Stability analysis of GMAW based on multi-scale entropy and genetic optimized support vector machine

Journal Pre-proofs Stability Analysis of GMAW Based on Multi-scale Entropy and Genetic Optimized Support Vector Machine Yong Huang, Dongqing Yang, Kehong Wang, Lei Wang, Qi Zhou PII: DOI: Reference:

S0263-2241(19)31146-7 https://doi.org/10.1016/j.measurement.2019.107282 MEASUR 107282

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Measurement

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Please cite this article as: Y. Huang, D. Yang, K. Wang, L. Wang, Q. Zhou, Stability Analysis of GMAW Based on Multi-scale Entropy and Genetic Optimized Support Vector Machine, Measurement (2019), doi: https://doi.org/ 10.1016/j.measurement.2019.107282

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Stability Analysis of GMAW Based on Multi-scale Entropy and Genetic Optimized Support Vector Machine Yong Huanga,b, Dongqing Yanga,b*, Kehong Wanga,b, Lei Wanga,b, Qi Zhoua,b (a. School of Material Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China) (b.Key Laboratory of Controlled Arc Intelligent Additive Manufacturing, Nanjing University of Science and Technology, Nanjing 210094, China)

Abstract: The gas metal arc welding is a complex chaotic dynamic process. To study the relationship between arc electrical signal and welding stability, the multi-scale entropy method was introduced to analyze the current signals under different welding process parameters. Under the short-circuiting droplet transition mode, the larger shielding gas flow rate led to, the more stable welding and the smaller amplitude of multi-scale entropy curves. When the welding current parameter increased gradually, the droplet transition mode changed, and the amplitude of multi-scale entropy curves increased. As the welding voltage rose, the droplet transfer frequency decreased and the multi-scale entropy increased. Furthermore, the four-class prediction of welding forming quality was studied by combining with the genetic algorithm-based support vector machine (GA-SVM). The multi-scale entropy distribution was closely related to the type and stability of short-circuiting transfer in the welding process. Keywords: Gas metal arc welding; Current signal; Multi-scale entropy; GA-SVM

1. Introduction The gas metal arc welding (GMAW) is widely used in practical industrial manufacturing owing to its high efficiency, low cost and wide positional adaptability. The arc electrical signals, weld pool image, spectral signals and sound signals generated during the welding process are closely related to the welding stability and quality [1-2]. Due to the complexity of arc droplet transition, the welding stability is susceptible to parameter conditions and external factors. The welding stability and varying arc signals have been extensively studied with certain achievements. Quinn et al. [3] analyzed the mean and standard deviation in GMAW, such as current, voltage, resistance and short-circuiting frequency. Pal et al. [4] extracted neurowavelet packet components of current signals in the pulse gas shielded welding, and combined with artificial neural network to predict the welding joint strength. Moreover, the adaptive signal decomposition, which is local mean decomposition in the alternating-current square wave submerged arc welding, was combined with support vector machine (SVM) to evaluate its stability [5]. Pal et al. [6] used a sound sensor and an arc sensor to monitor the welding penetration, and correlated the statistical features with welding bead characteristics. Wang et al. [7] used wavelet packet transform to decompose the welding sound signals in gas tungsten arc welding, and extracted 128 features through statistical processing to classify the welding penetration along with an artificial neural network. Han et al. [8] collected five types of spectrum signals in GMAW, and thereby well classified the metal transfer mode. With suitable spectral processing zones, Li et al. [9] detected the welding bead face discontinuity, poor weld and porosity in the gas tungsten arc welding. Huang et al. [10] used the features of six

more spectral signals to build a welding quality prediction model based on the extreme learning machine for Al-Mg alloy in arc welding. Liu et al. [11] investigated the relationship of weld pool dimensions of keyhole behaviors with plasma arc welding current, welding speed and plasma gas flow rate to describe the weld pool behaviors along with the keyhole evolution. Wang et al. [12] used laser to avoid the interference of arc light and determine the surface information of the weld pool, and reconstructed the weld pool surface. Xu et al. [13] applied a purpose-built vision sensor to develop the welding seam tracking system for improving the welding quality in robotic GMAW. The GMAW has chaotic and fractal characteristics, Tolle et al. [14] used approximate entropy and Lyapunov exponent to classify the droplet transfer mode from globular to spray and then to streaming transition. He et al. [15] applied the maximum Lyapunov exponent of current signals for the alternating-current square wave submerged arc welding, and extracted the features to study the welding stability. Yao et al. [16] analyzed the sample entropy for the double-wire pulse gas shielded welding and thereby evaluated the welding stability. However, the sample entropy can depict the complexity degree of time series only at a single scale. In addition to rich information at a single scale, the time series of complex system also contains other important information at a larger scale. Therefore, the entropy value on a single scale is incomplete, and the larger-scale information of the time series should be considered, so as to better analyze the complexity of the time series. Based on the sample entropy theory and by introducing the scale factor, Costa et al. [17] proposed the multi-scale entropy (MSE), and defined it as the sample entropy curve of a time series at different scales. The MSE curve can describe the ability of a time series at different scales to generate new patterns when embedded dimensional changes. In general, a time series with a larger MSE than another time series on most scales is considered to be more complex. At present, MSE is widely used in mechanical fault diagnosis [18-21], biomedicine [22-24] and other industries. Zhang et al. [25] employed MSE for feature extraction and fault recognition of three different faults, and combined with the neuro-fuzzy inference system to classify the bearing fault. Gao et al. [26] used MSE to extract the feature of load mean decomposition of wind turbine vibration signals, and combined with the least square SVM to accurately classify the fault type. Zhang et al. [27] applied MSE to analyze the fault vibration signals, and combined with SVM to classify the fault type. Clearly, the combination of MSE and pattern recognition contributes to classification and prediction. In this study, MSE was applied to study the complexity degree of welding electrical signals at different scales, and probed into the relationship between the welding stability and the MSE curves under different process conditions. At the same time, MSE was used as a feature to distinguish the welding forming quality. Genetic algorithm (GA) is a very effective parameter optimization method and can optimize the parameters of the SVM pattern recognition model [28, 29]. Thus, MSE was combined with GA-SVM to classify several types of forming quality. The remainder of the paper was organized as follows: In Section 2, MSE and GA-SVM were introduced. In Section 3, the experimental of GMAW, and the MSE

results obtained from arc current signals were discussed. In Section 4, the classification of forming quality, and the method based on MSE and GA-SVM. Finally, the conclusions were drawn.

2. MSE and GA-SVM 2. 1 MSE The calculation of MSE mainly consists of two parts: (1) calculation of sample entropy and (2) multi-scaling of a time series [30]. For the original time series 𝑥(1),𝑥(2),…,𝑥(𝑁) containing a total of N points, its sample entropy is calculated in 6 steps: 1) Form a set of m-dimensional vectors in sequential order (1) 𝑋（𝑖） = [𝑥(𝑖),𝑥(𝑖 + 1),…,𝑥(𝑖 + 𝑚 ― 1)], 𝑖 = 1,2,…,𝑁 ― 𝑚 +1 2) Find the largest difference between two vectors 𝑋(𝑖) and 𝑋(𝑗) and define it as the distance d[𝑋(𝑖),𝑋(𝑗)] between them: (2) d[𝑋（𝑖）,𝑋(𝑗)] = max[|𝑥(𝑖 + 𝑘) ― 𝑥(𝑗 + 𝑘)|], 𝑘 = 0,1,2,…,𝑚 ―1 where j=1,2,…,N-m+1, and j≠i. 3) Given the threshold r, summarize the number of d[𝑋(𝑖),𝑋(𝑗)] at i where d[𝑋 (𝑖),𝑋(𝑗)] is less than r, and determine 𝐶𝑚 𝑖 , which is the ratio of this number to the total number N-m: 𝐶𝑚 𝑖 =

{the numble of 𝑑[𝑋(𝑖),𝑋(𝑗) < 𝑟]} 𝑁―𝑚

， 𝑖 = 1,2,…,𝑁 ― 𝑚 + 1

(3)

4) Calculate the average 𝐶𝑚 𝑖 for all i: 𝑚

𝐶𝑚 𝑖

(4)

C (𝑟) = ∑(𝑁 ― 𝑚 + 1)，i = 1,2,…N ― m + 1

5) Add the dimension m by 1 to m+1, and repeat steps (1) to (4) to get C𝑚 + 1(𝑟). 6) Determine the sample entropy of this time series as follows: 𝑆𝑎𝑚𝑝𝐸𝑛(𝑚,𝑟,𝑁) = ― ln (C𝑚 + 1(𝑟)) ln (C𝑚(𝑟))

(5)

Since the sample entropy at a single scale does not take into account the characteristics of the time series when the scale factor is greater than 1, the time series may contain the characteristic information of the complex system at a large scale. For this reason, the multi-scale coarse granulation way is introduced to analyze the trend of the sample entropy curve changing at multiple scales. The steps of the MSE algorithm are as follows: 1) Assume the original data is 𝑥(1),𝑥(2),…,x(𝑁) in length of N. Set the scale factor value 𝜏, and construct the coarse grained sequence {𝑦(𝜏)} as follows: 𝑥(𝑖) 𝜏 𝑖 = (𝑗 ― 1)𝜏 + 1,…,𝑗𝜏 𝑗 = 1,2,…𝑁/𝜏 𝑦(𝜏) 𝑗 =∑

(7)

The length of each time series is equal to the length N of the original time series divided by 𝜏. 2) Calculate the sample entropy values of the coarse-grained sequence {𝑦(𝜏)} separately for different values of 𝜏. The above process is the sample entropy analysis at multiple scales, called MSE analysis. Namely, the scale factor is 1 when the time series is the original time series.

Figure 1 shows the coarse granulation process at scale factors of 2 and 3. From the MSE analysis, the sample entropy characterizes the irregularity of a time series on the minimum scale (scale factor of 1). The time series with larger entropy is more complicated, where the time series with smaller entropy is simpler and more similar. MSE defines the sample entropy curve of a time series under different values of the scale factor, which characterizes the complexity of the time series at different scales and the ability to generate new patterns when dimension changes. In general, if the time series with larger MSE than another time series at mostly scales is considered to be more complex. X(1) X(2) X(3) X(4) X(5)

X(6)

X(k-1) X(k) ···

···

Scaleτ=2 y2(1)

y2(3)

y2(2)

y2(k/2)

X(1) X(2) X(3) X(4) X(5) X(6)

X(k-1) X(k) X(k+1) ···

···

Scaleτ=3 y3(1)

y3(2)

y3(k/3)

Fig.1 The multi-scale coarse granulation of time series

2.2 Principle of SVM The main idea of SVM is to map-transform the input vectors of nonlinear samples into high-dimensional feature space, search for a global optimal hyperplane in the feature space, and classify the samples [31]. Let the sample training set be 𝑇 = {(𝑥1, 𝑦1),...,(𝑥𝑙,𝑦𝑙)} where the 𝑥𝑖 ∈ 𝑅𝑛 is the eigenvector, and 𝑦𝑖 ∈ {1, ― 1} (i=1, 2, ..., l) is the category label. In the case of linear separable, there is an optimal hyperplane that completely separates the two types of samples: 𝜔∙𝑥+𝑏=0 (8) where x is the input vector, 𝜔 is adjustable weight vector, and b is bias. Figure 2 shows two types of two-dimensional linear separable diagrams. The circle and the star represent two different types of training samples, and H is a separate classification line with two types of errors. H1 and H2 are separated from each other. At the closest point of the classification line, the line is parallel to the classification line H, the distance between H1 and H2 is the classification interval (Margin) of the two types. The so-called optimal classification line refers to the classification line can separate the two types of no error and maximize the interval between the two types. The former guarantees the minimal risk of experience and the latter minimizes the confidence risk, thus minimizing the structuring risk.

Support vector

Margin Support vector

H2 H H1 Support vector

Fig.2 The classification of data by SVM

The problem of seeking the optimal classification surface can be transformed into the one of solving the quadratic programming. The objective function set constraints are as follows: 1 𝑙 (9) min (2‖𝜔‖2 + 𝐶∑𝑖 = 1𝜉𝑖) 𝜔,b

(10) s.t.𝑦𝑖( < 𝑥𝑖,𝜔 > +𝑏) ≥ 1 ― 𝜁𝑖 where 𝜔 is the weight vector, 𝑏 is the bias, 〈 ∙ 〉 is the inner product; ζ𝑖 is the non-negative slack variable that measures the deviation of the data point; C is the error penalty factor, and a larger C means a larger penalty for wrong classification. According to Mercer's theorem, the nonlinear classification can be realized by using different kernel function 𝐾(𝑥,𝑥𝑖), and the Lagrange multiplier α𝑖 is introduced. The decision function of the optimal classification hyperplane can be expressed as: 𝑙

𝑓(𝑥) = sgn[∑𝑖 = 1𝛼𝑖𝑦𝑖𝐾(𝑥,𝑥𝑖) + 𝑏]

(11)

The radial basis function, which is commonly preferred to other kernel functions and is excellent for classification, is used as the basic kernel function of SVM:

{

K(x,𝑥𝑖) = exp ―

|𝑥 ― 𝑥𝑖|2 𝑔2

}

(12)

As a pattern recognition algorithm based on the statistical learning theory, SVM has many unique advantages in solving small samples, non-linearity and high-dimensional pattern recognition. The time complexity of the algorithm is O(d*n2), where d is the dimension of the input eigenvector and n is the number of training sets. The calculated time consumption is relatively shorter compared to other classification algorithms, so it is often used in the classification of samples. 2.3 Principle of GA GA is an intelligent optimization algorithm based on the genetic and evolutionary processes of the nature [32]. GA introduces the biological evolution theory of ‘survival of the fittest’ into the coding tandem population formed by optimization parameters, and sifts individuals by selection, crossover and mutation according to the fitness function, so as to retain the individuals with good fitness, eliminate those with poor fitness and then inherit new populations. The information of the new generation is better than that of the previous generation. The above process is

repeated until the optimization criteria are satisfied. The time complexity of the algorithm mainly depends on the fitness function, maximum generation and maximum population, and the calculated time consumption is relatively longer. The GA process is shown in Figure 3, and its basic steps are as follows: (1) With some coding methods (e.g. binary coding and real coding), generate a group of initial individuals as an initial population, in which each initial individual represents an initial solution to the problem; (2) Calculate the fitness of each individual in the population; (3) Carry out selection, crossover and mutation operations to form the next generation population; (4) If the optimization criteria are met, output the results; otherwise, go back to step 2 and repeat the above process. Start Initial population Mutation

Caculate fitness values

Optimization criteria

Crossover N

Selection

Y

optimal result

Fig.3 The process of GA

2.4 GA-SVM When SVM is used for classification prediction, the choice of the kernel function parameter g and error penalty factor C has a greatly influences the classification result. It is necessary to select appropriate parameter values to obtain more accurate classification. The common SVM obtains appropriate values of g and C through the grid search method, but the values are often not the optimal [33]. GA with the parallel global optimization ability can be used to optimize the parameters of SVM [34], which can improve the classification accuracy. The process of GA to optimize the SVM parameters is shown in Figure 4, and its basic steps are as follows: (1) With the radial basis function, binary-code K and C to randomly generate the initial population; (2) Decode the gene strings in the population, obtain the values of K and C, and substitute them into the SVM model for training; (3) Calculate the fitness value of the individual population, the fitness function is based on classification accuracy of the trained SVM, and the classification accuracy of a trained SVM is tested by testing feature subsets and calculated by classification accuracy; (4) Discriminate whether the termination condition of the algorithm is satisfied,

and yes, stop the loop and output the optimal combination of parameters, and if not; (5) Perform selection, crossover and mutation operations to generate a new population, and return to step 2 to start a new cycle until the optimal solution is obtained. Initial value of g, C Coding g,C in parameters population Randomize initial parameters population

Training subset

Train SVM Model Caculate fitness values Stop Condition

New population GA operation: Selection Crossover Mutation N

Y

Testing subset

Test SVM Model with optimal parameters Classfication result

Fig.4 The process of SVM parameters being optimized by GA

3. Experiment and MSE Analysis According to the theoretical method above, we analyzed the relationship between short-circuiting GMAW stability and process parameters. The equipment (Figure 5) consists of a Motorman robot and a RD-350A welding machine under the following conditions: the shielding gas of 20% CO2 + 80% Ar, the wire diameter of 1.2 mm, the wire extension of 15 mm, and specimens of Q235 steel plates of 400 × 200 × 6 mm3. The sampling rate of arc signals is 10 KHz.

Metal Pool + MAG welding machine

Hall Sensor _

Hall Sensor

Current signal

signals

Welding Torch

worktable Voltage signal

Protection Model

Acqusition Card Computer

Fig.5 The robot welding system of GMAW

3.1 MSE analysis of different shielding gas flow rates The shielding gas flow is one of the influencing factors on the welding stability. During the welding process, the main function of the shielding gas is to provide charged particles and isolate the outside air. Under the condition of given, then wave-controlled short-circuiting transition welding tests were conducted at given voltage of 16.5 V, current of 80 A, welding speed of 40 cm/min, and shielding gas flow rate of 1, 5 or 15 L/min. The collected local current waveform in Figure 6 implies the welding process is a short-circuiting transition.

(a) Current signal at gas flow of 1 L/min

(b) Current signal at gas flow of 5 L/min

(c) Current signal at gas flow of 15 L/min

(d) MSE of different gas flow rates

Fig.6 MSE of current signals at different shielding gas flow rates

The time-domain waveform showed that the stability of the welding current signals differed among different air flow rates, so the relationship between the sample entropy values was considered. Three sets of data were taken from the above three collected welding current signals, and their mean sample entropy was calculated (Table 1). Clearly, the sample entropy of the welding current signals differed among different stability conditions (Table 1). At the shielding gas rate of 1 L/min, the sample entropy changed greatly, indicating the influence of insufficient gas flow further complicated the electric signal fluctuation of the welding arc, and the values of different sample segments varied greatly. With the increase of shielding gas rate, the protective gas was enhanced, the arc became more stable, and the fluctuation complexity of the arc current signal as well as the variation of the values among different samples decreased. The mean sample entropy showed that when the gas flow rate was sufficient, the sample entropy declined (Table 1). With the increase of the shielding gas flow rate, the signal complexity and the degree of irregularity both declined. When the embedding dimension m changed, the probability of generating a new mode and thus the sample entropy both dropped. However, the distinction between entropy values was not obvious along with the shielding gas flow rates. Thus, MSE was introduced to analyze the welding stability. Table.1 Sample entropy of current signals at different shielding gas flow rates Samples

Gas flow 1 L/min

Gas flow 5 L/min

Gas flow 15 L/min

1 2 3 Mean

0.0807 0.1833 0.1634 0.1424

0.0560 0.0592 0.0882 0.0678

0.0426 0.0409 0.0417 0.0417

For the welding current signals under the above three different gas flow rates, the same nine samples were still considered, and their MSEs were calculated. The 15 multi-scale factors and the MSEs were drawn (Figure 6(d)). Compared to the sample entropy, MSE can clearly and intuitively reflect the changes of welding stability along with the gas flow rates. The MSE curve was small when the welding process was stable, and tended to become a horizontal straight line as the scale factor increased. Therefore, the MSE realized the analysis of the welding stability. As the shielding gas flow rates differed, the welding process became more stable and the MSE curve was smaller. 3.2 MSE analysis of different voltage parameters The frequency of droplet transfer is one of the important factors affecting the short-circuiting GMAW stability. During the welding process, the higher frequency of droplet transfer made the welding process more stable. To obtain different droplet transfer frequencies, we carried out welding tests at the voltage of 16.5, 18.4 or 23.0 V at a given current of 80 A, gas flow rate of 15 L/min and welding speed of 40 cm/min. The collected local current waveform (Figure 7) shows that the frequency of

the droplet transfer decreases in turn.

(a) Current signal at voltage of 16.5 V

(b) Current signal at voltage of 18.4 V

(c) Current signal at voltage of 23.0 V

(d) MSE of different voltages

Fig.7 MSE of current signals at different voltages

The time-domain waveform showed when the voltage increased, the frequency of the arc droplet transition decreased due to the difference in arc length, which in turn caused a change in the welding stability. Three sets of data were taken from the above three collected welding current signals, and their MSEs were calculated. The 15 multi-scale factors and the MSEs were drawn (Figure 7(d)). The MSE curve was smaller when the welding process was stable, and turned into a horizontal straight line as the scale factor increased. Therefore, the MSE achieved the stability analysis of the welding process under varying short-circuiting droplet transit frequency. The more stable welding process led to, a smaller MSE curve. 3.3 MSE analysis of different current parameters The type of the droplet transfer mode is one of the performances of welding stability. During the GMAW process, as the current increased, the type of droplet transition changed from a stable short-circuiting transition to a spray droplet transition, and the middle was an unstable current parameter interval, which was a large droplet mixture transition. To obtain different types of droplet transfer, we conducted welding tests at the gas flow rate of 15 L/min, welding speed of 40 cm/min, and the self-matching voltage and the current of 80, 220 or 330 A. The acquired local current waveform (Figure 8) showed the type of droplet transfer changed significantly.

(a) Current signal at current of 80 A

(b) Current signal at current of 220 A

(c) Current signal at current of 330 A

(d) MSE of different currents

Fig.8 MSE of current signals at different currents

The time-domain waveform in Figure 8 showed the increase of current caused a change in the type of the droplet transfer mode, which caused a change of welding stability. Three sets of data were taken from the above three collected welding current signals, and their MSEs were calculated. The 15 multi-scale factors and MSEs were drawn (Figure 8(d)). As the scale factor increased, it tended to be a horizontal straight line. Therefore, MSE realized the analysis of different droplet transfer types. Under different current parameters, the more stable welding process led to a smaller MSE curve.

4. Forming quality classification of MSE and GA-SVM The above MSE curve change trend under different parameters implies there are certain differences in MSE distribution under different welding process stability, which can be used as a characteristic quantity to characterize the stability and forming quality of the welding process. MSE was used as a feature input vector for GA-SVM to classify the the stability and forming quality of the welding process. Figure 9 shows the welding tests to get four types of welding seam quality, including porous, good, uneven, and spatter welding seams, which were classified as label 1, 2, 3 and 4 respectively.

(a) Porous welding seam

(b) Good welding seam

(c) Uneven welding seam

(d) Spattered welding seam (red circle: spatter) Fig. 9 Four types of welding seam quality

Of the totally 213 samples, 105 samples were selected into the common SVM and GA-SVM models for training, and the other 108 samples into the trained common SVM and GA-SVM models for testing. An Intel(R) Core(TM) i5-2430M CPU @ 2.40GHz, RAM=4.00G computer was used for algorithm calculation. To obtain optimal parameters, the time consumption of common SVM was 10.756 seconds and that of GA-SVM was 28.883 seconds. The time required for GA to find the optimal parameters of SVM was relatively longer. The recognition results were shown in Figures 10, 11 and Table 2. Through classification and prediction of multiple sets of samples, GA-SVM achieved a better classification result. Calculation of the fitness value for 200 evaluated generations shows the optimized parameters are c = 93.5754 and g = 1.01576. The parameters of the common SVM are c = 111.43 and g = 2.2974. The recognition accuracy of GA-SVM is 92.36%, indicating it can achieve higher accuracy than the common SVM in the forming quality classification of the welding process. Table.2 Comparison of common SVM and GA-SVM

Model

Test sets

Correct number

Accuracy rate

SVM GA-SVM

144 144

127 133

88.19% 92.36%

(a) Grid search

(b) Classification label

Fig.10 Forming quality classification of common SVM

(a) Fitness

(b) Classification label

Fig.11 Forming quality classification of GA-SVM

5. Conclusions MSE was used to analyze the arc electric signals under short-circuiting transfer process of GMAW, and then combined with GA-SVM to classify the weld seam forming quality, the following conclusions are obtained. (1) Compared to the sample entropy, the MSE characterized the complexity of a time series at different scale and can clearly and intuitively reflect the changes of welding stability under different parameters. (2) MSE can be used to characterize the welding stability. As the scale factor increased, the MSE curve distribution of the arc electrical signals became a horizontal straight line. When the welding process was more stable, the MSE distribution was smaller. (3) Based on the MSE change characteristics under different welding process and

combined with GA-SVM, a four-classification prediction model under forming quality was established. The classification quality was improved, and a better classification effect was achieved in comparison with the common SVM. In the future, we will conduct a feature selection study of multi-scale entropy, reduce the computational complexity, and combine it with other pattern recognition methods for online diagnosis of welding processes.

Acknowledgments This work is supported by the key project of National Natural Science Foundation of (no 51805266, 51905273), is gratefully acknowledged.

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First author: Yong Huang email: [email protected] corresponding author: Dongqing Yang email: [email protected] other author: Kehong Wang email: [email protected] other author: Lei Wang email: [email protected] other author: Qi Zhou email:[email protected]

Stability Analysis of GMAW (Gas Metal Arc Welding) Based on Multi-scale Entropy (MSE) and Genetic Optimized Support Vector Machine (GA-SVM)

Highlights 1) Compared to the sample entropy, the MSE can intuitively reflect GMAW stability. 2) As the scale increased, the MSE distribution became a horizontal straight line. 3) When the welding process was more stable, the MSE distribution was smaller. 4) Based on the MSE and GA-SVM, a four-classification model was established. Declaration of interest statement The authors declared that they have no conflicts of interest to this work. We

declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. Conflict of interest Form The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.