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A study on on-line mathematical model to control of bead width for arc welding process

Son J. S., Lee J. P., Park M. H., Kim D. H., Jin B. J., Ki K. S., Kim C. G., Kim I. S.

Archives of Materials Science and Engineering

2015

Vol. 72, nr 2

78--85

Purpose: Recently, not only robotic welders have replaced human welders in many welding applications, but also reasonable seam tracking systems are commercially available. However, fully adequate control systems have not been developed due to a lack of reliable sensors and mathematical models that correlate welding parameters to the bead geometry for the automated welding process. Design /meth o d o lo g y/ap p ro ach : In this paper, two on-line empirical models using multiple regression analysis are proposed in order to be applicable for the prediction of bead width. For development of the proposed models, an attempt has been made to apply for a several methods. For the more accurate prediction, the prediction variables are first used to the surface temperatures measured using infrared thermometers with the welding parameters (welding current, arc voltage) because the surface temperature are strongly related to the formation of the bead geometry. The developed models are applied to monitor and control the bead width as welding quality. Findings: The developed two on-line empirical models are able to predict the optimal welding parameters required to achieve desired bead width and weld criteria, help the development of automatic control system and expert system and establish guidelines and criteria for the most effective joint design. Research lim ita tio n s /im p lic a tio n s : This research was concentrated to develop on the on-line empirical models that can predict bead width in robotic GMA welding process. The developed empirical models can only be employed to control the bead width for butt welding. O rig in ality /va lu e : It has been realized that with the use of the developed algorithms, the prediction of bead width becomes much simpler to even a novice user who has no prior knowledge of the robotic GMA welding process and optimization techniques.