A mobile sensing system for real-time 3D weld pool surface measurement in manual GTAW

Zhang, Wei Jie; Xiao, Jun Jun; Zhang, Yu Ming

doi:10.1088/0957-0233/27/4/045102

Cited by 14 publications

(3 citation statements)

References 31 publications

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“…The visual sensor is able to obtain comprehensive molten pool information and exhibit a high sensing accuracy. The welding process information with visual sensing is obtained widely, providing the data for achieving closed-loop control of the welding process 8 .H Cao et al 9 used infrared visual sensor to detect defects on TIG welding. However, the molten pool defects are unable to be detected accurately due to the molten pool temperature falling outside the range of infrared imaging temperature.…”

Section: Introductionmentioning

confidence: 99%

Defects detection of GMAW process based on convolutional neural network algorithm

Li,

Ma,

Duan

et al. 2023

Sci Rep

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It is significant to predict welding quality during gas metal arc welding process. The welding defect detection algorithm has been developed based on convolutional neural network (CNN). The sensing system and image processing algorithm for molten pools has been developed. It overcomes the interference caused by the arc light to obtain clear images of the molten pool's boundaries. The molten pools images are used to build up training set and test set for training and testing the CNN model. The model is designed to extract the visual features of molten pool images to predict the penetration state, the welding crater, and slags. Through optimizing the network parameters such as kernel-size, batch-size and learning rate, the prediction accuracy is higher than 95%. Moreover, the model enhances additional focus on the welding crater based on the welder experience. The mechanisms between molten pool characteristics and welding defects were analyzed based on the welder experience and the visual features of the model. It is found that the model judges the occurrence of burn-through with the black hole in the middle zone of the molten pool. When the surface pores are generated, the model exhibits a strong response to circular voids in the semi-solid region at the trailing end of the molten pool. The size and shape of fusion holes exhibit a strong correlation with the molten state. When the shape of the crater does not appear concave, it often signifies excessive penetration. It contributes to enhancing the algorithm's robustness during various welding scenarios.

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Section: Introductionmentioning

confidence: 99%

Defects detection of GMAW process based on convolutional neural network algorithm

Li,

Ma,

Duan

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The 3D reconstruction method for welding pool can be categorized into the 3D structured light method [6], shape from shading method [7], and binocular stereo vision method [8]. To avoid arc interference during measurement, Zhang et al [9] proposed a novel-structured laser vision system for reconstructing 3D welding pool surface. In their experiments, a structured laser was projected onto the welding pool surface and was specularly reflected onto a preset imaging plane.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Reconstruction of Welding Pool Surface by Binocular Vision

Chen

2021

Chin. J. Mech. Eng.

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Current research of binocular vision systems mainly need to resolve the camera’s intrinsic parameters before the reconstruction of three-dimensional (3D) objects. The classical Zhang’ calibration is hardly to calculate all errors caused by perspective distortion and lens distortion. Also, the image-matching algorithm of the binocular vision system still needs to be improved to accelerate the reconstruction speed of welding pool surfaces. In this paper, a preset coordinate system was utilized for camera calibration instead of Zhang’ calibration. The binocular vision system was modified to capture images of welding pool surfaces by suppressing the strong arc interference during gas metal arc welding. Combining and improving the algorithms of speeded up robust features, binary robust invariant scalable keypoints, and KAZE, the feature information of points (i.e., RGB values, pixel coordinates) was extracted as the feature vector of the welding pool surface. Based on the characteristics of the welding images, a mismatch-elimination algorithm was developed to increase the accuracy of image-matching algorithms. The world coordinates of matching feature points were calculated to reconstruct the 3D shape of the welding pool surface. The effectiveness and accuracy of the reconstruction of welding pool surfaces were verified by experimental results. This research proposes the development of binocular vision algorithms that can reconstruct the surface of welding pools accurately to realize intelligent welding control systems in the future.

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“…熔透控制是实现焊接质量控制和焊接过程自动化的焦点和难点，而实现熔透控制关键在于如何有效地获取表征焊接熔透状态的信号 [1] 。熔池图像包含着最为丰富的信息，能够反映焊接过程的稳定性、焊接熔透状态等情况 [2] 。电弧焊中，一个熟练 20180904 收到初稿，20190520 收到修改稿的焊工通过观察液体熔池表面进行判断，进而调整焊接参数，控制焊接过程达到预定的熔透状态，从而得到合适的焊缝形状和焊接质量 [3][4] 。如果通过视觉检测系统模拟人工操作，通过正面熔池检测预测熔透状态，进而实现闭环控制，具有很大的实用价值 [5] 。人们在将人工神经网络用于实际熔透预测研究中进行了许多探索。肯塔基大学的 KOVACEVIC 等 [6] 基于神经网络建立了熔池尺寸信息与背面熔宽的模型。山东大学高进强 [7] 使用普通 CCD 摄像机拍摄出 GTAW 熔池图像，并提取熔池宽度、熔池半长、熔池后部面积和熔池后拖角等参数，基于神经网络建立正面熔池几何形状参数与背面熔宽的关系模型。刘新锋等 [8][9]…”

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GTAW Penetration Prediction Model Based on Convolution Neural Network Algorithm

Li¹,

Liu²,

Xie³

et al. 2019

Journal of Mechanical Engineering

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：The penetration state can be reflected by the information of the molten pool, but it is difficult to establish a function between the molten pool and the penetration state. To solve this problem, a penetration prediction model based on convolution neural network (CNN) is proposed. Based on the introduction of CNN principle, a molten pool sensing system based on passive vision is designed to collect 2D images of the molten pool. The acquired images are preprocessed to generate the training set and test set for CNN training and testing. Then the prediction mode is built, and the network parameters such as learning rate, batch-size and iterations are optimized. It is found that the model can achieve the best comprehensive performance in prediction accuracy and training time when the size of the first layer convolutional kernel is 99  and the last layer contained 64 convolutional kernels. After training the model with the training set, the trained model is used to predict the penetration state on the test set, and the prediction accuracy is higher than 96%. By visualizing the feature mapping of the prediction model, it is found that the model predicts the penetration state through the features of edge, the position of reflective point and the molten pool tail, thus explaining how the model the judges the penetration state.

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A mobile sensing system for real-time 3D weld pool surface measurement in manual GTAW

Cited by 14 publications

References 31 publications

Defects detection of GMAW process based on convolutional neural network algorithm

Defects detection of GMAW process based on convolutional neural network algorithm

Three-Dimensional Reconstruction of Welding Pool Surface by Binocular Vision

GTAW Penetration Prediction Model Based on Convolution Neural Network Algorithm

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