A Novel 3D Seam Extraction Method Based on Multi-Functional Sensor for V-Type Weld Seam

Yang, Lei; Liu, Yanhong

doi:10.1109/access.2019.2944884

Cited by 12 publications

(5 citation statements)

References 30 publications

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“…As robot technology and vision technology continue to advance, vision-guided robotic automatic welding has gradually been integrated into factory production [1]. In this context, the weld seam extraction algorithm plays a crucial role in achieving automated welding [2] [3].…”

Section: Introductionmentioning

confidence: 99%

A novel seam extraction method based on nearest point for non-groove butt weld seam

Ma,

Wang,

Zhang

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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Section: Introductionmentioning

confidence: 99%

A novel seam extraction method based on nearest point for non-groove butt weld seam

Ma,

Wang,

Zhang

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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“…In the welding process, thermal deformation of the welded parts can easily lead to the failure of trajectory planning using linear or arc interpolation, resulting in deviation of the welding trajectory. Therefore, many scholars have applied advanced sensor technology to welding robots to achieve real-time correction of welding trajectories [ 4 , 5 , 6 ]. Among various sensors, active optical-vision sensors are more commonly used in welding tracking due to their ability to overcome complex environmental interference using light sources compared to passive vision sensors that use natural light or arc light generated during welding.…”

Section: Introductionmentioning

confidence: 99%

YOLO-Weld: A Modified YOLOv5-Based Weld Feature Detection Network for Extreme Weld Noise

Gao

Fan

et al. 2023

Sensors

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Weld feature point detection is a key technology for welding trajectory planning and tracking. Existing two-stage detection methods and conventional convolutional neural network (CNN)-based approaches encounter performance bottlenecks under extreme welding noise conditions. To better obtain accurate weld feature point locations in high-noise environments, we propose a feature point detection network, YOLO-Weld, based on an improved You Only Look Once version 5 (YOLOv5). By introducing the reparameterized convolutional neural network (RepVGG) module, the network structure is optimized, enhancing detection speed. The utilization of a normalization-based attention module (NAM) in the network enhances the network’s perception of feature points. A lightweight decoupled head, RD-Head, is designed to improve classification and regression accuracy. Furthermore, a welding noise generation method is proposed, increasing the model’s robustness in extreme noise environments. Finally, the model is tested on a custom dataset of five weld types, demonstrating better performance than two-stage detection methods and conventional CNN approaches. The proposed model can accurately detect feature points in high-noise environments while meeting real-time welding requirements. In terms of the model’s performance, the average error of detecting feature points in images is 2.100 pixels, while the average error in the world coordinate system is 0.114 mm, sufficiently meeting the accuracy needs of various practical welding tasks.

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“…Tan et al [8] proposed a single-frame large-field welding seam position method combining polarization three-dimensional (3D) reconstruction and linear structured light imaging. Yang et al [9,10] developed an algorithm to predict the weald seam future point using digital light processing techniques to acquire the point cloud of planar welding workpieces.…”

Section: Introductionmentioning

confidence: 99%

Laser Profilometry Application in Welding Geometrical Characterization

Martín-Béjar,

Cañero-Nieto,

Solano Martos

et al. 2023

KEM

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Welding processes are commonly used in the industry for the manufacture of large parts or due to their complex geometry that does not allow the part to be manufactured as a whole. Nevertheless, the union can show worst mechanical properties than the rest of the piece, affecting negatively its service behavior, so it is necessary evaluate weald seam to ensure the correct process application. Electrical welding operations are commonly used due to the reduced equipment size or their possibilities application in numerous metallic materials. Notwithstanding, different variables have to be taken into account during the metal deposition, as intensity or speed deposition, among others. Weald seam geometrical evaluation is usually utilized to validate the union surface conditions. Despite this, surface irregularities caused during the process make its difficult to measure correctly with conventional equipment. However, laser profilometry is a non-contact technique that can be used to generate 3D profile of weald seam, facilitating its measurement with high accuracy. Therefore, in this work an initial analysis of the influence of material deposition speed and arc welding intensity on the weald seam geometry will be carried out using a laser profilometry equipment. In addition, to ensure a correct information acquisition, the laser profilometer requires a constant speed movement in relation with the weald seam analyzed, so new equipment has been manufactured, using additive manufacturing techniques, to support the profilometer throughout the information acquisition process.

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A Novel 3D Seam Extraction Method Based on Multi-Functional Sensor for V-Type Weld Seam

Cited by 12 publications

References 30 publications

A novel seam extraction method based on nearest point for non-groove butt weld seam

A novel seam extraction method based on nearest point for non-groove butt weld seam

YOLO-Weld: A Modified YOLOv5-Based Weld Feature Detection Network for Extreme Weld Noise

Laser Profilometry Application in Welding Geometrical Characterization

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