A Smart Monitoring System for Automatic Welding Defect Detection

2019 ◽  
Vol 66 (12) ◽  
pp. 9641-9650 ◽  
Author(s):  
Paolo Sassi ◽  
Paolo Tripicchio ◽  
Carlo Alberto Avizzano
IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 125929-125938 ◽  
Author(s):  
Feng Duan ◽  
Shifan Yin ◽  
Peipei Song ◽  
Wenkai Zhang ◽  
Chi Zhu ◽  
...  

2021 ◽  
Vol 120 ◽  
pp. 102435
Author(s):  
Lei Yang ◽  
Huaixin Wang ◽  
Benyan Huo ◽  
Fangyuan Li ◽  
Yanhong Liu

2019 ◽  
Vol 9 (24) ◽  
pp. 5481 ◽  
Author(s):  
Peizhuo Zhai ◽  
Songbai Xue ◽  
Tao Chen ◽  
Jianhao Wang ◽  
Yu Tao

Pulsed gas metal arc welding (GMAW) is widely applied in industrial manufacturing. The use of pulsed GMAW was found superior to the traditional direct-current (DC) welding method with respect to spatter, welding performance, and adaptability of all-position welding. These features are closely related to the special pulsed projected metal transfer process. In this paper, a monitoring system based on a high-speed camera and laser backlight is proposed. High-quality images with clear droplets and a translucent arc can be obtained at the same time. Furthermore, a novel image-processing algorithm is proposed in this paper, which was successfully applied to remove the interference of the arc. As a result, the edge and region of droplets were precisely extracted, which is not possible using only the threshold method. Based on the algorithm, centroid coordinates of undetached and detached droplets can be calculated, and more parameters of the kinematic characteristics of droplets can be derived, such as velocity, acceleration, external force, and momentum. The proposed monitoring system and image-processing algorithm give a simple and feasible way to investigate kinematic characteristics, which can provide a new method for possible applications in studying mathematic descriptions of droplet flight trajectory and developing a precise automatic welding system.


2006 ◽  
Vol 39 (5) ◽  
pp. 356-360 ◽  
Author(s):  
J. Mirapeix ◽  
A. Cobo ◽  
O.M. Conde ◽  
C. Jaúregui ◽  
J.M. López-Higuera

2007 ◽  
Vol 10-12 ◽  
pp. 543-547 ◽  
Author(s):  
Ying Yin ◽  
G.Y. Tian ◽  
Guo Fu Yin ◽  
A.M. Luo

Radiography inspection (X-ray or gamma ray) is one of the most commonly used Non-destructive Evaluation (NDE) methods. More and more digital X-ray imaging is used for medical diagnosis, security screening, or industrial inspection, which is important for e-manufacturing. In this paper, we firstly introduced an automatic welding defect inspection system for X-ray image evaluation, defect image database and applications of Artificial Neural Networks (ANNs) for NDE. Then, feature extraction and selection methods are used for defect representation. Seven categories of geometric features were defined and selected to represent characteristics of different kinds of welding defect. Finally, a feed-forward backpropagation neural network is implemented for the purpose of defect classification. The performance of the proposed methods are tested and discussed.


2021 ◽  
Author(s):  
Abdallah Amine Melakhsou ◽  
Mireille Batton-Hubert

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