Vision based neurofuzzy logic control of weld pool geometry

Abstract

The geometry of the weld pool contains accurate, instantaneous information about welding quality. Thus, weld pool sensing and control plays a significant role in automated arc welding. Previous studies have focused on inferring penetration through models and controlling penetration by various methods, such as adaptive control, model based fuzzy logic, etc. In the present work, a weld pool imaging system employing a LaserStrobe (tradename) high shutter speed camera is used to obtain contrasting images and eliminate arcing interference. Two image processing tools based on edge detection and connectivity analysis extract online information about the weld pool length and width. A neurofuzzy control system elicited from both human experience and experimental results has been developed to control the welding current and welding speed in real time based on changes in weld pool dimensions. Closed loop control of welding speed is used to achieve desirable weld pool geometry.

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