Real-time seam defect identification for Al alloys in robotic arc welding using optical spectroscopy and integrating learning

Journal Pre-proofs Real-time seam defect identification for Al alloys in robotic arc welding using optical spectroscopy and integrating learning Zhifen Zhang, Wenjing Ren, Zhe Yang, Guangrui Wen PII: DOI: Reference:

S0263-2241(20)30083-X https://doi.org/10.1016/j.measurement.2020.107546 MEASUR 107546

To appear in:

Measurement

Received Date: Revised Date: Accepted Date:

28 May 2019 20 January 2020 22 January 2020

Please cite this article as: Z. Zhang, W. Ren, Z. Yang, G. Wen, Real-time seam defect identification for Al alloys in robotic arc welding using optical spectroscopy and integrating learning, Measurement (2020), doi: https://doi.org/ 10.1016/j.measurement.2020.107546

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Real-time seam defect identification for Al alloys in robotic arc welding using optical spectroscopy and integrating learning Zhifen Zhang Wenjing Ren Zhe Yang Guangrui Wen* Institute of aero-engine, School of Mechanical Engineering, Xi’an Jiao Tong University, Xi’an 710049 China

Abstract Accurate on-line weld defect detection in robotic arc welding manufacturing is still challenging, due to the complexity and diversity of weld defects. In this study, a new real-time defect identification method is proposed for Al alloys in robotic arc welding, using arc optical

spectroscopy and an integrated learning method. Spectrum feature was extracted, based on the absolute coefficients of the principal components. Feature importance was quantitatively evaluated using the mean decrease accuracy of Principal Component Analysis-Random Forest (PCA-RF).

A new indicator, e.g., Importance Factor, was proposed, based on the variance of the out-ofbag test error of RF to select the optimal feature subset. The proposed PCA-RF proved to effectively identify five classes of weld defects with better performance than support vector machine and back propagation neural network. Finally, the selection pattern of spectrum feature subset was investigated, before revealing the correlation mechanism of the selected lines spectrum and weld process.

Keywords: optical spectroscopy, aluminum alloy welding, defect on-line identification, principal component analysis, random forest

1. Introduction Aluminum alloys is one of the most important materials in lightweight manufacturing for military fighters, large military transport aircrafts, carrier rockets, space vehicles, automobiles and so on. Al alloy of Gas Tungsten Arc Welding (GTAW), also used for Additive Manufacturing (AM), has been considered as the main forming manufacturing technology because of its key features. In these industries, quality of the welding product has always been of the utmost concern, from the point of manufacturing to the point of equipment service process. For the inspection and control of the welding quality, offline manual Non-Dstructive Testing (NDT) (X-ray, ultrasonic, etc.) method is adopted, after welding, which obviously

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lengthens the production cycle, reduces the production efficiency, and increases the manufacturing costs. Moreover, in cases of welds with quite complex structure or coating processing, it is difficult to inspect the quality and defects using traditional NDT methods. Therefore, research of on-line monitoring and detecting of weld quality is of essence and a high necessity. One reason is that this recorded information, during the manufacturing process, can be used to trace back the defect during its whole lifetime. Other reason is that online detection of welding surface defects or even internal defects, can provide key information for the welding robot to take active control measures or adjust the main parameters of the welding process to prevent defects from further developing, thus, reducing the welding defects, and optimizing the manufacturing process. More importantly, the accuracy and efficiency of traditional NDT can be greatly improved, as well as the quality stability and reliability of the robot welding manufacturing. However, because of the complexity of the welding process and randomness of the process interference, various weld defects, such as surface, metallurgical, process-induced, and inner defects, are usually inevitable. For instance, defects due to under penetration, over penetration, and burning through can greatly weaken the strength of the welding joint, while are closely related to the wire feeding. Besides, if the wire feeding and the weld pool are interrupted, defects, such as poor surface quality or inner porosity, typical in Al alloys, might occur. Therefore, real-time monitoring of the welding process and seam quality is of great importance in terms of timely detection of seam defects, improvement in the stability of the welding quality and manufacturing efficiency, and last, in terms of promoting the intelligent welding manufacturing.

1.1 State of art for robotic welding monitoring Various kinds of sensing technologies have been applied and investigated for on-line monitoring and defects detecting of Al alloys in robotic welding. Chen and Wu [1] introduced the concept of intelligence into welding, e.g., welding intelligent manufacturing. H. Huang et al. [2] studied in-situ measurement of welding distortion, based on digital image correlation on thin plates, during Tungsten Inert Gas Welding process. Liu and Zhang [3] innovatively proposed the vision-based sensing system using laser generator and dot matrix structured light

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and researched its applications for weld penetration controlling in GTAW process. Chen [4] innovatively used the reflection of arc light in Al alloy seam and proposed a reflection model to calculate the index of the weld pool surface height from the passive vision image during GTAW. Furthermore, the backside width of the welding seam was estimated by means of two supervised machine learning methods, including linear regression and bagging trees. Zhang et al. [5] investigated the sensing of welding arc audible sound and its application in seam penetration classification for Al alloys in robotic welding. Online control of welding penetration via arc sound signal for pulse GTAW was achieved by Lv et al. [6]. NDT technology was applied for on-line monitoring of the welding process, especially for the inner defects. Acoustic emission can acquire the strain wave inside the welding material and was used by Fang et al. [7] to monitor the cold cracking of steel in gas metal arc welding. Recently, acoustic emission sensing was applied by Taheri et al. [8] for in situ monitoring of the additive manufacturing (AM) process. However, AE sensors need to be mounted closely to the metal surface, which limits its application due to the damaging high temperature. Calta et al. [9] developed the in situ X-ray measurements for laser powder bed fusion additive manufacturing process. Their results have revealed pore formation, phase transition and so on. Moreover, the X-ray equipment can be complex and it is difficult to develop, even with limitations, thus making it still difficult to achieve industrial implementation. Multisensory fusion technology might combine the advantages of different sensors and provide more accurate estimation of the real-time welding quality. You et al. [10] fused the visible light radiation, ultraviolet radiation and visual sensing to identify weld defects in laser welding. Zhang et al. [11] achieved better prediction accuracy of weld defects by combining the audible sound, arc voltage and arc light optical emission on the feature-level.

1.2 Optical spectroscopy for real-time welding monitoring During the dynamic welding process, arc light emission, e.g., arc optical spectrum, is one of the most important information sources for on-line welding monitoring and defects detecting. Compared to visual sensing, audible sound and arc electrical signal, the approach of optical spectrum has many advantages such as non-contact mounting, high sensitivity to weld process, source of abundant information.

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Monitoring and controlling of laser-drilling process were investigated by Chao-Ching Ho et al.[12, 13] by in-situ measuring the plasma optical emission and plane or interdigital electrodes in the external electric field respectively. The variation of light brightness from laser-induced plasma was extracted to control the laser percussion drilling process after the thorough correlation research between the increased drilling depth and the optical signal output. J. Mirapeix et al. [14] correlated the ratio of line-to-continuum for Al I emission line at 396.15 nm to Al contribution of weld plate, in order to monitor the seam quality of Usibor blanks in laser-welding process. Song et al. [15] extracted the feature parameters using Al/Ti lineintensity ratio and applied it to evaluate the Al concentration for laser additive manufacturing (AM), based on support vector regression. Zhang et al. [16] found that the statistic features extracted from several spectrum bands of interests (SOI) have good correlation with the surface oxidation defects of Al alloy in GTAW. Yu et al. [17] investigated the real-time detection of seam porosity for Al alloys in GTAW, using optical spectrum. The intensity ratio between H I line (656.28 nm) and Ar I line (641.63 nm) was found to be able to decrease the pulse interference and the non-hydrogen factor, while it also correlated with the inner porosity. Furthermore, Huang et al. [18]used K-medoids algorithm to select spectral lines that have more sensitivity to the inner porosity. Huang et al. [19] used empirical mode decomposition to eliminate the influence of pulse current, before distinguishing small porosity from no porosity, based on the two hydrogen ratio features. Recently, Huang et al. [20] established the models for bubble nucleation and growth, while the influence of welding current on the critical nucleation radius was investigated. The developed physical model shows good consistency with the metallographic observation results. Zhang et al. [21] studied the correlation between the arc spectrum feature and characterization results for the inner porosity for Al alloy in arc welding.

1.3 Application of machine learning Literature [22] applied deep neural network to predict the solidification cracking susceptibility for stainless steels, which displayed better performance than shallow neural network and, support vector machine after pre-training and fine-tuning of DNN. Random forest [23] was introduced to evaluate the microstructural parameters and find out the important one affecting the mechanical properties of composites. Di Wu[24] combined the t-

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SNE and deep belief network method to identify the seam penetration states of variable polarity plasma arc welding based on the visual and acoustic signals. For the increasing need of powderbed fusion AM quality monitoring, literature [25] proposed and compared two intelligent classification methods PCA-SVM and CNN in terms of accurate quality level identification. Deep learning has displayed its powerful capability in classification issues, however, its need of large amount of training data also limits the application, especially for the defect detection. It is known that for a good prediction and classification model, certain number of training data set is necessary as well as its balance between different kinds of samples. But the sample of defects are usually much less than normal ones the normal ones. Integrating learning is one of the most practical methods in the area of pattern recognition. It integrates several algorithms together including feature selection, multiple classifiers, and decision fusion. In this paper, random forest was applied and was improved by proposing a new integrating structure to achieve the on-line defects detection of robotic welding with higher accuracy.

1.4 Problems and contributions In a complex real welding environment, more than one type of defects and interruptions might occur simultaneously. However, those defects might show similar features in monitoring signals, which makes it more difficult to achieve accurate defect detection in real-time. Therefore, establishing a large spectrum feature knowledge base of various defects, studying the effect of all types of defects on the spectrum signal, and focus on the key spectrum information are in high and immediate demand. These are the essential preconditions for establishing correlation with the welding quality and then precise defect detection during the welding process. In this case, we are facing another problem, which is to accurately select the respective feature for each type of defect. However, the utilization of abundant information on spectrum emission is inadequate, because only a limited number of spectrum lines is selected. Hence, integrating a learning method is highly recommended to solve the accurate identification of various defects among the massive data of the welding process. In this study, a new real-time defect identification method, e.g., PCA-RF, was proposed for Al alloy in robotic arc welding, using arc optical spectroscopy, based on the integrated learning

method of random forest. Traditional feature extraction, selection, classification and model

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optimization were significantly simplified by integrating them into PCA-RF together. The proposed feature set, based on the PCA absolute coefficients, showed little pulse interference in the pulsed GTAW process. Furthermore, the indicator of Importance Factor (IF) was introduced, based on the OOB test error of RF, to qualitatively evaluate the importance of each spectrum feature and select the optimal feature subset. After careful comparison, the dimensions of high feature space were reduced from 2466D to 84D, while the classification accuracy was improved from 79.3% to 91.8%. Next, PCA-RF proved to be able to identify five classes of weld defects, including under penetration, surface contamination, inner porosity, wire stuck and couple defects, performing better than SVM and BP. Finally, the selection pattern of spectrum feature subset, based on PCA-RF, was analyzed and summarized into three rules. An extensive analysis was conducted about the correlation mechanism of the selected lines spectrum and the weld process. An interesting conclusion was that the larger concentration gradient of Fe, between the wire and the base material, might cause greater variation in the line spectrum emission of Fe I (407.84 nm).

2. Experimental setup and materials 2.1 Experimental setup Fig.1 shows the robotic welding experiment setup for Al alloy in GTAW, which consists of a robotic welding system, a process-controlling unit, and multiple signal acquisition systems. The welding process and the running speed were controlled by the Yaskawa industrial robot, while the trigger of the welding arc, the signal acquiring process and parameters recording were controlled by a custom-made in-house monitoring system, in LabVIEW environment. This monitoring system can also regulate the wire feeding speed and the welding current, as well as set the signals sampling frequency. As displayed in Fig. 1(b), AvaSpec-1350F-USB2 optical CCD spectrometer, including an optical probe, a fiber, and a spectrometer was used for the optical spectrum signal acquisition. Its wavelength ranges from 350 nm to 1100 nm with 1,350 pixels and a minimum integration time of 0.7 ms. The probe position plays an important role in the acquired spectrum signal because the spectrometer is sensitive to it. To solve this problem, during the welding process, the probe was slowly moved in the horizontal and vertical direction, in order to determine the optimal acquisition position. Fig. 1(b) shows that as the probe was

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moved downwards, the acquired intensity of the Mg I spectrum line (383.83 nm) was partially out of range, because of high arc light intensity. Also, the spectrum intensity dramatically decreased when the probe was moved slightly up or down, which indicates that the spectrometer is more sensitive in the vertical direction, owing to the blocking of the welding torch. Therefore, it is better to register the arc light spectrum emission within the collectable area, as marked in Fig. 1(c). On the other hand, the variation in the spectrum intensity was not significant during the motion from left to right, because the probe was facing the welding arc area with no blocking. At the final experimental setup, the probe was fixed across the center of the welding arc at a distance of 10 cm, whereas a 10% dimmer was fixed before the probe. An integration time of 1.5 ms was set, in order to guarantee a spectrum intensity of about 2/3 of the maximum scale. The sampling frequency of the arc spectrum signal was 60 Hz, according to the consecutive acquisition model. Once the probe position was determined, it was rigidly fastened before the welding commenced, so as to remain constant during the welding process.

a) Robot welding experimental system with optical spectrum acquisition Overrange

Left-right Up-down

Intensity of Mg I

60000 50000

Collectable area

40000 30000 20000 10000 0 -10000 2

4

6

8

10

12

14

16

18

20

Welding time(s)

b) Position adjustment c) Spectrum intensity curves when moving the probe Fig. 1. Welding experiment system for robotic GTAW

2.2 Materials and experiment

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In Table 1, Al alloy, feeding wire materials and several key welding parameters are listed. Table 2 shows the component weight for the welding material. In the case of 5A06 Al alloy, the weights of components were tested using energy-dispersive spectrometer (EDS), for both the base material and the weldment. However, the amount of Fe was too small to be detected in 5A06 Al alloy, before the welding, but it was 0.44 after the welding and filling of SGAlMg6 wire feeding. Table 3 lists all other welding experiment parameters. Fig. 2 shows a view of the experiment designs, where spot welding without feeding was performed after starting arc, in order to preheat the plate. Post nondestructive testing was carried out using X-rays. For the defect designing, 5A06 Al alloy with Y-grooved butt welding was used for normal welding. Multiple defects were simulated to study on-line defect detection, such as under penetration, surface and inner porosity, and abnormal wire feeding. Specifically, surface contamination and surface porosity defects were artificially generated using a contamination preset on the plate surface, resulting in different degrees of porosity, according to the amount of contamination. Inner porosity defect was obtained by not removing the surface oxidation film before welding. Under penetration defect was generated by not using spot welding preheating. Table 1 Welding materials and main parameters Welding material Al–Mg alloy (5A06)

Plate

Welding wire

Wire feed

size

type/diameter

speed

(mm)

(mm)

(cm/min)

300 ×

SG-AlMg6

50 × 4

Φ1.6

Welding form

Peak

Welding

current

speed

(A)

(cm/min)

Y-grooved 0, 12

butt

Experiment purpose Multiple

240

16

welding

defect detection

Table 2 Component weight for the welding plate and wire (wt. %) 5A06 5A06 weldment SG-AlMg6

Al balance balance balance

Fe 0.44 0.38

Mg 1.9 2.68 5.5-6.5

Mn 0.12

Si 1.07 0.22

Zn 0.05

Ti 0.11

Data source EDS-tested post welding Manufacturer data

Table 3 Welding experiment parameters Welding parameters

Value

Welding parameters

Value

Pulse frequency (Hz)

1

Electrode diameter (mm)

3.2

9

Base current (A)

50

Wire feeding position

Right forward

Pulse duty ratio (%)

50

Feeding angle (°)

45

Ar flow (L/min)

15

Arc length (mm)

4

AC frequency (Hz)

50

Spot welding preheating (s)

8

a) Welding experiments with Y-grooved butt welding b) Weldment and its test results Fig. 2 Design of the welding experiments and post welding test

3. Theoretical background 3.1 PCA Principal Component Analysis (PCA) [26] is one of the most widely used data dimensionality reduction algorithm. In this study, it was used to evaluate and select the line spectra using its principle components (PC) vector and coefficients. The algorithm in detail is given below. Step 1. The input of PCA should be a vector including certain number of sampled signals under different conditions. For this case, vector X(N,D) is prepared as the input, where D is the dimension of the spectrum signal; each one representing the spectrum wavelength. N is the total sampling number for real-time processing. Step 2. The eigen structure decomposition is calculated as: λiei = Qei,

(1)

Where, Q = XXT is the covariance matrix and λi is the eigenvalue associated with the eigenvector ei. Step 3. The key of PCA is the linear transformation using the calculated eigenvalues ei from Step 2. Each dimension feature, e.g., principle component, has an engenvalue while the D dimension of the new components can be acquired. W is the linear transformation vector, which can map the input D-dimensional feature space onto a p-dimensional subspace. For the

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purpose of feature reduction, the p dimension of the new feature space, should be smaller than D. The new feature vectors Y are described as follows: Yj = WTxi;

(j:1; ...; P; i:1,...1322)

(2)

The eigenvalues ei, e.g., PC coefficients, are in descending order and are the columns of the W vector. So, the new principle components can be obtained from the top vectors of Y. p is the dimension number of the new feature space. Usually p is equal to 3, considering most of the information can be represented by the new top 3 principle components. In this paper, PCA and its PC coefficient were used as the index to quantitatively evaluate their sensitivity to weld defects and the correlation between those one thousand pixels of the spectrum. 3.2 Random forest Random forest (RF) [27] is one of the main methods in integrated learning and machine learning. It has a wide range of applications in various classification and regression problems. Cerrada et al. [28] combined genetic algorithms with random forest to diagnose the multiple classes of gear fault. Compared to other classification methods, including logistic model tree (LMT) and classification and regression tree (CART) models, RF has shown the best results in map landslide prediction, according to the report of Chen et al [29]. Manavalan et al. [30] applied RF to the area of pharmacology to predict the anti-inflammatory peptides. The basic unit of RF is the decision tree, as seen in Fig.3, which has several good characteristics, such as low computational complexity, fast prediction process, and easy model display. The algorithm of random forest can be described as follows: Step 1. Supposing the size of the forest to be constructed is k and the size of the training set is N. Each tree is resampled according to the Bootstrap method, and is sampled k times independently. Each time N samples are randomly selected, k training data can form S sets, and they are IID (independent and identically distributed). Step 2. Each autonomous sample set is used to build a classification tree. Let the characteristic dimension of the sample be M, so the growth process of the single tree is as follows: m features are randomly selected from the M features, and the decision tree selects the best split from each of the m features every time decision tree is splitting. Each tree is allowed

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to grow sufficiently until the purity of each leaf node is minimized, with no pruning process involved. Step 3. According to the constructed classifier, a new unknown sample is predicted, and the classification result of the unknown sample is determined according to a simple majority voting method applied on the voting result of each tree classifier.

Fig. 3 Schematic of random forest for weld defect detection In the random forest model, there are two indicators of importance concerning the characteristics. One is based on the OOB (out of bag) error method, called MDA (Mean Decrease Accuracy), which randomly smashes the eigenvalues of the sample data outside the bag, then retests the OOB error of each tree. The MDA is the average difference of the two OOB tests errors. And its formula is as follows: Ja ( x j ) 

Where:

yi

1 1 x (  I (hk j ( i )  yi )  I (hk (i)  yi ))  k Bk C Bk iBk

(3)

is the classification label in the i-th OOB, I is an indication function, and hk ( i ) is

a classification label of the sample i predicted by the data set

Bk

x

.

hk j ( i )

is a classification

label after replacing feature. The second importance indicator is based on the Gini impure method, called MDG (Mean Decrease Gini), which is calculating the difference between the Gini index before the classification and the Gini index after the classification. The Gini index expression is: n

n

i 1

i 1

Gini(p)   pk ( 1  pk )  1   pk2

(4)

12

Where pk is the probability of the nth category among the n categories. Its formula is as follows: J b ( x j )  Gini (D) - ((

D | D1 | )Gini (D1 )  2 Gini (D2 )) D |D|

(5) Where D1、D2 are the two categories in which the set D is divided into, based on the features. It can be seen that the indicators in the random forest model are able to evaluate the importance of input features. The larger the indicator is, the higher the importance degree of the features is. Both methods evaluating the importance degree of the features are based on the quality of the classification results.

4. Methods and results 4.1 Preprocessing of spectrum Fig. 3 shows signals acquired during the welding process, including pulsed AC-square wave welding current signal and arc spectrum, in the peak and base currents. This type of pulsed current, which often appears in aerospace industry applications, can control the heat input well, achieving a better seam shape. On the other hand, it brings more complexity into the monitoring signal and thus should be eliminated. Fig. 3(b) shows that the arc spectrum signal consists of the line and the background spectrums, generated by the liquid weld pool, tungsten electrode, and melted filling wire. The line spectrum and the background spectrum were separated by means of lower enveloping, as displayed in Fig. 4. First, the minimum value is found to obtain the lower envelop of the original spectrum. Next, the line spectrum can be separated by subtracting the lower envelop curve. The separated line spectra, from 360 nm to 1100 nm, were

Spectrum emssion counts

considered as the input to PCA for further processing.

Welding peak current Welding base current

10000

5000

0

400

500

600 700 800 Wavelength / nm

900

1000

1100

13

a) Pulsed AC- square wave welding current signal b) Arc spectrum in the peak and base currents Fig. 3 Signals acquired during the welding process

Spectral emission intensity/counts

15000

Original spectral signal Second lower enveloping spectral lines

10000

5000

0 300

400

500

600

700

800

Wavelength/nm

900

1000

1100

Fig. 4 Separation of the line spectrum from the background spectrum 4.2 PCA on spectrum In order to automatically select the line spectra that are closely related to the weld quality and defects, PCA was first performed on the spectrum data from the welding seam of 5A06, during butt welding process, as it contains multiple welding states, including without feeding, with feeding, porosity, and contamination of the oxidation film. As shown in Fig. 5(a), concerning this welding seam, the total sample number of the arc spectrum was 3308, whereas a dimension of 1350 corresponds to the spectrum wavelength.

a) Input of PCA

b) Coefficient of PCs

Fig.5 Selected metal line spectrum using PCA Based on the theory of PCA, as described in Section 3.1, the original dimension of the

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spectrum signal can be reduced from 1350 to 3. First, the covariance matrix and the PC eigenvector of the spectrum sample, as shown in Fig. 5(a), were calculated. Next, the three new PC features were obtained by a linear combination of each dimension, using the coefficients of the PC eigenvector. As a result, the first new PC feature had the largest variance and included the most important information about the original data. The second PC feature had smaller variance, etc. All PC features were determined by the PC coefficients. Moreover, since the input spectrum sample of PCA contained data from a dynamic welding process with multiple weld defects, the coefficients of each line spectrum, for the three new PCs, that is, ei, are considered as the quantitative indices in evaluation of the sensitivity of the line spectrum to weld defects, as displayed in Fig. 5(b). Observing Fig. 5(b), it is evident that in PC1, Ar I line spectrum shows higher value than PC2 metal line spectrum, including Al, Mg, and Fe as the main components. The PC coefficients are the key for the linear combination and feature reduction. The higher the coefficient value is, the more sensitive the line spectrum is to weld defects. Thus, it is reasonable that the coefficient of Ar I line spectrum, being the first PCs of the welding arc, is higher. It might be related to weld defects due to under penetration or burning through conditions that change the welding arc length and type significantly. As far as the second PC is concerned, it can be seen that metal elements are other PCs, which have the opposite effect or cooling effect on the welding arc, as a large part of metal elements absorb the arc heat. For the hydrogen spectrum, it is confirmed to be related to the porosity of Al alloys [17], even though its coefficient is quite low. Hence, those line spectra might be closely related to weld defects due to conditions like interference of wire feeding and weld pool contamination.

4.2 Feature extraction for weld defects Fig. 6 shows the diagram of the feature extraction algorithm based on the selected line spectra as well as their capability in defect detection based on PCA. Three lines spectra with higher PC coefficients, including Fe I (407.84 nm), H I (656.28 nm) and Ar I (763.3 nm), were selected for analysis of their correlation to weld defects, such as porosity, abnormal penetration and abnormal wire feeding.

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Eliminate the background spectrum of arc spectrum signal by two-times lower enveloping;

Preprocessing of arc spectrum signal

Spectrum signal X(N,D) as input vector of PCA , N = 30, is the sampling number of spectrum txt files, D=1350, is the dimension of spectrum signal; Obtain the eigen structure decomposition λiei = Qei (i:1;...p) , where p sets to 3 is the reduced dimension number;

PCA on arc spectrum

Extract the coefficient vector, ei (i:1,...3) in descending order based on eigenvalue, where e is the coefficient, i is the number of PC ;

Correlation to multiple defects

Obtain the absolute coefficient value of H I (656.28 nm) at 530th dimension, |Hei|(i=1) ;

Surface porosity

Inner porosity

Obtain the absolute coefficient value of Fe I (407.84 nm) at 105th dimension,|Feei|(i=2) ;

Wire feeding struck

Weld pool contamination

Obtain the absolute coefficient value of Ar I (763.3 nm) at 718th dimension,|Arei|(i=1) ;

Under penetration

Over penetration

Fig.6 Feature extraction for multiple defects based on PCA One of the main defects for Al alloy in arc welding is porosity, which might greatly affect the strength of weld products and is not tolerated under strict standard requirements. The root cause of porosity is the great difference in the solubility of H atoms in liquid and solid weld pools. Due to the high randomness and complexity of robotic welding, the porosity cannot be effectively controlled and is randomly generated. Fig. 7(a) shows the acquired spectrum and the H I (656.28 nm) line spectrum enlarged, where its higher intensity is obvious when surface porosity occurs. Fig. 7(b) presents the feature extraction steps in the case of seam porosity, based on H I (656.28 nm) line spectrum. As it can be seen, the original intensity of H I has good correlation with the surface porosity at great pulse interference. Then, using the absolute value of the PC coefficient of the H I spectrum, the performance of defect detection is greatly improved. Concluding, the proposed PCA feature, e.g., the absolute values of PC coefficients can effectively decrease the noise of the PC coefficients and demonstrate better results than the original PC coefficients. In addition, the proposed method has no need to eliminate pulse interference and it is easy to implement in potential industry applications.

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a) Spectrum signal for weld porosity

b) Absolute PC coefficient of HI (656.28 nm) Fig. 7 PCA feature improvement

a) Surface porosity coupled with over penetration b) Surface contamination Fig. 8 Correlation of PCA features to different weld defects Fig. 8 displays the correlation of PCA features from Fe I (407.84 nm), H I (656.28 nm) and Ar I (763.3 nm) to the same welding process and weld defects. In Fig. 8(a), it can be seen that

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the surface porosity occurs along with over penetration defects, during which the porosity detection curve shows the corresponding spikes as well as the penetration curve. In addition, the wire feeding detection curve displays a clear response to the porosity detection, possibly due to the fact that the preset contamination absorbed a part of the arc heat and decreased the emission intensity of the Fe I line spectrum. Therefore, the detection curve showed similar spikes with that of the case of wire stuck. Fig. 8(b) demonstrates the surface contamination detection results in the case where wire feeding was stuck for a part of the seam. Furthermore, the detection curves indicate that, when defects occur, the width of the Fe I spectrum feature curve is much larger than that of H I and Ar I. By calculating the statistic feature, e.g., kurtosis, their response to defects was evaluated. The Fe I spectrum feature has the smallest value of kurtosis, among the other two features, which indicates that the Fe I emission was affected longer by the defects.

4.3 Defects identification using PCA-RF The frame of the proposed PCA-RF is displayed in Fig.9, where it can be seen that traditional feature extraction, selection and model optimization were greatly simplified and integrated into PCA-RF together. Traditional feature extraction in the time domain and frequency domain is no longer necessary, as well as pulse interference elimination. Only PCA processing on spectrum was needed and was adequate for further classification. Besides, Random Forest has integrated feature selection, evaluation and classification. Moreover, its selection results can qualitatively evaluate each spectrum elements and can be used to reveal their higher correlation to weld process and defects.

Fig.9 Diagram of the proposed PCA-RF

The input of PCA-RF, as shown in Fig.9, includes 30 samples of spectrum with 1322 dimension. After its PCA processing, the coefficient vector from the first PC and the second PC are both considered as the input feature of the RF model. The sampling rate of the spectrum

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is 60 Hz. PCA and feature extraction are performed on every 30 samples. Therefore, the seam is inspected at every 1.3 cm by means of the proposed method, during the welding process. The output class of PCA-RF is six. Normal seam, coupled defects with surface contamination and over penetration, wire stuck, surface contamination, inner porosity and under penetration are included. Their detailed sample number for training and testing are listed in Table. 4. For the training and testing, the total number of all samples is 817, including 670 for training and 147 for testing. Table 4 Data description and preparing for PCA-RF Seam description

Seam appearance

Class (number of samples)

Normal seam

1(402)

Coupled defects

2(55)

Wire stuck

3(113)

Surface contamination

4(51)

Inner porosity

5(79)

Under penetration

6(89)

In order to quantitatively evaluate the importance of each spectrum feature, a new indicator based on RF was proposed, e.g., Important Factor (IF), which is obtained by calculating the variance of OOB test error. As introduced in the theory of RF, Mean Decrease Accuracy is the average of difference of the two OOB test errors. The variance of MDA can evaluate the fluctuation of OOB test error. Specifically, when a new feature was added into the RF model, if the OOB test error showed small variation, it meant that this new feature had little influence on the model performance and was not important. On the contrary, if the OOB test error showed greater fluctuation, then, the new feature was of high importance. The IF can be used as a threshold to select optimal feature subset and reduce the feature dimension. Concluding, the higher IF is, the more important the feature is.

19

3

x 10

-3

Rate of OOB misclassified(IF)

2.5

Feature selected when IF>0.0020(12D)

2 IF>0.0015(36D)

1.5 IF>0.0009(84D)

1 IF>0.0005(143D) IF>0.0004(165D) 0.5

IF>0.0003(213D)

0 0

300

600

900

1200

1500

1800

2100

2400

2700

Number of spectrum PCA feature

Fig. 10 Feature importance and selection based on PCA-RF

Fig. 10 gives the result of OOB test error in the training and testing of the PCA-RF model. The X axis can correspond to each wavelength of spectral elements and refers to the coefficient vector derived from first and second PC. Y axis represents the importance of each feature parameters. As seen in Fig. 10, the IF value was changed from 0.0003 to 0.002, leading to feature dimension decreasing from 2466D to 12D. Fig. 11 has displayed ten times testing results of the PCA-RF model, where the highest accuracy of the PCA-RF defect identification algorithm was achieved from a 84D feature set, while the original feature set of 2466D showed the lowest accuracy. The performance of each model was evaluated by standard deviation and mean accuracy, as listed in Table 5, where it can be seen that the model with the 84D feature set has the highest accuracy of 0.918 and the lowest standard deviation of 0.018. Interesting enough, the feature space of neither the higher nor the lower dimension has shown the lower accuracy and higher fluctuation. For higher feature dimension, like 213D, 165D, feature redundancy might damage the model classification performance. On the other hand, lower feature dimension contributes to insufficient or inadequate information for accurate defect identification.

20

1

Test accuracy of RF

0.95 0.9 0.85 0.8 0.75 0.7

Original PCA feature with 2466D PCA feature reduction with 84D PCA feature reduction with 12D

0.65 1

2

3

4

5

6

7

8

9

10

Number of testing

Fig. 11 Test curves of PCA-RF with different feature dimension Table 5 Performance of PCA-RF with different feature set dimension Threshold value of IF

None

0.0003

0.0004

0.0005

0.0009

0.0015

0.0020

Dimension of feature set

2644D

213D

165D

143D

84D

36D

12D

Mean Accuracy

0.729

0.908

0.905

0.918

0.918

0.915

0.885

Standard deviation

0.046

0.287

0.030

0.0241

0.018

0.0193

0.039

After feature selection and optimization of feature subset, the final PCA-RF was established based on a 84D feature space. In Fig.12, the results of feature importance of each feature (Fig.12-a), testing (Fig.12-b) with 93.2% level of accuracy and decision trees (Fig.12c) are displayed. Studying Fig. 12(a), it is hard to trace back to each spectrum wavelength and component, but more detailed information will be presented and discussed in the next section. In Fig. 12(b), defect classes of No.1, 2 and No. 6 were misclassified. Specifically, normal seam was misidentified as coupled defect. Before model optimization, normal seams were mostly misclassified into defect no.2, no. 4 or no. 6. Compared to this poor classification, the proposed model performance has been greatly improved with lower error and lower standard deviation. In Fig. 12(c), the test error has been decreasing along the increasing number of decision trees and has become stable at about 300 decision trees. In addition, PCA-RF has demonstrated higher accuracy of defect identification, compared to SVM and BP, while the input feature dimension decreased from 2644D to 12D, as seen in Fig 13.

21

Rate of OOB misclassified

基 于 OOB误 分 率

0.05 0.04 0.03 0.02 0.01 0

0

10

20

30

40

50

60

70

80

90

70

80

90

Number of spectrum PCA feature

a) Feature importance

20

6 Lable Tested result

15

4

0.25

10

OOB test error

下降值

Class of samples

5

5

3 0

2

1

0

0.3

0

20

10

40

60

20

80

100

30

120

40

140150

0.2 0.15 50

0.1 特征 0.05

0

60

100

200

300

400

500

Number of decision trees

Tested samples

b) Testing results

c) Iteration of decision trees

Fig. 12 Defect identification based on PCA-RF with 84D feature set dimension 1

Test accuracy

0.9 0.8 0.7 0.6 0.5 PCA-RF SVM BP

0.4 2644D

213D

165D

143D

84D

36D

12D

Input feature dimension of spectrum PCA

Fig. 13 Comparison with different classification models

5. Discussions Table 6 gives the 12 spectrum features selected, based on IF value of 0.002 using PCARF, wherein the possible chemical element, corresponding number of pixels, wavelength and their IF value are listed. It can be seen that most of the spectrum selected from the first PC vector of PCA were Ar I lines spectrum, while all of the elements were features adjacent to metal lines spectrum in the second PC vector of PCA. These findings are in agreement with the results of PCA on spectrum in Section 3.2. The Ar I line spectrum is the first main component inside the welding arc plume, whereas the metal element spectrum emission is the other kind

22

of component, which has the opposite or cooling effect on the welding arc, as a large part of metal elements absorb the arc heat. Table 6 12D Spectrum subset selected by PCA-RF Chemical element

Observed wavelength

IF value

694

751.16 nm

0.122

828

827.04 nm

0.100

874

852.92 nm

0.039

717

763.51 nm

0.072

802

811.53 nm

0.093

857

842.46 nm

0.09

HI

531

657.84 nm

0.130

Fe I

105

408.93 nm

0.037

Adjacent to Al I

129

422.55 nm

0.054

86

397.66 nm

0.042

105

408.93 nm

0.160

106

409.52nm

0.025

Components Ar I selected from First PC of PCA

Components Adjacent selected from second to Al I PC of PCA Adjacent to Fe I

Number of pixels

Fig. 14 shows the selected features in the original representation of wavelength-spectrum intensity and the selection pattern is summarized as follows: 1)

Not consistent with the rule of the higher the spectrum intensity is, the more important the feature is; nonetheless, the selected lines usually have higher intensity. For instance, as listed in Table 6, the line of Ar I (763.51nm) has the highest intensity with the lower IF value. In comparison, H I (657.84nm) and Ar I (827.04 nm) with the lower intensity, have shown the higher IF value, which means that intensity alone does not influence the defect identification process. It is possible that the object of classification might also have a certain effect on the feature selection.

2)

The background spectral intensities are low and stable, as seen in Fig.14(a), such as Ar I (811.53nm), Ar I (827.04 nm), Ar I (842.46 nm) and Ar I (852.92 nm). Considering the

23

complex formation mechanism of arc spectrum signal in the welding process, less interference from the background might provide better quality in defect information and avoid the nonlinear interference. Based on Fig. 14(b), the selected spectrum features from the second PC vector were neither the lines spectrum nor the peak, which are different from the Ar I and H I features, as selected from first PC vector. In combination with the data of Table 6, it can be seen that those features are mostly adjacent to the metal line spectrum, probably because their fluctuation is smaller than the spectrum peak. x 10

4

ArI (763.51nm)

Spectrum intensity / counts

3

Ar I (811.53nm)

2.5 Ar I (751.16nm) 2 Ar I (842.46nm) 1.5 1

H I (657.84nm)

Ar I (827.04nm)

Ar I (852.92nm)

0.5

550

600

650

700

750

800

850

900

950

Number of pixels

(a) Selected subset of Ar I and HI lines spectrum

9000

Spectrum intensity/counts

3)

422.55nm

8000 7000

408.93nm

6000 5000

397.66nm X: 129 Y: 4532

4000 X: 105 Y: 3481

3000 X: 86 Y: 2753

2000 1000 80

90

100

110

120

Number of pixels

130

140

150

160

170

(b) Selected spectrum subset adjacent to metal lines spectrum Fig. 14 Selected feature subset based on PCA-RF

Metal spectrum emission contains vast plasma spectrum information about the chemical

24

elements, such as Al, Fe, Mg, and Mn, which mainly come from welding tungsten, welding wire, and base material. They are the products of the dynamic interaction between the welding arc, wire, and weld pool, while also closely related to the welding quality and defects. In addition, Table 6 shows that, 105 pixel has been selected from both first and second PC vector, while its IF has the highest value of 0.16, indicating a higher importance of this element. After careful identification of line spectrum, based on NIST database, 105 pixel can be determined as Fe I with the wavelength of 407.84 nm. To reveal the hidden pattern of Fe I line spectrum, the original spectrum signal from 382.2 nm to 429.64 nm, in Y-grooved butt-welded Al alloy 5A06, was carefully reviewed and displayed in Fig. 15. It can be seen that a new line spectrum of Fe I (407.84 nm) appeared, as the wire feeding speed was increased from 0 cm/min to 16 cm/min, because the spectrum peak shifted to a new wavelength. This happened possibly due to the larger concentration gradient of Fe, between the wire and the base material. As presented in Table 2, the weight of Fe for the wire material of SG-AlMg6 was 0.38, whereas Fe was not detected in the base material. Al, as the main component of the welding wire and base material, did not show much singularity. As the weight difference between the wire and the base material is not significant, the behavior of the wire feeding might not cause high concentration gradient of Al. However, Mg might be suffering from increased burning loss because of its lower melting point, compared to that of Fe. Therefore, the high temperature of the welding arc caused considerable Mg burning, while no similar phenomena for Fe, under similar behavior of the wire feeding, were observed. Therefore, the new line spectrum of Fe I, generated in the welding process of 5A06 alloy, when the wire feeding commenced, might be related to the larger concentration gradient of Fe.

25

Mg I(383.83nm)

66000

Spot welding without wire feeding

44000

No feeding in Peak current Feeding in Peak current

Spectrum intensity (counts)

16000 14000

Fe I(407.84nm)

12000 10000

Al I(396.15nm)

8000 6000

Spectrum intensity (counts)

22000 0

Al I(396.15nm)

54000

Start wire feeding

36000 18000 0

Fe I(407.84nm) 57000

Mg I(383.83nm)

38000

4000

19000

2000 380 384 388 392 396 400 404 408 412 416 420 424 428

Wavelength (nm)

0

0

10

20

30

40

Welding time(s)

50

60

a) Selected metal lines spectrum b) Metal spectrum feature curves for the same welding process Fig.15 Selected line spectrum based on PCA-RF and its correlation to weld process

6. Conclusions In this study, a new real-time defect identification method, e.g., PCA-RF, was proposed for Al alloy in robotic arc welding, using arc optical spectroscopy. First, PCA was applied to the arc

spectrum to reveal hidden mechanisms of the dynamic welding process, before defect feature extraction and detection. It was proposed that spectrum features, the absolute values of the PC coefficients of Fe I (407.84 nm), H I (656.28 nm), and Ar I (763.3 nm), have a clear response to multiple weld defects. Then, traditional feature extraction, selection, classification and model optimization were integrated into PCA-RF algorithm, simplifying the respective steps. Furthermore, an indicator, e.g., Importance Factor (IF), was proposed, based on the OOB test error of RF, to qualitatively evaluate the importance of each spectrum feature and reduce the high dimension of feature space. Five classes of weld defects or abnormalities, including under penetration, surface contamination, inner porosity, wire stuck and couple defect, can be effectively identified on a normal seam, using PCA-RF with the mean accuracy of 91.8% and an 84D feature subset, which also outperforms SVM and BP. The selection pattern of spectrum feature subset has been revealed and summarized into three rules. Moreover, an in depth analysis of the selected metal lines spectrum showed that increased concentration gradient of

26

Fe, between the wire and the base material, might cause a greater variation in the line spectrum emission of Fe I (407.84 nm).

Acknowledgement The work was supported by National Natural Science Foundation of China, No.51605372, China postdoctoral science foundation funding, No.2018T111052, No.2016M602805, National Natural Science Foundation of China, No. 51775409, the Program for New Century Excellent Talents in University (NCET-13-0461).

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CRediT author statement Zhifen Zhang: Conceptualization, Methodology, Writing- Original draft preparation- Reviewing and Editing Wenjing Ren: Experiment, Data curation Zhe Yang: Validation, Software Guangrui Wen: Supervision

30

31

Highlights 1) PCA-RF was proposed for on-line defect detection in arc welding. 2) The classification accuracy was improved from 79.3% to 91.8%. 3) Feature importance was qualitatively evaluated and selection pattern was given. 4) Higher gradient of Fe might cause the greater change of Fe I(407.84 nm)