Robot welding seam tracking system research basing on image identify

2019 ◽  
Author(s):  
Hongwei Sun ◽  
Tao Ma ◽  
zheng zhang ◽  
Maochuan Wu
Keyword(s):  
Tracking System ◽  
Seam Tracking ◽  
System Research ◽  
Welding Seam ◽  
Robot Welding

Robot welding seam tracking method based on passive vision for thin plate closed-gap butt welding

2009 ◽  
Vol 48 (9-12) ◽  
pp. 945-953 ◽  
Author(s):  
Hongbo Ma ◽  
Shanchun Wei ◽  
Zhongxi Sheng ◽  
Tao Lin ◽  
Shanben Chen
Keyword(s):  
Thin Plate ◽  
Seam Tracking ◽  
Welding Seam ◽  
Butt Welding ◽  
Tracking Method ◽  
Robot Welding

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.

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.

Signal Analysis of Magnetic Control Seam Tracking Based on the Hilbert-Huang Transform

2014 ◽  
Vol 529 ◽  
pp. 559-563 ◽  
Author(s):  
Bo Hong ◽  
Ai Jun Xu ◽  
Jian Liu ◽  
Xiang Wen Li
Keyword(s):  
Signal Analysis ◽  
Tracking System ◽  
Seam Tracking ◽  
Good Effect ◽  
Time Signal ◽  
Magnetic Control ◽  
Intrinsic Mode Functions ◽  
Welding Seam ◽  
Hilbert Huang Transform ◽  
Time Frequency

According to the fact that the nonlinear magnetic control welding signal is not smooth, this paper proposes a signal extraction and an analytical method of the system based on Hilbert-Huang transform magnetic control arc seam tracking sensor. First, the magnetic control to track the signal motivated by cycle is decomposed into several intrinsic mode functions from high frequency to low frequency component by using the empirical mode decomposition. On the basis of the Hilbert marginal spectrum of each component, distribution of time-frequency transform to each component can effectively restrain cross terms and extract the real-time signal dynamic law reflecting magnetic control seam tracking. This method used in a certain experimental platform for magnetic control arc welding seam tracking sensor platform signal analysis, has produced a good effect and extracted the seam tracking signal, which can offer more valuable information and help to further reveal the frequency and spectrum characteristics of various interference sources in the weld automatic tracking system. Furthermore, It also provides a theoretical basis for establishing the welding signals with excitation source as well as a new nonlinear model.

Sign in / Sign up

Export Citation Format

Share Document