A real-time passive vision system for robotic arc welding

Liu, Jinchao; Fan, Zhun; Olsen, Søren I.; Christensen, Kim Hardam; Kristensen, Jakob

doi:10.1109/coase.2015.7294109

Cited by 11 publications

(2 citation statements)

References 10 publications

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“…The geometry parameters of the weld pool were detected by the image processing algorithm. 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. Since the weld pool is dynamic and its image is affected by the arc, reliable extraction of geometry parameters is a major research focus [28].…”

Section: Introductionmentioning

confidence: 99%

Penetration Estimation of GMA Backing Welding Based on Weld Pool Geometry Parameters

Huang

Xue

Huang

et al. 2019

Chin. J. Mech. Eng.

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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.

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Section: Introductionmentioning

confidence: 99%

Penetration Estimation of GMA Backing Welding Based on Weld Pool Geometry Parameters

Huang

Xue

Huang

et al. 2019

Chin. J. Mech. Eng.

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“…It uses the arc light to illuminate the scene; however, this process requires an accurate calibration of the camera position and a precise selection of a narrow-band filter to reduce the arc light [7]. A few research works using passive vision have been reported to enhance and automate welding processes [8], [9], or extract different features [10]- [12] from the weld pool for gas metal arc welding (GMAW) based AM. Recent research from Zhan et al [13] introduced a method of using passive vision system for monitoring the wire deviation during the WAAM process.…”

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confidence: 99%

A Passive Imaging System for Geometry Measurement for the Plasma Arc Welding Process

comas

Diao

Ding

et al. 2017

IEEE Trans. Ind. Electron.

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Automatic and flexible geometry measurement of the weld pool surface can help better understand the complex welding processes and even provide feedback to better control this process. Most of existing imaging systems use an additional source of illumination to remove the light interference coming from the welding arc but it is usually costly. This paper introduces a novel low-cost optical-sensor-based monitoring system working under passive mode to monitor the wire + arc additive manufacture process, particularly for plasma arc welding. Initially, configurations and parameters of camera are investigated to achieve good visualization of weld pool. A novel camera calibration methodology using the nozzle of a computer numerical control (CNC) machine is then proposed for this imaging system, allowing estimation of the camera position with respect to the inspecting surface and its orientation in an easy-to-use approach. The verification tests show that the average error of the calibration is less than 1 pixel. As a case study, an image analysis routine is proposed to measure the width of the bead during the welding process. The results show that the proposed system is effective to measure the dimension of weld pool. Index Terms-Additive manufacturing (AM), camera calibration, edge detection, plasma arc welding (PAW), wire + arc additive manufacture (WAAM). I. INTRODUCTION A DDITIVE manufacturing (AM) is a process of threedimensional (3-D) printing in which parts are built by adding materials layer by layer. Compared to traditional subtrac

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Vision-Based Seam Tracking in Robotic Welding: A Review of Recent Research

Wang

2020

Transactions on Intelligent Welding Manufacturing

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A real-time passive vision system for robotic arc welding

Cited by 11 publications

References 10 publications

Penetration Estimation of GMA Backing Welding Based on Weld Pool Geometry Parameters

Penetration Estimation of GMA Backing Welding Based on Weld Pool Geometry Parameters

A Passive Imaging System for Geometry Measurement for the Plasma Arc Welding Process

Vision-Based Seam Tracking in Robotic Welding: A Review of Recent Research

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