PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Statistical characteristics of Gas Metal Arc Welding (GMAW) soundSipei ZHAO1; Xiaojun QIU1; Ian BURNETT1; Malcolm RIGBY2; Anthony LELE2
1

Centre for Audio, Acoustics and Vibration, Faculty of Engineering and IT, University of Technology Sydney,
Australia
2Sound Intuition Ltd. Pty., Australia

ABSTRACTGas Metal Arc Welding (GMAW) is an arc welding process to join two or more metal materials throughfusion, where an electric arc is formed between a consumable electrode and the base metal. It has beenreported that expert GMAW welders can direct the welding arc type based on the welding sound, andpsychoacoustic experiments show that the welding performance is significantly degraded without the
acoustic feedback to the welders. In addition, identifying the metal transfer mode based on the welding soundis critical for automatic GMAW process monitoring, quality control and a training pathway for competency.
However, the research on the generation and characteristics of the welding sound is still rare. In this paper,
the welding sound is measured simultaneously with the welding current at different metal transfer modes toinvestigate the unique characteristics of welding sound. The welding sound consists of many impulses
corresponding to the current leap. The envelope of the impulse responses is estimated based on the soundpressure signal for statistical analysis. It is found that the probability density function of the peak soundpressure, impulse interval and event duration can be well modelled by the Burr distribution. The findings canbe used to classify the metal transfer mode from its welding sound.
Keywords: Welding Sound, GMAW, Arc

1. INTRODUCTION
Arc welding is used to join two or more materials through fusion so that the joint exhibits asufficient strength and fracture toughness (1). Gas Metal Arc Welding (GMAW) is widely used inindustry due to its high metal deposition and ease of automation with better weld quality (2). The
acoustic emissions from a GMAW process contain information on the weld quality and can beutilized for online monitoring, inspection and quality control and training of welding processes.
The ultrasonic acoustic emission from welding was first measured to detect the defects in theweld (3). Saini and Floyd (1998) developed an online welding quality control system based on
audible sound, where the time domain features were used to detect deviations from ideal arc while
the frequency domain features showed some promise for metal transfer mode classification (4).
Similarly, various time and frequency domain features have been explored for welding quality
monitoring and control based on sound signals (5,6). In addition, different signal processing
techniques have been utilized to enhance the welding sound signals for better robustness, such as the
wavelet package transformation (7) and the Hilbert Huang transform (8).
In addition, advanced machine learning algorithms have also been used for GMAW processmonitoring using welding sound signals (5). The random forest algorithm was used to classify 3categories of welding quality, where the inputs for the decision tree algorithm were the statisticalfeatures extracted from the welding sound signals (9). The artificial neural network was employedfor online welding quality monitoring, and a prediction rate of 80-90% for penetration degree wasachieved based on training with a large data set (10). The Gaussian Mixture Model (GMM) was
utilized to identify the metal transfer mode from the welding sound signals and a 10 -fold crossvalidation shows a 90% accuracy (11).
In summary, the significance of acoustic sensing in monitoring the arc welding processes has
been identified by many researchers. However, the existing research relies on general audio features ,17594

sipei.zhao@uts.edu.auwhich are not dedicated for welding sound signals. In a different approach, this paper explores theunique statistical characteristics of the welding sound. The welding sound for different metal transfer
modes is measured simultaneously with the welding current. The welding sound is found to consistof a sequence of impulse responses, each of which corresponds to a welding current pulse and metal
droplet shape. The envelope of the impulse responses in the welding sound is estimated for statisticalanalysis. It is shown that the probability density function of the peak sound pressure, the impulseinterval and the event duration obey the Burr distribution. The model parameters vary for thewelding sound measured at different metal transfer modes, which is promising for automatic metaltransfer mode classification in the future.

2. EXPERIMENTS
The experimental setup is illustrated in Figure 1. A Lincoln Electric PowerWave C300 welder wasused in the experiments, where the torch was positive and the negative cable was connected to thework bench. During the welding, the torch was fixed and the work bench was moving, which wascontrolled by a servo motor and the travel speed could be set manually. A GRAS 40PH free fieldmicrophone was hanged 1.0 m above the weld pool to record the welding sound. The microphone wasconnected to an NI 9234 Sound and Vibration module. An LEM HTA 300-S current sensor was
installed around the torch cable to measure the welding current through the torch. The current sensor
was connected to an NI 9215 Analogue Voltage Input module. Both the NI 9234 and NI 9215 modules
were installed in an NI cDAQ 9185 chassis, which was connected to a DELL Optiplex 7060 computerwith a professional DELL P2918H monitor.

Microphone

NI 9234
Torch

Base metal

NI cDAQ 9185

NI 9215Work bench

WelderCurrent sensor

DELL Optiplex desktop computer

Figure 1 – Diagram of the experimental setup.
In GMAW, there are many different metal transfer modes, depending on various operationalvariables, such as the welding current, composition of shielding gas, electrode extension, ambientpressure, polarity and welding material (11,12). The welding current is the most used variable for thewelders to adjust for obtaining the desired metal droplet transfer mode (12). Identifying the metaltransfer mode is critical for process monitoring and quality control of GMAW (11). Therefore, the pure
contact transfer and the pure free flight mode were produced in the experiments to investigate thestatistical difference between the both. In addition to the natural GMAW, a Pulsed-GMAW mode wasalso performed in the experiments, where a controlled pulsed current waveform was used. Therefore,
three metal transfer modes are studied in this paper which aligns with an academic weld droplet mode
designation .

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A software with a user interface is developed with LabWindows/CVI 2017 to acquire, display andstore the signals in synchronisation. The sampling rate for the sound and current signals are 51.2 kHz
and 3.2 kHz, respectively. The measured sound pressure and current signal are shown in Figure 2 forthe pure contact transfer mode. The current signal shows that the welding current increases from a basevalue of approximately 85 A to a peak value of approximately 280 A first and then slumps to the basevalue. The increase in the current value shows the build-up of the droplet attached to the electrodewhile the peak current value corresponds to the short-circuiting phase of the contact metal transfer,where the droplet is big enough to connect the electrode to the base metal (13). The droplet detaches
from the electrode to the base metal and the current slumps to the base value again, which is called thearcing phase (13). The process repeats and a sequence of current peaks is formed during the wholewelding process.
Figure 2 demonstrates that an acoustic impulse is formed immediately after each current peak,indicating that the welding sound is produced by the quick energy release in the short-circuiting phase.
The detailed sound generation mechanism is beyond the scope of this paper and will be investigated infuture. In this paper, the statistical characteristics of the sound impulses are studied, e.g., the peaksound pressure in the impulses and the impulse interval as illustrated in Figure 2(a). In addition, theevent duration is defined to describe the time length of the cycling that corresponds to the increase anddecrease in the current value, as denoted by the green rectangle in Figure 2. The peak sound pressure,the impulse interval and the event duration can be calculated from the envelope of the sound signals, as
depicted in the following section. The correspondence between the sound and current signals for othermetal transfer modes are similar and not shown here for the sake of brevity.

Figure 2 – (a) Sound pressure and (b) welding current measured during the contact transfer GMAW.

3. STATISTICAL ANALYSIS AND DISCUSSIONS
To calculate the peak sound pressure, impulse interval and event duration for the sound impulses inthe sound signal, the envelope of the absolute sound pressure is first estimated. Figure 3(a) shows thesound signal for the pure contact transfer mode and Figure 3(b) illustrates the absolute sound signal
with the estimated envelopes (red curve). The envelope refers to the smooth curve outlining theacoustic impulses in the welding sound signal. The peak value in the envelope denotes the peak soundpressure, the time interval between two consecutive peaks denotes the impulse interval, and the time
period between two consecutive valleys denotes the event duration. To describe the statisticalcharacteristics of these variables, the probability density function is e stimated and fitted to various
statistical distribution model. It was found that the Burr distribution shows excellent performance infitting the probability density function. The Burr distribution is defined as (14)݂ሺ‫ݔ‬ȁߙǡ ܿǡ ݇ሻ ൌ݇ܿሺ‫ݔ‬Τߙሻ௖ିଵߙሾͳ ൅ ሺ‫ݔ‬Τߙሻ௖ ሿ௞ାଵwhere x is the variable and D, c and k are three positive function parameters.

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(1)

3.1 Contact transfer mode
A typical sound signal for the contact transfer mode is shown in Figure 3(a) and the corresponding
absolute sound pressure and the estimated envelope is illustrated in Figure 3(b). The histogram of thepeak sound pressure is shown in Figure 4(a), which is not symmetric and cannot be modelled by anormal distribution. Instead, the Burr distribution can be used the fit the histogram very well, as shownby the red curve in Figure 4(a). Similarly, the distributions of the impulse interval and the event
duration are also asymmetrical, as shown in Figure 4(b) and (c), respectively. The probability
distributions of the impulse interval and the event duration are very similar because they are actuallypertinent to each other, as shown in Figure 2(a).

Figure 3 – (a) Measured sound pressure and (b) the estimated envelope on the absolute sound pressure.

(a)

(b)

(c)

Figure 4 – Histogram and fitted Burr distribution for (a) sound pressure, (b) impulse interval and (c) event
duration for the welding sound at short-circuiting mode.
To describe the asymmetry in the probability distribution, the skewness is calculated as (15)‫ܧ‬ሺ‫ ݔ‬െ ߤሻଷ(2)ߪଷ
where P and V denote the mean and standard deviation of the variable x, respectively, and E()
denotes the expectation. A positive skewness indicates that the distribution is tailed to the right whilea negative skewness depicts a tail on the left. The skewness of a perfect symmetrical distribution
such as the normal distribution is zero.
The mean, standard deviation and the skewness of the peak sound pressure, the impulse interval andthe event duration are shown in the first row of Table 1. The mean and standard deviation of the peak
‫ݏ‬ൌ

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sound pressure are 38.7 Pa and 9.5 Pa, respectively. The skewness is 0.8, indicating that the probability
distribution is slightly tailed to the right, as shown in Figure 4(a). The mean and standard deviation of
the impulse interval and the event duration are almost the same because they are pertinent as discussed
above. The skewness of the impulse interval and the event duration is larger than that of the peak soundpressure, which stand for a longer tail to the right, as illustrated in Figure s 4(b) and (c).
Table 1 – Statistics of the welding sound event based on the envelope estimation
Variables

Sound Pressure

Impulse Interval

Event Duration

Statistics

P

V

s

P

V

s

P

V

s

Contact transfer

38.7

9.5

0.8

27.6

4.1

1.5

27.6

4.2

2.0

Free flight

8.6

4.8

2.9

32.7

9.9

1.7

32.6

10.3

1.5

Pulsed-GMAW

8.2

1.1

0.1

13.9

0.5

-0.1

13.9

0.8

0.0

3.2 Free flight mode
The typical sound signal for the pure free flight mode is shown in Figure 5(a) and the estimatedenvelope (red curve) of the absolute sound pressure is illustrated in Figure 5(b). By comparing Figure5(b) with Figure 3(b), it is clear that the peak sound pressure level for the free flight mode is much
lower than that for the contact transfer mode. In addition, the acoustic impulses are irregular as theimpulse interval varies much with time. These differences can be clearly observed from the probability
distribution, as shown in Figure 6.

Figure 5 – (a) Measured sound pressure and (b) the estimated envelope on the absolute sound pressure.
Most of the peak sound pressure in Figure 6(a) is smaller than that in Figure 4(a) and the
distribution is more heavily skewed to the right. This leads to a smaller mean value and a larger
skewness compared to the peak sound pressure distribution for the contact transfer mode, as depicted
in Table 1. On the other hand, the acoustic impulses are irregularly distributed, as evidenced by the
waveform in Figure 5 and the wider distribution of the impulse interval and the event duration in
Figure 6(b) and (c). This can be quantized by the standard deviation, as depicted in Table 1. The
standard deviation is 9.9 and 10.3 for the impulse interval and the event duration at the free flight mode,
which are much larger than that at the contact transfer mode.
In summary, the peak sound pressure of the free flight mode is much smaller than that of the contact
transfer mode, and the acoustic impulses are irregularly distributed as evidenced by the larger standard
deviation of the impulse interval and the event duration.

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(a)

(b)

(c)

Figure 6 – Histogram and fitted Burr Distribution for (a) sound pressure, (b) impulse interval and (c) event
duration for the welding sound at free-flight mode.
3.3 Pulsed-GMAW
Pulsed-GMAW is often used to improve weld quality as well as productivity in thin sheet metalindustries (16). A Pulse-GMAW was also performed in the experiments at the pure contact transfer mode.
The typical sound signal is shown in Figure 7(a) and the estimated envelope for the acoustic impulses isshown in Figure 7(b) based on the absolute sound pressure. It is clear that the acoustic impulses are very
regularly distributed and the impulse interval is smaller than the contact transfer mode in Figure 3 and thefree flight mode in Figure 5 under natural GMAW. The probability distribution of the peak sound pressurein Figure 8(a) shows that the sound pressure is concentrated to a very thin region, indicating the standard
deviation is quite small. In addition, the distribution is almost symmetrical, so the skewness is as small as
0.1 as shown in Table 1.
Similarly, the probability distributions of the impulse interval and the event duration are also
concentrated in a small region and almost symmetrical, so the standard deviation and the skewness are very
close to zero, as depicted in Table 1. In addition, the mean impulse interval and event duration are smaller
than that at the contact transfer mode and free flight mode under natural GMAW. These uniquecharacteristics shed light on distinguishing the Pulsed-GMAW from the contact transfer mode and the free
flight mode under natural GMAW based on sound signals.

Figure 7 – (a) Measured sound pressure and (b) the estimated envelope on the absolute sound pressure.

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(a)

(b)

(c)

Figure 8 – Histogram and fitted Burr Distribution for (a) sound pressure, (b) impulse interval and (c) event
duration for the welding sound at pulse mode.
In summary, the difference between different metal transfer modes in GMAW can be clearly seen fromthe statistical analysis of the welding sound signals, which consist of many acoustic impulses. In thewelding sound recorded at the free flight mode, the impulses are irregularly distributed with time, as
evidenced by the wide spread of the histograms of the impulse interval and event duration and the large
value of the standard deviation. In contrast, in the welding sound produced by Pulsed-GMAW, the acoustic
impulses are regular with an almost constant impulse interval, so the histograms of the impulse interval andthe event duration are concentrated in a small region and the standard deviation is very small. The
characteristics of the welding sound recorded at the contact transfer mode lie in between, but the peaksound pressure level is much higher than both the free flight mode and the Pulsed-GMAW.
In addition to the mean value, standard deviation and skewness, the model parameters of the Burr
distribution in Equation (1) are also different for different metal transfer mode, as summarized in Table 2.
The relationship between the model parameters and the shape of the histogram is not intuitive from
Equation (1), but Table 2 shows clearly that the model parameters are remarkably different, as thehistograms in Figures 4, 6 and 8. These different characteristics can be used as the dedicated features for
welding sound signals to distinguish the metal transfer mode automatically, which will be investigated in a
future work. The limitation of the current research is that the analysis is based on the clean welding sound.
The effect of the background noise in the factory environment on the statistical characteristics of welding
sound will be investigated in future.
Table 2 – Model parameters of the Burr distribution for different welding soundVariables

Sound Pressure

Impulse Interval

Event Duration

Parameters

D

c

k

D

c

k

D

c

k

Contact transfer

36.8

7.5

0.9

24.5

22.2

0.3

25.3

18.4

0.5

Free flight

7.9

3.7

1.1

27.1

9.1

0.5

26.7

8.5

0.5

Pulse-GMAW

8.8

10.5

1.8

13.9

48.1

1.2

13.9

29.8

1.0

4. CONCLUSIONSThis paper explores the statistical characteristics of the welding sound recorded at different metaltransfer modes, which aims to find out the dedicated features for the welding sound to create a datum
to automatically distinguish metal transfer modes. The probability distr ibution of the peak soundpressure, the impulse interval and the event duration were calculated based on the estimated envelopeof the absolute sound signals and were found to be modelled well by the Burr distribution. Different
characteristics of the welding sound at different metal transfer modes can be clearly observed from theshape of the histograms, the model parameters and the statistics such as the mean, standard deviationand skewness. These unique characteristics are promising to be used as the dedicated features for welding
sound signals to distinguish the metal transfer mode automatically, which will be investigated in a future
study. The physical generation mechanism of the welding sound at different metal transfer modes will7600

be investigated in the future.

ACKNOWLEDGEMENTS
This research is supported under the Australian Research Council’s Linkage Project (No.
LP160100616) and the UTS FEIT Tech Lab Blue Sky Grant.

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