Research evolution on intelligentized technologies for arc welding process

Chen, S.B.; Lv, Na

doi:10.1016/j.jmapro.2013.07.002

Cited by 112 publications

(30 citation statements)

References 30 publications

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“…In Ref. [44,45], arc sensing and vision sensing were combined to realize seam tracking. & Augment reality (AR) is an important application field of vision technology.…”

Section: Discussionmentioning

confidence: 99%

Three-dimensional vision applications in GTAW process modeling and control

Wang

2015

Int J Adv Manuf Technol

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In gas tungsten arc welding (GTAW), the weld pool is the major source of information that can be used to assure the production of the desired weld penetration-the most critical factor determining the weld integrity. To meet this challenge, various sensing technologies, modeling methods, and control strategies have been studied, and artificial intelligence technologies were applied to improve system intelligence. The GTAW process analysis is given first. Then, a short introduction on weld pool sensing technologies is presented, where three-dimensional (3D) vision sensing is a very active orientation. Furthermore, weld pool description model and characteristic parameter model are also discussed, where intelligent algorithms were used generally. Besides, dynamic modeling and penetration control strategies, especially intelligent control strategies are presented. At last, the discussion about development of the GTAW penetration control product is analyzed briefly.

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“…In Ref. [44,45], arc sensing and vision sensing were combined to realize seam tracking. & Augment reality (AR) is an important application field of vision technology.…”

Section: Discussionmentioning

confidence: 99%

Three-dimensional vision applications in GTAW process modeling and control

Wang

2015

Int J Adv Manuf Technol

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“…Currently, the two most common operating modes for welding robots, namely, the teaching mode and the off-line programming mode, do not depend on sensor measurements during welding; the welding trajectories are set in advance by workers, and the robot moves in accordance with the desired trajectory. These two modes are suitable for use in a standardized, modular, strictly coordinated welding system [1][2][3][4]. However, in actual welding operations, the welding environment might not be static.…”

Section: Introductionmentioning

confidence: 99%

A Weld Joint Type Identification Method for Visual Sensor Based on Image Features and SVM

Zeng

Cao

Peng

et al. 2020

Sensors

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In the field of welding robotics, visual sensors, which are mainly composed of a camera and a laser, have proven to be promising devices because of their high precision, good stability, and high safety factor. In real welding environments, there are various kinds of weld joints due to the diversity of the workpieces. The location algorithms for different weld joint types are different, and the welding parameters applied in welding are also different. It is very inefficient to manually change the image processing algorithm and welding parameters according to the weld joint type before each welding task. Therefore, it will greatly improve the efficiency and automation of the welding system if a visual sensor can automatically identify the weld joint before welding. However, there are few studies regarding these problems and the accuracy and applicability of existing methods are not strong. Therefore, a weld joint identification method for visual sensor based on image features and support vector machine (SVM) is proposed in this paper. The deformation of laser around a weld joint is taken as recognition information. Two kinds of features are extracted as feature vectors to enrich the identification information. Subsequently, based on the extracted feature vectors, the optimal SVM model for weld joint type identification is established. A comparative study of proposed and conventional strategies for weld joint identification is carried out via a contrast experiment and a robustness testing experiment. The experimental results show that the identification accuracy rate achieves 98.4%. The validity and robustness of the proposed method are verified.

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“…As a result, collecting the most accurate and comprehensive fusion features from dynamic welding process becomes the primary and necessary step for real-time monitoring of the welding quality [1]. Therefore, various signal processing methods have been used for information of arc welding process, such as processing algorithms for arc voltage, current, pool, acoustic, visual, spectrum, thermic, optical, mechanical information in order to obtain the effectual characteristics of arc welding process for real-time control of weld quality [2].…”

Section: Introductionmentioning

confidence: 99%

Parametric analysis of the stringer bead reinforcement area and prediction of the number of beads in a single-V butt joint, using the robotic Flux-cored arc welding technique

Kazasidis

Pantelis

2016

Int J Adv Manuf Technol

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In the present study, a new-developed method which leads to the determination of the number of beads in the single-V butt weld joint, is introduced and applied to robotic Flux-cored arc welding technique. The correlation between the wire feed rate, the wire variables, the torch welding speed, and the bead reinforcement area is investigated and determined via a mathematical formula. The investigation includes a theoretical section where the method is analyzed and several confirmation experiments where the theoretical function of the bead reinforcement area is verified. Moreover, the method is applied to a multipass weld of steel plates with preselected welding parameters, through the determination of a proposed weld profile based on the fundamental principles, standards, and rules of the welding technology. The methodology can be also expanded in the case of multipass welding joints with a different edge preparation such as J, U, or double-V in butt and tee weld joints.

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Research evolution on intelligentized technologies for arc welding process

Cited by 112 publications

References 30 publications

Three-dimensional vision applications in GTAW process modeling and control

Three-dimensional vision applications in GTAW process modeling and control

A Weld Joint Type Identification Method for Visual Sensor Based on Image Features and SVM

Parametric analysis of the stringer bead reinforcement area and prediction of the number of beads in a single-V butt joint, using the robotic Flux-cored arc welding technique

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