scholarly journals Advanced Automatic Welding System for Offshore Pipeline System with Seam Tracking Function

2020 ◽  
Vol 10 (1) ◽  
pp. 324 ◽  
Author(s):  
Jin-Hyeong Park ◽  
Hyeong-Soon Moon
Keyword(s):  
Tracking System ◽  
Seam Tracking ◽  
Step Motor ◽  
Pipeline System ◽  
Automatic Welding ◽  
Welding Seam ◽  
Control Module ◽  
Motor Module ◽  
Welding Productivity ◽  
Welding System

Automatic welding technology is a solution to increase welding productivity and improve welding quality in offshore pipe welding. To increase welding productivity, it is necessary to save time during the assembly/disassembly of the guide track from the welding carriage and pipe to move the next station. The guide track consists of a pneumatic system that does not separate the welding carriage, and two welding carriages operate on a half-pipe joint to increase productivity. These welding carriages automatically operate under the controller command. An automatic welding system consists of a DC motor module, a step motor module, a welding control module, a welding monitoring module, and a central control module. The control systems incorporate control modules and transmit commands to each module for an automatic welding system. In order to minimize the inevitable misalignment between the centerline of the welding seam and the welding torch for each welding pass, a moving average algorithm for seam tracking is proposed, which was proven to be suitable for the root pass, filling pass, and cap pass. Welding experiments were also carried out to verify the validity of the weld seam tracking system.

Kalman Filtering for Seam Tracking in Master-Slave Robot Remote Welding System

2011 ◽  
Vol 314-316 ◽  
pp. 1005-1008
Author(s):  
Hong Tang Chen ◽  
Hai Chao Li ◽  
Hong Ming Gao ◽  
Lin Wu
Keyword(s):  
Kalman Filtering ◽  
Seam Tracking ◽  
Experimental Results ◽  
Welding Quality ◽  
Welding Seam ◽  
Tracking Process ◽  
Key Factor ◽  
Remote Welding ◽  
Welding System

Welding seam tracking precision is a key factor influencing welding quality for master-slave robot remote welding system. However, it does not satisfy the welding requirement due to significant noises. To eliminate the influence of noises upon the seam tracking precision and improve the seam tracking precision, a master-slave robot remote welding system was built and Kalman filtering (KF) was applied to the seam tracking process. The experimental results show that the KF eliminated the influence of noises upon the seam tracking precision and improved the seam tracking precision.

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

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.

Development on Tandem GMA Welding System using Seam Tracking System in Pipe Line

2014 ◽  
Vol 31 (11) ◽  
pp. 1007-1013
Author(s):  
JongPyo Lee ◽  
JiHye Lee ◽  
MinHo Park ◽  
CheolKyun Park ◽  
IllSoo Kim
Keyword(s):  
Gma Welding ◽  
Tracking System ◽  
Seam Tracking ◽  
Pipe Line ◽  
Welding System

Automatic Welding Seam Tracking and Identification

2017 ◽  
Vol 64 (9) ◽  
pp. 7261-7271 ◽  
Author(s):  
Xinde Li ◽  
Xianghui Li ◽  
Shuzhi Sam Ge ◽  
Mohammad Omar Khyam ◽  
Chaomin Luo
Keyword(s):  
Seam Tracking ◽  
Automatic Welding ◽  
Welding Seam

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.

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