Linear Mathematical Model for Seam Tracking with an Arc Sensor in P-GMAW Processes

Liu, Wenji; Li, Liangyu; Hong, Ying-Yi; Jianfeng, Yue

doi:10.3390/s17030591

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…In recent years, great efforts have been put to develop the automated and high-precision welding equipment to achieve high quality welded joints consistently. The important parts of this kind of automated welding equipment include the seam tracking system [3][4][5][6], the weld penetration monitoring system [7][8][9] and the control system. Desirably, the seam tracking system or the monitoring system and control system are in a closed loop.…”

Section: Of 16mentioning

confidence: 99%

Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding

et al. 2018

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There is a strong correlation between the geometry of the weld pool surface and the degree of penetration in arc welding. To measure the geometry of the weld pool surface robustly, many structured light laser line based monitoring systems have been proposed in recent years. The geometry of the specular weld pool could be computed from the reflected laser lines based on different principles. The prerequisite of accurate computation of the weld pool surface is to segment the reflected laser lines robustly and efficiently. To find the most effective segmentation solutions for the images captured with different welding parameters, different image processing algorithms are combined to form eight approaches and these approaches are compared both qualitatively and quantitatively in this paper. In particular, the gradient detection filter, the difference method and the GLCM (grey level co-occurrence matrix) are used to remove the uneven background. The spline fitting enhancement method is used to remove the fuzziness. The slope difference distribution-based threshold selection method is used to segment the laser lines from the background. Both qualitative and quantitative experiments are conducted to evaluate the accuracy and the efficiency of the proposed approaches extensively.

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Section: Of 16mentioning

confidence: 99%

Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding

et al. 2018

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“…To overcome the difficulties encountered by welding robots in teaching and offline programming mode operations, many studies have been performed to explore methods that can make robotic welding simpler and more effective [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Among these methods, welding seam trajectory recognition is used for welding path planning and welding seam tracing is a significant method for improving the efficiency and flexibility of robotic welding.…”

Section: Introductionmentioning

confidence: 99%

“…Having been extensively applied to robotic welding due to its high accuracy and non-contact, vision sensing is considered one of the most promising welding seam trajectory recognition technologies [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. Great attention has been paid to welding seam detection methods based on structured light vision sensing [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. Hascoet et al [ 23 ] first used a single-line laser vision sensor to detect V-shaped groove shape information, then generated a torch path based on this information, and finally proposed a welding strategy for automated welding of ships.…”

Section: Introductionmentioning

confidence: 99%

Welding Seam Trajectory Recognition for Automated Skip Welding Guidance of a Spatially Intermittent Welding Seam Based on Laser Vision Sensor

Hong

Gao

et al. 2020

Sensors

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To solve the problems of low teaching programming efficiency and poor flexibility in robot welding of complex box girder structures, a method of seam trajectory recognition based on laser scanning displacement sensing was proposed for automated guidance of a welding torch in the skip welding of a spatially intermittent welding seam. Firstly, a laser scanning displacement sensing system for measuring angles adaptively is developed to detect corner features of complex structures. Secondly, a weld trajectory recognition algorithm based on Euclidean distance discrimination is proposed. The algorithm extracts the shape features by constructing the characteristic triangle of the weld trajectory, and then processes the set of shape features by discrete Fourier analysis to solve the feature vector used to describe the shape. Finally, based on the Euclidean distance between the feature vector of the test sample and the class matching library, the class to which the sample belongs is identified to distinguish the weld trajectory. The experimental results show that the classification accuracy rate of four typical spatial discontinuous welds in complex box girder structure is 100%. The overall processing time for weld trajectory detection and classification does not exceed 65 ms. Based on this method, the field test was completed in the folding special container production line. The results show that the system proposed in this paper can accurately identify discontinuous welds during high-speed metal active gas arc welding (MAG) welding with a welding speed of 1.2 m/min, and guide the welding torch to automatically complete the skip welding, which greatly improves the welding manufacturing efficiency and quality stability in the processing of complex box girder components. This method does not require a time-consuming pre-welding teaching programming and visual inspection system calibration, and provides a new technical approach for highly efficient and flexible welding manufacturing of discontinuous welding seams of complex structures, which is expected to be applied to the welding manufacturing of core components in heavy and large industries such as port cranes, large logistics transportation equipment, and rail transit.

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“…they analyzed the peak, average, base, and pulse frequency components 20 . In addition, Liu et al studied seam tracking using arc sound 21 .…”

mentioning

confidence: 99%

Development of seam tracking device in asynchronous tandem welding with arc sensing

Seo

Kim

et al. 2022

Sci Rep

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Tandem welding is extensively used for welding large structures, such as ships and plants, for increased welding speed and volume. Seam tracking is essential because of a large amount of thermal deformation. However, in tandem welding, arc interference causes current and voltage to vary non-uniformly, leading to difficulties in seam tracking. Therefore, in this study, an optimal signal was identified for seam tracking in tandem welding and evaluated. To select the seam-tracking signal, an algorithm was developed that separates the welding signal into peak, average, and base. Based on the collected data, regression and signal-to-noise ratio analyses were performed to identify a suitable seam-tracking signal. To trace the welding line based on the selected signal, the welding signal was checked by weaving on the V-groove specimen. As a result, the current area difference of the welding signal generated between the left and right parts of the center of the V-groove could be calculated. An algorithm and equipment for seam tracking were constructed using the area difference of the welding current. Finally, the seam tracking system was verified by conducting an actual test using the equipment to which the algorithm was applied.

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Linear Mathematical Model for Seam Tracking with an Arc Sensor in P-GMAW Processes

Cited by 7 publications

References 11 publications

Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding

Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding

Welding Seam Trajectory Recognition for Automated Skip Welding Guidance of a Spatially Intermittent Welding Seam Based on Laser Vision Sensor

Development of seam tracking device in asynchronous tandem welding with arc sensing

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