Digital Twin-driven smart manufacturing: Connotation,reference model,applications and research issues

This paper reviews the recent development of Digital Twin technologies in manufacturing systems and processes, to analyze the connotation, application scenarios, and research issues of Digital Twin-driven smart manufacturing in the context of Industry 4.0. To understand Digital Twin and its future potential in manufacturing, we summarized the definition and state-of-the-art development outcomes of Digital Twin. Existing technologies for developing a Digital Twin for smart manufacturing are reviewed under a Digital Twin reference model to systematize the development methodology for Digital Twin. Representative applications are reviewed with a focus on the alignment with the proposed reference model. Outstanding research issues of developing Digital Twins for smart manufacturing are identified at the end of the paper.     

Digital twin-based designing of the configuration,motion, control,and optimization model of a flow-type smart manufacturing system

Digital twins can achieve hardware-in-the-loop simulation of both physical equipment and cyber model, which could be used to avoid the considerable cost of manufacturing system reconfiguration if the design deficiencies are found in the deployment process of the traditional irreversible design approach. Based on the digital twin technology, a quad-play CMCO (i.e., Configuration design-Motion planning-Control development-Optimization decoupling) design architecture is put forward for the design of the flow-type smart manufacturing system in the Industry 4.0 context. The iteration logic of the CMCO design model is expounded. Two key enabling technologies for enabling the customized and software-defined design of flow-type smart manufacturing systems are presented, including the generalized encapsulation of the quad-play CMCO model and the digital twin technique. A prototype of a digital twin-based manufacturing system design platform, named Digital Twin System, is presented based on the CMCO model. The digital twin-based design platform is verified with a case study of the hollow glass smart manufacturing system. The result shows that the Digital Twin System-based design approach is feasible and efficient.     

Digital twin modeling method based on biomimicry for machining aerospace components

High-performance aerospace component manufacturing requires stringent in-process geometrical and performance-based quality control. Real-time observation, understanding and control of machining processes are integral to optimizing the machining strategies of aerospace component manufacturing. Digital Twin can be used to model, monitor and control the machining process by fusing multi-dimensional in-context machining process data, such as changes in geometry, material properties and machining parameters. However, there is a lack of systematic and efficient Digital Twin modeling method that can adaptively develop high-fidelity multi-scale and multi-dimensional Digital Twins of machining processes. Aiming at addressing this challenge, we proposed a Digital Twin modeling method based on biomimicry principles that can adaptively construct a multi-physics digital twin of the machining process. With this approach, we developed multiple Digital Twin sub-models, e.g., geometry model, behavior model and process model. These Digital Twin sub-models can interact with each other and compose an integrated true representation of the physical machining process. To demonstrate the effectiveness of the proposed biomimicry-based Digital Twin modeling method, we tested the method in monitoring and controlling the machining process of an air rudder.     

A generic tri-model-based approach for product-level digital twin development in a smart manufacturing environment

Smart manufacturing, as an emerging manufacturing paradigm, leverages massive in-context data from manufacturing systems for intelligent decision makings. In such context, Cyber-Physical Systems (CPS) play a key role in digitizing manufacturing systems and integrating multiple systems together for collaborative works. Amongst different levels of smartness and connectedness of CPS, Digital Twin (DT), as an exact digital copy of a physical object or system including its properties and relationship with the environment, has a significant impact on realizing smart manufacturing. A DT constantly synchronizes with its physical system and provides real-time high-fidelity simulations of the system and offers ubiquitous control over the system. Despite its great advantages, few works have been discussed about DT reference models, let alone a generic manner to establish it for smart manufacturing. Aiming to fill the gap, this research introduces a generic CPS system architecture for DT establishment in smart manufacturing with a novel tri-model-based approach (i.e. digital model, computational model and graph-based model) for product-level DT development. The tri-model works concurrently to simulate real-world physical behaviour and characteristics of the digital model. To validate the proposed architecture and approach, a case study of an open source 3D printer DT establishment is further conducted. Conclusions and future works are also highlighted to provide insightful knowledge to both academia and industries at last.     

Mass Personalisation as a Service in Industry 4.0: A Resilient Response Case Study

The Fourth Industrial Revolution (Industry 4.0) leads to mass personalisation as an emerging manufacturing paradigm. Mass personalisation focuses on uniquely made products to individuals at scale. Global challenges encourage mass personalisation manufacturing with efficiency competitive to mass production. Driven by individualisation as a trend and enabled by increasing digitalisation, mass personalisation can go beyond today’s mass customisation. This paper aims to introduce Mass Personalisation as a Service (MPaaS) to address unique and complex requirements at scale by harnessing Industry 4.0 technologies, including Internet of Things, Additive Manufacturing, Big Data, Cloud Manufacturing, Digital Twin, and Blockchain. A case study for the implementation of MPaaS in personalised face masks is presented. The workforce with constant exposure to contaminants requires personal protective equipment (PPE), such as facemasks, for longer hours resulting in pressure-related ulcers. This prolonged use of PPE highlights the importance of personalisation to avoid ulcers and other related health concerns. Most studies have used Additive Manufacturing for individualisation and cloud capabilities for large-scale manufacturing. This study develops a framework and mathematical model to demonstrate the capability of the proposed solution to address one of the most critical challenges by making personalised face masks as an essential PPE in the critical industrial environment.     

Digital Twin-driven machining process for thin-walled part manufacturing

Thin-walled parts are widely used in the aerospace, shipbuilding, and automotive industry, but due to its unique structure and high accuracy requirements, which leads to an increase in scrapped parts, high cost in production, and a more extended period in the trial machining process. However, to adapt to fast production cycles and increase the efficiency of thin-walled parts machining, this paper presents a Digital Twin-driven thin-walled part manufacturing framework to allow the machine operator to manage the product changes, make the start-up phases faster and more accurate. The framework has three parts: preparation, machining, and measurement, driven by Digital Twin technologies in detail. By establishing and updating the workpiece Digital Twin under a different status, various manufacturing information and data can be integrated and available to machine operators and other Digital Twins. It can serve as a guideline for establishing the machine tool and workpiece Digital Twin and integrating them into the machining process. It provides the machine operator opportunities to interact with both the physical manufacturing process and its digital data in real-time. The digital representation of the physical process can support them to manage the trial machining from different aspects. In addition, a demonstrative case study is presented to explain the implementation of this framework in a real manufacturing environment.     

Applying digital twins for the management of information in turnaround event operations in commercial airports

The aerospace sector is one of the many sectors in which large amounts of data are generated. Thanks to the evolution of technology, these data can be exploited in several ways to improve the operation and management of industrial processes. However, to achieve this goal, it is necessary to define architectures and data models that allow to manage and homogenise the heterogeneous data collected. In this paper, we present an Airport Digital Twin Reference Conceptualisation’s and data model based on FIWARE Generic Enablers and the Next Generation Service Interfaces-Linked Data standard. Concretely, we particularise the Airport Digital Twin to improve the efficiency of flight turnaround events. The architecture proposed is validated in the Aberdeen International Airport with the aim of reducing delays in commercial flights. The implementation includes an application that shows the real state of the airport, combining two-dimensional and three-dimensional virtual reality representations of the stands, and a mobile application that helps ground operators to schedule departure and arrival flights.     

构建数字城市的元数据服务体系

Metadata is one of the six key technologies of Digital Earth,which is also an important aspect in buildingand implementing the Digital City. Based on the technologies and standards of Web Services ,the author came up witha metadata services architecture in the distributed heterogeneous network environment of Digital City. In this paper,the metadata services architecture is described and explained in detail,including the related standards,technologies,and the practical experiences in the ‘Metadata Sharing Network Project of Digital Beijing‘ as well. It also points out that, based on the Web service ,to build the metadata service architecture will be practically meaningful for organizing,managing and sharing the information resources in Digital City.     

A Digital Twin approach based on nonparametric Bayesian network for complex system health monitoring

This paper proposes a Digital Twin approach for health monitoring. In this approach, a Digital Twin model based on nonparametric Bayesian network is constructed to denote the dynamic degradation process of health state and the propagation of epistemic uncertainty. Then, a real-time model updating strategy based on improved Gaussian particle filter (GPF) and Dirichlet process mixture model (DPMM) is presented to enhance the model adaptability. On one hand, for those parameters in the nonparametric Bayesian network with prior models, the improved GPF is used to update them in real time. On the other hand, for parameters lacking a prior model, DPMM is proposed to learn hidden variables, which adaptively update the model structure and greatly reduce uncertainty. Experiments on the electro-optical system are conducted to validate the feasibility of the Digital Twin approach and verify the effectiveness of the nonparametric Bayesian network. The results of comparative experiments prove that the Digital Twin approach based on nonparametric Bayesian Network has a good model self-learning ability, which improves the accuracy of health monitoring.     

Digital Twin-driven online anomaly detection for an automation system based on edge intelligence

Accurate anomaly detection is critical to the early detection of potential failures of industrial systems and proactive maintenance schedule management. There are some existing challenges to achieve efficient and reliable anomaly detection of an automation system: (1) transmitting large amounts of data collected from the system to data processing components; (2) applying both historical data and real-time data for anomaly detection. This paper proposes a novel Digital Twin-driven anomaly detection framework that enables real-time health monitoring of industrial systems and anomaly prediction. Our framework, adopting the visionary edge AI or edge intelligence (EI) philosophy, provides a feasible approach to ensuring high-performance anomaly detection via implementing Digital Twin technologies in a dynamic industrial edge/cloud network. Edge-based Digital Twin allows efficient data processing by providing computing and storage capabilities on edge devices. A proof-of-concept prototype is developed on a LiBr absorption chiller to demonstrate the framework and technologies' feasibility. The case study shows that the proposed method can detect anomalies at an early stage.     

A deep learning-enhanced Digital Twin framework for improving safety and reliability in human–robot collaborative manufacturing

In Industry 5.0, Digital Twins bring in flexibility and efficiency for smart manufacturing. Recently, the success of artificial intelligence techniques such as deep learning has led to their adoption in manufacturing and especially in human–robot collaboration. Collaborative manufacturing tasks involving human operators and robots pose significant safety and reliability concerns. In response to these concerns, a deep learning-enhanced Digital Twin framework is introduced through which human operators and robots can be detected and their actions can be classified during the manufacturing process, enabling autonomous decision making by the robot control system. Developed using Unreal Engine 4, our Digital Twin framework complies with the Robotics Operating System specification, and supports synchronous control and communication between the Digital Twin and the physical system. In our framework, a fully-supervised detector based on a faster region-based convolutional neural network is firstly trained on synthetic data generated by the Digital Twin, and then tested on the physical system to demonstrate the effectiveness of the proposed Digital Twin-based framework. To ensure safety and reliability, a semi-supervised detector is further designed to bridge the gap between the twin system and the physical system, and improved performance is achieved by the semi-supervised detector compared to the fully-supervised detector that is simply trained on either synthetic data or real data. The evaluation of the framework in multiple scenarios in which human operators collaborate with a Universal Robot 10 shows that it can accurately detect the human and robot, and classify their actions under a variety of conditions. The data from this evaluation have been made publicly available, and can be widely used for research and operational purposes. Additionally, a semi-automated annotation tool from the Digital Twin framework is published to benefit the collaborative robotics community.     

A bio-inspired LIDA cognitive-based Digital Twin architecture for unmanned maintenance of machine tools

Affected by COVID-19, the maintenance process of machine tools is significantly hindered, while unmanned maintenance becomes an emerging trend in such background. So far, three challenges, namely, the dependence on maintenance experts, the dynamic maintenance environments, and unsynchronized interactions between physical and information sides, exist as the main obstacles in its widespread applications. In order to fill this gap, a bio-inspired LIDA cognitive-based Digital Twin architecture is proposed, so as to achieve unmanned maintenance of machine tools through a self-constructed, self-evaluated, and self-optimized manner. A three phases process in the architecture, including the physical phase, virtual phase, and service phase, is further introduced to support the cognitive cycle for unmanned maintenance of machine tools. An illustrative example is depicted in the unmanned fault diagnosis on the rolling bearing of a drilling platform, which validates the feasibility and advantages of the proposed architecture. As an explorative study, it is wished that this work provides useful insights for unmanned maintenance of machine tools in a dynamic production environment.     

云计算虚拟化技术的发展与趋势

云计算是一种融合了多项计算机技术的以数据和处理能力为中心的密集型计算模式,其中以虚拟化、分布式数据存储、分布式并发编程模型、大规模数据管理和分布式资源管理技术最为关键。经过十多年的发展,云计算技术已经从发展培育期步入快速成长期,越来越多的企业已经开始使用云计算服务。与此同时,云计算的核心技术也在发生着巨大的变化,新一代的技术正在改进甚至取代前一代技术。容器虚拟化技术以其轻便、灵活和快速部署等特性对传统的基于虚拟机的虚拟化技术带来了颠覆性的挑战,正在改变着基础设施即服务（IaaS）平台和平台即服务（PaaS）平台的架构和实现。对容器虚拟化技术进行深入介绍,并通过分析和比较阐述容器虚拟化技术和虚拟机虚拟化技术各自的优势、适应场景和亟待解决的问题,然后对云计算虚拟化技术的下一步研究方向和发展趋势进行展望。     

生态流量监管“四预”业务应用探索

为提高重点河湖生态流量动态监管能力，针对各地服务支撑生态流量监管业务系统与数字孪生流域建设提出的“四统一”不相适应，以及生态流量监测预警、达标判别、统计分析、流量预测、成因分析、调度方案预演、预警响应等全过程监管能力不足等问题，本文围绕业务管理需求，提出了生态流量监管总体方案以及“四预”业务应用总体架构，提出了包括数据底板、模型库、知识库等内容的数字孪生平台建设思路以及基于图数据库的生态流量预警知识图谱构建技术，提出了包括监测告警、预测预警、水量调度预演、会商服务等功能的应用系统模块建设思路，梳理了生态流量监管“四预”业务流程，可为当前数字孪生流域建设先行先试和水资源管理与调配系统中的生态流量监管业务“四预”能力建设提供参考和借鉴意义。     

A multi-agent and cloud-edge orchestration framework of digital twin for distributed production control

The demands for mass individualization and networked collaborative manufacturing are increasing, bringing significant challenges to effectively organizing idle distributed manufacturing resources. To improve production efficiency and applicability in the distributed manufacturing environment, this paper proposes a multi-agent and cloud-edge orchestration framework for production control. A multi-agent system is established both at the cloud and the edge to achieve the operation mechanism of cloud-edge orchestration. By leveraging Digital Twin (DT) technology and Industrial Internet of Things (IIoT), real-time status data of the distributed manufacturing resources are collected and processed to perform the decision-making and manufacturing execution by the corresponding agent with permission. Based on the generated data of distributed shop floors and factories, the cloud production line model is established to support the optimal configuration of the distributed idle manufacturing resources by applying a systematic evaluation method and digital twin technology, which reflects the actual manufacturing scenario of the whole production process. In addition, a rescheduling decision prediction model for distributed control adjustment on the cloud is developed, which is driven by Convolutional Neural Network (CNN) combined with Bi-directional Long Short-Term Memory (BiLSTM) and attention mechanism. A self-adaptive strategy that makes the real-time exceptions results available on the cloud production line for holistic rescheduling decisions is brought to make the distributed manufacturing resources intelligent enough to address the influences of different degrees of exceptions at the edge. The applicability and efficiency of the proposed framework are verified through a design case.     

数字城市应用服务平台体系结构研究

数字城市源于数字地球的概念,本质上是基于计算机网络和城市信息资源的开放的、复杂的适应系统(关于数字城市的概念,作者已在《数字城市,创建21世纪的智能服务平台》中论述,该文在《计算机科学》2002年No.7发表)。而随着Internet的发展,Web服务将成为第二代Internet的主要内容和发展趋势,多层B/S结构的Web系统将发展到开放性服务系统。因此,网络应用发展的总趋势是实现从数据、信息到服务的交换、共享与互操作。另一方面,目前大多数的城市信息系统和城市信息网站     

流域水文信息系统研究与实践综述

流域水文信息系统是地理信息系统、互联网和数据库等技术与水文学研究的结合,集成观测和环境信息的水文信息系统已经成为“数字地球”技术在流域科学方面的重要应用。研究回顾了流域科学的研究方法、发展现状和技术背景,提出当前流域科学研究迫切需要建立水文信息系统,而水文信息系统的重点任务是流域数据建模与数据共享服务。针对目前Arc Hydro水文地理数据模型和开源“协同促进水文科学发展大学联盟”--水文信息系统(CUAHSI\|HIS)的研究进展,提出了一个适合于黑河流域的在线流域水文信息系统设计即“数字黑河”在线门户,集成流域地理数据、观测数据和数据共享服务于一体的流域水文信息系统将有效推动流域科学各方面的研究。     

An IMS-based integration architecture for WiMax/LTE handover

To enable seamless handovers for broadband networks, many researchers have addressed the integration of heterogeneous access technologies to provide users with always-on connectivity. Currently, there are several researches reported in the literature that discuss the integration of beyond 3G networks such as 3GPP Long Term Evolution LTE and mobile WiMax networks. They mainly focused on providing mobile users with seamless mobility when switching between heterogeneous access networks. In this context, many solutions for integration architecture have been proposed with mobility management considerations such as loose and tight coupling, IP Multimedia Subsystem IMS-based architecture and Evolved Packet Core EPC-based solutions for the purpose of providing mobile users with seamless handovers. In this paper, we present the different integration solutions and propose an integration architecture for WiMax and LTE access technologies with EPC as core network and IMS for service provisioning. A vertical handover VHO scheme is presented based on cross-layer approach that enables vertical handover with less handover latency and signaling cost.     

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.