Monitoring of root gap change based on electrical signals of flux-cored arc welding using random convolution kernel transform

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.

KEYWORDS:

• Flux-cored arc welding
• root-pass welding
• gap monitoring
• electrical signals
• time-series
• machine learning
• random convolutional kernel transform (ROCKET)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The raw/processed data required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study.

Additional information

Funding

This work was supported by the Korea Evaluation Institute of Industrial Technology (KEIT) (No. 20014796), the Korea Institute of Energy Technology Evaluation and Planning (KETEP) (No. 20213030030190) funded by the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea, and the research fund of Hanyang University (HY-202200000003359).