A laser back-lighting based metal transfer monitoring system for robotic gas metal arc welding. (April 2016)

• Record Type: Journal Article
• Authors: Wang, Zhenzhou
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 38(2016)
• Page Start: 52
• Page End: 66
• Publication Date: 2016-04
• Subjects: GMAW -- Modeling -- Image analysis -- Machine vision -- Threshold selection -- Segmentation; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2015.10.004
• Languages: English
• ISSNs: 0736-5845

Abstract

Abstract: Gas metal arc welding (GMAW) can be considered the most widely used process in automated welding due to its high productivity. However, its solid to liquid metal transfer also complicates the process and causes fumes and spatters. It needs to be better understood and controlled for assured weld quality, improved process stability, and reduced fumes, spatters, and energy consumption. For automated robotic gas metal arc welding, automatic and efficient image processing algorithms are required to extract the metal transfer robustly. In addition, the machine vision apparatus in real welding environments should be compact and easy to handle. To this end, a simplified laser back-lighting based monitoring system is proposed to measure the metal transfer in this paper. To facilitate the image analysis, the arc light and the image are modeled based on the physical laws. A double-threshold method is proposed to segment the image robustly with a linear membership assigned to the fuzzy edge region. To compute the two thresholds accurately and simultaneously, slope difference is calculated for the histogram distribution and the gray-scale positions with the largest and second largest peaks are selected as the two thresholds respectively. Experimental results verified the effectiveness of the on line monitoring system and the subsequent automatic image processing methods. Highlights: A metal transfer monitoring system is proposed and implemented. The captured image by the monitoring system is modeled. Effective image processing algorithms are proposed to compute the metal transfer. The real time processing ability of the system is validated by experiments.