Feasibility study of acoustic signals for on-line monitoring in short circuit gas metal arc welding

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International Journal of Machine Tools & Manufacture 44 (2004) 555–561

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Feasibility study of acoustic signals for on-line monitoring in shortcircuit gas metal arc welding

Ladislav Grad a, Janez Grumb,�, Ivan Polajnar b, Janez Marko Slabe b

a Fotona d.d., Stegne 7, Ljubljana, Sloveniab Faculty of Mechanical Engineering, University of Ljubljana, Askerceva 6, P.O.B. 394, Ljubljana, Slovenia

Received 4 August 2003; received in revised form 6 October 2003; accepted 15 October 2003

Abstract

The acoustic waves produced by the gas metal arc welding (GMAW) contain information about the behaviour of the arc col-umn, the molten pool and droplet transfer. In this study measurements of acoustic waves generated during GMAW process wereperformed. Acoustic waves were measured in the surrounding air and in the parts being welded by employing a microphone andPZT sensor. To evaluate influences on sound generation extensive experiments were performed with two different unalloyed car-bon steels: DIN RSt13 with 0.1% C and DIN Ck45 with 0.46% C, using two types of shielding gas: CO2 and gas mixture by itsbrand name Crystal (90% Ar, 10% CO2) and welding on a slope to vary the distance between welding nozzle and welding part.Acoustic signals were processed to obtain time domain and frequency domain descriptors. Some relationships between descriptorsand the weld process characteristics were investigated. Results indicate that the arc sound exhibits distinct characteristics for eachwelding situation and that the main source of acoustic waves in short circuit metal transfer mode is arc reignition. From acousticsignals one can easily assess process stability and detect welding conditions resulting in weld defects.# 2003 Elsevier Ltd. All rights reserved.

Keywords: Arc welding; Metal transfer mode; Airborne sound; Acoustic emission

1. Introduction

On-line quality control in automated welding opera-

tions is an important factor contributing to higher pro-

ductivity, lower costs and greater reliability of the

welded components. However, on-line inspection tech-

niques and feedback control are not yet broadly imple-

mented in industry. Much scientific work has been

performed to verify the suitability of different arc sig-

nals for on-line monitoring. As researchers continue to

refine these systems there has been increasing interest

in the mechanisms by which the signals are produced

and their relationship to the various physical processes

occurring in the welding process. Furthermore different

algorithms of signal analysis have been checked to

assure desired sensitivity and precision [1–10]. Pro-

posed methods are often either too complicated for

implementation in industry or not sufficiently accurate.Three levels of on-line quality control have been

articulated by the industry. In the first level one should

be able to automatically on line detect production of

bad welds. In the second level one should be able to

locate type of fault and reasons for faulty weld pro-

duction like changes in welding process induced by dis-

turbances in shielding gas delivery, changes in wire feed

rate and welding geometry, etc. In the third level one

should be able to correct welding parameters during

the welding process to assure proper weld quality.The lack of reliable non-contact, non-destructive, on-

line sensors with the ability to detect defects as they

form and with the capacity to operate at high tempera-

tures and in harsh environments is a considerable

obstacle to fully automated robotic welding. This paper

presents a non-contact automated data acquisition sys-

tem for monitoring a gas metal arc welding (GMAW)

process based on arc acoustics.

556 L. Grad et al. / International Journal of Machine Tools & Manufacture 44 (2004) 555–561

An experienced welder can evaluate an arc welding

process by listening to the sound emitted during weld-

ing [1]. The acoustic signal produced by the GMAW

contains information about the behaviour of the arc

column, the molten pool and droplet transfer. Taking

into account that today industry has high demands on

the welding process reliability and controllability and

that much effort is used to on line predict and control

the quality of welds, it is surprisingly only a few pub-

lished studies in which acoustic waves are regarded as a

source of information for process monitoring.In 1967, Erdmann-Jesnitzer et al. [2] published the

first study of acoustic waves generated during GMAW.

They found out that the pressure of produced sound

increases with the arc length and welding current. In

late 1970s and early 1980s Arata et al. [3,4] performed

important measurements on which base it was found

out that the sound travelling into the specimen and

into the surrounding air influence the welding process

by affecting the behaviour of molten pool. Some

attempts to use acoustic signals for on-line monitoring

of submerged arc welding process were presented by

Mayer [5] in 1987. Rostek [6] in 1990 used computer-

aided acoustic pattern recognition to prove monitoring

capabilities of acoustic signals. Grad et al. [7] in 1996

developed a monitoring method using different statisti-

cal parameters to assess process stability. In 2001,

Wang et al. [8] developed an acoustic method for

detecting the behaviour of the keyhole effect of plasma-

arc welding. In 2002, Miller et al. [9] presented a non-

contact automated data acquisition system for moni-

toring a robotic GMAW process based on laser ultra-

sonic technology. However, the usefulness of their

method is mainly in capturing geometry of the weld.

Despite the gaining of knowledge through researchindustry does not show much interest in employingacoustic monitoring techniques. One of reasons is thatacoustic phenomena for different welding conditions, aswell as the correlation with final weld properties is notwell understood. It is evident that the complexity of therelation between acoustic signal and weld quality repelsbroader investigation on this topic. To bridge some ofthe existing gap in scientific knowledge and industrialneed, measurements of acoustic signals during GMAWprocess, which included two different shielding gases,two different specimen materials and welding on aslope, have been performed. GMAW process was cho-sen due to its widespread use in automatic weldingsystems.

2. Experiments

Experiments were performed with the experimentalset-up shown in Fig. 1. A power source ISKRA E-450was used with control unit UNIMAG E-6 and weldingtrolley E-11 on which the welding head was fixed. Theconsumable electrode was VAC 60, U ¼ 1:2 mm. ACO2 and gas mixture (90% Ar and 10% CO2) under itscommercial name of Crystal was used as the shieldinggas. Welding was performed using two different voltagesettings: (a) voltage setting 6 with Uw ¼ 19 V; and (b)voltage setting 8 with Uw ¼ 21 V. Other welding para-meters were chosen to assure short circuit metal trans-fer and varied in the following ranges: wire speedvwire ¼ 3 4:5 m=min, trolley speed vtrolley ¼ 35 40 mm=s,

welding current Iw ¼ 110 130 A, gas fluxQgas ¼ 9 l=min.

For welding flat specimens with length l = 270 mm,width b = 25 mm and thickness d = 3 mm were pre-pared. They were made of two different steels:

Fig. 1. Experimental set-up.

L. Grad et al. / International Journal of Machine Tools & Manufacture 44 (2004) 555–561 557

(a) L
ow-carbon steel DIN RSt13 (0.1% C, 0.45% Mn,up to 0.030% P and up to 0.03% S) having goodweldability; and
(b) M
edium-carbon steel DIN Ck45 (0.46% C, 0.65%Mn, up to 0.2% Si, up to 0.045% S and up to0.045% P) having poorer weldability.
By placing the flat specimen under angle a with regardto horizontal base (Fig. 1) conditions that wire exten-sion length varied from 2 to 16 mm were ensured andthus industrial practice which often incurs changes inwire extension length was followed.The welding process was on line monitored by

measuring welding current and acoustic waveforms.Welding current was measured by employing a shuntresistor with characteristic 60 mV/250 A. Acousticwaveforms were detected in the surrounding air by amicrophone B&K 4134 (Bruel & Kjær) with reson-ance frequency at 23 kHz and negative polarization.Microphone was fixed on welding trolley E-11 in away that constant distance L between microphoneand welding arc was maintained. The microphone sig-nal was amplified by an preamplifier B&K 2636 andtransferred to PC by employing PCI analogue todigital converter card with acquisition sampling rate48 kHz.Acoustic emission in the welding part was measured

by a piezoelectric sensor (PZT) with resonance fre-quency at 2.8 MHz. The sensor was fixed to the weld-ing specimen using a wave guide. Signals were firsttransferred to the oscilloscope HP 54111 and then toPC.Using a PC, all signals were processed and compared

with welding process and weld characteristics. To char-acterize welding current signals maximum welding cur-rent Iw,peak and frequency of short circuit metal transfermMT all calculated in a 2-s time interval were used. Tocharacterize microphone signals an average of arcreignition sound peak amplitudes YM,peak was used. Toanalyse acoustic signals in frequency domain FFT(Y(t))was calculated. The stability of the process was char-acterized by statistical parameter of acoustic signalscalled kurtosis.

3. Results

3.1. Shape of acoustic signal

Typical acoustic signals measured in the air and inthe welding part and also corresponding welding cur-rent are shown in Fig. 2. It can easily be observed thatmicrophone acoustic signals peaks are well synchro-nized with short circuits. Signals measured with PZTsensor are more complex and no direct correlation withthe welding current was observed in the time domain.

3.2. Generation of acoustic waves in the air

In gas-shielded arc welding, sources of the sound

spreading in the surrounding air are:

. Changes in arc dimensions and geometry (fromshort circuiting to arc oscillation);

. Changes in arc intensity;

. Metal transfer; and

. Oscillations of the molten pool.

A closer look at one of the short circuits (Fig. 3)

reveals that two significant sound packages were travel-

ling in the air. The first one was generated when a

short circuit occurred, while the other was generated at

reignition of the arc. Due to the distance L of 35 cm

between microphone and welding arc, a time delay tdof 1.1 ms exists for each package. Regarding polariza-

tion of the microphone, it means that in the case of arc

extinction implosion occurs and in the case of arc

Fig. 2. Typical measured signals (base material Ck45: (a) Uw ¼ 19 V

and (b) Uw ¼ 21 V; vwire ¼ 3 m=min, vtrolley ¼ 35 mm=s, Qgas ¼9 l=min).


reignition explosion occurs. Substantial differences inamplitudes between both packages exist. Absolutevalue of reignition peak can be more than 10 times lar-ger than the corresponding sound of arc extinction.Thus the sound of the metal droplet transfer with arcreignition is much louder than the short circuitingsound. At the beginning of short circuiting, the arclength is reduced approximately simultaneously withthe feeding of welding wire and arc simply diminishesuntil it goes out without inducing significant changes inthe surrounding air. On the contrary reignition of arcoccurs in a more dramatic way, because the initial lengthof the arc is relatively high and the process itself issimilar to explosion which produces strong shock waves.

3.3. Generation of acoustic waves in the welding parts

Many studies have been published in the past inwhich acoustic emission was measured in the weldingparts during welding and just after welding. With suchmeasurements valuable information was obtained onthe formation and growth of cracks [10], liberation ofinternal tension, microstructure changes [11], etc. How-ever, if acoustic waves are measured during weldingprocess considerable noise is superposed [5]. Acousticwaves in welding parts during the welding process are aresult of different processes such as:

. Metal droplet transfer;

. Flow of the molten pool;

. Microstructure phase changes;

. Liberation of internal tension;

. Dilatations; and

. Plastic deformation.

Acoustic waves produced after welding are the resultof reverse processes, i.e. microstructure phase changes,

liberation of internal tension, dilatations and plasticdeformations. Acoustic emission produced by metaldroplet transfer seems to be much stronger in compari-son to acoustic emission from other sources mentionedabove. In this paper the cumulative effect of all citedcontributions was measured.

3.4. Influence of the shielding gas type

If welding with higher welding current, reignitionwill produce louder sound due to greater energyreleased in the arc column. Furthermore intensity ofarc reignition sound depends on the length of the arccolumn, which is closely connected with the ionisationenergy of the shielding gas. In our experiments, whengas mixture Crystal was used, higher welding currentwas achieved and consequently higher amplitudes ofmicrophone signals were observed as well (Fig. 4).Considering these results it can be concluded that anymonitoring algorithm should take into account the dif-ferences in microphone signal amplitudes that are theresult of shielding gas type.

3.5. Influence of the wire extension length (WEL)

When different WELs were used a substantial differ-ence in acoustic signal amplitudes measured by micro-phone was observed (Fig. 5). For example in the caseof welding Ck45 at 16 mm WEL, the average peakamplitude of arc reignition sound was �YYM; peak ¼0:109 V. By using a 13 mm WEL the welding currentremained practically unchanged. However, the averagepeak amplitude of arc reignition sound dropped to�YYM; peak ¼ 0:063 V. Below 12 mm WEL there is a very

small difference in average sound peak amplitudes.However, in Fig. 5 differences in cross-section can be

Fig. 4. Influence of shielding gas type (CO2 and gas mixture Crys-

tal) on average peak amplitude of arc reignition sound measured by

microphone (base material (a) RSt13 and (b) Ck45).

Fig. 3. Typical microphone signal measured during one short circuit

with corresponding welding current signal (base material Ck45,

Uw ¼ 21 V; vwire ¼ 3 m=min; vtrolley ¼ 35 mm=s; Qgas ¼ 9 l=min).


observed. The amount of melted material in the case of

the longer WEL is larger.

3.6. Influence of the material of the welding parts

Results obtained with welding parts made of RSt13

(0.1% C, 0.45% Mn) and Ck45 (0.46% C, 0.65% Mn)

show very similar peak amplitudes of measured acous-

tic signals:

�YYM; peak ¼ 0:44 V ðRSt13Þ and�YYM; peak ¼ 0:32 V ðCk45Þ:

Differences in peak amplitudes are too small compar-

ing to random distribution of signals peak amplitudes

to be directly used for on-line monitoring. Thus the

peak amplitudes of acoustic signal which are generated

by the arc reignition process does not depend strongly

on the type of welding part material and consequently

relatively small differences in chemical compositions

between RSt13 and Ck45 cannot be detected using this

method. Furthermore, despite similar acoustic signals

differences in cross-sections between these two materi-

als might be substantial (Fig. 6).

3.7. Influence of longer arc extinguished timeand burn-through

The most severe faults in welding are extinguishmentof the arc for a longer period and burn-through. Theirinfluence on weld and on acoustic signal is clearly seenfrom the Figs. 7 and 8. Photography of weld in Fig. 7shows that extinguishment of arc for a longer periodresults in non-uniform and discontinuous weld andthus in dramatically reduced quality of the weld. In themicrophone signal shown in the diagram above thephotography this irregularity is observed as a longsilence between two successive arc reignitions. We alsoobserved that longer extinguishment of the arc is oftenpreceded by non-regular arc reignitions as seen in thediagram as well. Acoustic signal is very accurate in themonitoring of the arc extinguishment. Larger irregula-rities in acoustic signal which are connected withdefects in the weld are previously announced by smal-ler irregularities in acoustic signal. This phenomenoncan be used as a base to develop a method to act ontime and prevent occurrence of substantial faults in theweld.Burn-through events as shown in Fig. 8 are severe

cases of weld irregularities resulting in bad welds. Inmicrophone signals burn-through events are demon-strated as longer extinguishment of the arc followed byhigh peak of arc reignition. Unfortunately burn-through happens without a previous announcement inmicrophone signal.

3.8. Analysis in frequency domain

PZT and microphone signals were analysed in thefrequency domain. A typical power spectral density(PSD) of PZT signals in frequency domain is shown inFig. 9. To find out the frequency of short circuit metaltransfer mPZT,MT a closer look was given to the low fre-quency range. A PSD peak can be observed at the fre-quency near the average frequency of metal droplettransfer �mmMT calculated from measured welding currentsignal. Taking into account that there is a random

Fig. 5. Influence of WEL on average arc reignition sound peak

amplitude and on metalographic structure of weld cross-section (base

material Ck45; shielding gas CO2).

hemical compositions of welding parts on weld cross-section (base material, (a) RSt1
Fig. 6. Influence of different c 3 and (b) Ck45; filler material
VAC 60, shielding gas Crystal, WEL ¼ 16 mm).


distribution of metal droplet transfer frequency around

average frequency, it is inferred that a peak in PSD

presents a frequency of metal droplet transfer as well.A shape of PSD around the peak might be taken as

a measure of process stability. Sometimes the peak is

clear and very narrow distribution is shown, but some-

times broad distribution with one or two local peaks

appears. The latter indicates that the process of metal

droplet transfer is not stable, i.e. frequency of metal

droplet transfer varies a lot, which is in generally not

desired. Similar but less accurate correlation can beobserved with microphone signals.

3.9. Characterisation of the process stability

Microphone and PZT signals might be characterisedin the time domain by different statistical measures. Ithas been shown [7] that signal kurtosis a4, which quan-tifies the sharpness of the peak of a signal distributionmight be used to monitor the stability of the weldingprocess. It is a dimensionless parameter defined as thefourth moment of the signal [12]:

a4 ¼m4

m22

ð1Þ

where mr is rth statistical moment of signal Y (YM orYPZT) with N discrete values Yj around the mean valueof the signal �YY :

mr ¼

PN

j¼1

ðYj � Y Þr

N: ð2Þ

With the increase of the metal droplet transfer frequency,the signal kurtosis increases (Fig. 10), which means thatat higher frequencies the process becomes more stableand consequently the quality of weld more uniform.

4. Conclusions

All manual welders use the welding arc sound as acriterion to exhibit the stability of arc welding process.By employing sound in the control of the welding pro-cess small changes in the process can be detected.Nevertheless, according to our knowledge, acousticwaves have not been used to monitor the welding pro-cess in an industrial environment. To better understandthis paradox, extensive experiments were performed bymeasuring acoustic waves in the surrounding air and inthe parts being welded by employing a microphone andPZT sensor.The results indicate that the arc sound produced

during the GMAW process are mainly produced byshort circuiting and arc reignition. The type of shield-ing gas substantially influences acoustic parameters.Wire extension length has substantial influence only atlengths larger than 12 mm. On the contrary, significantinfluence on acoustic signals by the amount of carboncontent in the test-pieces, was not observed. Further-more, acoustic emission produced by metal droplettransfer seems to be much stronger than the acousticemission generated by microstructure changes.All discrepancies that resulted from arc non-

regularity or produced nonregularities in arc behavioursuch as extinguishing of the arc and burn-throughevents, which have a dramatic influence on weld qual-

Fig. 7. Influence of long arc extinguishment on microphone signal

waveform and weld appearance.

Fig. 8. Influence of burn-through occurrence on microphone signal

waveform and weld appearance.


ity, are clearly monitored by acoustic signals. Thus theacoustic method is mainly useful to assess welding pro-cess stability and to detect mentioned severe dis-crepancies in arc behaviour.

References

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(1968).

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Untersuchungen am Schweislichtbogen, Schw. und Schn. 19 (3)

(1967) 95–100.

[3] Y. Arata, Investigation on welding arc sound, Report 1, IIW

Doc.S.G.212-451-79 (1979).

[4] Y. Arata, Investigation on welding arc sound: vibration analysis

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[5] J.L. Mayer, Application of acoustic emission to in process moni-

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[8] Y.W. Wang, P.S. Zhao, Noncontact acoustic analysis monitor-

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mated real-time data acquisition system for robotic weld quality

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[10] W.D. Jolly, Acoustic emission exposes cracks during welding,

Weld. J 48 (1969) 21–27.

[11] G. Frommeyer, K. Wolitz, Schallemissionsanalyse der martensi-

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(9) (1980) 613–616.

[12] M.R. Spiegel, Statistics, Schaum’s outlines series, McGraw Hill,

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PZT signal: (a) captured frequency range; and (b) low frequency range with denoted measur
Fig. 9. Typical PSD of ed average frequency of
metal droplet transfer.

. Typical dependence of kurtosis on metal transfer frequency (a) microphone signals; (b) PZT s
Fig. 10 ignals.

Documents

Feasibility study of acoustic signals for on-line monitoring in short circuit gas metal arc welding