A Study on the Defect Detection Algorithm by Interval Statistical Processing Method of Arc Welding Waveform

• Published in: Journal of Welding and Joining Vol. 39; no. 1; pp. 74 - 80
• Main Authors: Ju, Woo-Hyeon, Ryu, Hyeong-Chang, Lim, Kyeong-Seob, Lee, Jong-Jung, Park, Yong-Hwan, Cho, Sang-Myung
• Format: Journal Article
• Language: English
• Published: 대한용접·접합학회 28.02.2021; 대한용접접합학회
• Subjects: 기계공학; Waveform; FCAW; Interval statistical processing method; Defect detection
• ISSN: 2466-2232; 2466-2100
• DOI: 10.5781/JWJ.2021.39.1.9

Defects in flux cored arc welding (FCAW) using CO2 gas not only deteriorate the quality of the welded part, but also increase the overall quality cost (Q-cost) due to the need for maintenance and welding. Destructive inspection and non-destructive inspection are two methods used to detect defects, but they are costly and time consuming. An alternate and advanced technique of detecting defects is by using a welding waveform. However, when the un processed welding waveform is checked, it is difficult to distinguish between normal and abnormal waveforms ac cording to the metal transfer mode. This is because the waveforms are significantly different based on whether or not a short circuit occurs. Therefore, an algorithm that can detect defects from waveforms, independent of the pres ence of a short circuit, is required. The developed algorithm should be able to detect defects using welding waveforms. In this study, we provide a method to detect defects by using the interval statistical processing method, according to the time series of welding data displayed in the welding monitoring system. KCI Citation Count: 0