基于数字孪生的航空发动机低压涡轮单元体对接技术研究

提出一种基于数字孪生的航空发动机低压涡轮单元体对接技术,以某型航空发动机总装装配的低压涡轮单元体对接安装关键过程为对象,采用数字孪生技术,通过对环境、工艺过程中的物理对象建模,并使用多传感器进行模型与物理对象之间数据映射与互联,实现航空发动机低压涡轮单元体对接工艺过程与3D虚拟对接仿真过程的物理融合、模型融合、数据融合。通过数据在虚拟仿真环境中的可视化展示与分析,实时预警及决策,并借助物理终端控制实现低压涡轮单元体对接安装过程的实时位姿调整,提高了真实对接过程的可视性、可达性、可操作性和可预测性。基于数字孪生的低压涡轮单元体对接技术可保证在复杂装配条件、高精度要求下,真实单元体装配过程的无磕碰对接,减少操作人员劳动强度。     

基于动力学的张拉整体柔性臂数字孪生系统

连续型机器人因其具有柔顺大变形、灵巧运动等特点,已成为未来提升机器人安全性和交互性的发展趋势,而数字孪生是实现机器人-环境-人之间共融共存的重要技术保障.本文以张拉整体连续型柔性臂为研究对象,结合数字孪生和虚拟仿真等技术,让张拉整体柔性臂在虚拟空间和实际物理空间中得以深度融合.搭建数据通讯架构实现数据实时传输和驱动,以提升柔性臂与人的协同工作效率,并可在复杂的环境中通过碰撞检测反馈实现动态避障.进一步,开发了一款基于动力学的张拉整体柔性臂数字孪生系统,并通过虚实双向操控验证了所建系统的有效性,为机器人远程智能监测与控制提供了参考.     

面向配电网终端设备的数字孪生映射方法研究

提出了一种面向配电网终端设备的数字孪生映射方法,推动电网企业数字化转型和电力系统自动化,降低成本与安全风险。方法利用虚拟数字孪生空间映射电网物理设备,实现对区域配电网多类型设备故障的实时诊断。首先,对配电网终端设备数据进行压缩转换和级联映射,提取感知终端监测信息与物理拓扑连接关系。其次,采用改进的轻量级Yolov4网络模型进行设备类型识别与分割,结合物理拓扑图进行数字孪生映射,形成设备孪生模型。最后,设计基于卷积注意力机制的状态评估模型,充分考虑区域设备关联与故障特征,实现对设备孪生模型的故障评估及物理空间设备的状态反馈。上述创新方法有望为电力行业带来更高效、安全的运行模式,推进电网智能化发展。     

工业互联网支持下的数字孪生车间

随着信息时代的发展,数字孪生技术已成为实现智能制造的方法之一。首先说明了数字孪生的产生背景,数字孪生技术是实现信息空间和物理空间信息数据融合的重要方法、手段。接着从模型、数据、应用等不同方面,对智能车间数字孪生技术进行了探讨,同时比对了不同公司对数字孪生技术在车间应用的侧重点。通过比较智能车间相关概念,得出了数字孪生车间是一种新的车间运行模式,其关键问题是实时数据传输。工业互联网是一种将人、数据和设备等连接起来的网络技术,通过物联网等技术可以实现实时数据传输。分析了工业互联网技术的发展及工业互联网技术对实现数字孪生车间的技术支持,最后对数字孪生技术的发展进行了展望。随着数字孪生技术在车间中的运用,数字孪生车间必将成为未来智能车间的一种重要的实现方式。     

基于一致性度量的数字孪生模型实时自修正

数字孪生技术解决了信息物理世界的融合难题,在工业互联网领域里获得了十分广泛的应用。为解决数字孪生与物理实体的动态修正问题,本文提出一种基于一致性度量的数字孪生模型实时自修正方法。利用数据变化快慢将模型分为渐变模型和快速模型2个部分,构建参数快速搜索方法,结合拉丁超立方全局搜索和贪婪局部搜索,并引入迭代更新机制,实现物理实体和数字孪生体的一致性度量。实验结果表明,数字孪生模型通过优化模型可调参数的选取过程,改善可调参数选取随机性的问题,实现模型与物理实体高度一致性,达到了模型实时自修正要求。     

一个多机器人制造系统的设计与实现

本文构建了一个包含多个机器人和自动化物流单元的柔性制造系统，其中机器人部分包括一个自动导引小车和两台装配机器人，物流单元部分包括一个小型立体仓库和一条自动传输线．系统采用递阶分散式的体系结构，每个作业单元均有独立的控制器，并通过串行接口实现与主控计算机的实时通讯．在对系统作业任务进行有限状态机建模的基础上，采用基于事件的控制思想实现了多个作业单元的协调控制，提高了系统的柔性和鲁棒性，实验结果也证明了系统设计的有效性．     

基于数字孪生技术的三维智能化系统设计

为了提升三维智能化水平,设计基于数字孪生技术的三维智能化系统。通过现场层管理数字孪生体映射的物理实体与监测对象,为三维智能化系统提供需要的基础数据;感知层利用温度传感器与颜色传感器等,感知现场层管理的物理实体相关数据;数据存储层利用分布式文件与关系型数据库,存储感知的数据;功能层利用数据预处理模块缺失值处理存储的数据;层次化建模模块,依据缺失值处理后的数据,建立物理实体的几何模型与属性模型,融合几何模型与属性模型,得到孪生体模型;孪生体模型驱动单元,利用三维模型驱动方法,驱动孪生体模型,实现物理实体与孪生体的同步映射,得到孪生数据;仿真与预测模块依据孪生数据,可视化监测与分析物理实体的工作状态;通过应用层为用户实时呈现物理实体的动作仿真与实时监控结果。实验证明：该系统可有效感知物理实体的相关数据,建立孪生体模型;该系统可有效实现物理实体的可视化管理,提升三维智能化水平。     

实时在线监控系统的三维可视化方案

为解决核反应堆辐射及不可接近环境下工人运维的技术难题,融合新一代信息技术,将数字孪生技术与工业互联技术相结合,提出了一套三维数据可视化实现方案,解决了数字孪生应用中三维模型呈现、实时数据对接、模型算法软件对接,以及数据可视化等关键问题。首先,利用3D Studio Max、SolidWorks等建模软件构建三维模型,并结合前端设计、虚拟场景渲染等技术,实现数字孪生可视化场景构建;其次,基于Node. js运行环境搭建WebSocket服务器,并通过Node-EPICS事件驱动读取实验物理和工业控制系统（EPICS）的过程变量,实现对设备支持层数据更新事件的监听;最后,利用Socket. io套接字创建双向数据通道实现服务器到Unity客户端的实时数据传输。使用该方案构建的反应堆三维监控系统现已应用于中国科学院（CAS）钍基熔盐堆（TMSR）核能项目,该系统实现了核反应设备结构及物理特性在虚拟环境中的数字映射,具备网络通信、数据显示等功能,且数据更新周期达到100 ms,有助于监控人员掌握现场实际情况与指导运维检修。     

基于接触状态感知发育的机器人柔性装配方法

为了提高工业机器人装配的实时性、自适应性和鲁棒性,借鉴人类后天感知学习方式,提出一种基于接触状态感知发育的柔性装配方法.采用机器人末端的位姿和力/力矩来描述装配接触状态,结合支持向量数据描述和改进极限学习机对接触状态感知发育,形成可自我更新成长的经验知识库,预测机器人的装配动作,完成柔性装配任务.为验证所提出方法的有效性,以小型断路器卡合装配为例进行实验,实验结果表明,采用接触状态感知发育可实现装配经验知识库的自我更新,完成机器人的柔性装配,验证了所提出方法的可行性和有效性.     

数字孪生: 跨界赋能于多领域智能的新应用

随着大数据、5G、人工智能、CPS、云计算、物联网技术的发展与交叉融合, 使得世界朝着数字化、智能化方向发展. 数字孪生是以物理实体为原型建立多维虚拟模型, 通过安装在物理本体上的传感器实时反馈数据, 并结合以往的历史数据和人工智能技术, 最后利用软件分析并呈现. 由于数字孪生技术能与多个先进理念, 如: 工业4.0、航空航天、智慧城市、智慧医疗等良好的融合并应用, 这使其成为多个行业的热门研究方向与主要驱动技术, 在各行各业都有很大的发展空间. 本文首先阐述了数字孪生技术的基本概念, 梳理了数字孪生技术的发展脉络, 进一步理清了数字孪生技术与CPS技术之间的关系, 并介绍了数字孪生技术的研究现状. 其次, 介绍了数字孪生的关键技术即多维多尺度建模, 孪生数据管理和虚拟呈现. 最后, 探讨了数字孪生技术在智慧工厂领域、智慧城市领域、孪生医疗领域、航空航天领域的应用发展和方向, 并从方案、特点、关键技术等角度介绍了本研究团队在智慧工厂领域对原稳加热炉设备的数字孪生应用案例.     

柔索牵引式力觉交互机器人控制策略

为了让航天员在没有太空真实环境的地面上模拟太空环境进行虚拟作业训练,设计了一种与虚拟现实（VR）技术相结合的柔索牵引式力觉交互机器人．首先,根据微重力环境中物体的运动特性设计机器人的构型,建立移动平台、驱动单元、人推物体运动过程的动力学模型并进行运动学分析．然后,针对系统冗余驱动及力控制任务,提出一种复合控制策略,即以柔索长度变化为速度控制内环,力的外环控制为力／速混合控制．最后,分别进行单柔索加载和人机系统力觉交互仿真分析,分析结果表明该控制策略可以使柔索驱动单元降低10%的恒力跟随误差并能稳定地跟随余弦力的变化,验证了该控制策略对多余力抑制的有效性．     

A digital twin-driven approach towards traceability and dynamic control for processing quality

Processing quality is the basis for ensuring product quality, and reflects the development needs and application value of realizing intelligent manufacturing. Aiming at the low efficiency of quality problems traceability, poor timeliness and unpredictability of quality control in the machining process, digital twin technology can provide a new intelligent solution based on interaction and integration between physical workshop and virtual workshop. Therefore, a digital twin-driven approach towards traceability and dynamic control for processing quality is proposed in the paper. Firstly, a Bayesian network model for the analysis of factors affecting processing quality (BN_PQ) is introduced, which determines the relevance and influence weight of each factor to processing quality. Secondly, in order to integrate multi-source heterogeneous data to trace the processing quality, a multi-level scalable information model and association mechanism are established. Moreover, the construction method of the IoT system in manufacturing unit for dynamic control of processing quality are introduced, in which the collection method of real-time data is discussed. The contents of digital twin data for processing quality constraints (DTD_PQ) and the management method are elaborated. Then, the digital twin-driven dynamic control method of processing quality is proposed. The conceptual model of the digital twin database and the operating logic for dynamic control of processing quality are described in detail. Finally, the interactive operation and core technologies of DTD_PQ towards traceability and dynamic control of processing quality are analyzed. By choosing examples of machining the connecting rod of diesel engine and the prototype system that has been developed, the effectiveness of the proposed method is verified.     

Digital twin-based smart assembly process design and application framework for complex products and its case study

With rapid advances in new generation information technologies, digital twin (DT), and cyber-physical system, smart assembly has become a core focus for intelligent manufacturing in the fourth industrial evolution. Deep integration between information and physical worlds is a key phase to develop smart assembly process design that bridge the gap between product assembly design and manufacturing. This paper presents a digital twin reference model for smart assembly process design, and proposes an application framework for DT-based smart assembly with three layers. Product assembly station components are detailed in the physical space layer; two main modules, communication connection and data processing, are introduced in the interaction layer; and we discuss working mechanisms of assembly process planning, simulation, predication, and control management in the virtual space layer in detail. A case study shows the proposed approach application for an experimental simplified satellite assembly case using the DT-based assembly application system (DT-AAS) to verify the proposed application framework and method effectiveness.     

Bi-level dynamic scheduling architecture based on service unit digital twin agents

Pure reactive scheduling is one of the core technologies to solve the complex dynamic disturbance factors in real-time. The emergence of CPS, digital twin, cloud computing, big data and other new technologies based on the industrial Internet enables information acquisition and pure reactive scheduling more practical to some extent. However, how to build a new architecture to solve the problems which traditional dynamic scheduling methods cannot solve becomes a new research challenge. Therefore, this paper designs a new bi-level distributed dynamic workshop scheduling architecture, which is based on the workshop digital twin scheduling agent and multiple service unit digital twin scheduling agents.Within this architecture, scheduling a physical workshop is decomposed to the whole workshop scheduling in the first level and its service unit scheduling in the second level. On the first level, the whole workshop scheduling is executed by its virtual workshop coordination (scheduling) agent embedded with the workshop digital twin consisting of multi-service unit digital twins. On the second level, each service unit scheduling coordinated by the first level scheduling is executed in a distributed way by the corresponding service unit scheduling agent associated with its service unit digital twin. The benefits of the new architecture include (1) if a dynamic scheduling only requires a single service unit scheduling, it will then be performed in the corresponding service unit scheduling without involving other service units, which will make the scheduling locally, simply and robustly. (2) when a dynamic scheduling requires changes in multiple service units in a coordinated way, the first level scheduling will be executed and then coordinate the second level service unit scheduling accordingly. This divide-and-then-conquer strategy will make the scheduling easier and practical.The proposed architecture has been tested to illustrate its feasibility and practicality.     

Digital twin-assisted fault diagnosis system for robot joints with insufficient data

The robot joint is an important component of the construction robot, and its fault diagnosis can ensure the exact execution of building jobs, stable operation, and timely prevention of probable safety mishaps. However, deep learning-based fault diagnosis needs a multitude of measured fault data, which is difficult to obtain for various reasons. To solve the problem of insufficient data, a digital twin-assisted fault diagnosis system for robot joints is proposed. First, a simplified dynamics model of the robot joint is developed to generate the virtual entity data which can be used as the X-domain data for the digital twin model. Second, a CycleGAN-based digital twin model is proposed to map the virtual entity (X-domain) data to the physical entity (Y-domain) utilizing only a small amount of measured data. In the end, a test-rig for the robot joint is built to simulate the robot's working conditions, and the CNN-ResNet classifier is utilized to verify the effectiveness of the simulated data generated by the digital twin model. The results show that the fault diagnosis accuracy can be increased from 32.5% to 98.86% utilizing only 400 sets of measured data.     

Intelligent scheduling of a feature-process-machine tool supernetwork based on digital twin workshop

Modern manufacturing enterprises are shifting toward multi-variety and small-batch production. By optimizing scheduling, both transit and waiting times within the production process can be shortened. This study integrates the advantages of a digital twin and supernetwork to develop an intelligent scheduling method for workshops to rapidly and efficiently generate process plans. By establishing the supernetwork model of a feature-process-machine tool in the digital twin workshop, the centralized and classified management of multiple data types can be realized. A feature similarity matrix is used to cluster similar attribute data in the feature layer subnetwork to realize rapid correspondence of multi-source association information among feature-process-machine tools. Through similarity calculations of decomposed features and the mapping relationships of the supernetwork, production scheduling schemes can be rapidly and efficiently formulated. A virtual workshop is also used to simulate and optimize the scheduling scheme to realize intelligent workshop scheduling. Finally, the efficiency of the proposed intelligent scheduling strategy is verified by using a case study of an aeroengine gear production workshop.     

小型断路器柔性装配中视觉识别系统的设计与应用

现代制造业对小型断路器（MCB）生产过程的效率和精度要求都在不断提高,传统的人工装配效率低且装配质量参差不齐,而传统基于振动盘上料的自动装配技术限制了制造的柔性化水平。针对上述问题以及未来的市场需求,提出了一种基于机器视觉的小型断路器柔性装配系统,该系统搭建专用的视觉识别模块,通过VGG-16架构的深度学习分类器和特征模板匹配方法,对小型断路器零件的种类、位置坐标、当前姿态进行识别,并将识别结果发送给工业机器人控制器,指导工业机器人对不同型号产品的不同零件类型通过机器人夹爪的灵活切换来完成不同的装配任务。实验表明,该系统对零件种类识别准确率为99.8%,坐标偏差在±0.3mm以内,旋转角度偏差在±0.8°以内,达到了MCB装配的精度要求,符合柔性化制造的需求。     

基于数字孪生的智能车间系统仿真加速测试方法

为解决当前制造系统软件可靠性仿真测试时间长、测试环境难以搭建等问题,提出采用数字孪生技术与智能车间系统仿真加速测试相结合的方法;建立智能车间高保真数字孪生模型替代现实生产车间系统用于制造系统软件的可靠性仿真测试,首先要构建包含产品、设备资源、工艺流程等系统级仿真模型;同时,为仿真车间生产事件流程,在模型中,还需结合生产实际情况,设置设备间通信协议、通信数据以及生产线事件及队列顺序,真实模拟系统运行环境;通过构建步进电机产线数字孪生模型,仿真加工装配流程,运行智能车间系统软件,采用仿真时钟推进机制开展加速测试,验证了该方法的有效性和实用性,对开展工业系统软件高保真快速测试评估具有一定的借鉴意义。     

Robot teaching system based on hand-robot contact state detection and motion intention recognition

This paper presents a robot teaching system based on hand-robot contact state detection and human motion intent recognition. The system can detect the contact state of the hand-robot joint and extracts motion intention information from the human surface electromyography (sEMG) signals to control the robot's motion. First, a hand-robot contact state detection method is proposed based on the fusion of the virtual robot environment with the physical environment. With the use of a target detection algorithm, the position of the human hand in the color image of the physical environment can be identified and its pixel coordinates can be calculated. Meanwhile, the synthetic images of the virtual robot environment are combined with those of the physical robot scene to determine whether the human hand is in contact with the robot. Besides, a human motion intention recognition model based on deep learning is designed to recognize human motion intention with the input of sEMG signals. Moreover, a robot motion mode selection module is built to control the robot for single-axis motion, linear motion, or repositioning motion by combining the hand-robot contact state and human motion intention. The experimental results indicate that the proposed system can perform online robot teaching for the three motion modes.