Apparent defect recognition of gas metal arc welding based on infrared vision

XIA Weisheng; GONG Fujian; YANG Rongguo; WAN Chaizhi; YANG Yunzhen

doi:10.12073/j.hjxb.20190928001

TRANSACTIONS OF THE CHINA WELDING INSTITUTION > 2020 > 41(3): 69-73. > DOI: 10.12073/j.hjxb.20190928001

XIA Weisheng, GONG Fujian, YANG Rongguo, WAN Chaizhi, YANG Yunzhen. Apparent defect recognition of gas metal arc welding based on infrared vision[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2020, 41(3): 69-73. DOI: 10.12073/j.hjxb.20190928001

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Apparent defect recognition of gas metal arc welding based on infrared vision

1.
State Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan,430074, China
2.
R&D Center, Shanxi North Machine-Building Co., Ltd., Taiyuan,030009, China
3.
Wuhan University of Technology, Wuhan,430070, China

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Received Date: September 27, 2019
Available Online: July 12, 2020

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Abstract

Abstract

The visual monitoring of welding process and welding defect identification are vital to the intelligent control of welding processes. In this paper, a mid-wave infrared CCD was used to acquire the infrared images of the welding pool on-line during the welding process. The images captured are preprocessed by the improved filtering algorithm and image enhancement algorithm. To obtain the temperature distribution information of the welding pool, the relationship between the gray value in infrared image and the temperature is established based on temperature calibration of the used thermocouple. The improved edge extraction algorithm is used to extract the characteristic parameters of the welding pool. Then the identification algorithm of welding defect is developed. The results of verified experiments show that the proposed algorithm has good practicability and accuracy in the on-line identification of welding shape, burn-through and unmelted defects.
- infrared vision,
- gas metal arc welding,
- image processing,
- apparent defect,
- pattern recognition

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References (15)

References

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Apparent defect recognition of gas metal arc welding based on infrared vision