Int J Adv Manuf Technol (2016) 86:557–564
DOI 10.1007/s00170-015-8159-y
ORIGINAL ARTICLE
A study on the arc characteristics of underwater wet
welding process
Bo Chen 1 & Caiwang Tan 1 & Jicai Feng 1
Received: 14 September 2015 / Accepted: 24 November 2015 / Published online: 16 December 2015
# Springer-Verlag London 2015
Abstract To control the underwater wet welding quality, an
arc sensor was used to obtain electrical information of underwater wet welding, and the information was analyzed to find
the characteristics that could reflect the arc stability. Shortcircuit time and frequency distributions were compared between underwater wet weld and CO2 weld; sensitivity analysis
method was used to find the factors that influenced the
welding quality. Experiment results showed that the arc stability of underwater wet weld was worse than CO2 weld, and
arc voltage and weld speed played important roles in underwater wet weld quality. Sensitivity model for underwater wet
weld arc stability was constructed; factors that had important
influences on underwater wet welding quality were investigated, and this laid the foundation for controlling the underwater
wet welding process quality.
Keywords Underwater wet welding . Arc characteristics .
Weld automation . Weld sensor
1 Introduction
With the fast development of the exploitation of marine resources, underwater welding technology is becoming more
and more important [1, 2]. Underwater welding technology
includes the dry chamber welding, portable dry spot welding,
and wet welding [3]; among them, underwater wet welding
* Bo Chen
chenber21@gmail.com
1
Key laboratory of special welding technology of Shandong Province,
Harbin Institute of Technology at Weihai, No 2 West Wenhua Road,
Weihai 264209, People’s Republic of China
has the advantage of easy operation, flexible application, and
low cost; it is gaining more and more attention nowadays [4].
Underwater wet welding technology can be divided into underwater shielded metal arc welding (SMAW) and flux-cored
arc welding (FCAW). SMAW is usually done by divers; the
welding rod needs to be changed frequently during the
welding process because of the limit of the welding rod, so
it is difficult to realize automated welding. FCAW uses fluxcored wire to do the welding work, and the available welding
time is much longer; it is a promising underwater wet welding
method to be used in automated welding. Underwater wet
welding has very rigorous work environment; the work piece
is directly put in the water, and the arc is only protected by the
bubbles and steam generated by the burn of welding materials
and work piece, accompanied by the influence of watercooling and pressure, the underwater wet welding process
has very poor weld bead forming [5]. The welding quality of
underwater welding is much worse than the welding done
onshore.
To control the underwater weld quality and understand
the forming process of underwater wet welding bead, different information that can reflect welding quality should be
obtained during the welding process. Sensors that have been
currently used in underwater wet welding include acoustic
sensor [6–8], visual sensor [9–12], and arc sensor. Because
stable welding arc is the foundation for obtaining fine
welding quality, it will be helpful to study the arc information to control the weld quality. Some researchers have studied the arc characteristics in hyperbaric underwater welding
[13, 14], and some researchers have studied the arc characteristics of SMAW in underwater welding [15, 16].
However, few researches about arc characteristics have
been done on underwater wet welding using FCAW; most
researches about underwater FCAW were focused on the
weld seam-tracking technologies to date [9, 17–19].
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Int J Adv Manuf Technol (2016) 86:557–564
Fig. 1 Experimental system
IPC
Protect
circuit
Control
circuit
Weld power
Wire feeder
Water tank
3D motion
plat form
Weld torch
Work piece
Current
Hall sensor
To understand the arc characteristics of underwater wet
welding process by FCAW, an arc sensor was used to study
the arc voltage and current information in underwater wet
welding. Algorithms were developed to obtain the statistical
characteristics of the welding voltage and current, and arc
stability was analyzed by using the reciprocal of the variable
coefficients, and the relationship between the welding parameters and arc stability was analyzed.
The main objective of this research was to study the influence factors that influence the underwater wet weld bead and
find the characteristics that could reflect the arc stability. The
rest of the paper was organized as follows: section 2 briefly
introduced the experimental system; section 3 compared the
electrical signal characteristics between CO2 welding and underwater wet welding by analyzing the short-circuit time; section 4 used the sensitivity method to analyze the underwater
wet welding process, and the sensitivity model was constructed to analyze the influence on underwater arc welding arc
stability; and a conclusion was made in Section 5.
2 Experiment setup
Experiment of underwater wet welding was conducted in
a water tank, in which the welding method was processed.
The schematic diagram of the experiment system was
shown in Fig. 1; the system mainly comprised a welding
power source, a wire feeder, a three-dimensional motion
platform, an industrial personal computer (IPC) in which
there were one data acquisition card and one motion control card, and a current hall sensor and a protect circuit.
The motion control card was used to control the threedimensional motion platform for controlling the welding
speed; the current hall sensor and the protect circuit were
used to obtain the weld current and voltage information,
and the information was obtained by the acquisition card
in the IPC; the sampling rates of the weld voltage and
current were 20 KHz.
Before welding, water was poured into the water tank until
the water surface was about 0.2 m higher than the work piece
surface. Hyundai supercored 71 self-shielded flux-cored wire
of 1.2-mm diameter was used to deposit bead-on-plate welds
on Q235 steel plate with a dimension of 300×50×6 mm. The
chemical composition and mechanical properties of the filler
materials were shown in Table 1.
Figure 2 showed one weld bead of underwater wet weld by
using FCAW, and the welding voltage was 24 V; the welding
current was 240 A. For the purpose of comparison, CO2 weld
with the same welding parameters was conducted with the
CO2 flow rate 15 l/min, and the experiment result was shown
in Fig. 3.
Table 1 Chemical composition and mechanical properties of
supercored 71
w(C)/%
w(Mn)/%
w(Si)/%
σ0.2(MPa)
σb(MPa)
δ(%)
0.03
1.45
0.55
540
590
26
Fig. 2 Weld bead of underwater wet weld
Int J Adv Manuf Technol (2016) 86:557–564
Fig. 3 Weld bead of CO2 welding in open air
3 Analysis of underwater weld electrical signals
From Figs. 2 and 3, it could be seen that the underwater wet
welding bead forming was much worse than the weld bead of
CO2 welding. This was caused by the rigorous underwater
environment, because in the underwater environment, the
cooling rate was much higher, and the arc was not as stable
as the arc in the atmospheric environment. Figure 4 shows the
current and voltage waveform of the underwater welding, and
Fig. 5 shows the current and voltage waveform of the CO2
welding; from the figures, it could be seen that changes of the
waveforms of underwater welding was very dramatic; the variation of the underwater weld waveform was much bigger
than the CO2 welding.
3.1 Analysis of short-circuit time and arcing time
From Figs. 4 and 5, it could be seen that the underwater
welding arc was not as stable as the CO2 welding; there were
lots of interruption arc and arc striking. These are one of the
reasons for its inaesthetic forming. Short-circuit time and arc
burning time are two parameters that could reflect the arc
burning process, so the short-circuit time and arc burning time
Fig. 4 Current and voltage waveform of underwater wet welding
559
were first calculated. To obtain the short-circuit time and arc
burning time, the following algorithm was designed.
First, a voltage threshold UT and a current threshold IT
were set according to lots of experiments, and a shortcircuit time threshold TN and a sampling threshold CT
were set to distinguish transient short-circuit process and
normal short-circuit process. The obtained voltage and
current data were first transformed to the real value.
Then, they were compared with the set thresholds. First,
the voltage was compared; if the ith sample data met the
following requirements: Ui+1 < UT and Ui−1 > UT, then it
was the start data of a short-circuit; and if the ith sampling
data met the following requirements: Ui+1 >UT and Ui−1 <
UT, then it was the end of the short-circuit time, as shown
in Fig. 6. All the start and end data of the short circuit
could be obtained by the above method, and the start
position of the start data were stored as N2 and the end
data were stored as N3, and it should be assured that the
current value of N3 should be bigger than IT to ensure the
reliability of the judgment and avoid the influence of signal fluctuations. The data between N2 and N3 were the
data during the short-circuit time, and the data between
N3 and the next N2 were the data during the arc burning
time. The short-circuit time and arc burning time could be
obtained by the sapling rate.
The welding travel speeds of the above experiment for
underwater wet welding and CO2 welding were both 6 mm/
s, and the total acquisition times of the two experiments were
both 11.67 s. The short-circuit time of the above experiment
could be obtained by the above method; there were 202 times
short-circuit transfers during the underwater wet welding process, and the average short-circuit time was 10.5 ms and 12
times arc blowouts; and there were 212 times short-circuit
transfers during the CO2 welding process; the average shortcircuit time was 4.84 ms, and no arc blowout existed.
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Int J Adv Manuf Technol (2016) 86:557–564
Fig. 5 Current and voltage waveform of CO2 welding
3.2 Analysis of probability density distribution
Probability analysis is another commonly used method for
analyzing the welding process. Figure 7 showed the probability distribution of the welding voltage. From Fig. 7, it
could be seen that there were two salient on the waveform; the small salient represented the short-circuit period, and the big salient represented the arc burning period.
The steeper the salient and the more symmetrical the salient, it meant that the distribution of the voltage was
narrower and the arc was more stable. From the figure,
it could also be seen that the short-circuit voltage of underwater welding was smaller than the voltage of CO2
welding, and the arc burning voltage of underwater
welding was bigger than the CO2 welding; this meant that
the voltage probability density distribution was more dispersive, so the arc stability was worse.
Fig. 6 Process for finding the
short-circuit data
Figure 8 showed the short-circuit time frequency distribution of CO2 welding and underwater wet welding; the
top figure was the frequency distribution of CO2 welding,
while the bottom figure was the underwater wet welding.
The deviation and variance of the short-circuit time could
be used to describe its uniformity; the deviation and variance of the CO2 welding were 1.818 and 4.121 s, and the
deviation and variance of underwater wet welding were
2.528 and 5.384 s; it meant that the short-circuit time of
CO2 welding was more stable. From the figure, it could
also be seen that the short-circuit time of underwater wet
welding centered within 7–15 s, while the short-circuit
time of CO2 welding centered within 4–7 s; it meant that
the short-circuit time of underwater welding covered a
long range and was not as stable as the CO2 welding.
This could be also used to explain the reason of the instability of underwater wet welding.
30
Voltage/V
25
20
15
10
5
0
200
400
600
800
1000
1200
Sampling No.
1400
1600
1800
2000
2200
Int J Adv Manuf Technol (2016) 86:557–564
561
Fig. 7 Voltage probability
density distribution
10
0
n[%]
10
CO2 weld
Underwater wet weld
2
10
10
-2
-4
0
5
10
15
20
25
30
35
40
45
U/V
4 Analysis of arc stability
was fixed, higher welding voltage was needed to obtain a
stable welding arc. In underwater wet welding, the welding
current range was narrower than the CO2 welding; this meant
that in underwater welding, the matching degree between
welding voltage and welding current required more strict
conditions.
4.1 Arc stability analysis based on variable coefficient
The obtained voltage was stored in the computer as a data set;
the fluctuation of the data could be used to analyze the stability
of the process; variation and variable coefficient (the ratio
between the variance and mean value) could be used to judge
the fluctuation [20]. The smaller the variation and the variable
coefficient, the more stable the arc. Usually, the coefficient
value was very small, and its reciprocal δ was used to judge
the arc stability, the bigger the δ value, the more stable the arc.
Experiments were done to compare the differences between CO2 welding and underwater wet welding. Table 2
showed the variable coefficient analysis of CO2 welding and
underwater wet welding. From the table, it could be seen that
when the weld current was kept const, the bigger the welding
voltage, the more stable the arc, and when the welding voltage
was kept constant, the bigger the welding current, the less
stable the arc, and the stability of underwater wet welding
changed much bigger. It meant that, when the weld current
Frequency
Fig. 8 Short-circuit time
frequency distribution
40
35
30
25
20
15
10
5
0
0
1
2
3
4
4.2 Range analysis of underwater wet welding
From the above analysis, it could be seen that the arc stability
of underwater wet welding was not stable compared to CO2
welding; to analyze the influence of different parameters on
the arc stability in underwater wet welding and to control the
welding quality, orthogonal experiments were done. Besides
welding current (I) and welding voltage (U), welding speed
(V) and the contact tube-to-work distance (D) were also considered. Table 3 showed the welding parameters and the corresponding reciprocal of variable coefficients.
Table 4 showed the calculated δ values under the same
level of every parameter, and range value R was the range
of different parameters; it was calculated by the maximum
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Frequency
Time/ms
40
35
30
25
20
15
10
5
0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Time/ms
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Table 2
Analysis of variable coefficients
Table 4
No.
Weld current
Weld voltage
CO2 welding
1
200
24
15.5025
3.2121
2
220
24
12.1172
2.2891
3
4
240
260
24
24
7.8109
6.8796
2.2695
2.6262
5
280
24
5.5090
2.3613
6
7
240
240
20
22
2.6030
4.4433
1.4175
1.7046
8
9
240
240
26
28
19.1751
22.1062
5.6983
10.6189
Mean value and range value of different parameters
U
I
D
V
Mean value 1
1.68
4.125
3.396
2.32
Mean value 2
2.664
3.039
2.804
3.807
Mean value 3
Mean value 4
3.394
4.971
3.039
2.452
3.066
3.443
3.055
3.528
Range R
3.291
1.673
0.639
1.487
Underwater
wet welding
and minimum mean values of every parameter, and the
range value could be used to judge the influences of different parameters on the arc stability. From Table 4, it
could be seen that the arc voltage had the biggest impact
on the arc stability, and the contact tube-to-work distance
had the least impact. According to the influence of the
four parameters on arc stability, it could be seen that within the scope of proper welding conditions, the smaller the
welding current, the bigger the welding voltage, the faster
the welding speed, the welding arc will be more stable.
Combining with the previous analysis, it could be seen
that the welding voltage played an important role in the
arc stability.
4.3 Sensitivity analysis of underwater wet welding
Mathematical model of underwater wet welding for arc stability could be constructed using multiple curvilinear regression
analysis. The mathematical model simulating the relationship
between the reciprocal of variable coefficients (δ) and the
welding parameters (I, U, v, D) could be obtained by Eq. (1)
δ ¼ ea0 I a1 U a2 ν a3 Da4
ð1Þ
Taking the natural logarithm of Eq. (1), the above equation
could be expressed by the following linear mathematical form:
lnδ ¼ a0 þ a1 lnI þ a2 lnU þ a3 lnν þ a4 lnD
ð2Þ
The regression coefficients of the above empirical formula
could be calculated using a Matlab program, according to the
experimental data shown in Table 3. Substituting these coefficients into Eq. (1), the following empirical formula could be
obtained:
δ ¼ e−3:0963 I −1:7773 U 4:2187 ν 0:5826 D−0:2244
Table 3
Orthogonal experiment of underwater wet welding
No.
U (V)
I (A)
D (mm)
V (mm/s)
Reciprocal of variable
coefficients (δ)
1
2
3
4
5
6
7
8
22
22
22
22
24
24
24
24
220
240
260
280
220
240
260
280
11
13
15
17
13
11
17
15
4
5
6
7
6
7
4
5
1.7354
1.6892
1.8570
1.4384
2.9831
3.5903
1.8801
2.2015
9
10
11
12
13
14
15
16
26
26
26
26
28
28
28
28
220
240
260
280
220
240
260
280
15
17
11
13
17
15
13
11
7
6
5
4
5
4
7
6
4.4719
3.1459
4.0263
1.9339
7.3092
3.7319
4.6101
4.2336
ð3Þ
The adequacy of the model and the significance of coefficients were tested by applying the analysis of variance technique and F test. Table 5 shows the R-square statistic, the F
statistic and p value for the full model. The R-square is 0.9005
while the p value<0.05; it is evident that the model was adequate. To ensure the accuracy of the developed equations and
survey the spread of the values, results were again plotted using
scatter graph. This graph of measured vs calculated values of
the reciprocal of variable coefficients is presented in Fig. 9. The
line of best fit for plotted points was also drawn using regression computation. It could be seen that the measure values and
the calculated values by Eq. (3) had a good linear relationship.
Sensitivity analysis was a method that could be used to
judge the key influence factors [21–23]; this method was used
to analyze the influence degree of different welding parameters
Table 5 Variance analysis for mathematical models for the reciprocal
of variable coefficients
Statistics
R-square
F statistic
p value
δ
0.9005
24.8792
0
Int J Adv Manuf Technol (2016) 86:557–564
563
based on these empirical equations. The sensitivities of
welding parameters on arc stability could be qualified by the
derivation of the sensitivity equation. If the arc stability with
respect to a certain parameter was positive, the arc stability
will increase as this parameter increases, whereas negative
sensitivities state the opposite.
Substituting orthogonal experiment parameters into
Eqs. 4–7, the sensitivity values for corresponding welding
parameters were obtained. Figure 10 shows the obtained results; from the figure, it could be seen that weld current and
tube-to–work distance had comparatively less impact on the
arc stability, and weld voltage and weld speed had more impact on arc stability.
Fig. 9 Accuracy of the calculated reciprocal of variable coefficients with
respect to measured data
on arc stability. The sensitivity equations for various parameters
on the arc stability was obtained by partially differentiating
Eq. (3), and the reciprocal of variable coefficients sensitivity
with respect to various welding parameters were obtained as
follows:
∂δ
4:2178e−3:0963 U 3:2178 v0:5826
¼
∂U
I 1:7773 D0:2244
ð4Þ
∂δ −1:7773e−3:0963 U 4:2178 v0:5826
¼
∂I
I 2:7773 D0:2244
ð5Þ
∂δ 0:58263e−3:0963 U 4:2178 v−0:4174
¼
∂v
I 1:7773 D0:2244
ð6Þ
∂δ
−0:2244e−3:0963 U 4:2178 v0:5826
¼
∂D
I 1:7773 D−1:2244
ð7Þ
The purpose of the analysis was to show the effect of
welding parameters by the direct sensitivity analysis technique
Fig. 10 Histogram of
sensitivities of arc stability on
welding parameters
5 Conclusions
Arc sensor was used to obtain the welding current and voltage
information in underwater wet welding, and the information
was compared with the CO 2 welding. Algorithms for
obtaining the short-circuit time was proposed, and the differences between underwater wet welding and CO2 welding
were compared based on the algorithm. Experiment results
showed that the arc stability of underwater wet welding was
much worse than CO2 welding. Arc stability was analyzed
based on the reciprocal of variable of coefficients, and it was
found that arc voltage plays a positive role on the arc stability
in underwater wet welding; to improve the arc voltage properly, the arc stability could be improved. Welding current and
contact tube-to-work distance have negative influence on the
arc stability.
This is a work in process; underwater wet welding is a
complex process which has many different characteristics
564
compared with atmospheric in-air welding because of the water environment. In our future work, other sensors such as
spectrometer, high-speed video camera will be used to monitor the process to obtain more information reflecting the
welding quality, so as to find the fundamental influence factors for underwater welding quality.
Acknowledgments This work was supported by the National Natural
Science Foundation of China under grant no. 51105103 and China Postdoctoral Science Foundation under grant nos. 2012 M510945 and 2013
T60362, Project (HIT.NSRIF.2015115) supported by the Natural Scientific Research Innovation Foundation in Harbin Institute of Technology.
Int J Adv Manuf Technol (2016) 86:557–564
9.
10.
11.
12.
13.
14.
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