OPTIMIZATION OF MACHINING PARAMETERS OF OHNS STEEL …€¦ · To achieve good machining stability, the following ranges of machining current recommended for different positions of

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International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 7, July 2017, pp. 805–821, Article ID: IJMET_08_07_090

Available online at http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=7

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© IAEME Publication Scopus Indexed

OPTIMIZATION OF MACHINING

PARAMETERS OF OHNS STEEL BY USING

EDM

V. Ramesh

Assistant Professor, Mechanical Engineering,

Veltech Dr RR & Dr SR University, Avadi, Chennai, India

P. Anand

Associate Professor, Mechanical Engineering,

Veltech Dr RR & Dr SR University, Avadi, Chennai, India

M. Soundar

Assistant Professor, Mechanical Engineering,

SRM University Ramapuram Campus, Chennai, India

ABSTRACT

The correct selection of manufacturing conditions is one of the most important

aspects to take into consideration in the majority of manufacturing processes and,

particularly, in processes related to Electrical Discharge Machining (EDM). It is a

capable of machining geometrically complex or hard material components, that are

precise and difficult-to-machine such as heat treated tool steels, composites, super

alloys, ceramics, carbides, heat resistant steels etc. being widely used in die and mould

making industries, aerospace, aeronautics and nuclear industries. OHNS-EN-31 is a

high car bon alloy steel which achieves high degree of hardness with compressive

strength and abrasive resistance. OHNS-EN-31 steel, which is popularly used in

automotive type applications, like axle, bearings, spindle and moulding dies etc. In this

paper we have tried to investigate effect of machining parameter such as discharge

current, pulse on time, and pulse of time on MRR in EDM while machining OHNS-EN-

31 STEEL using Cu tool . A well-designed experimental scheme was used to reduce the

total number of experiments. Parts of the experiment were conducted with the L18

orthogonal array based on the Taguchi method. The results of analysis of variance

(ANOVA) indicate that the proposed mathematical model can be adequately describe

the performance within the limit of factors being studied. The optimal set of process

parameters has also been predicted to maximize the MRR.

Key words: OHNS, EDM, TWR, MRR.

V. Ramesh, P. Anand and M. Soundar


Cite this Article: V. Ramesh, P. Anand and M. Soundar, Optimization of Machining

Parameters of OHNS Steel by Using EDM, International Journal of Mechanical

Engineering and Technology, 8(7), 2017, pp. 805–821.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=7

INTRODUCTION

The history of EDM Machining Techniques goes as far back as the 1770s when it was

discovered by an English Scientist. However, Electrical Discharge Machining was not fully

taken advantage of until 1943 when Russian scientists learned how the erosive effects of the

technique could be controlled and used for machining purposes. When it was originally

observed by Joseph Priestly in1770, EDM Machining was very imprecise and riddled with

failures. Commercially developed in the mid-1970s, wire EDM began to be a viable technique

that helped shape the metal working industry we see today. In the mid-1980s.The EDM

techniques were transferred to a machine tool. This migration made EDM more widely

available and appealing over traditional machining processes. The new concept of

manufacturing uses non-conventional energy sources like sound, light, mechanical, chemical,

electrical, electrons and ions. With the industrial and technological growth, development of

harder and difficult to machine materials, which find wide application in aerospace, nuclear

engineering and other industries owing to their high strength to weight ratio, hardness and heat

resistance qualities has been witnessed. New developments in the field of material science have

led to new engineering metallic materials, composite materials and high tech ceramics having

good mechanical properties and thermal characteristics as well as sufficient electrical

conductivity so that they can readily be machined by spark erosion. Non-traditional machining

has grown out of the need to machine these exotic materials. The machining processes are non-

traditional in the sense that they do not employ traditional tools for metal removal and instead

they directly use other forms of energy. The problems of high complexity in shape, size and

higher demand for product accuracy and surface finish can be solved through non-traditional

methods. Currently, non-traditional processes possess virtually unlimited capabilities except for

volumetric material removal rates, for which great advances have been made in the past few

years to increase the material removal rates. As removal rate increases, the cost effectiveness

of operations also increase, stimulating ever greater uses of non-traditional process. The

Electrical Discharge Machining process is employed widely for making tools, dies and other

precision parts. EDM has been replacing drilling, milling, grinding and other traditional

machining operations and is now a well-established machining option in many manufacturing

industries throughout the world. And is capable of machining geometrically complex or hard

material components, that are precise and difficult-to-machine such as heat treated tool steels,

composites, super alloys, ceramics, carbides, heat resistant steels etc. being widely used in die

and mould making industries, aerospace, aeronautics and nuclear industries. Electric Discharge

Machining has also made its presence felt in the new fields such as sports, medical and surgical,

instruments, optical, including automotive R&D areas. Electro Discharge Machining (EDM) is

an electro-thermal non-traditional machining Process, where electrical energy is used to

generate electrical spark and material removal mainly occurs due to thermal energy of the spark.

EDM can be used to machine difficult geometries in small batches or even on job-shop basis.

Work material to be machined by EDM has to be electrically conductive.

OHNS steel is an important tool and die material, mainly because of its high strength, high

hardness, and high wear resistance. It has a high specific strength due to that it cannot be easily

machinable by conventional machining techniques. EDM is a non-conventional machining

process that removes material by thermal erosion, such as melting and vaporization of material.

To understand the machining characteristics of OHNS steel by EDM were explored in this

Optimization of Machining Parameters of OHNS Steel by Using EDM


experimental study. Pichai Janmanee et al. (2012) studied considers the effect of a copper-

graphite electrode material on tungsten carbide work pieces during machining by EDM. The

experiment found that by increasing the discharge current there was led to the more material

removal rate (MRR) and more electrode wear ratio (EWR). Dilshad Ahmad Khan et al. (2011)

discussed the effect of tool polarity on the machining of silver steel by electric discharge

machining. They concluded that direct polarity is suitable for higher MRR and lower relative

EWR, but reverse polarity gives better surface finish. N.Arunkumar et al. (2012) presented the

results of experimental work carried out in EDM of EN31 using three different tool materials

namely copper, aluminium and EN24. They concluded that copper undergoes less tool wear

rate and very high material removal rate.

EXPERIMENT AND DATA COLLECTION

Experiments are designed by using DOE. There are various important parameter of EDM.

(a) Spark On-time (pulse time or Ton): The duration of time (μs) the current is allowed to

flow per cycle. Material removal is directly proportional to the amount of energy applied during

this on-time. This energy is really controlled by the peak current and the length of the on- time.

(b) Spark Off-time (pause time or Toff): The duration of time (μs) between the sparks (that

is to say, off-time). This time allows the molten material to solidify and to be wash out of the

arc gap. This parameter is to affect the speed and the stability of the cut. Thus, if the off-time is

too short, it will cause sparks to be unstable.

(c) Arc gap (or gap): The Arc gap is distance between the electrode and work piece during the

process of EDM. It may be called as spark gap. Spark gap can be maintained by servo feed

system.

(d) Discharge current (current Ip): Discharge current is directly proportional to the Material

removal rate.

(e) Duty cycle (t): It is a percentage of the on-time relative to the total cycle time. This

parameter is calculated by dividing the on-time by the total cycle time (on-time pulse off- time).

(f) Voltage (V): It is a potential that can be measure by volt it is also effect to the material

removal rate and allowed to per cycle. Voltage is given by in this experiment is 50V.

(g) Diameter of electrode (D): It is the electrode of Cu-tube there are two different size of

diameter 4mm and 6mm in this paper. This tool is used not only as an electrode but also for

internal flushing.

EXPERIMENTAL SETUP

Section – I

Machine Tool

A) Technical Data:

* Height : 2,075 mm

* Width : 1,230 mm

* Depth : 1,035 mm

* Net Weight: 800 Kg.



Section – II

Power Supply Unit

A) Technical Data:

1. Electrical data

* Type : ELECTRONICA MODEL

* Supply Voltage : 415 V, 3 Ph., 50 Hz.

* Taps : 380 V, 415 V, 440 V.

* Mains Voltage Tolerance : + 10%

* Connected Load (KVA) : 3 KVA

* Power Factor : 0.8

2. Working Parameters

* Machining Current Max. : 70 Amps

* B.Pulse current : 2 Amps

* Open Gap O/V : 140 + 5%

* Current Range Selection : 10 Selection

1 = 1 Amp

2 = 2 Amps

3-10 = 4 Amps

* B.Pulse Current : 2 Selection

1 = 1 Amp

1 = 1 Amp

* Pulse on Duration : 2 to 1000 us.

* Weight : 250 Kg. Approx.

SELECTION OF EROSIVE PULSE PARAMETER

According to the requirement of machining rate, surface finish, over cut and electrode, the

positions of following switches are selected:

a) Position of Push wheel (Pulse 'ON') time

b) Position of Push wheel (Pulse 'OFF') time

c) Current selection switches

The setting of erosive pulse parameters could be obtained by selection of Pulse 'ON' time,

Pulse 'OFF' time and Average Machining Current. It is then possible to adopt with precision

the electrical parameters of pulse to the required for the machining conditions. All these

adjustments influences the performance parameters such as – conditions. All these adjustments

influences the performance parameters such as -

a) Machining speed and hence rate of removal

b) Electrode tool wear

c) Quality of machined surface

d) Extent of overcut



The various technology charts represent the relation between the performance parameters

and the erosive pulse parameters.

(A)PULSE 'ON' TIME

Different positions of switch Pulse 'ON' time are used for different machining rates as follows:

a) Position 39 to 99 : Level 3

b) Position 12 to 38 : Level 2

c) Position 1 to 11 : Level 1

To achieve good machining stability, the following ranges of machining current

recommended for different positions of Push Wheel (Pulse 'ON') Time.

(B) PULSE 'OFF' TIME

The pulse duration can be changed from minimum position (Position 9) to maximum (Position

1) in 9 positions by push wheel (Pulse 'OFF') time. Thus, one can obtain a full range of pulse

duration from a minimum of 6 s to a maximum of 1680 s which largely covers the duration

limits used in a Pulse Generator with a total power of 3 KVA. Decreasing the Pulse 'OFF' time,

switch reduces the machining rate with a drastic increasing in the relative electrode tool wear.

Too short a Pulse duration (T ON) position 3, 2 and 1, with copper electrode and steel work

piece results in excessive accumulation of carbon in the machining zone with a subsequent

instability of the machining process.

(C) MACHINING CURRENT

The increase in machining power is obtained by increasing the average machining current,

indicated by Ammeter Gap Current and controlled by turning on one by one the switches.

At the beginning of machining, the active electrode surface area is relatively small due to

particular shape or misalignment of the electrode and the job at the start of machining. So it is

always recommended to start with a low machining current, i.e., with a low machining power

and then increase the current gradually. This is important in order to avoid frequent

interruptions in machining process due to the excessive power being used for small area.

Frequent interruption in the process reduces working speed, and unless a corrective action is

taken the process remain in the state of instability.

After machining to turn off the current selection switches without fail. With this, it is

assured that the machine will restart under proper conditions in its next setting.

(E) FLUSHING

The product of Spark Erosion have to be removed from the work gap. The process by which

this is accomplished is known as flushing.

(F) OPERATIONAL DATA

When a work piece is machined by EDM process, in order to attain optimum material removal

rate and minimum electrode wear values of large number of parameters must be taken into

consideration which can be derived only by experience.

It also indicates the performance of the system under specified standard operating

conditions given below and may vary within + 5% from generator to generator.



EXPERIMENTAL SETUP

Section – I

Machine Tool

A) Technical Data

* Height : 2,075 mm

* Width : 1,230 mm

* Depth : 1,035 mm

* Net Weight: 800 Kg.

Section – II

Power Supply Unit

A) Technical Data:

1. Electrical Data:

* Type : ELECTRONICA MODEL

* Supply Voltage : 415 V, 3 Ph., 50 Hz.

* Taps : 380 V, 415 V, 440 V.

* Mains Voltage Tolerance : + 10%

* Connected Load (KVA) : 3 KVA

* Power Factor : 0.8

2. Working Parameters

* Machining Current Max. : 70 Amps

* B.Pulse current :2 Amps

* Open Gap O/V :140 + 5%

* Current Range Selection : 10 Selection

1 = 1 Amp

2 = 2 Amps

3-10 = 4 Amps

* B.Pulse Current : 2 Selection

1 = 1 Amp

1 = 1 Amp

* Pulse on Duration : 2 to 1000 us.

* Weight : 250 Kg. Approx.



SELECTION OF EROSIVE PULSE PARAMETER

According to the requirement of machining rate, surface finish, over cut and electrode, the

positions of following switches are selected:

a) Position of Push wheel (Pulse 'ON') time

b) Position of Push wheel (Pulse 'OFF') time

c) Current selection switches

The setting of erosive pulse parameters could be obtained by selection of Pulse 'ON' time,

Pulse 'OFF' time and Average Machining Current. It is then possible to adopt with precision

the electrical parameters of pulse to the required for the machining conditions. All these

adjustments influences the performance parameters such as – conditions. All these adjustments

influences the performance parameters such as -

a) Machining speed and hence rate of removal

b) Electrode tool wear

c) Quality of machined surface

d) Extent of overcut

The various technology charts represent the relation between the performance parameters

and the erosive pulse parameters.

(A)PULSE 'ON' TIME

Different positions of switch Pulse 'ON' time are used for different machining rates as follows:

a) Position 39 to 99 : Level 3

b) Position 12 to 38 : Level 2

c) Position 1 to 11 : Level 1

To achieve good machining stability, the following ranges of machining current

recommended for different positions of Push Wheel (Pulse 'ON') Time.

(B) PULSE 'OFF' TIME

The pulse duration can be changed from minimum position (Position 9) to maximum (Position

1) in 9 positions by push wheel (Pulse 'OFF') time. Thus, one can obtain a full range of pulse

duration from a minimum of 6 s to a maximum of 1680 s which largely covers the duration

limits used in a Pulse Generator with a total power of 3 KVA. Decreasing the Pulse 'OFF' time,

switch reduces the machining rate with a drastic increasing in the relative electrode tool wear.

Too short a Pulse duration (T ON) position 3, 2 and 1, with copper electrode and steel work

piece results in excessive accumulation of carbon in the machining zone with a subsequent

instability of the machining process.

(C) MACHINING CURRENT

The increase in machining power is obtained by increasing the average machining current,

indicated by Ammeter Gap Current and controlled by turning on one by one the switches. At

the beginning of machining, the active electrode surface area is relatively small due to particular

shape or misalignment of the electrode and the job at the start of machining. So it is always

recommended to start with a low machining current, i.e., with a low machining power and then

increase the current gradually. This is important in order to avoid frequent interruptions in

machining process due to the excessive power being used for small area. Frequent interruption

in the process reduces working speed, and unless a corrective action is taken the process remain



in the state of instability. After machining to turn off the current selection switches without fail.

With this, it is assured that the machine will restart under proper conditions in its next setting.

(D) FLUSHING

The product of Spark Erosion have to be removed from the work gap. The process by which

this is accomplished is known as flushing.

(E) OPERATIONAL DATA

When a work piece is machined by EDM process, in order to attain optimum material removal

rate and minimum electrode wear values of large number of parameters must be taken into

consideration which can be derived only by experience. It also indicates the performance of the

system under specified standard operating conditions given below and may vary within + 5%

from generator to generator.

EXPERIMENT DETAILS

Figure 1 Showing Experimental Setup

Experiments will be conduct based on RSM and ANOVA method. The four factor i.e.

Current, Pulse ON, Voltage Gap and Flushing Pressure were selected for conducting

experiment with three levels each.

This have three phases for the calculation and optimization, they are

• Planning phase.

• Conducting phase.

• Analysing phase.

PLANNING PHASE

In this phase following things are to be planned for the experiment.

I. Selection of process variables and there levels for EDM.

II. Selection of work piece material

III. Design of experiment (DOE)



A. Selection of process variables and three levels

Selection of process variables

CONTROL FACTOR SYMBOL FACTOR

Current I(amp) A

Pulse on-time Ton(µs) B

Voltage V(volt) C

Pressure P(kg/cm2) D

Level of parameters

CONTROL FACTORS LEVEL 1 LEVEL 2 LEVEL 3 UNITS

A 12 26 40 Amp

B 65 75 85 μs

C 30 35 40 μs

D 0.1 0.15 .2 Kg/min

B. Selection of work piece material

The material used for the experiments is grade OHNS EN-31 steel, which is popularly used in

automotive type applications, like axle, bearings, spindle and moulding dies etc. OHNS steel

refers to a variety of carbon and alloy steels that are particularly well-suited to be made into

tools. Their suitability comes from their distinctive hardness, resistance to abrasion, their ability

to hold a cutting edge, and/or their resistance to deformation at elevated temperatures (red-

hardness). Generally used in a heat-treated state. Many high carbon tool steels are also more

resistant to corrosion due to their higher ratios of elements such as vanadium and niobium. With

a carbon content between 0.7% and 1.5%, tool steels are manufactured under carefully

controlled conditions to produce the required quality. The manganese content is often kept low

to minimize the possibility of cracking during water quenching. However, proper heat treating

of these steels is important for adequate performance, and there are many suppliers who provide

tooling blanks intended for oil quenching.

C. Response surface methodology/ DOE

Response surface methodology (RSM) is a collection of mathematical and statistical techniques

for empirical model building. By careful design of experiments, the objective is to optimize a

response (output variable) which is influenced by several independent variables (input

variables). An experiment is a series of tests, called runs, in which changes are made in the

input variables in order to identify the reasons for changes in the output response. Originally,

RSM was developed to model experimental responses (Box and Draper, 1987), and then

migrated into the modelling of numerical experiments. The difference is in the type of error

generated by the response. In physical experiments, inaccuracy can be due, for example, to

measurement errors while, in computer experiments, numerical noise is a result of incomplete

convergence of iterative processes, round-off errors or the discrete representation of continuous

physical phenomena (Giunta et al., 1996; van Campen et al., 1990, Toropov et al., 1996). In

RSM, the errors are assumed to be random. The application of RSM to design optimization is

aimed at reducing the cost of expensive analysis methods (e.g. finite element method or CFD

analysis) and their associated numerical noise. The problem can be approximated as described

with smooth functions that improve the convergence of the optimization process because they

reduce the effects of noise and they allow for the use of derivative-based algorithms. Venter et

al. (1996) have discussed the advantages of using RSM for design optimization applications.



1. Approximate model function

Generally, the structure of the relationship between the response and the independent variables

is unknown. The first step in RSM is to find a suitable approximation to the true relationship.

The most common forms are low-order polynomials (first or second-order).

In this thesis a new approach using genetic programming is suggested. The advantage is

that the structure of the approximation is not assumed in advance, but is given as part of the

solution, thus leading to a function structure of the best possible quality. In addition, the

complexity of the function is not limited to a polynomial but can be generalised with the

inclusion of any mathematical operator (e.g. trigonometric functions), depending on the

engineering understanding of the problem. The regression coefficients included in the

approximation model are called the tuning parameters and are estimated by minimizing the

sum of squares of the errors (Box and Draper, 1987):

2. Design of experiments

An important aspect of RSM is the design of experiments (Box and Draper, 1987), usually

abbreviated as DoE. These strategies were originally developed for the model fitting of physical

experiments, but can also be applied to numerical experiments. The objective of DoE is the

selection of the points where the response should be evaluated.

Most of the criteria for optimal design of experiments are associated with the mathematical

model of the process. Generally, these mathematical models are polynomials with an unknown

structure, so the corresponding experiments are designed only for every particular problem. The

choice of the design of experiments can have a large influence on the accuracy of the

approximation and the cost of constructing the response surface.

In a traditional DoE, screening experiments are performed in the early stages of the process,

when it is likely that many of the design variables initially considered have little or no effect on

the response. The purpose is to identify the design variables that have large effects for further

investigation. Genetic Programming has shown good screening properties (Gilbert et al., 1998),

as will be demonstrated which suggests that both the selection of the relevant design variables

and the identification of the model can be carried out at the same time.

A detailed description of the design of experiments theory can be found in Box and Draper

(1987), Myers and Montgomery (1995) and Montgomery (1997), among many others. Schoofs

(1987) has reviewed the application of experimental design to structural optimization, Unal et

al. (1996) discussed the use of several designs for response surface methodology and

multidisciplinary design optimization and Simpson et al. (1997) presented a complete review

of the use of statistics in design. A particular combination of runs defines an experimental

design. The possible settings of each independent variable in the Ndimensional space are called

levels. A comparison of different methodologies is given in the next section.

2.1. Full factorial design

To construct an approximation model that can capture interactions between N design variables,

a full factorial approach (Montgomery, 1997) may be necessary to Response surface

methodology investigate all possible combinations. A factorial experiment is an experimental

strategy in which design variables are varied together, instead of one at a time.

The lower and upper bounds of each of N design variables in the optimization problem needs

to be defined. The allowable range is then discretized at different levels. If each of the variables

is defined at only the lower and upper bounds (two levels), the experimental design is called

2N full factorial. Similarly, if the midpoints are included, the design is called 3N full factorial

and shown in Figure 3.2.



2.2. Central composite design

A second-order model can be constructed efficiently with central composite designs (CCD)

(Montgomery, 1997). CCD are first-order (2N) designs augmented by additional centre and

axial points to allow estimation of the tuning parameters of a second-order model. Figure 3.4

shows a CCD for 3 design variables.

CONDUCTING PHASE

After DOE 14 experiments are carried out in electrode discharge machining. After each

experiment MRR is calculated. A quality characteristic for MRR is larger is better. The readings

which noted during each cycle are as follows along with their current, pulse on-time, voltage

and flushing pressure.

I Ton V P MRR(mm3/min) TWR(mm3/min)

25 82 40 0.15 254.0512095 158.984534

23 85 41 0.15 238.8535032 209.2633929

24 88 40 0.14 187.3360809 229.7794118

40 96 39 0.2 244.977952 128.7774725

40 90 40 0.18 249.7814412 153.1862745

37 80 35 0.18 299.7377295 157.5630252

15 60 35 0.1 254.7770701 186.0119048

16 55 30 0.1 299.7377295 218.837535

12 55 30 0.1 445.8598726 279.0178571

13 50 30 0.1 424.6284501 265.7312925

ANALYSIS PHASE

The output characteristic, MRR & TWR are analysed by software Design Expert 8 and is

formed, which shows the percentage contribution of each influencing factor on MRR & TWR.

Main effect plot for means and main effect plots for S-N ratio are plotted with the help of

software Design Expert 8. The observations made by the software for every steps are as follows:

DESIGN SUMMARY

File Version 8.0.7.1

Study Type Response Surface Runs 29

Design Type Box-Behnken Blocks No Blocks

Design Model Quadratic Build Time (ms) 1.43

EVALUATION

RESULT

4 Factors: A, B, C, D Design Matrix Evaluation for Response Surface Quadratic Model

No aliases found for Quadratic Model Aliases are calculated based on your response

selection, taking into account missing data points, if necessary. Watch for aliases among terms

you need to estimate. Degrees of Freedom for Evaluation Model 14 Residuals 14

Lack of Fit 10

Pure Error 4

Corr Total 28

A recommendation is a minimum of 3 lack of fit df and 4 df for pure error. This ensures a

valid lack of fit test. Fewer df will lead to a test that may not detect lack of fit. Standard errors

should be similar within type of coefficient. Smaller is beter. Ideal VIF is 1.0. VIFs above 10



are cause for alarm, indicating coefficients are poorly estimated due to multicollinearity. Ideal

Ri-squared is 0.0. High Ri-squared means terms are correlated with each other, possibly leading

to poor models. If the design has multi linear constraints multi collinearity will exist to a greater

degree, thus increasing the VIFs and the Ri-squareds, rendering these statistics useless. Power

is an inappropriate tool to evaluate response surface designs. Use precision-based metrics

provided in this program via fraction of design space (FDS) statistics. Click on the Graphs

button at the top of this screen, look for the [?] button on the FDS Tool. Be sure to set the Model

(on previous screen) to be an estimate of the terms you expect to be significant.

Measures Derived From the (X'X)-1 Matrix

StdL Point type

1 0.5833 IBFact

2 0.5833 IBFact

3 0.5833 IBFact

4 0.5833 IBFact

5 0.5833 IBFact

6 0.5833 IBFact

7 0.5833 IBFact

8 0.5833 IBFact

9 0.5833 IBFact

10 0.5833 IBFact

11 0.5833 IBFact

12 0.5833 IBFact

13 0.5833 IBFact

14 0.5833 IBFact

15 0.5833 IBFact

16 0.5833 IBFact

17 0.5833 IBFact

18 0.5833 IBFact

19 0.5833 IBFact

20 0.5833 IBFact

21 0.5833 IBFact

22 0.5833 IBFact

23 0.5833 IBFact

24 0.5833 IBFact

25 0.2000 Center

26 0.2000 Center

27 0.2000 Center

28 0.2000 Center

29 0.2000 Center

Average = 0.5172

Watch for leverages close to 1.0. Consider replicating these points or make sure they are

run very carefully.



GRAPHS

ANALYSIS PROCESS

After you have entered your response data in the Design layout view, choose a response by

clicking on the corresponding node under analysis. Now following are the steps displayed as

buttons across the top of the view:

• Transformation. Select response node and choose transformation

• A. fit summary (RSM/mix). Use this to evaluate models for RSM and mixture.

• B. effects (Factorials). Choose significant effects from graph or list.

• Model (RS/Mix). Choose model order and desired terms from the list.

• Analysis of Variance (ANOVA). Analyse the chosen model and view results.

• Diagnostics. Evaluate model fil and transformation choice with graphs.

• Model Graphs. Use this to interpret and evaluate your model.

Final Equation in Terms of Coded Factors:

MRR =+160.61+ (107.62 * A) – (1.31 * B) - (0.24 * C) - (6.88 * D) – (4.68 * A * B) – (1.02 * A * C)

+ (0.93 * A * D) - (0.35 * B * C) – (1.33 * B * D) + (7.71 * C * D) + (59.82 * A2) + (7.54 * B2) +

(1.03 * C2) - (8.89 * D2)

Final Equation in Terms of Actual Factors

MRR = +327.07767 + (-6.85447 * current) + (-1.48760* pulse on time) + (-6.11488 * voltage) + (-

17.17298 * pressure) + (-0.014534 * current * pulse on time) + (-0.013208* current * voltage) +

(+1.33361* current * pressure) + (-2.77372E-003* pulse on time * voltage) + (-1.15422* pulse on

time * pressure) +

(+28.04636* voltage * pressure) + (+0.30522 * current2) + (+0.01424* pulse on time2) +

(+0.033942 * voltage2) + (-3554.77225 * pressure2)

The Diagnostics Case Statistics Report has been moved to the Diagnostics Node. In the

Diagnostics Node, Select Case Statistics from the View Menu.

Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the:

1) Normal probability plot of the student zed residuals to check for normality of residuals.

2) Student zed residuals versus predicted values to check for constant error.

3) Externally Student zed Residuals to look for outliers, i.e., influential values.

4) Box-Cox plot for power transformations.



If all the model statistics and diagnostic plots are OK, finish up with the Model Graphs icon.

DIAGNOSTICS

MODEL GRAPHS



Final Equation in Terms of Coded Factors:

TWR = +25.80 + (20.04 * A) + (0.55 * B) - (0.057 * C) - (2.92 * D)

Final Equation in Terms of Actual Factors:

TWR = - 4.03128 + (1.43112 * current) + (0.024027 * pulse on time) – (0.010277 * voltage) –

(58.45367 * pressure)

The Diagnostics Case Statistics Report has been moved to the Diagnostics Node.

In the Diagnostics Node, Select Case Statistics from the View Menu.

Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the:

1) Normal probability plot of the studentized residuals to check for normality of residuals.

2) Studentized residuals versus predicted values to check for constant error.

3) Externally Studentized Residuals to look for outliers, i.e., influential values.

4) Box-Cox plot for power transformations.

If all the model statistics and diagnostic plots are OK, finish up with the Model Graphs icon.

DIAGNOSTICS



MODEL GRAPHS

OPTIMIZATION

• Numerical optimization – set goals for each response then click on solutions to generate optimal

conditions.

• Graphical optimization – set minimum or maximum limits for each response then create an

overlay graph highlighting the area of operability.

• Point prediction – enter your desired operating conditions and discover your predicted response

values with confidence intervals.

• Confirmation – compare the results predicted by the models to overcome of a confirmation

experiment.

NUMERICAL OPTIMIZATION

GRAPH for the selected reading



CONCLUSIONS

Following conclusions were made for optimum MRR and TWR during the machining of OHNS

steel on EDM.

1. 1. For OHNS steel optimum machining condition for material removal rate (MRR) During

machining on EDM were Current, Pulse on, Voltage Gap and flushing pressure with positive

polarity.

2. 2. For OHNS steel optimum machining condition for better hardness were, Current (12 amp.),

Pulse-on (73 μs), voltage gap (35.50 volt) and flushing pressure (0.20 kg/cm2).

3. 3. MRR increases with increase of current

4. 4. MRR first increases and then after getting a peak value it decreases with increase of Ton.

5. 5. MRR increases partially and then decreases with increase of Toff.

6. 6. Optimum value of MRR is calculated as1.6348mm3/min.

REFERENCES

[1] Dhar, S Purohit, R., Saini, n., Sharma, a. and Kumar, G.H., 2007. Mathematical modeling

of electric discharge machining of cast Al-4Cu-6Si alloy-10 wt. % SICP composites.

Journal of Materials Processing Technology, 193(1-3), 24-29.

[2] Karthikeyan R, Lakshmi Narayanan, P.R. and Naagarazan, R.S., 1999. Mathematical

modeling for electric discharge machining of aluminium-silicon carbide particulate

composites. Journal of Materials Processing Technology, 87(1-3), 59-63.

[3] El-Taweel, T.A., 2009. Multi-response optimization of EDM with Al-Cu-Si-tic P/M

composite electrode. International Journal of Advanced Manufacturing Technology, 44(1-

2), 100-113.

[4] Mohan, B., Rajadurai, A. and Satyanarayana, K.G., 2002. Effect of sic and rotation of

electrode on electric discharge machining of Al-sic composite. Journal of Materials

Processing Technology, 124(3), 297-304.

[5] Lin, y, Cheng, C. Su, B.-. and Hwang, L, 2006. Machining characteristics and optimization

of machining parameters of SKH 57 high-speed steel using electrical-discharge machining

based on Taguchi method. Materials and Manufacturing Processes, 21(8), 922-929.

[6] J. Simao, H.G. Lee, D.K. Aspinwall, R.C. Dewes, and E.M. Aspinwall 2003.Workpiece

surface modification using electrical discharge machining, 43 (2003) 121– 128.

[7] Dr. S. Sreenivasulu, M. Venkatesulu, T. Vijaya Kumar Comparisons of Machining

Parameters in Electro Discharge Machining of Aluminum 6082 and Hybrid Nano Metal

Matrix Composite. International Journal of Mechanical Engineering and Technology, 8(5),

2017, pp. 784–790.

[8] Chandra Shekar, N B D Pattar and Y Vijaya Kumar, Design and Study of the Effect of

Multiple Machining Parameters in Turning of AL6063T6 Using Taguchi Method.

International Journal of Design and Manufacturing Technology 7(3), 2016, pp. 12–18.

[9] D. Jeya Prakash, P. Vijaya Kumar, P. Renuka Devi, M. Ganesan and N. Baskar,

Optimization of Electrical Discharge Machining Parameters For Machining of Titanium

Grade 2 Using Design of Experiments Approach, International Journal of Mechanical

Engineering and Technology, 8(4), 2017, pp. 413-423

[10] Singh, P.N., Raghukandan, K., Rathinasabapathi, M. and Pai, B.C., 2004.Electric discharge

machining of Al-10%sicp as-cast metal matrix composites. Journal of Materials Processing

Technology, 155-156(1-3), 1653-1657.

Documents

OPTIMIZATION OF MACHINING PARAMETERS OF OHNS STEEL …€¦ · To achieve good machining stability, the following ranges of machining current recommended for different positions of