Multidirectional magneto-optical imaging system for weld defects inspection

Gao, Xiangdong; Li, Yanfeng; Zhou, Xiao-Hu; Dai, Xinxin; Zhang, Yanxi; You, Deyong; Zhang, Nanfeng

doi:10.1016/j.optlaseng.2019.105812

Cited by 18 publications

(9 citation statements)

References 24 publications

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“…Hence, the classifcation task is completed accurately at a faster rate. Te output images of the Proposed method are shown in Figure 3 with the numbering from [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Image segmentation is generally used to identify objects and boundaries (lines, curves, etc.)…”

Section: Resultsmentioning

confidence: 99%

“…Lei et al [12] Infuence of thermal efect on droplet transfer (cold metal transfer (CMT) laser welding) Analysis with a high-speed camera and brightness curves Huang et al [13] Welding defect identifcation (laser welding) Te defect identifcation through electrical signals of laser plasma and plasma fumes acquired by a high-speed camera Gao et al [14] To identify invisible weld defects Magneto-optical imaging system and grayscale curves Hamade and Baydoun [4] To identify wormhole defects in welded lap joints. X-ray computed tomography (CT) scan and Otsu segmentation…”

Section: Coaxial Infrared Pyrometermentioning

confidence: 99%

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Plug Valve Surface Defects Identification through Nondestructive Testing and Fuzzy Deep-Learning Algorithm for Metal Porosity and Surface Evaluation

Jacintha

Karthikeyan

Sivaprakasam

2023

Journal of Engineering

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This paper addresses the detection and identification of flaws in Plug valves. The Plug valve thermal image is acquired using a Fluke thermal camera [TiS20]. Thermal images of the Plug valve are used for the identification of flaws such as Crack, Porosity, Corrosion, and Internal defects. The thermal images detect the surface flaws and never subsurface flaws in Plug valves. The subsurface flaws detection is a challenging problem in valve inspection. In this paper, the thermal images obtained after the dye penetrates the surface valve detect the surface flaws more efficiently after applying the Fuzzy Deep Learning Algorithms. DyePenetrating Test (DPT) combined with Infrared Thermography is proposed to identify heat flux changes and flaws in the faulty metal surface of Plug valves. In DPT, thinned paint is employed on the metal surface that displays metal porosity and even fine cracks. After DPT, thermal images of the Plug valve are processed through the Fuzzy Deep Learning Algorithm to evaluate flaws. The Fuzzy Algorithm is utilized prior to Deep Learning to simplify and speed up the classification task. The flaws are identified using Slicing operation and the following parametric quantities such as Accuracy, Mathew’s Correlation Coefficient (MCC), Local Self-Similarity Descriptor (LSS), Precision/Recall, F-measure, and Jaccard Index. The parametric quantities depict corresponding variations with regard to surface coarseness and metal flaws. The DPT and Fuzzy Deep Learning Algorithm identify metal defects with 80.67% accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Coaxial Infrared Pyrometermentioning

confidence: 99%

Plug Valve Surface Defects Identification through Nondestructive Testing and Fuzzy Deep-Learning Algorithm for Metal Porosity and Surface Evaluation

Jacintha

Karthikeyan

Sivaprakasam

2023

Journal of Engineering

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“…Some reported works by Gao et al [205]- [207] analyzed the principle of MOI technique for defect detection, which converts the magnetic field distribution into a MO visual image of natural weld defect during laser welding process. Further, Gao et al [208] proposed a multi-directional MOI technique based on induced rotating magnetic field to solve the problem of directional detection of MO imaging under alternating magnetic field excitation. Multi-directional defect detection experiments clearly demonstrate the performance of the monitoring system under the rotating magnetic field excitation.…”

Section: Off-axial Visual Sensingmentioning

confidence: 99%

Progress and perspectives of in-situ optical monitoring in laser beam welding: Sensing, characterization and modeling

Zhang

et al. 2022

Journal of Manufacturing Processes

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“…Magneto optical imaging is a detection technology based on Faraday magneto-optical effect. When light passes through a medium with special magneto-optical properties, if a magnetic field is applied to it along the light propagation direction, the polarization plane of light will deflect [9] .…”

Section: Faraday Effectmentioning

confidence: 99%

Research on rail crack detection technology based on magneto-optical imaging principle

et al. 2022

J. Phys.: Conf. Ser.

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The detection of rail top crack is of great significance in ensuring the safe operation of railway. Based on the principle of magnetic flux leakage detection and Faraday magneto-optical effect, a high-resolution and non-destructive magneto-optical imaging detection method is proposed in this paper. This paper analyses the principle of magneto-optical imaging, establishes a rail magnetic flux leakage detection experimental system based on magneto-optical effect, and detects the rail specimen with crack through experiment. The experimental results show that the change of crack width and depth will affect the magneto-optical imaging results, and the minimum crack size that can be detected by this experimental system is 0.4mm wide and 0.5mm deep. This paper realizes the detection of rail crack defects by using magneto-optical imaging method, and provides the basis for building an efficient, portable and high-resolution rail non-destructive testing equipment.

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Multidirectional magneto-optical imaging system for weld defects inspection

Cited by 18 publications

References 24 publications

Plug Valve Surface Defects Identification through Nondestructive Testing and Fuzzy Deep-Learning Algorithm for Metal Porosity and Surface Evaluation

Plug Valve Surface Defects Identification through Nondestructive Testing and Fuzzy Deep-Learning Algorithm for Metal Porosity and Surface Evaluation

Progress and perspectives of in-situ optical monitoring in laser beam welding: Sensing, characterization and modeling

Research on rail crack detection technology based on magneto-optical imaging principle

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