criteria Decision Making (MCDM) techniques. The objective of this research work is to identify the best tool work combination which will satisfy the contradictory responses of

The present research study is done on the Die Sinking EDM (Electronica make). In this research work the workpiece material, tool material, current and pulse on time (POT) are varied simultaneously. The tools considered here are Copper, Aluminum, and brass. While the workpiece material used are High Carbon High Chro-

mium (HCHCr) steel, Hot Die Steel (HDS) and Oil Hardened Nitride Steel

piece materials are expressed by their respective density as in **Table 1**.

where a1, b1, c1 and d1 are constant terms:

þ f1 ∗ Machining Time

**138**

(OHNS). During experimentation, the current is varied in three levels as 10 amps, 15 amps and 20 amps and the Pulse on Time (POT) are varied in three levels as 800 μsec, 1600 μsec, and 2000 μsec respectively. Other parameters which have a significant effect on the process are kept constant. Here kerosene is used as the dieelectric fluid, voltage is kept at 85 Volts, pulse off time is set at 800 μsec, depth of cut considered is 2 mm and spark gap is 5 mm. During the experimental run, various parameters are varied simultaneously by following the L9 Orthogonal Array (OA) so that the experimental time and as well as experimental cost can be reduced to a great extent. To analyze the data statistically the tool materials and also work-

From the experimental run the regression equitation is obtained for the MRR,

MRR ¼ a1 þ b1 ∗ Tool Density þ c1 ∗W*=*P Density þ d1 ∗Current þ e1 ∗ POT

(1)

D.O. Hebb first introduced neural plasticity-based learning and then finally

backpropagation is developed by Werbos in 1975 [2].

*Computational Optimization Techniques and Applications*

key technology for this application.

high MRR and low TWR.

**2. Experimental design**

*Density of the tool and workpiece materials.*

From the same experimental run Tool Wear Rate (TWR) is obtained where a2, b2, c2 and d2 are constant terms:

$$\begin{array}{l} \text{TWR} = \text{a2} + \text{b2} \ast \text{Tool Density} + \text{c2} \ast \text{W/P Density} + \text{d2} \ast \text{Current} + \text{e2} \ast \text{POT} \\ \text{ + f2} \ast \text{Machining Time} \end{array}$$

Here Artificial Neural Network (ANN) is utilized to test and validate the experimental data. Then the responses Material Removal Rate (MRR) and Tool Wear Rate (TWR) are optimized by Genetic Algorithm (GA) in 'MATLAB R2015a' environment. The global optimal solution is then calculated by the Multi-Criteria Decision Making (MCDM) technique by applying Fuzzy set theory. Finally, the calculated data are analyzed by actual experimentation to validate the final result.
