Narrow-seam identification and deviation detection in keyhole deep-penetration TIG welding

Zhang, Baori; Shi, Yonghua; Gu, Shuqing

doi:10.1007/s00170-018-3089-0

Cited by 30 publications

(7 citation statements)

References 24 publications

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“…In order to verify the effectiveness of the proposed method, the Roberts operator, Prewitt operator, Sobel operator, Canny operator, fuzzy-Sobel operator (Sivaranjani and Kalaiselvi, 2021 ), and bilateral filter based Canny operator (Zhang et al, 2019 ) are used for comparison. The experimental results are shown in Figure 6 , and it can be seen that most methods are unable to segment the closed image edge completely.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Dynamic Video Image Segmentation Based on Dual Channel Convolutional Kernel and Multi-Frame Feature Fusion

Chen

2022

Front. Neurorobot.

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The color image of the fire hole is key for the working condition identification of the aluminum electrolysis cell (AEC). However, the image of the fire hole is difficult for image segmentation due to the nonuniform distributed illuminated background and oblique beam radiation. Thus, a joint dual channel convolution kernel (DCCK) and multi-frame feature fusion (MFF) method is developed to achieve dynamic fire hole video image segmentation. Considering the invalid or extra texture disturbances in the edge feature images, the DCCK is used to select the effective edge features. Since the obtained edge features of the fire hole are not completely closed, the MFF algorithm is further applied to complement the missing portion of the edge. This method can assist to obtain the complete fire hole image of the AEC. The experiment results demonstrate that the proposed method has higher precision, recall rate, and lower boundary redundancy rate with well segmented image edge for the aid of working condition identification of the AEC.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Dynamic Video Image Segmentation Based on Dual Channel Convolutional Kernel and Multi-Frame Feature Fusion

Chen

2022

Front. Neurorobot.

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“…In narrow gap welding, the groove width and weld central position usually vary due to the groove processing error, assembling error and welding thermal deformation, which leads to uneven sidewall penetrations and inconsistent bead surface [ 15 , 16 , 17 ]. To avoid poor weld formation due to groove variation, several passive visual sensing detection methods have been proposed [ 18 , 19 , 20 , 21 , 22 ]. Yamazaki et al [ 18 ] used a CMOS camera to capture infrared images of the welding zone and detected the width and central position of the narrow gap laser welding groove using brightness distribution analysis, but it is difficult to adaptively determine a threshold for the gradient of the brightness distribution curve with the occurrence of laser plume and welding spatter.…”

Section: Introductionmentioning

confidence: 99%

Infrared Visual Sensing Detection of Groove Width for Swing Arc Narrow Gap Welding

Wang

et al. 2022

Sensors

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To solve the current problem of poor weld formation due to groove width variation in swing arc narrow gap welding, an infrared passive visual sensing detection approach was developed in this work to measure groove width under intense welding interferences. This approach, called global pattern recognition, includes self-adaptive positioning of the ROI window, equal division thresholding and in situ dynamic clustering algorithms. Accordingly, the self-adaptive positioning method filters several of the nearest values of the arc’s highest point of the vertical coordinate and groove’s same-side edge position to determine the origin coordinates of the ROI window; the equal division thresholding algorithm then divides and processes the ROI window image to extract the groove edge and forms a raw data distribution of groove width in the data window. The in situ dynamic clustering algorithm dynamically classifies the preprocessed data in situ and finally detects the value of the groove width from the remaining true data. Experimental results show that the equal division thresholding algorithm can effectively reduce the influences of arc light and welding fume on the extraction of the groove edge. The in situ dynamic clustering algorithm can avoid disturbances from simulated welding spatters with diameters less than 2.19 mm, thus realizing the high-precision detection of the actual groove width and demonstrating stronger environmental adaptability of the proposed global pattern recognition approach.

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“…Although seam area prediction models and seam center extraction algorithms proposed by Zhang et al [ 20 , 21 , 22 ] could extract the motion reference point to a large extent, the accuracy of these models and algorithms was not 100%. Due to the irresistible, dynamic, irregular and uncertain interference in the acquisition of accurate feature points, there was a high probability that the original laser image lacked valid information, and thus the authentic seam feature points were not obtained, which would lead to the chaos of motion program and was why the technology suddenly failed during the welding process.…”

Section: Introductionmentioning

confidence: 99%

Research on Trajectory Recognition and Control Technology of Real-Time Tracking Welding

Zhao

Zhang

Wang

et al. 2022

Sensors

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Real-time tracking welding with the assistance of structured light vision enhances the intelligence of robotic welding, which significantly shortens teaching time and guarantees accuracy for user-customized product welding. However, the robustness of most image processing algorithms is deficient during welding practice, and the security regime for tracking welding is not considered in most trajectory recognition and control algorithms. For these two problems, an adaptive feature extraction algorithm was proposed, which can accurately extract the seam center from the continuous, discontinuous or fluctuating laser stripes identified and located by the CNN model, while the prior model can quickly remove a large amount of noise and interference except the stripes, greatly improving the extraction accuracy and processing speed of the algorithm. Additionally, the embedded Pauta criterion was used to segmentally process the center point data stream and to cyclically eliminate outliers and further ensure the accuracy of the welding reference point. Experimental results showed that under the guarantee of the above-mentioned seam center point extraction and correction algorithms, the tracking average error was 0.1 mm, and even if abnormal trajectory points existed, they did not cause welding torch shaking, system interruption or other accidents.

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Narrow-seam identification and deviation detection in keyhole deep-penetration TIG welding

Cited by 30 publications

References 24 publications

Dynamic Video Image Segmentation Based on Dual Channel Convolutional Kernel and Multi-Frame Feature Fusion

Dynamic Video Image Segmentation Based on Dual Channel Convolutional Kernel and Multi-Frame Feature Fusion

Infrared Visual Sensing Detection of Groove Width for Swing Arc Narrow Gap Welding

Research on Trajectory Recognition and Control Technology of Real-Time Tracking Welding

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