1164-1169The goal of this paper is to study the characteristics of the butt joint imperfections with different types of joint shapes (curve, straight and tooth saw work piece) according to their class categories (good welds, excess welds, insufficient welds and no welds). The work piece is placed in a center position on the workbench. The distance between camera and workpiece is set as 300 mm during welding imperfections process and the entire work piece image is taken from the same distance to maintain the accuracy. The input feature vector is determined by feature co-occurrence matrix consisting of energy, correlation, homogeneity and contrast both no scaled and scaled by 0.5. Results show that no welds class categories exhibit higher homogeneity compared to the other class categories. This is because the homogeneity value depends on bright and dark parts of a certain size and also include some changes from dark to bright. Meanwhile, insufficient welds class categories produced larger contrast value, but good weld class categories recorded higher contrast value

Ahmad, A.

Aras, M.S.M.

Kamis, Z.

Rashid, M.Z.A.

Shah, H.N.M.

Sulaiman, M.

English

The goal of this paper is to study the characteristics of the butt joint imperfections with different types of joint shapes (curve, straight and tooth saw work piece) according to their class categories (good welds, excess welds, insufficient welds and no welds). The work piece is placed in a center position on the workbench. The distance between camera and workpiece is set as 300 mm during welding imperfections process and the entire work piece image is taken from the same distance to maintain the accuracy. The input feature vector is determined by feature co-occurrence matrix consisting of energy, correlation, homogeneity and contrast both no scaled and scaled by 0.5. Results show that no welds class categories exhibit higher homogeneity compared to the other class categories. This is because the homogeneity value depends on bright and dark parts of a certain size and also include some changes from dark to bright. Meanwhile, insufficient welds class categories produced larger contrast value, but good weld class categories recorded higher contrast value

Mohd Shah, Hairol Nizam

Kamis, Zalina

Sulaiman, Marizan

Ab Rashid, Mohd Zamzuri

Mohd Aras, Mohd Shahrieel

Ahmad, Azhar

Universiti Teknikal Malaysia Melaka (UTeM) Repository

Characteristics Of Butt Welding Imperfections Joint Using Co-Occurrence Matrix

NOPR

Indian Journal of Geo Marine Sciences Vol. 48 (07), July 2019, pp. 1164-1169       Characteristics of butt welding imperfections joint using  co-occurrence matrix  H.N.M. Shah*, Z. Kamis, M. Sulaiman, M.Z.A. Rashid, M.S.M. Aras, & A. Ahmad Center for Robotics and Industrial Automation (CERIA), Fakulti Kejuruteraan Elektrik,  Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia. *[E-mail: hnizami@utem.edu.my] The goal of this paper is to study the characteristics of the butt joint imperfections with different types of joint shapes (curve, straight and tooth saw work piece) according to their class categories (good welds, excess welds, insufficient welds and no welds). The work piece is placed in a center position on the workbench. The distance between camera and workpiece is set as 300 mm during welding imperfections process and the entire work piece image is taken from the same distance to maintain the accuracy. The input feature vector is determined by feature co-occurrence matrix consisting of energy, correlation, homogeneity and contrast both no scaled and scaled by 0.5. Results show that no welds class categories exhibit higher homogeneity compared to the other class categories. This is because the homogeneity value depends on bright and dark parts of a certain size and also include some changes from dark to bright. Meanwhile, insufficient welds class categories produced larger contrast value, but good weld class categories recorded higher contrast value. [Keywords: Co-occurrence Matrix; Butt joint; Imperfections welding] Introduction Previous work in the classification of weld defects noticed that this issue has been investigated widely in different ways. Many researchers have used radiographic image rather than CCD camera only as the input acquisition integrated with external light source or control the welding environment to reduce the noise. Most of the researchers used geometrical parameter in feature extraction which includes size, location, attribute, scale invariant1,2 and shape of the weld defects. Other parameters can be applied to interpret the data, such as gray value and linguistic description. Linguistic description is done by human expert where it needs the experience and skill to observe the welds defect linguistic description. Perfect knowledge of the geometry of the weld defects is an important step which is essential to appreciate the quality of the weld.  The other aspect of interest here is how to categorize the weld defects. Many of the previous works discuss weld categories in terms of type of welds, defect shape, welds flaw and defect information such as width, location and position. There are several methods to inspect the weld defect, such as statistical tools3, neural networks4,5, fuzzy interferences system6 fitted line profiles, and average gray level characteristic. Statistical approaches are usually implemented using classifiers, such as bayers7,9, decision tree8, support vector machine10,14 and neuro-fuzzy classification (NEFCLASS)7.  In this paper, we studied the characteristics of butt welding imperfections joint by using co-occurrence matrix. The study was based on work piece sample images taken from welding robot. The imperfection joint classes are divided into four class categories, namely, good welds, excess welds, insufficient welds and no welds. To extract the parameters from the work piece, we used co-occurrence matrix approach in scaled and no scaled images. The features parameters were then evaluated and compared with each imperfections class categories related to the co-occurrence matrix consisting of energy, correlation, homogeneity and contrast.  Methodology Process overview The characteristics of butt joint imperfections in this paper are determined by four steps as show in Fig. 1. The first step is image acquisition where the imperfections image is captured from a CCD camera at a top of workpiece. Next step is to calculate a co-occurrence matrix consisting of energy, correlation, homogeneity and contrast. This step is done without using filtering techniques and the region of interest SNAH et al: WELDING IMPERFECTIONS JOINT USING CO-OCCURRENCE MATRIX   1165(ROI) is not required. Then the gray value features driven for both no scaled and scaled by 0.5 obtained from co-occurrence matrix. The final step is to group the value of characteristics into the imperfections categories, namely, good welds, excess welds, insufficient welds and no welds.  Feature extraction Feature selection is the most critical step as the success of classifiers depends on the correct feature set15,16. The feature value is obtained from the object description in numerical form where there are many ways to select the features. The process to choose the desirable features from the initial set of the features list is called feature selection17. Thus, in this paper, features were selected by using co-occurrence matrix. Note that, this features value contains the gray level value either from its pixels or values from neighbors. Fig. 2 shows how to generate features by using  co-occurrence matrix for both no scaled and scaled  factor by 0.5. The additional features with images factor scaled by 0.5 is applied to determine  the highest probability of the butt joint welding imperfection. Co-occurrence matrix To determine the co-occurrence matrix from butt welding imperfection joint images relationship between the gray value and its neighboring values need to be defined. The co-occurrence matrix contains two probability values that appear next to each other. The values depend on the direction   is used and the distance d at which the matrix is computed. The value of the butt welding imperfection joint image is originally referred to the grayscale value of the specified pixel. Based on the co-occurrence matrix there are four features that can be derived from co-occurrence matrices. The features are: Energy (calculates the sum of the square elements in the coincidence matrix), correlation (measures the linear dependence of the gray levels on the neighboring pixels), homogeneity (measures the closeness of the distribution of elements in the co-occurrence matrix to its diagonal), and contrast (measures the intensity contrast between neighboring pixels). The formula for the features indicated by the direction (0, 45, 90, 135 degrees) and distance as shown in Equations 1 to 4 where width of co-occurrence matrix is given as width and entry of co-occurrence matrix as .ijc   widthjiijcEnergy0,2   …(1)                widthji ijwidthji ijwidthji ijwidthji ijwidthji ijwidthji ijwidthji ijccjicciiccjjciinCorrelatio0,20,0,20,0, 0,0, …(2)    widthjiijcjiyHomogeneit0,211   …(3)   widthjiijcjiContrast0,2   …(4)  A calculation of the co-occurrence matrices was performed using 6 (neighboring pixel) distance and the directions  =0 and  =90 at which the matrix is computed. The co-occurrence matrices have been scaled to 8-bit (256 gray levels) with image size 533x400 pixel. The explanation of gray level co-occurrence matrix (GLCM) features characteristics is shown in Table 1.   Results and Discussion The system consists of CCD camera, frame grabbers, monitor, computer, and light source. Butt welding imperfection joint images are  captured  by  a   Fig. 1 — Methodology for overall process    Fig. 2 — Block diagram for generate features INDIAN J. MAR. SCI., VOL. 48, NO. 07, JULY 2019   1166  Fig. 3 — Butt welding imperfection joint system set-up    Fig. 4 — imperfections class categories: (a) straight, (b) tooth saw, and (c) curved  Basler camera with 67 frames per second 2 MP resolution. The workpiece are placed in a center position on the workbench. The distance between camera and workpiece is set as 300 mm during welding imperfections process and the entire workpiece image is taken from the same distance to maintain the accuracy by adjusted additional light source in control room environment. The system set-up is shown in Fig. 3. The butt welding imperfections joint was verified by four imperfections class categories: Good welds, excess welds, insufficient welds and no welds  as shown in Fig. 4. The output results were based  on  three  imperfection  images   data   in   each   class  Table 2 — Sample result of co-occurrence matrix features for straight welding joint shape  Feature Good Weld Excess Weld Direction  = 0  =90  = 0  =90 Energy 0.0532903 0.0525691 0.0532903 0.0525691 Correlation 0.94991 0.946928 0.94991 0.946928 Homogeneity 0.673412 0.671247 0.673412 0.671247 Contrast 9.21824 9.84254 9.21824 9.84254  Insufficient Weld No Weld Direction  = 0  =90  = 0  =90 Energy 0.05882 0.0576135 0.05882 0.0576135 Correlation 0.938968 0.938205 0.938968 0.938205 Homogeneity 0.670267 0.66622 0.670267 0.66622 Contrast 11.0421 11.2672 11.0421 11.2672   Table 3 — Sample result of co-occurrence matrix features for tooth saw welding joint shape  Feature Good Weld Excess Weld Direction  = 0  =90  = 0  =90 Energy 0.0646059 0.0635119 0.0658506 0.0638784 Correlation 0.944401 0.944736 0.921889 0.92183 Homogeneity 0.676406 0.673715 0.650836 0.644023 Contrast 9.66717 9.69389 13.946 14.0735  Insufficient Weld No Weld Direction  = 0  =90  = 0  =90 Energy 0.0780737 0.0759898 0.0478025 0.0456672 Correlation 0.928958 0.923911 0.950107 0.945327 Homogeneity 0.668728 0.664688 0.66429 0.656474 Contrast 12.2663 13.252 8.3102 9.16955  categories for butt joint type shapes. A results sample of a calculation of co-occurrence matrices for four common characteristics of each class categories of butt welding imperfections joint is shown in  Table 2 to Table 4 The matrix occurence consists of gray value of corelation, homogeneity, contrast and energy for  both no scaled and scaled by 0.5. Fig. 5 to Fig. 7 show  Table 1 — Gray level co-occurrence matrix (GLCM) features characteristics Co-occurrence matrix features Description Energy Energy is actually the short form for uniformity of energy and indicates that all kinds of gray valuecombinations are present or if certain gray value combinations are dominant Correlation  Correlation defines the measure of dependency between neighboring values. High correlation values imply a linear relationship between pixel pairs.  Homogeneity  Homogeneity is high if large areas in an image have the same gray values. It measures texture homogeneity and receives higher values for smaller differences in gray values of pixel pairs. Contrast  The contrast represented the value between two pixels that is multiplied and squared. A lowContrast value is attributed to low contrast of image texture. SNAH et al: WELDING IMPERFECTIONS JOINT USING CO-OCCURRENCE MATRIX   1167the matrix occurence data of the butt welding imperfections joint image. The data are taken from three sample images of each imperfections categories. Fig. 5(a), 6(a) and 7(a) show the homogeneity  results of butt welding imperfections joint. Homogeneity is high if large areas in an image have the same gray values. It measures texture homogeneity and receives higher values for smaller differences in gray values of pixel pairs. No weld class categories exhibit higher homogeneity compared to the other class categories. This is because the homogeneity value depends on bright and dark parts of a certain size but also include some changes from dark to bright. However, a uncolored image has the highest homogeneity  value. Meanwhile, if the matrix shows a thin line from top left to right bottom, this means that the image is blurry which is why the values are so close together. The two correlation values of the same image are very different depending on the rotation of the image where  =0° causes a low correlation and  =90° has a high correlation. It is clearly show in Figures 5(b), 6(b) and 7(b). The co-ocurrence matrix energy results are shown in Figures 5(c), 6(c) and 7(c). From the figures, it is clear that the energy is actually the short form for uniformity of energy and indicates that all kinds of gray value combinations are present or if certain gray value combinations are dominant. An image with a constant gray value has the highest energy. It  can have values between 0 and 1,  with  higher  values    Table 4 — Sample result of co-occurrence matrix features for curve welding joint shape Feature Good Weld Excess Weld Direction  = 0  =90  = 0  =90 Energy 0.0734787 0.0714337 0.0835966 0.0819113 Correlation 0.946625 0.944468 0.913001 0.91084 Homogeneity 0.680538 0.677662 0.675731 0.672015 Contrast 10.4674 10.9823 15.4024 15.9263  Insufficient Weld No Weld Direction  = 0  =90  = 0  =90 Energy 0.0976121 0.0958199 0.0560018 0.0541501 Correlation 0.932256 0.931668 0.965653 0.961555 Homogeneity 0.700197 0.696605 0.703902 0.697859 Contrast 12.5807 12.7951 5.97442 6.73168  Fig. 5 — Co-occurence matrix data for butt joint imperfection welding in straight type  INDIAN J. MAR. SCI., VOL. 48, NO. 07, JULY 2019   1168     Fig. 6 — Co-occurence matrix data for butt joint imperfection welding in tooth saw type    Fig. 7 — Co-occurence matrix data for butt joint imperfection welding in curve type  representing a more uniform texture, when the gray level distribution has either a constant or periodic form. A high energy emerges if there are only few gray value combinations, and the change is always according to the same scheme, while a little energy represent the changes between pixels where there is no uniformity.  The contrast represented the value between two pixels that is multiplied and squared. A low contrast value is attributed to low contrast of image texture. In butt welding imperfections joint, insufficient welds class produced larger contrast value but good weld class recorded higher contrast value. This shows that the contrast value includes the frequency as well as SNAH et al: WELDING IMPERFECTIONS JOINT USING CO-OCCURRENCE MATRIX   1169the intensity. It is clearly shown in Fig. 5(d), 6(d)  and 7(d).  Conclusion The characteristics of butt welding imperfections joint for straight, curve and tooth saw type of joint shapes were investigated according to the class categories, namely, good welds, excess welds, insufficient welds, and no welds. Input feature vector is determined by co-occurrence matrix consisting of energy, correlation, homogeneity and contrast with no scaled and scaled by 0.5. The results shows no weld class categories exhibit higher homogeneity compared to the other class categories. This is because the homogeneity value depends on bright and dark parts of a certain size and also include some changes from dark to bright. The insufficient welds class produced larger contrast value, but good weld class recorded higher contrast value. This shows that the contrast value includes the frequency as well as the intensity.  Acknowledgement The authors are grateful for the support granted by by Center for Robotics and Industrial Automation, Universiti Teknikal Malaysia Melaka (UTeM) in conducting this research through grant JURNAL/2018/FKE/Q00007 and Ministry of Higher Education.  References 1 Shah, H.N.M., Sulaiman, M., Shukor, A.Z., Rashid, M.Z.A., Vision based Identification and Classification of Weld Defects in Welding Environments: A Review., Indian Journals of Science and Technology., 9(2016):1-5.  2 Lowe, D. G., Object recognition from local scale-invariant features., International Conference on Computer Vision., (1999): 1150-1157.  3 Zhang, M. and Cheng, W., 2015. 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Characteristics of butt welding imperfections joint using  co-occurrence matrix

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Characteristics of butt welding imperfections joint using co-occurrence matrix

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