基于特勒根定理的风场送出线路新型纵联保护

双馈风机和永磁直驱风机广泛应用于陆上风电场。受控制策略影响,送出线路故障时风场侧故障特性与同步机电源差异显著,使传统纵联保护性能下降。特勒根定理建立于基尔霍夫电压、电流定律,仅与电路拓扑结构有关,对含线性/非线性、时变/时不变元件的电路均能适用。将特勒根定理应用到陆上风场送出系统,建立了满足定理应用要求的线路故障拓扑结构图。以此为基础,分析了能够区分内、外部故障的特征量,提出了一种基于故障前后线路两端电压电流的新型纵联保护动作判据。利用风场多个故障案例的录波数据和基于PSCAD的仿真数据对所提判据进行验证及对比分析,证明了所提纵联保护方法的优越性和可行性。     

AR-based deep learning for real-time inspection of cable brackets in aircraft

In the process of aircraft assembly, there exist numerous and ubiquitous cable brackets that shall be installed on frames and subsequently need to be manually verified with CAD models. Such a task is usually performed by special operators, hence is time-consuming, labor-intensive, and error-prone. In order to save the inspection time and increase the reliability of results, many researchers attempt to develop intelligent inspection systems using robotic, AR, or AI technologies. However, there is no comprehensive method to achieve enough portability, intelligence, efficiency, and accuracy while providing intuitive task assistance for inspectors in real time. In this paper, a combined AR+AI system is introduced to assist brackets inspection in a more intelligent yet efficient manner. Especially, AR-based Mask R-CNN is proposed by skillfully integrating markerless AR into deep learning-based instance segmentation to generate more accurate and fewer region proposals, and thus alleviates the computation load of the deep learning program. Based on this, brackets segmentation can be performed robustly and efficiently on mobile devices such as smartphones or tablets. By using the proposed system, CAD model checking can be automatically performed between the segmented physical brackets and the corresponding virtual brackets rendered by AR in real time. Furthermore, the inspection results can be directly projected on the corresponding physical brackets for the convenience of maintenance. To verify the feasibility of the proposed method, experiments are carried out on a full-scale mock-up of C919 aircraft main landing gear cabin. The experimental results indicate that the inspection accuracy is up to 97.1%. Finally, the system has been deployed in the real C919 aircraft final-assembly workshop. The preliminary evaluation reveals that the proposed real-time AR-assisted intelligent inspection approach is effective and promising for large-scale industrial applications.     

A deep learning-enabled human-cyber-physical fusion method towards human-robot collaborative assembly

Human-robot collaborative (HRC) assembly has become popular in recent years. It takes full advantage of the strength, repeatability and accuracy of robots and the high-level cognition, flexibility and adaptability of humans to achieve an ergonomic working environment with better overall productivity. However, HRC assembly is still in its infancy nowadays. How to ensure the safety and efficiency of HRC assembly while reducing assembly failures caused by human errors is challenging. To address the current challenges, this paper proposes a novel human-cyber-physical assembly system (HCPaS) framework, which combines the powerful perception and control capacity of digital twin with the virtual-reality interaction capacity of augmented reality (AR) to achieve a safe and efficient HRC environment. Based on the framework, a deep learning-enabled fusion method of HCPaS is proposed from the perspective of robot-level fusion and part-level fusion. Robot-level fusion perceives the pose of robots with the combination of PointNet and iterative closest point (ICP) algorithm, where the status of robots together with their surroundings could be registered into AR environment to improve the human's cognitive ability of complex assembly environment, thus ensuring the safe HRC assembly. Part-level fusion recognizes the type and pose of parts being assembled with a parallel network that takes an extended Pixel-wise Voting Network (PVNet) as the base architecture, on which assembly sequence/process information of the part could be registered into AR environment to provide smart guidance for manual work to avoid human errors. Eventually, experimental results demonstrate the effectiveness and efficiency of the approach.     

Improved genetic algorithm based on multi-layer encoding approach for integrated process planning and scheduling problem

Integrated process planning and scheduling (IPPS) is of great significance for modern manufacturing enterprises to achieve high efficiency in manufacturing and maximize resource utilization. In this paper, the integration strategy and solution method of IPPS problem are deeply studied, and an improved genetic algorithm based on multi-layer encoding (IGA-ML) is proposed to solve the IPPS problem. Firstly, considering the interaction ability between the two subsystems and the multi-flexibility characteristics of the IPPS problem, a new multi-layer integrated encoding method is designed. The encoding method includes feature layer, operation layer, machine layer and scheduling layer, which respectively correspond to the four sub-problems of IPPS problem, which provides a premise for a more flexible and deeper exploration in the solution space. Then, based on the coupling characteristics of process planning and shop scheduling, six evolutionary operators are designed to change the four-layer coding interdependently and independently. Two crossover operators change the population coding in the unit of jobs, and search the solution space globally. The four mutation operators change the population coding in the unit of gene and search the solution space locally. The six operators are used in series and iteratively optimized to ensure a fine balance between the global exploration ability and the local exploitation ability of the algorithm. Finally, performance of IGA-ML is verified by testing on 44 examples of 14 benchmarks. The experimental results show that the proposed algorithm can find better solutions (better than the optimal solutions found so far) on some problems, and it is an effective method to solve the IPPS problem with the maximum completion time as the optimization goal.     

Neural reactive path planning with Riemannian motion policies for robotic silicone sealing

Due to its excellent chemical and mechanical properties, silicone sealing has been widely used in many industries. Currently, the majority of these sealing tasks are performed by human workers. Hence, they are susceptible to labor shortage problems. The use of vision-guided robotic systems is a feasible alternative to automate these types of repetitive and tedious manipulation tasks. In this paper, we present the development of a new method to automate silicone sealing with robotic manipulators. To this end, we propose a novel neural path planning framework that leverages fractional-order differentiation for robust seam detection with vision and a Riemannian motion policy for effectively learning the manipulation of a sealing gun. Optimal control commands can be computed analytically by designing a deep neural network that predicts the acceleration and associated Riemannian metric of the sealing gun from feedback signals. The performance of our new methodology is experimentally validated with a robotic platform conducting multiple silicone sealing tasks in unstructured situations. The reported results demonstrate that compared with directly predicting the control commands, our neural path planner achieves a more generalizable performance on unseen workpieces and is more robust to human/environment disturbances.     

Digital twin-driven manufacturing equipment development

Currently, expectations of shorter time-to-market and improved product performance are placing greater demands on manufacturing companies. However, the optimization and redesign work between the design stage and the prototype design and manufacturing stage in the traditional product development process lengthens the required product development cycle time (which lasts up to several years in extreme cases). The manufacturing phase for the physical prototype of the product is especially time-consuming and costly. The above reasons make the common product development process increasingly unable to meet the demands of market needs. Motivated by this need, the digital twin (DT)-driven manufacturing equipment (ME) development method is studied in this paper. This method contains three main core elements of the design method based on axiomatic design (AD) theory, the construction of DT models related to ME development, and DT-based validation analysis. The advantage of this method is that it can incorporate the physical prototype manufacturing stage into the digital space with the high-fidelity model provided by the DT technology, which ensures the confidentiality of the design scheme validation while freeing it from the physical prototype stage. This avoids the cost of physical prototyping, shortens the product development cycle, and improves the efficiency of new ME development. At the end of this paper, a case study of the development of a virtual machining dynamic performance test bench (VM-TB) is carried out to show the implementation flow of this proposed method, and its operability and effectiveness are verified.     

Differential geometry modeling and application of roller pose and trajectory of robot roller hemming for complex curved surface-curved edge panels

Roller hemming is a relatively new process used to achieve high-precision assembly of auto-body enclosure panels. During the process of roller hemming, accuracy of the roller pose and trajectory affects the hemming quality of the product. The traditional passive method based on robot teaching to determine the pose of the roller is inefficient and time-consuming. In these studies, we proposed an active method for solving roller pose and trajectory based on differential geometry for curved surface-curved edge geometric characteristics of auto-body enclosure panels and multi-pass reciprocating motions of the roller. Firstly, the local coordinate system of the die was constructed based on the Frenet Frame according to the normal vector of the surface of die and the tangent vector of the curved die edge. Secondly, the coordinate system of the die, diameter of the roller, TCP-RTP value, and inclination of the roller were combined to form the roller pose based on a homogeneous transformation matrix. Based on the obtained trajectory curve of the roller reference point, the equal chord deviation error method was used to analyze the roller trajectory. Finally, a roller pose and trajectory solving algorithm was developed based and implemented using PYTHON to obtain the positions and poses of the roller at several discrete reference points. ABAQUS software was subsequently utilized to complete modeling of the roller pose and trajectory. This research supports the multi-field mechanical simulation of robot roller hemming for curved surface-curved edge panels and provides support for determining roller pose and kinematic trajectory of industrial robot roller hemming for curved surface-curved edge panels.     

Mitigating Surface Deficiencies of Perovskite Single Crystals Enables Efficient Solar Cells with Enhanced Moisture and Reverse-Bias Stability

Metal halide perovskite single crystals are promising for diverse optoelectronic applications due to their outstanding properties. In comparison to the bulk, the crystal surface suffers from high defect density and is moisture sensitive; however, surface modification strategies of perovskite single crystals are relatively deficient. Herein, solar cells based on methylammonium lead triiodide (MAPbI₃) thin single crystals are selected as a prototype to improve single-crystal perovskite devices by surface modification. The surface trap passivation and protection against moisture of MAPbI₃ thin single crystals are achieved by one bifunctional molecule 3-mercaptopropyl(dimethoxy)methylsilane (MDMS). The sulfur atom of MDMS can coordinate with bare Pb²⁺ of MAPbI₃ single crystals to reduce surface defect density and nonradiative recombination. As a result, the modified devices show a remarkable efficiency of 22.2%, which is the highest value for single-crystal MAPbI₃ solar cells. Moreover, MDMS modification mitigates surface ion migration, leading to enhanced reverse-bias stability. Finally, the cross-link of silane molecules forms a protective layer on the crystal surface, which results in enhanced moisture stability of both materials and devices. This work provides an effective way for surface modification of perovskite single crystals, which is important for improving the performance of single-crystal perovskite solar cells, photodetectors, X-ray detectors, etc.     

50 kW双透平气浮转子系统动力学特性研究

本文以海洋温差能发电为背景,设计了以气体轴承为支撑的50 kW双透平转子系统。应用ANSYS分析软件,对50 kW双透平转子系统进行动力学仿真计算分析,首先采用模态分析法求得了转子系统各阶临界转速及固有频率;其次,基于模态分析进行谐响应分析,得到叶轮径向幅频特性曲线,验证了转子系统能保持稳定运行;最后,对叶轮进行了离心应力分析。结果表明:双透平转子系统的临界转速为16 241.0 r/min,设计的工作转速符合安全裕度;系统在受到外界激振力时叶轮不会与喷嘴发生碰撞;叶轮的最大变形发生在叶尖处,且从叶片顶部到根部逐渐减小,最大应力发生在叶片顶端靠近轴孔的根部,叶轮在工作时不会损坏叶片。研究结果可为双透平试验与结构优化提供一定的理论依据。     

考虑新能源不确定性的互联电网交换功率鲁棒优化模型

高比例新能源及其不确定性增加了多区域交流互联系统交换功率控制的难度,为保证区域间联络线传输功率的协调分布及其波动相对均衡,提出了一种考虑互联电网外特性的交换功率稳定控制模型。考虑互联系统自动发电控制(AGC)参与因子与联络线潮流分布的关联关系,推导并构建了互联系统AGC下的潮流模型。进而,以联络线功率波动幅值最小为目标函数,建立了基于鲁棒优化的区域交换功率稳定控制模型,并提出了一种基于松弛法的协同演化算法对该模型进行求解。以双IEEE 14节点和IEEE 118节点测试系统为例进行仿真分析,将无优化与鲁棒优化计算结果进行对比,结果表明鲁棒优化可以有效应对新能源随机出力的影响,对于外特性的稳定性具有明显改善效果,并验证了所提算法的有效性和实用性。