An adaptive orbital system based on laser vision sensor for pipeline GMAW welding

Silva, Régis Henrique Gonçalves e; Galeazzi, Daniel; Schwedersky, Mateus Barancelli; Mendonça, Felippe Kalil; Bonamigo, Alberto Viviani; Marques, Cleber

doi:10.1007/s40430-021-03081-7

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…Once the most appropriate arc welding with its corresponding parameters has been defined, we must bear in mind that during the welding process, unforeseen events may occur that may produce various defects in the weld. Before carrying out the welding operations, it is advisable to calibrate the robot itself and the system to be used [28] and to take into account the availability of a classification of the quality of the weld [29] as well as a classification of possible defects that may appear. These classifications, generally based on databases or neural networks, have a very high degree of accuracy in the detection of defects by means of machine learning algorithms or decision tree algorithms [30] to establish a correlation between current and voltage signatures [31] through radiographic image processing based on ANFIS adaptive networks [32].…”

Section: Inspection Of Welding Performancementioning

confidence: 99%

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Advances in Robotic Welding for Metallic Materials: Application of Inspection, Modeling, Monitoring and Automation Techniques

Curiel

Veiga

Suárez³

et al. 2023

Metals

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The robotic welding manufacturing of metal parts is a very important process, especially in heavy industries such as shipbuilding, oil and gas, automotive, and aerospace. There is a great variety of different techniques for manufacturing by robotic welding, and the welding operations are always in a constant process of evolution, as any advance can be significant to avoid defects during the welding process. Although a great deal of research work has been carried out in recent years, thanks to which results and reviews have been presented on this subject, the main aim of this publication is to define and review works that show the advances in the main inspection, modeling, monitoring, and automated operations during the welding process to avoid, or predictively identify, any possible defect in order to obtain an optimum degree of quality in the welding.

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Section: Inspection Of Welding Performancementioning

confidence: 99%

“…• Robot [28] and tool [87] trajectory control to avoid deviations before the start of welding operations and to avoid possible dimensional errors.…”

mentioning

confidence: 99%

Advances in Robotic Welding for Metallic Materials: Application of Inspection, Modeling, Monitoring and Automation Techniques

Curiel

Veiga

Suárez³

et al. 2023

Metals

View full text Add to dashboard Cite

show abstract

“…In this regard, Zhou et al [7] introduced a laser vision tracking system in automatic pipeline welding. Silva et al [23,24] employed laser sensing techniques to control the trajectory of the welding torch during the root layer weld in pipeline welding, and achieved accurate results compared with the conventional manually controlled system. Studies show that the weld tracking accuracy and real-time performance based on vision sensors are prone to interferences originating from equipment (e.g.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a two-stage algorithm for NG-GMAW seam tracking and oscillation width adaptation in pipeline welding

Liu,

Xue,

Zhou

et al. 2023

Science and Technology of Welding and Joining

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Seam tracking and oscillation width adaptation could effectively reduce the sidewall fusion defects in automatic narrow-gap gas metal arc welding (NG-GMAW). This study proposes a twostage algorithm to realise seam tracking and oscillation width adaptation in NG-GMAW pipeline welding. The first stage involves using the principal component analysis (PCA) eigenvectors to adjust the oscillating centre, enabling seam tracking and oscillation width adaptation for the hot and fill layer. In the second stage, dataset was prepared based on the tracking data from the first stage, which can guide the welding torch trajectory for the cap layer. Experimental results show that the proposed scheme achieves seam tracking and oscillation width adaptation with an accuracy of 0.1 mm.

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AI Enhanced Processing of Large Parts: Volume Monitoring and Control of Robotic Gluing Application

Prezas,

Arkouli,

Michalos

et al. 2024

Lecture Notes in Mechanical Engineering

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An adaptive orbital system based on laser vision sensor for pipeline GMAW welding

Cited by 7 publications

References 16 publications

Advances in Robotic Welding for Metallic Materials: Application of Inspection, Modeling, Monitoring and Automation Techniques

Advances in Robotic Welding for Metallic Materials: Application of Inspection, Modeling, Monitoring and Automation Techniques

Implementation of a two-stage algorithm for NG-GMAW seam tracking and oscillation width adaptation in pipeline welding

AI Enhanced Processing of Large Parts: Volume Monitoring and Control of Robotic Gluing Application

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