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Controlled metal transfer in gas metal arc welding (GMAW) implies controllable weld quality. To understand, analyse and control the metal transfer process, the droplet should be monitored and tracked. To process the metal transfer images in double-electrode GMAW (DE-GMAW), a novel modification of GMAW, a brightness-based algorithm is proposed to locate the droplet and compute the droplet size automatically. Although this algorithm can locate the droplet with adequate accuracy, its accuracy in droplet size computation needs improvements. To this end, the correlation among adjacent images due to the droplet development is taken advantage of to improve the algorithm. Experimental results verified that the improved algorithm can automatically locate the droplets and compute the droplet size with an adequate accuracy.

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Abstract-Next generation gas metal arc welding (GAMW) machines require the rapid metal transfer process be accurately monitored using a high speed vision system and be feedback controlled. However, the necessity for high frame rate reduces the resolution achievable and bright welding arc makes it difficult to clearly image the metal transfer process. Processing of images for real-time monitoring of metal transfer process is thus challenging. To address this challenge, the authors analyzed the characteristics of metal transfer images in a novel modification of GAMW, referred to as double-electrode GMAW, and proposed an algorithm consisting of a system of effective steps to extract the needed droplet feedback information from high frame-rate low-resolution metal transfer images. Experimental results verified the effectiveness of the proposed algorithm in automatically locating the droplet and computing the droplet size with an adequate accuracy.Note to Practitioners-Monitoring of metal transfer process is a fundamental step toward intelligent control of gas metal arc welding process and its modifications. However, the metal transfer rate may exceed over hundred hertz and its monitoring requires high frame rate images so that the resolution of the image is relatively low. Because of the low resolution and harsh welding environment, automated processing of the images for droplet identification and computation is challenging. This paper proposes a system of solutions to process the low resolution images to obtain robust and accurate estimation of the droplet location and size. Experiments verified the effectiveness of the proposed solutions and future work will focus on algorithm optimization and high speed processor implementation to improve the speed for real-time control of droplet trajectory and size which are required for future precision manufacturing applications.

Controlled metal transfer in gas metal arc welding (GMAW) and its modifications including the double-electrode GMAW implies controllable heat and mass inputs and better assured weld quality. To understand, analyze, and control the metal transfer process, the droplet should be monitored in real time. Due to the fast development and transfer of the droplet, the monitoring speed is a key. A tracking method that takes advantage of results from previous images to speed the processing is advantageous. In this paper, Kalman Filter tracking and Least Square Match tracking algorithms are developed to track a droplet in the innovative double-electrode GMAW after its original position is identified. Experimental results showed that the Kalman Filtering algorithm is not suitable for this application due to the limited life span of each droplet. Instead, the Least Square Match algorithm is effective in tracking a droplet if a universal droplet template can be found and defined. However, there are no universal templates suitable for all the droplets. Hence, a real time template updating and LSM tracking method is proposed to track the droplet effectively. Experimental results verified its tracking accuracy.