scholarly journals A Novel Seam Tracking Technique with a Four-Step Method and Experimental Investigation of Robotic Welding Oriented to Complex Welding Seam

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3067
Author(s):  
Gong Zhang ◽  
Yuhang Zhang ◽  
Shuaihua Tuo ◽  
Zhicheng Hou ◽  
Wenlin Yang ◽  
...  
Keyword(s):  
Seam Tracking ◽  
Laser Sensor ◽  
Feature Points ◽  
Welding Robot ◽  
Robotic Welding ◽  
Step Method ◽  
Welding Seam ◽  
Precise Positioning ◽  
Tracking Technique ◽  
Joint Width

The seam tracking operation is essential for extracting welding seam characteristics which can instruct the motion of a welding robot along the welding seam path. The chief tasks for seam tracking would be divided into three partitions. First, starting and ending points detection, then, weld edge detection, followed by joint width measurement, and, lastly, welding path position determination with respect to welding robot co-ordinate frame. A novel seam tracking technique with a four-step method is introduced. A laser sensor is used to scan grooves to obtain profile data, and the data are processed by a filtering algorithm to smooth the noise. The second derivative algorithm is proposed to initially position the feature points, and then linear fitting is performed to achieve precise positioning. The groove data are transformed into the robot’s welding path through sensor pose calibration, which could realize real-time seam tracking. Experimental demonstration was carried out to verify the tracking effect of both straight and curved welding seams. Results show that the average deviations in the X direction are about 0.628 mm and 0.736 mm during the initial positioning of feature points. After precise positioning, the average deviations are reduced to 0.387 mm and 0.429 mm. These promising results show that the tracking errors are decreased by up to 38.38% and 41.71%, respectively. Moreover, the average deviations in both X and Z direction of both straight and curved welding seams are no more than 0.5 mm, after precise positioning. Therefore, the proposed seam tracking method with four steps is feasible and effective, and provides a reference for future seam tracking research.

Welding seam recognition and tracking for a novel mobile welding robot based on multi-layer sensing strategy

2021 ◽  
Author(s):  
Chao Liu ◽  
Hui Wang ◽  
Yu Huang ◽  
Youmin Rong ◽  
Jie Meng ◽  
...  
Keyword(s):  
Seam Tracking ◽  
Sensor System ◽  
Vision Sensor ◽  
Welding Robot ◽  
Welding Seam ◽  
Localization And Mapping ◽  
Laser Vision Sensor ◽  
Tracking Ability ◽  
Mobile Welding Robot ◽  
Laser Vision

Abstract Mobile welding robot with adaptive seam tracking ability can greatly improve the welding efficiency and quality, which has been extensively studied. To further improve the automation in multiple station welding, a novel intelligent mobile welding robot consists of a four-wheeled mobile platform and a collaborative manipulator is developed. Under the support of simultaneous localization and mapping (SLAM) technology, the robot is capable of automatically navigating to different stations to perform welding operation. To automatically detect the welding seam, a composite sensor system including an RGB-D camera and a laser vision sensor is creatively applied. Based on the sensor system, the multi-layer sensing strategy is performed to ensure the welding seam can be detected and tracked with high precision. By applying hybrid filter to the RGB-D camera measurement, the initial welding seam could be effectively extracted. Then a novel welding start point detection method is proposed. Meanwhile, to guarantee the tracking quality, a robust welding seam tracking algorithm based on laser vision sensor is presented to eliminate the tracking discrepancy caused by the platform parking error, through which the tracking trajectory can be corrected in real-time. The experimental results show that the robot can autonomously detect and track the welding seam effectively in different station. Also, the multiple station welding efficiency can be improved and quality can also be guaranteed.

Study on Seam Tracking System Based on Stripe Type Laser Sensor and Welding Robot

2015 ◽  
Vol 42 (5) ◽  
pp. 0502005
Author(s):  
李琳 Li Lin ◽  
林炳强 Lin Bingqiang ◽  
邹焱飚 Zou Yanbiao
Keyword(s):  
Tracking System ◽  
Seam Tracking ◽  
Laser Sensor ◽  
Welding Robot

Modern tracking methods of the weld seams for robotic welding based on laser vision

2021 ◽  
Author(s):  
Keyword(s):  
Control System ◽  
Seam Tracking ◽  
Automatic Welding ◽  
Robotic Welding ◽  
Welding Seam ◽  
Advantages And Disadvantages ◽  
Laser Sensors ◽  
Vision Sensors ◽  
Laser Vision ◽  
Weld Seams

Laser sensors with various technologies used to track weld seams during welding operations are discussed in detail Laser vision sensors provide full automation of welding robotic systems and real-time process monitoring. Reasonable selection of the control system for a robotic welding system with laser vision is represented. Based on the analysis of the advantages and disadvantages, the practical application of laser vision sensors in the process of automatic welding is predicted. Keywords weld seam tracking; laser vision sensor; robotic welding; seam recognition; pre-processing of images; structure of the control system

Seam tracking investigation via striped line laser sensor

2017 ◽  
Vol 44 (5) ◽  
pp. 609-617 ◽  
Author(s):  
Yanbiao Zou ◽  
Jinchao Li ◽  
Xiangzhi Chen
Keyword(s):  
Machine Vision ◽  
Object Tracking ◽  
Seam Tracking ◽  
Cosine Similarity ◽  
Feature Points ◽  
Robot Arm ◽  
Coordinate Systems ◽  
Welding Seam ◽  
Content Type ◽  
Experiment Platform

Purpose This paper aims to propose a set of six-axis robot arm welding seam tracking experiment platform based on Halcon machine vision library to resolve the curve seam tracking issue. Design/methodology/approach Robot-based and image coordinate systems are converted based on the mathematical model of the three-dimensional measurement of structured light vision and conversion relations between robot-based and camera coordinate systems. An object tracking algorithm via weighted local cosine similarity is adopted to detect the seam feature points to prevent effectively the interference from arc and spatter. This algorithm models the target state variable and corresponding observation vector within the Bayes framework and finds the optimal region with highest similarity to the image-selected modules using cosine similarity. Findings The paper tests the approach and the experimental results show that using metal inert-gas (MIG) welding with maximum welding current of 200A can achieve real-time accurate curve seam tracking under strong arc light and splash. Minimal distance between laser stripe and welding molten pool can reach 15 mm, and sensor sampling frequency can reach 50 Hz. Originality/value Designing a set of six-axis robot arm welding seam tracking experiment platform with a system of structured light sensor based on Halcon machine vision library; and adding an object tracking algorithm to seam tracking system to detect image feature points. By this technology, this system can track the curve seam while welding.

A Fuzzy Controller Design of Seam Tracking for Welding Robot

2012 ◽  
Vol 442 ◽  
pp. 370-374 ◽  
Author(s):  
Yong Qiang Wu ◽  
Zhong Hu Yuan ◽  
Jia Han Wang
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Tracking Control ◽  
Weld Seam ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Seam Tracking ◽  
Welding Robot ◽  
Welding Seam ◽  
Welding Torch

Kinematics model of welding robot is built in the paper. An improved fuzzy controller (Fuzzy-P) for welding robot mobile platform is designed based on analyzing seam tracking control system. The domain of fuzzy control should not be set too big in order to make system smooth, but the system must respond rapidly. P control can respond rapidly. When weld seam deviation is big,it adopts P control while seam deviation is small, it adopts Fuzzy-P control. The simulation result shows that the improved controller is effective for 45°broken line; the welding torch is able to track the welding seam well.

scholarly journals Semi-Supervised Training for Positioning of Welding Seams

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7309
Author(s):  
Wenbin Zhang ◽  
Jochen Lang
Keyword(s):  
Supervised Learning ◽  
Deep Neural Networks ◽  
Image Resolution ◽  
Learning Approaches ◽  
Welding Robot ◽  
Robotic Welding ◽  
Welding Seam ◽  
Classification Tasks ◽  
Point Detection ◽  
Imaging Conditions

Robotic welding often uses vision-based measurement to find the correct placement of the welding seam. Traditional machine vision methods work well in many cases but lack robustness when faced with variations in the manufacturing process or in the imaging conditions. While supervised deep neural networks have been successful in increasing accuracy and robustness in many real-world measurement applications, their success relies on labeled data. In this paper, we employ semi-supervised learning to simultaneously increase accuracy and robustness while avoiding expensive and time-consuming labeling efforts by a domain expert. While semi-supervised learning approaches for various image classification tasks exist, we purpose a novel algorithm for semi-supervised key-point detection for seam placement by a welding robot. We demonstrate that our approach can work robustly with as few as fifteen labeled images. In addition, our method utilizes full image resolution to enhance the accuracy of the key-point detection in seam placement.

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