Study of Dynamic Features of Surface Plasma in High-Power Disk Laser Welding

Wang, Teng; Gao, Xiangdong; Katayama, Seiji; Jin, Xiaoli

doi:10.1088/1009-0630/14/3/11

Cited by 33 publications

(13 citation statements)

References 19 publications

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“…Basically, laser welding process monitoring will focus on the characteristics of the molten pool, keyhole, plume, spatters and the radiation signal generated from the welding position. [9][10][11][12][13][14] The most common defects that appear during the laser welding process are crack, porosity, incomplete penetration, undercut, underfill and spatters. 15,16 Typical structure of monitoring system for laser welding Unlike that of traditional welding technology, energy transmission during laser welding is mainly carried out by the laser beam, which travels through the optical fibre and lens and is then shone on the surface of the material.…”

Section: Basic Mechanism Of Laser Welding Monitoring Principles Of La...mentioning

confidence: 99%

Review of laser welding monitoring

You

Gao

Katayama

2014

Science and Technology of Welding and Joining

192

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Laser welding, as a highly efficient processing technology, has been widely applied to manufacturing industry. This paper makes an overview on real time monitoring of laser welding. It begins with a detailed introduction to six typical sensors (photodiode, visual, spectrometer, acoustical sensor, pyrometer, plasma charge sensor) in laser welding detection. Then it makes a review on multi-sensor fusion technology in both laser welding monitoring and adaptive control. Last, subjects for future research concerning welding monitoring and control have been proposed. The paper concludes that the real-time monitoring of laser welding can provide a great amount of valid information about welding status to help effectively identify weld defects and realize adaptive control.

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Section: Basic Mechanism Of Laser Welding Monitoring Principles Of La...mentioning

confidence: 99%

Review of laser welding monitoring

You

Gao

Katayama

2014

Science and Technology of Welding and Joining

192

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“…At present, most of the ceramic tile manufacturers quality testing is still done manually, manual testing time-consuming and laborious and poor stability. Therefore, the research on image processing and machine vision has always been popular [1][2][3][4][5]. Although the traditional visual inspection method has many applications, its adaptability and accuracy are weak.…”

Section: Introductionmentioning

confidence: 99%

Detection of ceramic tile defects based on improved YOLOv5

Liu

Gao²,

Xiang-dong

2023

Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023)

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Aiming at the problem that the defect detection algorithm of ceramic tile is not much and the performance is not enough to meet the industrial requirements, this paper proposes a ceramic tile defect detection algorithm based on improved yolov5. The improvement scheme proposed in this paper includes the introduction of CNB module and Ghost convolution, and the improvement of spatial pyramid pooling. Through repetitive experiments on the collected tile data set, it can be confirmed that the proposed modules have obvious improvement effects on the accuracy, real-time and lightweight of the model. The mAP of the improved algorithm in this paper reaches 73.1 % under the premise of meeting the industrial demand in terms of speed and memory usage, which is 6.2 % higher than the original yolov5 model.

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“…At present, surface defects detection methods mainly include traditional image processing methods and deep learning methods [1][2][3]. Traditional image processing methods detect targets through edge detection, threshold segmentation, feature histogram, classical machine learning methods [2,[4][5][6][7][8][9][10] (support vector machine, k-Nearest Neighbor method and Naive Bayes, neural network, decision tree, etc.). Literature [11] constructed neural network to detect welding defects under alternating/rotating magnetic field, and the test detection accuracy is 94.1%.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Detection Algorithm for Surface Defects of Automobile Door Seals

2022

Teh. vjesn.

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The surface defects of automobile door seals are mainly detected manually at present, which is costly and has low efficiency. Therefore, a deep learning automatic detection algorithm of automobile door seals is studied in this paper. At first, the defects are classified and the data set is made according to the geometric characteristics of the defects. While enhancing the data set, the K-means clustering algorithm is used to cluster the target annotation frame in the data set, and the anchor that matches with the surface defect size of the seal is obtained. Finally, in view of the characteristics of large variation range of defect size, the target detection algorithm YOLOV3 is selected as the basic framework. Meanwhile, considering the high proportion of small and medium-sized targets in defects, the output scale is introduced at 4 times of the sampling position of YOLOV3 backbone network. In order to further enhance the correlation between the extracted features and the channel, spatial position and coordinate position, the feature fusion and attention mechanism module is constructed. The test results show that the mean of the average precision is improved by 4.81% compared with YOLOV3.

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Study of Dynamic Features of Surface Plasma in High-Power Disk Laser Welding

Cited by 33 publications

References 19 publications

Review of laser welding monitoring

Review of laser welding monitoring

Detection of ceramic tile defects based on improved YOLOv5

Deep Learning Detection Algorithm for Surface Defects of Automobile Door Seals

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