Robotics and Computer-integrated Manufacturing最新文献

In the machining of complex parts with free-formed surfaces, robots are widely employed due to their advantages of a large operating space and high flexibility. The industrial robot with 6 degrees-of-freedom (DOF) has an extra redundant degree of freedom around the tool axis, which does not affect the tool pose related to the workpieces but influences the robot's joint configuration. The motion performance and machining efficiency can be improved by optimizing the redundant angle. Based on this, an analytical path smoothing algorithm for 6-DOF robots with the consideration of redundant kinematics is proposed to improve the robot's dynamic performance. The tool tip position and orientation are fit with the analytical 5th degree Pythagorean-Hodograph (PH) spline to satisfy C² continuity, respectively. Therefore, the 5th degree polynomial spline with minimum acceleration is adopted to fit the redundant rotation angle and the tool tip position arc length. Then the tool path position spline, tool orientation spline, and redundant rotary angle spline are all synchronized to the tool position displacement, which makes it convenient to do interpolation along the tool path. The minimum time feed planning method considering the joint dynamic constraints is adopted to interpolate motion commands. Experimental results show that the motion efficiency of the robot in the same test path increases by 33.97 % compared with the regular spline without considering acceleration of the redundant angle spline. Meanwhile, the proposed tool path smoothing method effectively mitigates the joint vibration with a maximum reduction of 65.05 %, without sacrificing motion accuracy.

Traditional robot trajectory planning and programming methods often struggle to adapt to changing working requirements, leading to repeated programming in manufacturing processes. To address these challenges, a trajectory learning and modification method based on improved Dynamic Movement Primitives (DMPs), called FDC-DMP, is proposed. The method introduces an improved force-controlled dynamic coupling term (FDCT) that uses virtual force as coupling force. This enhancement enables precise and flexible shape modifications within the target trajectory range. The paper also dissects the core dynamic systems of DMP to achieve the reproduction and generalization of both robot position and pose trajectories. The practical feasibility of the proposed method in manufacturing is demonstrated through two case studies on trajectory planning for bus body polishing.

The use of laser beam welding with robotic manipulators is expanding towards wider industrial applications as the system availability increases with reduced capital costs. Conventionally, laser welding requires high positioning and coupling accuracy. Due to the variability in the part geometry and positioning, as well as the thermal deformation that may occur during the process, joint position and fit-up are not always acceptable nor predictable a-priori if simple fixtures are used. This makes the passage from virtual CAD/CAM environment to real production site not trivial, limiting applications where short part preparations are a need like small-batch productions. Solutions that render the laser welding operations feasible for production series with non-stringent tolerances are required to serve a wider range of industrial applications. Such solutions should be able to track the seam as well as tolerating variable gaps formed between the parts to be joined. In this work, an online correction for robot trajectory based on a greyscale coaxial vision system with external illumination and an adaptive wobbling strategy are proposed as means to increase the overall flexibility of a manufacturing plant. The underlying vision algorithm and control architectures are presented; the robustness of the system to poor illumination conditions and variable reflection conditions is also discussed. The developed solution employed two control loops: the first is able to change the robot pose to follow varying trajectories; the second, able to vary the amplitude of circular wobbling as a function of the gap formed in butt-joint welds. Demonstrator cases on butt-joint welds with AISI 301 stainless steel with increased complexity were used to test the efficacy of the solution. The system was successfully tested on 2 mm thick, planar stainless-steel sheets at a maximum welding speed of 25 mm/s and yielded a maximum positioning and yaw-orientation errors of respectively 0.325 mm and 4.5°. Continuous welds could be achieved with up to 1 mm gaps and variable seam position with the developed control method. The acceptable weld quality could be maintained up to 0.6 mm gap in the employed autogenous welding configuration.

Industry 5.0 aims at establishing an inclusive, smart and sustainable production process that encourages human creativity and expertise by leveraging enhanced automation and machine intelligence. Collaborative robotics, or “cobotics”,is a major enabling technology of Industry 5.0, which aspires at improving human dexterity by elevating robots to extensions of human capabilities and, ultimately, even as team members. A pivotal element that has the potential to operate as an interface for the teaming aspiration of Industry 5.0 is the adoption of novel technologies such as virtual reality (VR), augmented reality (AR), mixed reality (MR) and haptics, together known as “augmentation”. Industry 5.0 also benefit from Digital Twins (DTs), which are digital representations of a physical assets that serves as their counterpart — or twins. Another essential component of Industry 5.0 is artificial intelligence (AI), which has the potential to create a more intelligent and efficient manufacturing process. In this study, a systematic review of the state of the art is presented to explore the synergies between cobots, DTs, augmentation, and Industry 5.0 for smart manufacturing. To the best of the author’s knowledge, this is the first attempt in the literature to provide a comprehensive review of the synergies between the various components of Industry 5.0. This work aims at increasing the global efforts to realize the large variety of application possibilities offered by Industry 5.0 and to provide an up-to-date reference as a stepping-stone for new research and development within this field.

Due to their inherent characteristics, robots inevitably suffer from pose errors, and accurate prediction is the key to error compensation, which facilitates the application of robots in high-precision scenarios. Existing studies almost follow the points-view, and the compensation effect depends entirely on the accuracy of the point prediction, which leads to overconfident prediction results. In order to quantify the pose errors uncertainty and achieve more accurate prediction, a method to quantify of uncertainty in robot pose errors and calibration of reliable compensation values is proposed in this paper. In the proposed method, a distribution-free joint prediction model is designed to realize the simultaneous prediction of points and uncertainty intervals. Based on this, the reliable compensation value calibration strategy is innovatively proposed. The proposed method is verified on five tasks including spatial motions, constant load and milling processing, showing accurate joint prediction capability and reliable accuracy improvement. In addition, through online compensation experiments, the pose errors are reduced by 90 %, which promotes the application of robots in higher-precision scenarios.

The Jacobian model is a prevalent tool for error compensation in multi-axis parallel mechanisms. However, discrepancies between the model's nominal and actual geometrical parameters, combined with equivalent replacements and high-order rounding in the modeling process, lead to equation solving challenges and modeling errors. These inaccuracies result in residual errors in the Jacobian model compensation. To address these problems, this paper proposes an optimal Jacobian correction approach. This is based on a geometrical parameter singularized Jacobian correction model, and a module for the evaluation of coupling errors for multi-axis parallel mechanisms was incorporated. Instead of relying on iterative processes, a singularized geometrical error solution method (SESM) was developed. Through this method, precise derivation of the Jacobian correction parameters is ensured, effectively addressing the indefinite equation challenge and partial posture non-solution problem. Moreover, modeling errors resulting from equivalent infinitesimal replacements and the overlooking of high-order minor values are compensated for by the SESM. It was observed that varying singularized geometrical parameters in the Jacobian model can produce different coupling effects and compensation outcomes. Therefore, a sensitivity-based error predictive evaluation method (EPEM) was introduced. By this method, the optimal correction parameter of the Jacobian model across the entire workspace is identified, ensuring precise pose error compensation. The proposed method was validated using a three-axis parallel mechanism. Through these tests, its superior efficacy was revealed. In comparison to the traditional uncorrected Jacobian compensation, reductions in position and orientation errors by 64.93% and 55.29%, respectively, were achieved. This method provides a new approach for error modeling, equation solving, and parameter correction for multi-axis mechanism error compensation and precision equipment development.

Welding is a method of realizing material connections, and the development of modern sensing technology is pushing this traditional process towards automation and intelligence. Among many sensing methods, visual sensing stands out with its advantages of non-contact, fast response and economic benefits, etc. This paper provides a comprehensive review of visualization methods in the context of specific welding processes in the following five aspects. The problem of IWP location is summarized from two directions of active and passive vision. Weld seam identification and tracking methods are discussed in detail based on the morphological characteristics of the weld seam. The feasibility of different weld path planning methods is analyzed based on the point cloud information and the composite vision information. Two types of monitoring means based on infrared sensing and visible light sensing are summarized taking into account the thermal and morphological characteristics of the weld pool, and welding defect detection technology is summarized by comparing the intelligent detection algorithms and the traditional detection algorithms. Finally, by combining the existing developments in computer technology, composite sensing technology, machine learning technology, and multi-robot control technology, the article concludes with a summary and trends in the development of automated welding technologies.

A production workshop is equipped with diverse equipment and machinery, and manufacturing data generated by different equipment show certain differences. Therefore, developing a method with an information presentation capability for a digital twin workshop system (DTWS) can be challenging due to the numerous information sources and types. The level of detail provided for a DTWS is directly related to the management comprehension of the production status of a physical workshop. Thus, arranging and presenting different data effectively represents a significant challenge. To address this challenge, this study proposes an equipment information self-reflection method with expandability for DTWSs. First, a generic equipment information model with an expandability is developed. This model encompasses all relevant data types in a workshop. The analysis of these data is conducted from two perspectives: workshop equipment categorization and equipment internal composition. The analysis results are used for the generation of extensible styles for property elements and function components. For the DTWSs, these two perspectives offer a standardized specification for equipment information presentation. In addition, a self-reflection method is introduced to address the demand for generating diverse information types ranging from equipment-based to extensible types. The system framework and operational flow of this method are explained in detail. To verify the proposed method, this paper conducts a practical case study using the DTWorks software development kit to simulate a real workshop. The proposed self-reflection method is implemented into the DTWorks. The disparities in equipment information types are analyzed for both pre- and post-implementation of the proposed method. The obtained results demonstrate the efficiency and advantages of the proposed self-reflection method. Finally, the results show that the proposed method exhibits advantageous performance regarding intricate business contexts.

During the powder compaction process, process parameters are required for product quality prediction. However, the inadequacy of compaction data leads to difficulties in constructing models for quality prediction. Meanwhile, the existing data generation methods can only generate required data partially, and fail to generate data for extreme operating conditions and difficult-to-measure quality parameters. To address this issue, a digital twin (DT) enhanced quality prediction method for powder compaction process is presented in this paper. First, a DT model of the powder compaction process with multiple dimensions is constructed and validated. Then, to solve the data inadequacy problem, data of process parameters are generated through an orthogonal experimental design, and are imported into the DT model to generate quality parameters, so as to obtain the virtual data. Finally, the quality prediction for the powder compaction process is achieved by the generative adversarial network-deep neural network (GAN-DNN) method. The effectiveness of the generated virtual data and the GAN-DNN method is verified through experimental comparison. On top of point-to-point validation, a quality prediction system applied in a powder compaction line is developed and implemented to demonstrate the end-to-end practicability of the proposed method.

Industrial robots (IRs), as the core equipment of intelligent manufacturing, play increasingly important roles in various industrial scenarios such as assembly, welding, handling, and spraying, significantly improving production efficiency and product quality. The massive popularization and application of IRs have brought about a sharp increase in energy consumption (EC), and the modeling and optimization of EC is becoming imperative. In this paper, a general energy modeling network DM-PLM for serial IRs based on Dynamic Model (DM) and Power Loss Model (PLM) is proposed by integrating the prior knowledge of IR power composition and dynamic mechanism, enabling efficient and accurate modeling of dynamics, power, and EC under multi-load conditions. Considering the force transmission characteristics of serial robots, this paper proposes an improved bidirectional recurrent neural network (BiRNN) to model the joint dynamics. Additionally, a power loss model based on the ResNet convolutional neural network is employed. Experiments are carried out with a KUKA KR210 heavy-duty robot and a UR5 collaborative robot. The results show that the DM-PLM model incorporating the physical mechanism priors achieves 97 %, 98 %, and 99 % modeling accuracy in joint torques, total power, and EC for both robots under multi-load conditions. In addition, the proposed DM-PLM model is applied to the EC optimization of KUKA KR210 through trajectory planning, which achieves over 30 % EC reduction with the genetic algorithm, providing an effective approach to improving the energy efficiency of serial IRs.