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Arc welding

About: Arc welding is a research topic. Over the lifetime, 25393 publications have been published within this topic receiving 168182 citations.


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Journal ArticleDOI
TL;DR: An innovative 3-D vision sensing system is used to measure the characteristic parameters of the weld pool in real-time in gas tungsten arc welding and it is confirmed that the developed control system is effective in achieving the desired weld joint penetration under various disturbances and initial conditions.
Abstract: Skilled welders can estimate and control the weld joint penetration, which is primarily measured by the backside bead width, based on weld pool observation. This suggests that an advanced control system could be developed to control the weld joint penetration by emulating the estimation and decisionmaking process of the human welder. In this paper an innovative 3-D vision sensing system is used to measure the characteristic parameters of the weld pool in real-time in gas tungsten arc welding. The measured characteristic parameters are used to estimate the backside bead width, using an adaptive neuro-fuzzy inference system (ANFIS) as an emulation of skilled welder. Dynamic experiments are conducted to establish the model that relates the backside bead width to the welding current and speed. The dynamic linear model is first constructed and the modeling result is analyzed. The linear model is then improved by incorporating a nonlinear operating point modeled by an ANFIS. Because the weld pool needs to gradually change, being controlled by a skilled welder, a model predictive control is used to follow a trajectory to reach the desired backside bead width and the control increment is penalized. Because the weld pool is not supposed to change in an extremely large range, the resultant model predictive control is actually linear and an analytical solution is derived. Welding experiments confirm that the developed control system is effective in achieving the desired weld joint penetration under various disturbances and initial conditions.

115 citations

01 Jan 2003
TL;DR: In this paper, the authors proposed a simple model to predict the onset and type of humping defect, and demonstrated that premature solidification of this thin layer initiates split bead, parallel humping, tunnel porosity, and unercutting.
Abstract: An explanation for penetra- tion and defect generation in the weld pool at high currents is proposed. In this regime, the arc pressure pushes the molten metal to the rear of the weld pool, creating a thin layer of liquid metal under the arc. Premature solidification of this thin layer initiates humping, split bead, parallel humping, tunnel porosity, and un- dercutting. The thin nature of the liquid layer is the cause of increased penetration at high currents. We propose a simple model to predict the onset and type of humping defect.

115 citations

Journal ArticleDOI
TL;DR: In this paper, a method for the automatic identification and location of welding seams for robotic welding using computer vision is presented, which can provide a 3D Cartesian accuracy of within ± 1mm which is acceptable in most robotic arc welding applications.
Abstract: One of the main difficulties in using robotic welding in low to medium volume manufacturing or repair work is the time taken to programme the robot to weld a new part. It is often cheaper and more efficient to weld the parts manually. This paper presents a method for the automatic identification and location of welding seams for robotic welding using computer vision. The use of computer vision in welding faces some difficult challenges such as poor contrast, textureless images, reflections and imperfections on the surface of the steel such as scratches. The methods developed in the paper enables the robust identification of narrow weld seams for ferrous materials combined with reliable image matching and triangulation through the use of 2D homography. The proposed algorithms are validated through experiments using an industrial welding robot in a workshop environment. The results show that this method can provide a 3D Cartesian accuracy of within ±1 mm which is acceptable in most robotic arc welding applications.

114 citations

Journal ArticleDOI
TL;DR: In this paper, the authors developed the curvilinear and linear equations to predict the bead penetration with reasonable accuracy in the CO 2 arc welding process and developed the process control algorithms to identify the various problems that result from the welding process, and establish criteria for effective joint design.

114 citations

Journal Article
TL;DR: In this paper, the relationship between welding penetration and pool depression has been investigated, and three distinct regimes of weld penetration on weld current were found. But none of the three regimes of welding penetration were considered in this paper.
Abstract: Weld pool depression, arc force, weld penetration, and their interrelations have been studied as a function of welding current. Pool depression and welding arc force have been measured simultaneously using a recently developed technique. The authors found quadratic dependence of arc force on current, confirming similar findings in previous studies. Pool depression is essentially zero below a threshold level of current (200 A in this experiment) and then increases quadratically with current. A perfectly linear relation between arc force and pool depression was found in the current range from 200 to 350 A, with pool depression onset at about 0.35 g force (0.34 [center dot] 10[sup [minus]2]N). The total surface tension and gravitational forces were calculated, from the measured surface topography, and found to be about five times that required to balance the arc force at 300 A. Thus electromagnetic and hydrodynamic forces must be taken into account to explain the measured levels of pool depression. The relation between weld penetration and pool depression for different welding currents has been established. Three distinct regimes of weld penetration on weld current were found.

114 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202396
2022186
2021303
2020685
2019807
2018922