Abstract
This paper presents a novel system for the automatic analysis of a hybrid welding process. High-speed imaging and laser illumination are used to measure the regularity of electric arc frequency and flight directions of filler metal droplets. A fuzzy c-means clustering method is used to detect arcs and segment the video sequences. The droplets are localized by combining principal component analysis and a support vector machine classifier. The flight of a droplet is tracked using Kalman filtering. Experiments indicate that the system is able to track the flights of droplets and to determine the regularity of the arc frequency with a high accuracy if the imaging conditions are stable.
Original language | English |
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Pages (from-to) | 103-118 |
Number of pages | 16 |
Journal | MACHINE VISION AND APPLICATIONS |
Volume | 20 |
Issue number | 2 |
DOIs | |
Publication status | Published - Feb 2009 |
MoE publication type | A1 Journal article-refereed |
Keywords
- Fuzzy c-means clustering
- Hybrid welding
- Kalman tracking
- Principal component analysis
- Support vector machine