Welding seam tracking in robotic gas metal arc welding

Xu, Yanling; Lv, Na; Gu, Fang; Du, Shaofeng; Zhao, Wenjun; Ye, Zhen; Chen, Shanben

doi:10.1016/j.jmatprotec.2017.04.025

Cited by 84 publications

(23 citation statements)

References 12 publications

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“…Passive vision is considered a practical way of monitoring weld pool characteristics and it is also cheaper than active vision, being preferred for monitoring and control welding processes (Wu and Chen, 2007). At the work published by Xu et al (2017), the authors also considered passive vision a cheaper solution and it provides enough information about the environment which can overcome the problem of tracking systems based on laser sensors (Xu et al, 2014).…”

Section: Vision In Welding and Waammentioning

confidence: 99%

“…Making a comparison between the measurement done through the camera and a 3D scanner of the bead, achieving good results in the proposed measurement method. Xu et al (2017) also used passive vision for seam tracking with a GMAW process for ensuring the correct position of the torch during welding. It is possible to notice that passive vision in widely during welding and material deposition.…”

Section: Introductionmentioning

confidence: 99%

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Weld Bead Width Measurement in a GMAW WAAM System by using Passive Vision

Couto

Costa

Leite³

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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The development and integration of Wire Arc Additive Manufacturing (WAAM) systems is nowadays a topic of growing interest. Industries are starting to focus on deploying this technology due to its vast capability of producing different types of parts and creating new possibilities for engineering design. Measuring the process characteristics is crucial in a WAAM system, because it helps to ensure that the build up was done according as planned. In this work, it was developed a passive vision-based monitoring method to measure the metal bead width in a WAAM process. The deposition was carried out by using a carbon steel wire with GMAW process, a Motoman HP20 robot arm and a welding torch device. A Xiris XVC-1000camera was used for visual inspection and mounted in a suitable configuration to minimize arc's noise. The experimental results show that it is possible to measure the bead deposition width in real time with a satisfactory accuracy using monocular cameras. Therefore, it is a feasible solution to be used in WAAM systems with the advantage of being relatively low-cost, as compared to other active vision equipment.

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Section: Vision In Welding and Waammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Weld Bead Width Measurement in a GMAW WAAM System by using Passive Vision

Couto

Costa

Leite³

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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“…Therefore, without sufficient teaching time, the robotic welding of such components cannot be realized. With the disadvantages of low programming efficiency and lack of flexibility [ 9 , 10 , 11 ], it is difficult to adapt robotic welding to the efficient and flexible production of complex box girders in small quantities.…”

Section: Introductionmentioning

confidence: 99%

Welding Seam Trajectory Recognition for Automated Skip Welding Guidance of a Spatially Intermittent Welding Seam Based on Laser Vision Sensor

Hong

Gao

et al. 2020

Sensors

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To solve the problems of low teaching programming efficiency and poor flexibility in robot welding of complex box girder structures, a method of seam trajectory recognition based on laser scanning displacement sensing was proposed for automated guidance of a welding torch in the skip welding of a spatially intermittent welding seam. Firstly, a laser scanning displacement sensing system for measuring angles adaptively is developed to detect corner features of complex structures. Secondly, a weld trajectory recognition algorithm based on Euclidean distance discrimination is proposed. The algorithm extracts the shape features by constructing the characteristic triangle of the weld trajectory, and then processes the set of shape features by discrete Fourier analysis to solve the feature vector used to describe the shape. Finally, based on the Euclidean distance between the feature vector of the test sample and the class matching library, the class to which the sample belongs is identified to distinguish the weld trajectory. The experimental results show that the classification accuracy rate of four typical spatial discontinuous welds in complex box girder structure is 100%. The overall processing time for weld trajectory detection and classification does not exceed 65 ms. Based on this method, the field test was completed in the folding special container production line. The results show that the system proposed in this paper can accurately identify discontinuous welds during high-speed metal active gas arc welding (MAG) welding with a welding speed of 1.2 m/min, and guide the welding torch to automatically complete the skip welding, which greatly improves the welding manufacturing efficiency and quality stability in the processing of complex box girder components. This method does not require a time-consuming pre-welding teaching programming and visual inspection system calibration, and provides a new technical approach for highly efficient and flexible welding manufacturing of discontinuous welding seams of complex structures, which is expected to be applied to the welding manufacturing of core components in heavy and large industries such as port cranes, large logistics transportation equipment, and rail transit.

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“…Fang et al [15,16] used a vision sensor with structured laser light to detect the fillet seam and designed a self-tuning fuzzy logic seam tracking control system. Xu et al [17] proposed a seam tracking system based on vision sensor with structured laser light to improve the welding quality of robotic gas metal arc welding. In [18], a seam tracking system based on a vision sensor was designed for welding a thin plate, in which a decoupled visual measurement method was presented at horizontal and vertical directions.…”

Section: Introductionmentioning

confidence: 99%

A Vision-Based Robotic Laser Welding System for Insulated Mugs with Fuzzy Seam Tracking Control

Fang

Weng²,

Zhang

et al. 2019

Symmetry

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The symmetrical insulated mug is composed of two layers. The two ends of the two layers form the mouth and bottom seams of the insulated mug. The weld quality of the two seams is very important to keep the vacuum degree of the air between the two layers, which is vital for the heat-insulating property of the mug. Due to the narrow seam, laser welding is used. Since laser welding has high demand on the relative position of the seam and the laser torch, a vision-based seam tracking system is designed. Before welding is started, the vision sensor scans the seam and feature sample points are collected. A reconstruction algorithm is proposed to form the image containing the seam. Then, a least square fitting (LSF) method combined with random sample consensus (RANSAC) method is proposed to detect the smooth seam from the sample points. In the welding process, a seam tracking system with fuzzy logic control method is presented to keep the torch precisely on the seam. Finally, full experiments are conducted in the welding factory of the insulated mugs to verify the effectiveness of the proposed system and method.

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Welding seam tracking in robotic gas metal arc welding

Cited by 84 publications

References 12 publications

Weld Bead Width Measurement in a GMAW WAAM System by using Passive Vision

Weld Bead Width Measurement in a GMAW WAAM System by using Passive Vision

Welding Seam Trajectory Recognition for Automated Skip Welding Guidance of a Spatially Intermittent Welding Seam Based on Laser Vision Sensor

A Vision-Based Robotic Laser Welding System for Insulated Mugs with Fuzzy Seam Tracking Control

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