Use of sensors in robotic welding for controlling the weld quality leads to replacement of manual welding operation in dangerous work environment in presence of high temperature and fumes even in small or medium scale enterprises. The seam tracking operation is very essential for extracting weld seam position which can be fed to robot controller for instructing robot along the weld seam path. The seam tracking operation can be executed by different types of sensors having their own merits and demerits. In this paper, different sensors and techniques used for seam tracking task in robotic welding have been discussed in detail. Each sensor has different method or technology of weld seam feature extraction which have been described by different authors in different ways. The chief tasks for seam tracking have been found to be weld starting and end point detection, weld edge detection, joint width measurement and weld path position determination with respect to robot co-ordinate frame. Thus sensors have a very important role in robotic welding for fully automating the system with in process real time monitoring of weld process parameters with the sensor feedback. In further discussion the practical use of different sensors in industries with a comparison of their advantages and disadvantages have been discussed. This Paper presents the role of sensors in robotic welding and a detail study of methodologies of weld seam position and geometry feature extraction by different sensors typically used for weld seam tracking.

Advances in weld seam tracking techniques for robotic welding: A review

Real-time seam tracking control technology during welding robot GTAW process based on passive vision sensor

Due to ever increasing demand in precision in robotic welding automation and its inherent technical difficulties, seam tracking has become the research hotspot. This paper introduces the research in application of computer vision technology for real-time seam tracking in robotic gas tungsten arc welding (GTAW) and gas metal arc welding (GMAW). The key aspect in using vision techniques to track welding seams is to acquire clear real-time weld images and to process them accurately. This is directly related to the precision of seam tracking. In order to further improve the accuracy of seam tracking, in this paper, a set of special vision system has been designed firstly, which can acquire clear and steady real-time weld images. By analyzing the features of weld images, a new and improved edge detection algorithm was proposed to detect the edges in weld images, and more accurately extract the seam and pool characteristic parameters. The image processing precision was verified through the experiments. Results showed that the precision of this vision based tracking technology can be controlled to be within ?0.17mm and ?0.3mm in robotic GTAW and GMAW, respectively. Using computer vision technology, we have solved some problems of seam tracking in robotic welding automation.We have designed a set of special vision sensor system for seam tracking and it has worked very well.A new improved Canny edge detection algorithm was proposed to detect the edges of weld image.We have further improved the precision of image processing in seam tracking.This paper systematically studied the application of computer vision technology in robotic GTAW and GMAW.

Computer vision technology for seam tracking in robotic GTAW and GMAW

https://works.bepress.com/jing-wu/29/download/

Research evolution on intelligentized technologies for arc welding process

Control of 3D weld pool surface

A passive vision sensor is added to a teaching and playback welding robot for pulsed gas tungsten arc welding, which is normally used in the welding of thin plate closed-gap butts. This paper presents a seam tracking method based on this sensor through period visual measurement of the offset between the torch and the seam center. A robust image processing algorithm is developed to extract the seam center. A kind of ARX(auto-regressive with exogenous input) model is studied to describe the relationship between the rectifying voltage and the offset. The fuzzy PID(proportional-integral-derivative) seam tracking controller is analyzed and designed in consideration of various offsets based on the ARX model. The experimental results on straight line weld display a good seam tracking capability of the proposed method.

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

Purpose – The control of weld penetration in gas tungsten arc welding (GTAW) is required for a “teach and playback” robot to overcome the gap variation in the welding process. This paper aims to investigate this subject.Design/methodology/approach – This paper presents a robotic system based on the real‐time vision measurement. The primary objective has been to demonstrate the feasibility of using vision‐based image processing to measure the seam gap in real‐time and adjust welding current and wire‐feed rate to realize the penetration control during the robot‐welding process.Findings – The paper finds that vision‐based measurement of the seam gap can be used in the welding robot, in real‐time, to control weld penetration. It helps the “teach and playback” robot to adjust the welding procedures according to the gap variation.Research limitations/implications – The system requires that the seam edges can be accurately identified using a correlation method.Practical implications – The system is applicable to...

Research on weld pool control of welding robot with computer vision

Purpose – The purpose of this paper is to develop a kind of low cost measuring system based on binocular vision sensor to detect both the weld pool geometry and root gap simultaneously for robot welding process.Design/methodology/approach – Two normal charge coupled device cameras are used for capturing clear images from two directions; one of them is used to measure the root gap and another one is used to measure the geometric parameters of the weld pool. Efforts are made from both hardware and software aspects to decrease the strong interferences in pulsed gas tungsten arc welding process, so that clear and steady images can be obtained. The grey level distribution characteristics of root gap edge and weld pool edge in images are analyzed and utilized for developing the image processing algorithms.Findings – A solid foundation for seam tracking and penetration control of robot welding process can be established based on the binocular vision sensor.Practical implications – The results show that the algor...

Binocular vision system for both weld pool and root gap in robot welding process

Purpose – Recognition and guidance of initial welding position (IWP) is one of the most important steps of automatic welding process, also a key technology of autonomous welding process. The purpose of this paper is to advance an improved Harris Algorithm and grey scale scanning method (GSCM) to raise the precision of image processing.Design/methodology/approach – Through the configuration of “single camera and double positions,” a new set of image processing algorithms is adopted to extract feature points by using the pattern of rough location and subtle extraction, so as to restructure three‐dimensional information to guide robot move to IWP in the practical welding environment.Findings – Experiments showed that mean square errors (MSEs) in X, Y, Z‐directions for both flat butt joint and flat flange are 0.4491, 0.8178, 1.4797, and 0.5398, 0.4861, 1.1071 mm, respectively.Research limitations/implications – It has a limitation in providing guidance for only one step, and would be more accurate if fraction...

Autonomous guidance of initial welding position with “single camera and double positions” method

The invention provides an arc welding robot welding monitoring system which is based on visual sensing in the arc welding robot technical field. The invention comprises a welding robot, a robot controller, a visual sensing system, an interface circuit device, a main control computer, and a dual inverter arc welding power source; wherein, the visual sensing system dynamically collects the images ofa welding pool and transmits the images to the main control computer which receives the image information which is provided by the visual sensor and achieves the image processing; furthermore, the main control computer adjusts the dual inverter arc welding power source and controls the welding robot through the interface circuit device according to the processing results; the interface circuit device consists of a simulation signal output sub-module, a welding switch and process state detection sub-module and a robot controller general I/O sub-module; the welding robot receives a moving instruction signal which is emitted by the main control computer, and the welding is achieved by moving a welding gun. The invention improves the monitoring capability of the user to the welding process ofthe arc welding robot, and widens the application range of the robots in the welding automation field.

Hongbo Ma

Papers

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

Research on weld pool control of welding robot with computer vision

Binocular vision system for both weld pool and root gap in robot welding process

Autonomous guidance of initial welding position with “single camera and double positions” method

Arc welding robot jointing monitoring system based vision sensing