During laser surface treatment real time process information like melt pool size, shape, surface integrity (porosity, cracks) and brightness can reflect the quality of the process and is important for quality control. This paper studies the use of real time image processing (or machine vision) for the on-line monitoring and closed loop control of melt pool parameters via a supervisory computer. Relationships between processing quality and image parameters are investigated. The algorithms/heuristics and effects of the adaptive control and process monitoring are presented.

Topics
Quality assurance, Image processing, Artificial intelligence, Cognitive science, Real-time process
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© 1994 Laser Institute of America.
1994
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