scholarly journals Research on Welding Seam Tracking System of Car Body Welding Robot for Aluminum Alloy EMU

2018 ◽  
Vol 08 (03) ◽  
pp. 410-419
Author(s):  
宇 付
Keyword(s):  
Aluminum Alloy ◽  
Tracking System ◽  
Seam Tracking ◽  
Welding Robot ◽  
Welding Seam ◽  
Car Body

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

Design of DSP-Based Automatic Seam Tracking Underwater System

2014 ◽  
Vol 644-650 ◽  
pp. 845-848
Author(s):  
Fu Yang ◽  
Wen Ming Zhang ◽  
Wan Cai Jiao
Keyword(s):  
Tracking System ◽  
Low Cost ◽  
Human Machine Interface ◽  
Seam Tracking ◽  
Welding Seam ◽  
Automatic Tracking ◽  
Underwater Welding ◽  
Effect Of Water ◽  
Machine Interface ◽  
Hardware Structure

It is high difficult to control the underwater welding because of the effect of water and the leak proofness of the weld devices which is a troubling problem. In this paper, a DSP-based automatic seam tracking system for underwater welding is designed. This system has the advantages of simple hardware structure, low-cost, rich function software, friendly human-machine interface, and easily realizing. And the work of this paper can be used for further research in underwater welding seam automatic tracking.

A Robust Detector for Automated Welding Seam Tracking System

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Yanbiao Zou ◽  
Mingquan Zhu ◽  
Xiangzhi Chen
Keyword(s):  
Object Detection ◽  
Weld Seam ◽  
Tracking System ◽  
Welding Current ◽  
Detection Algorithm ◽  
Seam Tracking ◽  
Detection Accuracy ◽  
Single Shot ◽  
Welding Seam ◽  
Laser Vision

Abstract Accurate locating of the weld seam under strong noise is the biggest challenge for automated welding. In this paper, we construct a robust seam detector on the framework of deep learning object detection algorithm. The representative object algorithm, a single shot multibox detector (SSD), is studied to establish the seam detector framework. The improved SSD is applied to seam detection. Under the SSD object detection framework, combined with the characteristics of the seam detection task, the multifeature combination network (MFCN) is proposed. The network comprehensively utilizes the local information and global information carried by the multilayer features to detect a weld seam and realizes the rapid and accurate detection of the weld seam. To solve the problem of single-frame seam image detection algorithm failure under continuous super-strong noise, the sequence image multifeature combination network (SMFCN) is proposed based on the MFCN detector. The recurrent neural network (RNN) is used to learn the temporal context information of convolutional features to accurately detect the seam under continuous super-noise. Experimental results show that the proposed seam detectors are extremely robust. The SMFCN can maintain extremely high detection accuracy under continuous super-strong noise. The welding results show that the laser vision seam tracking system using the SMFCN can ensure that the welding precision meets industrial requirements under a welding current of 150 A.

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.

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