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International journal of advanced production and industrial engineering

IJAPIE-2018-01-126, Vol 3 (1), 21-26

IJAPIE Connecting

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(1Northern India Engineering College, Shastri Park, Delhi – 110053

2Maharaja Agrasen Institute of Technology, Delhi, -110086, India)

[email protected],

| IJAPIE | ISSN: 2455–8419 | www.ijapie.org | Vol. 3 | Issue. 1 | 2018 | 21 |

Optimization of Temperature variations on Steel Grade EN-18 using Pin-on-disc

Method Srikant Rana

1, Sumit Kumar

1, Ramakant Rana

2

Abstract : In this era, technology has the main role and have tremendous number of industrial machines which are also used in

daily life. Steel is the basic industrial material used in all sectors. From all the Steel, Tool Steel have been extensively used and has

many applications in today’s technological industries. Tool Steel Grade EN-18 is used for the experimentation in this research. It

has noteworthy range of physical properties which can be imparted to various field. Experiments were conducted for analysing the

temperature variations on Steel Grade EN-18 using a pin-on-disc wear test rig as per ASTM specification G99. The temperature

rises at the contact area when there is relative motion between two mating surfaces. This motion also results in the failure of the

components. Experiments have been carried out to study and analyse the temperature variations of various material with respect to

tool steel, while the operational parameters were normal load, sliding velocity of pin w.r.t. rotating disk at room temperature and

different materials. Based on the experiments the dependency of temperature variations is found on applied load, sliding speed and

material.

Keywords: Friction, Wear, Speed, Pin-On-Disc, Taguchi, Optimization.

I. INTRODUCTION Mild steel is the most common form of steel and its price is

relatively low. The properties provided by this material are

acceptable for many applications. Mild steel has a relatively

low tensile strength, but it is cheap and malleable; surface

hardness can be increased through carburizing. It is often

used when large quantities of steel are needed [1-4].

In 1798, Rumford’s boring cannon experiment for detection

of frictional heat generation proved that mechanical work can

be converted into heat.

This experiment laid the foundation of experimental analysis

of mechanical equivalent of heat. However, no attempt was

made to measure the mechanical equivalent of heat

numerically. Over more than half a century later the

mechanical equivalence of heat was successfully established

by Joule [2] by envisioning a calorimetric method. Taylor

and Quinney’s [3] study of the generation of heat

accompanied by plastic strain strengthened the concept of

mechanical equivalence. They measured the temperature of

various specimens under tensile test during the formation of

creep and they found that the major amount of plastic energy,

used for deforming the specimen, gets converted into heat. In

metal cutting application, Taylor [4] studied the relation

between cutting velocity and tool life, and developed a tool

life equation. He also invented a tough, wear resistant, heat

resistant, and hard tool material (HSS) which is still in use.

Since then, scientists and researchers have been developing

various methods and techniques to estimate the temperature

variations at various points of tool, chip, and work piece.

II. EXPERIMENTAL SETUP Set up used in the study of wear test is capable of creating

reproducible abrasive wear situation accessing the abrasive

wear resistance of the prepared samples. It consists of a pin

on disc, loading panel and controller. The entire test was

carried out using a “pin on disk” machine with normal

condition. The condition has 40-50% relative humidity and at

room temperature. A high precision temperature measuring

device (accuracy ±0.01OC.) has been used to measure the

temperature of pin contact [Fluke Thermal image Camera,

Ti300]. In the due course of the experiment.

Fig. 1: Test-Rig used to Monitor temperature variations for

the Wear Tests

Srikant Rana et al.,

International Journal of Advanced Production and Industrial Engineering


III. DESIGN OF EXPERIMENT Table 1: Selected Parameters and their Level

Symbol

Process

Parameter

s

Unit

Leve

l 1 Leve

l 2 Leve

l 3

A Materials - Brass

Mild

Steel

Al

B Load Kg 2 5 8 C Speed Rpm

1000 1300

1600

IV. LAYOUT OF EXPERIMENT

Fig. 2: Tool Steel (Raw Material) used in Wear and Friction

testing.

The sliding distance was kept at 1.0 km for all tests various

levels of the load speed and pin material were listed in table.

Table 2: Composition of Tool Steel

MATERIAL COMPOSITION (%)

C

0.85-1.00

Mn

1.00-1.40

Si

0.50

Cr

0.40-0.60

Ni

0.30

W

0.40-0.60

V

0.30

Cu

0.25

P

0.03

S

0.03

In every experimentation, at least 3-5 values of temperature

were taken by clicking the photographs using Fluke Thermal

Image Camera, Ti300 as shown in Fig. 1. [12- 15]. The

maximum of each set was selected for the temperature

variations. These values were noted down in L9 OA for

Taguchi optimization techniques. Figure 3 (a) & (b) to Figure

11 (a) & (b) shows the temperature variation in each

experiment runs.

Table 3: L9 Orthogonal Array Input Parameters

S.NO.

Materials

Load

Speed

1

Brass

2

1000

2

Brass

5

1300

3

Brass

8

1600

4

Mild Steel

2

1300

5

Mild Steel

5

1600

6

Mild Steel

8

1000

7

Aluminium

2

1600

8

Aluminium

5

1000

9

Aluminium

8

1300

V. RESULT It is clear from the Fig. 4 that Brass has less thermal variation

as compared to Aluminium and Mild Steel [6, 7]. The wear

rate increases with the increase in Disc Speed but the

variation in temperature is more sudden. The major amount

of temperature occurs due to sticking of the material due to

the heat generated at the frictional contact [5, 8-11].

1) Temperature was lowest when: - Brass was used.

- Load is lowest i.e. 2kg

- Speed is highest i.e. at 1600 R.P.M.

Table 4: Mean values of Temperature Variations

S.NO. Materials Load Speed Temp.

(K)

1 Brass 2 1000 297.0 2 Brass 5 1300 312.3 3 Brass 8 1600 361.3 4 Mild Steel 2 1300 327.0 5 Mild Steel 5 1600 363.7 6 Mild Steel 8 1000 456.4 7 Aluminium 2 1600 430.3 8 Aluminium 5 1000 454.1 9 Aluminium 8 1300 599.5

Table 5: Response Table for Means for Temperature

Level Material Load Speed

1 323.5 351.4 402.5

2 382.4 376.7 412.9

3 494.6 472.4 385.1

Delta 171.1 121.0 27.8

Rank 1 2 3










Fig. 12: Main of Means effect plots for Temperature

Variation

Fig. 13: Main of S/N Ratio effect plots for Temperature

Variation

Fig. 14: Contour Plot of “Temperature vs Load& Speed”

Fig. 15: Probabilistic Curve of Temperature Variations

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