Through arc sensing for reciprocating wire feed gas metal arc welding

Kim, Cheol-Hee

doi:10.1177/0954405414537244

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…This classic approach utilizes control algorithms for seam tracking during welding, enhancing the adaptability of robotic systems to tolerate tolerances and variations in joint geometry. Kim [17] extended this concept by developing an arc sensor for reciprocating wire feed gas metal arc welding, leveraging voltage waveform characteristics to achieve torch height sensing and seam tracking.…”

Section: Introductionmentioning

confidence: 99%

Intelligent and Adaptive System for Welding Process Automation in T-Shaped Joints

Aldalur,

Suárez,

Curiel

et al. 2023

Metals

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The automation of welding processes requires the use of automated systems and equipment, in many cases industrial robotic systems, to carry out welding processes that previously required human intervention. Automation in the industry offers numerous advantages, such as increased efficiency and productivity, cost reduction, improved product quality, increased flexibility and safety, and greater adaptability of companies to market changes. The field of welding automation is currently undergoing a period of profound change due to a combination of technological, regulatory, and economic factors worldwide. Nowadays, the most relevant aspect of the welding industry is meeting customer requirements by satisfying their needs. To achieve this, the automation of the welding process through sensors and control algorithms ensures the quality of the parts and prevents errors, such as porosity, unfused areas, deformations, and excessive heat. This paper proposes an intelligent and adaptive system based on the measurement of welding joints using laser scanning and the subsequent analysis of the obtained point cloud to adapt welding trajectories. This study focuses on the optimization of T-joints under specific welding conditions and is intended as an initial implementation of the algorithm, thus establishing a basis to be worked on further for a broader welding application.

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Section: Introductionmentioning

confidence: 99%

Intelligent and Adaptive System for Welding Process Automation in T-Shaped Joints

Aldalur,

Suárez,

Curiel

et al. 2023

Metals

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“…However, the present study focuses on tandem-type welding, which uses two arc plasmas simultaneously; this method requires a different approach for arc sensing as arc interference occasionally occurs between the two wires 22 . The signal for seam tracking is affected by arc interference, making it difficult to track the welding line using the existing method.…”

mentioning

confidence: 99%

Development of seam tracking device in asynchronous tandem welding with arc sensing

Seo

Kim

et al. 2022

Sci Rep

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Tandem welding is extensively used for welding large structures, such as ships and plants, for increased welding speed and volume. Seam tracking is essential because of a large amount of thermal deformation. However, in tandem welding, arc interference causes current and voltage to vary non-uniformly, leading to difficulties in seam tracking. Therefore, in this study, an optimal signal was identified for seam tracking in tandem welding and evaluated. To select the seam-tracking signal, an algorithm was developed that separates the welding signal into peak, average, and base. Based on the collected data, regression and signal-to-noise ratio analyses were performed to identify a suitable seam-tracking signal. To trace the welding line based on the selected signal, the welding signal was checked by weaving on the V-groove specimen. As a result, the current area difference of the welding signal generated between the left and right parts of the center of the V-groove could be calculated. An algorithm and equipment for seam tracking were constructed using the area difference of the welding current. Finally, the seam tracking system was verified by conducting an actual test using the equipment to which the algorithm was applied.

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Weld deviation detection based on wide dynamic range vision sensor in MAG welding process

Guo

Shi

et al. 2016

Int J Adv Manuf Technol

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Through arc sensing for reciprocating wire feed gas metal arc welding

Cited by 8 publications

References 18 publications

Intelligent and Adaptive System for Welding Process Automation in T-Shaped Joints

Intelligent and Adaptive System for Welding Process Automation in T-Shaped Joints

Development of seam tracking device in asynchronous tandem welding with arc sensing

Weld deviation detection based on wide dynamic range vision sensor in MAG welding process

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