Weld Surface Imperfection Detection by 3D Reconstruction of Laser Displacement Sensing

Yang, Pengcheng; Hu, Dan; Wang, Congyi; Zhang, Yanxi; You, Deyong; Gao, Xiangdong; Zhang, Nanfeng

doi:10.1109/icmcce51767.2020.00457

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…Researchers have demonstrated that combining deep-learning-based approaches with edge computing can help to develop diverse application systems [22,23]. Edge computing can also be used in combination with cloud computing that provides on-demand services for intensive calculation [24][25][26][27][28]. In order to complete the inspection task in a hash industrial production environment, a more sophisticated system can be created by integrating advanced information technologies such as the Internet of Things (IoT), virtual simulation and cloud-edge computing.…”

Section: Introductionmentioning

confidence: 99%

A Novel Robotic-Vision-Based Defect Inspection System for Bracket Weldments in a Cloud–Edge Coordination Environment

Wang

Liu

et al. 2023

Sustainability

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Arc-welding robots are widely used in the production of automotive bracket parts. The large amounts of fumes and toxic gases generated during arc welding can affect the inspection results, as well as causing health problems, and the product needs to be sent to an additional checkpoint for manual inspection. In this work, the framework of a robotic-vision-based defect inspection system was proposed and developed in a cloud–edge computing environment, which can drastically reduce the manual labor required for visual inspection, minimizing the risks associated with human error and accidents. Firstly, a passive vision sensor was installed on the end joint of the arc-welding robot, the imaging module was designed to capture bracket weldments images after the arc-welding process, and datasets with qualified images were created in the production line for deep-learning-based research on steel surface defects. To enhance the detection precision, a redesigned lightweight inspection network was then employed, while a fast computation speed was ensured through the utilization of a cloud–edge-computing computational framework. Finally, virtual simulation and Internet of Things technologies were adopted to develop the inspection and control software in order to monitor the whole process remotely. The experimental results demonstrate that the proposed approach can realize the faster identification of quality issues, achieving higher steel production efficiency and economic profits.

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Section: Introductionmentioning

confidence: 99%

A Novel Robotic-Vision-Based Defect Inspection System for Bracket Weldments in a Cloud–Edge Coordination Environment

Wang

Liu

et al. 2023

Sustainability

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“…A. Dawda et al [12] proposed an automatic defect detection technology combining laser line projection and stereo vision, which effectively reduced the influence of high-precision surfaces on the accuracy of 3D reconstruction. Yang et al [13] processed the data required for defect detection and 3D reconstruction based on laser scanning and adopted a BP neural network to filter the obtained point cloud data, effectively reducing the complexity of the 3D reconstruction of the weld surface. At present, research on the 3D reconstruction of surface defects mostly focuses on the identification of defects, while the quantitative analyses for defect classification and statistics are relatively few.…”

Section: Introductionmentioning

confidence: 99%

A Method for Extracting a Laser Center Line Based on an Improved Grayscale Center of Gravity Method: Application on the 3D Reconstruction of Battery Film Defects

Yao,

Wang,

et al. 2023

Applied Sciences

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Extraction of the laser fringe center line is a key step in the 3D reconstruction of linear structured light, the accuracy of which is directly related to the quality of the 3D model. A laser center line extraction method based on an improved gray center of gravity method is proposed to solve the problem of low extraction accuracy. Firstly, a smoothing method is used to eliminate the flat top of the laser line, and the Gaussian curve is adopted to fit the peak position of the curve. Then, the gray threshold is set to automatically extract the laser linewidth, and based on the window opening, the grayscale center of gravity method is improved to extract the coordinates of the center pixel for the second time. Finally, experiments show that the average absolute error of the improved laser line extraction method is 0.026 pixels, which is 2.3 times lower than the gray center of gravity method, 1.9 times lower than the curve fitting method, and the standard error can reach 0.005 pixels. Compared with the gray center of gravity method and the curve fitting method, the influence of gray value change on the center line extraction is more fully considered, and the center of the light strip can be extracted more accurately, achieving sub-pixel accuracy.

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“…The NDTs have been combined with mathematical morphology, neural networks, fuzzy logic, wavelets and stereo vision for automatic flaw identification but using the top-view data [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ] with very advanced tools for computation. The comparisons between NDT uses are the determination of alternative configuration for the equipment [ 20 ], the comparison of the acoustical echoes [ 21 ] or testing the capability of the different NDTs when working on simple or complex geometries [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Advanced Comparison of Phased Array and X-rays in the Inspection of Metallic Welding

et al. 2022

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The most common nondestructive weld inspection technique is X-rays and, since a few years ago, the ultrasound-based phased array. Their comparison has been done from the top view of both, with the result that the phased array is much more efficient in discovering flaws. From the last studies of the authors, a welding flaw can be three-dimensionally reconstructed from the sectorial phased array information. The same methodology is applied to compare quantitatively X-rays and phased array on 15 metal inert/active (MIG/MAG) welding specimens covering pores, slag intrusion and cracks. The results can be summarized in the correlation of the top views and in the correlation profiles between the X-ray top-view and the reconstructed top-view at the depths from phased array in the weld. The maximum correlation is the depth when the flaw in the X-ray looks like that in the phased array records at some depth, leading to an effective quantitative comparison of X-rays and phased array.

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Weld Surface Imperfection Detection by 3D Reconstruction of Laser Displacement Sensing

Cited by 7 publications

References 4 publications

A Novel Robotic-Vision-Based Defect Inspection System for Bracket Weldments in a Cloud–Edge Coordination Environment

A Novel Robotic-Vision-Based Defect Inspection System for Bracket Weldments in a Cloud–Edge Coordination Environment

A Method for Extracting a Laser Center Line Based on an Improved Grayscale Center of Gravity Method: Application on the 3D Reconstruction of Battery Film Defects

Advanced Comparison of Phased Array and X-rays in the Inspection of Metallic Welding

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