Real-Time Prediction of Welding Penetration Mode and Depth Based on Visual Characteristics of Weld Pool in GMAW Process

Yu, Rongwei; Han, Jing; Zhao, Zhuang; Bai, Lianfa

doi:10.1109/access.2020.2990902

Cited by 15 publications

(5 citation statements)

References 8 publications

(8 reference statements)

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“…A model of laser welding quality prediction based on different input parameters was established by Petkovic 38 . Yu et al 39 proposed a real-time prediction method of welding penetration mode and depth based on two-dimensional visual characteristics of the weld pool. Other prediction models: A neuro-fuzzy model for the prediction and classification of the defects in the fused zone was built by Casalino et al 40 .…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Prediction and optimization method for welding quality of components in ship construction

Liu,

Cheng,

Jing

et al. 2024

Sci Rep

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Welding process, as one of the crucial industrial technologies in ship construction, accounts for approximately 70% of the workload and costs account for approximately 40% of the total cost. The existing welding quality prediction methods have hypothetical premises and subjective factors, which cannot meet the dynamic control requirements of intelligent welding for processing quality. Aiming at the low efficiency of quality prediction problems poor timeliness and unpredictability of quality control in ship assembly-welding process, a data and model driven welding quality prediction method is proposed. Firstly, the influence factors of welding quality are analyzed and the correlation mechanism between process parameters and quality is determined. According to the analysis results, a stable and reliable data collection architecture is established. The elements of welding process monitoring are also determined based on the feature dimensionality reduction method. To improve the accuracy of welding quality prediction, the prediction model is constructed by fusing the adaptive simulated annealing, the particle swarm optimization, and the back propagation neural network algorithms. Finally, the effectiveness of the prediction method is verified through 74 sets of plate welding experiments, the prediction accuracy reaches over 90%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Prediction and optimization method for welding quality of components in ship construction

Liu,

Cheng,

Jing

et al. 2024

Sci Rep

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show abstract

“…After setting the welding parameters, the current waveform was collected in the actual welding process. Figure 5 [22] shows the welding current waveform of the CMT process under certain welding parameters, while, Figure 6 [22] shows all images collected by color CCD within a CMT cycle. Figures 5 and 6 show that the CMT cycle was approximately 14 ms, at the peak stage of CMT process.…”

Section: Definition and Extraction Of Visual Feature Parametersmentioning

confidence: 99%

Prediction of Weld Reinforcement Based on Vision Sensing in GMA Additive Manufacturing Process

Zhao

Bai

et al. 2020

Metals

Self Cite

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In the gas-metal-arc (GMA) additive manufacturing process, the shape of the molten pool, the temperature field of the workpiece and the heat dissipation conditions change with the increase of cladding layers, which can affect the dimensional accuracy of the workpiece; hence, it is necessary to monitor the additive manufacturing process online. At present, there is little research about formation-dimension monitoring in the GMA additive manufacturing process; in this paper, weld reinforcement prediction in the GMA additive manufacturing process was conducted, the visual-sensing system for molten pool was established, and a laser locating system was designed to match every frame of the molten pool image with the actual weld location. Extracting the shape and location features of the molten pool as visual features, on the basis of a back-propagation (BP) neural network, we developed the prediction model for weld reinforcement in the GMA additive manufacturing process. Experiment results showed that the model could accurately predict weld reinforcement. By changing the cooling time between adjacent cladding layers, the generalization ability of the prediction model was further verified.

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“…The visual weld pool image features necessary for dynamic welding processes are often imprecise because of the prevalence of spatter, smoke, and arc intensity during the welding of hull structures, which, in turn, impede the accuracy of the information and diminish the efficiency of welding quality monitoring. Recent research on the GMAW welding process has already proposed the application of these newest machine-learning techniques, like support vector machines (SVM) [19], convolutional neural network (CNN) [20], deep-learning techniques [21], and fuzzy-clustering analysis [16]. However, as shown in [21], there are still few investigations based on the research developed in the field and the potential of these techniques.…”

Section: Introductionmentioning

confidence: 99%

Image Processing and Feature Extraction for Hull Structure GMAW Based on Weld Pool Visual Sensing

et al. 2023

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Image processing and feature information extraction based on the visual perception of the weld pool are considered essential components of intelligent welding quality monitoring of hull structure gas metal arc welding (GMAW). The unstable characteristics, such as large spatter, much smoke, and strong arc light during hull structure GMAW, lead to the blurring of image acquisition and the difficulty of contour extraction of the weld pool. The present study is aimed at addressing the practical issues from two perspectives, i.e., a spectrum-visual-sensing acquisition system and an image-processing and feature extraction algorithm. First of all, by analyzing the light energy distribution law and acquiring the optical parameters relevant to the cut-off composite dimming and near-infrared narrowband filtering, spectral sensing is employed in establishing models of arc light radiation to detect the strength of continuously distinctive spectral lines. Besides, an appropriate high-speed charge-coupled device (CCD) camera is selected to build a visual acquisition system, which can reduce the external interference of the arc light on the image acquisition of the weld pool. Afterwards, the implementation of an image-processing fusion model based on the spatial information fuzzy C-means (FCM) clustering analysis and Sobel edge detection operator accompanies the investigation of the geometric aspects of the weld pool image. In terms of clear segmentation of the interest region, the edge detection and accurate extraction of the target contour are successfully obtained. In the subsequent section, the Hough transform analysis is adopted to establish the geometric feature extraction model of the weld pool, with corner detection, conversion, and camera calibration as the core technology. Additionally, the left and right views of the image contour are calibrated to achieve the lossless conversion of corner pixels and physical coordinates. Finally, three other distinct image-processing methods are designed to compare the segmentation effect of the edge contour with the fusion model, and then, the extraction accuracy of the geometric features of the weld pool is verified. The interference of the arc light and smoke has been demonstrated to be substantially diminished, which is attributable to the visual-sensing system during image acquisition of the weld pool. The results of edge fusion of the weld pool image show that based on the GMAW using the FCM-Sobel fusion method, a superior extraction accuracy of geometric features characterized by smoothness, continuousness, no breakpoints, and less noise has fulfilled the engineering requirements.

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Real-Time Prediction of Welding Penetration Mode and Depth Based on Visual Characteristics of Weld Pool in GMAW Process

Cited by 15 publications

References 8 publications

Prediction and optimization method for welding quality of components in ship construction

Prediction and optimization method for welding quality of components in ship construction

Prediction of Weld Reinforcement Based on Vision Sensing in GMA Additive Manufacturing Process

Image Processing and Feature Extraction for Hull Structure GMAW Based on Weld Pool Visual Sensing

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