Abstract
Weld bead geometry features (WBGFs) such as the bead width, height, area, and center of gravity are the common factors for weighing welding quality control. The effective modeling of these WBGFs contributes to implementing timely decision making of welding process parameters to improve welding quality and enhance automatic levels. In this work, a dynamic modeling method of WBGFs is presented based on machine vision and learning in multipass gas metal arc welding (GMAW) with typical joints. A laser vision sensing system is used to detect weld seam profiles (WSPs) during the GMAW process. A novel WSP extraction method is proposed using scale-invariant feature transform and machine learning. The feature points of the extracted WSP, namely the boundary points of the weld beads, are identified with slope mutation detection and number supervision. In order to stabilize the modeling process, a fault detection and diagnosis method is implemented with cubic exponential smoothing, and the diagnostic accuracy is within 1.50 pixels. A linear interpolation method is presented to implement sub pixel discrimination of the weld bead before modeling WBGFs. With the effective feature points and the extracted WSP, a scheme of modeling the area, center of gravity, and all-position width and height of the weld bead is presented. Experimental results show that the proposed method in this work adapts to the variable features of the weld beads in thick plate GMAW with T-joints and butt/lap joints. This work can provide more evidence to control the weld formation in a thick plate GMAW in real time.
Highlights
The gas metal arc welding (GMAW) process has become a popular method for joining the thick plates of steel structures and been used in the shipbuilding industry, wire arc additive manufacturing [1], pipe manufacturing [2,3], etc
Automatic and intelligent welding technologies have been widely applied to the thick plate GMAW process, such as automatic seam tracking [4] and weld formation monitoring, to enhance welding quality and efficiency [5]
This paper presented an effective Weld bead geometry features (WBGFs) modeling method during the multipass GAMW process with T-joints and butt joints based on machine vision and learning
Summary
The gas metal arc welding (GMAW) process has become a popular method for joining the thick plates of steel structures and been used in the shipbuilding industry, wire arc additive manufacturing [1], pipe manufacturing [2,3], etc. In thick plate GMAW, the online monitoring of weld formation for each pass is necessary for weldment quality control, and welding process. Weld bead geometry features (WBGFs) can reflect the welding process parameters and are effective evidence to suggest how to optimize the latter. This is because the relationship between WBGFs and weld process parameters can be built with various models [9,10,11]. The real-time modeling of WBGFs is the really demanded technology for the effective control of weld formation especially and accurate metal deposition [12,13] during the multipass welding process
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