Abstract
This paper proposes a study on the comparison of particle swarm optimization with genetic algorithm for molten pool detection in fixed aluminum pipe welding. The research wasconducted for welding of aluminum alloy Al6063S-T6 with a controlled welding speed and aCharge-couple Device (CCD) camera as vision sensor. Omnivision-based monitoring using ahyperboloidal mirror was used to detect the molten pool. In this paper, we propose an optimizedbrightness range for detecting the molten pool edge using particle swarm optimization andcompare the results to genetic algorithm. The values of the brightness range were applied to thereal time control system using fuzzy inference system. Both optimization methods showed good results on the edge detection of the molten pool. The results of experiments with control show the effectiveness of the image processing algorithm and control process.
| Original language | English |
|---|---|
| Pages (from-to) | 74-83 |
| Number of pages | 10 |
| Journal | International Journal of Technology |
| Volume | 2 |
| Issue number | 1 |
| Publication status | Published - Jan 2011 |
Keywords
- Fixed aluminum pipe welding
- Fuzzy inference system
- Genetic algorithm
- Molten pool detection
- Particle swarm optimization