Upload
others
View
3
Download
0
Embed Size (px)
Citation preview
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 12 (2018) pp. 10298-10309
© Research India Publications. http://www.ripublication.com
10298
Development of Mathematical Model to Predict Weld Bead Geometry and
Parameters Optimization for Pulsed MIG Welding Using Statistical Design
of Experiments
Yerriswamy Wooluru1, Swamy D R 2, Jagadish Rangaswamy 3
1JSS Academy of Technical Education, Bangalore-560060, India.
2 JSS Academy of Technical Education, Bangalore-560060, India.
34527 Pitch Pine Lane West, Ypsilanti, Michigan 48197, USA
Abstract
Pulsed MIG welding process offers spray metal transfer at low
average currents, high metal deposition rate and less
distortion. Study the effect of Pulsed MIG welding process
variables on the bead geometry is inevitable to enhance
mechanical properties of weld bead geometry. This can be
achieved by developing mathematical models to predict the
weld bead dimensions in terms of process parameters. Design
of experiments tool was used to develop models for pulsed
MIG welding process. The models so developed can be used
to predict the weld bead geometry.
Keywords: Process parameter optimization, Design of
Experiments, ANOVA, weld bead geometry.
INTRODUCTION
Pulsed MIG welding maintains an arc at low current and
superimposes short periodic pulses (30 – 300 Hz) of high
current in order to detach and transfer single drops of molten
metal from the electrode to the weld pool. The pulsing of the
current at such high and low levels results in mean current.
Parameters in pulsed MIG welding are: Peak current (Ip),
Peak duration (Tp), Base current (Ib), Base current duration
(Tb) and Wire feed speed (Ws).In Pulsed MIG welding
process a square wave current pulses are used as shown in
Figure 1.
Figure 1. Pulsed MIG welding current wave form
It is very important to evaluate the effect of MIG welding
process variables upon weld bead geometry. The weld
quality depends on number of independent variable factors.
Their effects on dependent factors are evaluated through
empirical investigation. In practice, a small number of
controllable variables contribute to a vital share of the effect
of the product quality. These variables do not necessarily
produce a constant effect on the product. The question
would therefore arise as to how efficiently and economically
the contribution of each of these factors can be assessed
individually and also collectively to produce the total effect
on the weld quality. An approach that fulfills these
requirements is available in the statistically designed
experiments.
LITERATURE REVIEW
Christensen [1] formulated no dimensional factors to
relate bead dimensions with the operating parameters.
Chandel [2] demonstrated the theoretical predictions of
the effect of current, electrode polarity, diameter, and
electrode extension on the melting rate, bead height, and
bead width and weld penetration, submerged arc welding
(SAW). Markelj and Tusek [3] modeled the current and
voltage in Tungsten inert Gas welding as quadratic
polynomials of sheet thickness. The results were
presented for algorithmic optimization in the case of T-
joint with fillet weld. Kim [4] distinguished experimental
data obtained for weld bead geometry with those
obtained from empirical formulae in gas metal arc
welding(GMAW).Patel et al. [5], evaluated the welding
parameters for Metallic inert Gas welding and Tungsten
inert Gas welding by Taguchi’s method. It was concluded
that the welding current was most significant parameter for
both the welding operation. Ghazvinloo H.R. et al.[6],
analyzed robotic MIG welding of AA6061’sproperties like
fatigue life, impact and bead penetration properties under
the effect of welding speed, voltage and current. Pradip et
al. [7], have investigate the effects of welding process
parameters of Gas Metal Arc Welding (GMAW) on tensile
strengths of SS 3Cr12 steel material specimen. Duhan et al.
[8], have developed a response surface model to predict
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 12 (2018) pp. 10298-10309
© Research India Publications. http://www.ripublication.com
10299
tensile strength of inert gas metal arc welding of AISI 50110
(EN 31) high carbon steel joint. M. Aghakhani et al. [9],
have done work on optimization of gas metal arc welding
process parameter for increase quality and productivity of
weldment. Kumar [10] studied TIG parameters and pitting
corrosion of Al-alloys using ANOVA, regression analysis
and mathematical models. He found that Peak current (Ip)
and frequency have direct and base current (Ib) and pulse on
time have inverse relation to pitting. Ratnayake and Vik
[11] suggested a methodology to recognize the most
frequently appearing imperfect and defective welds by
grouping based on welding procedure specifications (WPSs)
that contribute to the highest level of quality deterioration.
Joshi [12] studied the MIG and TIG welding. The authors
have used the Design of Experiment method for conducting
the experiments and data analysis. Palani [13] studying TIG
parameters of Al-65032 using Taguchi and mathematical
models found that welding speed impacts weld strength and
percentage elongation more than other parameters.
Statistical design of experiments (DOE) concept has been
successfully applied to many welding situations This may
be achieved by the development of mathematical
expressions, which can be fed into a computer, relating the
bead dimensions to the important welding parameter
affecting these dimensions Also, optimization of the
welding parameters to control and obtain the required shape
and quality of weld beads is possible with these expressions
[14, 15, and 16].
PLAN OF INVESTIGATION
The work was planned to be carried out in the following steps:
1. Identifying important process control variables
2. Finding upper and lower limits of control variables
viz., Pulse current (Ip), Pulse duration (Tp), Wire
feed speed (Ws), Background current (Ib) and
background current duration.
3. Selection of optimization parameter.
4. Development of design matrix.
5. Conducting experiment as per design matrix.
6. Recording responses viz Penetration (P), Width of
the weld bead (W) and Dilution (D).
7. Develop mathematical model to predict weld bead
geometry
8. Determining the co-efficient of the model using
DOE software
9. Check the adequacy of the models
10. Optimize the welding process parameters
IDENTIFYING THE IMPORTANT PROCESS
CONTROL VARIABLES AND FINDING LIMITS
In pulsed MIG welding process, there are many variables
affecting the weld bead geometry .However it was
planned to study the effects of those parameters which
affect definitely the mode of metal transfer and the
amount of heat input to the work piece and thus determine
the bead geometry and shape relationships. Based on their
importance the welding variables have been put in to two
groups as follows.
Primary variables:
Pulse current, Pulse time, Wire feed rate, Welding speed,
feed wire diameter, shielding composition.
Secondary variables:
Background current ,Back ground current duration, mean
current, Volt-ampere ratio ,Shielding gas flow rate ,Torch
angle ,Electrode polarity, Pre heat condition ,burn back,
rise time, Inductance level, short circuit current, Arc
length and electrode stick out. Out of these parameters
only important variables were chosen which has no
dependence on each other .Based on these consideration
pulse current (IP), Pulse time (Tp), Welding speed (Ws),
Background current (Ib), and background current duration
(Tb) were chosen as the control variables. Factors and
their levels are given in Table 1.
WELDING FACTORS AND THEIR LIMITS
Table 1. Factors and their levels
Sl.
No.
Factors Unit Notations Lower
Limit
Basic
Level
Upper
Level
Variation
interval
1 pulse
current
Amps A 300 350 400 50
2 Welding
speed
mm/min B 200 250 300 50
3 Pulse time ms C 2 4 6 2
4 Background
current
Amps D 50 75 100 25
5 Background
current time
period
ms E 5 7.5 10 2.5
The limiting values of the welding parameters and constants
of the experiments were set based on welding knowledge and
experience.
SELECTION OF THE EXPERIMENTAL CONSTANTS
The following welding operational parameters and conditions
were made constant during the experiments.
Plate thickness: 12 mm, V-groove with900 included angle
Feed wire diameter: 1.2 mm
Torch to work angle: 900
Gas flow rate: 20 lit/min
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 12 (2018) pp. 10298-10309
© Research India Publications. http://www.ripublication.com
10300
Nozzle to plate distance: 20 mm
Pre heat: Nil
SELECTION OF OPTIMIZATION PARAMETER
Optimization parameter is the reaction (response) to the action
of the factors (pulse current, Pulse time, Welding speed,
Background current and Background current time period)
determining the behavior of the system being studied. An
optimization parameter should be:
1. Effective from the view point of reaching a goal
2. Universal in nature
3. Quantitative and expressed by a single number
4. Statistically effective
5. With a physical meaning, simple and easy to
calculate.
Based on the above considerations the following parameters
are selected:
Penetration (P):
Penetration is the depth to which the base metal has been
melted. It is normally a function of arc pressure. Arc pressure
is decided from current density. Therefore, welding current is
an important parameter deciding penetration besides the
welding current also decides the deposition rate. Therefore the
heat transferred through the droplets and deposited mass also
has some influence on penetration. Depth of penetration is
also influenced by the nature of the shielding gas .In pulsed
MIG welding although the mean current decides the
deposition rate and penetration the magnitude of the pulse
current also has some influence on the penetration. Generally
higher pulse current gives slightly higher penetration with
larger pulse current can be attributed to the higher arc force
and higher force due to the impinging droplet. The depth of
penetration is also influenced by welding speed.
Width of the weld bead (W):
In consumable arc welding process such as MIG and FCAW
the bead width is controlled by deposition rate welding speed
and also the fluidity of the molten pool. in FCAW processes
the thermo physical property of the molten flux also
contribute to the bead formation. However in pulsed MIG
welding using solid wire the bead width is largely controlled
by the mean current decreased with increasing the welding
speed.
Bead wetting Angle:
It is an important aspect of the deposited weld metal. Bead
wetting angle is particularly important in case of multi pass
welding and surfacing applications as larger bead wetting
angle can lead to inter pass lack of fusion defects and slag
entrapment. In fluxed process like FCAW and SAW the bead
wetting angle can affect the slag detachability .Therefore the
bead wetting angle is an important aspect in V- groove
wetting as well as surfacing .However the effect of current on
bead wetting angle appears to be limited when compare to the
effect of welding speed on wetting angle.
Dilution:
(D) Dilution can be defined as the ratio of the plate fusion
area to the total area. It may also be used as rough measure of
susceptibility to fusion defects and as a mean of comparing
fusion characteristics with other processes. As a direct
measure it is of interest in surfacing applications and also
gives an idea about the inter pass refinement in the multilayer
welding.
FRACTIONAL FACTORIAL DESIGN MATRIX
A five factor, two levels, fractional factorial design with
interaction effect is as shown in Table 2 to conduct the
experiments.
Table 2. Design Matrix
Std
Order
Run
Order
Center
Pt
Blocks A B C D E AB AC AD AE BC BD BE CD CE DE
32 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
19 2 1 1 -1 1 -1 -1 -1 -1 1 1 1 -1 -1 -1 1 1 1
15 3 1 1 -1 1 1 1 -1 -1 -1 -1 1 1 1 -1 1 -1 -1
23 4 1 1 -1 1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1
27 5 1 1 -1 1 -1 1 1 -1 1 -1 -1 -1 1 1 -1 -1 1
9 6 1 1 -1 -1 -1 1 -1 1 1 -1 1 1 -1 1 -1 1 -1
4 7 1 1 1 1 -1 -1 1 1 -1 -1 1 -1 -1 1 1 1 -1
3 8 1 1 -1 1 -1 -1 -1 -1 1 1 1 -1 -1 -1 1 1 1
31 9 1 1 -1 1 1 1 -1 -1 -1 -1 1 1 1 -1 1 -1 -1
11 10 1 1 -1 1 -1 1 1 -1 1 -1 -1 -1 1 1 -1 -1 1
14 11 1 1 1 -1 1 1 -1 -1 1 1 -1 -1 -1 1 1 -1 -1
25 12 1 1 -1 -1 -1 1 -1 1 1 -1 1 1 -1 1 -1 1 -1
1 13 1 1 -1 -1 -1 -1 1 1 1 1 -1 1 1 -1 1 -1 -1
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 12 (2018) pp. 10298-10309
© Research India Publications. http://www.ripublication.com
10301
Std
Order
Run
Order
Center
Pt
Blocks A B C D E AB AC AD AE BC BD BE CD CE DE
10 14 1 1 1 -1 -1 1 1 -1 -1 1 1 1 -1 -1 -1 -1 1
7 15 1 1 -1 1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1
26 16 1 1 1 -1 -1 1 1 -1 -1 1 1 1 -1 -1 -1 -1 1
12 17 1 1 1 1 -1 1 -1 1 -1 1 -1 -1 1 -1 -1 1 -1
24 18 1 1 1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 -1 1
8 19 1 1 1 1 1 -1 -1 1 1 -1 -1 1 -1 -1 -1 -1 1
16 20 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
17 21 1 1 -1 -1 -1 -1 1 1 1 1 -1 1 1 -1 1 -1 -1
30 22 1 1 1 -1 1 1 -1 -1 1 1 -1 -1 -1 1 1 -1 -1
21 23 1 1 -1 -1 1 -1 -1 1 -1 1 1 -1 1 1 -1 -1 1
2 24 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 1 1 1 1
13 25 1 1 -1 -1 1 1 1 1 -1 -1 -1 -1 -1 -1 1 1 1
6 26 1 1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 -1 1 -1
22 27 1 1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 -1 1 -1
18 28 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 1 1 1 1
20 29 1 1 1 1 -1 -1 1 1 -1 -1 1 -1 -1 1 1 -1 -1
29 30 1 1 -1 -1 1 1 1 1 1 -1 -1 -1 -1 -1 1 1 1
28 31 1 1 1 1 -1 1 -1 1 -1 1 -1 -1 1 -1 -1 1 -1
5 32 1 1 -1 -1 1 -1 -1 1 -1 1 1 -1 1 1 -1 -1 1
EXPERIMENTAL DETAILS
Steel plates of size 150x 75 mm were used for welding trials.
The required number of plates was cut from 12 mm thick
carbon steel (BMC-16) structural steel plate. V- Grooves with
900 included angle were cut into machined on the center of the
specimen and depth of the V- groove was limited to 6mm
only. The specimen was fixed on the carriage in vertical
position. The M450 PS programmable MIG power source was
used in this experiment. The weld beads were deposited using
the welding conditions stipulated by the design matrix as
shown in the Table 2 .The weld runs were made at random to
avoid systematic errors in the result. The experimental setup
with gas flow meter and wire feeder used for bead on plate
experiment in pulsed MIG is shown in figure 3, 4, and 5.It
Consists of a traveling carriage with a table for supporting the
test plate. The carriage speed is continuously varied from 1
mm/sec to 20 mm/sec. A frame held the welding torch above
the table and it was provided with horizontal and vertical
movements for setting the nozzle to job distance. The
experimental set up used for welding in vertical position.
Thought the experiment the test plates were kept stationary
and the welding torch was moved. Vertical and horizontal
movements were provided for the torch. The correct standoff
distance and also for proper centering of the torch with
reference to the V-Groove were made. The welding gun was
allowed to cool to room temperature and spatter was cleaned
from the nozzle after each weld run. Two samples were cut
transverse to the weld bead from each welded plate then
milling, surface grinding, polishing and etching for
examination using 10% nital. Weld bead profiles were
sketched with the help of a profile projector at a magnification
of X10.Areas and dimensions of interest were measured using
a planimeter and scale. The weld bead dimensions and shape
relations were tabulated against their respective welding
conditions in the coded forms as shown in Table 4 .The
tabulated values are averages from two samples from each
specimen. The magnified bead profiles for different welding
trials were made and a sample bead profile is shown in Figure
2.
Figure 2. Weld bead profile
Electrode wire:
The copper coated electrode wire of diameter 1.2 mm and its
nominal composition of the weld metal as supplied by the
manufacturer used in this experiment are as shown in Table 3
Table 3. Chemical composition of electrode material
Carbon 0.05- 0,08%
Silicon 0.4- 0.6%
Manganese 0.03% (Max)
Phosphorus 0.03%
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 12 (2018) pp. 10298-10309
© Research India Publications. http://www.ripublication.com
10302
Gas supply and wire feeder:
The smith’s gas unit used in the study is a pressure flow
device capable of proportionately mixing Argon and
CO2ininfinitely varying proportions. The gas flow rate and gas
mixing ratio were set using separate knobs provided in the
front panel. In this study the gas flow rate was kept at 20
lit/min. The mixing accuracy as reported by the manufacturer
is about + or – 2% of the full scale.
Figure 3. Gas flow meter
Figure 4. Wire feeder
Figure 5. Experimental setup
The wire feeder unit employed is a millermatic four roll drive
feed unit .It provides a continuously variable wire feed speed
from about 1.0 m/mm to 18 m/min.
CONDUCTION OF EXPERIMENT AS PER DESIGN
MATRIX
Sixteen experimental runs were conducted as per the 2 5-1
fractional factorial design matrix at random to avoid any
systematic error creeping into the system. The surface plates
were cross-sectioned at their midpoints to obtain test
specimens. The values of the response are given in Table 4.
Table 4. Design matrix with responses
Std
Order
Run
Order
Center
Pt
Blocks A B C D E Penetration
‘mm’
Weld bead
width in ‘mm’
Dilution
in %
32 1 1 1 1 1 1 1 1 0.75 11.9 43.9
19 2 1 1 -1 1 -1 -1 -1 0.87 8.6 47.9
15 3 1 1 -1 1 1 1 -1 1.09 10 55.53
23 4 1 1 -1 1 1 -1 1 0.88 6.8 45.12
27 5 1 1 -1 1 -1 1 1 0.56 7.5 73.68
9 6 1 1 -1 -1 -1 1 -1 0.81 10 35.11
4 7 1 1 1 1 -1 -1 1 0.83 7.2 45.56
3 8 1 1 -1 1 -1 -1 -1 0.51 8.0 48.0
31 9 1 1 -1 1 1 1 -1 1.13 9.2 56.9
11 10 1 1 -1 1 -1 1 1 0.54 7.0 56.7
14 11 1 1 1 -1 1 1 -1 0.64 6.8 73.4
25 12 1 1 -1 -1 -1 1 -1 0.87 11.0 34.71
1 13 1 1 -1 -1 -1 -1 1 0.64 6.8 65.16
10 14 1 1 1 -1 -1 1 1 0.59 10.3 40.0
7 15 1 1 -1 1 1 -1 1 0.84 6.0 43.85
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 12 (2018) pp. 10298-10309
© Research India Publications. http://www.ripublication.com
10303
Std
Order
Run
Order
Center
Pt
Blocks A B C D E Penetration
‘mm’
Weld bead
width in ‘mm’
Dilution
in %
26 16 1 1 1 -1 -1 1 1 0.67 11.3 38.9
12 17 1 1 1 1 -1 1 -1 0.79 11.5 44.47
24 18 1 1 1 1 1 -1 -1 0.84 6.8 76.56
8 19 1 1 1 1 1 -1 -1 0.88 6.6 76.6
16 20 1 1 1 1 1 1 1 0.79 11.2 45.02
17 21 1 1 -1 -1 -1 -1 1 0.72 6.0 65.0
30 22 1 1 1 -1 1 1 -1 1.02 6.0 73.68
21 23 1 1 -1 -1 1 -1 -1 0.54 9.5 81.58
2 24 1 1 1 -1 -1 -1 -1 0.69 10.0 40.0
13 25 1 1 -1 -1 1 1 1 0.91 11.2 50.0
6 26 1 1 1 -1 1 -1 1 0.73 11.2 39.1
22 27 1 1 1 -1 1 -1 1 0.75 12 41.68
18 28 1 1 1 -1 -1 -1 -1 0.71 10.6 40.1
20 29 1 1 1 1 -1 -1 1 0.85 8.0 47.6
29 30 1 1 -1 -1 1 1 1 0.87 11.9 52.28
28 31 1 1 1 1 -1 1 -1 0.75 11.0 44.8
5 32 1 1 -1 -1 1 -1 -1 0.84 8.9 80.0
MATHEMATICAL MODEL
The study attempts relate the important welding process
parameters to process output characteristics, through
developing empirical regression models for various target
parameters. Linear Regression function is fitted to the data
and the coefficient values are found using regression
analysis with the help of MINITAB statistical software.
The developed mathematical models are accurately
representing the actual pulsed MIG welding process.
NORMAL PROBABILITY PLOT FOR PENETRATION,
WELD BEAD WIDTH AND DILUTION
Validating ANOVA Assumptions It is necessary to check the
assumptions of ANOVA before draw conclusions. There are
three assumptions in ANOVA analysis: normality, constant
variance, and independence. Normality assumptions have
been checked.
Figure 6. Normality plot for Penetration, weld bead width and Dilution
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 12 (2018) pp. 10298-10309
© Research India Publications. http://www.ripublication.com
10304
The normality plot of the residuals above shows that the
residuals follow a normal distribution in all the three cases. It
indicates significant and insignificant factors and residuals
follow a straight line. According to the above normal plots C
and DE are significant for penetration, C, E, BC, CD, are
insignificant for weld bead width and B, BE, CD are
insignificant for Dilution case.
PARETO PLOT FOR PENETRATION, WELD BEAD
WIDTH AND DILUTION MODEL
Pareto chart shows the same results. Since some of the terms
are insignificant, we can drop these terms in the model.
Figure 7. Pareto chart for Penetration, weld bead width and Dilution
Table 5. ANOVA for penetration
Source DF Adj SS Adj MS F-Value P-Value
Model 15 0.506487 0.033766 2.76 0.026
Linear 5 0.215462 0.043092 3.52 0.025
A 1 0.003613 0.003613 0.30 0.594
B 1 0.025312 0.025312 2.07 0.170
C 1 0.137812 0.137812 11.26 0.004
D 1 0.013613 0.013613 1.11 0.307
E 1 0.035113 0.035113 2.87 0.110
2-Way
Interactions
10 0.291025 0.029102 2.38 0.059
A*B 1 0.006613 0.006613 0.54 0.473
A*C 1 0.035113 0.035113 2.87 0.110
A*D 1 0.046513 0.046513 3.80 0.069
A*E 1 0.003612 0.003612 0.30 0.594
B*C 1 0.025313 0.025313 2.07 0.170
B*D 1 0.023113 0.023113 1.89 0.188
B*E 1 0.010512 0.010512 0.86 0.368
C*D 1 0.040612 0.040612 3.32 0.087
C*E 1 0.000613 0.000613 0.05 0.826
D*E 1 0.099012 0.099012 8.09 0.012
Error 16 0.195800 0.012238
Total 31 0.702287
Table 6. Regression coefficients with P-values
Term Effect Coef SE Coef T-Value P-Value VIF
Constant 0.7781 0.0196 39.79 0.000
A -0.0213 -0.0106 0.0196 -0.54 0.594 1.00
B 0.0563 0.0281 0.0196 1.44 0.170 1.00
C 0.1312 0.0656 0.0196 3.36 0.004 1.00
D 0.0413 0.0206 0.0196 1.05 0.307 1.00
E -0.0662 -0.0331 0.0196 -1.69 0.110 1.00
A*B 0.0288 0.0144 0.0196 0.74 0.473 1.00
A*C -0.0662 -0.0331 0.0196 -1.69 0.110 1.00
A*D -0.0762 -0.0381 0.0196 -1.95 0.069 1.00
A*E 0.0212 0.0106 0.0196 0.54 0.594 1.00
B*C 0.0562 0.0281 0.0196 1.44 0.170 1.00
B*D -0.0537 -0.0269 0.0196 -1.37 0.188 1.00
B*E -0.0362 -0.0181 0.0196 -0.93 0.368 1.00
C*D 0.0713 0.0356 0.0196 1.82 0.087 1.00
C*E 0.0088 0.0044 0.0196 0.22 0.826 1.00
D*E -0.1112 -0.0556 0.0196 -2.84 0.012 1.00
Table 7. Model summary –Penetration
S R-sq R-sq(adj) R-sq(pred)
0.110623 72.12% 45.98% 0.00%
Discussion: Table 6 shows the estimation coefficient
(Coef.) of each variable term in a regression model for
penetration along with the corresponding standard deviation
(SD coef), t-statistics (t-Stat) and probability (P) values
determined at 5% significance level. Variable terms with P <
0.05, are C, and DE which are considered statistically
significant for penetration of weld bead geometry. Therefore,
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 12 (2018) pp. 10298-10309
© Research India Publications. http://www.ripublication.com
10305
a second-order model was built to describe the behavior of
each response, followed by the optimization stage to find the
best setting for each factor. The second-order models for
penetration of weld bead geometry in terms of coded variables
with all significant terms are given in Equation (1).The value
of R2 was 72.12%. This means that regression model provided
an explanation of the relationship between independent factors
and the response. The associated p-value for the model was
lower than 0.05 (i.e. α = 0.05, or 95% confidence) which
indicated that the model was considered to be moderately
statistically significant.
Table 8. ANOVA for weld bead width
Source DF Adj SS Adj MS F-Value P-Value
Model 15 126.735 8.4490 32.03 0.000
Linear 5 33.692 6.7385 25.55 0.000
A 1 6.125 6.1250 23.22 0.000
B 1 8.201 8.2013 31.09 0.000
C 1 0.045 0.0450 0.17 0.685
D 1 19.220 19.2200 72.87 0.000
E 1 0.101 0.1013 0.38 0.544
2-Way
Interactions
10 93.042 9.3042 35.28 0.000
A*B 1 2.101 2.1012 7.97 0.012
A*C 1 8.000 8.0000 30.33 0.000
A*D 1 2.880 2.8800 10.92 0.004
A*E 1 20.801 20.8013 78.87 0.000
B*C 1 0.101 0.1013 0.38 0.544
B*D 1 9.901 9.9012 37.54 0.000
B*E 1 6.125 6.1250 23.22 0.000
C*D 1 0.500 0.5000 1.90 0.188
C*E 1 38.281 38.2812 145.14 0.000
D*E 1 4.351 4.3513 16.50 0.001
Error 16 4.220 0.2638
Total 31 130.955
Table 9. Regression coefficients with P-values
(Weld bead width)
Term Effect Coef SE Coef T-Value P-Value VIF
Constant 9.0875 0.0908 100.10 0.000
A 0.8750 0.4375 0.0908 4.82 0.000 1.00
B -1.0125 -0.5063 0.0908 -5.58 0.000 1.00
C 0.0750 0.0375 0.0908 0.41 0.685 1.00
D 1.5500 0.7750 0.0908 8.54 0.000 1.00
E 0.1125 0.0563 0.0908 0.62 0.544 1.00
A*B 0.5125 0.2563 0.0908 2.82 0.012 1.00
A*C -1.0000 -0.5000 0.0908 -5.51 0.000 1.00
A*D -0.6000 -0.3000 0.0908 -3.30 0.004 1.00
A*E 1.6125 0.8063 0.0908 8.88 0.000 1.00
B*C -0.1125 -0.0563 0.0908 -0.62 0.544 1.00
B*D 1.1125 0.5562 0.0908 6.13 0.000 1.00
B*E -0.8750 -0.4375 0.0908 -4.82 0.000 1.00
C*D -0.2500 -0.1250 0.0908 -1.38 0.188 1.00
C*E 2.1875 1.0937 0.0908 12.05 0.000 1.00
D*E 0.7375 0.3688 0.0908 4.06 0.001 1.00
Table 10. Model summary – weld bead width
S R-sq R-sq(adj) R-sq (pred)
0.513566 96.78% 93.76% 87.11%
Discussion: Table 9 shows the estimation coefficient
(Coef) of each variable term in a regression model for weld
bead width along with the corresponding standard deviation
(SD coef), t-statistics (t-Stat) and probability (P) values
determined at 5% significance level. Variable terms with P
< 0.05, A, B, D, AB, AC, AD, AE, BD, BE, CE, DE are
considered statistically significant for weld bead width of
weld bead geometry. The second-order models for weld bead
width of weld bead geometry in terms of coded variables
with all significant terms are given in Equation (2).The
value of R2 was 93.76%. This means that regression model
provided an explanation of the relationship between
independent factors and the response. The associated p-value
for the model was lower than 0.05 (i.e. α = 0.05, or 95%
confidence) which indicated that the model was considered to
be statistically significant.
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 12 (2018) pp. 10298-10309
© Research India Publications. http://www.ripublication.com
10306
Table 11. ANOVA for Dilution
Source DF Adj SS Adj MS F-Value P-Value
Model 15 6354.93 423.66 43.29 0.000
Linear 5 1627.60 325.52 33.26 0.000
A 1 200.75 200.75 20.51 0.000
B 1 0.07 0.07 0.01 0.934
C 1 876.86 876.86 89.60 0.000
D 1 130.94 130.94 13.38 0.002
E 1 418.98 418.98 42.81 0.000
2-Way
Interactions
10 4727.33 472.73 48.30 0.000
A*B 1 170.25 170.25 17.40 0.001
A*C 1 250.38 250.38 25.58 0.000
A*D 1 107.57 107.57 10.99 0.004
A*E 1 611.71 611.71 62.50 0.000
B*C 1 299.94 299.94 30.65 0.000
B*D 1 61.47 61.47 6.28 0.023
B*E 1 9.17 9.17 0.94 0.347
C*D 1 0.25 0.25 0.03 0.875
C*E 1 3018.84 3018.84 308.46 0.000
D*E 1 197.76 197.76 20.21 0.000
Error 16 156.59 9.79
Total 31 6511.52
Table 12. Regression coefficients with P-values (Dilution)
Term Effect Coef SE Coef T-Value P-Value VIF
Constant 9.0875 0.0908 100.10 0.000
A 0.8750 0.4375 0.0908 4.82 0.000 1.00
B -1.0125 -
0.5063
0.0908 -5.58 0.000 1.00
C 0.0750 0.0375 0.0908 0.41 0.685 1.00
D 1.5500 0.7750 0.0908 8.54 0.000 1.00
E 0.1125 0.0563 0.0908 0.62 0.544 1.00
A*B 0.5125 0.2563 0.0908 2.82 0.012 1.00
A*C -1.0000 -
0.5000
0.0908 -5.51 0.000 1.00
A*D -0.6000 -
0.3000
0.0908 -3.30 0.004 1.00
A*E 1.6125 0.8063 0.0908 8.88 0.000 1.00
B*C -0.1125 -
0.0563
0.0908 -0.62 0.544 1.00
B*D 1.1125 0.5562 0.0908 6.13 0.000 1.00
B*E -0.8750 -
0.4375
0.0908 -4.82 0.000 1.00
C*D -0.2500 -
0.1250
0.0908 -1.38 0.188 1.00
C*E 2.1875 1.0937 0.0908 12.05 0.000 1.00
D*E 0.7375 0.3688 0.0908 4.06 0.001 1.00
Table 13. Model summary – Dilution
S R-sq R-sq(adj) R-sq(pred)
0.513566 96.78% 93.76% 87.11%
Discussion :Table 12 shows the estimation coefficient
(Coef) of each variable term in are regression model for
weld bead width along with the corresponding standard
deviation (SD coef), t-statistics (t-Stat) and probability (P)
values determined at 5% significance level. Variable terms
with P < 0.05, A, C,D, E,AB, AC, AD, AE, BC,BD,CE,
DE are considered statistically significant for dilution for
weld bead geometry. The second-order models for weld bead
width of weld bead geometry in terms of coded variables
with all significant terms are given in Equation (3).The
value of R2 was 96.78%. This means that regression model
provided an explanation of the relationship between
independent factors and the response. The associated p-value
for the model was lower than 0.05 (i.e. α = 0.05, or 95%
confidence) which indicated that the model was considered to
be statistically significant.
EFFECTS AND INTERACTION OF PROCESS
VARIABLES:
MAIN EFFECTS AND INTERACTION OF PROCESS
VARIABLES ON DEPTH OF PENETRATION :
Figure 8. Illustration of main and interaction effect on
Penetration
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 12 (2018) pp. 10298-10309
© Research India Publications. http://www.ripublication.com
10307
MAIN EFFECTS AND INTERACTION OF PROCESS
VARIABLES ON PENETRATION
From Figure 8, it is evident that Penetration (P) increases with
an increase in pulse time (C) and as the Back ground current
(D) increases, penetration (P) decreases considerably. Factors
A, B and C will not enhance the penetration much as they are
insignificant. There is an interaction effect between
background current (D) and background current time period
(E), as the background current (D) increases ,penetration (P)
decreases.
Figure 9. Illustration of main and interaction effect on weld
bead width
MAIN EFFECTS AND INTERACTION OF PROCESS
VARIABLES ON WELD BEAD WIDTH (W)
From Figure 9, it is evident that weld bead width (W)
increases with an increase in pulse current (A ) and back
ground current (D) and as the Welding speed (B) increases,
weld bead width (W) decreases considerably. Increase the
Factors Pulse time (C)and background current (D) will not
enhance the weld bead width at all. There is an interaction
effect between factors Pulse current and pulse time (AC),
Pulse current and background current time period (AE),
Welding speed and background current period (BE), Pulse
time and background current period (CE),background current
and background current period(DE).
MAIN EFFECTS AND INTERACTION OF PROCESS
VARIABLES ON DILUTION
Figure 10. Illustration of main and interaction effect on
Dilution
From Figure 10, it is evident that Dilution (D) increases with
an increase in pulse time (C).As Pulse time (A),background
current (D) background current time period (E) increases, the
dilution decreases. There is no effect of welding speed (B) on
Dilution when welding speed varies from lower level to
higher level. There is an interaction effect between factors
Pulse current and background current time period (AE), Pulse
time and background current period (CE).
MATHEMATICAL MODELS IN CODED FORM
After testing adequacy and significance of each coefficient of
the regression equations we obtained the linear mathematical
models for penetration, weld bead width and dilution in the
coaded form as follows.
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 12 (2018) pp. 10298-10309
© Research India Publications. http://www.ripublication.com
10308
Penetration = 0.7781 - 0.0106 A + 0.0281 B + 0.0656 C + 0.0206 D - 0.0331 E + 0.0144 A*B -
0.0331 A*C - 0.0381 A*D + 0.0106 A*E + 0.0281 B*C - 0.0269 B*D - 0.0181 B*E
+ 0.0356 C*D + 0.0044 C*E - 0.0556 D*E (1)
Weld bead width = 9.0875 + 0.4375 A - 0.5063 B + 0.0375 C + 0.7750 D + 0.0563 E + 0.2563 A*B -
0.5000 A*C - 0.3000 A*D + 0.8063 A*E - 0.0563 B*C + 0.5562 B*D - 0.4375 B*E -
0.1250 C*D + 1.0937 C*E + 0.3688 D*E (2)
Dilution = 53.215 - 2.505 A + 0.047 B + 5.235 C - 2.023 D - 3.618 E + 2.307 A*B + 2.797 A*C
+ 1.833 A*D - 4.372 A*E - 3.062 B*C + 1.386 B*D + 0.535 B*E - 0.088 C*D - 9.713 C*E
+ 2.486 D*E (3)
In calculating the predicted value of the optimization
parameters, the coaded values of the factors are inserted in to
the mathematical model.
Rule 1.If an interaction effect has a positive sign, then for
increasing the optimization parameter, what is required is a
simultaneous increase or decrease of the value of the factors,
for example the combination X1= +1 and X2 = +1 or X1= -1
and X2 = -1.
Rule 2.If the interaction effect has a negative sign, then to
increase the optimization parameter the factors should
simultaneously change in different directions for example the
combination X1= +1 and X2 = -1 or X1= -1 and X2 = +1.
Table 14. Prediction of weld bead geometry for a treatment
using the above model
Sl.
no.
A B C D E Dilution
in %
Weld bead
width in
‘mm’
Penetration
in’ mm’
1 -1 -1 1 1 -1 65.15 9.25 1.04
CONCLUSION
Fractional factorial technique can be effectively utilized to
design the experiments for pulsed MIG welding.
Mathematical models based on regression and analysis of
variance technique can be an effective tool for prediction of
weld bead geometry and shape relations. The developed
models can also be utilized for predicting the values of
controlled variables for achieving desired weld bead profile.
These equations can be usefully employed for control of weld
bead contours in mechanized and robotic welding system. The
models can provide a picture of interaction between control
factors and response factors. The suitable ranges of pulse
current, pulse time, welding speed ,background current and
back ground current time period used for the experiment
provide a useful data ,which can be utilized for making bead
in V- groove plate of medium thickness(10-15 mm).The
established welding parameters for 12 mm thick plate with 6
mm depth V- groove (90 0 included angle) can be employed to
get good quality of weld bead.
REFERENCES
[1] N.Christensen, V. Davies, & K. Gjermundsen,
1965 "Distribution of temperature in arc welding",
Br Weld J vol. 12 (2), pp.54–75,.
[2] R.S. Chandel, H.P. Seow, F.L. Cheong, 1997
"Effect of increasing deposition rate on the bead
geometry of submerged arc welds", J Mater Process Technol, vol.72, pp.124–128,.
[3] F. Markelj, J. Tusek, 2001"Algorithmic
optimization of parameters in tungsten inert gas
welding of stainless-steel sheet", Sci Technol Weld Join vol.6 (6), pp.375–382,.
[4] I.S. Kim, K.J. Son, Y.S. Yang, P.K. Yarlagadda,
2003"Sensitivity analysis for process parameters
in GMA welding processes using a factorial design
method", Int. J Mach Tools Manuf. vol.43,
pp.763– 769,.
[5] C. N. Patel and Chaudhary, S., (2013), “Parametric
Optimization of Weld Strength of Metal Inert Gas
Welding and Tungsten Inert Gas Welding by using
Analysis of Variance and Grey Relational Analysis”,
International Journal of Research in Modern
Engineering and Emerging Technology, Vol. 1,
No.3.
[6] Ghazvinloo H.R., Honarbakhsh-Raouf A. and
Shadfar N., 2010 , " Effect of arc voltage, welding
current and welding speed on fatigue life, impact
energy and bead penetration of AA6061 joints
produced by robotic MIG welding". Indian Journal of
Science and Technology, Vol.3.
[7] Pradip D. Chaudhari and Nitin N. More, (2014),
“Effect of welding process parameters on tensile
strength”, IOSR journal of engineering, Vol. 04,
Issue 05, pp. - 01-05.
[8] Rajkumar Duhan and Rajesh Nandal, 2013
“Maximizing tensile strength in AISI 50110 (En 31)
welded joints using gas metal arc (GMAW)
welding”, International journal of engineering
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 12 (2018) pp. 10298-10309
© Research India Publications. http://www.ripublication.com
10309
sciences paradigms and researches, October , Vol.
08, Issue 01, pp.-41-57.
[9] M. Aghakhani, E. Mehrdad, and E. Hayati, 2011
“Parametric optimization of gas metal arc welding
process by Taguchi method on weld dilution”,
International Journal of Modeling and Optimization,
August, Vol. 1, No. 3.
[10] Kumar, T. S., V. Balasubramanian and M. Y.
Sanavullah. 2007 Influences of Pulsed Current
Tungsten Inert Gas Welding Parameters on the
Tensile Properties of AA 6061 Aluminium Alloy.
Materials and Design, 28(7): 2080 – 2092.
[11] Ratnayake, R. M. Chandima and K. T. Vik. 2012
Quality Surveillance Methodology for Pipe Welding:
An Industrial Case Study”, International Journal of
Performability engineering, November 8(6): 635-
643.
[12] Joshi, Jay, ManthanThakkar, and SahilVora.2014,
Parametric Optimization of Metal Inert Gas Welding
and Tungsten Inert Gas Welding By Using Analysis
of Variance and Grey Relational Analysis.
International Journal of Science and Research (IJSR),
Vol. 3.
[13] Palani, P. K., and M. Saju. Modelling And
Optimization of Process Parameters For Tig Welding
Of Aluminium-65032 Using Response Surface
Methodology. International Journal of Engineering
Research and Applications, March-April 2013; 3 (2):
230 – 236.
[14] H. S. Gurev and R. D. Stout,1969 Solidification
Phenomena in Inert -gas Metal Arc Welds, Welding
Journal, 42(7) , 298-310.
[15] K. Srinivasulu Reddy, 2013 Optimization &
Prediction of Welding Parameters and Weld Bead
Geometry In Sub-merged Arc Welding, International
Journal of Applied Engineering Research And
Development, 3(3) , 1-6.
[16] G.Haragopal, P. V. R. Ravindra Reddy, G. Chandra
Mohan Reddy and J. V. Subrahmanyam, 2011,
Parametric Design for MIG Welding of Al-65032
Alloy Using Taguchi Technique, Journal of
Scientific and Industrial Research, 70,844-858.
[17] D.C. Montgomery, E.A. Peck, G.G. Vining, 2003
"Introduction to Linear Regression Analysis".
Third ed., Wiley, New York.