Real-time Seam Research Articles

Real-time image processing for vision-based weld seam tracking in robotic GMAW

Image capturing and processing is important in using vision sensor to effectively track the weld seam and control the weld quality in robotic gas metal arc welding (GMAW). Using vision techniques to track weld seam, the key is to acquire clear weld images and process them accurately. In this paper, a method for real-time image capturing and processing is presented for the application in robotic seam tracking. By analyzing the characteristic of robotic GMAW, the real-time weld images are captured clearly by the passive vision sensor. Utilizing the main characteristics of the gray gradient in the weld image, a new improved Canny edge detection algorithm was proposed to detect the edges of weld image and extract the seam and pool characteristic parameters. The image processing precision was further verified by using the random welding experiments. Results showed that the precision range of the image processing can be controlled to be within ±0.3 mm in robotic GMAW, which can meet the requirement of real-time seam tracking.

Design of High Accuracy Robot Welding Machine for a Cone Bottom

This paper designed a special welding machine for an aluminum cone bottom workpiece. This machine contains highly accurate positioner system, laser tracking system, and robotic welding devices. It is used to weld the transverse seams and the longitudinal seams of the workpiece. The interaction of welding robot with positioner and the real-time seam correcting can ensure high quality of welding.

An image acquisition system for real-time seam tracking

Gas metal arc welding (GMAW) process is one of the most widespread welding processes used in industries for their excellent quality, reliability, productivity, and cost-effectiveness. To develop an automatic GMAW system, vision capability in the system is a necessary component supplying real-time information about weld pool and seam tracking. In this research work, an automatic seam tracking system is presented, where the automatic tracking of welding path and torch positioning are performed by a newly developed image acquisition system. The system aims to add a vision capability to the GMAW system. A CCD camera is configured with a welding torch to acquire real-time images. The acquired images are processed through newly developed software for real-time detection of welding seam location and characteristics. The software encapsulates the acquired image input facility, image filtering technique, strategy to measure the seam gap, strategy to position torch at the starting point of welding, user interface for automatic guide, and the strategy to correct the torch movements. The seam recognition accuracy was verified during several welding experiments on linear weld seam. Real-time measurements of the seam gap and the seam tracking have achieved a high accuracy.

Seam Tracking Monitoring during High-Power Fiber Laser Welding

Accurate detection of weld seam deviations from the desired path during laser welding process facilitates seam tracking monitoring and is thus of primary concern in performing successful weld. This paper proposes a novel method for real-time seam tracking monitoring during high-power fiber laser welding, as it investigates into the techniques of infrared image sensing and visual recognition. Molten pool images were taken by a high-speed camera arranged in the off-axis direction of laser beam. Accumulative grayscale variation based on a molten pool thermal distribution was extracted and used to determine the deviations between the laser beam focus and the weld seam center. The proposed algorithm was experimented on a butt joint laser welding of Type 304 stainless steel plates at a continuous wave fiber laser power of 10 kW.

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

This paper presents a technology about real-time seam tracking, which is necessary to overcome the deficiencies of the teaching-playback welding robots in seam tracking control during gas tungsten arc welding (GTAW) process. A set of vision sensor system has been designed for the welding robot, which can acquire clear and steady welding images. By analyzing the features of welding images, a new improved Canny algorithm has been proposed to detect the edges of seam and pool, and extract the characteristic parameters of welding images. Based on the analysis of the characteristic of the real-time seam tracking, a segmented self-adaptive PID controller is introduced to the system, and some experiments have been done to testify whether the accuracy of the technology can meet the requirements of quality control of seam forming.

Infrared image recognition for seam tracking monitoring during fiber laser welding

Seam-tracking ability of a laser-welding system is important for welding process, and the accurate detection of deviations between the laser-beam focus and the weld seam position is prerequisite for seam-tracking control. Infrared image sensing and visual recognition techniques for real-time seam tracking monitoring during high-power fiber laser welding is researched to improve the accuracy of seam-tracking ability. Molten pool images are caught by an infrared sensitive high speed camera arranged off-axis orientation of a laser-welding head which is fixed to a robot. Through the image processing, the feature detection of a near-infrared image is used in visual tracking. The gray-value gradient of near-infrared image is calculated and the keyhole margin of a molten pool is also detected. Combining the gradient and keyhole margin of a molten pool image, the thermal gradient parameter based on the thermal distribution of a molten pool is extracted. As a visual feature in robot control system, this parameter can be used to determine the deviations between the laser-beam focus and the weld-seam center. In comparison with direct detection of the narrow gap position, this parameter can be measured easily and the delay error resulted from the forerun of the sensor can be eliminated. This provides a practical approach to detect the deviations and the possibility to adjust the laser-beam focus position in real time, which can sensibly promote seam tracking accuracy. The proposed algorithm is tested during a butt-joint laser welding of Type 304 austenitic stainless steel plates at a continuous wave fiber laser power of 6kW and 10kW. Its effectiveness is confirmed by the welding experiments.

Real-Time Seam Tracking Technology of Welding Robot with Visual Sensing

A seam tracking system with visual sensing free from calibration was developed for the robot applied in gas tungsten arc welding. A visual sensor with double-layer filter system was researched for the different levels of the welding current. An image processing in C+?+ language was developed to extract the seam trajectory and the offset of the torch to the seam in the welding images of aluminum alloys plates with grooves. The rectifying rule of the robot used in this paper was found based on the experimental data, and the seam tracking controller was also analyzed and constructed. The experimental results on straight line seam and curve seam demonstrated the efficiency of the proposal seam tracking system.

Real-time seam tracking for robotic laser welding using trajectory-based control

In this paper a real-time seam tracking algorithm is proposed that can cope with the accuracy demands of robotic laser welding. A trajectory-based control architecture is presented, which had to be developed for this seam tracking algorithm. Cartesian locations (position and orientation) are added to the robot trajectory during the robot motion. In this way, sensor information obtained during the robot motion is used to generate the robot trajectory while moving. Experiments have been performed to prove the tracking capabilities of the seam tracking algorithm.

Design of Robot Welding Seam Tracking System with Structured Light Vision

Robot welding is an important developing direction of welding automation and intelligentization, and automatic seam tracking technology is one of principal research domains. Nowadays, seam tracking system with structured light vision becomes a hot research. Structured light vision seam tracking products abroad are generally very expensive and can only be applied on special occasions. In China, the research of structured light vision seam tracking system is still just on the stage of experiments. A robot real-time seam tracking system with line structured light vision is designed. The hardware system is set up, a filtering method for line structure seam image is improved, and compared with common filtering, it has better effect and characteristic of real time. Two methods, fast template matching and fast Hough transform, to recognize the image coordinates of seam center are improved. Two new image recognition methods, structure element matching and corner detecting, are proposed. The comparison of seam image recognition shows that fast template matching and corner detecting are more precise and stable than the other two methods, and corner detecting is the best in real time. A simultaneous calibration for camera parameters and robot hand-eye is also proposed, and calculation shows that the calibration is effective and feasible. The robot seam tracking tests for linear and folded lap-joint are performed, which are based on the above four image recognition methods, and the results indicate that four image recognition methods are all applicable to real-time seam tracking, and the whole system sufficed for the requirements of real-time seam tracking. Automatic seam tracking with line structured light vision is feasible and has good versatility.

Three Dimension Curve Welding Seam Modeling for Seam Tracking

A novel modeling method for 3D curve welding seam is presented in this paper based on a welding robot for large workpiece. The 3D curve welding seam tracking is implemented by macro and micro motion which can control translational motion of torch in a large range and implement accurate seam tracking in small range. As such, the self-teaching seam model and real-time seam model are established to accurately control position and orientation of torch. The self-teaching model is obtained by a cubic B-spline interpolation function. Then a continuous position model of a V-type seam is established by using the cubic parameter B-spline least square method. The orientation model of the seam is also established by using the position model and coordinates of seam characteristic points. These three models form the base of macro and micro translational motion control and real-time tracking control. Furthermore, in order to compare the performances of the different position modeling method, simulations have been carried out to produce the modeling results of cubic parameter B-spline least square method and polynomial least square method. The effectiveness of these seam model has been verified.

Weld bead placement system for multipass welding

A transputer-based laser triangulation vision system has been developed for use with automated tungsten inert gas (TIG) welding. The object of the vision system is to provide real-time seam tracking and weld bead profile monitoring at all stages of the multipass welding process to allow control of the welding process. The laser triangulation technique was used to measure the 3-D geometric profile in a single pass. The electronic image processing hardware was based on a vision system developed in-house by the University of Liverpool called the Transputer Image Processing System (TIPS). A prototype sensor system has been developed and used to track the position of the two corner positions of a V-preparation joint and to measure the weld profile between these positions as the multipass welding progresses. The concept and the design of the system are presented.

An adaptive real-time intelligent seam tracking system

Abstract The concept of a real-time seam tracking system for welding automation and the initial design for building a prototype is presented. The Adaptive Real-time Intelligent Seam Tracker (ARTIST) uses a robot held, laser based vision system for automation of arc welding processes, and is currently under development at the Applied Research Laboratory of the Pennsylvania State University. The design of ARTIST builds upon the concept of a zero-pass technique. The 3D information of seam geometry is collected and processed for real-time guidance and control of the welding torch trailing behind the laser based vision sensor. This zero-pass concept eliminates the need for preprogramming of the weld path and thus, potentially enhances the welding cycle time for small batches. The ARTIST is designed to support multipass arc welding and to handle any tack welds which are encountered during the seam welding operation.