Vision-Based Seam Tracking in Robotic Welding: A Review of Recent Research

Wang, Ziheng; Xu, Yanling

doi:10.1007/978-981-13-8192-8_3

Cited by 11 publications

(2 citation statements)

References 51 publications

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“…The features of EAW machines, such as current, voltage, and frequency, have undergone significant evolution over time. While older machines typically operated with low voltage and high current, modern machines are now able to work with high voltage and low current, resulting in more precise and higher quality welds [6]. Moreover, the frequency response of EAW machines has witnessed a remarkable advancement, with the advent of machines designed to function at diverse frequency amplitudes catering to the distinct application requirements.…”

Section: Introductionmentioning

confidence: 99%

Estimating Average Power of Welding Process With Emitted Noises Based on Adaptive Neuro Fuzzy Inference System

et al. 2023

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In this study, the average power consumption of an electrode welding machine during the welding process was estimated using the features of the sound emitted during welding. First, the instantaneous values of electrode current and voltage and the sound emitted during the welding process were recorded simultaneously. The minimum, maximum, average, root mean square (RMS), and energy values of the sound data were found and feature extraction was performed, and the instantaneous power and average power values were calculated using the instantaneous current and voltage values. Three Adaptive Neuro-Fuzzy Inference Systems (ANFIS) using the sound features as inputs and average power values as outputs were created, and their results were compared. The average power values consumed during the welding process have been successfully estimated at a rate of 87-95%.

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Section: Introductionmentioning

confidence: 99%

Estimating Average Power of Welding Process With Emitted Noises Based on Adaptive Neuro Fuzzy Inference System

et al. 2023

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“…This can lead to greatly differing weld positions, and both the welding robot teachingplayback mode and offline programming will face very large work 2 . Weld tracking technology has emerged as a solution to the problems caused by nonstandard workpieces 3 . A laser vision sensor (LVS) is typically installed ahead of the welding gun along the movement direction, and the weld seam information is acquired in advance through a sensor.…”

Section: Introductionmentioning

confidence: 99%

A Guidance System for Robotic Welding Based on an Improved YOLOv5 Algorithm with a RealSense Depth Camera

Li,

Huang,

Xue

et al. 2023

Preprint

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Vision-based automatic welding guidance technology plays an essential role in robotic welding. A laser vision sensor (LVS) relies on manual intervention to guide the robot when near the workpiece, which reducesthe autonomy of the welding robot and productivity. To solve this problem, a robot welding guidance system based on an improved YOLOv5 algorithm with a RealSense Depth Camera was proposed. A coordinate attention (CA) module was embedded in the original YOLOv5 algorithm to improve the accuracy of weld groove detection. The center of the predicted frame of the weld groove in the pixel plane was combined with the depth information acquired by a RealSense depth camera to calculate the actual position of the weld groove. Subsequently, the robot was guided to approach and move over the workpiece. Then, the LVS was used to guide the welding torch installed at the end of the robot to move along the centerline of the weld groove and complete welding tasks. The feasibility of the proposed method was verified by experiments. The maximum error was 2.9 mm in guiding experiments conducted with a distance of 300 mm between the depth camera and the workpiece. The percentage error was within 2% in guidance experiments conducted with distances from 0.3 m to 2 m. The system combines the advantages of the depth camera for accurate positioning within a large field and the LVS for high accuracy. Once the position of the weld groove of the workpiece to be welded has been determined, the LVS combined with the robot can easily track the weld groove and realize the welding operation without manual intervention.

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Recent developments in computer vision and artificial intelligence aided intelligent robotic welding applications

Eren

Demir

Mıstıkoğlu

2023

Int J Adv Manuf Technol

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Vision-Based Seam Tracking in Robotic Welding: A Review of Recent Research

Cited by 11 publications

References 51 publications

Estimating Average Power of Welding Process With Emitted Noises Based on Adaptive Neuro Fuzzy Inference System

Estimating Average Power of Welding Process With Emitted Noises Based on Adaptive Neuro Fuzzy Inference System

A Guidance System for Robotic Welding Based on an Improved YOLOv5 Algorithm with a RealSense Depth Camera

Recent developments in computer vision and artificial intelligence aided intelligent robotic welding applications

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