Seam Tracking Algorithm Research Articles

Anti-noise weld seam tracking algorithm based on spatio-temporal memory mechanism

In laser vision-based robotic welding, existing seam tracking methods are seriously affected by noise and lack effective utilization of spatio-temporal information during welding process. To address this, this paper proposes an anti-noise weld seam tracking algorithm based on spatio-temporal memory mechanism. First, a memory mechanism with welding history memory information is designed, which can better adapt to seam appearance changes and noise interference during welding. Subsequently, a laser stripe tracker based on spatio-temporal memory is constructed, it enables the current frame to adaptively search historical valid information to eliminate effects of noise and appearance changes. The experimental results show that the proposed method has an average response speed of 43 FPS and an average tracking error of 1.35 pixels for various weld seams. The actual tracking error is less than 0.6 mm. The introduction of memory mechanism improves the weld tracking accuracy by 52.1 % compared to a single tracking template.

Research on welding seam tracking algorithm for automatic welding process of X-shaped tip of concrete piles using laser distance sensor

AbstractThe manufacturing of the X-shaped tip of prestressed centrifugal concrete piles is nowadays done half automatically by combining the manual worker and the automatic welding robot. To make this welding process full automatically, the welding seam tracking algorithm is considered. There are many types of sensors that can be used to detect the welding seam such as vision sensor, laser vision sensor, arc sensor, or touch sensor. Each type of sensor has its advantages and disadvantages. In this paper, an algorithm for welding seam tracking using laser distance sensor is proposed. Firstly, the fundamental mathematics theory of the algorithm is presented. Next, the positioning table system supports the procedure is designed and manufactured. The object of this research is the fillet joint because of the characteristics of the X-shaped tip of the concrete piles. This paper proposes a new method to determine the welding trajectory of the tip using laser distance sensor. After that, the experimental results are received to verify the proposed idea. Finally, the improved proposal of the algorithm is considered to increase the accuracy of the suggested algorithm.

Development of a vision system integrated with industrial robots for online weld seam tracking

This paper develops a laser-structured light vision system for the industrial welding manipulator. The designed structured light vision system consisting of a line laser projector and an industrial camera is a decent 3D object data acquisition platform. By applying the principle of triangulation, the depth value of the object can be obtained via the laser plane in the captured image. According to hand-eye calibration and the principle of triangulation, the 3D position of the objects in the robot coordinate system is calculated. Based on the discriminated convolution tracker and deep reinforcement learning that increases the accuracy and processing time of the tracker, allowing simultaneous scanning for seam data acquisition and welding along the weld path, a real-time seam tracking algorithm is developed to handle the square-groove butt joint with small gaps (i.e., <1 mm). Experiments are undertaken using a Yaskawa industrial welding robot integrating the designed laser-structured light vision system. Experiment results show the significant performance of the developed system in welding square-groove butt joints with curved profiles and small gaps, i.e., the welding absolute mean error is 0.31 mm, whereas in the case of using convolution tracker only, the welding absolute mean error 1.7 mm.

Image restoration based on vector quantization for robotic automatic welding

For the purpose of robot automatic welding based on a laser stripe welding seam tracking system, a weld noise image restoration algorithm based on vector quantization and synthetic fractal noise is proposed to solve the problems caused by strong noise interference and limited noise dataset in welding seam tracking. The weld noise image restoration is a process of noise loss. To achieve this process, a weld noise image restoration learning model based on feature encoding, vector quantization, and feature decoding is constructed, which includes feature encoding to extract image features, vector quantization to produce noise loss, and feature decoding to complete image restoration. To enhance the generalization ability of the weld noise image restoration model and overcome the difficulty of limited data collection in on-site welding, a synthetic welding noise model is proposed based on fractal theory, multidimensional Gaussian distribution, and random region generation algorithm. A large-scale training dataset is generated by randomly initializing parameters, and the potential mapping relationship between the weld noise image and the noise-free image is constructed. Compared with the limited dataset obtained in on-site welding, the synthetic dataset makes the image restoration model more generalizable, and the similarity between the restored welding seam image and the original image reaches 0.85. In the welding seam tracking experiment, for multiple sets of different data, the average tracking accuracy of the welding seam tracking algorithm based on image restoration is 0.2 mm, which verifies that the proposed image restoration algorithm can improve the robustness of automatic welding.

Unified seam tracking algorithm via three-point weld representation for autonomous robotic welding

Autonomous robotic welding based on real-time seam tracking has shown excellent prospects in the field of intelligent manufacturing. In previous research, real-time seam tracking requires teaching trajectories, which has significant limitations. Moreover, as different types of welds have different shapes, their geometric distribution also exists in various forms such as straight lines, planar curves, and spatial curves, which brings significant challenges to the visual recognition of welds. The use of existing methods for tracking specific types of welds has limited applications. Currently, the existing methods are carried out for tracking specific types of welds, resulting in limited application scope. To handle various types of weld joints, a unified tracking paradigm named the three-point seam tracking algorithm (TSTA) is proposed in this study. First, a feature description method using three feature points is established to define different weld joints. The feature points are employed to reconstruct the position and attitude of welds, which allows the method to track welds of arbitrary geometries. Subsequently, a recognition method for the feature points combining morphology extraction and kernel correlation filter (KCF) tracker is designed. Combined with their respective advantages, the algorithm can identify the three feature points accurately and robustly in strong-noise environments. Extensive experiments including actual welding were conducted to verify the superiority of the proposed TSTA. Eleven representative types of weld seams were tested and all of them could be tracked in real time without any prior information. Compared with similar studies, the proposed recognition algorithm currently achieves the highest accuracy and best robustness. The TSTA provides a complete solution in autonomous robotic welding for unknown welds and shows excellent application prospects.

Real-time seam tracking during narrow gap GMAW process based on the wide dynamic vision sensing method

Welding torch horizontal seam tracking is a crucial condition to realize automatic welding of narrow gap gas metal arc welding (NG-GMAW) robots. In the present study, a real-time welding seam tracking technology based on the passive vision sensor is proposed. To this end, a wide dynamic range (WDR) vision sensor is used to capture real-time welding images. In order to realize real-time identification of the welding torch center deviation in the process of swinging arc in a welding bead, a welding seam tracking algorithm based on the window information fusion (WIF) is proposed. In this regard, the local feature moving window recognition (LMWR) algorithm is applied to extract the groove center. Based on the improved welding image processing program, the weld pool center and welding wire center are prepared for seam tracking. In this regard, considering the trifling variation of groove and weld pool center, the welding horizontal deviation is obtained by combining them with welding wire center calculation. Aiming at establishing a correlation between consecutive images, the Kalman filter is used to estimate the deviation optimally. The tracking algorithm is successfully verified in the weld bead with a horizontal deviation, and the tracking accuracy of ±0.47 mm is achieved. The performed analyses demonstrate that the developed algorithm meets the requirements of real-time seam tracking in the GMAW process of the welding robot.

Intelligent Seam Tracking of an Ultranarrow Gap During K-TIG Welding: A Hybrid CNN and Adaptive ROI Operation Algorithm

Deep-penetration keyhole tungsten inert gas (K-TIG) welding uses a large current for welding. The extraction of the deviation between the position of the welding torch and the centerline of the weld is the prerequisite for the realization of seam tracking. However, K-TIG welded workpieces are often assembled without a groove and the gap between each weld is super narrow (0.2–1 mm), making it difficult to accurately identify the seam. In addition, traditional methods of identifying seams based on a fixed region of interest (ROI) are likely to lead to seam tracking failures when the type of weld changes. A narrow gap seam tracking algorithm suitable for different weld shapes is developed to address these factors and increase the robustness. To minimize the interference caused by strong arc lights, the welding images are captured by an high dynamic range (HDR) camera. A you only look once (YOLOv5)-based object detection algorithm is used to determine the position of the torch by the center of the keyhole entrance. Then, an image processing algorithm based on the adaptive ROI operation is used to adaptively extract the weld centerline and obtain the weld deviations. The experimental results proved that the welding deviation detected by the algorithm fluctuated within ±0.122 mm and that the average error was within ±0.043 mm. Moreover, the real-time performance of the algorithm can reach up to 45 frames per second (FPS), which is sufficient to meet the real-time and accuracy requirements of the K-TIG seam tracking system.

Welding seam recognition and tracking for a novel mobile welding robot based on multi-layer sensing strategy

That a mobile welding robot with an adaptive seam tracking ability can greatly improve welding efficiency and quality has been extensively studied. To further improve automation in multiple-station welding, we developed a novel intelligent mobile welding robot consisting of a four-wheeled mobile platform and a collaborative manipulator. With the support of simultaneous localization and mapping (SLAM) technology, the robot is capable of automatically navigating to different stations to perform welding operations. To automatically detect the welding seam, a composite sensor system including an RGB-D camera and a laser vision sensor is creatively applied. Based on the sensor system, a multi-layer sensing strategy is performed to ensure that the welding seam can be detected and tracked with high precision. By applying a hybrid filter to the RGB-D camera measurement, the initial welding seam could be effectively extracted. Next, a novel welding start point detection method is proposed. Meanwhile, to guarantee the tracking quality, a robust welding seam tracking algorithm based on laser vision sensor is presented, to eliminate the tracking discrepancy caused by the platform parking error, through which the tracking trajectory can be corrected in real-time. The experimental results show that the robot can autonomously detect and track the welding seam effectively at different welding stations. Also, multiple-station welding efficiency can be improved and quality can also be guaranteed.

A Study on Development of Seam Tracking Algorithm in Robotic GMA Welding Process

A Study on Development of Seam Tracking Algorithm in Robotic GMA Welding Process

Virtual reality human-robot collaborative welding: A case study of weaving gas tungsten arc welding

To combine the advantages of humans (adaptive intelligence) and robots (higher movement accuracy and physical limitation), we propose a virtual reality human-robot collaborative welding system which allows them to collaborate with each other to complete welding tasks. A common welding method, weaving gas tungsten arc welding (GTAW) is presented as a case study. In this system, a 6-degree of freedom (6-DoF) robot UR-5 carrying the welding torch works as the final performer of weaving welding. A virtual welding environment based on HTC VIVE is built where humans can observe the working scene via a motion-tracked headset without being onsite physically. A mapping model between arc voltage, arc length and welding current is established, and based on this model a seam tracking algorithm is developed which allows the robot to track the weld seam automatically. The robot is pre-programmed to weave the welding torch across the weld seam. The robot travelling along the weld seam is fully controlled by the human operator via motion-tracked handle per the observed working scene. This teleoperation style allows the humans to adjust the travel speed adaptively and freely as needed without suffering onsite danger. The proposed collaborative welding system combines the advantages of humans and robots together, utilizing the intelligence and adaptability of the human operator with precise movement obtained from robots. The welding experiments show that the welded workpiece from human-robot collaboration has better performance compared with that from either humans or robots separately and demonstrated the effectiveness of the proposed virtual reality human-robot collaborative welding system.

Automatic seam detection and tracking system for robots based on laser vision

Detecting weld seams and accurately locating them from time-series images that contain strong noise pollution are difficult and lead to reduced tracking accuracy. To address this problem, this paper presents a seam detection and tracking algorithm from the perspective of seam feature extraction and seam detection and positioning. The proposed image-processing algorithm employs the powerful feature expression capability and self-learning function of the deep convolutional neural network. A real-time weld seam searching and positioning strategy based on the multi-correlation filter cooperative detection mechanism is proposed in consideration of the continuity of the motion of the feature points of adjacent frames and the correlation of laser stripe structural information. Experimental results show that the sensor's measurement frequency can reach 20 Hz, the average absolute tracking error of straight or curved welds is less than 0.25 mm, and the maximum tracking error does not exceed 1 mm in an environment with strong arc and splash noise. Moreover, the welding torch end runs smoothly during welding. The proposed strategy can meet high-quality welding requirements.

An Optimal Image Processing Method for Simultaneous Detection of Weld Seam Position and Weld Gap in Robotic Arc Welding

For robot path planning the weld seam positions need to be known in advance as the industrial robot generally work in teach and playback mode. In this paper, a vision sensor has been utilized for automation of robotic welding path planning. A seam tracking algorithm has been proposed for a butt type of weld joint with varying weld gap for effective measurement of weld path positions and weld gap simultaneously. For this first an image acquisition algorithm technique has been proposed for capturing of image of weld seam in gray scale mode. Then in image processing at first one pattern matching algorithm for tracking of weld seam path is performed. Then different edge detection techniques have been applied to find the most efficient edge detection method for obtaining the characteristics of weld seam edge. Then best edge fitting method has been applied to fit the edges along the weld seam edge and the pixel values on the edges were measured. The weld gap and the midpoint between edges points are measured simultaneously by vision assistant toolbox in LabVIEW software background.

Robust Welding Seam Tracking and Recognition

In the process of automatic welding based on structured light vision, the precise localization of the welding seam in an image has an important influence on the quality of the welding. However, in practice, there is much interference, such as spatter and arc, which introduces great challenges for accurate welding seam localization. In this paper, we considered welding seam localization problem as visual target tracking and based on that, we proposed a robust welding seam tracking algorithm. Prior to the start of welding, the seam is separated using a cumulative gray frequency, which is utilized to adaptively determine the initial position and size of the search window. During the welding process, large seam motion range may result in only a portion of the welding seam exists in the search window. To prevent that, a tracking-by-detection method is used to calculate the location of the search window. Usually, the intersection of seam and noise, e.g., spatter, has a severe influence on the accuracy of feature points localization. In order to solve this problem, a sequence gravity method (SGM) for extracting a smoother center line of welding seam is proposed, which is able to reduce the impact of interference. The double-threshold recursive least square method is used to fit the curve obtained by SGM with the aim of improving the real-time performance and accuracy of the system. Finally, the superiority of the proposed algorithm is well demonstrated by comparison with other solutions for seam tracking and recognition through extensive experiments.

Development of an automatic orbital welding system with robust weaving width control and a seam-tracking function for narrow grooves

From a broad point of view, seam tracking has been one of the main challenges with respect to welding automation. Several attempts at automation have been successful in seam tracking with a fixed weaving width. As a solution for seam-tracking methods for varying groove widths, visual sensors such as CCD cameras have been adopted. Although these vision-sensing techniques can achieve high accuracy, their weakness is that a well-prepared vision sensor environment is needed to obtain high-quality visual measurements and that these measurements can be easily affected by the significant noises present in industrial areas. This paper presents an alternative approach using an arc sensor to measure weld widths and positions for weld seam tracking. Arc sensing is less sensitive than vision sensors, which are susceptible to environmental noises, and requires no additional costs except for the measurement apparatus for detecting the arc voltage or welding current. In this study, a special measurement device for the arc voltage is also developed to enhance the reliability of the seam-tracking sensor. Based on a well-known seam-tracking algorithm, an automatic weaving width control algorithm is proposed which can predict weld position more accurately. The usefulness of the weaving width control is verified through an example in which it is applied to gas tungsten arc welding (GTAW).

Seam tracking algorithm based on multi-information sensor of vision and arc

With the rapid development of weld sensor technology and signal processing technology,more and more researchers begin to pay attention to the research of the welding process quality control.The multi-sensor information fusion technology is an important method for welding quality control.The technology is applied to the automatic control process of MIG pulse welding quality.This paper captured different description information for the effective feature extraction and transmission with the vision sensor and the arc sensor,which used in multi-sensor information fusion algorithm for seam tracking.The vision sensor obtained image information by industrial Charg-Coupled Device(CCD) for the lateral deviation information of the weld torch,the arc sensor obtained current information by data acquisition card for the height complementary deviation information and lateral redundancy information of the weld torch.The complementary and redundant information of two sensors were used to sensor information fusion on feature level for lateral and height deviation control.The redundant information fusion achieved visual image de-noising,while the complementary information fusion can further improve the seam tracking precision.The experimental results show that the proposed algorithm can improve the welding quality,and thus verify its efficiency and rationality.

A new seam-tracking algorithm through characteristic-point detection for a portable welding robot

The welding task in double-hulled structures in shipyards and in steel-frame structures is hazardous and difficult due to the toxic gas and limited workspace. Therefore, many efforts have been undertaken for automation. The main challenge for automation is the development of a simple and robust seam-tracking algorithm that can be applied to a portable welding robot that operates under irregular and diverse task conditions in the workspace. We developed a seam-tracking algorithm for weaving weld path planning using a laser displacement sensor. The goal of the proposed algorithm is to detect the seam of single-butt welding with manually tack-welded non-zero gaps. The focus is on keeping the algorithm simple and affordable so that it can be applied to portable robots that operate in hazardous fields. The algorithm consists of four steps: scanning, filtering, generation of the reference points, and path planning. In the scanning process, the depth data of a cross-section of the seam profile is obtained. Next, a Gaussian filter is used to remove noise from the raw data. A differential characteristic-point detection algorithm is applied to the filtered data to detect the reference points that represent the shape and location of the gap to be welded. Finally, path planning for single-V butt multi-pass welding is done based on the detected reference points. A portable four-axis welding robot is built using the developed algorithm. The algorithm is validated through welding experiments regarding a single-V butt welding task with a manually tack-welded non-zero gap.

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.

An Algorithm for Seam Tracking Applications

Seam tracing is currently accomplished by exploiting special features of the robot and a priori knowledge of the seam geometry. This paper demonstrates the feasibility of tracking a seam in real time. A general-purpose seam tracing algo rithm is developed for implementation on a robot with six de grees of freedom. The algorithm is motivated by a physical interpretation of the T6and d T6matrices and the assumption that three-dimensional seam data are available. In the past, the d T6matrix and inverse Jacobian solutions have been used to compute the differential changes in the joint angles. The inverse Jacobian is used to introduce an iterative algo rithm to compute both large and small changes in the joint variables. The versatile seam tracking algorithm can be applied to a multitude of robotic seam tracking activities such as gluing, surface grinding, and flame cutting.