Search Results (220)

In response to the challenges associated with weld treatment during the on-site corrosion protection of hydraulic steel gates, this paper proposes a method utilizing a magnetic adsorption climbing robot to perform corrosion protection operations. Firstly, a magnetic adsorption climbing robot with a multi-wheel independent drive configuration is proposed as a mobile platform. The robot body consists of six joint modules, with the two middle joints featuring adjustable suspension. The joints are connected in series via an EtherCAT bus communication system. Secondly, the kinematic model of the climbing robot is analyzed and a PID trajectory tracking control method is designed, based on the kinematic model and trajectory deviation information collected by the vision system. Subsequently, the proposed kinematic model and trajectory tracking control method are validated through Python3 simulation and actual operation tests on a curved trajectory, demonstrating the rationality of the designed PID controller and control parameters. Finally, an intelligent software system for weld defect detection based on computer vision is developed. This system is demonstrated to conduct defect detection on images of the current weld position using a trained model. Full article

Traditional industrial robot programming methods often pose high usage thresholds due to their inherent complexity and lack of standardization. Manufacturers typically employ proprietary programming languages or user interfaces, resulting in steep learning curves and limited interoperability. Moreover, conventional systems generally lack capabilities for remote control and real-time status monitoring. In this study, a novel approach is proposed by integrating digital twin technology with traditional robot control methodologies to establish a virtual–real mapping architecture. A high-precision and efficient digital twin-based control platform for industrial robots is developed using the Unity3D (2022.3.53f1c1) engine, offering enhanced visualization, interaction, and system adaptability. The high-precision twin environment is constructed from the three dimensions of the physical layer, digital layer, and information fusion layer. The system adopts the socket communication mechanism based on TCP/IP protocol to realize the real-time acquisition of robot state information and the synchronous issuance of control commands, and constructs the virtual–real bidirectional mapping mechanism. The Unity3D platform is integrated to develop a visual human–computer interaction interface, and the user-oriented graphical interface and modular command system effectively reduce the threshold of robot use. A spatially curved part welding experiment is carried out to verify the adaptability and control accuracy of the system in complex trajectory tracking and flexible welding tasks, and the experimental results show that the system has high accuracy as well as good interactivity and stability. Full article

With the rise of digital twin technology, the application of digital twin technology to industrial automation provides a new direction for the digital transformation of the global smart manufacturing industry. In order to further improve production efficiency, as well as realize enterprise digital empowerment, this paper takes a welding robot arm as the research object and constructs a welding robot arm digital twin system. Using three-dimensional modeling technology and model rendering, the welding robot arm digital twin simulation environment was built. Parent–child hierarchy and particle effects were used to truly restore the movement characteristics of the robot arm and the welding effect, with the help of TCP communication and Bluetooth communication to realize data transmission between the virtual segment and the physical end. A variety of UI components were used to design the human–machine interaction interface of the digital twin system, ultimately realizing the data-driven digital twin system. Finally, according to the digital twin maturity model constructed by Prof. Tao Fei’s team, the system was scored using five dimensions and 19 evaluation factors. After testing the system, we found that the combination of digital twin technology and automation is feasible and achieves the expected results. Full article

Welding robots play a crucial role in manufacturing industries, where minimizing energy consumption (EC) is increasingly important for enhancing efficiency and reducing operational costs. This study presents a data-driven approach to model and optimize EC in welding robot systems, utilizing a dataset generated from real-world measurements of robot EC during various motions and integrated with trajectory data. A predictive model was developed using an extreme gradient boosting (XGBoost) regression technique focused on joint torque data, which achieved a mean absolute percentage error (MAPE) of 1.86%. Furthermore, trajectory optimization was achieved by adjusting the spatial position of the workpiece, effectively reducing EC. To solve the optimization problem, an improved whale optimization algorithm (IWOA) was employed. Experimental validations with a welding robot demonstrate that the proposed method not only accurately predicted EC with a MAPE of 2.66% but also reduced the robot system’s EC by 6.72%, outperforming the traditional method focused solely on joint motor EC, which achieved a 4.08% reduction. These results confirm the efficacy of the proposed approach, underscoring its potential for broad application in robotic systems to achieve significant energy savings. Full article

Friction stir spot welding (FSSW) is a solid-state joining technique increasingly favored in industries requiring high-quality, defect-free welds in lightweight and durable structures, such as the automotive, aerospace, and marine industries. This review examines the current advancements in FSSW, focusing on the relationships between microstructure, properties, and performance under load. FSSW offers numerous benefits over traditional welding, particularly for joining both similar and dissimilar materials. Key process parameters, including tool design, rotational speed, axial force, and dwell time, are discussed for their impact on weld quality. Innovations in robotics are enhancing FSSW’s accuracy and efficiency, while numerical simulations aid in optimizing process parameters and predicting material behavior. The addition of nano/microparticles, such as carbon nanotubes and graphene, has further improved weld strength and thermal stability. This review identifies areas for future research, including refining robotic programming, using artificial intelligence for autonomous welding, and exploring nano/microparticle reinforcement in FSSW composites. FSSW continues to advance solid-state joining technologies, providing critical insights for optimizing weld quality in sheet material applications. Full article

Robotic tack welding poses challenges in path optimization due to local optimum entrapment, limited adaptability, and high-dimensional complexity. To overcome these challenges, a Lévy flight-enhanced improved whale optimization algorithm (LF-IWOA) was developed. The algorithm combines elite opposition-based learning (EOBL), differential evolution (DE), and Lévy flight (LF) to improve global exploration capability, increase population diversity, and improve convergence. Additionally, a dynamic trajectory optimization model is designed to consider joint-level constraints, including velocity, acceleration, and jerk. The performance of LF-IWOA was evaluated using two industrial workpieces with varying welding point distributions. Comparative experiments with metaheuristic algorithms, such as the genetic algorithm (GA), WOA and other recent nature-inspired methods, show that LF-IWOA consistently achieves shorter paths and faster convergence. For Workpiece 1, the algorithm reduces the welding path by up to 25.53% compared to the genetic algorithm, with an average reduction of 14.82% across benchmarks. For Workpiece 2, the optimized path is 18.41% shorter than the baseline. Moreover, the dynamic trajectory optimization strategy decreases execution time by 26.83% and reduces mechanical energy consumption by 15.40% while maintaining smooth and stable joint motion. Experimental results demonstrated the effectiveness and practical applicability of the LF-IWOA in robotic welding tasks. Full article

To address the challenges of redundant point cloud processing and insufficient robustness under complex working conditions in existing teaching-free methods, this study proposes a dual-modal perception framework termed “2D image autonomous recognition and 3D point cloud precise planning”, which integrates stereo vision and deep learning. First, an improved U-Net deep learning model is developed, where VGG16 serves as the backbone network and a dual-channel attention module (DAM) is incorporated, achieving robust weld segmentation with a mean intersection over union (mIoU) of 0.887 and an F1-Score of 0.940. Next, the weld centerline is extracted using the Zhang–Suen skeleton refinement algorithm, and weld feature points are obtained through polynomial fitting optimization to establish cross-modal mapping between 2D pixels and 3D point clouds. Finally, a groove feature point extraction algorithm based on improved RANSAC combined with an equal-area weld bead filling strategy is designed to enable multi-layer and multi-bead robot trajectory planning, achieving a mean absolute error (MAE) of 0.238 mm in feature point positioning. Experimental results demonstrate that the method maintains high accuracy under complex working conditions such as noise interference and groove deformation, achieving a system accuracy of 0.208 mm and weld width fluctuation within ±0.15 mm, thereby significantly improving the autonomy and robustness of robot trajectory planning. Full article

Although structural aluminium alloys have many advantages (low self-weight, corrosion resistance, 100% recyclable), they are associated with some conservative design methods in Eurocode 9. Conservative reductions in aluminium’s mechanical properties in the welded connection zone and the limitations of extruded aluminium members (the relatively small dimensions and uniform shape of the profile over the length) significantly limit the use of aluminium in load-bearing structures. This paper summarises the background, planned activities, and preliminary results of the ongoing REAL-fit project. The aim of the project is to conduct comprehensive interdisciplinary research on the feasibility of applying innovative automated (robotic) welding technologies and reliable design methods for aluminium welded members, joints, and entire structural systems. In this paper, the shortcomings of the current design approach are identified, and experimental, numerical, and reliability-based methodology for possible improvements is proposed. Furthermore, the project considers the integration of the advanced direct design method (DDM) with the methods of life cycle assessment (LCA) and life cycle cost analysis (LCCA) as a possible direction for establishing a more holistic evaluation framework. This is precisely one of the project’s ultimate goals, which will assess the reliability and sustainability of economical aluminium structures throughout their life cycle. Full article

Automated and robotic welding have become standard practices in manufacturing, requiring precise control to maintain weld quality without relying on skilled welders. In Tungsten Inert Gas (TIG) welding, monitoring the weld pool is crucial for ensuring the necessary weld penetration, which is vital for maintaining weld integrity. Real-time observation is essential to prevent defects and improve weld quality. Various sensing technologies have been developed to address this need, with vision-based systems showing particular effectiveness in enhancing welding quality and productivity within the framework of Industry 4.0. This review looks at the latest technologies for monitoring weld pools and bead shapes. It covers methods like using Complementary Metal-Oxide Semiconductors (CMOS) to take clear images of the melt pool for better process identification, Active Appearance Model (AAM) to capture 3D images of the weld pool for accurate penetration measurement, and Charge-Coupled Devices (CCD) and Laser-Induced Breakdown Spectroscopy (LIBS) to analyze plasma spectra and create material composition graphs. Full article

Welding robots are essential in modern manufacturing, providing high precision and efficiency in welding processes. To optimize their performance and minimize errors, accurate simulation of their behavior is crucial. This paper presents a novel approach to enhance the simulation of welding robots using the Virtual, Augmented, and Mixed Reality (VAM) simulation platform. The VAM platform offers a dynamic and versatile environment that enables a detailed and realistic representation of welding robot actions, interactions, and responses. By integrating VAM with existing simulation techniques, we aim to improve the fidelity and realism of the simulations. Furthermore, to accelerate the learning and optimization of the welding robot’s behavior, we incorporate deep reinforcement learning (DRL) techniques. Specifically, DRL is utilized for task offloading and trajectory planning, allowing the robot to make intelligent decisions in real-time. This integration not only enhances the simulation’s accuracy but also improves the robot’s operational efficiency in smart manufacturing environments. Our approach demonstrates the potential of combining advanced simulation platforms with machine learning to advance the capabilities of industrial robots. In addition, experimental results show that ANFIS achieves higher accuracy and faster convergence compared to traditional control strategies such as PID and FLC. Full article

Traditional offline programming has limitations for large parts with significant machining or assembly deviations. This study proposes a 3D scanning-assisted method that generates accurate STereoLithography (STL) models and enables multi-layer multi-bead welding trajectory planning for large intersecting line workpieces. The proposed framework implements a robust STL model processing pipeline incorporating Random Sample Consensus (RANSAC)-based cylindrical approximation, cross-sectional slicing, and automated feature detection to achieve high-precision groove feature recognition. For asymmetric variable-section grooves, a multi-layer and multi-pass path-planning algorithm based on template affine projection transformation is developed to ensure accurate deposition of welds along complex geometric contours. Experimental validation demonstrates sub-millimeter trajectory accuracy (positional errors < 1.0 mm), meeting stringent arc welding specifications and substantially expanding the applicability of offline programming systems. Full article

This study addresses the critical challenges of conductor structure fusing, thermal management failure, and thermal runaway risks in lithium-ion batteries under extreme high-amperage discharge conditions. By integrating theoretical analysis, multiphysics coupling simulations, and experimental validation, the research systematically investigates the overcurrent capability of lithium battery conductor structures. A novel current–thermal structure coupled finite element model was developed to analyze the dynamic relationship between key parameters, specifically overcurrent cross-sectional area and contact area, and their influence on temperature gradient distribution. Experimental results confirm the model’s accuracy, revealing that under extreme high-amperage conditions, increasing the conductor cross-sectional area by 50% only marginally extends the battery’s current-carrying duration from 0.75 s to 0.8 s. This limited enhancement is attributed to rapid heat generation, which restricts the effectiveness of increasing the cross-sectional area alone. Instead, optimizing the conductor structure by modifying the heat conduction path, which involves a similar increase in the cross-sectional area and an additional 60% increase in contact area through the addition of a welding reinforcement structure, achieves thermal equilibrium. The optimized design achieves a current-carrying duration of 1.73 s, which is 230% of the duration of the traditional configuration. This work establishes a scalable framework for enhancing the thermal–electrical performance of lithium-ion batteries, providing a theoretical foundation for structural optimization and offering significant methodological support for advancing research in high-power battery design, with potential applications in electric vehicles, renewable energy systems, and industrial robotics. Full article

The Sparrow Search Algorithm (SSA), proposed by Jiankai Xue in 2020, is a swarm intelligence optimization algorithm that has received extensive attention due to its powerful optimization-seeking ability and rapid convergence. However, similar to other swarm intelligence algorithms, the SSA has the problem of being prone to falling into local optimal solutions during the optimization process, which limits its application effectiveness. To overcome this limitation, this paper proposes a Modified Sparrow Search Algorithm (MSSA), which enhances the algorithm’s performance by integrating three optimization strategies. Specifically, the Latin Hypercube Sampling (LHS) method is employed to achieve a uniform distribution of the initial population, laying a solid foundation for global search. An adaptive weighting mechanism is introduced in the producer update phase to dynamically adjust the search step size, effectively reducing the risk of the algorithm falling into local optima in later iterations. Meanwhile, the cat mapping perturbation and Cauchy mutation operations are integrated to further enhance the algorithm’s global exploration ability and local development efficiency, accelerating the convergence process and improving the quality of the solutions. This study systematically validates the performance of the MSSA through multi-dimensional experiments. The MSSA demonstrates excellent optimization performance on 23 benchmark test functions and the CEC2019 standard test function set. Its application to three practical engineering problems, namely the design of welded beams, reducers, and cantilever beams, successfully verifies the effectiveness of the algorithm in real-world scenarios. By comparing it with deterministic algorithms such as DIRET and BIRMIN, and based on the five-dimensional test functions generated by the GKLS generator, the global optimization ability of the MSSA is thoroughly evaluated. In addition, the successful application of the MSSA to the problem of robot path planning further highlights its application advantages in complex practical scenarios. Experimental results show that, compared with the original SSA, the MSSA has achieved significant improvements in terms of convergence speed, optimization accuracy, and robustness, providing new ideas and methods for the research and practical application of swarm intelligence optimization algorithms. Full article

This study proposes an improved stiffness ellipsoid method to enhance the stiffness and precision of robotic arms in friction stir welding (FSW) operations. The method involves establishing a joint stiffness model through static identification experiments and developing a novel stiffness index derived from the improved stiffness ellipsoid method. This index provides a refined metric for evaluating the robot’s performance under variable loads during FSW. Simulation experiments demonstrate significant improvements in welding trajectory precision and computational efficiency. The findings highlight the potential of this method to elevate FW quality and consistency. Full article

In aerospace and new-energy vehicle manufacturing, there is an increasing demand for the high-quality joining of large, curved aluminum alloy structures. This study presents a robotic friction stir welding (RFSW) system employing a force/position hybrid control. An eight-axis linkage platform integrates an electric spindle, multidimensional force sensors, and a laser displacement sensor, ensuring trajectory coordination between the robot and the positioner. By combining long-range constant displacement with small-range constant pressure—supplemented by an adaptive transition algorithm—the system regulates the axial stirring depth and downward force. The experimental results confirm that this approach effectively compensates for robotic flexibility, keeping weld depth and pressure deviations within 5%, significantly improving seam quality. Further welding verification was performed on typical curved panels for aerospace applications, and the results demonstrated strong adaptability under high-load, multi-DOF conditions, without crack formation. This research could advance the field toward more robust, automated, and adaptive RFSW solutions for aerospace, automotive, and other high-end manufacturing applications. Full article