Abstract
Penetration estimation is a prerequisite of the automation of backing welding based on vision sensing technology. However, the arc interference in welding process leads to the difficulties of extracting the weld pool characteristic information, which brings great challenges to the penetration estimation. At present, most researches focus on the extraction of weld pool geometry parameters, and the visual sensing systems are complex in structure and complicated in the image processing algorithms. The research of penetration estimation based on weld pool geometry parameters is still in the exploratory stage. The purpose of this paper is to research the relationship between the weld pool geometry parameters and the penetration during backing welding and to estimate penetration using the weld pool geometry parameters. A passive vision sensing test system for gas metal arc (GMA) backing welding was established. An image processing algorithm was developed to extract the weld pool geometry parameters, namely, the area, maximum width and length, half-length, length-width ratio and advancing contact angle (simplified as AWP, MWWP, MLWP, HLWP, LWR and ACA, respectively). The corresponding relationships between the weld pool geometry parameters and the penetration state were explored by analysing their changes with the welding current and speed. The distribution of the weld pool geometry parameters corresponding to penetration was determined. When the AWP of the weld pool is within a certain range and the values of LWR and ACA are close to their maximum and minimum respectively, the penetration is in good condition. A mathematical model with the weld pool geometry parameters as independent variables and the back-bead width (the indicator of the penetration state) as a dependent variable was established based on multivariable linear regression analysis, and relevant statistical tests were carried out. Multivariable linear regression equations for the weld pool geometry parameters and the back-bead width were deduced according to the variations in the current and speed, and the equations can be used to estimate the penetration of backing welding. The study provides a solution to penetration estimation of GMA backing welding based on automatic vision sensing.
Highlights
With the development of industrial automation, intelligent and digital technology, automatic welding technology is widely used in the petrochemical industry [1], construction machinery [2], shipbuilding [3], marine engineering [4], aerospace [5], rail transportation and Quality problems, such as partial penetration or excessive penetration, are often encountered in the backing welding process, and manual backing welding by skilled welders is still required to ensure the welding quality of butt welding [10], which seriously restricts theHuang et al Chin
3 Test Results and Analysis Image acquisition of the weld pool was carried out during the welding process, and the geometric characteristics were extracted by an image processing algorithm
The area of the weld pool (AWP), length-width ratio (LWR), advancing contact angle (ACA), maximum length of the weld pool (MLWP) and maximum width of the weld pool (MWWP) are discussed in this paper
Summary
With the development of industrial automation, intelligent and digital technology, automatic welding technology is widely used in the petrochemical industry [1], construction machinery [2], shipbuilding [3], marine engineering [4], aerospace [5], rail transportation and. Related research has been carried out at the Lanzhou University of Technology, where Zhang et al [19] proposed a laser vision method using a low-power fractional laser to cover the entire pool surface and a high-speed camera to capture the laser image reflected from the molten metal surface. Liu et al [27] presented a real-time passive machine vision system for weld pool sensing in robotic arc welding and proposed a detection algorithm to extract the geometrical profile of the weld pool. Welding (GMAW) weld pool was conducted with weld pool images captured by a passive visual sensing method, and the relationships between geometric parameters and the penetration state at different welding currents and speeds were analysed. Two Mako G-192B/C (produced by Allied Vision, Germany) cameras were utilized for image acquisition of the front and back of the weld pool, and the camera was an industrial Gigabit Ethernet camera with a high
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