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

In this paper, an intelligent seam-tracking system is developed for fillet weld workpiece. Structured light vision is employed to detect the deviation of the welding torch. Two stripe lines are formed on the surface of the fillet weld workpiece by the structured laser light. Their intercepts are selected as the image feature, which can result in an approximately uncoupled relationship between the image feature and the movement of adjustment mechanism. A two-step feature-extraction method is designed to robustly detect the two lines in image. In order to get a favorable tracking precision in the welding process, a new self-tuning fuzzy controller is designed. Its input and output scaling factors are tuned according to its active working region. Moreover, a supervisory level is introduced into the control system to limit the output pulses in order to guarantee steady and safe tracking. Finally, experiments are well conducted to verify the performance of the proposed system and methods.

A Vision-Based Self-Tuning Fuzzy Controller for Fillet Weld Seam Tracking

A novel circular laser three-dimensional (3D) scanner is developed instead of a one-dimensional laser spot scanner (1D) and two-dimensional (2D) striped laser light. Using the proposed circular laser scanner, a seam tracking system is constructed, and architecture based on the Visual C++ and RAPID languages which determines the cooperation among image processing modules is carried out. Finally, real seam tracking experiments are investigated and the error analysis is carried out. The results show that: (1) the proposed system can realize seam tracking; (2) the precision of this system is affected by the light scanning system, calibration results, image processing, and the welding seam 3D algorithm—the tracking accuracy is satisfied for robotic arc welding; (3) this system can be used in a welding robot system as a weld seam finder, flatness detector, and weld seam tracker.

A visual seam tracking system for robotic arc welding

Intelligent robotic welding involves replicating the role of a manual professional welder to adaptively control the welding process. This is necessary to achieve accurate, fast and high-quality welding process in addition to the challenging factors for humans to operate in the welding environment. Therefore, robotic welding exists since the early days of robotics and it is still an active research area. This is why there have been numerous researches in this area for a very long time. Among various techniques proposed by researchers for the adaptive control of the robotic welding process, vision-based control is the most popular due to its non-invasiveness. Therefore, in this paper, we review, analyse and categorise the proposed vision-based techniques with the aim of covering the different image processing and feature extraction aspect of the techniques. The focus is mainly on the active vision system where various image processing techniques have been utilised in extracting the welding seam features. The challenges and difficulties to extract seam features in active vision system have been highlighted. The trends and new approaches have been indicated in order to provide a comprehensive source for researchers who are planning to carry out research related to the intelligent robot vision techniques for welding automation.

Welding seam profiling techniques based on active vision sensing for intelligent robotic welding

A new laser stripe sensor system for the automation of welding processes in heavy industries is presented. Conventional laser stripe sensors use expensive multiprocessor boards with up to six digital signal processors (DSP) to cope with the high data rate of 14 Mbyte/s delivered by the sensor's CCD-camera. A new solution using only one highly integrated logic IC (FPGA) has been developed offering the same performance as multiprocessor solutions at a drastically lower price making laser stripe sensors attractive for cost sensitive applications in the heavy industries. Integrating the FPGA in the sensor head facilitates data transmission to the sensor computer as the data rate is decreased by a factor of 700 and can thus be transmitted as a robust RS485 serial signal over large distances. For the detection of the location of the welding groove a new robust fuzzy algorithm has been developed that guarantees a satisfying tracking result even when the welding gap geometry strongly varies or is distorted by noise. Using this new method, investigations carried out on industrial samples from the ship-building industry showed that the fuzzy approach was able to cope with strong seam geometry variations and proved to be superior to conventional approaches.

Robust laser-stripe sensor for automated weld-seam-tracking in the shipbuilding industry

This paper presents a robotic seam tracking system which is aimed at achieving robustness against some welding noises such as arc glares, welding spatters, fumes etc. In particularly, a syntactic analysis is used to improve the extraction reliability of the joint features. The joint features thus obtained are used to extract the 3-dimensional information of the weld joint and then achieve the robot path correction. To show the performance of the developed system, a series of experiments on joint feature detection and robotic seam tracking are conducted for different types of weld joints. The results exhibit that the system is very robust to various welding noises as well as variations in appearance of weld joint and workpiece.

Young Tak Son

Papers

A robust method for vision-based seam tracking in robotic arc welding

A robust visual seam tracking system for robotic arc welding