Monitoring of root gap change based on electrical signals of flux-cored arc welding using random convolution kernel transform
- Authors
- Jang, Junmyoung; Lee, Jaeheon; Lee, Jaeyoung; Park, Sang Rin; Kim, Jin-young; Kim, Young-Beom; Lee, Seung Hwan
- Issue Date
- Nov-2023
- Publisher
- TAYLOR & FRANCIS LTD
- Keywords
- Flux-cored arc welding; root-pass welding; gap monitoring; electrical signals; time-series; machine learning; random convolutional kernel transform (ROCKET)
- Citation
- SCIENCE AND TECHNOLOGY OF WELDING AND JOINING, v.28, no.8, pp.738 - 746
- Indexed
- SCIE
SCOPUS
- Journal Title
- SCIENCE AND TECHNOLOGY OF WELDING AND JOINING
- Volume
- 28
- Number
- 8
- Start Page
- 738
- End Page
- 746
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/193095
- DOI
- 10.1080/13621718.2023.2219081
- ISSN
- 1362-1718
- Abstract
- A monitoring technique for detecting changes in the root gap of butt joints during the flux-cored arc welding (FCAW) was proposed. FCAW experiments were conducted for both increasing and decreasing root gap conditions, and current and voltage were measured during the root-pass welding. The measured time series signals were used as input data for training Random Convolution Kernel Transform (ROCKET) algorithm, which consists of a feature extractor with multiple random kernels, and a linear classifier. A univariate model using current and voltage, respectively, and a multivariate model using both were compared, and the multivariate model showed the highest classification accuracy of 96.2%. Moreover, the classification errors were investigated by correlating the geometry of the root bead with the measured signals.
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