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Investigation on arc sound and metal transfer modes for on-line monitoring in pulsed gas metal arc welding
Journal of Materials Processing Technology 210 (2010) 1397–1410
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Investigation on arc sound and metal transfer modes for on-line monitoring in pulsed gas metal arc welding Kamal Pal, Sandip Bhattacharya, Surjya K. Pal ∗ Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
a r t i c l e
i n f o
Article history: Received 14 January 2009 Received in revised form 26 March 2010 Accepted 29 March 2010
Keywords: P-GMAW Metal transfer mode Arc acoustics Pulse shape
a b s t r a c t The arc sound was found to be strongly related to both process parameters and weld quality, like voltage and current signals, in gas metal arc welding. In this investigation, the acquired welding arc sound signal along with current and voltage signals were analyzed in time domain as well as frequency domain to correlate them with the various process parameters and metal transfer modes. The arc sound of continuous as well as pulsed gas metal arc welding at various process parameters was also compared. A major variation of auxiliary arc sound frequency peaks was observed due to change of pulse shape as evidenced by frequency domain analysis. The arc sound was also used to detect welding defects. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Gas metal arc welding (GMAW) is widely used in industry due to its high metal deposition and ease of automation with better weld quality. Pulsed GMAW (P-GMAW) is often used to improve weld quality as well as productivity in thin sheet metal industries (Chen et al., 2005). It is a variation of constant current (or voltage) welding process which involves cycling of the welding current (or voltage) from a high level to a low level at a desired pulse frequency. The pulse shape also affects the heat input as well as weld characteristics in P-GMAW (Joseph et al., 2005). Manufacturing companies often face weld quality problems due to poor arc stability. The arc stability depends on material transfer behavior and arc length variations in P-GMAW. A stable welding process must have a uniform material transfer with minimal spatter (Hermans and Ouden, 1999). The optimum arc stability in PGMAW is obtained for one droplet per pulse (ODPP) condition with a droplet size close to that of the electrode wire diameter (Amin, 1983). Miranda et al. (2007) developed a control system capable of automatically adjusting pulse parameters to acheive stable ODPP metal transfer. The droplet detachment is also related to its oscillation behavior, which is also affected by pulse parameters (Wu et al., 2004). Palani and Murugan (2006, 2007) proposed the various aspects of pulse parameters and their selection to obtain good
∗ Corresponding author. Tel.: +91 3222 282996; fax: +91 3222 255303. E-mail addresses: [email protected] (K. Pal), [email protected] (S. Bhattacharya), [email protected] (S.K. Pal). 0924-0136/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jmatprotec.2010.03.029
quality welds. Ghosh et al. (2006) developed an analytical model to provide a theoretical background of the effect of pulse parameters on metal transfer modes in P-GMAW. There are a lot of uncontrollable factors like contamination and environmental conditions in GMAW. Therefore, the real time monitoring and control is highly required to overcome the timeconsuming and costly off-line inspection in modern automated welding environments. Various intelligent modeling, analytical and simulation techniques such as soft-computing tools, finite element methods and thermal models developed to monitor GMAW. But, the actual process parameters vary dynamically. Therefore, various advanced (i.e., reliable, non-contact and non-destructive) online sensors are required. These sensors can be utilized to acquire actual values of process parameters to be used as inputs to the developed model to improve the predictability, optimization and control capability in real time. Current and voltage sensors are considered to be the most reliable, simple and competitive (Li et al., 2000). The metal transfer modes were identified with a pattern-recognition system using these sensor signals (GuoMing et al., 2003). Pal et al. (2007, 2008) developed various soft-computing models for the monitoring of weld quality using these sensor signals. But, these two arc sensors are not sufficient to completely characterize the process. Therefore, visual inspection, artificial vision sensing, infrared sensing, radiographic sensing, etc. are also used. The infrared sensor was used to monitor the weld centerline temperature (Santos et al., 2000) and weld penetration depth (Wikle III et al., 2001) during arc welding. It is very difficult to grasp the dynamic welding characteristics, especially near the arc due to very high temperature, spatter formation, fumes, etc. The arc
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acoustics and ultrasonic sensing may be useful to overcome these difficulties. The acoustic waves produced in GMAW are related to the behavior of the arc, the molten weld pool and metal droplet transfer mode. Erdmann-Jesnitzer et al. (1967) and Jolly (1969) confirmed the relevance of acoustic waves produced during GMAW. Arata et al. (1979a, 1979b, 1980) tried to correlate the welding sound with various process parameters in CO2 arc welding, continuous GMAW and pulsed gas tungsten arc welding (P-GTAW). Drouet and Nadeau (1982) developed an arc length monitoring technique with an acoustic voltmeter. Mansoor and Huissoon (1997) investigated the same phenomenon with both time and frequency domain analyses to correlate it with metal transfer modes in GMAW. Grad and Kralj (1996) and Grad et al. (2004) evaluated the arc welding process stability using various statistical parameters of acquired sound signals and established the theoretical and experimental base of arc acoustic signals to monitor GMAW in industrial environments. Fan et al. (2001) concluded that CO2 welding arc sound energy and spatter loss were proportional to each other. Deuster and Sinz (1983), Mayer (1987) and Wang and Zhao (2001) proposed acoustic emission monitoring systems in submerged arc welding (SAW). Futamata et al. (1984) analyzed the plasma jet sound using transferred arc in plasma arc welding (PAW). Saad et al. (2006) identified the cutting mode from keyhole mode using power spectral density of welding sound acoustics with artificial neural network (ANN) in variable polarity PAW. There are various back-ground industrial noise sources that may hinder the proper analysis of emitted welding noise signals. But, these problems may be suitably overcome and acoustic monitoring ˇ might be better than other techniques (Cudina and Prezelj, 2003). Ogawa et al. (1995) observed that the burn-through condition in CO2 arc welding can only be identified by arc sound. Luksa (2003) correlated sound emissions generated in the GMAW process with signals registered in the arc circuit during various metal transfer ˇ modes. Cudina et al. (2008) proposed two types of noise generating mechanisms namely sound impulses for arc extinction and arc re-ignition and turbulent noise due to metal transfer in shortcircuit GMAW. Luo et al. (2005) used frequency domain and wavelet analysis of acoustic signals to identify weld defects in laser welding by ANN. Tam and Huissoon (2005) proposed psycho-acoustic experiments to better understand the welding acoustic characteristics using spectral acoustic reliance of professional welders. Poopat and Warinsiriruk (2006) used the time and frequency domain features to identify the metal transfer modes and defects in GMAW. Lin and Fischer (1995) also used arc welding sound acoustics in ANN
models for the prediction of weld bead geometry and degree of spatter. There is a strong relationship between arc sound signal and process parameters as well as weld quality in various GMAW processes, especially in CO2 welding and short-circuit GMAW. In this investigation, the welding sound was acquired in GMAW, which was further analyzed in time domain as well as frequency domain. Initially, the arc sound signal along with current and voltage signals were compared between P-GMAW and continuous GMAW. These sensor signals were used to correlate it with various metal transfer modes at various pulse voltage conditions in P-GMAW. The measured peak temperature also used to indicate metal transfer stability. Finally, an attempt was made to correlate the various metal transfer modes with sound signal analysis in time domain as well as frequency domain. 2. Experiments In this work, a constant voltage P-GMAW machine was used. The power source and control unit were TRANSARC 500 and FRONIUS VR131, respectively. The experiments were carried out on mild steel plate (7.5 mm thick) using copper coated mild steel filler electrode wire (ESAB, S-6 wire, 1.2 mm diameter) in bead-on-plate method. The electrode wire was fed to the welding gun by a fourroller drive system. The current (Hall-effect current sensor LEM, model LT 500S), voltage, sound sensors and infrared pyrometer were used. The experimental setup is shown in Fig. 1. The measured voltage was stepped down in 1:11 ratio before being fed to the A/D card. A microphone (B&K, 4189) with attached pre-amplifier (2669L) and a conditioning amplifier (Nexus 2690, combined sensitivity 316 mV/Pa) was positioned at a distance of one meter from the welding torch as shown. This microphone with a frequency bandwidth of 6.3 Hz to 20 kHz and dynamic sound level range of 14.6 dB to 146 dB is highly feasible to monitor arc welding sound in this case. An infrared pyrometer (Omegascope, OS5233) was focused on the plate surface at a distance of 20 mm from the arc. The signals were acquired using two A/D cards (National Instruments, USB-6210) to two Intel Pentium-4 PCs using LabVIEW 7.1 data acquisition interface at a sampling frequency of 40 kHz (for current, voltage and sound signals) and 10 Hz (for pyrometer), respectively. A high speed camera (MiDAS 2.0) was also used to capture real time welding images at 4000 frames per second. The chemical composition of work piece material was obtained by optical emission spectroscopy (OES) analysis (Table 1). The plates were cleaned with brush and alcohol solutions for better weldablity. Pure argon (99.9%) was used as shielding gas. Six pro-
Fig. 1. Schematic diagram of the experimental set-up.
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Table 1 Chemical composition (weight percentage) of the base plate. C
Si
Mn
P
S
Ta
Cr
Cu
0.149
0.108
0.358
0.072
0.073
0.018
0.015
0.032
Table 2a Process parameters and their values in continuous GMAW and P- GMAW (constant pulse parameters) (1st set of experiment). Process parameter
Parameter values
Arc mean voltage (V) Welding speed (mm/s) Wire feed rate (m/min)
26 2.5, 3.6, 5.6, 7.5 6, 7, 8, 9
cess parameters, namely welding speed (S), wire feed rate (F) along with four pulse voltage parameters (pulse frequency (fp ), pulse ontime (tp ), peak voltage (Vp ) and back-ground voltage (Vb ) were considered in this work. Initially, the welding speed and wire feed rate were varied in both continuous GMAW as well as P-GMAW, maintaining a constant mean voltage (26 V). The arc stability is related to the ratio of peak current to back-ground current (Ip /Ib ) as well as pulse shape (or duty factor) in P-GMAW (Ghosh et al., 2006). So, three pulse variables, namely voltage ratio (Vp /Vb ), pulse frequency (fp ) and duty factor (Dp ) were considered, which were varied one at a time keeping the other two parameters constant. The voltage ratio (Vp /Vb ) and pulse voltage frequency (fp ) were varied (within acceptable weld range) at two mean voltage conditions (26 V and 20 V) with two acceptable wire feed rates (6 and 7, and 7 and 8 m/min, respectively) as shown in Tables 2a and 2b. These two mean voltage conditions were chosen, so that the mean current would be lower and higher than transition current (current for the change of metal transfer mode from globular to spray) respectively. The pulse voltage duty factor (Dp ) was then varied at a constant Vp , Vb , fp with two combinations of wire feed rate as before. The detailed process parameter settings of 65 number of experiments with corresponding mean and RMS value of arc power (Pm and Prms ), measured peak temperature (Tp ), sound statistical values viz. RMS value of sound (Srms ) and sound kurtosis (Sk ), are presented in Tables 3a and 3b. 3. Results RMS (or mean) current increased linearly with wire feed rate in both pulse and continuous modes at a particular mean voltage Fig. 2. The weld bead was found to be much smoother in pulse mode of voltage condition than continuous mode due to less amount of spatter, evidenced by high metal deposition efficiency in P-GMAW (Pal et al., 2009).
Fig. 2. Variation of RMS current with wire feed rate.
One major arc sound peak was observed at arc re-energized point of time, i.e., when voltage changes from low to high (same as arc re-ignition in short circuit GMAW), as achieved in P-GTAW (Arata et al., 1980). Similarly, a minor sound peak was observed when voltage dropped from high to low as shown in case of high pulse voltage condition (Fig. 3a). But, this minor sound pressure peak could not be distinguished from turbulent metal transfer sound, especially in case of droplet transfer mode or unstable arc conditions due to metal droplet transfer sound or spatter sound. The metal droplet transfer sound was found to be significantly higher than re-energized arc sound in case of low pulse voltage condition as shown in Fig. 3b. The arc shape variation (high speed camera images) with a typical pulse voltage variation along with sensor signals (sound peaks) at high mean voltage condition is shown in Fig. 4 But, the metal transfer mode identification is limited with the video image due to glare of arc (Ghosh et al., 2007). The droplet detachment (or globular transfer mode) was also observed at low mean voltage conditions evidenced by voltage fluctuations with corresponding sound peaks, which may also be higher than previous re-energized sound peaks. However, it could not properly identify the metal transfer mode using high speed video images. The arc sound pressure indicates the metal transfer modes in GMAW (Mansoor and Huissoon, 1997).Two statistical parameters (RMS and kurtosis) were computed from the raw welding sound signals. The RMS value arc sound pressure value related with degree of metal transfer. The sound kurtosis indicated the degree of sharpness of arc sound signal, which was found to be related to the mode of metal transfer. High sound kurtosis indicated droplet or globular metal transfer with spatters rather than ODPP (in pulsed conditions) or spray mode. The RMS and kurtosis value of arc sound signal along with current and voltage signals were used to indicate the mode of metal transfer. The measured peak temperature
Table 2b Process parameters (pulse) and their values in P- GMAW (2nd set of experiment). Process parameter
Parameter values
Fixed
Variable
Welding speed (5.6 mm/s)
Wire feed rate (m/min)
at Vm = 20 V at Vm = 26 V
6, 7 7, 8
Voltage ratio
at Vm = 20 V at Vm = 26 V
1.5, 2, 2.25, 2.5, 3
Pulse frequency (Hz)
at Vm = 20 V at Vm = 26 V
50, 100, 150, 200, 250
Pulse duty factor
at Vp = 36 V and Vb = 16 V, at Vp = 27.8 V and Vb = 12.2 V
0.3, 0.4, 0.5, 0.6, 0.7
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Table 3a Detailed experimental design matrix with responses (continuous GMAW). Expt. no
Wire feed rate (m/min)
Welding speed (mm/s)
Arc voltage (V)
Mean arc power (kW)
RMS arc power (kW)
RMS arc sound (V)
Arc sound kurtosis
Measured peak temperature (◦ C)
1 2 3 4 5 6 7
7 7 7 7 6 8 9
2.5 3.6 5.6 7.5 5.6 5.6 5.6
26 26 26 26 26 26 26
4.27 4.38 4.44 4.5 4.14 4.66 4.97
5.11 4.89 5.04 4.96 4.6 5.16 5.66
0.598 0.552 0.53 0.505 0.571 0.601 0.572
63.73 74.85 81.93 78.67 52.62 61.33 68.56
786 657 473 485 395 570 615
was also acquired and correlated with the stability of metal transfer. The acquired time domain data of all sensor signals is shown in Table 3. A typical measured temperature variation profile is shown (Fig. 5). 3.1. Time domain analysis of arc sound, arc power and weld temperature The arc sound kurtosis indicates the sharpness of sound signal during metal transfer in P-GMAW (Pal et al., 2009). In this work, RMS and kurtosis values of arc sound were processed and correlated with metal transfer behavior in continuous GMAW, as well as PGMAW. The current and voltage signals along with arc power and measured peak temperature was also used to identify the metal transfer modes. 3.1.1. Effect of welding speed The mean arc power increased slightly with the welding speed (Table 3) as observed earlier (Nouri et al., 2007). The arc sound pressure is mainly related to the metal transfer behavior as well as degree of spatter generation at constant voltage condition (Arata et al., 1979b). The arc sound pressure reduces with welding speed at low arc power (Arata et al., 1979a). In this work also, the RMS value of sound pressure reduced with an increase of welding speed due to reduced metal transfer. But, it was found to be very high at low welding speed (2.5 mm/s) in continuous GMAW due to huge spatter with unstable metal transfer. Whereas, it was significantly less in P-GMAW due to bridging or short-circuiting metal transfer caused by reduced electrode to plate gap with high arc power as shown in Fig. 6a (Iordachescu and Quintino, 2008). This is evident from the sensors’ time domain signals of the corresponding experiments. The sound kurtosis value decreased (less peaked) gradually with increase in table speed in case of P-GMAW due to the transition to more stable spray transfer. In case of continuous GMAW, an initial increasing trend was observed after which it stabilized (Fig. 6b). The probable reason was an increase of metal droplet size with reduced spatter due to higher arc power. The measured peak temperature reduced typically (experiment #8) with welding speed due to lower average heat input to the weld (Table 3). However, it was not significantly reduced at higher welding speeds, especially in P-GMAW due to two opposite effects of reduced weld heat content and better metal transfer stability. 3.1.2. Effect of wire feed rate The mean (as well as RMS) value of arc power increased with wire feed rate due to higher welding current at same arc voltage conditions (Table 3). The RMS value of arc sound was found to increase smoothly with increase in wire feed rate due to more metal transfer (Fig. 6c). But, it was higher at very low wire feed rate (6 m/min), especially in P-GMAW, possibly due to high spatter. Generally, the metal transfer is regular, if wire feed rate is same as burn-off rate in GMAW (Palani and Murugan, 2007). The RMS value of arc sound was found to be reduced when the wire burn-off rate criteria was satisfied. It also indicated a stable metal transfer over
consecutive current (or voltage) pulses evidenced by time domain sensors’ signals. The sound signal kurtosis increased for both continuous and pulsed GMAW with wire feed rate due to increase of metal droplet size at constant mean voltage condition (Fig. 6d) as achieved earlier in short-circuit GMAW (Grad et al., 2004). The voltage (or current) fluctuations at pulse on-time were observed for higher wire feed rate which also indicated the same. The measured peak temperature increased with wire feed rate due to higher heat content of metal droplets influenced by higher arc power (Table 3). Thus, it also showed an identification of metal transfer rate and its transfer mode. 3.1.3. Effect of pulse parameters on metal transfer modes The effect of various pulse voltage parameters on metal transfer behavior was investigated by studying 3–5 consecutive pulses of voltage and current with corresponding acquired sound signal in P-GMAW. In pulse current welding there are mainly two types of metal transfer modes, namely one droplet per pulse (ODPP) and multiple droplets per pulse (MDPP) with or without metal transfer at base current depending on pulse parameters (Ghosh et al., 2007). There was a variation of metal transfer behavior with an increase of peak voltage or background voltage, as the voltage ratio (Vp /Vb ) was varied, which directly influenced the arc stability (Ghosh et al., 2007). As voltage ratio increased, the magnitude of arc sound peaks (at arc re-energized points) increased as it is proportional to the effective voltage amplitude. The metal transfer mode was found to be changed from globular with various size of metal droplets (unstable arc) at Vp /Vb = 1.5 to ODPP with smaller droplets at Vp /Vb = 2.25 to fully spray at Vp /Vb = 3, in case of low mean voltage condition (Vm = 20 V), evidenced by acquired voltage and current with corresponding sound signals peaks. However, in case of high mean voltage condition (Vm = 26 V) the metal transfer changed from ODPP to smaller sized MDPP to spray with an enhancement of voltage ratio from 1.5 to 3. This occurred due to higher peak voltage. The metal transfer mode was regular with higher voltage ratio up to a certain extent (2.5–3.0) for a particular welding mean voltage (constant pulse parameters) and wire feed rate combination. But the metal transfer stability then reduced with further increase of voltage ratio due to high voltage fluctuation. The arc sensors’ signal fluctuations due to metal transfer was found to be increased with an increase of wire feed rate keeping mean voltage constant, indicating larger sized metal droplet or more metal transfer. Thus, the metal transfer stability was found to be significantly disturbed at higher wire feed rate. The variation of metal transfer mode and arc stability with the variation of two pulse parameters simultaneously at different wire feed rates is somewhat complex. As the voltage pulse frequency is increased with same duty factor (50%), the pulse ontime reduces. There were irregular globular droplets with unstable mode of metal transfer (at fp = 50–100 Hz with tp = 10–5 ms), which changed to somewhat stable (at fp = 150 Hz with tp = 3.33 ms) and then to smaller droplets with stable arc (at fp = 200–250 Hz
Table 3b Detailed experimental design matrix with responses (P-GMAW). Mean arc voltage (V)
Peak voltage Back-ground (V) voltage (V)
Voltage ratio
Pulse duty factor
Pulse frequency (Hz)
8 9 9a 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
2.5 3.6 3.6 5.6 7.5 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6
26 26 26 26 26 26 26 26 20 20 20 20 20 20 20 20 26 26 26 26 26 26 26 26 20 20 20 20 20 20 20 20 20 20 26 26 26 26 26 26 26 26 16.8 18.4 21.6 23.2 16.8 18.4 21.6 23.2 22 24 28 30 22 24 28 30
36 36 36 36 36 36 36 36 24 26.8 28.6 30 24 26.8 28.6 30 31.2 34.8 37.2 39 31.2 34.8 37.2 39 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.8 36 36 36 36 36 36 36 36 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.8 36 36 36 36 36 36 36 36
2.25 2.25 2.25 2.25 2.25 2.25 2.25 2.25 1.5 2.03 2.51 3 1.5 2.03 2.51 3 1.5 2.02 2.51 3 1.5 2.02 2.51 3 2.28 2.28 2.28 2.28 2.28 2.28 2.28 2.28 2.28 2.28 2.25 2.25 2.25 2.25 2.25 2.25 2.25 2.25 2.28 2.28 2.28 2.28 2.28 2.28 2.28 2.28 2.25 2.25 2.25 2.25 2.25 2.25 2.25 2.25
0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.4 0.6 0.7 0.3 0.4 0.6 0.7 0.3 0.4 0.6 0.7 0.3 0.4 0.6 0.7
100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 50 100 150 200 250 50 100 150 200 250 50 150 200 250 50 150 200 250 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
7 7 7 7 7 6 8 9 6 6 6 6 7 7 7 7 7 7 7 7 8 8 8 8 6 6 6 6 6 7 7 7 7 7 7 7 7 7 8 8 8 8 6 6 6 6 7 7 7 7 7 7 7 7 8 8 8 8
16 16 16 16 16 16 16 16 16 13.2 11.4 10 16 13.2 11.4 10 20.8 17.2 14.8 13 20.8 17.2 14.8 13 12.2 12.2 12.2 12.2 12.2 12.2 12.2 12.2 12.2 12.2 16 16 16 16 16 16 16 16 12.2 12.2 12.2 12.2 12.2 12.2 12.2 12.2 16 16 16 16 16 16 16 16
Pulse on-time (ms) 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 10 5 3.3 2.5 2 10 5 3.3 2.5 2 10 3.3 2.5 2 10 3.3 2.5 2 3 4 6 7 3 4 6 7 3 4 6 7 3 4 6 7
Mean arc power (kW)
RMS arc power (kW)
RMS arc sound (V)
Arc sound kurtosis
Measured peak temperature (◦ C)
5.06 5.2 5.21 5.29 5.3 5.08 5.66 5.73 3.15 3.23 2.81 3.33 3.21 3.42 3.42 3.49 4.75 5.26 5.5 5.82 4.94 5.56 5.63 5.95 2.39 3.29 3.37 3.51 3.6 3.3 3.5 3.5 3.7 3.83 5.09 5.39 5.43 5.37 5.19 5.47 5.6 5.51 2.56 2.9 3.87 4.18 2.76 3.04 3.95 4.38 4.07 4.52 5.95 6.54 4.08 4.65 6.14 6.64
6.06 6.23 6.2 6.17 6.26 5.85 6.76 6.94 4.26 4.14 4.06 4.41 4.09 4.47 4.6 4.67 5.17 6.09 6.54 7.15 5.48 6.43 6.78 7.38 3.97 4.21 4.36 4.71 5 4.42 4.63 4.43 5.08 5.49 6.07 6.31 6.24 6.01 6.27 6.34 6.5 6.27 3.64 3.87 5.34 5.57 3.94 4.06 5.32 5.89 5.38 5.53 6.63 7.03 5.47 5.8 6.95 7.18
0.476 0.557 0.552 0.546 0.53 0.914 0.638 0.616 0.55 0.566 0.536 0.599 0.515 0.573 0.597 0.666 0.424 0.532 0.563 0.664 0.613 0.564 0.532 0.609 0.537 0.49 0.538 0.537 0.523 0.584 0.576 0.484 0.572 0.558 0.532 0.571 0.547 0.496 0.56 0.422 0.481 0.46 0.444 0.52 0.538 0.501 0.438 0.512 0.589 0.533 0.579 0.464 0.771 0.815 0.561 0.602 0.56 0.498
20.08 8.849 7.444 −0.38 −0.26 −0.74 3.555 14 73.27 65.55 56.99 36.6 93.4 66.88 48.62 40.44 28.49 1.531 0.156 1.297 28.32 4.069 1.047 0.512 69.08 72.44 83.79 89.18 80.78 63.74 62.55 84.44 78.78 74.13 1.378 1.529 0.11 1.837 9.672 0.3 0.845 9.676 73.39 80.11 65.63 68.37 111.4 71.39 63.99 62.41 25.96 3.857 −0.35 −0.22 41.02 26.62 3.279 0.351
861 663 598 627 550 577 698 700 385 369 413 477 296 458 458 408 626 595 572 661 593 710 708 746 368 373 410 370 331 455 470 476 473 377 646 705 634 558 720 674 661 507 419 318 353 431 326 341 416 451 339 491 574 566 495 510 585 547
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Welding speed (mm/s)
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Expt. no Wire feed rate (m/min)
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Fig. 3. Variation of arc sound peaks in case of (a) high voltage and (b) low voltage in P-GMAW.
Fig. 4. Variation of arc shape with pulse voltage (current) and corresponding sound peaks.
K. Pal et al. / Journal of Materials Processing Technology 210 (2010) 1397–1410
Fig. 5. A typical weld surface temperature profile (expt # 8).
with tp = 2.5–2 ms) with an increase of pulse frequency (keeping duty factor or pulse shape constant) at Vm = 20 V and F = 6 m/min (Fig. 7a–c, respectively). The variation of metal transfer mode changed from irregular MDPP (50–100 Hz) to stable ODPP (150 Hz), and to spray (200–250 Hz), at Vm = 26 V and F = 8 m/min (Fig. 7d–f, respectively), as achieved in earlier work (Praveen et al., 2006). The regularity of metal transfer was found to be improved with lower wire feed rate in this case. The metal transfer occurred at pulse off-time as per voltage, current and corresponding sound signal fluctuations in case of low frequency pulses. This was found especially for low voltage with higher wire feed rate condition as observed earlier with high speed videography (Ghosh et al., 2007). The metal transfer mode variation occurred with the variation of duty factor at constant peak voltage, background voltage and pulse frequency. The metal transfer mode changed from globular with various sized droplets (low duty factor) to smaller ODPP and fine droplets with little instability to unstable smaller MDPP at Vm = 20 V and F = 7 m/min (Fig. 8a–c, respectively). However, the
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metal transfer irregularity was somewhat reduced at lower wire feed rate F = 6 m/min, keeping mean voltage constant at 20 V. The variation was similar but shifted towards higher duty factor in case of higher mean voltage condition. The metal transfer mode changed from irregular droplets to ODPP with smaller sized droplets, and to completely spray with an increase of duty factor from 0.3 to 0.7 at Vm = 26 V and F = 7 m/min (Fig. 8d–f, respectively). It changed from unstable globular droplets to stable ODPP to spray at very high duty factor in case of high wire feed rate (F = 8 m/min), as achieved in earlier work (Choi et al., 1998). The metal transfer also occurred at pulse off-time in case of low duty factor pulses having higher background voltage, especially for low pulse voltage and higher wire feed rate condition as observed earlier with high speed camera (Ghosh et al., 2007). However, the metal transfer in pulse off-time disturbed the arc stability as per time domain sensors’ signals. 3.1.4. Effect of voltage ratio The mean arc power increased gradually with voltage ratio (Table 3). The RMS of sound signal was found to increase with the voltage ratio (Fig. 9a), especially at a wire feed rate of 7 m/min. The sound pressure level also increased with voltage amplitude (Vp –Vb ) in P-GTAW, as observed by Arata et al. (1980). The RMS value of sound was found to fluctuate at high wire feed rate (8 m/min) at Vm = 26 V and low wire feed rate (6 m/min) at Vm = 20 V, due to irregular metal transfer caused by improper electrode wire burn-off rate. When the wire feed rate was 7 m/min, the magnitude of sound signal RMS was found to be greater for low mean voltage conditions due to droplet metal transfer mode as compared to spray transfer in high mean voltage condition (Fig. 9a). The arc sound kurtosis was found to reduce considerably when the voltage ratio increased from 1.5 to 2.0, but thereafter decreased gradually with further enhancement of this ratio (Fig. 9b). The magnitude of the sound signal kurtosis was much higher in the case of low mean voltage conditions, as compared to high mean voltage
Fig. 6. Variation of RMS value (Srms ) and kurtosis (Sk ) of arc sound with welding speed (S) and wire feed rate (F). (a) Srms with S, (b) Sk with S, (c) Srms with F, (d) Sk with F.
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Fig. 7. Variation of metal transfer modes with pulse frequency (fp ). (a) 50 Hz, (b) 150 Hz, (c) 250 Hz for Vm = 20 V, F = 6 m/min, Vp /Vb = 2.2–2.3 and Dp = 0.5, (d) 50 Hz, (e) 150 Hz, (f) 250 Hz for Vm = 26 V, F = 8 m/min, Vp /Vb = 2.2–2.3 and Dp = 0.5.
conditions due to a change in the mode of metal transfer, discussed in Section 3.1.3. The measured peak temperature gradually increased with an increase of voltage ratio at constant mean voltage condition in most of the cases due to stable metal transfer (Table 3) (Pal et al., 2009). However, a little drop or fluctuation indicated the change of arc stability influenced by metal transfer behavior. The metal droplet temperature increases with the ratio of peak to back-ground current (Ip /Ib ) at a constant mean current (Randhawa et al., 2000). It was probably the reason for higher measured peak temperature with voltage ratio in this case. The average measured peak temperature was higher at higher mean voltage (Vm = 26 V) condition as expected. This showed an indication of stable spray (or ODPP) mode of metal transfer at this voltage rather than unstable globular mode.
3.1.5. Effect of pulse frequency The mean arc power increased smoothly with pulse frequency (Table 3). The variation of sound RMS with pulse frequency is shown in (Fig. 9c). It was higher at irregular metal transfer and lower at stable ODPP metal transfer mode keeping mean voltage constant, which almost followed the change of metal transfer behavior as explained in Section 3.1.3. The sound kurtosis was low at higher welding voltage due to stable arc with spray mode of metal transfer, whereas it was much more in case of low voltage conditions due to droplet (or globu-
lar) mode of metal transfer (Fig. 9d) as discussed in Section 3.1.3. There exists an optimum frequency range for stable metal transfer at each welding voltage condition as shown by Kim and Eagar (1993). The measured peak temperature was almost same with the variation of pulse frequency at low mean voltage condition, whereas it reduced smoothly at high mean voltage condition (Table 3). There was a significant reduction of measured peak temperature at high pulse frequency (200–250 Hz), especially in case of high mean voltage (26 V) condition due to fully spray mode of metal transfer as discussed in Section 3.1.3.
3.1.6. Effect of duty factor (or mean voltage) The mean (and RMS) arc power was found to increase linearly with duty factor (Table 3) due to an increase in mean voltage. The RMS value of sound signal initially increased (due to bigger size metal droplet transfer modes) and then decreased (due to spray transfer) with further increase of duty factor or mean voltage (Fig. 10a). Thereafter, the RMS value of sound signal again increased beyond the stable point (in case of high mean voltage with low wire feed rate) due to higher burn-off rate than wire feed rate, which reduces the arc stability, as shown in earlier work (Arata et al., 1979a). The arc sound pressure may also increase with an increase of arc voltage in this case (Arata et al., 1979b).
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Fig. 8. Variation of metal transfer modes with duty factor (Dp ). (a) 0.3, (b) 0.5, (c) 0.7 for VP = 27.8 V, Vb = 12.2 V, F = 7 m/min, Vp /Vb = 2.2-2.3 and fp = 100 Hz, (d) 0.3, (e) 0.5, (f) 0.7 for Vp = 36 V, Vb = 16 V, F = 7 m/min, Vp /Vb = 2.2–2.3 and fp = 100 Hz.
The sound signal kurtosis reduced initially and then remained almost constant (Fig. 10b). The sound kurtosis value was much less for high voltage conditions, which indicates spray mode of metal transfer (as compared to droplet or globular metal transfer in case of low voltage conditions). A stable arc was established at a lower duty factor in case of lower wire feed rate.
The measured peak temperature increased with duty factor of 0.5 in most of the cases due to higher arc power, and then reduced due to poor metal transfer stability (Table 3). It was found to be higher at duty factor of 0.5, specifically in case of high pulse voltage condition due to better metal transfer stability as explained in Section 3.1.3. Thus, measured peak
Fig. 9. Variation of RMS value (Srms ) and kurtosis (Sk ) of arc sound with voltage ratio (Vp /Vb ) and pulse frequency (fp ). (a) Srms with Vp /Vb , (b) Sk with Vp /Vb , (c) Srms with fp , (d) Sk with fp .
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Fig. 10. Variation of RMS value (Srms ) and kurtosis (Sk ) of arc sound with duty factor (Dp ). (a) Srms with Dp , (b) Sk with Dp , .
temperature was also found to be an indicator of metal transfer mode. 3.2. Frequency domain analysis of arc sound signal
in GMAW. The metal transfer modes and its transfer rate (or metal droplet sizes) may be identified with arc sound frequency and its corresponding power (or amplitude) in fast Fourier transform (FFT) spectrum.
The arc sound signal was also analyzed in the frequency domain for proper understanding of variation of metal transfer behavior (Mansoor and Huissoon, 1997) and welding defect classification (Luo et al., 2005) as shown by earlier research. It showed various arc sound frequency peaks, which also indicated the degree of metal transfer stability or arc stability (Arata et al., 1979a, 1979b)
3.2.1. Continuous GMAW There were various low frequency arc sound peaks at low welding speed, which were found to be predominantly changed to somewhat monotonous and less number of peaks with an increase of the welding speed. It was due to stable metal transfer affected by higher arc intensity at higher welding speed. However, the metal
Fig. 11. Comparison of FFT between continuous GMAW and P- GMAW. (a) and (d) voltage FFT, (b) and (e) current FFT, (c) and (f) sound FFT .
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Fig. 12. Variation of sound FFT with various pulse frequency (fp ) in P-GMAW. (a) 50 Hz, (b) 150 Hz, (c) 250 Hz for Vm = 20 V, F = 6 m/min, Vp /Vb = 2.2–2.3 and Dp = 0.5, (d) 50 Hz, (e) 150 Hz, (f) 250 Hz for Vm = 26 V, F = 8 m/min, Vp /Vb = 2.2–2.3 and Dp = 0.5.
transfer stability was poor with higher wire feed rate indicated by more number of frequency peaks in voltage, current and sound signals’ frequency spectrum. In continuous GMAW, since the voltage and current FFT, both having various low frequency peaks were clubbed together, the metal transfer behavior could not be properly indicated as shown in Fig. 11a and b. However, the arc sound FFT showed a sharp peak at 50 Hz (experiment #2) with various high frequency peaks, which properly indicate the metal transfer frequency, as shown in Fig. 11c. Thus, the major frequency peak was also found to be 50 Hz to 100 Hz in most of the cases, indicating more metal transfer at this frequency range. However, the metal transfer behavior was unstable with various size of metal droplets, i.e., combination of globular and small metal droplets. 3.2.2. P-GMAW The arc stability depends not only on the arc power, but also on metal transfer modes and its regularity (Ghosh et al., 2006). The arc stability increased with monotonous sound peaks, with higher welding speed in case of pulsed welding conditions, which showed higher deposition efficiency (Pal et al., 2009). The sound peak power was found to be reduced at higher welding speed due to the reduced metal droplet size with higher welding speed. The sound peak power first, monotonically increased with an increase of wire feed rate (spray mode of metal transfer), and then reduced with more number of sound frequency peaks (spray with
smaller droplets) and finally, significantly increased (droplet mode of metal transfer) as before with further increase of wire feed rate. The FFT analysis of the arc sound signal indicated that there were various sound peaks of different frequencies in continuous GMAW, which drastically changed to a highly monotonous spectrum (high peak at pulse voltage frequency) in P-GMAW. It indicated that there were various sizes of metal droplets or unstable metal transfer with considerable spatter in continuous mode. The voltage and current FFT showed major peak at input pulse frequency of 100 Hz in P- GMAW as shown in Fig. 11d and e, whereas there were two major peaks at 100 Hz (mainly due to arc re-energized sound) and 500 Hz (mainly due to metal transfer sound) for corresponding sound FFT, as shown in Fig. 11f. Thus, the metal transfer mode mostly at 500 Hz showed five or more number of metal droplet transfer per pulse or spray mode of metal transfer. The voltage and current FFTs were found to be almost monotonous to input pulse frequency which could not clearly identify the metal transfer mode (or frequency). The sound FFT peak power significantly increased with an increase of voltage ratio at each voltage and wire feed rate condition. This variation first increased swiftly and then gradually increased or remained almost constant with further increase of voltage ratio. There was a major metal transfer sound frequency at about 50 Hz, 100 Hz, 300 Hz and 800 Hz at Vp /Vb = 1.5, in case of low mean voltage condition, indicating unstable various modes of metal transfer as found from time domain signals. However, arc sound FFT contained only one
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Fig. 13. Variation of sound FFT with various duty factor (Dp ) in P-GMAW. (a) 0.3, (b) 0.5, (c) 0.7 for Vp = 27.8 V, Vb = 12.2 V, F = 7 m/min, Vp /Vb = 2.2–2.3 and fp = 100 Hz, (d) 0.3, (e) 0.5, (f) 0.7 for VP = 36 V, Vb = 16 V, F = 7 m/min, Vp /Vb = 2.2–2.3 and fp = 100 Hz.
major metal transfer sound at 500 Hz at higher voltage ratio which showed fully spray mode of metal transfer even at low mean voltage condition. It also indicated that the variation of metal droplet size reduced with voltage ratio, evidenced by the monotonous arc sound peak at 500 Hz. There were no low frequency arc sound peaks at high mean voltage condition even for low voltage ratio. It clearly indicated that there was ODPP to spray mode of metal transfer at high mean voltage condition. There were two major metal transfer sound peaks at about 150 Hz and 650 Hz for pulse voltage frequency 50 Hz in case of high as well as low mean voltage conditions as shown in Fig. 12. Various frequency peaks at low mean voltage condition showed irregular metal transfer, which was found to be significantly reduced at high voltage condition. However, the arc sound FFT was found to be highly monotonous at 150 Hz for pulse voltage frequency of 150 Hz, indicating stable ODPP mode of metal transfer. Thus, the major metal transfer frequency was around 750 Hz at pulse voltage frequency of 250 Hz. Therefore, it may be concluded that the metal transfer mode changed from irregular various sized metal droplets to stable ODPP to spray with an enhancement of pulse frequency. There was one significant major sound peak at 200 Hz with various frequency peaks at very low (0.3) as well as very high (0.7) duty factor, which indicated MDPP in both high and low mean voltage conditions as shown in Fig. 13. The metal transfer stability and its rate were poor at low voltage condition which was evidenced by various sound frequency peaks and low power in arc sound FFT, respectively. However, the major metal transfer frequency was
300 Hz and above as per sound FFT at duty factor of 0.5 as shown Fig. 13. An interesting result was observed due to variation of duty factor (or pulse on-time) as well as mean voltage. The minor peaks at even multiples as well as odd multiples of major peak frequency were observed due to variation of duty factor from 50% duty factor as achieved earlier in case of ‘Sawtooth’ pulse current shape (Arata et al., 1980). This was due to the variation of pulse shape, especially with less duty factor (30%) or high duty factor (70%). 3.3. Weld defect monitoring with arc sound analysis Sound signal was found to be a good indicator of weld defects ˇ (Grad et al., 2004; Cudina and Prezelj, 2003). There are various types of weld defects due to improper welding conditions, such as insufficient voltage or current. One significant defect (blow hole) was found due to poor condition of wire electrode, as shown in Fig. 14 in experiment # 9), which was identified with corresponding variation of arc sound signal kurtosis. So, this experiment was repeated (experiment # 9a) again with good electrode, which showed better weld without any defects. Initially, a comparison was made using important time domain statistical sensors’ values like RMS value of welding voltage, current, power, sound, measured peak temperature as well as arc sound kurtosis for these two experiments. The variations were found to be insignificant to monitor the defect. Then, the defected weld bead was divided into four regions (Fig. 14) and corresponding
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Fig. 14. Welding defect monitoring with sound kurtosis (expt # 9).
sensors’ time domain average values were calculated. The variation of arc sound kurtosis was found to be significantly reduced in region 3 with respect to other regions due to no metal transfer. The corroded electrode wire burnt off instantly within few milliseconds which showed very low arc sound kurtosis. Thus, the arc sound kurtosis was found to be a better indicator of blow hole defect.
4. Conclusions The arc sound comprised of arc re-energized sound, metal transfer sound and spatter generation sound in GMAW. The metal transfer modes were identified from the arc sound analysis, along with current and voltage sensor signals. The continuous mode GMAW arc sound contained various peaks of different frequencies due to irregular metal transfer, which can be improved with stable ODPP conditions in P-GMAW. There were two major impulse sounds generated at arc re-energized and arc weakening points. The former was distinguished remarkably at any pulse voltage setting, but the latter was disturbed due to metal transfer with spatter generation sounds. The RMS value and kurtosis of arc sound are strongly related to pulse voltage parameters, which can be used to indicate the metal transfer modes. There was a major variation of arc sound peak frequencies due to variation of
pulse shape (or duty factor), which also affected the metal transfer modes. Welding sound signal was found to be a good indicator of weld defects. Acknowledgements The authors wish to acknowledge the assistance and support provided by the Welding Laboratory of Mechanical Engineering department, and Steel Technology Centre, IIT Kharagpur. They would also like to express earnest gratitude to Professor S. Roy, Mechanical Engineering Department, IIT Kharagpur, for his support in capturing the image with the high speed camera. References Amin, M., 1983. Pulsed current parameters for stability and controlled metal transfer in arc welding. Metal Constr. 15, 272–278. Arata, Y., Inoue, K., Futamata, M., Toh, T., 1979a. Investigation on welding arc sound (Report I)—effect of welding method and welding condition of welding arc sound. Trans. JWRI 8, 25–31. Arata, Y., Inoue, K., Futamata, M., Toh, T., 1979b. Investigation on welding arc sound (Report II)—evaluation by hearing acuity and some characteristics of arc sound. Trans. JWRI 8, 33–38. Arata, Y., Inoue, K., Futamata, M., Toh, T., 1980. Investigation on welding arc sound (Report III)—effects of current waveforms on TIG welding arc sound. Trans. JWRI 9, 25–30. Chen, M.A., Wu, C.S., Li, S.K., Zhang, Y.M., 2005. Analysis of active control of metal transfer in modified pulsed GMAW. Sci. Technol. Weld. Join. 12, 10–14.
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Mansoor, A., Huissoon, J.P., 1997. An investigation of the arc sound produced during GMA welding. MASc. Thesis, University of Waterloo. Mayer, J.L., 1987. Application of acoustic emission to in process monitoring of submerged arc welding. IIW Doc V-WG3, 29–87. Miranda, H.C.D., Scotti, A., Ferraresi, V.A., 2007. Identification and control of metal transfer in pulsed GMAW using optical sensor. Sci. Tech. Weld. Join. 12, 249– 257. Nouri, M., Abdollah-zadehy, A., Malek, F., 2007. Effect of welding parameters on dilution and weld bead geometry in cladding. J. Mater. Sci. Technol. 23, 817– 822. Ogawa, Y., Ogawa, H.Y., Koga, H., 1995. Analysis of acoustic signals on CO2 arc welding. In: Proc. 5th Int. Offshore Polar Eng. Conf., The Hague, The Netherlands, pp. 131–136. Pal, S., Pal, S.K., Samantaray, A.K., 2007. Radial basis function neural network model based prediction of weld plate distortion due to pulsed metal inert gas welding. Sci. Technol. Weld. Join. 12, 725–731. Pal, S., Pal, S.K., Samantaray, A.K., 2008. A neuro-wavelet packet analysis based on current signature for weld joint strength prediction in a pulsed metal inert gas welding process. Sci. Technol. Weld. Join. 13, 638–645. Pal, K., Bhattacharya, S., Pal, S.K., 2009. Prediction of metal deposition from arc sound and weld temperature signatures in pulsed MIG welding. Int. J. Adv. Manuf. Technol. 45, 1113–1130. Palani, P.K., Murugan, N., 2006. Selection of parameters of pulsed current gas metal arc welding (review). J. Mater. Proc. Techn. 172, 1–10. Palani, P.K., Murugan, N., 2007. Modeling and simulation of wire feed rate for steady current and pulsed current gas metal arc welding using 317L flux cored wire. Int. J. Adv. Manuf. Techn. 34, 1111–1119. Poopat, B., Warinsiriruk, E., 2006. Acoustic signal analysis for classification of transfer mode in GMAW by noncontact sensing technique. Songklanakarin J. Sci. Technol. 28, 829–840. Praveen, P., Kang, M.J., Prasad, Y.K.D.V., 2006. Behaviour of metal transfer modes in pulse gas metal arc welding of aluminium. In: Proc. Adv. Mater. Process. Technol. Conf., Las Vegas, USA, pp. 1–8. Randhawa, H.S., Ghosh, P.K., Gupta, S.R., 2000. Some basic aspects of geometrical characteristics of pulsed current vertical-up GMA weld. ISIJ Int. 40, 71–76. Saad, E., Wang, H., Kovacevic, R., 2006. Classification of molten pool modes in variable polarity plasma arc welding based on acoustic signature. J. Mat. Proc. Technol. 174, 127–136. Santos, T.O., Caetano, R.B., Lemos, J.M., Coito, F.J., 2000. Multipredictive adaptive control of arc welding trailing centerline temperature. IEEE Trans. Control Syst. Technol. 8, 159–169. Tam, J., Huissoon, J., 2005. Developing psycho-acoustic experiments in gas metal arc welding. In: Proc. IEEE Inter. Conf. Mechatronics Automation, Niagara Falls, Canada, pp. 1112–1117. Wang, Y., Zhao, P., 2001. Plasma-arc welding sound signature for on-line quality control. ISIJ Int. 41, 164–167. Wikle III, H.C., Kottilingam, S., Zee, R.H., Chin, B.A., 2001. Infrared sensing techniques for penetration depth control of the submerged arc welding process. J. Mater. Proc. Tech. 113, 228–233. Wu, C.S., Chen, M.A., Li, S.K., 2004. Analysis of excited droplet oscillation and detachment in active control of metal transfer. Comput. Mater. Sci. 31, 147– 154.
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Journal of Materials Processing Technology journal homepage: www.elsevier.com/locate/jmatprotec
Investigation on arc sound and metal transfer modes for on-line monitoring in pulsed gas metal arc welding Kamal Pal, Sandip Bhattacharya, Surjya K. Pal ∗ Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
a r t i c l e
i n f o
Article history: Received 14 January 2009 Received in revised form 26 March 2010 Accepted 29 March 2010
Keywords: P-GMAW Metal transfer mode Arc acoustics Pulse shape
a b s t r a c t The arc sound was found to be strongly related to both process parameters and weld quality, like voltage and current signals, in gas metal arc welding. In this investigation, the acquired welding arc sound signal along with current and voltage signals were analyzed in time domain as well as frequency domain to correlate them with the various process parameters and metal transfer modes. The arc sound of continuous as well as pulsed gas metal arc welding at various process parameters was also compared. A major variation of auxiliary arc sound frequency peaks was observed due to change of pulse shape as evidenced by frequency domain analysis. The arc sound was also used to detect welding defects. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Gas metal arc welding (GMAW) is widely used in industry due to its high metal deposition and ease of automation with better weld quality. Pulsed GMAW (P-GMAW) is often used to improve weld quality as well as productivity in thin sheet metal industries (Chen et al., 2005). It is a variation of constant current (or voltage) welding process which involves cycling of the welding current (or voltage) from a high level to a low level at a desired pulse frequency. The pulse shape also affects the heat input as well as weld characteristics in P-GMAW (Joseph et al., 2005). Manufacturing companies often face weld quality problems due to poor arc stability. The arc stability depends on material transfer behavior and arc length variations in P-GMAW. A stable welding process must have a uniform material transfer with minimal spatter (Hermans and Ouden, 1999). The optimum arc stability in PGMAW is obtained for one droplet per pulse (ODPP) condition with a droplet size close to that of the electrode wire diameter (Amin, 1983). Miranda et al. (2007) developed a control system capable of automatically adjusting pulse parameters to acheive stable ODPP metal transfer. The droplet detachment is also related to its oscillation behavior, which is also affected by pulse parameters (Wu et al., 2004). Palani and Murugan (2006, 2007) proposed the various aspects of pulse parameters and their selection to obtain good
∗ Corresponding author. Tel.: +91 3222 282996; fax: +91 3222 255303. E-mail addresses: [email protected] (K. Pal), [email protected] (S. Bhattacharya), [email protected] (S.K. Pal). 0924-0136/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jmatprotec.2010.03.029
quality welds. Ghosh et al. (2006) developed an analytical model to provide a theoretical background of the effect of pulse parameters on metal transfer modes in P-GMAW. There are a lot of uncontrollable factors like contamination and environmental conditions in GMAW. Therefore, the real time monitoring and control is highly required to overcome the timeconsuming and costly off-line inspection in modern automated welding environments. Various intelligent modeling, analytical and simulation techniques such as soft-computing tools, finite element methods and thermal models developed to monitor GMAW. But, the actual process parameters vary dynamically. Therefore, various advanced (i.e., reliable, non-contact and non-destructive) online sensors are required. These sensors can be utilized to acquire actual values of process parameters to be used as inputs to the developed model to improve the predictability, optimization and control capability in real time. Current and voltage sensors are considered to be the most reliable, simple and competitive (Li et al., 2000). The metal transfer modes were identified with a pattern-recognition system using these sensor signals (GuoMing et al., 2003). Pal et al. (2007, 2008) developed various soft-computing models for the monitoring of weld quality using these sensor signals. But, these two arc sensors are not sufficient to completely characterize the process. Therefore, visual inspection, artificial vision sensing, infrared sensing, radiographic sensing, etc. are also used. The infrared sensor was used to monitor the weld centerline temperature (Santos et al., 2000) and weld penetration depth (Wikle III et al., 2001) during arc welding. It is very difficult to grasp the dynamic welding characteristics, especially near the arc due to very high temperature, spatter formation, fumes, etc. The arc
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acoustics and ultrasonic sensing may be useful to overcome these difficulties. The acoustic waves produced in GMAW are related to the behavior of the arc, the molten weld pool and metal droplet transfer mode. Erdmann-Jesnitzer et al. (1967) and Jolly (1969) confirmed the relevance of acoustic waves produced during GMAW. Arata et al. (1979a, 1979b, 1980) tried to correlate the welding sound with various process parameters in CO2 arc welding, continuous GMAW and pulsed gas tungsten arc welding (P-GTAW). Drouet and Nadeau (1982) developed an arc length monitoring technique with an acoustic voltmeter. Mansoor and Huissoon (1997) investigated the same phenomenon with both time and frequency domain analyses to correlate it with metal transfer modes in GMAW. Grad and Kralj (1996) and Grad et al. (2004) evaluated the arc welding process stability using various statistical parameters of acquired sound signals and established the theoretical and experimental base of arc acoustic signals to monitor GMAW in industrial environments. Fan et al. (2001) concluded that CO2 welding arc sound energy and spatter loss were proportional to each other. Deuster and Sinz (1983), Mayer (1987) and Wang and Zhao (2001) proposed acoustic emission monitoring systems in submerged arc welding (SAW). Futamata et al. (1984) analyzed the plasma jet sound using transferred arc in plasma arc welding (PAW). Saad et al. (2006) identified the cutting mode from keyhole mode using power spectral density of welding sound acoustics with artificial neural network (ANN) in variable polarity PAW. There are various back-ground industrial noise sources that may hinder the proper analysis of emitted welding noise signals. But, these problems may be suitably overcome and acoustic monitoring ˇ might be better than other techniques (Cudina and Prezelj, 2003). Ogawa et al. (1995) observed that the burn-through condition in CO2 arc welding can only be identified by arc sound. Luksa (2003) correlated sound emissions generated in the GMAW process with signals registered in the arc circuit during various metal transfer ˇ modes. Cudina et al. (2008) proposed two types of noise generating mechanisms namely sound impulses for arc extinction and arc re-ignition and turbulent noise due to metal transfer in shortcircuit GMAW. Luo et al. (2005) used frequency domain and wavelet analysis of acoustic signals to identify weld defects in laser welding by ANN. Tam and Huissoon (2005) proposed psycho-acoustic experiments to better understand the welding acoustic characteristics using spectral acoustic reliance of professional welders. Poopat and Warinsiriruk (2006) used the time and frequency domain features to identify the metal transfer modes and defects in GMAW. Lin and Fischer (1995) also used arc welding sound acoustics in ANN
models for the prediction of weld bead geometry and degree of spatter. There is a strong relationship between arc sound signal and process parameters as well as weld quality in various GMAW processes, especially in CO2 welding and short-circuit GMAW. In this investigation, the welding sound was acquired in GMAW, which was further analyzed in time domain as well as frequency domain. Initially, the arc sound signal along with current and voltage signals were compared between P-GMAW and continuous GMAW. These sensor signals were used to correlate it with various metal transfer modes at various pulse voltage conditions in P-GMAW. The measured peak temperature also used to indicate metal transfer stability. Finally, an attempt was made to correlate the various metal transfer modes with sound signal analysis in time domain as well as frequency domain. 2. Experiments In this work, a constant voltage P-GMAW machine was used. The power source and control unit were TRANSARC 500 and FRONIUS VR131, respectively. The experiments were carried out on mild steel plate (7.5 mm thick) using copper coated mild steel filler electrode wire (ESAB, S-6 wire, 1.2 mm diameter) in bead-on-plate method. The electrode wire was fed to the welding gun by a fourroller drive system. The current (Hall-effect current sensor LEM, model LT 500S), voltage, sound sensors and infrared pyrometer were used. The experimental setup is shown in Fig. 1. The measured voltage was stepped down in 1:11 ratio before being fed to the A/D card. A microphone (B&K, 4189) with attached pre-amplifier (2669L) and a conditioning amplifier (Nexus 2690, combined sensitivity 316 mV/Pa) was positioned at a distance of one meter from the welding torch as shown. This microphone with a frequency bandwidth of 6.3 Hz to 20 kHz and dynamic sound level range of 14.6 dB to 146 dB is highly feasible to monitor arc welding sound in this case. An infrared pyrometer (Omegascope, OS5233) was focused on the plate surface at a distance of 20 mm from the arc. The signals were acquired using two A/D cards (National Instruments, USB-6210) to two Intel Pentium-4 PCs using LabVIEW 7.1 data acquisition interface at a sampling frequency of 40 kHz (for current, voltage and sound signals) and 10 Hz (for pyrometer), respectively. A high speed camera (MiDAS 2.0) was also used to capture real time welding images at 4000 frames per second. The chemical composition of work piece material was obtained by optical emission spectroscopy (OES) analysis (Table 1). The plates were cleaned with brush and alcohol solutions for better weldablity. Pure argon (99.9%) was used as shielding gas. Six pro-
Fig. 1. Schematic diagram of the experimental set-up.
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Table 1 Chemical composition (weight percentage) of the base plate. C
Si
Mn
P
S
Ta
Cr
Cu
0.149
0.108
0.358
0.072
0.073
0.018
0.015
0.032
Table 2a Process parameters and their values in continuous GMAW and P- GMAW (constant pulse parameters) (1st set of experiment). Process parameter
Parameter values
Arc mean voltage (V) Welding speed (mm/s) Wire feed rate (m/min)
26 2.5, 3.6, 5.6, 7.5 6, 7, 8, 9
cess parameters, namely welding speed (S), wire feed rate (F) along with four pulse voltage parameters (pulse frequency (fp ), pulse ontime (tp ), peak voltage (Vp ) and back-ground voltage (Vb ) were considered in this work. Initially, the welding speed and wire feed rate were varied in both continuous GMAW as well as P-GMAW, maintaining a constant mean voltage (26 V). The arc stability is related to the ratio of peak current to back-ground current (Ip /Ib ) as well as pulse shape (or duty factor) in P-GMAW (Ghosh et al., 2006). So, three pulse variables, namely voltage ratio (Vp /Vb ), pulse frequency (fp ) and duty factor (Dp ) were considered, which were varied one at a time keeping the other two parameters constant. The voltage ratio (Vp /Vb ) and pulse voltage frequency (fp ) were varied (within acceptable weld range) at two mean voltage conditions (26 V and 20 V) with two acceptable wire feed rates (6 and 7, and 7 and 8 m/min, respectively) as shown in Tables 2a and 2b. These two mean voltage conditions were chosen, so that the mean current would be lower and higher than transition current (current for the change of metal transfer mode from globular to spray) respectively. The pulse voltage duty factor (Dp ) was then varied at a constant Vp , Vb , fp with two combinations of wire feed rate as before. The detailed process parameter settings of 65 number of experiments with corresponding mean and RMS value of arc power (Pm and Prms ), measured peak temperature (Tp ), sound statistical values viz. RMS value of sound (Srms ) and sound kurtosis (Sk ), are presented in Tables 3a and 3b. 3. Results RMS (or mean) current increased linearly with wire feed rate in both pulse and continuous modes at a particular mean voltage Fig. 2. The weld bead was found to be much smoother in pulse mode of voltage condition than continuous mode due to less amount of spatter, evidenced by high metal deposition efficiency in P-GMAW (Pal et al., 2009).
Fig. 2. Variation of RMS current with wire feed rate.
One major arc sound peak was observed at arc re-energized point of time, i.e., when voltage changes from low to high (same as arc re-ignition in short circuit GMAW), as achieved in P-GTAW (Arata et al., 1980). Similarly, a minor sound peak was observed when voltage dropped from high to low as shown in case of high pulse voltage condition (Fig. 3a). But, this minor sound pressure peak could not be distinguished from turbulent metal transfer sound, especially in case of droplet transfer mode or unstable arc conditions due to metal droplet transfer sound or spatter sound. The metal droplet transfer sound was found to be significantly higher than re-energized arc sound in case of low pulse voltage condition as shown in Fig. 3b. The arc shape variation (high speed camera images) with a typical pulse voltage variation along with sensor signals (sound peaks) at high mean voltage condition is shown in Fig. 4 But, the metal transfer mode identification is limited with the video image due to glare of arc (Ghosh et al., 2007). The droplet detachment (or globular transfer mode) was also observed at low mean voltage conditions evidenced by voltage fluctuations with corresponding sound peaks, which may also be higher than previous re-energized sound peaks. However, it could not properly identify the metal transfer mode using high speed video images. The arc sound pressure indicates the metal transfer modes in GMAW (Mansoor and Huissoon, 1997).Two statistical parameters (RMS and kurtosis) were computed from the raw welding sound signals. The RMS value arc sound pressure value related with degree of metal transfer. The sound kurtosis indicated the degree of sharpness of arc sound signal, which was found to be related to the mode of metal transfer. High sound kurtosis indicated droplet or globular metal transfer with spatters rather than ODPP (in pulsed conditions) or spray mode. The RMS and kurtosis value of arc sound signal along with current and voltage signals were used to indicate the mode of metal transfer. The measured peak temperature
Table 2b Process parameters (pulse) and their values in P- GMAW (2nd set of experiment). Process parameter
Parameter values
Fixed
Variable
Welding speed (5.6 mm/s)
Wire feed rate (m/min)
at Vm = 20 V at Vm = 26 V
6, 7 7, 8
Voltage ratio
at Vm = 20 V at Vm = 26 V
1.5, 2, 2.25, 2.5, 3
Pulse frequency (Hz)
at Vm = 20 V at Vm = 26 V
50, 100, 150, 200, 250
Pulse duty factor
at Vp = 36 V and Vb = 16 V, at Vp = 27.8 V and Vb = 12.2 V
0.3, 0.4, 0.5, 0.6, 0.7
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Table 3a Detailed experimental design matrix with responses (continuous GMAW). Expt. no
Wire feed rate (m/min)
Welding speed (mm/s)
Arc voltage (V)
Mean arc power (kW)
RMS arc power (kW)
RMS arc sound (V)
Arc sound kurtosis
Measured peak temperature (◦ C)
1 2 3 4 5 6 7
7 7 7 7 6 8 9
2.5 3.6 5.6 7.5 5.6 5.6 5.6
26 26 26 26 26 26 26
4.27 4.38 4.44 4.5 4.14 4.66 4.97
5.11 4.89 5.04 4.96 4.6 5.16 5.66
0.598 0.552 0.53 0.505 0.571 0.601 0.572
63.73 74.85 81.93 78.67 52.62 61.33 68.56
786 657 473 485 395 570 615
was also acquired and correlated with the stability of metal transfer. The acquired time domain data of all sensor signals is shown in Table 3. A typical measured temperature variation profile is shown (Fig. 5). 3.1. Time domain analysis of arc sound, arc power and weld temperature The arc sound kurtosis indicates the sharpness of sound signal during metal transfer in P-GMAW (Pal et al., 2009). In this work, RMS and kurtosis values of arc sound were processed and correlated with metal transfer behavior in continuous GMAW, as well as PGMAW. The current and voltage signals along with arc power and measured peak temperature was also used to identify the metal transfer modes. 3.1.1. Effect of welding speed The mean arc power increased slightly with the welding speed (Table 3) as observed earlier (Nouri et al., 2007). The arc sound pressure is mainly related to the metal transfer behavior as well as degree of spatter generation at constant voltage condition (Arata et al., 1979b). The arc sound pressure reduces with welding speed at low arc power (Arata et al., 1979a). In this work also, the RMS value of sound pressure reduced with an increase of welding speed due to reduced metal transfer. But, it was found to be very high at low welding speed (2.5 mm/s) in continuous GMAW due to huge spatter with unstable metal transfer. Whereas, it was significantly less in P-GMAW due to bridging or short-circuiting metal transfer caused by reduced electrode to plate gap with high arc power as shown in Fig. 6a (Iordachescu and Quintino, 2008). This is evident from the sensors’ time domain signals of the corresponding experiments. The sound kurtosis value decreased (less peaked) gradually with increase in table speed in case of P-GMAW due to the transition to more stable spray transfer. In case of continuous GMAW, an initial increasing trend was observed after which it stabilized (Fig. 6b). The probable reason was an increase of metal droplet size with reduced spatter due to higher arc power. The measured peak temperature reduced typically (experiment #8) with welding speed due to lower average heat input to the weld (Table 3). However, it was not significantly reduced at higher welding speeds, especially in P-GMAW due to two opposite effects of reduced weld heat content and better metal transfer stability. 3.1.2. Effect of wire feed rate The mean (as well as RMS) value of arc power increased with wire feed rate due to higher welding current at same arc voltage conditions (Table 3). The RMS value of arc sound was found to increase smoothly with increase in wire feed rate due to more metal transfer (Fig. 6c). But, it was higher at very low wire feed rate (6 m/min), especially in P-GMAW, possibly due to high spatter. Generally, the metal transfer is regular, if wire feed rate is same as burn-off rate in GMAW (Palani and Murugan, 2007). The RMS value of arc sound was found to be reduced when the wire burn-off rate criteria was satisfied. It also indicated a stable metal transfer over
consecutive current (or voltage) pulses evidenced by time domain sensors’ signals. The sound signal kurtosis increased for both continuous and pulsed GMAW with wire feed rate due to increase of metal droplet size at constant mean voltage condition (Fig. 6d) as achieved earlier in short-circuit GMAW (Grad et al., 2004). The voltage (or current) fluctuations at pulse on-time were observed for higher wire feed rate which also indicated the same. The measured peak temperature increased with wire feed rate due to higher heat content of metal droplets influenced by higher arc power (Table 3). Thus, it also showed an identification of metal transfer rate and its transfer mode. 3.1.3. Effect of pulse parameters on metal transfer modes The effect of various pulse voltage parameters on metal transfer behavior was investigated by studying 3–5 consecutive pulses of voltage and current with corresponding acquired sound signal in P-GMAW. In pulse current welding there are mainly two types of metal transfer modes, namely one droplet per pulse (ODPP) and multiple droplets per pulse (MDPP) with or without metal transfer at base current depending on pulse parameters (Ghosh et al., 2007). There was a variation of metal transfer behavior with an increase of peak voltage or background voltage, as the voltage ratio (Vp /Vb ) was varied, which directly influenced the arc stability (Ghosh et al., 2007). As voltage ratio increased, the magnitude of arc sound peaks (at arc re-energized points) increased as it is proportional to the effective voltage amplitude. The metal transfer mode was found to be changed from globular with various size of metal droplets (unstable arc) at Vp /Vb = 1.5 to ODPP with smaller droplets at Vp /Vb = 2.25 to fully spray at Vp /Vb = 3, in case of low mean voltage condition (Vm = 20 V), evidenced by acquired voltage and current with corresponding sound signals peaks. However, in case of high mean voltage condition (Vm = 26 V) the metal transfer changed from ODPP to smaller sized MDPP to spray with an enhancement of voltage ratio from 1.5 to 3. This occurred due to higher peak voltage. The metal transfer mode was regular with higher voltage ratio up to a certain extent (2.5–3.0) for a particular welding mean voltage (constant pulse parameters) and wire feed rate combination. But the metal transfer stability then reduced with further increase of voltage ratio due to high voltage fluctuation. The arc sensors’ signal fluctuations due to metal transfer was found to be increased with an increase of wire feed rate keeping mean voltage constant, indicating larger sized metal droplet or more metal transfer. Thus, the metal transfer stability was found to be significantly disturbed at higher wire feed rate. The variation of metal transfer mode and arc stability with the variation of two pulse parameters simultaneously at different wire feed rates is somewhat complex. As the voltage pulse frequency is increased with same duty factor (50%), the pulse ontime reduces. There were irregular globular droplets with unstable mode of metal transfer (at fp = 50–100 Hz with tp = 10–5 ms), which changed to somewhat stable (at fp = 150 Hz with tp = 3.33 ms) and then to smaller droplets with stable arc (at fp = 200–250 Hz
Table 3b Detailed experimental design matrix with responses (P-GMAW). Mean arc voltage (V)
Peak voltage Back-ground (V) voltage (V)
Voltage ratio
Pulse duty factor
Pulse frequency (Hz)
8 9 9a 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
2.5 3.6 3.6 5.6 7.5 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6
26 26 26 26 26 26 26 26 20 20 20 20 20 20 20 20 26 26 26 26 26 26 26 26 20 20 20 20 20 20 20 20 20 20 26 26 26 26 26 26 26 26 16.8 18.4 21.6 23.2 16.8 18.4 21.6 23.2 22 24 28 30 22 24 28 30
36 36 36 36 36 36 36 36 24 26.8 28.6 30 24 26.8 28.6 30 31.2 34.8 37.2 39 31.2 34.8 37.2 39 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.8 36 36 36 36 36 36 36 36 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.8 36 36 36 36 36 36 36 36
2.25 2.25 2.25 2.25 2.25 2.25 2.25 2.25 1.5 2.03 2.51 3 1.5 2.03 2.51 3 1.5 2.02 2.51 3 1.5 2.02 2.51 3 2.28 2.28 2.28 2.28 2.28 2.28 2.28 2.28 2.28 2.28 2.25 2.25 2.25 2.25 2.25 2.25 2.25 2.25 2.28 2.28 2.28 2.28 2.28 2.28 2.28 2.28 2.25 2.25 2.25 2.25 2.25 2.25 2.25 2.25
0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.4 0.6 0.7 0.3 0.4 0.6 0.7 0.3 0.4 0.6 0.7 0.3 0.4 0.6 0.7
100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 50 100 150 200 250 50 100 150 200 250 50 150 200 250 50 150 200 250 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
7 7 7 7 7 6 8 9 6 6 6 6 7 7 7 7 7 7 7 7 8 8 8 8 6 6 6 6 6 7 7 7 7 7 7 7 7 7 8 8 8 8 6 6 6 6 7 7 7 7 7 7 7 7 8 8 8 8
16 16 16 16 16 16 16 16 16 13.2 11.4 10 16 13.2 11.4 10 20.8 17.2 14.8 13 20.8 17.2 14.8 13 12.2 12.2 12.2 12.2 12.2 12.2 12.2 12.2 12.2 12.2 16 16 16 16 16 16 16 16 12.2 12.2 12.2 12.2 12.2 12.2 12.2 12.2 16 16 16 16 16 16 16 16
Pulse on-time (ms) 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 10 5 3.3 2.5 2 10 5 3.3 2.5 2 10 3.3 2.5 2 10 3.3 2.5 2 3 4 6 7 3 4 6 7 3 4 6 7 3 4 6 7
Mean arc power (kW)
RMS arc power (kW)
RMS arc sound (V)
Arc sound kurtosis
Measured peak temperature (◦ C)
5.06 5.2 5.21 5.29 5.3 5.08 5.66 5.73 3.15 3.23 2.81 3.33 3.21 3.42 3.42 3.49 4.75 5.26 5.5 5.82 4.94 5.56 5.63 5.95 2.39 3.29 3.37 3.51 3.6 3.3 3.5 3.5 3.7 3.83 5.09 5.39 5.43 5.37 5.19 5.47 5.6 5.51 2.56 2.9 3.87 4.18 2.76 3.04 3.95 4.38 4.07 4.52 5.95 6.54 4.08 4.65 6.14 6.64
6.06 6.23 6.2 6.17 6.26 5.85 6.76 6.94 4.26 4.14 4.06 4.41 4.09 4.47 4.6 4.67 5.17 6.09 6.54 7.15 5.48 6.43 6.78 7.38 3.97 4.21 4.36 4.71 5 4.42 4.63 4.43 5.08 5.49 6.07 6.31 6.24 6.01 6.27 6.34 6.5 6.27 3.64 3.87 5.34 5.57 3.94 4.06 5.32 5.89 5.38 5.53 6.63 7.03 5.47 5.8 6.95 7.18
0.476 0.557 0.552 0.546 0.53 0.914 0.638 0.616 0.55 0.566 0.536 0.599 0.515 0.573 0.597 0.666 0.424 0.532 0.563 0.664 0.613 0.564 0.532 0.609 0.537 0.49 0.538 0.537 0.523 0.584 0.576 0.484 0.572 0.558 0.532 0.571 0.547 0.496 0.56 0.422 0.481 0.46 0.444 0.52 0.538 0.501 0.438 0.512 0.589 0.533 0.579 0.464 0.771 0.815 0.561 0.602 0.56 0.498
20.08 8.849 7.444 −0.38 −0.26 −0.74 3.555 14 73.27 65.55 56.99 36.6 93.4 66.88 48.62 40.44 28.49 1.531 0.156 1.297 28.32 4.069 1.047 0.512 69.08 72.44 83.79 89.18 80.78 63.74 62.55 84.44 78.78 74.13 1.378 1.529 0.11 1.837 9.672 0.3 0.845 9.676 73.39 80.11 65.63 68.37 111.4 71.39 63.99 62.41 25.96 3.857 −0.35 −0.22 41.02 26.62 3.279 0.351
861 663 598 627 550 577 698 700 385 369 413 477 296 458 458 408 626 595 572 661 593 710 708 746 368 373 410 370 331 455 470 476 473 377 646 705 634 558 720 674 661 507 419 318 353 431 326 341 416 451 339 491 574 566 495 510 585 547
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Expt. no Wire feed rate (m/min)
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Fig. 3. Variation of arc sound peaks in case of (a) high voltage and (b) low voltage in P-GMAW.
Fig. 4. Variation of arc shape with pulse voltage (current) and corresponding sound peaks.
K. Pal et al. / Journal of Materials Processing Technology 210 (2010) 1397–1410
Fig. 5. A typical weld surface temperature profile (expt # 8).
with tp = 2.5–2 ms) with an increase of pulse frequency (keeping duty factor or pulse shape constant) at Vm = 20 V and F = 6 m/min (Fig. 7a–c, respectively). The variation of metal transfer mode changed from irregular MDPP (50–100 Hz) to stable ODPP (150 Hz), and to spray (200–250 Hz), at Vm = 26 V and F = 8 m/min (Fig. 7d–f, respectively), as achieved in earlier work (Praveen et al., 2006). The regularity of metal transfer was found to be improved with lower wire feed rate in this case. The metal transfer occurred at pulse off-time as per voltage, current and corresponding sound signal fluctuations in case of low frequency pulses. This was found especially for low voltage with higher wire feed rate condition as observed earlier with high speed videography (Ghosh et al., 2007). The metal transfer mode variation occurred with the variation of duty factor at constant peak voltage, background voltage and pulse frequency. The metal transfer mode changed from globular with various sized droplets (low duty factor) to smaller ODPP and fine droplets with little instability to unstable smaller MDPP at Vm = 20 V and F = 7 m/min (Fig. 8a–c, respectively). However, the
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metal transfer irregularity was somewhat reduced at lower wire feed rate F = 6 m/min, keeping mean voltage constant at 20 V. The variation was similar but shifted towards higher duty factor in case of higher mean voltage condition. The metal transfer mode changed from irregular droplets to ODPP with smaller sized droplets, and to completely spray with an increase of duty factor from 0.3 to 0.7 at Vm = 26 V and F = 7 m/min (Fig. 8d–f, respectively). It changed from unstable globular droplets to stable ODPP to spray at very high duty factor in case of high wire feed rate (F = 8 m/min), as achieved in earlier work (Choi et al., 1998). The metal transfer also occurred at pulse off-time in case of low duty factor pulses having higher background voltage, especially for low pulse voltage and higher wire feed rate condition as observed earlier with high speed camera (Ghosh et al., 2007). However, the metal transfer in pulse off-time disturbed the arc stability as per time domain sensors’ signals. 3.1.4. Effect of voltage ratio The mean arc power increased gradually with voltage ratio (Table 3). The RMS of sound signal was found to increase with the voltage ratio (Fig. 9a), especially at a wire feed rate of 7 m/min. The sound pressure level also increased with voltage amplitude (Vp –Vb ) in P-GTAW, as observed by Arata et al. (1980). The RMS value of sound was found to fluctuate at high wire feed rate (8 m/min) at Vm = 26 V and low wire feed rate (6 m/min) at Vm = 20 V, due to irregular metal transfer caused by improper electrode wire burn-off rate. When the wire feed rate was 7 m/min, the magnitude of sound signal RMS was found to be greater for low mean voltage conditions due to droplet metal transfer mode as compared to spray transfer in high mean voltage condition (Fig. 9a). The arc sound kurtosis was found to reduce considerably when the voltage ratio increased from 1.5 to 2.0, but thereafter decreased gradually with further enhancement of this ratio (Fig. 9b). The magnitude of the sound signal kurtosis was much higher in the case of low mean voltage conditions, as compared to high mean voltage
Fig. 6. Variation of RMS value (Srms ) and kurtosis (Sk ) of arc sound with welding speed (S) and wire feed rate (F). (a) Srms with S, (b) Sk with S, (c) Srms with F, (d) Sk with F.
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Fig. 7. Variation of metal transfer modes with pulse frequency (fp ). (a) 50 Hz, (b) 150 Hz, (c) 250 Hz for Vm = 20 V, F = 6 m/min, Vp /Vb = 2.2–2.3 and Dp = 0.5, (d) 50 Hz, (e) 150 Hz, (f) 250 Hz for Vm = 26 V, F = 8 m/min, Vp /Vb = 2.2–2.3 and Dp = 0.5.
conditions due to a change in the mode of metal transfer, discussed in Section 3.1.3. The measured peak temperature gradually increased with an increase of voltage ratio at constant mean voltage condition in most of the cases due to stable metal transfer (Table 3) (Pal et al., 2009). However, a little drop or fluctuation indicated the change of arc stability influenced by metal transfer behavior. The metal droplet temperature increases with the ratio of peak to back-ground current (Ip /Ib ) at a constant mean current (Randhawa et al., 2000). It was probably the reason for higher measured peak temperature with voltage ratio in this case. The average measured peak temperature was higher at higher mean voltage (Vm = 26 V) condition as expected. This showed an indication of stable spray (or ODPP) mode of metal transfer at this voltage rather than unstable globular mode.
3.1.5. Effect of pulse frequency The mean arc power increased smoothly with pulse frequency (Table 3). The variation of sound RMS with pulse frequency is shown in (Fig. 9c). It was higher at irregular metal transfer and lower at stable ODPP metal transfer mode keeping mean voltage constant, which almost followed the change of metal transfer behavior as explained in Section 3.1.3. The sound kurtosis was low at higher welding voltage due to stable arc with spray mode of metal transfer, whereas it was much more in case of low voltage conditions due to droplet (or globu-
lar) mode of metal transfer (Fig. 9d) as discussed in Section 3.1.3. There exists an optimum frequency range for stable metal transfer at each welding voltage condition as shown by Kim and Eagar (1993). The measured peak temperature was almost same with the variation of pulse frequency at low mean voltage condition, whereas it reduced smoothly at high mean voltage condition (Table 3). There was a significant reduction of measured peak temperature at high pulse frequency (200–250 Hz), especially in case of high mean voltage (26 V) condition due to fully spray mode of metal transfer as discussed in Section 3.1.3.
3.1.6. Effect of duty factor (or mean voltage) The mean (and RMS) arc power was found to increase linearly with duty factor (Table 3) due to an increase in mean voltage. The RMS value of sound signal initially increased (due to bigger size metal droplet transfer modes) and then decreased (due to spray transfer) with further increase of duty factor or mean voltage (Fig. 10a). Thereafter, the RMS value of sound signal again increased beyond the stable point (in case of high mean voltage with low wire feed rate) due to higher burn-off rate than wire feed rate, which reduces the arc stability, as shown in earlier work (Arata et al., 1979a). The arc sound pressure may also increase with an increase of arc voltage in this case (Arata et al., 1979b).
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Fig. 8. Variation of metal transfer modes with duty factor (Dp ). (a) 0.3, (b) 0.5, (c) 0.7 for VP = 27.8 V, Vb = 12.2 V, F = 7 m/min, Vp /Vb = 2.2-2.3 and fp = 100 Hz, (d) 0.3, (e) 0.5, (f) 0.7 for Vp = 36 V, Vb = 16 V, F = 7 m/min, Vp /Vb = 2.2–2.3 and fp = 100 Hz.
The sound signal kurtosis reduced initially and then remained almost constant (Fig. 10b). The sound kurtosis value was much less for high voltage conditions, which indicates spray mode of metal transfer (as compared to droplet or globular metal transfer in case of low voltage conditions). A stable arc was established at a lower duty factor in case of lower wire feed rate.
The measured peak temperature increased with duty factor of 0.5 in most of the cases due to higher arc power, and then reduced due to poor metal transfer stability (Table 3). It was found to be higher at duty factor of 0.5, specifically in case of high pulse voltage condition due to better metal transfer stability as explained in Section 3.1.3. Thus, measured peak
Fig. 9. Variation of RMS value (Srms ) and kurtosis (Sk ) of arc sound with voltage ratio (Vp /Vb ) and pulse frequency (fp ). (a) Srms with Vp /Vb , (b) Sk with Vp /Vb , (c) Srms with fp , (d) Sk with fp .
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Fig. 10. Variation of RMS value (Srms ) and kurtosis (Sk ) of arc sound with duty factor (Dp ). (a) Srms with Dp , (b) Sk with Dp , .
temperature was also found to be an indicator of metal transfer mode. 3.2. Frequency domain analysis of arc sound signal
in GMAW. The metal transfer modes and its transfer rate (or metal droplet sizes) may be identified with arc sound frequency and its corresponding power (or amplitude) in fast Fourier transform (FFT) spectrum.
The arc sound signal was also analyzed in the frequency domain for proper understanding of variation of metal transfer behavior (Mansoor and Huissoon, 1997) and welding defect classification (Luo et al., 2005) as shown by earlier research. It showed various arc sound frequency peaks, which also indicated the degree of metal transfer stability or arc stability (Arata et al., 1979a, 1979b)
3.2.1. Continuous GMAW There were various low frequency arc sound peaks at low welding speed, which were found to be predominantly changed to somewhat monotonous and less number of peaks with an increase of the welding speed. It was due to stable metal transfer affected by higher arc intensity at higher welding speed. However, the metal
Fig. 11. Comparison of FFT between continuous GMAW and P- GMAW. (a) and (d) voltage FFT, (b) and (e) current FFT, (c) and (f) sound FFT .
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Fig. 12. Variation of sound FFT with various pulse frequency (fp ) in P-GMAW. (a) 50 Hz, (b) 150 Hz, (c) 250 Hz for Vm = 20 V, F = 6 m/min, Vp /Vb = 2.2–2.3 and Dp = 0.5, (d) 50 Hz, (e) 150 Hz, (f) 250 Hz for Vm = 26 V, F = 8 m/min, Vp /Vb = 2.2–2.3 and Dp = 0.5.
transfer stability was poor with higher wire feed rate indicated by more number of frequency peaks in voltage, current and sound signals’ frequency spectrum. In continuous GMAW, since the voltage and current FFT, both having various low frequency peaks were clubbed together, the metal transfer behavior could not be properly indicated as shown in Fig. 11a and b. However, the arc sound FFT showed a sharp peak at 50 Hz (experiment #2) with various high frequency peaks, which properly indicate the metal transfer frequency, as shown in Fig. 11c. Thus, the major frequency peak was also found to be 50 Hz to 100 Hz in most of the cases, indicating more metal transfer at this frequency range. However, the metal transfer behavior was unstable with various size of metal droplets, i.e., combination of globular and small metal droplets. 3.2.2. P-GMAW The arc stability depends not only on the arc power, but also on metal transfer modes and its regularity (Ghosh et al., 2006). The arc stability increased with monotonous sound peaks, with higher welding speed in case of pulsed welding conditions, which showed higher deposition efficiency (Pal et al., 2009). The sound peak power was found to be reduced at higher welding speed due to the reduced metal droplet size with higher welding speed. The sound peak power first, monotonically increased with an increase of wire feed rate (spray mode of metal transfer), and then reduced with more number of sound frequency peaks (spray with
smaller droplets) and finally, significantly increased (droplet mode of metal transfer) as before with further increase of wire feed rate. The FFT analysis of the arc sound signal indicated that there were various sound peaks of different frequencies in continuous GMAW, which drastically changed to a highly monotonous spectrum (high peak at pulse voltage frequency) in P-GMAW. It indicated that there were various sizes of metal droplets or unstable metal transfer with considerable spatter in continuous mode. The voltage and current FFT showed major peak at input pulse frequency of 100 Hz in P- GMAW as shown in Fig. 11d and e, whereas there were two major peaks at 100 Hz (mainly due to arc re-energized sound) and 500 Hz (mainly due to metal transfer sound) for corresponding sound FFT, as shown in Fig. 11f. Thus, the metal transfer mode mostly at 500 Hz showed five or more number of metal droplet transfer per pulse or spray mode of metal transfer. The voltage and current FFTs were found to be almost monotonous to input pulse frequency which could not clearly identify the metal transfer mode (or frequency). The sound FFT peak power significantly increased with an increase of voltage ratio at each voltage and wire feed rate condition. This variation first increased swiftly and then gradually increased or remained almost constant with further increase of voltage ratio. There was a major metal transfer sound frequency at about 50 Hz, 100 Hz, 300 Hz and 800 Hz at Vp /Vb = 1.5, in case of low mean voltage condition, indicating unstable various modes of metal transfer as found from time domain signals. However, arc sound FFT contained only one
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Fig. 13. Variation of sound FFT with various duty factor (Dp ) in P-GMAW. (a) 0.3, (b) 0.5, (c) 0.7 for Vp = 27.8 V, Vb = 12.2 V, F = 7 m/min, Vp /Vb = 2.2–2.3 and fp = 100 Hz, (d) 0.3, (e) 0.5, (f) 0.7 for VP = 36 V, Vb = 16 V, F = 7 m/min, Vp /Vb = 2.2–2.3 and fp = 100 Hz.
major metal transfer sound at 500 Hz at higher voltage ratio which showed fully spray mode of metal transfer even at low mean voltage condition. It also indicated that the variation of metal droplet size reduced with voltage ratio, evidenced by the monotonous arc sound peak at 500 Hz. There were no low frequency arc sound peaks at high mean voltage condition even for low voltage ratio. It clearly indicated that there was ODPP to spray mode of metal transfer at high mean voltage condition. There were two major metal transfer sound peaks at about 150 Hz and 650 Hz for pulse voltage frequency 50 Hz in case of high as well as low mean voltage conditions as shown in Fig. 12. Various frequency peaks at low mean voltage condition showed irregular metal transfer, which was found to be significantly reduced at high voltage condition. However, the arc sound FFT was found to be highly monotonous at 150 Hz for pulse voltage frequency of 150 Hz, indicating stable ODPP mode of metal transfer. Thus, the major metal transfer frequency was around 750 Hz at pulse voltage frequency of 250 Hz. Therefore, it may be concluded that the metal transfer mode changed from irregular various sized metal droplets to stable ODPP to spray with an enhancement of pulse frequency. There was one significant major sound peak at 200 Hz with various frequency peaks at very low (0.3) as well as very high (0.7) duty factor, which indicated MDPP in both high and low mean voltage conditions as shown in Fig. 13. The metal transfer stability and its rate were poor at low voltage condition which was evidenced by various sound frequency peaks and low power in arc sound FFT, respectively. However, the major metal transfer frequency was
300 Hz and above as per sound FFT at duty factor of 0.5 as shown Fig. 13. An interesting result was observed due to variation of duty factor (or pulse on-time) as well as mean voltage. The minor peaks at even multiples as well as odd multiples of major peak frequency were observed due to variation of duty factor from 50% duty factor as achieved earlier in case of ‘Sawtooth’ pulse current shape (Arata et al., 1980). This was due to the variation of pulse shape, especially with less duty factor (30%) or high duty factor (70%). 3.3. Weld defect monitoring with arc sound analysis Sound signal was found to be a good indicator of weld defects ˇ (Grad et al., 2004; Cudina and Prezelj, 2003). There are various types of weld defects due to improper welding conditions, such as insufficient voltage or current. One significant defect (blow hole) was found due to poor condition of wire electrode, as shown in Fig. 14 in experiment # 9), which was identified with corresponding variation of arc sound signal kurtosis. So, this experiment was repeated (experiment # 9a) again with good electrode, which showed better weld without any defects. Initially, a comparison was made using important time domain statistical sensors’ values like RMS value of welding voltage, current, power, sound, measured peak temperature as well as arc sound kurtosis for these two experiments. The variations were found to be insignificant to monitor the defect. Then, the defected weld bead was divided into four regions (Fig. 14) and corresponding
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Fig. 14. Welding defect monitoring with sound kurtosis (expt # 9).
sensors’ time domain average values were calculated. The variation of arc sound kurtosis was found to be significantly reduced in region 3 with respect to other regions due to no metal transfer. The corroded electrode wire burnt off instantly within few milliseconds which showed very low arc sound kurtosis. Thus, the arc sound kurtosis was found to be a better indicator of blow hole defect.
4. Conclusions The arc sound comprised of arc re-energized sound, metal transfer sound and spatter generation sound in GMAW. The metal transfer modes were identified from the arc sound analysis, along with current and voltage sensor signals. The continuous mode GMAW arc sound contained various peaks of different frequencies due to irregular metal transfer, which can be improved with stable ODPP conditions in P-GMAW. There were two major impulse sounds generated at arc re-energized and arc weakening points. The former was distinguished remarkably at any pulse voltage setting, but the latter was disturbed due to metal transfer with spatter generation sounds. The RMS value and kurtosis of arc sound are strongly related to pulse voltage parameters, which can be used to indicate the metal transfer modes. There was a major variation of arc sound peak frequencies due to variation of
pulse shape (or duty factor), which also affected the metal transfer modes. Welding sound signal was found to be a good indicator of weld defects. Acknowledgements The authors wish to acknowledge the assistance and support provided by the Welding Laboratory of Mechanical Engineering department, and Steel Technology Centre, IIT Kharagpur. They would also like to express earnest gratitude to Professor S. Roy, Mechanical Engineering Department, IIT Kharagpur, for his support in capturing the image with the high speed camera. References Amin, M., 1983. Pulsed current parameters for stability and controlled metal transfer in arc welding. Metal Constr. 15, 272–278. Arata, Y., Inoue, K., Futamata, M., Toh, T., 1979a. Investigation on welding arc sound (Report I)—effect of welding method and welding condition of welding arc sound. Trans. JWRI 8, 25–31. Arata, Y., Inoue, K., Futamata, M., Toh, T., 1979b. Investigation on welding arc sound (Report II)—evaluation by hearing acuity and some characteristics of arc sound. Trans. JWRI 8, 33–38. Arata, Y., Inoue, K., Futamata, M., Toh, T., 1980. Investigation on welding arc sound (Report III)—effects of current waveforms on TIG welding arc sound. Trans. JWRI 9, 25–30. Chen, M.A., Wu, C.S., Li, S.K., Zhang, Y.M., 2005. Analysis of active control of metal transfer in modified pulsed GMAW. Sci. Technol. Weld. Join. 12, 10–14.
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