Cellular Neural Network (CNN) based control algorithms for omnidirectional laser welding processes: Experimental results
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
The high dynamics of laser beam welding (LBW) in several manufacturing processes ranging from automobile production to precision mechanics requires the introduction of new fast real time controls. In the last few years, algorithms for the control of constant-orientation LBW processes have been introduced. Nevertheless, some real life processes are also performed changing the welding orientation during the process. In this paper experimental results obtained by the use of a new CNN based strategy for the control of curved welding seams are discussed. It is based on the real time adjustment of the laser power by the detection of the full penetration hole in process images. The control algorithm has been implemented on the Eye-RIS system v1.2 leading to a visual closed loop control solution with controlling rates up to 6 kHz.
Details
Original language | English |
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Title of host publication | 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications, CNNA 2010 |
Publisher | IEEE Computer Society, Washington |
ISBN (print) | 9781424466795 |
Publication status | Published - 29 Mar 2010 |
Peer-reviewed | Yes |
Publication series
Series | IEEE International Workshop on Cellular Nanoscale Networks and their Applications (CNNA) |
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Conference
Title | 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications, CNNA 2010 |
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Duration | 3 - 5 February 2010 |
City | Berkeley, CA |
Country | United States of America |
External IDs
ORCID | /0000-0001-7436-0103/work/142240304 |
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Keywords
ASJC Scopus subject areas
Keywords
- Closed loop systems, CNN, Laser welding, SIMD processor, System application and experience