Performance Prediction of Electric Discharge

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ScienceDirect Materials Today: Proceedings 5 (2018) 3770–3780

www.materialstoday.com/proceedings

ICMPC 2017

Performance Prediction of Electric Discharge Machining of Inconel-718 using Artificial Neural Network Vishnu P a, Santhosh Kumar N b, M Manohar c# a

PG student, Dept. of Mech. Engg. NSS College of Engineering, Palakkad - 678008, India Professor, Dept. of Mech. Engg. NSS College of Engineering, Palakkad - 678008, India c Scientist/ Engineer, Vikram Sarabhai Space Centre,ISRO, Thiruvananthapuram - 695022, India b

Abstract Inconel-718 is a nickel based alloy which displays very high yield, tensile and creep rupture properties at temperatures of up to 978 K. This alloy has extensive applications in jet engines, high speed air frame parts, power plant turbine components, automobile engine components and high temperature fasteners. Inconel-718 is classified under ‘difficult to machine’ material using traditional techniques. This paper deals with a non-traditional approach of machining Inconel-718 using Electric Discharge Machining (EDM). Experiments were designed and conducted according to Taguchi’s L18 orthogonal array. Experiments were carried out under different cutting conditions of polarity, pulse on time, pulse off time and peak current. Electrolytic copper was used as tool electrodes. The researchers propose analytical models that simulate the machining conditions to understand the role of contributing factors and their interaction effects and establish cause and effect relationships among various factors and desired product quality requirements. An Artificial Neural Network (ANN) is a branch of Artificial Intelligence (AI) which represents a human brain that tries to simulate its learning process. To predict the performance characteristics namely Material Removal Rate (MRR), Surface Roughness (SR) and Tool Wear Rate (TWR), ANN models were developed using backpropagation algorithms. Sufficient level of fitness was observed for the trained model. A comparison was made between experimental response values and the predicted values. The prediction accuracies were found to be sufficiently high which indicates the effectiveness of the model. Keywords:EDM, Inconel-718, ANN, optimisation

* Corresponding author. Tel:9495200949

E-mail address: [email protected]

2214-7853© 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of 7th International Conference of Materials Processing and Characterization.

Vishnu P et al./ Materials Today: Proceedings 5 (2018) 3770–3780

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1. Introduction There is a continuous industrial demand for advanced materials having high hardness, temperature resistance, and high strength to weight ratio for use in mould and die making, aerospace components, medical appliances and automotive components. There is focus to arrive at new manufacturing techniques meeting productivity quality of machined surface and bringing down the cost, while machining such materials. Electrical Discharge machining (EDM) is an unconventional machining process, in general adopted to produce components with complex profiles that are difficult to be achieved through conventional manufacturing processes. The process uses thermal energy of the spark to machine electrically conductive parts regardless of the hardness of the work material. Like in any other process in EDM also, selection of process variables and fixing the appropriate range of parameters to machine every product is deciding the quality of product and in turn the design requirements. It is essential to understand the process intricacies, the process variables and the factors influencing the output parameters. Considerable research on different approaches were found for optimising EDM process parameters to bring out the desired output viz. Material Removal Rate (MRR), Electrode Wear Rate (EWR), Wear ratio (WR) and Surface Roughness (Ra). Input variables like peak current, current intensity, pulse-on time, pulse-off time, electrode material, electrode size and its bottom profile are tailored for every work piece material and optimised to get the desired output. In the manufacturing process industries, modeling a specific process is essential to understand the effect of contributing factors and the role of process parameters in the responses (output) of the process, which further enables the researcher to optimize the process. Machining processes are in general complex in nature for developing suitable analytical models and during the course of developing such analytical models, researchers proceed with many assumptions which often contradict the reality. Sometimes, it is difficult to adjust the parameters of the models according to the actual situation of the machining process. Due to the complexity of the machining process, it is difficult to achieve optimization and to perform optimal control of the process and to achieve these, many analytical approaches are evolved. Each analytical approach evolved so far, has its own strengths and short-comings for specific problems and in similar lines, neural networks are also evolved and being adopted in practice for specific problems. Neural networks which can map the input/output relationships and possess massive parallel computing capability, are serving in research area on machining processes, to predict the responses, to understand the role of input variables and their interacting effects and finally arrive at optimization level of the process. Neural network models associated with artificial intelligence are known as artificial neural networks (ANN) which are simple mathematical models in the form of defining a function f ( x) = Y or a distribution over or both and . In some cases, models are associated with a specific learning algorithm or learning rule. In general, the use of the term ANN model indicates the definition of a group of such functions (where the units of group are obtained by input parameters (process variables), weightage-factors and or specific construction features such as the number of neurons, number of layers and their connectivity. This work presents the details of the experiments carried out for data generation, building the ANN Models and their validation. These models can be used either for predicting the output for a chosen set of input variables or to get a specific desired output, finding the set of input variables to be chosen. Studies show that selection of process variables and fixing the appropriate parameter to machine every product is deciding the quality of product, in turn the design requirements [1, 2]. It is essential to understand the process intricacies, the variables interacting the process and the factors influencing the output parameters; for increasing the flexibility of the process much need to be improved by which a component could be made with many easier ways, by consumption of less energy thereby making the process more economical. Even though EDM technology has been established to be very efficient in machining out complex shapes and also to machine hard materials, there are several problems associated with this machining process. The success of electric discharge machined components in real applications relies on the understanding of material removal mechanisms and the relationship between the EDM parameters and the formation of surface and sub-surface damages. Surface and sub-surface damages need to be investigated as they provide key information on the mechanisms of material removal. Effect of parameters like discharge current and pulse-on time on the surface roughness of the machined surface of ceramiccomposites has been established [3].

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Electrode material, geometry of the electrode, Dielectric fluid, work material are the factors in EDM and the process parameters like current, gap voltage, spark on-time, spark of-time, duty cycle, work piece polarity are the influencing attributes, to decide the output of the machined work piece [4-7]. The performance of EDM is usually evaluated by the output parameters namely material removal rate (MRR), electrode wear rate (EWR), wear ratio (WR), machined surface roughness (Ra) etc., It is desirable to obtain higher MRR with lower EWR, WR and Ra [4]. Crack formation on the work piece can be attributed to the presence of the residual stresses induced during the machining processes. Studies have been conducted to investigate the relationship between machining conditions and surface cracking with an objective of establishing machining conditions which prevent the occurrence of such cracks. Relationship of crack critical line (CCL) between work material and electrode diameter was deduced, indicating crack zone and no crack zone [5]. Machining parameters do have an effect on the formation of recast layer, white layer, surface roughness of the machined surface, density of cracks while machining AISI D5 tool steel [6]. The characteristics of the recast layer formed at the machined surface of the work piece have a great relationship with the type of dielectric used during EDM. Formation of micro-cracks and micro-voids within the recast layer, formation of oxides and carbides in the recast layer and the roughness of the machined surface are attributed to the type of dielectric used in EDM [8]. 2. Experimental Details 2.1. Work-piece and tool Inconel 718 alloy in the form of extruded rod in the Annealed condition having square cross section was chosen as the work-piece material. Specimens for EDM were sliced to a size of 16 mm square (shown in Figure -1) with a thickness of 10 mm using Wire EDM. The composition of Inconel-718 is given in Table 1. Table-1. Chemical composition of Inconel-718 Element

Value (%)

Nickel (plus Cobalt)

50.00-55.00

Chromium

17.00-21.00

Niobium (plus Tantalum)

4.75-5.50

Molybdenum

2.80-3.30

Titanium

0.65-1.15

Aluminium

0.20-0.80

Cobalt

1.00 max.

Carbon

0.08 max

Manganese

0.35 max.

Silicon

0.35 max.

Phosphorus

0.015 max.

Sulphur

0.015 max.

Boron

0.006 max.

Copper

0.30 max.

Iron

Balance


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The physical and mechanical properties are given in table – 2. Table 2.

Physical and mechanical properties of Inconel-718

Property

Value

Density

8193 kg/m3

Melting Range

1609 K

Ultimate Tensile Strength

1241 MPa

Yield Strength (0.2% Offset)

1034 MPa

Hardness

341 BHN

Material for Tool electrode used for EDM process needs to be electrically conductive. There are a wide range of materials that can be used to manufacture electrodes such as Brass, tungsten carbides, electrolytic copper, coppertungsten alloys, silver-tungsten alloy, tellurium-copper alloys, copper-graphite alloys, graphite. For the present study, electrode made of electrolytic copper having machining interface of 10 mm dia. was used. Tool electrodes used for the study are shown in Figure 2.

Figure. 1

Figure 2.

Work specimens

Tool Electrode

2.2. Experimental setup All experiments were conducted on Mitsubishi EA-8 die sinking machine. Experimental set-up showing the tool and work-piece are shown in Figure 3. 2.3. EDM parameters

Figure 3. Experimental setup

For proper selection of the process parameters that will yield the desired output, a good knowledge of the input parameters influencing such output is essential. Importantly, predicting such an output performance through analytical models reduces process iterations [9]. In this regard, essential steps include identification of factors that are to be included and varied in the experiments and determining their levels. Accordingly, the process parameters namely, polarity, peak current (Amps), pulse on time (µs) and pulse off time (µs) were considered for the study. Applied voltage and flushing pressure were kept constant. Output parameters (responses) chosen for assessing the process performance were electrode wear rate, material removal rate and surface finish (Ra).

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Statistical design of experiments (DOE) refers to the process of planning the experiment so that the appropriate data can be analyzed by statistical methods, resulting in valid and objective conclusions. DOE methods such as factorial design, response surface methodology and Taguchi methods are now widely used in optimizing the machining parameters in place of one-factor-at-a-time experimental approach. Design of Experiments’ (DOE) techniques enables the designers to determine simultaneously the individual and interactive effects of many input factors that could influence the output results in any process. In Taguchi method, for reducing the number of experiments and to determine the optimal cutting parameters orthogonal array is introduced. Taguchi design of experiments (DOE) methods incorporate orthogonal arrays to minimize the number of experiments required to determine the effect of process parameters upon the responses of the process [10, 11]. Based on the preliminary studies conducted, three levels of the chosen parameters viz. Pulse-on time (Ton), Pulse-off time (Toff) and Peak current (Ip) were considered. Polarity has two levels only. Other process variables like dielectric fluid flushing pressure and applied voltage were kept constant for all the experiments. Taguchi L18 orthogonal array was designed for carrying out experiments to machine Inconel 718 specimens and is shown in Table -3. It is a mixed level design having one process variable in two levels and the other three variables in three levels. Table 3 Experiment factors and their levels Levels Parameters

1

2

3

Polarity (+/-)

+

-

Pulse on-time, Ton (µ sec)

50

100

Pulse off-time, Toff (µ sec)

30

60

90

Peak Current, Ip (A)

5

10

15

150

2.4. Performance measures in EDM In Electric Discharge Machining, removal by localized melting and vaporisation of workpiece material by the high energy spark generated between tool electrode and work-piece. To obtain a specific geometry, the EDM tool is guided along the desired path very close to the work; ideally it should not touch the workpiece. In this way, a large number of sparks (current discharges) occur, each contributing to the removal of material from both tool and workpiece. While material is being removed, small craters on the surface are formed. The size of the craters is a Figure – 4 function of the process parameters set Typical craters (100X) for the job in operation [12]. They can be varying in dimensions from the nano-scale (in micro-EDM operations) to some hundreds of micrometers in roughing conditions. The presence of these small craters on the workpiece results in the gradual erosion of the electrode. This erosion of the electrode is referred to as tool-wear. Tool-wear is an important factor since it affects the shape and the dimensional accuracy of the features produced in the workpiece. Tool wear is related to the melting point of the tool electrode material. Strategies are needed to


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neutralise the detrimental effect of the tool-wear on the geometry of the work-piece. One possibility is that of continuously monitoring the tool contact surface and when in adverse, replacing the tool-electrode during the machining operation. Material Removal Rate (MRR) and Tool Wear Rate (TWR) is calculated by the weight loss method using precision electronic balance weighing machine with an accuracy of 1 mg. MRR is calculated by measuring the weight loss of workpiece and tool as per the equation (1).

MRR =

(Wb − Wa )

(1)

t

Where Wb and Wa are the weight of work-piece before and after machining respectively. Time, t = time taken for machining in minutes. TWR is calculated by measuring the weight loss of tool as per the equation (2).

TWR =

(Tb − Ta ) t

(2)

Tb and Ta are the weights of tool before and after machining respectively and ‘t’ is the machining time in minutes. The measurement of Centre Line Average (Ra) value of Surface Roughness was made using Mitutoyo Surftest SJ-201, with cut-off length = 0.25 mm. Measuring force was set as 0.75 mN. Three measurements were taken in each region to ensure that the variation was controlled and well within the normal dispersion. It can be assured that the roughness measurements reported, represented the stabilized machined region. It is further stated here that the measured surface roughness (Ra) values indicate that their dispersion is within a narrow range and hence the EDM process carried out is a fairly stabilized one with minimum uncertainties. 2.5. Experimental results The results obtained from the experiments are shown in the Table 4. Table - 4. Experimental results Exp no.

Polarity

Pulse on (µsec)

Pulse off (µsec)

Input current (A)

MRR (mg/min)

TWR (mg/min)

Ra (µm)

1

+

50

30

5

8.3333

0.1667

3.5

2

+

50

60

10

27.5

0.25

4.02

3

+

50

90

15

44.6667

0.3333

4.96

4

+

100

30

5

7.3333

0.1667

2.76

5

+

100

60

10

31.3333

0.25

4.44

6

+

100

90

15

101.3333

0.6667

6.1

7

+

150

30

10

64.5

0.4167

4.82

8

+

150

60

15

136.1667

0.8333

5.67

9

+

150

90

5

4.3333

0.0833

2.16

10

-

50

30

15

0.5

0.6667

1.85

11

-

50

60

5

0.5

0.5833

1.56

12

-

50

90

10

0.8333

0.8333

1.33

13

-

100

30

10

1.5

1.5

1.75

14

-

100

60

15

1.3333

1.4167

2.23

15

-

100

90

5

0.8333

0.75

2.08

16

-

150

30

15

2.6667

2.5833

2.21

17

-

150

60

5

1

1.1667

1.98

18

-

150

90

10

2.5

2.3333

1.58

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3. Modeling of EDM Process There is a requirement of theoretical models that simulate the manufacturing process and establish ‘cause and effect’ relationships between the input parameters and desired process output [11]. A model can be defined as an abstract system which is equivalent to the real system with respect to key properties and characteristics and is used for investigations, calculations, explanation or demonstration purposes, which would otherwise be too expensive or not possible. A model permits general statements about elements, structure and behavior of a section of reality [14 - 16]. Today, most of the researchers dealing with modeling in machining perform it for its predictive ability. Once the model is established, important output parameters (response) of machining can be worked out without performing the machining operation [16-18]. The trial-and-error approach is far more laborious, costly and time- consuming. With modeling, optimization is achieved, resources are spared and cost is reduced. The above statements do not mean that experimental work is obsolete; for developing the model and for validating them by actual testing and comparisons, experimental data in real conditions are needed. However, once the model is established, it reduces subsequent experimental work considerably. Furthermore, modeling and experiments together add to the understanding of fundamental issues of machining theory. This is a feedback-loop serving as an aid for machining research since better understanding of the influencing parameters in the process results in evolution of better models and further optimising the process [9]. To develop the ability to predict the process, understanding the process is the first step. Modeling is an engineering necessity and also a scientific challenge that are needed for the benefit of machining industry. ANNs have been developed as generalizations of mathematical models of human cognition or neural biology. An ANN is an information processing system that displays similar behavior to that of its biological analog. It is essentially a mathematical model that mimics the human reasoning and neurobiology [19-21]. ANNs are mostly used for pattern recognition, pattern association and classification, constrained optimization and systems modeling with applications ranging from simple signal processing to medical diagnosis [21-23]. 3.1 Network architecture The artificial neural networks are made of inter connecting neurons which may share some properties of biological neurons. ANN is an information processing paradigm that is inspired by procedure in the biological nervous system. Neural networks are non-linear mapping systems that consist of simple processors which are called neurons, linked by weighed connections. Each neuron has inputs and generates an output that can be seen as the reflection of local information that is stored in connections. The output signal of a neuron is fed to other neurons as input signals via interconnections. The common type of ANN consists of 3 layers viz., Input layer, Hidden layer and Output layer. A layer of input units is connected to a layer of hidden units which in turn is connected to the layer of output units. Patterns are presented to the networks from the input layer, which communicate to one or more hidden layers where the actual processing is done through a system of weighed connections. The hidden layers are then linked to an output layer. A layer is defined as group of parallel neurons without any interactions between them. 3.1. Building the neural network Back propagation feed forward neural network (BPNN) and Levenberg–Marquardt algorithm (LMA) are used to build and train the network. In the present work neural network model was developed for Material Removal Rate, Tool Wear Rate and Surface roughness. In this model, the network consists of four input parameters (variables) and three output parameters (responses). The network consists of one input layer, one hidden layer and one output layer. The selection of number of neurons in the hidden layer is usually a model-dependent. The number of hidden layer neurons is decided by trial and error method on the basis of the improvement in the error with increasing number of hidden nodes. In the proposed model, hidden layer has five neurons, whereas input and output layers have four and one neurons, respectively. To train each network, learning rate (ƞ) and momentum constant (α) of 0.05 and 0.9 respectively were used, the activation function of hidden and output neurons was selected as a hyperbolic tangent,


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and the performance function was MSE (mean square error) and the number of epochs was set as 100000. The dividing function used is dividerand function.

FIGURE-5

:

General

b : input variables w : weight assigned to input variables k : number of input variables c : bias d : sum of weighted input variables and bias e : output The following procedure was followed to train and develop the model. Step 1 Input and output data were normalised within the range (0, 1). While normalising, equal range of the data was maintained. Step 2: Experimental data was randomly chosen for training the ANN model. Data for training the model was kept maximum (12 sets) to improve solution accuracy. Remaining set of data (6 sets) was used for validating the developed model. Step 3: Normalized input and output parameters were assigned as the input and target values for the model. Step 4: By selecting network type, training function, performance function, number of layers and assigning the properties like the number of neurons and transfer function for each layer, the model was created. Step 5: Parameters such as number of epochs, goal, learning rate etc, were chosen through iterations to improve the generalization ability and the network was trained. Step 6: By comparing the error between normalized training data and predicted data and establishing the correlation between data sets, the model was trained. If the error had been within the tolerance limits, network was frozen. Otherwise the process from step 4 was repeated till to get the prediction within minimum variation. Step 7: Optimum ANN model was selected by comparing mean absolute prediction error and correlation coefficient. Step 8 Model was used for testing with the validation data and verified to observe the same trend. Step 9 Predicted data and the input variables were denormalised for adoption and practical usage. 3.2 Prediction of output responses Using the developed model, predictions were made by taking the training data set one by one. The predictions were tabulated and compared with the corresponding experimental results.

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Further verification of the model was done by taking the validation data set. Comparison of predicted values of MRR, TWR and SR was made with that of experimental values. For easy understanding of the trend of variation, this has been brought out as plots and shown in figure 6, 7 and 8 respectively.

Figure-6 Time series plot of measured MRR and predicted MRR

Figure-7

Time series plot of measured MRR and predicted MRR

Time Series Plot of Surface Roughness (SR) Measured SR Predicted SR

6

SR (µm)

5

4

3

2

1 2

4

6

8

10

12

14

16

18

No. of experiments

Figure 8. Time series plot of measured SR and predicted SR

The absolute error is found for MRR, TWR and SR using equation (3) given below.


 | Experimental value - Predicted value|  Absolute error, % =   *100 Experimental value  

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(3)

The mean prediction accuracy of the ANN for MRR = 98.82 %, TWR = 88.02 %, SR = 93.97 %. This shows the overall effectiveness of the trained model. Verification of the developed model was done with the 6 sets of data and the fitness of the model is observed same as that of the training data set. 4. Conclusions The ANN models developed for electrical discharge machining of Inconel 718 alloy is having high order of fitness quality within the training data sets and also for the validation data sets. Hence the models are found to be dependable and can be used for all practical purposes in the shop floor for choosing the set of machining parameters to meet the output requirements of the machinist. Conversely, to get the specific output quality, set of input variables to be followed can also be selected using the developed model. This model shall be a reference to the shop floor requirements. This model is useful in optimizing the process parameters, depending on the requirement like MRR or surface roughness. Hence, this is considered as an optimization tool also. References [1]

[2]

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[9]

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