Establishing empirical relations to predict grain size and hardness of pulsed current micro plasma arc welded SS 304L sheets

2012 American Transactions on Engineering & Applied Sciences

American Transactions on
Engineering and Applied Sciences

http://TuEngr.com/ATEAS, http://Get.to/Research

Establishing Empirical Relations to Predict Grain Size
and Hardness of Pulsed Current Micro Plasma Arc
Welded SS 304L Sheets
a* b
Kondapalli Siva Prasad , Chalamalasetti Srinivasa Rao , and
c
Damera Nageswara Rao
a
Department of Mechanical Engineering, Anil Neerukonda Institute of Technology and Sciences,
Visakhapatnam, INDIA
b
Department of Mechanical Engineering, Andhra University,Visakhapatnam, INDIA
c
Centurion University of Technology & Management, Odisha, INDIA

ARTICLEINFO A B S T RA C T
Article history: SS 304L, an austenitic Chromium-Nickel stainless steel
Received 23 August 2011
Received in revised form offering the optimum combination of corrosion resistance, strength
01 December 2011 and ductility, is favorable for many mechanical components. The
Accepted 25 December 2011 low carbon content reduces susceptibility to carbide precipitation
Available online
26 December 2011 during welding. In case of single pass welding of thinner section of
Keywords: this alloy, pulsed current micro plasma arc welding was found
Pulsed current micro plasma beneficial due to its advantages over the conventional continuous
arc welding, current process. The paper focuses on developing mathematical
SS304L, models to predict grain size and hardness of pulsed current micro
grain size, plasma arc welded SS304L joints. Four factors, five level, central
hardness, composite rotatable design matrix is used to optimize the number of
Design of Experiments, experiments. The mathematical models have been developed by
ANOVA. response surface method. The adequacy of the models is checked by
ANOVA technique. By using the developed mathematical models,
grain size and hardness of the joints can be predicted with 99%
confidence level. Contour plots are drawn to study the interaction
effect of pulsed current micro plasma arc welding parameters on
fusion zone grain size and hardness of SS304L steel.
2012 American Transactions on Engineering and Applied
Sciences.
*Corresponding author ( Kondapalli Siva Prasad). Tel/Fax: +91-9849212391. E-mail
address: kspanits@gmail.com. 2012. American Transactions on Engineering &
Applied Sciences. Volume 1 No.1 ISSN 2229-1652 eISSN 2229-1660. Online Available
57
at http://TUENGR.COM/ATEAS/V01/57-74.pdf

1. Introduction
In welding processes, the input parameters have greater influence on the mechanical properties
of the weld joints. By varying the input process parameters, the output could be changed with
significant variation in their mechanical properties. Accordingly, welding is usually selected to get
a welded joint with excellent mechanical properties. To determine these welding combinations that
would lead to excellent mechanical properties, different methods and approaches have been used.
Various optimization methods can be applied to define the desired output variables through
developing mathematical models to specify the relationship between the input parameters and
output variables. One of the most widely used methods to solve this problem is response surface
methodology (RSM), in which the unknown mechanism with an appropriate empirical model is
approximated, being the function of representing a response surface method

Welding thin sheets is quite different from welding thick sections, because during welding of
thin sheets many problems are experienced. These problems are usually linked with heat input.
Fusion welding generally involves joining of metals by application of heat for melting of metals to
be joined. Almost all the conventional arc welding processes offer high heat input, which in turn
leads to various problems such as burn through or melt trough, distortion, porosity, buckling
warping and twisting of welded sheets, grain coarsening , evaporation of useful elements present
in coating of the sheets, joint gap variation during welding, fume generation form coated sheets etc.
Use of proper welding process, procedure and technique is one tool to address this issue
(Balasubramanian et.al, 2010). Micro Plasma arc Welding (MPAW) is a good process for joining
thin sheet, but it suffers high equipment cost compared to GTAW. However it is more economical
when compare with Laser Beam welding and Electron Beam Welding processes.

Pulsed current MPAW involves cycling the welding current at selected regular frequency. The
maximum current is selected to give adequate penetration and bead contour, while the minimum is
set at a level sufficient to maintain a stable arc (Balasubramanian et.al, 2006 and Madusudhana
et.al, 1997). This permits arc energy to be used effectively to fuse a spot of controlled dimensions
in a short time producing the weld as a series of overlapping nuggets. By contrast, in constant
current welding, the heat required to melt the base material is supplied only during the peak current
pulses allowing the heat to dissipate into the base material leading to narrower heat affected zone

58 Kondapalli Siva Prasad, Ch.Srinivasa Rao, and D.Nageswara Rao

(HAZ). Advantages include improved bead contours, greater tolerance to heat sink variations,
lower heat input requirements, reduced residual stresses and distortion, refinement of fusion zone
microstructure and reduced with of HAZ. There are four independent parameters that influence the
process are peak current, back current, pulse and pulse width.

From the literature review (Zhang and Niu, 2000, Sheng-Chai Chi and LI-Chang Hsu, 2001,
Hsiao et.al, 2008, Siva et.al, 2008, Lakshinarayana et.al, 2008, Balasubramanian et.al, 2009,
Srimath and Muragan, 2011) it is understood that in most of the works reported the effect of
welding current, arc voltage, welding speed, wire feed rate, magnitude of ion gas flow, torch
stand-off, plasma gas flow rate on weld quality characteristics like front melting width, back
melting width, weld reinforcement, welding groove root penetration, welding groove width,
front-side undercut are considered. However much effort was not made to develop mathematical
models to predict the same especially when welding thin sheets in a flat position. Hence an
attempt is made to correlate important pulsed current MPAW process parameters to grain size and
hardness of the weld joints by developing mathematical models by using statistical tools such as
design of experiments, analysis of variance and regression analysis.

2. Literature review on Response Surface Method
Response Surface Method or commonly known as RSM is an anthology of statistical and
mathematical methods that are helpful in generating improved methods and optimizing a welding
process. RSM is more frequently used in analyzing the relationships and the influences of input
parameters on the responses. The method was introduced by G. E. P. Box and K. B. Wilson in
1951. The main idea of RSM is to use a set of designed experiments to obtain an optimal response.
Box and Wilson used first-degree polynomial model to obtain DOE through RSM and
acknowledged that the model is only an approximation and is easy to estimate and apply, even
when little information is known about the process. Response Surface Regression method is an
assortment of mathematical and statistical techniques useful for modeling and analyzing
experiments in which a response variable is influenced by several independent variables. It
explores the relationships between several independent variables and one or more response

59

variables; the response variable can be graphically viewed as a function of the process variables (or
independent variables) and this graphical perspective of the problem has led to the term Response
Surface Method (Myers and Montgomery, 2002). RSM is applied to fit the acquired model to the
desired model when random factors are present and it may fit linear or quadratic models to describe
the response in terms of the independent variables and then search for the optimal settings for the
independent variables by performing an optimization step. According to (Clurkin and Rosen,
2002), the RSM was constructed to check the model part accuracy which uses the build time as
function of the process variables and other parameters. According to (Asiabanpour et.al, 2006)
developed the regression model that describes the relationship between the factors and the
composite desirability. RSM also improves the analyst’s understanding of the sensitivity between
independent and dependent variables (Bauer et.al, 1999). With RSM, the relationship between the
independent variables and the responses can be quantified (Kechagias, 2007). RSM is an
experimental strategy and have been employed by research and development personnel in the
industry, with considerable success in a wide variety of situations to obtain solutions for
complicated problems.

The following two designs are widely used for fitting a quadratic model in RSM.

2.1 Central Composite Designs
Central composite designs (CCDs), also known as Box-Wilson designs, are appropriate for
calibrating the full quadratic models described in Response Surface Models. There are three types
of CCDs, namely, circumscribed, inscribed and faced. The geometry of CCD’s is shown in the
Figure 1.

Figure 1: Circumscribed, inscribed and faced designs.


Each design consists of a factorial design (the corners of a cube) together with center and star
points that allow estimation of second-order effects. For a full quadratic model with n factors,
CCDs have enough design points to estimate the (n+2)(n+1)/2 coefficients in a full quadratic
model with n factors.

The type of CCD used (the position of the factorial and star points) is determined by the
number of factors and by the desired properties of the design. Table 1 summarizes some
important properties. A design is rotatable if the prediction variance depends only on the distance
of the design point from the center of the design.

Table 1: Comparison of CCD’s.
Design Rotatable Factor Uses Accuracy of Estimates
Levels Points
Outside ±1
Circumscribed Yes 5 Yes Good over entire design space
(CCC)
Inscribed Yes 5 No Good over central subset of design space
(CCI)
Faced (CCF) No 3 No Fair over entire design space; poor for
pure quadratic coefficients

2.2 BoxBehnken Designs
Box-Behnken designs (Figure 2) are used to calibrate full quadratic models. These are
rotatable and for a small number of factors (four or less), require fewer runs than CCDs. By
avoiding the corners of the design space, they allow experimenters to work around extreme factor
combinations. Like an inscribed CCD, however, extremes are then poorly estimated.

Figure 2: Box-Behnken design
61

3. Experimental Procedure
Austenitic stainless steel (SS304L) sheets of 100 x 150 x 0.25mm are welded autogenously
with square butt joint without edge preparation. The chemical composition of SS304L stainless
steel sheet is given in Table 2. High purity argon gas (99.99%) is used as a shielding gas and a
trailing gas right after welding to prevent absorption of oxygen and nitrogen from the atmosphere.
The welding has been carried out under the welding conditions presented in Table 3. From the
literature (Balasubramaniam et.al, 2007, Balasubramaniam et.al, 2008, Balasubramaniam et.al,
2009, Balasubramaniam et.al, 2010) it is understood that in pulsed current arc welding processes,
four important factors namely peak current, back current, pulse and pulse width are dominating
over other factors. In the present work the above four factors of pulsed current MPAW are chosen
and their values are presented in Table 4. A large number of trail experiments were carried out
using 0.25mm thick SS304L sheets to find out the feasible working limits of pulsed current MPAW
process parameters. Due to wide range of factors, it has been decided to use four factors, five
levels, rotatable central composite design matrix to perform the number of experiments for
investigation. Table 5 indicates the 31 set of coded conditions used to form the design matrix. The
first sixteen experimental conditions (rows) have been formed for main effects. The next eight
experimental conditions are called as corner points and the last seven experimental conditions are
known as center points. The method of designing such matrix is dealt elsewhere (Montgomery,
1991, Box et.al,1978). For the convenience of recording and processing the experimental data, the
upper and lower levels of the factors are coded as +2 and -2, respectively and the coded values of
any intermediate levels can be calculated by using Equation (1) (Ravindra and Parmar, 1987).

Xi = 2[2X-(Xmax + Xmin)] / (Xmax – Xmin) (1)

Where Xi is the required coded value of a parameter X. The X is any value of the parameter
from Xmin to Xmax, where Xmin is the lower limit of the parameter and Xmax is the upper limit of the
parameter.

Table 2: Chemical composition of SS304L (weight %).
C Si Mn P S Cr Ni Mo Ti N
0.021 0.35 1.27 0.030 0.001 18.10 8.02 -- -- 0.053


Table 3: Welding conditions.
Power source Secheron Micro Plasma Arc Machine
(Model: PLASMAFIX 50E)
Polarity DCEN
Mode of operation Pulse mode
Electrode 2% thoriated tungsten electrode
Electrode Diameter 1mm
Plasma gas Argon and Hydrogen
Plasma gas flow rate 6 Lpm
Shielding gas Argon
Shielding gas flow rate 0.4 Lpm
Purging gas Argon
Purging gas flow rate 0.4 Lpm
Copper Nozzle diameter 1mm
Nozzle to plate distance 1mm
Welding speed 260mm/min
Torch Position Vertical
Operation type Automatic

Table 4: Important factors and their levels.
Levels
SI No Input Factor Units -2 -1 0 +1 +2
1 Peak Current Amps 6 6.5 7 7.5 8
2 Back Current Amps 3 3.5 4 4.5 5
3 Pulse No’s/sec 20 30 40 50 60
4 Pulse width % 30 40 50 60 70

4. Recording the responses

4.1 Measurement of grain size
Three metallurgical samples are cut from each joint, with the first sample being located at
25mm behind the trailing edge of the crater at the end of the weld and mounted using Bakelite.
Sample preparation and mounting is done as per ASTM E 3-1 standard. The samples are surface
grounded using 120 grit size belt with the help of belt grinder, polished using grade 1/0 (245 mesh
size), grade 2/0( 425 mesh size) and grade 3/0 (515 mesh size) sand paper. The specimens are
further polished by using aluminum oxide initially and the by utilizing diamond paste and velvet

63

cloth in a polishing machine. The polished specimens are etched by using 10% Oxalic acid solution
to reveal the microstructure as per ASTM E407. Micrographs are taken using metallurgical
microscope (Make: Carl Zeiss, Model: Axiovert 40MAT) at 100X magnification. The micrographs
of parent metal zone and weld fusion zone are shown in Figures 3 and 4.
Table 5: Design matrix and experimental results.
SI No Peak Current Back current Pulse Pulse width Grain Size Hardness
(Amps) (Amps) (No/sec) (%) (Micons) (VHN)
1 -1 -1 -1 -1 20.812 198
2 1 -1 -1 -1 30.226 190
3 -1 1 -1 -1 21.508 199
4 1 1 -1 -1 27.536 193
5 -1 -1 1 -1 27.323 193
6 1 -1 1 -1 25.206 195
7 -1 1 1 -1 25.994 195
8 1 1 1 -1 23.491 197
9 -1 -1 -1 1 26.290 194
10 1 -1 -1 1 29.835 190
11 -1 1 -1 1 20.605 200
12 1 1 -1 1 27.764 193
13 -1 -1 1 1 30.095 190
14 1 -1 1 1 26.109 194
15 -1 1 1 1 27.385 193
16 1 1 1 1 25.013 195
17 -2 0 0 0 20.788 196
18 2 0 0 0 25.830 195
19 0 -2 0 0 31.663 188
20 0 2 0 0 27.263 193
21 0 0 -2 0 25.270 195
22 0 0 2 0 26.030 194
23 0 0 0 -2 24.626 195
24 0 0 0 2 26.626 194
25 0 0 0 0 24.845 196
26 0 0 0 0 24.845 196
27 0 0 0 0 20.145 200
28 0 0 0 0 24.845 195
29 0 0 0 0 20.045 201
30 0 0 0 0 24.845 195
31 0 0 0 0 20.445 198

Grain size of parent metal and weld joint is measured by using Scanning Electron Microscope
(Make: INCA Penta FETx3, Model:7573). Figure 5 and Figure 6 indicates the measurement of
grain size for parent metal zone and weld fusion zone. Average values of grain size are presented
in Table 5.


Figure 3: Microstructure of parent metal zone Figure 4: Microstructure of weld fusion zone.

Figure 5: Grain size of parent metal. Figure 6: Grain size of weld fusion zone.

The grain size at the weld fusion zone is smaller than parent metal zone, which indicates sound
weld joint.

4.2 Measurement of hardness
Vickers’s micro hardness testing machine (Make: METSUZAWA CO LTD, JAPAN, Model:
MMT-X7) was used to measure the hardness at the weld fusion zone by applying a load of 0.5Kg as
per ASTM E384. Average values of three samples of each test case are presented in Table 5.

5. Developing mathematical models
In most RSM problems (Cochran and Cox, 1957, Barker, 1985, Montgomery,1991, Gardiner
and Gettinby,1998), the form of the relationship between the response (Y) and the independent
variables is unknown. Thus the first step in RSM is to find a suitable approximation for the true
65

functional relationship between the response and the set of independent variables.

Usually, a low order polynomial is some region of the independent variables is employed. If
the response is well modeled by a linear function of the independent variables then the
approximating function in the first order model.

Y = bo+∑bi xi +∈ (2)

If interaction terms are added to main effects or first order model, then we have a model
capable of representing some curvature in the response function.

Y = bo+∑bi xi + ∑∑bijxixj+∈ (3)

The curvature, of course, results from the twisting of the plane induced by the interaction term
βijxixj

Table 6: Estimated Regression Coefficients for grain size.
Term Coef SE Coef T P Remarks
Constant 22.8593 0.6453 35.424 0.000 Significant
Peak Current 1.0522 0.3485 3.019 0.008 Significant
Back Current -1.0583 0.3485 -3.037 0.008 Significant
Pulse 0.3150 0.3485 0.904 0.379 Insignificant
Pulse Width 0.6250 0.3485 1.793 0.092 Insignificant
Peak Current*Peak Current 0.1020 0.3193 0.320 0.753 Insignificant
Back Current*Back Current 1.6405 0.3193 5.138 0.000 Significant
Pulse*Pulse 0.6873 0.3193 2.153 0.047 Insignificant
Pulse Width*Pulse Width 0.6813 0.3193 2.134 0.049 Insignificant
Peak Current*Back Current 0.0910 0.4268 0.213 0.834 Insignificant
Peak Current*Pulse -2.3203 0.4268 -5.436 0.000 Significant
Peak Current*Pulse Width -0.4047 0.4268 -0.948 0.357 Insignificant
Back Current*Pulse 0.1813 0.4268 0.425 0.677 Insignificant
Back Current*Pulse Width -0.4078 0.4268 -0.955 0.354 Insignificant
Pulse*Pulse Width 0.1360 0.4268 0.319 0.754 Insignificant
S = 1.707 R-Sq = 84.2% R-Sq(adj) = 70.4%

There are going to be situations where the curvature in the response function is not adequately
modeled by Equation-3. In such cases, a logical model to consider is


Y = bo+∑bi xi +∑biixi2 + ∑∑bijxixj+∈ (4)

Where bii repesent pure second order or quadratic effects. Equation 4 is a second order
response surface model.

Using MINITAB 14 statistical software package, the significant coefficients were determined
and final models are developed using significant coefficients to estimate grain size and hardness
values of weld joint. The details of estimation of regression coefficients for grain size and
hardness are presented in Tables 6 and 7.

Table 7: Estimated Regression Coefficients for hardness.
Term Coef SE Coef T P Remarks
Constant 197.286 0.6410 307.801 0.000 Significant
Peak Current -0.708 0.3462 -2.046 0.058 Insignificant
Back Current 1.292 0.3462 3.731 0.002 Significant
Pulse -0.292 0.3462 -0.843 0.412 Insignificant
Pulse Width -0.542 0.3462 -1.565 0.137 Insignificant
Peak Current*Peak Current -0.353 0.3171 -1.112 0.283 Insignificant
Back Current*Back Current -1.603 0.3171 -5.054 0.000 Significant
Pulse*Pulse -0.603 0.3171 -1.900 0.076 Insignificant
Pulse Width*Pulse Width -0.603 0.3171 -1.900 0.076 Insignificant
Peak Current*Back Current -0.188 0.4240 -0.442 0.664 Insignificant
Peak Current*Pulse 2.188 0.4240 5.160 0.000 Significant
Peak Current*Pulse Width 0.312 0.4240 0.737 0.472 Insignificant
Back Current*Pulse -0.313 0.4240 -0.737 0.472 Insignificant
Back Current*Pulse Width 0.313 0.4240 0.737 0.472 Insignificant
Pulse*Pulse Width -0.313 0.4240 -0.737 0.472 Insignificant
S = 1.696 R-Sq = 83.2% R-Sq(adj) = 68.5%

The final mathematical models are given in terms of grain size and hardness as below:

Grain Size (G)
G = 22.859+1.052X1-1.058X2+0.315X3+0.625X4+1.640X22-2.320X1X3 (5)

67

Hardness (H)
H = 197.286-0.708X1+1.292X2-0.292X3-0.542X4-1.603X22+2.188X1X3 (6)

Where X1, X2, X3 and X4 are the coded values of peak current, back current, pulse and pulse
width.

Table 8: ANOVA test results for grain size and hardness.
Grain Size
Source DF Seq SS Adj SS Adj MS F P
Regression 14 249.023 249.023 17.7873 6.10 0.000
Linear 4 65.207 65.207 16.3018 5.59 0.005
Square 4 91.443 91.443 22.8608 7.84 0.001
Interaction 6 92.372 92.372 15.3954 5.28 0.004
Residual Error 16 46.639 46.639 2.9149
Lack-of-Fit 10 9.750 9.750 0.9750 0.16 0.994
Pure Error 6 36.889 36.889 6.1481
Total 30 295.661
Hardness
Source DF Seq SS Adj SS Adj MS F P
Regression 14 228.18 228.18 16.299 5.67 0.001
Linear 4 61.17 61.17 15.292 5.32 0.006
Square 4 83.64 83.64 20.910 7.27 0.002
Interaction 6 83.38 83.38 13.896 4.83 0.005
Residual Error 16 46.01 46.01 2.876
Lack-of-Fit 10 10.58 10.58 1.058 0.18 0.991
Pure Error 6 35.43 35.43 5.905
Total 30 274.19
Table value of Fisher’s ratio is 7.87 for 99% confidence level
Where DF =Degrees of Freedom, SS=Sum of Squares, F=Fisher’s ratio

6. Checking the adequacy of the developed models
The adequacy of the developed models was tested using the analysis of variance technique
(ANOVA). As per this technique, if the calculated value of the Fratio of the developed model is less
than the standard Fratio (from F-table) value at a desired level of confidence (say 99%), then the
model is said to be adequate within the confidence limit. ANOVA test results are presented in
Table 8 for all the models. From the table it is understood that the developed mathematical models
are found to be adequate at 99% confidence level. Coefficient of determination ‘ R2 ’ is used to
find how close the predicted and experimental values lie. The value of ‘ R2 ’ for the above

developed models is found to be about 0.84, which indicates good correlation exists between the
experimental values and predicted values.

Figures 7 and 8 indicate the scatter plots for grain size and hardness of the weld joint and
reveals that the actual and predicted values are close to each other with in the specified limits.

To validate the developed models further, one has to conduct validation tests and check for
repeatability. However in the present paper confirmation test results are not implemented.

Scatterplot of Grain Size Scatterplot of Hardness
32 202

200
30

198
28
196
Actual

Actual
26
194

24
192

22 190

20 188

20 22 24 26 28 30 32 189.0 190.5 192.0 193.5 195.0 196.5 198.0 199.5
Predicted Predicted

Figure 7: Scatter plot of Grain Size Figure 8: Scatter plot of Hardness

Main Effects Plot for Grain Size Main Effects Plot for Hardness
Peak Current Back Current Peak Current Back Current
196
30.0
194
27.5
192
25.0
190
22.5

20.0 188
6.0 6.5 7.0 7.5 8.0 3.0 3.5 4.0 4.5 5.0 6.0 6.5 7.0 7.5 8.0 3.0 3.5 4.0 4.5 5.0
Pulse Pulse Width Pulse Pulse Width
196
30.0
194
27.5
192
25.0
190
22.5

20.0 188
20 30 40 50 60 30 40 50 60 70 20 30 40 50 60 30 40 50 60 70

Figure 9: Variation of grain size. Figure: 10 Variation of hardness.

69

7. Effect of process variable on output responses

7.1 Main effect
The variation of grain size and hardness of SS304L welds with pulsed current MPAW input
process parameters are presented in Figures 9 and 10.
From Figures 9 and 10 it is clearly understood that grain size and hardness are inversely
proportional, i.e. smaller the grain size, higher the hardness of the weld joint.

7.2 Interaction effects
Contour plots play a very important role in the study of the response surface. By generating
contour plots using software (MINITAB14) for response surface analysis, the optimum is located
by characterizing the shape of the surface. If the counter patterning of circular shaped counters
occurs, it tends to suggest the independence of factor effects; while elliptical contours may indicate
factor interaction. Figures 11a and 11b represent the contour plots for grain size and Figures 11a
and 11b represents the contour plots for hardness.

From the contour plots, the interaction effect between the input process parameters and output
response can be clearly analysed.
Contour Plot of Grain Size vs Back Current, Peak Current Contour Plot of Grain Size vs Pulse Width, Pulse
5.0 70
Hold Values 28.5 Hold Values
28
24 Pulse 40
25.5 Peak Current 7
Pulse Width 50 Back Current 4

26 27.0
4.5 60
Back Current

Pulse Width

4.0 50
22

3.5 40
28
26

30 25.5
24.0
3.0 30
6.0 6.5 7.0 7.5 8.0 20 30 40 50 60
Peak Current Pulse

Figure 10a: Contour plot of Grain Size Figure 10b: Contour plot of Grain Size
(Peak current, Back current) (Pulse, Pulse width)

From Figures 10a and 10b it is understood that the grain size is more sensitive to changes in
pulse and pulse width than to changes in peak current and back current. Also from Figure 10a, the
grain size is more sensitive to changes in peak current than changes in pulse and pulse width.


Contour Plot of Hardness vs Back Current, Peak Current Contour Plot of Hardness vs Pulse Width, Pulse
5.0 70
Hold Values Hold Values
194 192
Pulse 40 Peak Current 7
Pulse Width 50 Back Current 4

196
4.5 60
194
Back Current

Pulse Width
4.0 50

196 194
3.5 40

192
190

3.0 30
6.0 6.5 7.0 7.5 8.0 20 30 40 50 60
Peak Current Pulse

Figure 11a: Contour plot of Hardness Figure 11b: Contour plot of Hardness
(Peak current, Back current) (Pulse, Pulse width)

From Figures 11a and 11b it is understood that the hardness is more sensitive to changes in
pulse and pulse width than to changes in peak current and back current. Also from Figure 11a, the
hardness is more sensitive to changes in peak current than changes in pulse and pulse width.

From the contour plots of grain size and hardness, it is understood that peak current and pulse
plays a major role in deciding the grain size and hardness of the weld joint. The decrease in
hardness is the result of the increased input heat associated with the use of higher peak current. The
formation of coarse grains in the fusion zone is responsible for the lower hardness of the weld
joints. Also increase in heat input results in slow cooling rate, which also contributes to longer time
for grain coarsening. The increase in hardness is because of grain refinement at fusion zone caused
by using pulsing current.

8. Conclusions
Empirical relations are developed to predict grain size and hardness of pulsed current micro
plasma arc welded SS304L sheets using response surface method. The developed model can be
effectively used to predict grain size and hardness of pulsed current micro plasma arc welded joints
at 99% confidence level. Contour plots are drawn and analysed that grain size and hardness are
more sensitive to peak current and pulse. Peak current is most important parameter as it affects the
grain size which signifies the hardness of weld joint. The decrease in hardness is because of

71

formation of coarse grains in the fusion zone. Increase in peak current increases the heat input
which results in slow cooling rate, which also contributes to longer time for grain coarsening.
Pulsing current helps to increase the hardness by refining the grains at the fusion zone. The
mathematical models are developed considering only four factors and five levels (peak current,
back current, pulse and pulse width). However one may consider more number of factors and their
levels to improve the mathematical model.

9 Acknowledgments
The authors would like to thank Shri. R.Gopla Krishnan, Director, M/s Metallic Bellows (I)
Pvt Ltd, Chennai, India for his support to carry out experimentation work.

9 References
Asiabanpour. B, Khoshnevis. B, and Palmer. K, (2006), Development of a rapid prototyping
system using response surface methodology. Journal of Quality and Reliability
Engineering International, 22,No.8, p.919.

Bauer.W.K, Parnell. S.G and Meyers. A.D, (1999), Response Surface Methodology as a
SensitivityAnalysis Tool in Decision Analysis. Journal of Multi-Criteria decision Analysis,
8, p.162.

Balasubramaniam.M, Jayabalan.V, Balasubramaniam.V,(2007), Response surface approach to
optimize the pulsed current gas tungsten arc welding parameters of Ti-6Al-4V titanium
alloy, METALS and MATERIALS International, 13, No.4,p.335.

Balasubramaniam.M, Jayabalan.V, Balasubramaniam.V, (2008), A mathematical model to predict
impact toughness of pulsed current gas tungsten arc welded titanium alloy, Int J Adv Manuf
Technol, 35, p.852.

Balasubramaniam.M, Jayabalan.V, Balasubramaniam.V, (2008), Optimizing pulsed current
parameters t o minimize corrosion rate in gas tungsten arc welde titanium alloy, Int J Adv
Mnauf Technol, 39, p.474.

Balasubramaniam.M, Jayabalan.V, Balasubramaniam.V, (2009), Prediction and optimization of
pulsed current gas tungsten arc welding process parameters to obtain sound weld pool
geometry in titanium alloy using lexicographic method, JMEPEG,18,p.871.

Balasubramanian.M, Jayabalan.V, Balasubramanian.V,(2010) Effect of process parameters of
pulsed current tungsten inert gas welding on weld pool geometry of titanium welds, Acta
Metall.Sin.(Engl. Lett.),23, No.4,p. 312.


Balasubramanian.V, Lakshminarayanan.A.K, Varahamoorthy.R and Babu.S, (2009), Application
of Response Surface Methodolody to Prediction of Dilution in Plasma Transferred Arc
Hardfacing of Stainless Steel on Carbon Steel , Science Direct, 16, No.1,p.44.

Balasubramanian.B, Jayabalan.V, Balasubramanian.V,(2006)Optimizing the Pulsed Current Gas
Tungsten Arc Welding Parameters, J Mater Sci Technol, 22,No.6, p.821.

Barker T B, Quality by experimental design,(1985), ASQC Quality Press, Marcel Dekker.

Box G EP, Hunter W H, Hunter J S,(1978), Statistics for experiments [M], New York: John Wiley
and Sons, p.112.

Cochran W G, Cox G M, (1957), Experimental Designs, John Wiley and Sons Inc, London.

Gardiner W P, Gettinby G, (1998), Experimental design techniques in statistical practice, Horwood
press, Chichester.

Hsiao.Y.F, Tarng.Y.S, and Wang. J,(2008), Huang Optimization of Plasma Arc Welding
Parameters by Using the Taguchi Method with the Grey Relational Analysis, Journal of
Materials and Manufacturing Processes, 23,p.51.

Kechagias. J, (2007), An experiment investigation of the surface roughness of parts produced by
LOM process. Rapid Prototyping Journal, 13,No.1, p.17.

LakshinarayanaA.K, Balasubramanian.V, Varahamoorthy.R and Babu.S, (2008), Predicted the
Dilution of Plasma Transferred Arc Hardfacing of Stellite on Carbon Steel using Response
Surface Methodology, Metals and Materials International, 14, No.6,p.779.

Madusudhana Reddy G, Gokhale A A, Prasad Rao K, (1997), Weld microstructure refinement in a
1441 grade aluminium-lithium alloy, Journal of Material Science, 32, No.5, p.4117.

Montgomery D.C,(1991), Design and analysis of experiments ,3rd Edition, New York, John Wiley
and Sons,p.291.

Myers, R., and Montgomery. D, (2002), Response Surface Methodology, 2nd ed. Wiley: New
York.

Mc Clurkin. J.E and Rosen, D.W, (2002), Computer-aided build style decision support for stereo
lithography. Rapid Prototyping Journal, 4, No.1, p. 4.

Ravindra J, Parmar R S, (1987),Mathematical model to predict weld bead geometry for flux cored
arc welding , Journal of Metal Construction, p.45.

Siva.K, Muragan.N, Logesh.R,(2008),Optimization of weld bead geometry in Plasma transferred
arc hardfacing austenitic stainless steel plates using genetic algorithm, Int J Adv Manuf
Technol, Volume 41, Numbers 1-2,p.24.

73

Sheng-Chai Chi, LI-Chang Hsu , (2001),A fuzzy Radial Basis Function Neural Network for
Predicting Multiple Quality characteristics of Plasma Arc Welding,
IEEE,0-7803-7078-3,No.01,p.2807.

Srimanth.N and Murugan.N , (2011), Prediction and Optimisation of Weld Bead Geometry of
Plasma Transferred Arc Hardfaced Valve Seat Rings, European Journal of Scientific
Research, 51, No2,p.285.

Zhang.D.K and Niu.J.T ,(2000),Application of Artificial Neural Network modeling to Plasma Arc
Welding of Aluminum alloys, Journal of Advanced Metallurgical Sciences, 13, No.1,
p.194.

K.Siva Prasad is an Assistant Professor of Department of Mechanical Engineering at Anil
Neerukonda Institute of Technology and Sciences, Visakhapatnam, India. He received his
bachelor degree from Osmania University, India and master degree from JNTU, Hyderabad, India.
He is also a part time scholar at Andhra University. He is a member of various professional bodies
like ISTE, FPSI, ISHRAE etc. His area of research is micro welding processes.
Dr. Ch.Srinivasa Rao is an Associate Professor in the Mechanical Engineering Department at
Andhra University, Visakhapatnam, India. He obtained his PhD degree from Andhra University,
Visakhapatnam, India. He has published his research papers in various International Journals and
conferences proceedings. He is a member of various professional bodies like ISTE, IE etc. His
area of interest is manufacturing sciences, rapid prototyping and robotics.

Professor Dr. D.Nageswara Rao is now Vice Chancellor, Centurion University of Technology &
Management, Odisha, INDIA. He obtained his PhD degree from Indian Institute of Technology
Delhi, India. He was the coordinator for Centre for Nanotechnology at Andhra University. He has
successfully completed various projects sponsored by DST, UGC, AICTE, NRB etc. His area of
research is manufacturing sciences and nanotechnology.

Peer Review: This article has been internationally peer-reviewed and accepted for publication
according to the guidelines given at the journal’s website.


Establishing empirical relations to predict grain size and hardness of pulsed current micro plasma arc welded SS 304L sheets

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (17)

Semelhante a Establishing empirical relations to predict grain size and hardness of pulsed current micro plasma arc welded SS 304L sheets

Semelhante a Establishing empirical relations to predict grain size and hardness of pulsed current micro plasma arc welded SS 304L sheets (20)

Mais de drboon

Mais de drboon (20)

Último

Último (20)

Establishing empirical relations to predict grain size and hardness of pulsed current micro plasma arc welded SS 304L sheets