Real-time monitoring of high-power disk laser welding based on support vector machine

2018 ◽  
Vol 94 ◽  
pp. 75-81 ◽  
Author(s):  
Juequan Chen ◽  
Teng Wang ◽  
Xiangdong Gao ◽  
Li Wei
Keyword(s):  
Support Vector Machine ◽  
Laser Welding ◽  
Real Time ◽  
High Power ◽  
Support Vector ◽  
Real Time Monitoring ◽  
Disk Laser

Real-time monitoring of high-power disk laser welding statuses based on deep learning framework

2019 ◽  
Vol 31 (4) ◽  
pp. 799-814 ◽  
Author(s):  
Yanxi Zhang ◽  
Deyong You ◽  
Xiangdong Gao ◽  
Congyi Wang ◽  
Yangjin Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Laser Welding ◽  
Real Time ◽  
High Power ◽  
Real Time Monitoring ◽  
Disk Laser ◽  
Learning Framework

scholarly journals Real-time Monitoring for Disk Laser Welding Based on Feature Selection and SVM

2017 ◽  
Vol 7 (9) ◽  
pp. 884 ◽  
Author(s):  
Teng Wang ◽  
Juequan Chen ◽  
Xiangdong Gao ◽  
Yuxin Qin
Keyword(s):  
Feature Selection ◽  
Laser Welding ◽  
Real Time ◽  
Real Time Monitoring ◽  
Disk Laser

Segmentation of Molten Pool Infrared Images Using Morphology during High Power Disk Laser Welding

2012 ◽  
Vol 201-202 ◽  
pp. 91-94
Author(s):  
Yan Xi Zhang ◽  
Xiang Dong Gao
Keyword(s):  
Laser Welding ◽  
High Power ◽  
Molten Pool ◽  
Welding Process ◽  
Histogram Equalization ◽  
Welding Quality ◽  
Infrared Images ◽  
Traditional Methods ◽  
Disk Laser ◽  
Laser Welding Process

Configuration of a molten pool is related to the laser welding quality. Analyzing the configuration of a molten pool is important to monitor the laser welding process. This paper proposes a method of segmentation of a molten pool and its shadow during high power disk laser welding, consequently provides the groundwork for reconstruction of the molten pool and analysis of welding quality. Subsection linear stretching histogram equalization was applied to enhance the contrast of the original images firstly, and then edge detection was used to highlight the edges. After that we used the morphology filtering method to produce the segmentation mask, and then combined the mask with the original images to get the final segmentation results. Also, the proposed method was compared with other traditional methods. The experimental results showed that our method not only could give better segmentation results and process large quantities images automatically, but also overcame the less-segmentation problems of traditional methods.

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