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Robotic welding requires a higher weld image resolution for easy weld identification; however, the higher the resolution, the higher the cost. Therefore, this paper proposes an improved CLAHE algorithm, which can not only effectively denoise and retain edge information but also improve the contrast of images. First, an improved bilateral filtering algorithm is used to process high-resolution images to remove noise while preserving edge details. Then, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm and Gaussian masking algorithm are used to enhance the enhanced image, and then differential processing is used to reduce the noise in the two images, while preserving the details of the image, enhancing the image contrast, and obtaining the final enhanced image. Finally, the effectiveness of the algorithm is verified by comparing the peak signal-to-noise ratio and structural similarity with other algorithms.

For the welding cell in the manufacturing process of large excavation motor arm workpieces, a system framework, based on a digital twin welding robot cell, is proposed and constructed in order to optimize the robotic collaboration process of the welding workstation with digital twin technology. For the automated welding cell, combined with the actual robotic welding process, the physical entity was digitally modeled in 3D, and the twin welding robot operating posture process beats and other data were updated in real time, through real-time interactive data drive, to achieve real-time synchronization and faithful mapping of the virtual twin as well as 3D visualization and monitoring of the system. For the robot welding process in the arc welding operation process, a mathematical model of the kinematics of the welding robot was established, and an optimization method for the placement planning of the initial welding position of the robot base was proposed, with the goal of smooth operation of the robot arm joints, which assist in the process simulation verification of the welding process through the virtual twin scenario. The implementation and validation process of welding process optimization, based on this digital twin framework, is introduced with a moving arm robot welding example.

With the rapid development of vision sensing, artificial intelligence, and robotics technology, one of the challenges we face is installing more advanced vision sensors on welding robots to achieve intelligent welding manufacturing and obtain high-quality welding components. Depth perception is one of the bottlenecks in the development of welding sensors. This review provides an assessment of active and passive sensing methods for depth perception and classifies and elaborates on the depth perception mechanisms based on monocular vision, binocular vision, and multi-view vision. It explores the principles and means of using deep learning for depth perception in robotic welding processes. Further, the application of welding robot visual perception in different industrial scenarios is summarized. Finally, the problems and countermeasures of welding robot visual perception technology are analyzed, and developments for the future are proposed. This review has analyzed a total of 2662 articles and cited 152 as references. The potential future research topics are suggested to include deep learning for object detection and recognition, transfer deep learning for welding robot adaptation, developing multi-modal sensor fusion, integrating models and hardware, and performing a comprehensive requirement analysis and system evaluation in collaboration with welding experts to design a multi-modal sensor fusion architecture.

The seam tracking operation is essential for extracting welding seam characteristics which can instruct the motion of a welding robot along the welding seam path. The chief tasks for seam tracking would be divided into three partitions. First, starting and ending points detection, then, weld edge detection, followed by joint width measurement, and, lastly, welding path position determination with respect to welding robot co-ordinate frame. A novel seam tracking technique with a four-step method is introduced. A laser sensor is used to scan grooves to obtain profile data, and the data are processed by a filtering algorithm to smooth the noise. The second derivative algorithm is proposed to initially position the feature points, and then linear fitting is performed to achieve precise positioning. The groove data are transformed into the robot’s welding path through sensor pose calibration, which could realize real-time seam tracking. Experimental demonstration was carried out to verify the tracking effect of both straight and curved welding seams. Results show that the average deviations in the X direction are about 0.628 mm and 0.736 mm during the initial positioning of feature points. After precise positioning, the average deviations are reduced to 0.387 mm and 0.429 mm. These promising results show that the tracking errors are decreased by up to 38.38% and 41.71%, respectively. Moreover, the average deviations in both X and Z direction of both straight and curved welding seams are no more than 0.5 mm, after precise positioning. Therefore, the proposed seam tracking method with four steps is feasible and effective, and provides a reference for future seam tracking research.

Underwater welding robots play a crucial role in addressing challenges such as low efficiency, suboptimal performance, and high risks associated with underwater welding operations. These robots face a dual challenge encompassing both hardware deployment and software algorithms. Recent years have seen significant interest in humanoid robots and artificial intelligence (AI) technologies, which hold promise as breakthrough solutions for advancing underwater welding capabilities. Firstly, this review delves into the hardware platforms envisioned for future underwater humanoid welding robots (UHWR), encompassing both underwater apparatus and terrestrial support equipment. Secondly, it provides an extensive overview of AI applications in underwater welding scenarios, particularly focusing on their implementation in UHWR. This includes detailed discussions on multi-sensor calibration, vision-based three-dimensional (3D) reconstruction, extraction of weld features, decision-making for weld repairs, robot trajectory planning, and motion planning for dual-arm robots. Through comparative analysis within the text, it becomes evident that AI significantly enhances capabilities such as underwater multi-sensor calibration, vision-based 3D reconstruction, and weld feature extraction. Moreover, AI shows substantial potential in tasks like underwater image enhancement, decision-making processes, robot trajectory planning, and dual-arm robot motion planning. Looking ahead, the development trajectory for AI in UHWR emphasizes multifunctional models, edge computing in compact models, and advanced decision-making technologies in expansive models.

Impellers are widely used in industrial equipment. Currently, the welding of impeller blades is mainly accomplished by manual welding. Aiming at the current situation of impeller production, this paper mainly introduces a novel welding path planning method based on point cloud for robotic welding of impeller blades. Firstly, in order to get rid of the traditional teaching-playback mode and offline programming method of welding robots, this paper adopts the scheme of the automatic welding path planning based on point cloud obtained by a three-dimensional vision structured light camera. To facilitate subsequent sampling and filtering of point cloud, a novel method for three-dimensional camera pose planning is proposed to accurately and efficiently obtain the point cloud and coordinates containing the welding seam information. After filtering the impeller point cloud, a novel algorithm for rough extraction of impeller blades welding seam scattered point cloud based on distance information is proposed. We use MATLAB simulation to choose a polynomial fitting method based on least squares to fit the welding seam scattered point cloud to adapt to the spatial characteristics and diversity of welding seam. Finally, we perform discrete interpolation on the fitted welding seam point cloud to realize the impeller blade welding path planning. Experimental results show that the proposed method can accurately and efficiently realize the welding path planning for impeller blades robotic welding and complete the welding task without teaching and programming before welding.

To meet the demand of different vehicle models welding task, as well as lower the cost of specialized welding facility, a kind of 3P2R arc welding robot has been developed and SolidWorks is used to set up the Three Dimensional (3D) solid model. The developed robot includes 3P2R body, controller, and a welding power supply. The direct kinematical model and inverse solution have been worked out basing on Denavit-Hartenberg (D-H) method, and the direct kinematical model and inverse solution are verified by experiment. This robot could also be used to burring or spray field with the actuator changed.

With the rapid development of artificial intelligence and intelligent manufacturing, the traditional teaching-playback mode and the off-line programming (OLP) mode cannot meet the flexible and fast modern manufacturing mode. Therefore, the intelligent welding robots have been widely developed and applied into the industrial production lines to improve the manufacturing efficiency. The sensing system of welding robots is one of the key technologies to realize the intelligent robot welding. Due to its unique characteristics of good robustness and high precision, the structured light sensor has been widely developed in the intelligent welding robots. To adapt to different measurement tasks of the welding robots, many researchers have designed different structured light sensors and integrated them into the intelligent welding robots. Therefore, the latest research and application work about the structured light sensors in the intelligent welding robots is analyzed and summarized, such as initial weld position identification, parameter extraction, seam tracking, monitoring of welding pool, and welding quality detection, to provide a comprehensive reference for researchers engaged in these related research work as much as possible.

The paper presents a comprehensive identification and assessment of occupational hazards related to the operation of robotic welding systems. New technology fundamentally improves quality and accuracy of welds, especially precise narrow seams connecting thin sheets or profiles. The paper draws attention to typical occupational hazards occurring in the workplace, as well as those resulting from the use of robotic welding devices. Risk assessment was performed using the commonly used RISK SCORE method. Moreover, as part of our own research, the employee’s reliability levels were determined by determining the probability of making a mistake. The human factor reliability study was carried out using HRA (Human Reliability Analysis) methods. The classic TESEO and HEART approaches have been expanded to include additional factors, such as human-machine interface (HMI), cognitive load, quality of documentation and procedures, and task ambiguity. This addition addresses a significant methodological gap in classic HRA analyses, which have so far overlooked these key aspects that influence the effectiveness and safety of human-robot interactions.

Feng, X.-B.; Gao, L.-S.; Tian, W.; Wei, R.; Wang, Z.-W., and Chen, Y., 2020. Application of wall climbing welding robot in automatic welding of island spherical tank. In: Qiu, Y.; Zhu, H., and Fang, X. (eds.), Current Advancements in Marine and Coastal Research for Technological and Sociological Applications. Journal of Coastal Research, Special Issue No. 107, pp. 1-4. Coconut Creek (Florida), ISSN 0749-0208. With the rapid development of China's energy industry, marine energy exploitation has become an important part of China's oil supply, which has adjusted China's energy structure. Therefore, in the exploitation of offshore oil such as islands, spherical tank engineering has been greatly affected, which requires us to improve the automatic welding technology of wall climbing robot. Through the automatic welding technology of climbing wall welding robot, we can improve the welding quality and efficiency of spherical tank, which will improve the all position welding technology of spherical tank in China. Therefore, the disadvantages of low efficiency of traditional electrode arc welding process can't meet the needs of offshore oil storage and transportation, which requires us to improve the automatic welding technology of spherical tank wall climbing welding robot, which has become the dream of Offshore Petrochemical construction. Firstly, this paper analyzes the application of spherical tank welding robot. Then, the fusion welding forming model based on BP neural network is studied. Finally, this paper analyzes the key technology trend in the future.