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,提出了产品数字孪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孪生和数字孪生体的定义,在此基础上进一步分析了数字孪生体全生命周期的内涵和特征.数字孪生体全生命...     

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.     

面向飞行器结构健康管理的数字孪生及应用研究综述

数字孪生一词起源于美国国防部对飞行器机体数字孪生的研究,目前已经成为全球制造业的前沿热点领域.解析了数字孪生的概念及其中的结构健康管理元素,提出了包含生命周期维、仿真精度维、智能程度维的数字孪生成熟度模型;梳理了面向结构健康管理的数字孪生关键技术,特别是4项关键的数字工程技术能力,包括多尺度建模、多物理特性建模、模型与...     

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.     

Enabling technologies and tools for digital twin

Digital twin is revolutionizing industry. Fired by sensor updates and history data, the sophisticated models can mirror almost every facet of a product, process or service. In the future, everything in the physical world would be replicated in the digital space through digital twin technology. As a cutting-edge technology, digital twin has received a lot of attention. However, digital twin is far from realizing their potential, which is a complex system and long-drawn process. Researchers must model all the different parts of the objects or systems. Varied types of data needed to be collected and merged. Many researchers and participators in engineering are not clear which technologies and tools should be used. 5-dimension digital twin model provides reference guidance for understanding and implementing digital twin. From the perspective of 5-dimension digital twin model, this paper tries to investigate and summarize the frequently-used enabling technologies and tools for digital twin to provide technologies and tools references for the applications of digital twin in the future.     

State-of-the-art of long-term preservation in product lifecycle management

Providing access to digital information for the indefinite future is the intention of long-term digital preservation systems. One application domain that certainly needs to implement such long-term digital preservation processes is the design and engineering industry. In this industry, products are designed, manufactured, and operated with the help of sophisticated software tools provided by product lifecycle management (PLM) systems. During all PLM phases, including geographically distributed cross-domain and cross-company collaboration, a huge amount of heterogeneous digital product data and metadata is created. Legal and economic requirements demand that this product data has to be archived and preserved for a long-time period. Unfortunately, the software that is able to interpret the data will become obsolete earlier than the data since the software and hardware lifecycle is relatively short-lived compared to a product lifecycle. Companies in the engineering industry begin to realize that their data is in danger of becoming unusable while the products are in operation for several decades. To address this issue, different academic and industrial initiatives have been initiated that try to solve this problem. This article provides an overview of these projects including their motivations, identified problems, and proposed solutions. The studied projects are also verified against a classification of important aspects regarding scope and functionality of digital preservation in the engineering industry. Finally, future research topics are identified.     

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.     

DT-II:Digital twin enhanced Industrial Internet reference framework towards smart manufacturing

In this paper, the interplay and relationship between digital twin and Industrial Internet are discussed at first. The sensing/transmission network capability, which is one of the main characteristics of Industrial Internet, can be a carrier for providing digital twin with a means of data acquisition and transmission. Conversely, with the capability of high-fidelity virtual modeling and simulation computing/analysis, digital twin evolving from lifecycle management for a single product to application in production/manufacturing in the shop-floor/enterprise, can further greatly enhance the simulation computing and analysis of Industrial Internet. This paper proposes a digital twin enhanced Industrial Internet (DT-II) reference framework towards smart manufacturing. To further illustrate the reference framework, the implementation and operation mechanism of DT-II is discussed from three perspectives, including product lifecycle level, intra-enterprise level and inter-enterprise level. Finally, steam turbine is taken as an example to illustrate the application scenes from above three perspectives under the circumstance of DT-II. The differences between with and without DT-II for design and development of steam turbine are also presented.     

A systematic review of digital twin about physical entities,virtual models,twin data,and applications

The digital twin is a crucial technology for realizing smart manufacturing and industrial digital transformation, which has received extensive attention and research from industry and academia. After 20 years of development, the application area of digital twins has been pervasive. Due to the diversity of application areas, various reference models and research methods have been presented for the components of the digital twin. Therefore, this paper provides systematic research of current studies on the basic components of the digital twin. This paper analyzed 117 articles from 2017 to 2022. By clarifying the relationship between the digital twin and the cyber-physical system, it first clarified the definition, characteristics, and application areas of the digital twin. On this basis, the research methodology of the core components of the digital twin (physical entities, virtual models, and twin data) is analyzed. At the same time, the application areas of digital twins are analyzed and delineated, and the application potential of the digital twin is explored. Finally, the research results and future research recommendations are presented.     

Secure sharing of big digital twin data for smart manufacturing based on blockchain

With the rapid development of digital twin technology, a large amount of digital twin data named as big digital twin data (BDTD), is generated in the lifecycle of equipment, which is supposed to be used in digital twin enabled applications. However, in the implementation of these applications, data sharing problem which is caused by the lack of data security as well as trust among stakeholders of equipment, limits data using value. It is a novel way to introduce blockchain technology into digital twin to solve the problem. However, current methods cannot fulfill the requirements of exponential growth and timely sharing of BDTD. Therefore, a blockchain-based framework for secure sharing of BDTD is proposed to solve the problems. Cloud storage is integrated into the framework, with which, BDTD is encrypted and stored in Cloud, while the hash of BDTD and transaction records are stored in blockchain. Some rules of generating new block are designed to improve the processing speed of blockchain. An algorithm for optimal sampling rate selection is presented to maximize total social benefits of the participants of BDTD sharing. Simulation results show that the algorithm is better than traditional method for maximizing the total social benefits. Furthermore, a protype system is developed and evaluated based on Fabric test network. Evaluation results show that BDTD can be shared securely multiple times per second through the framework, which demonstrates the feasibility of the framework in supporting timely sharing of BDTD.     

Architecture for modeling and simulation of technical systems along their lifecycle

Modeling and simulation is an established scientific and industrial method to support engineers in their work in all lifecycle phases—from first concepts or tender to operation and service—of a technical system. Due to the fact of increasing complexity of such systems, e.g. plants, cyber-physical systems and infrastructures, system simulation is rapidly gaining impact. In this paper, a simulation architecture is presented and discussed on three different industrial applications, which offers a client–server concept to master the challenges of a lifecycle spanning simulation framework. Looking ahead, open software concepts for modeling, simulation and optimization will be required to cover new co-simulation techniques and to realize distributed, for example web-based simulation environments and tools.     

数字孪生小浪底知识库建设研究与实践

随着大数据、人工智能等新技术的飞速发展，信息技术在水利工程运行管理中正发挥越来越重要的支撑作用。数字孪生小浪底作为水利部数字孪生水利建设的首批试点工程，其知识库的建设是数字孪生小浪底的核心组成部分，技术难度大，业务场景应用面临着较大挑战。本文针对数字孪生小浪底知识库的建设进行了系统性的研究与实践，采用了知识引擎、知识库和知识应用三部分组成的知识库架构体系，实现了基于水利知识的检索、问答、推荐和推理决策等多样化服务，并在数字孪生“四预”业务应用中进行实践和验证，为小浪底枢纽的工程安全和防汛调度等核心业务提供了重要支撑。本文数字孪生小浪底知识库建设为类似水利工程具有参考价值和实践意义。     

Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation

Digital twin (DT) and artificial intelligence (AI) technologies are powerful enablers for Industry 4.0 toward sustainable resilient manufacturing. Digital twins of machine tools and machining processes combine advanced digital techniques and production domain knowledge, facilitate the enhancement of agility, traceability, and resilience of production systems, and help machine tool builders achieve a paradigm shift from one-time products provision to on-going service delivery. However, the adaptability and accuracy of digital twins at the shopfloor level are restricted by heterogeneous data sources, modeling precision as well as uncertainties from dynamical industrial environments. This article proposes a novel modeling framework to address these inadequacies by in-depth integrating AI techniques and machine tool expertise using aggregated data along the product development process. A data processing procedure is constructed to contextualize metadata sources from the design, planning, manufacturing, and quality stages and link them into a digital thread. On this consistent data basis, a modeling pipeline is presented to incorporate production and machine tool prior knowledge into AI development pipeline, while considering the multi-fidelity nature of data sources in dynamic industrial circumstances. In terms of implementation, we first introduce our existing work for building digital twins of machine tool and manufacturing process. Within this infrastructure, we developed a hybrid learning-based digital twin for manufacturing process following proposed modeling framework and tested it in an external industrial project exemplarily for real-time workpiece quality monitoring. The result indicates that the proposed hybrid learning-based digital twin enables learning uncertainties of the interaction of machine tools and machining processes in real industrial environments, thus allows estimating and enhancing the modeling reliability, depending on the data quality and accessibility. Prospectively, it also contributes to the reparametrization of model parameters and to the adaptive process control.     

数字孪生驱动的汇流行星排故障预测研究综述

为了在第四次工业革命中抢占制高点,各国紧锣密鼓地进行着自己的信息化建设,数字孪生技术作为关键技术之一,可以实现物理世界与信息世界的交互,将该技术应用到装甲车辆汇流行星排的故障预测,可以实时预测车辆运行状态,有效降低了事故发生的概率,大大提高了车辆的安全性,对提高战斗力有重要意义;在综述数字孪生技术于故障预测研究方面的发展历程的基础上,针对装甲车辆汇流行星排实际工作过程中难以及时预测故障的问题,提出了4层数字孪生框架,即物理实体层、信息交互层、数据互动层和人机交互层,并阐述了每一层的具体功能要求,预期实现装甲车辆汇流行星排在发生故障前及时预警,从而达到提高设备使用寿命及驾驶安全性的目的 .     

A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment,interfaces and intelligence

Filling the gaps between virtual and physical systems will open new doors in Smart Manufacturing. This work proposes a data-driven approach to utilize digital transformation methods to automate smart manufacturing systems. This is fundamentally enabled by using a digital twin to represent manufacturing cells, simulate system behaviors, predict process faults, and adaptively control manipulated variables. First, the manufacturing cell is accommodated to environments such as computer-aided applications, industrial Product Lifecycle Management solutions, and control platforms for automation systems. Second, a network of interfaces between the environments is designed and implemented to enable communication between the digital world and physical manufacturing plant, so that near-synchronous controls can be achieved. Third, capabilities of some members in the family of Deep Reinforcement Learning (DRL) are discussed with manufacturing features within the context of Smart Manufacturing. Trained results for Deep Q Learning algorithms are finally presented in this work as a case study to incorporate DRL-based artificial intelligence to the industrial control process. As a result, developed control methodology, named Digital Engine, is expected to acquire process knowledges, schedule manufacturing tasks, identify optimal actions, and demonstrate control robustness. The authors show that integrating a smart agent into the industrial platforms further expands the usage of the system-level digital twin, where intelligent control algorithms are trained and verified upfront before deployed to the physical world for implementation. Moreover, DRL approach to automated manufacturing control problems under facile optimization environments will be a novel combination between data science and manufacturing industries.     

基于LabVIEW和MATLAB的数字孪生供热系统平台的设计与仿真

数字孪生技术充分利用物理结构、传感器更新、设备运行历史等数据通过集成多领域、多物理量、多可能性的模拟过程，在虚拟空间中进行镜像，以此表达相对应的实物装置的整个生命周期过程。从智慧供热的发展历史来看，依托现代工业系统理念，提出了基于"数字孪生"的智能供热系统结构。首先介绍了数字孪生的基本结构，给出了数字孪生的构建方式基于虚拟仪器Labview的大数据采集、处理、归档、仿真；然后以采集的数据为基础，得到供热系统的孪生模型，叙述了数字孪生技术解决的关键问题；最后，通过Labview仿真平台调用Matlab中神经网络智能算法，得到基于大数据采集以及经过智能算法优化后的参数，同时系统将参数反馈给物理实体设备，从而完成孪生模型的仿真、优化、反馈过程。通过热网系统优化仿真案例验证了Labview和Matlab混合编程在建立的孪生供热平台上、应用的有效性。     

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.     

Development of an edge computing-based cyber-physical machine tool

Digital twin is a virtual model that represents physical entities in a digital manner. By leveraging means of data to simulate the behavior of physical entities in the real environment, the functions of physical entities can be optimized and expanded, through virtual and real interaction feedback, data fusion, decision making, and optimization. Despite numerous researches on digital twin concept and its applications, scarcely any discusses about the computation efficiency of the twin established. In order to shorten the latency of mapping and reduce the high computation workload in the cloud, this paper develops a cyber-physical machine tool based on edge computing techniques, to realize remote sensing, real-time monitoring and scalable high-performance digital twin application. Furthermore, a novel edge computing algorithm is proposed to detect the abnormality of the edge data from two aspects: the unary outliers of the edge data itself and the multivariate parameter correlation among edge devices. The effectiveness of the application platform of the cyber-physical machine tool developed is verified by the prototype system and edge algorithm experiment.     

On the requirements of digital twin-driven autonomous maintenance

Autonomy has become a focal point for research and development in many industries. Whilst this was traditionally achieved by modelling self-engineering behaviours at the component-level, efforts are now being focused on the sub-system and system-level through advancements in artificial intelligence. Exploiting its benefits requires some innovative thinking to integrate overarching concepts from big data analysis, digitisation, sensing, optimisation, information technology, and systems engineering. With recent developments in Industry 4.0, machine learning and digital twin, there has been a growing interest in adapting these concepts to achieve autonomous maintenance; the automation of predictive maintenance scheduling directly from operational data and for in-built repair at the systems-level. However, there is still ambiguity whether state-of-the-art developments are truly autonomous or they simply automate a process.In light of this, it is important to present the current perspectives about where the technology stands today and indicate possible routes for the future. As a result, this effort focuses on recent trends in autonomous maintenance before moving on to discuss digital twin as a vehicle for decision making from the viewpoint of requirements, whilst the role of AI in assisting with this process is also explored. A suggested framework for integrating digital twin strategies within maintenance models is also discussed. Finally, the article looks towards future directions on the likely evolution and implications for its development as a sustainable technology.