*2.3.1 Statistical analysis to identify disturbances*

Knowing that a signal behaves according to a stochastic process, it is possible to determine a probabilistic model and apply some algorithms to process this signal. Hence, several works have focused on the study of the electrical signals at the moment of disturbance, using a statistical treatment.

Adolfsson and Bahrami [10] calculate the variance of weld voltage (every 1024 signals). The study validates the hypothesis that the instability of the process

particular the current. The welding equipment generates two levels of current. In the first, the base current (Ib) is kept low so that there is no transfer, but only the onset of wire fusion; in the second, the peak current (Ip) is higher than the globular transition current causing the transfer, under optimal operating conditions, of a single drop. Typical parameters in pulsed transfer are voltage 20–30 ⱱ and current

Another parameter that influences the stability of the process is the transition current, which changes the frequency and diameter of the transferred drops. In case of a given current of short-circuiting transition, the droplet transfer exists in the form of short-circuiting, and the welding is stable. When the welding current increases, the droplet transition changes from the short-circuiting mode to the mixed mode, so the welding process and electric signal become unstable.

On the other hand, the globular-spray transition current also presents instability; a big number of spatters but the arc is no longer extinguished. Studies show that with the increase of CO2 in the gas mixture, an increase of the transition current is

Finally, a peculiarity of the short-circuit transfer mode is the existence of regular contact between the electrode and the workpiece. Typical short-circuit parameters are voltage 16–22 ⱱ and current intensity 50–150 A. When the short circuit occurs, the arc is extinguished establishing two characteristic phases: the arcing period and the short-circuit period. Droplet growth occurs in the arcing period, whereas during the contact period, the metal is transferred. Also, the voltage and current oscillate to

high and low at the same frequency of the metal transfer (**Figure 7**).

intensity 100–300 A, as shown in **Figure 6**.

*Waveform factors (modified from [8]).*

produced.

**8**

**Figure 5.**

*Welding - Modern Topics*

**Figure 6.**

*Waveform factors of globular transfer mode [7].*

(caused by disturbances) correlates with a decrease in the variance of the weld voltage; in a similar manner, the short-circuit transfer rate decreased; conversely, no decrease occurs in the estimated variance of the weld current. The results obtained were used in the development of an online fault detection algorithm. This work shows a promising stability index but is only oriented to short-circuit transfer mode and was not extended to other transference modes. Note that the moments of disturbance were caused by making cuts in the workpiece and not varying input variables of the process such as wire feed speed, welding speed, and contact tip to work distance (CTWD), which also influences the stability.

Luksa [11] calculates the mean value of short circuit; the variance of welding current; the time of arc burning; and the short-circuit frequency values (every 2200 signals samples). He identifies two types of disturbances those caused by external factors such as grease and paint that affect the gas shield of the welding arc and a second group caused by variations in the wire extension. As was mentioned in the previous work, the author indicates that the variance of weld voltage decreased in the disturbance moment. But he also affirms that the short-circuit rates increase and optimal process stability can also occur during step disturbance, which contradicts the results found by [10]. An interesting contribution of this work is the study of the correct data window size since very large or small data window size can lead to erroneous stability results.

Finally, Wu et al. [12] used statistical process control (SPC), creating a sequential chart of the welding voltage and current (every 2000 signals). Coinciding with the index previously presented, a decrease in the estimated variance of the welding voltage occurs during the disturbance step. They also understand as a result an increase in the kurtosis for both the welding voltage and current. The results were generalized for the three main transferences modes and used in the construction of an SPC.

formulation are presented in **Table 1**. They also use the voltage waveform to predict the mode of metal transfer because more variations are observed in the voltage moving from spray to short-circuit transfer. They perform a generalization and propose a new index power ratio (PR) (Eq. 5, **Table 1**), used for identification of the metal transfer mode and arc stability. Finally, an online monitoring system was

Simpson [15] presents a stability index using an image processing method known as signature images. This index is calculated successively from the comparison of two images of dimensional histograms of the voltage and current data, allowing the detection of faults for the three main modes of metal transfer. Although it is a method of image processing which does not require high-speed cameras, instead, it is necessary for a good data acquisition system to work in realtime. Therefore, it can be considered as a cheap and feasible method to implement

Finally, the group Laprosolda of the Federal University of Uberlândia, Brazil [12, 13, 16, 17], in a similar approximation, based on numerical and statistical techniques, propose two indexes for the short-circuit transfer mode: the regularity index (IVcc) (Eq. 6, **Table 1**) criteria for quantifying the short-circuit transfer stability in the MIG/MAG welding process, taking into account the constancy of the short-circuit and open-arc times, and cutting frequency index (ΔFcc) (Eq. 7, **Table 1**) criteria to determinate the voltage regulation range that guarantees greater stability of metal transfer in GMAW short circuit. Using the parameters wire-electrode diameter, wire feed rate and drop diameter as a function of the wire diameter, they address metal transfer behavior (especially regarding the correlation between the stability of transfer mode and the welding defects). The use of these indices allowed the authors to test the correlation between the inductance; the regularity of the metallic transfer; and the influence of the variation in the contact tip to work distance (CTWD), with three different types of gases. In addition, the proposed indices have been widely used in other studies; some of them are discussed below.

created capable of predicting the status of the process.

in the industry.

**11**

**Table 1.**

*Summary of arc stability indexes.*

*Stability on the GMAW Process*

*DOI: http://dx.doi.org/10.5772/intechopen.90386*

## *2.3.2 Arc stability*

In 1988, the authors Mita et al. [9] enunciated the correlation between the stability of the arc and standard deviation of the arcing time; the standard deviation of short current; and the average value of short-circuit frequency. They used linear regression to prove this correlation and to create a new stability index. They showed that short-circuit frequency is influenced by several welding parameters mainly the wire feed rate and the arc voltage. Also, affirm that the stability of the process grows when the standard deviation of the short-circuit frequency decreases. However, the proposed index was tested in all current ranges, and the authors conclude that god arc stability can be obtained in all transfer modes.

Hermans and Ouden [13] propose a criterion for arc stability (Eq. 1, **Table 1**), based on the short-circuit frequency using the relationship between the arc time and the short-circuit time. To do this, they analyzed the behavior of the weld pool taking images with a high-speed camera. The authors concluded that the moment in which the oscillation frequency of the welding pool and the short-circuit frequency are synchronized, the greatest stability is reached.

Ogunbiyi and Norris [14] perform a summary of several criteria presented by other authors and propose three indexes to calculate the stability of the metal transfer. These indexes are Transfer index (Eq. 2, **Table 1**), transfer stability index (Eq. 3, **Table 1**) and dip consistency index (Eq. 4, **Table 1**), which are based on the correlation between metal transfer modes, arc stability and current waveform. The study confronts the three main modes of metal transfer, an advantage in relation to other studies. They calculate the indexes based on the relationship between minimum, mean, and maximum welding current. The indexes and the mathematical

## *Stability on the GMAW Process DOI: http://dx.doi.org/10.5772/intechopen.90386*


#### **Table 1.** *Summary of arc stability indexes.*

(caused by disturbances) correlates with a decrease in the variance of the weld voltage; in a similar manner, the short-circuit transfer rate decreased; conversely, no decrease occurs in the estimated variance of the weld current. The results obtained were used in the development of an online fault detection algorithm. This work shows a promising stability index but is only oriented to short-circuit transfer mode and was not extended to other transference modes. Note that the moments of disturbance were caused by making cuts in the workpiece and not varying input variables of the process such as wire feed speed, welding speed, and contact tip to

Luksa [11] calculates the mean value of short circuit; the variance of welding current; the time of arc burning; and the short-circuit frequency values (every 2200 signals samples). He identifies two types of disturbances those caused by external factors such as grease and paint that affect the gas shield of the welding arc and a second group caused by variations in the wire extension. As was mentioned in the previous work, the author indicates that the variance of weld voltage decreased in the disturbance moment. But he also affirms that the short-circuit rates increase and optimal process stability can also occur during step disturbance, which contradicts the results found by [10]. An interesting contribution of this work is the study of the correct data window size since very large or small data window size can lead to

Finally, Wu et al. [12] used statistical process control (SPC), creating a sequential chart of the welding voltage and current (every 2000 signals). Coinciding with the index previously presented, a decrease in the estimated variance of the welding voltage occurs during the disturbance step. They also understand as a result an increase in the kurtosis for both the welding voltage and current. The results were generalized for the three main transferences modes and used in the construction

In 1988, the authors Mita et al. [9] enunciated the correlation between the stability of the arc and standard deviation of the arcing time; the standard deviation of short current; and the average value of short-circuit frequency. They used linear regression to prove this correlation and to create a new stability index. They showed that short-circuit frequency is influenced by several welding parameters mainly the wire feed rate and the arc voltage. Also, affirm that the stability of the process grows when the standard deviation of the short-circuit frequency decreases. However, the proposed index was tested in all current ranges, and the authors conclude

Hermans and Ouden [13] propose a criterion for arc stability (Eq. 1, **Table 1**), based on the short-circuit frequency using the relationship between the arc time and the short-circuit time. To do this, they analyzed the behavior of the weld pool taking images with a high-speed camera. The authors concluded that the moment in which the oscillation frequency of the welding pool and the short-circuit frequency

Ogunbiyi and Norris [14] perform a summary of several criteria presented by other authors and propose three indexes to calculate the stability of the metal transfer. These indexes are Transfer index (Eq. 2, **Table 1**), transfer stability index (Eq. 3, **Table 1**) and dip consistency index (Eq. 4, **Table 1**), which are based on the correlation between metal transfer modes, arc stability and current waveform. The study confronts the three main modes of metal transfer, an advantage in relation to other studies. They calculate the indexes based on the relationship between minimum, mean, and maximum welding current. The indexes and the mathematical

that god arc stability can be obtained in all transfer modes.

are synchronized, the greatest stability is reached.

work distance (CTWD), which also influences the stability.

erroneous stability results.

*Welding - Modern Topics*

of an SPC.

**10**

*2.3.2 Arc stability*

formulation are presented in **Table 1**. They also use the voltage waveform to predict the mode of metal transfer because more variations are observed in the voltage moving from spray to short-circuit transfer. They perform a generalization and propose a new index power ratio (PR) (Eq. 5, **Table 1**), used for identification of the metal transfer mode and arc stability. Finally, an online monitoring system was created capable of predicting the status of the process.

Simpson [15] presents a stability index using an image processing method known as signature images. This index is calculated successively from the comparison of two images of dimensional histograms of the voltage and current data, allowing the detection of faults for the three main modes of metal transfer. Although it is a method of image processing which does not require high-speed cameras, instead, it is necessary for a good data acquisition system to work in realtime. Therefore, it can be considered as a cheap and feasible method to implement in the industry.

Finally, the group Laprosolda of the Federal University of Uberlândia, Brazil [12, 13, 16, 17], in a similar approximation, based on numerical and statistical techniques, propose two indexes for the short-circuit transfer mode: the regularity index (IVcc) (Eq. 6, **Table 1**) criteria for quantifying the short-circuit transfer stability in the MIG/MAG welding process, taking into account the constancy of the short-circuit and open-arc times, and cutting frequency index (ΔFcc) (Eq. 7, **Table 1**) criteria to determinate the voltage regulation range that guarantees greater stability of metal transfer in GMAW short circuit. Using the parameters wire-electrode diameter, wire feed rate and drop diameter as a function of the wire diameter, they address metal transfer behavior (especially regarding the correlation between the stability of transfer mode and the welding defects). The use of these indices allowed the authors to test the correlation between the inductance; the regularity of the metallic transfer; and the influence of the variation in the contact tip to work distance (CTWD), with three different types of gases. In addition, the proposed indices have been widely used in other studies; some of them are discussed below.

Souza [18] presents a work related to mapping the droplet transfer modes to help welders in the choice of the best welding setting parameters needed. The maps were proposed for spray and short-circuit transfer modes. They used the IVcc and ΔFcc parameters to allow focusing voltage range and to obtain transfer regions with proper operating characteristics for the short-circuit mode. The study demonstrates that the index has the characteristic of decreasing and then again increasing its value with increasing welding voltage. As smaller index values indicate better stability, it appears that the process has poor stability at very low and very high voltages.

the process classification in a stable and non-stable arc. It would be interesting in future works to get an integration of these techniques with supervised machine

Chu et al. [21] perform an analysis of power spectral density of the current and voltage signals also for processes with short-circuit transfer mode using Fourier transformation to do that. To determine if the testing processes were stable, a correlation was made between the weld bead geometry and the voltage and current values. They affirm that the welding process with a unique frequency corresponds to uniform welds and good weld surface quality, enabling the detection of stable

Cayo and Alfaro [22] make a comparison between time domain and frequency

S-GMAW welding process. Applying the two methods to the welding arc sound, the time domain was found to be the most appropriate technique. They also demonstrate that the acoustical ignitions frequency and short-circuit frequency decrease in regions of instability. The results obtained can be used for the development of an

Macías et al. [23] use image processing to analyze the image generated by the time-frequency diagram obtained from acoustic monitoring. Proving that the minimum standard deviation of the metal transfer weld indicates that the process is stable, as previously mentioned. The authors did not implement online monitoring but highlight the existing flexibility in terms of image processing and online signal processing. It should be noted that in future works, the authors integrate their results into a neural network with artificial intelligence to predict stability in the process. Then, it can be concluded that power spectral density is a powerful method for the

quantification of stability and allows to identify faults in the process through the detection of changes in the waveform frequency. Then, it can be concluded that power spectral density is a powerful method for the quantification of stability and allows to identify faults in the process through the detection of changes in the waveform frequency, being possible to correlate with the quality of the geometry of the weld bead. The current and voltage signals have also been used to create cyclograms that show the welding voltage as a function of welding current to obtain a process stability indicator. Cyclograms are a novel method for stability analysis in the welding process. They constitute a visual representation by graphs of the voltage values as a function of the current (**Figure 9**). It has been widely used as a stability

domain to define which is most appropriate to calculate the stability of the

learning algorithms to perform stability classification.

online system to identify regions of disturbances.

indicator for the short-circuit transfer mode.

*Representation of the cyclograms (modified from [24]).*

**Figure 9.**

**13**

ranges and areas with defects.

*Stability on the GMAW Process*

*DOI: http://dx.doi.org/10.5772/intechopen.90386*

Meneses [3] presents an implementation of a model that represents the GMAW process in orbital welding. She also developed a study of the metal transfer control, with the objective of achieving a high level of quality of welded joint in different conditions. The mentioned indices were used to make evaluating the hypothesis possible so that more short circuits had greater stability in the process. That allows users to choose a correct parameter setting depending on their needs, in order to obtain a stable transfer with appropriate welding conditions.

Costa [19] performed the validation of the stability on the welding process for the short-circuit transfer mode. The regularity index (IVcc) and cutting frequency (Fcc) index were used, and this was able to identify the tension levels that result in greater transfer regularity, lower level of spatter, higher deposition efficiency, and better surface quality of the weld bead. In the next step, they used the deposition performance and allowed to estimate the amount of material lost by slag and fumes, along with the amount of generated spatter. It was also able to evaluate the effects of the feed rate and the influence of the type of protection gas on the behavior of short circuits. Finally, he developed a thermal efficiency analysis where he concludes that there is no relationship between the values of thermal efficiency and the regularity of transfer.

In conclusion, those indices are powerful tools to determinate the stability in the GMAW process and can be monitored in real time. The short-circuit frequency is one of the most suitable parameters to determine stability in the short-circuit transference mode, either by correlating it with the oscillation frequency of the weld pool or by calculating its standard deviation. The so-called Vilarinho index developed by the group Laprosolda has been widely adopted in Brazil, and it is the index of stability for short-circuit transfer of which the largest number of references was found.

#### *2.3.3 Analysis of current and voltages waveforms*

The analysis of current and voltage waveforms is used in the same way as an indicator of stability. Power spectral density and time-frequency analysis methods were used and allowed the decomposition in time and frequency of the waveforms.

Adolfsson and Bahrami [10] used spectral domain analysis of measurement data to detect differences in the power spectral densities of the weld voltage and current in disturbance moments. It made the creation of an algorithm that detects changes in the frequencies and that enables the detection of faults possible. They also affirm that a decrease in the variance was reflected in a decrease in the area in the power spectral density. This work was discussed previously in Section 2.3.1.

Also, Huang et al. [20] used time-frequency entropy techniques to estimate the stability of short-circuiting gas metal arc welding, demonstrating that when the welding is more stable, the time-frequency entropy increases. To obtain the results, the authors made variations in the input variables such as current, voltage, and welding speed, demonstrating that it is possible to use this technique to define the parameters that provide more stability. Finally, the results can be used to perform

#### *Stability on the GMAW Process DOI: http://dx.doi.org/10.5772/intechopen.90386*

Souza [18] presents a work related to mapping the droplet transfer modes to help welders in the choice of the best welding setting parameters needed. The maps were proposed for spray and short-circuit transfer modes. They used the IVcc and ΔFcc parameters to allow focusing voltage range and to obtain transfer regions with proper operating characteristics for the short-circuit mode. The study demonstrates that the index has the characteristic of decreasing and then again increasing its value with increasing welding voltage. As smaller index values indicate better stability, it appears that the process has poor stability at very low and very high

Meneses [3] presents an implementation of a model that represents the GMAW process in orbital welding. She also developed a study of the metal transfer control, with the objective of achieving a high level of quality of welded joint in different conditions. The mentioned indices were used to make evaluating the hypothesis possible so that more short circuits had greater stability in the process. That allows users to choose a correct parameter setting depending on their needs, in order to

Costa [19] performed the validation of the stability on the welding process for the short-circuit transfer mode. The regularity index (IVcc) and cutting frequency (Fcc) index were used, and this was able to identify the tension levels that result in greater transfer regularity, lower level of spatter, higher deposition efficiency, and better surface quality of the weld bead. In the next step, they used the deposition performance and allowed to estimate the amount of material lost by slag and fumes, along with the amount of generated spatter. It was also able to evaluate the effects of the feed rate and the influence of the type of protection gas on the behavior of short circuits. Finally, he developed a thermal efficiency analysis where he concludes that there is no relationship between the values of thermal efficiency and the

In conclusion, those indices are powerful tools to determinate the stability in the GMAW process and can be monitored in real time. The short-circuit frequency is one of the most suitable parameters to determine stability in the short-circuit transference mode, either by correlating it with the oscillation frequency of the weld pool or by calculating its standard deviation. The so-called Vilarinho index developed by the group Laprosolda has been widely adopted in Brazil, and it is the index of stability for short-circuit transfer of which the largest number of references

The analysis of current and voltage waveforms is used in the same way as an indicator of stability. Power spectral density and time-frequency analysis methods were used and allowed the decomposition in time and frequency of the waveforms. Adolfsson and Bahrami [10] used spectral domain analysis of measurement data to detect differences in the power spectral densities of the weld voltage and current in disturbance moments. It made the creation of an algorithm that detects changes in the frequencies and that enables the detection of faults possible. They also affirm that a decrease in the variance was reflected in a decrease in the area in the power

Also, Huang et al. [20] used time-frequency entropy techniques to estimate the stability of short-circuiting gas metal arc welding, demonstrating that when the welding is more stable, the time-frequency entropy increases. To obtain the results, the authors made variations in the input variables such as current, voltage, and welding speed, demonstrating that it is possible to use this technique to define the parameters that provide more stability. Finally, the results can be used to perform

spectral density. This work was discussed previously in Section 2.3.1.

obtain a stable transfer with appropriate welding conditions.

voltages.

*Welding - Modern Topics*

regularity of transfer.

*2.3.3 Analysis of current and voltages waveforms*

was found.

**12**

the process classification in a stable and non-stable arc. It would be interesting in future works to get an integration of these techniques with supervised machine learning algorithms to perform stability classification.

Chu et al. [21] perform an analysis of power spectral density of the current and voltage signals also for processes with short-circuit transfer mode using Fourier transformation to do that. To determine if the testing processes were stable, a correlation was made between the weld bead geometry and the voltage and current values. They affirm that the welding process with a unique frequency corresponds to uniform welds and good weld surface quality, enabling the detection of stable ranges and areas with defects.

Cayo and Alfaro [22] make a comparison between time domain and frequency domain to define which is most appropriate to calculate the stability of the S-GMAW welding process. Applying the two methods to the welding arc sound, the time domain was found to be the most appropriate technique. They also demonstrate that the acoustical ignitions frequency and short-circuit frequency decrease in regions of instability. The results obtained can be used for the development of an online system to identify regions of disturbances.

Macías et al. [23] use image processing to analyze the image generated by the time-frequency diagram obtained from acoustic monitoring. Proving that the minimum standard deviation of the metal transfer weld indicates that the process is stable, as previously mentioned. The authors did not implement online monitoring but highlight the existing flexibility in terms of image processing and online signal processing. It should be noted that in future works, the authors integrate their results into a neural network with artificial intelligence to predict stability in the process.

Then, it can be concluded that power spectral density is a powerful method for the quantification of stability and allows to identify faults in the process through the detection of changes in the waveform frequency. Then, it can be concluded that power spectral density is a powerful method for the quantification of stability and allows to identify faults in the process through the detection of changes in the waveform frequency, being possible to correlate with the quality of the geometry of the weld bead.

The current and voltage signals have also been used to create cyclograms that show the welding voltage as a function of welding current to obtain a process stability indicator. Cyclograms are a novel method for stability analysis in the welding process. They constitute a visual representation by graphs of the voltage values as a function of the current (**Figure 9**). It has been widely used as a stability indicator for the short-circuit transfer mode.

**Figure 9.** *Representation of the cyclograms (modified from [24]).*

According to Moinuddin and Sharma [24], using the cyclograms it is possible to represent characteristics of droplet detachment and arc burning stage. The authors also carried out an analysis of probability density distribution of arc voltage, weld bead, and microstructure analysis for various welding conditions, allowing to extend the stability study to spray transfer mode. The study showed that there is a strong correlation between the microstructure and the stability of the arc. Besides, the different types of electrodes and their electrical conductivity capacity also has influence on the resulting microstructure in a welded bead. A stable arc produces greater penetration and improves melting efficiency. The authors mention that the study can be expanded taking into account other parameters such as electrode type, electrode extension, shield protection gas, welding speed, and other current modes such as pulsed.

it is recommended to detach one dropper short. Equally Pal et al. [30] affirm that in the pulsed welding processes, the detachment of the drop should occur during the pulses and the diameter of the drop should be similar to the diameter of the electrode. Finally, adequate control and study of the metallic transfer allow

The amount of spatters generated during the welding process has been another indicator widely used; the spatters are a product of instability in the arc and should be minimized. The largest amount of study is developed in the short-circuit area. The moment when the short circuit occurs and the arc is reset is when the largest number of spatters is produced. Also, if the mean of the short-circuit time is

Silva et al. [31] propose a criterion for the spattering index correlating spattering rate (S—Eq. 1, **Table 2**) and the deposition rate (D—Eq. 2, **Table 1**). The purpose was to demonstrate that the correct control of these indicators allows to choose

On the other hand, Kang and Rhee [32] develop statistical regression models to predict the amount of spatter in the short-circuit transfer for GMAW. It is shown, in the same way, that voltage and welding current waveforms can be satisfactorily used to predict the presence of spatters. Kang et al. [33] in a similar work use four different linear and nonlinear regression models composed of the waveform factors to develop the spatter prediction model. Proving that the amount of spatter depends on the number of arc extinctions, arc extinctions occur when the welding voltage is

guaranteeing the quality in the geometry of the welded bead.

appropriate parameters for any specific welding application.

*2.3.5 Spattering index*

*Stability on the GMAW Process*

*DOI: http://dx.doi.org/10.5772/intechopen.90386*

**Table 2.**

**15**

*Summary of transfer stability indexes.*

irregular, more spatters will be generated.

Cayo [25] uses the cyclograms to detect defects in the weld reflected in the arc and current–voltage signals. The cyclograms allowed to identify three types of disturbances, a variation of the stand of, presence of grease and absence of protection gas. Each type of defect showed changes in the cyclograms, allowing to analyze the changes in voltage and current. One of the advantages of the cyclograms is that it provides a visual result that allows a quick analysis of the values obtained in the process. Again a powerful stability indicator is shown, but it has been oriented only to the analysis of the short-circuit transfer mode.

Suban [26] uses this index to determine a more stable short-circuit material transfer. As a result, open arc, short-circuit, and spray transfer moments are identified depending on the type of gas used. In addition, the author performs an analysis of the probability distribution of voltage and current using Fourier analysis. Among the conclusions, the authors emphasize that with pure CO2, more stability is achieved. This method is simple and can be implemented in real time.

#### *2.3.4 Control of droplet size*

The control of droplet size ensures transfer stability. For measuring this variable, image processing, laser shadowing, and sound processing techniques are generally used. The appropriate control ensures proper transfer mode; increases the quality of welding, and decreases the number of defects. Large drops do not represent a suitable condition.

The transfer of the drop is dependent on welding current and arc voltage waveforms influenced by gravity force, electromagnetic force, plasma drag force, and surface tension. Suban [26] ensures that to maximize stability, the time between the transfers of two subsequent drops should always be the same.

Mousavi and Kulkarni [27, 28] demonstrate that a relationship between droplet detachment and statistical parameters of current exists, assuring that lesser standard deviation and coefficient to variation was considered to be of uniform droplet detachment and arc length uniformity.

Soderstrom and Mendez [29] use high-speed laser shadowgraphs and fast Fourier transform (FFT) of the voltage signal for droplet diameter and detachment frequency measurement. It has been found that a relationship between average droplet diameter and current for the different diameter electrodes exists. In addition, it states that the increase in CO2 above normal standards causes an erratic detachment.

Then it can be concluded that there is a correlation between the waveform of the current and the detachment of the drop. A lower coefficient of variation in the mean of the welding current represents uniformity in the detachment frequency. Additionally, for variable transfer time, the welding arc tends to be unstable and the current signals exhibit irregular behavior. In the case of short-circuit transfer mode, it is recommended to detach one dropper short. Equally Pal et al. [30] affirm that in the pulsed welding processes, the detachment of the drop should occur during the pulses and the diameter of the drop should be similar to the diameter of the electrode. Finally, adequate control and study of the metallic transfer allow guaranteeing the quality in the geometry of the welded bead.
