Date Available


Year of Publication


Degree Name

Master of Science in Electrical Engineering (MSEE)

Document Type





Electrical Engineering and Computer Science

First Advisor

Dr. Yuming Zhang


Human welders have long been able to monitor a weld pool and adjust welding parameters accordingly. Automated welding robots can provide consistent movement during the welding process, but lack the ability to monitor the weld pool. A vision system attached to the welding robot could provide a way to monitor the weld pool substantially faster than a human being. Previous vision systems to monitor weld pool surfaces have been developed, but their uses are limited since the system is fixed in place. The compact vision system developed in this research attaches directly to the welding torch, which provides no limitations in weld pool monitoring. This system takes advantage of the specular surface of a molten weld pool by reflecting a pattern of laser beams from the weld pool surface. The deformation of the laser beam after it reflects from the weld pool surface can provide clues to the weld pool shape, and thus the penetration of the weld. Image processing techniques and geometric optics are used to reconstruct a weld pool surface shape based on the image captured of the deformed laser pattern.