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

*Corresponding author ( Kondapalli Siva Prasad). Tel/Fax: +91-9849212391. E-mail address: [email protected]. 2012. American Transactions on Engineering & Applied Sciences. Volume 1 No.1 ISSN 2229-1652 eISSN 2229-1660. Online Available at http://TUENGR.COM/ATEAS/V01/57-74.pdf

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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 Kondapalli Siva Prasada*, Chalamalasetti Srinivasa Raob, and

Damera Nageswara Raoc

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 A R T I C L E I N F O

A B S T RA C T

Article history: Received 23 August 2011 Received in revised form 01 December 2011 Accepted 25 December 2011 Available online 26 December 2011 Keywords: Pulsed current micro plasma arc welding, SS304L, grain size, hardness, Design of Experiments, ANOVA.

SS 304L, an austenitic Chromium-Nickel stainless steel offering the optimum combination of corrosion resistance, strength and ductility, is favorable for many mechanical components. The low carbon content reduces susceptibility to carbide precipitation during welding. In case of single pass welding of thinner section of this alloy, pulsed current micro plasma arc welding was found beneficial due to its advantages over the conventional continuous current process. The paper focuses on developing mathematical models to predict grain size and hardness of pulsed current micro plasma arc welded SS304L joints. Four factors, five level, central composite rotatable design matrix is used to optimize the number of experiments. The mathematical models have been developed by 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.

2012 American Transactions on Engineering & Applied Sciences

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

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


59

(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


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.


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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 Levels

Uses Points Outside ±1

Accuracy of Estimates

Circumscribed (CCC)

Yes 5 Yes Good over entire design space

Inscribed (CCI)

Yes 5 No Good over central subset of design space

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


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


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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


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 (Amps)

Back current(Amps)

Pulse (No/sec)

Pulse width (%)

Grain Size (Micons)

Hardness (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.


65

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


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


67

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)


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


69

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.

Predicted

Act

ual

32302826242220

32

30

28

26

24

22

20

Scatterplot of Grain Size

Predicted

Act

ual

199.5198.0196.5195.0193.5192.0190.5189.0

202

200

198

196

194

192

190

188

Scatterplot of Hardness

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

8.07.57.06.56.0

30.0

27.5

25.0

22.5

20.05.04.54.03.53.0

6050403020

30.0

27.5

25.0

22.5

20.07060504030

Peak Current Back Current

Pulse Pulse Width

Main Effects Plot for Grain Size

8.07.57.06.56.0

196

194

192

190

188

5.04.54.03.53.0

6050403020

196

194

192

190

188

7060504030

Peak Current Back Current

Pulse Pulse Width

Main Effects Plot for Hardness

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


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.

Peak Current

Bac

k C

urre

nt

30

28

28

26

26

24

22

8.07.57.06.56.0

5.0

4.5

4.0

3.5

3.0

Hold ValuesPulse 40Pulse Width 50

Contour Plot of Grain Size vs Back Current, Peak Current

Pulse

Pul

se W

idth

28.5

27.0

25.5

25.5

24.0

6050403020

70

60

50

40

30

Hold ValuesPeak Current 7Back Current 4

Contour Plot of Grain Size vs Pulse Width, 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.


71

Peak Current

Bac

k C

urre

nt

196 194

194

192 190

8.07.57.06.56.0

5.0

4.5

4.0

3.5

3.0

Hold ValuesPulse 40Pulse Width 50

Contour Plot of Hardness vs Back Current, Peak Current

Pulse

Pul

se W

idth

196

194

192

6050403020

70

60

50

40

30

Hold ValuesPeak Current 7Back Current 4

Contour Plot of Hardness vs Pulse Width, 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


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.

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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.

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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.

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