Laser vision seam tracking system based on image processing and continuous convolution operator tracker

Zou, Yanbiao; Chen, Tao

doi:10.1016/j.optlaseng.2018.01.008

Cited by 90 publications

(28 citation statements)

References 6 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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“…Mainly this is done with the gas metal arc welding (GMAW) process or the submerged arc welding process [1]. Advancements of computer vision, control theory, robotics, machine learning and artificial intelligent [2], are applied to the complicated welding process by many researchers to understand the welding input and weld formation [3] and improve welding quality and efficiency [4,5] etc. However, it is the multipass welding process that makes thick plate GMAW manufacturing full of challenges because for this automated welding manufacturing process, weldment quality highly depends on stable seam tracking for each sampling time and each pass.…”

Section: Introductionmentioning

confidence: 99%

“…The first one involves sending the welding position to the robot controller and using the robot interpolation algorithm to control the robot's movement. In this case, how to obtain the accurate welding point in images is the key, such as the continuous convolution operator tracker [5], the Gaussian kernelized correlation filter [13] and the error smoothing filter [14]. In [15], a controller based on the kinematic and dynamic model of the mobile welding robot was designed to deal with the partial uncertainty and the disturbance of the welding process.…”

Section: Introductionmentioning

confidence: 99%

Stable 3D seam tracking for thick plate GMAW manufacturing with T-joints using Kalman filter and machine learning

He¹,

Zhang²,

Xiao³

et al. 2021

Preprint

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In this paper, a robust stable three-dimensional (3D) seam tracking method is investigate based on the Kalman filter (KF) and machine learning during the multipass gas metal arc welding process with a T-joint of 60 mm thickness. The laser vision sensor is used to profile the weld seam, and with the reference image captured before arcing a scheme is proposed to extract the variable weld seam profiles (WSPs) using scale-invariant feature transform and the clustering algorithm. An effective slope mutation detection method is presented to identify the feature points of the extracted WSP, namely the candidate welding positions. In order to lower the impact of fake welding positions on seam tracking, a Bayesian Network model is first built to implement fault detection and diagnosis for the visual feature measurement process using the involved process parameters and the trigger rule. A KF, as an estimator, is then established to further stabilize the tracking process combing with a self determination algorithm of the measurement result. With the visual calibration technology, 3D seam tracking is realized. Seam tracking results show that the proposed method overcomes the tremor of the tracking position and multiple fake candidate welding positions on tracking accuracy, and the tracking accuracy is 0.6 mm. This method provides potential industrial application value for industrial manufacturing with large-scale components.

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“…Visual systems are highly related to the automation of the welding processes, and vision-based monitoring systems have been mainly used and developed for tracking weld seam and inspecting weld quality [11][12][13]. For weld seam tracking, Zou et al [14,15] designed a laser vision seam tracking system that can determine weld feature points and obtain the three-dimensional (3D) coordinate values of these points in real-time based on the morphological image processing method and continuous convolution operator tracker (CCOT) object tracking algorithm. Zhang et al [16] designed a weld path autonomous programming system based on laser structured light scanning, and developed algorithms for processing the image data collected by a laser vision sensor (LVS) to obtain the 3D information of the examined workpiece based on multiple segment scanning.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Weld Quality Prediction Using a Laser Vision Sensor in a Lap Fillet Joint during Gas Metal Arc Welding

Lee

Hwang

Kim

et al. 2020

Sensors

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Nondestructive test (NDT) technology is required in the gas metal arc (GMA) welding process to secure weld robustness and to monitor the welding quality in real-time. In this study, a laser vision sensor (LVS) is designed and fabricated, and an image processing algorithm is developed and implemented to extract precise laser lines on tested welds. A camera calibration method based on a gyro sensor is used to cope with the complex motion of the welding robot. Data are obtained based on GMA welding experiments at various welding conditions for the estimation of quality prediction models. Deep neural network (DNN) models are developed based on external bead shapes and welding conditions to predict the internal bead shapes and the tensile strengths of welded joints.

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Laser vision seam tracking system based on image processing and continuous convolution operator tracker

Cited by 90 publications

References 6 publications

Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding

Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding

Stable 3D seam tracking for thick plate GMAW manufacturing with T-joints using Kalman filter and machine learning

Real-Time Weld Quality Prediction Using a Laser Vision Sensor in a Lap Fillet Joint during Gas Metal Arc Welding

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