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1 – 10 of 762Yanbiao Zou, Jinchao Li and Xiangzhi Chen
This paper aims to propose a set of six-axis robot arm welding seam tracking experiment platform based on Halcon machine vision library to resolve the curve seam tracking issue.
Abstract
Purpose
This paper aims to propose a set of six-axis robot arm welding seam tracking experiment platform based on Halcon machine vision library to resolve the curve seam tracking issue.
Design/methodology/approach
Robot-based and image coordinate systems are converted based on the mathematical model of the three-dimensional measurement of structured light vision and conversion relations between robot-based and camera coordinate systems. An object tracking algorithm via weighted local cosine similarity is adopted to detect the seam feature points to prevent effectively the interference from arc and spatter. This algorithm models the target state variable and corresponding observation vector within the Bayes framework and finds the optimal region with highest similarity to the image-selected modules using cosine similarity.
Findings
The paper tests the approach and the experimental results show that using metal inert-gas (MIG) welding with maximum welding current of 200A can achieve real-time accurate curve seam tracking under strong arc light and splash. Minimal distance between laser stripe and welding molten pool can reach 15 mm, and sensor sampling frequency can reach 50 Hz.
Originality/value
Designing a set of six-axis robot arm welding seam tracking experiment platform with a system of structured light sensor based on Halcon machine vision library; and adding an object tracking algorithm to seam tracking system to detect image feature points. By this technology, this system can track the curve seam while welding.
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Keywords
Mikael Fridenfalk and Gunnar Bolmsjö
This paper presents the design and validation of a universal 6D seam tracking system that reduces the need of accurate robot trajectory programming and geometrical databases in…
Abstract
This paper presents the design and validation of a universal 6D seam tracking system that reduces the need of accurate robot trajectory programming and geometrical databases in robotic laser scanning. The 6D seam tracking system was developed in the flexible unified simulation environment, integrating software prototyping with mechanical virtual prototyping, based on physical experiments. The validation experiments showed that this system was both robust and reliable and should be able to manage a radius of curvature less than 200 mm. In the pre‐scanning mode, a radius of curvature down to 2 mm was managed for pipe intersections at 3 scans/mm, using a laser scanner with an accuracy of 0.015 mm.
Shanchun Wei, Meng Kong, Tao Lin and Shanben Chen
This paper aims to develop a method to achieve automatic robotic welding and seam tracking so that three‐dimensional weld seam could be tracked without teaching and good welding…
Abstract
Purpose
This paper aims to develop a method to achieve automatic robotic welding and seam tracking so that three‐dimensional weld seam could be tracked without teaching and good welding formation could be accomplished.
Design/methodology/approach
Adaptive image processing method was used for various types of weld seam. Also the relationship between welding height and arc signal was calibrated. Through the decomposition and synthesis, three‐dimensional space type weld seam could be extracted and tracked well. The workpiece without teaching was finally tracked precisely and in a timely way with use of the fuzzy controller.
Findings
Composite sensing technology including arc and visual sensing had obvious advantages. Image processing method could be used for tracking plane weld seam efficiently while arc sensing could characterize welding height. Through the coupled controlling algorithm, arc sensing and visual sensing could be fused effectively.
Research limitations/implications
How to couple information more accurately and quickly was still one of the most important problems in composite sensing technology.
Practical implications
Composite sensing technology could reduce costs to achieve weld seam instead such expensive device as laser sensor. The simulating parts of scalloped segment of bottom board for rockets were tracked in the project. Once more adaptive algorithms were developed, more complicated practical workpieces could be dealt with in robotic welding which promotes the application of industry robots.
Originality/value
A useful method for three‐dimensional space type weld seam tracking without teaching was developed. The whole procedure of adaptive image processing method was simple but efficient and robust. The coupled controlling strategy addressed could accomplish seam tracking by composite sensing technology.
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Yanbiao Zou and Hengchang Zhou
This paper aims to propose a weld seam tracking method based on proximal policy optimization (PPO).
Abstract
Purpose
This paper aims to propose a weld seam tracking method based on proximal policy optimization (PPO).
Design/methodology/approach
By constructing a neural network based on PPO and using the reference image block and the image block to be detected as the dual-channel input of the network, the method predicts the translation relation between the two images and corrects the location of feature points in the weld image. The localization accuracy estimation network (LAE-Net) is built to update the reference image block during the welding process, which is helpful to reduce the tracking error.
Findings
Off-line simulation results show that the proposed algorithm has strong robustness and performs well on the test set of curved seam images with strong noise. In the welding experiment, the movement of welding torch is stable, the molten material is uniform and smooth and the welding error is small, which can meet the requirements of industrial production.
Originality/value
The idea of image registration is applied to weld seam tracking, and the weld seam tracking network is built on the basis of PPO. In order to further improve the tracking accuracy, the LAE-Net is constructed and the reference images can be updated.
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Yanling Xu, Huanwei Yu, Jiyong Zhong, Tao Lin and Shanben Chen
The purpose of this paper is to analyze the technology of capturing and processing weld images in real‐time, which is very important to the seam tracking and the weld quality…
Abstract
Purpose
The purpose of this paper is to analyze the technology of capturing and processing weld images in real‐time, which is very important to the seam tracking and the weld quality control during the robotic gas tungsten arc welding (GTAW) process.
Design/methodology/approach
By analyzing some main parameters on the effect of image capturing, a passive vision sensor for welding robot was designed in order to capture clear and steady welding images. Based on the analysis of the characteristic of the welding images, a new improved Canny algorithm was proposed to detect the edges of seam and pool, and extract the seam and pool characteristic parameters. Finally, the image processing precision was verified by the random welding experiments.
Findings
It was found that the seam and pool images can be clearly acquired by using the passive vision system, and the welding image characteristic parameters were accurately extracted through processing. The experiment results show that the precision range of the image processing can be controlled about within ±0.169 mm, which can completely meet the requirement of real‐time seam tracking for welding robot.
Research limitations/implications
This system will be applied to the industrial welding robot production during the GTAW process.
Originality/value
It is very important for the type of teaching‐playback robots with the passive vision that the real‐time images of seam and pool are acquired clearly and processed accurately during the robotic welding process, which helps determine follow‐up seam track and the control of welding quality.
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Chen Chen, Tingyang Chen, Zhenhua Cai, Chunnian Zeng and Xiaoyue Jin
The traditional vision system cannot automatically adjust the feature point extraction method according to the type of welding seam. In addition, the robot cannot self-correct the…
Abstract
Purpose
The traditional vision system cannot automatically adjust the feature point extraction method according to the type of welding seam. In addition, the robot cannot self-correct the laying position error or machining error. To solve this problem, this paper aims to propose a hierarchical visual model to achieve automatic arc welding guidance.
Design/methodology/approach
The hierarchical visual model proposed in this paper is divided into two layers: welding seam classification layer and feature point extraction layer. In the welding seam classification layer, the SegNet network model is trained to identify the welding seam type, and the prediction mask is obtained to segment the corresponding point clouds. In the feature point extraction layer, the scanning path is determined by the point cloud obtained from the upper layer to correct laying position error. The feature points extraction method is automatically determined to correct machining error based on the type of welding seam. Furthermore, the corresponding specific method to extract the feature points for each type of welding seam is proposed. The proposed visual model is experimentally validated, and the feature points extraction results as well as seam tracking error are finally analyzed.
Findings
The experimental results show that the algorithm can well accomplish welding seam classification, feature points extraction and seam tracking with high precision. The prediction mask accuracy is above 90% for three types of welding seam. The proposed feature points extraction method for each type of welding seam can achieve sub-pixel feature extraction. For the three types of welding seam, the maximum seam tracking error is 0.33–0.41 mm, and the average seam tracking error is 0.11–0.22 mm.
Originality/value
The main innovation of this paper is that a hierarchical visual model for robotic arc welding is proposed, which is suitable for various types of welding seam. The proposed visual model well achieves welding seam classification, feature point extraction and error correction, which improves the automation level of robot welding.
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John Ogbemhe and Khumbulani Mpofu
– The purpose of this paper is to review the progress made in arc welding automation using trajectory planning, seam tracking and control methodologies.
Abstract
Purpose
The purpose of this paper is to review the progress made in arc welding automation using trajectory planning, seam tracking and control methodologies.
Design/methodology/approach
This paper discusses key issues in trajectory planning towards achieving full automation of arc welding robots. The identified issues in trajectory planning are real-time control, optimization methods, seam tracking and control methodologies. Recent research is considered and brief conclusions are drawn.
Findings
The major difficulty towards realizing a fully intelligent robotic arc welding system remains an optimal blend and good understanding of trajectory planning, seam tracking and advanced control methodologies. An intelligent trajectory tracking ability is strongly required in robotic arc welding, due to the positional errors caused by several disturbances that prevent the development of quality welds. An exciting prospect will be the creation of an effective hybrid optimization technique which is expected to lead to new scientific knowledge by combining robotic systems with artificial intelligence.
Originality/value
This paper illustrates the vital role played by optimization methods for trajectory design in arc robotic welding automation, especially the non-gradient approaches (those based on certain characteristics and behaviour of biological, molecular, swarm of insects and neurobiological systems). Effective trajectory planning techniques leading to real-time control and sensing systems leading to seam tracking have also been studied.
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Jie Li, Jiyuan Wu, Chunlei Tu and Xingsong Wang
Automatic robots can improve the efficiency of liquefied petroleum gas (LPG) tank inspection and maintenance, but it is difficult to achieve high-precision spatial positioning and…
Abstract
Purpose
Automatic robots can improve the efficiency of liquefied petroleum gas (LPG) tank inspection and maintenance, but it is difficult to achieve high-precision spatial positioning and navigation on tank surfaces. The purpose of this paper is to develop a spatial positioning robotic system for tank inspection. The robot can accurately identify and track weld paths. The positioning system can complete robot’s spatial positioning on tank surfaces.
Design/methodology/approach
A tank inspection robot with curvature-adaptive transmission mechanisms is designed in this study. A weld path recognition method based on deep learning is proposed to accurately identify and extract weld paths. Integrated multiple sensors, the positioning system is developed to improve the robot’s spatial positioning accuracy. Experiments are conducted on a cylindrical tank to test weld seam tracking accuracy and spatial positioning performance of the robotic system. The practicality of the robotic system is then verified in field tests.
Findings
The robot can accurately identify and track weld seams with a maximum drift angle of 4° and a maximum offset distance of ±30 mm. The positioning system has excellent positioning accuracy and stability. The maximum angle and height errors are 3° and 0.08 m, respectively.
Originality/value
The positioning system can improve the autonomous performance of inspection robots and solve the problems of weld path recognition and spatial positioning. Application of the robotic system can promote the automatic inspection and maintenance of LPG tanks.
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Keywords
Zeng Jinle, Zou Yirong, Du Dong, Chang Baohua and Pan Jiluan
This paper aims to develop a feasible visual weld detection method to solve the problems in multi-layer welding detection (e.g. cover pass welding detection) for seam tracking and…
Abstract
Purpose
This paper aims to develop a feasible visual weld detection method to solve the problems in multi-layer welding detection (e.g. cover pass welding detection) for seam tracking and non-destructive testing. It seeks for an adaptive and accurate way to determine the edge between the seam and the base metal in the grayscale image of weld automatically. This paper tries to contribute to next-generation real-time robotic welding systems for multi-layer welding.
Design/methodology/approach
This paper opted for invariant moments to characterize the seam and the base metal for classification purposes. The properties of invariant moments, such as high degree of self-similarity and separation, affine invariance and repetition invariance, were discussed to verify the adaptability of the invariant moment in weld detection. Then, a weld detection method based on invariant moments was proposed to extract the edge between the seam and the base metal, including image division, invariant moment features extraction, K-Means adaptive thresholding, maximum connected domain detection and edge position extraction.
Findings
This paper highlights the significance of high degree of self-similarity and separation, affine invariance and repetition invariance of the invariant moment for weld detection. An adaptive, effective and accurate method is proposed to detect the edge between the seam and the base metal based on invariant moments.
Research limitations/implications
It is necessary to verify the applicability of the proposed method in variable welding conditions further. Further works will focus on the establishment of a real-time seam tracking system during the whole multi-layer/multi-pass welding process based on such adaptive visual features.
Practical implications
This paper includes the implications for development of an adaptive and real-time weld detection method, which is expected to be applied to online seam tracking in multi-layer welding.
Originality/value
This paper presents an accurate weld detection method in multi-layer welding, overcoming difficulties in effectiveness, adaptability and efficiency of existing weld detection methods.
Details
Keywords
B.B.V.L. Deepak, Raju M.V.A. Bahubalendruni, Ch A. Rao and Jalumuru Nalini
This paper aims to automate the welding operation that motion control, sensor integration and coordination with the welding power source. Therefore, there is a need for…
Abstract
Purpose
This paper aims to automate the welding operation that motion control, sensor integration and coordination with the welding power source. Therefore, there is a need for sophisticated technologies to control precisely the process in terms of positioning the welding torch, and controlling the welding parameters through the use of correct devices which are aided by appropriate control tools and techniques.
Design/methodology/approach
A new seam tracking methodology, named sewing technique, has been introduced for the welded joints available in computer-aided design (CAD) environment. This methodology gives the seam path by drawing a line through the adjacent centroids of curve fitted in the weld joint volume. Obtained geometric path and kinematic constraints are given as input to the modeled robot for performing welding operation followed by desired trajectory.
Findings
In this investigation, a novel and efficient weld seam technique has been developed to produce uniform welded joints. The key feature of this approach is that the initial and end positions of the weld seams can be obtained easily. Because of this, the robot can be controlled flexibly during welding operation.
Originality/value
This investigation deals with the development of an automated seam tracking methodology for the welded joints available in CAD environment. Validation of the developed methodology has been done through simulation results while performing welding operations for different weld profiles.
Details