A Study of Process Parameters of wear Characteristics of AISID2 Steel

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 01 | Jan -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1491
A Study of Process Parameters of Wear
Characteristics of AISID2 Steel
Harvinder Singh1
, Ramandeep Singh2
, Anish Goyal3
, Rupinder Singh4
1,2,3,4 Department of Mechanical Engineering, Chandigarh Group of Colleges, Landran Mohali
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Abstract: In sheet metal forming, the wear of deforming
dies continues to be a great concern to the automotive
industry as a result of increasing die maintenance cost
and scrap rate. The demand to reduce the use of
lubricants and increase tool life in sheet metal stamping
has resulted in increased research on the sliding contact
between the tool and the sheet material. Hence it has been
recognized that the deforming conditions, such as -
normal load, sliding speed, sliding time etc. affect the
performance of the operation to a greater extent. These
deforming parameters are required to be carefully
selected to optimize the economics and quality of
operations. This can be achieved by detailed
investigation and mathematical modeling of performance
as a function of sliding conditions using design of
experiments (DOE).The objective of the present work is to
assess the effect of the sliding parameters on the wear
of AISI D2 steel. It is used as the stamping die material in
many cold roll forming and other press working
industries. It is also known as high carbon & high
chromium steel. The wear experiments were performed
on pins of D2 steel and disks of Aluminium alloy 6061.
Design of experiment based on 2 level full factorial
design with three independent factors (normal load,
sliding speed, sliding time) has been used to develop
relationships for predicting weight loss of pins caused
by rubbing action. The weight
loss of pins has been measured within 10-4
g
precision.The ‘design expert 8.0.4.1’ software has been
used for the analysis. A prediction model has been
developed which indicates that interaction is present
between sliding parameters. Model adequacy tests were
conducted using ANOVA and the effects of various
parameters were investigated and presented in the form
of contour plots and 3D surface graphs. Numerical
optimization has been carried out considering all the
input parameters within range so as to minimize the
weight loss (wear volume). The findings of this study
would be beneficial to manufacturing industry making use
of AISI D2 steel for deforming dies.
INTRODUCTION
Among the property requirements of hot working dies, the
following can be considered to be the most important: hot
strength, thermal stability, ability to resist abrasion by the
work piece scales formed at the high temperatures of
working. Chromium containing steels hardened and
tempered largely fulfil these requirements and hence are
the first choice for the dies in the pressure die casting
industries. The sliding parameters such as normal load,
sliding speed, sliding distance etc., play a vital role in
controlling the wear of the die material.
The wear is the progressive loss or removal of
material from a surface. It has important technological and
economical significance because it changes the shape of
the tool and die interfaces and hence that of the work-
piece. Thus it affects the process, size & quality of the parts
produced.
1. SIGNIFICANCE OF TOPIC
General Engineering materials have limitations in
achieving optimum levels of strength, stiffness, density,
toughness and wear resistance. To overcome these
shortcomings, discontinuously reinforced aluminium metal
matrix composites are gaining importance due to their
high specific strength, high stiffness, low density and
good wear resistance and they have the potential to
replace their monolithic counterparts primarily in
automotive, aerospace and energy applications. The
aluminium 6061 alloy has the highest strength and
ductility of the aluminium alloys with excellent
machinability and good bearing and wear properties.
In pressure die casting industry, the wear of dies
continues to be a great concern to the automotive industry
as a result of increasing die maintenance cost and scrap
rate. The demand to reduce the use of lubricants and
increase life of dies in pressure die casting has resulted
in increased research on the sliding contact between the die
and aluminum alloys. The AISI D2 die steel are used as the
die material in many pressure die casting industries due
to its properties to withstand higher wear. However under

the actual working conditions, wear of these hot die
steels is a common problem in the pressure die casting
industry.
The wear is a process where interaction between
two surfaces or bounding faces of solids within the
working environment results in dimensional loss of one
solid, with or without any actual decoupling and loss of
material. Aspects of the working environment which
affect wear include loads and features such as
unidirectional sliding, reciprocating, rolling, and impact
loads, speed, temperature, but also different types of
counter- bodies such as solid, liquid or gas and type of
contact ranging between single phase or multiphase, in
which the last multiphase may combine liquid with
solid particles and gas bubbles.
Hence this chapter presents the importance of
sliding conditions between the hot die steel and the
material to be cast. Different sliding conditions such as
load, sliding speed, sliding time and sliding distance
play a vital role during the casting process. These
parameters have a major effect on wear of the die
material. This in turn affects the quality of
production, cost of production and production rate.
Therefore a judicious selection assumes significance.
2. PREPARATIONS OF SPECIMENS The
wear experiments were performed on pins, made of AISI
D2 steel and disk, made of aluminium 6061 alloy. All the
pins used in experimentation were 8 mm in diameter and
30 mm in length. All the disks used in experimentation
were 165 mm in diameter and 6 mm in thickness.
Chemical composition of AISI D2 steel and mild steel
was obtained by spectral analysis. The preparation of the
specimens was done at Gaurav engineering works,
Chandigarh.
3. DESIGN OF EXPERIMENTS
The parameters and their levels are shown in Table
3.3. Complete design layout for experiments and
experimental results are summarized in Table 3.4. This
demonstrates a total of 14 runs required for complete
experimentation. Fourteen experiments constitute 23
factorial point and six centre point.
Table 3.1 Parameters and their levels according to 2 level full factorial design
Factors Symbol Type Levels
Load (N) A Numeric 40 50 60
Speed (m/sec.) B Numeric 1 1.75 2.5
Time (min.) C Numeric 4 8 12
Table 3.2 Complete design layout and experimental results
Std. Run
Independentparameters Dependent parameters
A:Load (N) B:Speed (m/sec) C:Time (min) Weight loss (gm)
5 1 40 1 12 0.1213
7 2 40 2.5 12 0.0515
13 3 50 1.75 8 0.0653
10 4 50 1.75 8 0.0682
8 5 60 2.5 12 0.0686
14 6 50 1.75 8 0.0692
9 7 50 1.75 8 0.0652
4 8 60 2.5 4 0.0661
3 9 40 2.5 4 0.0256
11 10 50 1.75 8 0.0723
6 11 60 1 12 0.1241

2 12 60 1 4 0.0852
1 13 40 1 4 0.058
12 14 50 1.75 8 0.0672
4. WEIGHT LOSS MEASUREMENT
Wear is the progressive loss of material due to relative
motion between a pin tested and disk. The pins tested
were weighted before and after the test to within 10-4
g
to calculate the weight loss. The
volumetric loss of pin material can be determined via
change of the pin masses during the test.
WEARVOLUMELOSSINmm3
=
𝑊݁𝑖𝑔ℎssll
10∗ytilned
5. ANOVA analysis and development of prediction model
The ANOVA test for response surface model for weight loss (wear volume) is summarized in Table 5.1. This
analysis was carried out for a significance level of α = 0.05, i.e. for a confidence level of 95%.
Table 5.1 Resulting ANOVA table (partial sum of squares) for quadratic model (response:Weight loss)
Source Sum of
squares
degree of
freedom
Mean Square F Value p-value
Prob> F
Model 0.00806 6 0.001343 225.1131 0.0001
A-LOAD 0.000959 1 0.000959 160.7407 0.0001
B-SPEED 0.003907 1 0.003907 654.7599 0.0001
C-TIME 0.002132 1 0.002132 357.2761 0.0001
AB 9.52E-05 1 9.52E-05 15.95643 0.0072
AC 0.000286 1 0.000286 47.86008 0.0005
BC 0.000681 1 0.000681 114.0855 0.0001
Curvature 0.000175 1 0.000175 29.37196 0.0016
Residual 3.58E-05 6 5.97E-06
Lack of Fit 1.25E-07 1 1.25E-07 0.017517 0.8999
Pure Error 3.57E-05 5 7.14E-06
Cor Total 0.008271 13
Std. Dev. 0.002443 R-Squared 0.995577
Mean 0.071986 Adj R-Squared 0.991155
C.V. % 3.393516 Pred R-Squared 0.992821
PRESS 5.94E-05 Adeq Precision 53.47612
Table shows that the value of “Prob. > F” for
model is 0.0001 which is less than 0.05, that indicates
the model is significant, which is desirable as it indicates
that the terms in the model have a significant effect on
the response. In the same manner, the value of “Prob. >
F” for main effect of load, speed, time and two-level
interaction of load and speed, load and time, speed and
time are less than 0.05 so these terms are significant model
terms.
The value of “Prob. > F” for lack-of-fit is
0.8999 which is greater than 0.05 and it indicates the
insignificant lack of fit. If the model does not fit the data
well, this will be significant. The insignificant lack of fit
is desirable.
The R2
value (the measure of proportion of total
variability explained in the model) is equal to 0.995 or
close to 1, which is desirable. The adjusted R2
value is
equal to 0.99; it is particularly useful when comparing
models with different number of

terms. The result shows that the adjusted R2
value is very
close to the ordinary R2
value. Adequate precision value
is equal to 53.47; a ratio greater than
4 is desirable which indicates adequate model
discrimination. Adequate precision value compares the
range of the predicted values at the design points to the
average prediction error.
The regression model for weight loss in
terms of coded factors is shown as follows:
(5.2)
𝑤݁𝑖𝑔ℎ ݁݁݁݁‫ݐ‬ = 0.075 + 0.011 ∗ ݁ − 0.022 ∗ ݁ + 0.016 ∗݁ + 0.0034 ∗ ݁ ∗ ݁ − 0.006 ∗ ݁∗݁−
0.01∗݁∗݁(5.1)
While, the following equation is the empirical model
in terms of actual factors
𝑊݁𝑎 ݁݁݁݁݁ = −0.0234+0.0015𝐿݁𝑎݁ − 0.027
∗ 𝑆 ݁݁݁݁ + 0.016 ∗ ‫𝑖ݐ‬ ݁݁
+ 0.00046 ∗ ݁݁ 𝑎݁ ∗ ݁݁ 𝑖݁𝑖݁𝑔 ݁݁݁݁݁
−0.00015∗ 𝐿݁𝑎݁∗‫݁݁𝑖ݐ‬
+ 0.0037 ∗ 𝑆 ݁݁݁݁ ∗ ‫𝑖ݐ‬ ݁݁
Fig.4.11 Interaction plot between load and speed at time 8 min
Interaction plot between load and time at sliding speed 1.75

Fig.4.12 3D plot between load and speed at time 8 min
Fig.4.13 3D plot between load and sliding time at sliding speed1.75 m/s
Fig.4.14 3D plot between time and speed at load 50 N

6. OPTIMIZATION OF SLIDING
CONDITIONS
In the present study, the aim is to obtain the optimal values of
sliding parameters in order to minimize the value of weight
loss of AISI D2 steel pins. The Table 6.1 Constraints for
optimization of sliding conditions
constraints used during the optimization process are
summarized in Table 6.1.The optimal solutions are
reported in Table 6.2
Condition Units Goal Lower limit Upper limit
Ssliding speed m/s Is in range 1 2.5
Load N Is in range 40 60
Time min Is in range 4 12
Weight loss gm Minimize 0.0256 0.1241
Table 6.2 Optimization results
Solution
No.
Sliding speed
(m/s)
Load (N) Time
(min)
Wt. Loss
(gm)
Desirability Remarks
1 2.5 40 4.00 0.0255 1 Selected
7. CONFIRMATION EXPERIMENTS
Statistically developed mathematical model for
weight loss, given by equations 4.1 and 4.2 , has been
already validated through F-tests and lack-of-fit test. The
fitted model seems to be significant and the lack of fit
insignificant. The coefficient of variation (R2
) for model is
0.99, which indicates the model ability for making
predictions. This conclusion must be further supported
through the confirmation runs. A set of three confirmation
runs have been performed to verify the prediction ability of
the developed weight Table 4.4 Plan of confirmation
experiments and results
loss model. The values of weight loss obtained by
confirmation run and those predicted through the model
are compared in Table 4.4. The percentage error
between the experimental and the predicted values of
weight loss is found to be less than 5% per cent. In other
words, all the experimental values are within the 95
percent prediction interval, which clearly demonstrates
the accuracy of the models developed in this study.
Test No. Sliding Conditions Wt. Loss (gm) Error (%)
Speed (m/s) Load(N) Time (min) Predicted Experimental
1 2.5 40 4.00 0.0255 0.0247 3.2
2 2.5 45 4.00 0.0354 0.0371 4.58
3 2.34 40 4.00 0.0290 0.0300 0.33
CONCLUSION
The important conclusions drawn from the present work
are summarized as follows:
1.) The relationship between weight loss (wear volume)
and applied load, sliding speed, sliding time has been
developed. The predicted results are in good agreement
with the measured ones. These relationships are
applicable within the ranges of tested parameters.
2.) All the three independent parameters (load, speed, time)
seem to be the influential sliding parameters.
3.) The mathematical models developed clearly show that
the speed seems to be the most significant factor. 4.) The
weight loss (wear volume) increases with increasing
sliding time and load but decreases with increasing
sliding speed.
4.) The results of ANOVA and the confirmation runs
verify that the developed mathematical model for weight
loss (wear volume) show excellent fit and provide
predicted values of weight loss that are close to the
experimental values, with a 95 percent confidence level.
6.) The optimum result of weight loss has been observed
to be 0.0255gm, corresponding to normal load = 40N,
sliding speed = 2.5 m/s, sliding time = 4 min.
7. FUTURE SCOPE
 In this study mathematical modeling and
optimization has been attempted only for one
response variable i.e. weight loss.

 The work can be extended to consider more
response variables like force of friction, surface
roughness etc.
 Also more parameters such as temperature can
be introduced to have a better insight into the
process.
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A Study of Process Parameters of wear Characteristics of AISID2 Steel

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A Study of Process Parameters of wear Characteristics of AISID2 Steel