Comprehensive detection method of weld based on line structured light and weak magnetic detection

Ye, Jing; Xia, Guisuo; Hu, Biao; Cheng, Qiangqiang

doi:10.1117/12.2548425

Cited by 3 publications

(3 citation statements)

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“…The measurement error is within ±1.5 mm, demonstrating high extraction accuracy. Ye et al 20 . combined machine vision with nondestructive testing, which can quickly and accurately reflect the surface morphology of welds and improve the detection effectiveness of weak magnetic sensors for weld defects.…”

Section: Introductionmentioning

confidence: 99%

“…The measurement error is within AE1.5 mm, demonstrating high extraction accuracy. Ye et al 20 combined machine vision with nondestructive testing, which can quickly and accurately reflect the surface morphology of welds and improve the detection effectiveness of weak magnetic sensors for weld defects. Although the laser vision inspection systems mentioned above can be applied in various environments, their main focus is on single-line laser streak inspection, which leads to limited enhancements in speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

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High-efficiency flatness detection method for medium-thick steel plates based on multiline parallel laser vision

Xia,

Wang,

Wang

et al. 2023

Opt. Eng.

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.To resolve the disparity between accuracy and speed in the current surface flatness detection of medium and thick steel plates, which predominantly relies on the single-line laser vision method, we propose a high-efficiency flatness detection method for medium-thick steel plates based on multiline parallel laser vision. On the basis of building a flatness detection system, we present the adaptive cropping algorithm and improved variable threshold Otsu thresholding segmentation algorithm (IVT-Otsu) for image preprocessing. Then, the centerline of subpixel-level laser stripes is accurately extracted using a weighted quadratic grayscale center of gravity method. Meanwhile, a pruning algorithm-based multiline laser stripe center extraction method is proposed to address the phenomenon of spotting and branching in the center of multiline parallel laser, to efficiently obtain the surface depth information and flatness index of medium-thick steel plate. Results of the experiment indicate that the proposed method better fulfills the accuracy and efficiency requirements for online inspection when extracting the laser centers and calculating the flatness.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-efficiency flatness detection method for medium-thick steel plates based on multiline parallel laser vision

Xia,

Wang,

Wang

et al. 2023

Opt. Eng.

View full text Add to dashboard Cite

show abstract

“…There are three main methods of current non-destructive testing of welding quality: 1) online monitoring of the quality of the welding process; 2) non-destructive testing of internal defects of welding (radiation [5][6][7][8][9], ultrasonic, [10][11][12][13][14], etc. ); 3) detection of surface defects of welding (visual [15,16], structured light [17][18][19][20][21], etc.). The latter two inspection methods belong to post-weld inspection, and it is difficult to take advantage of the high speed and high efficiency of laser welding.…”

Section: Introductionmentioning

confidence: 99%

A New Method to Assess Fiber Laser Welding Quality of Stainless Steel 304 Based on Machine Vision and Hidden Markov Models

et al. 2020

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High-power fiber laser welding has a broad range of applications in industrial processing and modern intelligent manufacturing, but how to assess the quality of laser welding has always been a concern. In the welding process, the geometric feature of the keyhole generated by the high temperature of laser can directly reflect quality of the laser welding. By using machine vision to acquire images in real time and analyze related features, the quality of laser welding can be evaluated, which reduce the cost and time in later inspection. Nevertheless, due to the dynamic complexity of the keyhole as well as the blurring of the keyhole and full penetration hole boundary caused by metal vapor and spatter, identification of multiple type defects through machine vision is still a problem that requires a solution. This paper presents a novel laser welding defect classification method using keyhole boundary-based feature based on machine vision and Hidden Markov process. After effective segmenting the collected welding image, the shapes of the keyhole as well as the full penetration hole were automatically extracted using the characteristics of gray projection distribution and the Poisson extinction method. Subsequently, a pre-trained Hidden Markov Model was employed to establish the connection between the keyhole's geometry and the welding quality defects. Experiments indicate that our theory can efficiently and accurately extract the key geometric shapes. Welding experimental data involving stainless steel 304 verifies the feasibility of the classification theory, which can monitor welding quality and reveal potential porosity and penetration defects.

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Feature Extraction of Laser Welding Pool Image and Application in Welding Quality Identification

Gao

Zhong²,

Tang

et al. 2021

IEEE Access

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Comprehensive detection method of weld based on line structured light and weak magnetic detection

Cited by 3 publications

References 0 publications

High-efficiency flatness detection method for medium-thick steel plates based on multiline parallel laser vision

High-efficiency flatness detection method for medium-thick steel plates based on multiline parallel laser vision

A New Method to Assess Fiber Laser Welding Quality of Stainless Steel 304 Based on Machine Vision and Hidden Markov Models

Feature Extraction of Laser Welding Pool Image and Application in Welding Quality Identification

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