数字孪生与平行系统:发展现状、对比及展望

随着物联网、大数据、人工智能（Artificial intelligence,AI）等技术的发展,针对促进新一代信息技术与制造业深度融合、实现制造物理世界与信息世界交互与共融的需要,数字孪生和平行系统技术成为智能制造和复杂系统管理与控制领域研究的热点.本文对数字孪生和平行系统技术的基本概念、技术内涵、相关应用等进行了研究与总结,对比了两者之间的异同,并分析了两者的发展趋势,预期能够给复杂系统管理与控制领域的研究人员提供一定的参考和借鉴.     

基于多智能体的数字孪生及其在工业中应用的综述

数字孪生是一种将物理实体数字化的技术,通过建立虚拟的数字孪生模型模拟实际的物理过程,以便进行模拟仿真、数据分析和优化设计等操作.鉴于此,分析数字孪生技术在复杂工业生产中的发展历程和研究现状,并重点讨论其概念、国家相关重点研究的政策,以及数字孪生使能技术在各行业的应用.主要途径是分析和综述基于多智能体的数字孪生、基于数字孪生的设计、制造和运维、数字孪生的集成在智能制造中的应用相关的研究成果.此外,提出高炉连续生产数字孪生方案和大飞机多智能体离散制造方案,高炉模型包括成分场大模型和增量学习小模型,该模型可以为数字孪生在复杂流程工业中的应用提供带有增量补偿的机理与计算机视觉相结合的解决方案.在复杂工业制造中,数字孪生和多智能体技术可以提高生产效率和质量,减少能源消耗和废品产生,同时也能够降低复杂度、安全风险和成本.     

数字孪生驱动的火箭控制系统故障诊断研究综述

在第四次工业革命中,智能制造成为各国工业发展的重点方向,数字孪生技术作为一项新兴技术,能够有效实现物理信息的融合;将其应用于火箭控制系统的故障诊断和健康管理,能够进一步提高故障诊断的事前准确性,提升火箭发射的可靠性;本文对数字孪生技术在航天领域的研究现状进行归纳整理;首先梳理了NASA的数字孪生目标,国内领域按照设计、生产、支持服务阶段对数字孪生应用进行分类;其次,按照故障树、专家系统、神经网络、数据驱动的方法阐述控制系统故障诊断的研究现状;在介绍数字孪生驱动的健康管理方法的基础上,提出数字孪生驱动的火箭控制系统的故障诊断方法;详细介绍其基本组成框架,分析关键技术及应用难点,并提出数字孪生健康管理平台的基本流程;该方法预期实现火箭控制系统的事前诊断和维修策略的制定     

智能制造下产品数字孪生体全生命周期研究

针对信息物理融合系统在智能制造领域中的应用新范式,提出了产品数字李生体全生命周期的理论框架。数字孪生的出现为信息物理融合理念的实现提供可行的思路和途径。从数字孪生概念的发展背景出发,分别从物理对象和物理对象的发展过程分析数字李生和数字李生体的定义,在此基础上进一步分析了数字李生体全生命周期的内涵和特征。数字李生体全生命周期分为数字胚胎、数字化映射体和孪生体互长三个阶段,作为物理对象全生命周期的另一条生命线.可以为物理对象全生命周期演化提供指导。最后,利用数字孪生体全生命周期在智能制造背景下的产品全生命周期中发挥的作用映证数字李生体全生命周期的概念,为数字孪生体全生命周期在其他领域的应用提供参考。     

挖掘数字孪生高价值应用，驱动城市数字化“智变”

当前,数字孪生技术加速与经济社会深度融合,我国数字孪生应用场景成熟度进一步提升,智慧工业、智慧水利、智慧交通等中高价值场景成为行业应用热点。然而,数字孪生技术的应用开发深度和广度仍不足、高价值场景仍然较少,需要加大关键技术的研发投入,不断健全产业生态,多措并举培育高价值应用,进而推动城市的数智化发展,为城市的可持续发展和人们的生活带来更多便利和改善。本文基于近三年中国电信5G“绽放杯”应用场景案例梳理,探讨了数字孪生技术在城市数智化发展中的作用,分析了我国数字孪生应用发展情况及面临的挑战,并提出了针对性的研究建议,旨在推动数字孪生高价值应用的快速发展,为城市数智化发展提供参考。     

互联网+安全技术在某民企制造信息化中的应用

智能制造即制造业数字化、网络化和智能化,是我国制造业创新发展和加快建设制造强国的主攻方向.随着新一代人工智能技术与先进制造技术的深度融合,新一代智能制造的突破和广泛应用将重塑制造业技术体系、生产模式和产业生态,进而实现第四次工业革命.针对某民企实施中国制造2025存在自动化程度偏弱、智能制造数据量缺乏等问题,以及德国汽...     

基于数字孪生的卷烟制丝生产线设计的研究

在全球智能制造大背景下,提出一种基于数字孪生的卷烟制丝生产线智能化升级的设计方法,旨在于解决卷烟制丝生产过程中的一些痛点难点问题。参照数字孪生制造系统的标准框架,对制丝生产线各层级实体对象进行分析,提出为建立数字孪生体系需要对制丝生产线进行智能化升级的核心功能并做出相应设计。通过在制丝生产线一些典型场景的应用,来证明基于数字孪生的卷烟制丝生产线智能化升级的设计方法的可行性,同时希望通过发展数字孪生技术建立物理实体和数字孪生体之间从数化到共智的关系,为智能制造的发展提供一条不失为颇具前景的路径。     

基于产教融合的智能制造工程实践教学体系研究与实践

智能制造作为新一代信息通信技术与先进制造技术深度融合的融合体,开展产教融合型智能制造工程实践教学体系研究势在必行。基于产教融合理论体系,以西安航空学院校办工厂作为智能制造实践教学载体,进行新型教学体系探索,构建了产教融合型智能制造工程实践教学体系,进行了实践课程内容体系的改革和工程实践体系支撑体系建设,健全新建本科院校多元化实践教学体制。     

CAE技术与数字孪生流域关键技术融合应用趋势

为推动CAE技术与数字孪生流域关键技术的深度融合，分析水工建筑物CAE分析计算和数字孪生流域应用的特点，以及数字孪生流域在算据、算法和算力等3个方面的关键技术。得出CAE分析计算具有可获得性、可展示性、结构系统性的特点，以及数字孪生流域建设具有精确性、多维关联性、闭环性的特点，为CAE分析在数字孪生流域建设发展提供参考。     

和利时,做医药行业智能转型的推动者——2019医药行业智能制造解决方案分论坛在京成功举办

近年来,5G、大数据、人工智能、区块链等新一代信息技术与先进制造技术深度融合,正在重塑设计、制造、服务等产品全生命周期的各环节及其集成,催生新技术、新产品、新业态、新模式,也正在引发制造业,包括医药制造业在发展理念、制造模式等方面重大而深刻的变革。     

Digital twin-driven intelligent production line for automotive MEMS pressure sensors

The equipment and technological processes used in manufacturing electronic products are gradually being automated and networked. Currently, digital twin technology continues to evolve and mature. The electronics manufacturing industry is undergoing an intelligent and digital transformation. Micro-electro-mechanical system (MEMS) sensors have been widely used in the automotive field due to their small size, low cost, and high reliability. In this study, a new intelligent production line for automotive MEMS pressure sensors driven by digital twin is individually designed. The intelligent production line system consists of physical production lines, digital production lines, twin data, and data service systems. The technology of multi-source heterogeneous data acquisition is used to process and analyze data collected in real time in a physical production line. Based on the technology of parallel control, the physical and digital production lines are synchronized. To obtain optimal process parameters, a process database is established through the analysis of the key processes of the production line. Three types of automotive MEMS pressure sensors are successfully manufactured in the constructed digital twin-driven intelligent production line. The intelligent production line can realize 24-h unattended operation. The product yield is above 98 %, and the takt time is less than 16 s.     

The rapid construction method of the digital twin polymorphic model for discrete manufacturing workshop

The modeling process is resource-intensive, time-consuming, and expensive due to the large number and variety of production and auxiliary equipment in discrete manufacturing plants. The popular twin-model construction method based on a single type of equipment cannot meet the demand for rapid construction and high-quality model mapping in large discrete manufacturing plants. This paper proposes a strategy for developing a digital twin polymorphic model (DTPM) for discrete manufacturing workshops to meet complex manufacturing business scenarios, improve the richness of digital twin model varieties, construction efficiency and intelligence, and form an efficient equipment model construction method. The complete element information and real-time dynamic data of the minimal component unit (Functional Components, FC) of DTPM are clustered and analyzed. In addition, properties of the functional component information model are characterized from several dimensions, such as geometry, physics, and behavior. Furthermore, the FC with highly concentrated primary components is established based on the object-oriented derivation inheritance and attribute reuse hybrid drive technique. FC drastically reduces the duration of the digital twin model development process through encapsulation, inheritance, polymorphism, and other technologies. On this premise, the DTPM construction method that deeply decouples the physical structure and functional methods is proposed. DTPM integrates adaptive information interaction technology to accomplish intelligent data connection, dynamic data updating, and digitally accurate mapping of static-dynamic-virtual multidimensional models in discrete production workshops. Lastly, the method's validity is confirmed by creating a large discrete production workplace. The results indicate that the proposed technique may significantly enhance the model variety and building efficiency of large-scale digital twin workshop systems.     

流程工业数字孪生关键技术探讨

流程工业是制造业的重要组成部分, 是国民经济发展的重要基础, 主要包括化工、冶金、石化等行业, 其安全高效的生产对国家而言具有重要的战略意义. 然而, 流程工业物理化学变化反应复杂、流程间能质流严重耦合、多目标冲突、在线实验风险大, 给生产流程系统建模与高效协同优化带来极大困难, 严重制约了生产质量和资源利用率的进一步提升. 随着信息技术与人工智能的发展, 建立虚实结合、协同优化运行的流程工业数字孪生生产线所需技术逐渐成熟, 其在流程工业的应用价值与潜力日益凸显. 本文首先阐述数字孪生在流程工业应用的必要性与重要性, 并通过边界定义法将数字孪生与信息物理系统(Cyber-physical system, CPS)、工业互联网等概念进行对比分析,从而明确数字孪生的基本内涵与功能边界. 其次描述流程工业抽象模型和数字孪生理论模型间的映射关系, 并分析了如何用数字孪生技术解决流程工业系统建模与高效协同优化的瓶颈问题. 最后, 从数字孪生系统构建的角度探讨数字孪生发展的关键技术, 并以一条炼铁生产线为例, 展示数字孪生技术在实际工业中的应用解决方案.     

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.     

数字制造环境下的加工过程仿真验证技术研究

生产线数字制造环境是数字化工厂的核心，而加工过程的仿真与验证技术构成生产线数字系统的底层结构与制造过程数字化分析的主要内容。分析了目前加工过程在几何仿真与物理仿真方面的研究情况、研究方法与存在问题，就该项技术向生产线数字制造环境融合的关键技术，即综合设备数字样机的完整数字加工环境的建立及加工过程仿真与上层制造环境的信息集成等进行分析与研究。     

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。     

Digital twin-enabled machining process modeling

Considering the new generation of information technology, the digitalization and intellectualization of the machining process have become the major core in intelligent manufacturing. The complex and diverse requirements, as well as the processing sites force the machining sequence to move towards cyber-physical integration. This paper presents a multidimensional modeling approach for machining processes, by introducing Digital Twin (DT) technology. The method is oriented towards the design and execution phases of the machining process and is used to support intelligent machining. The working mechanism of modeling, simulation, prediction and control of machining process is described based on the interpretation of the modeling and application methods of machining process design, inspection process, fault diagnosis and quality prediction, as based on digital twin technology. Finally, key components of diesel engines are targeted as test objects, demonstrating increased material removal rate by 5.1%, reduced deformation by 22.98% and 30.13%, respectively, verifying the effectiveness of the applied framework and the proposed method.