Seam tracking and welding bead geometry analysis for autonomous welding robot

Soares, Luciane B.; Weis, Átila A.; Rodrigues, Ricardo; Drews, Paul; Guterres, Bruna; Botelho, Silvia Silva da Costa; Filho, Nelson Duarte

doi:10.1109/sbr-lars-r.2017.8215324

Cited by 19 publications

(2 citation statements)

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“…A higher-quality weld will emerge from the robot's ability to detect and rectify flaws in real-time thanks to AI and ML. The utilization of collaborative robots is a further development in the creation of welding robots (de Faria et al, 2018;Qiao et al, 2019;Soares et al, 2017;Majeed et al, 2018). These robots collaborate with humans to complete jobs, making them especially helpful in small-and medium-sized businesses (SMEs), where cost and space are important considerations (Liu et al, 2022;Sharma et al, 2020;Xu et al, 2022;Haldankar et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Health assessment of welding by-products in a linear welding automation: Temperature and smoke concentration measurements

Gupta,

Chouksey,

Pandey

2024

JART

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Due to its potential to improve production and weld quality, automated welding systems are growing in popularity in industrial applications. However, these technologies generate potentially dangerous by-products like welding gases and fumes. By evaluating the temperature range and smoke concentration near the welding area, this study intends to assess the health parameters of welding by-products in linear welding automation. A four-wheeled robot system equipped with joint detection and referencing technologies will be the research subject, which will also improve welding conditions to match industrial standards. Also, the study will look into how welding factors affect smoke concentration and temperature range. The findings of this study will shed light on the health risks posed by automated welding systems and guide the creation of safety regulations that will shield workers from hazardous consequences.

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Section: Introductionmentioning

confidence: 99%

Health assessment of welding by-products in a linear welding automation: Temperature and smoke concentration measurements

Gupta,

Chouksey,

Pandey

2024

JART

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“…To overcome the difficulties encountered by welding robots in teaching and offline programming mode operations, many studies have been performed to explore methods that can make robotic welding simpler and more effective [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Among these methods, welding seam trajectory recognition is used for welding path planning and welding seam tracing is a significant method for improving the efficiency and flexibility of robotic welding.…”

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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