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...     

A state-of-the-art survey on Augmented Reality-assisted Digital Twin for futuristic human-centric industry transformation

The combination of Augmented Reality (AR) and Digital Twin (DT) has begun to show its potential nowadays, leading to a growing research interest in both academia and industry. Especially under the current human-centric trend, AR embraces the potential to integrate operators into the new generation of Human Cyber–Physical System (HCPS), in which DT is a pillar component. Some review articles have focused on this topic and discussed the benefits of combining AR and DT, but all of them are limited to a specific domain. To fill the gap, this research conducts a state-of-the-art survey (till 17-July-2022) from the AR-assisted DT perspective across different sectors of the industrial field, covering a total of 118 selected publications. Firstly, application scenarios and functions of AR-assisted DT are summarized by following the engineering lifecycle, among which production process, service design, and Human–Machine Interaction (HMI) are hot topics. Then, improvements specifically brought by AR are analyzed according to three dimensions, namely virtual twin, hybrid twin, and cognitive twin, respectively. Finally, challenges and future perspectives of AR-assisted DT for futuristic human-centric industry transformation are proposed, including promoting product design, robotic-related works, cyber–physical interaction, and human ergonomics.     

Smart manufacturing scheduling: A literature review

Within the scheduling framework, the potential of digital twin (DT) technology, based on virtualisation and intelligent algorithms to simulate and optimise manufacturing, enables an interaction with processes and modifies their course of action in time synchrony in the event of disruptive events. This is a valuable capability for automating scheduling and confers it autonomy. Automatic and autonomous scheduling management can be encouraged by promoting the elimination of disruptions due to the appearance of defects, regardless of their origin. Hence the zero-defect manufacturing (ZDM) management model oriented towards zero-disturbance and zero-disruption objectives has barely been studied. Both strategies combine the optimisation of production processes by implementing DTs and promoting ZDM objectives to facilitate the modelling of automatic and autonomous scheduling systems. In this context, this particular vision of the scheduling process is called smart manufacturing scheduling (SMS). The aim of this paper is to review the existing scientific literature on the scheduling problem that considers the DT technology approach and the ZDM model to achieve self-management and reduce or eliminate the need for human intervention. Specifically, 68 research articles were identified and analysed. The main results of this paper are to: (i) find methodological trends to approach SMS models, where three trends were identified; i.e. using DT technology and the ZDM model, utilising other enabling digital technologies and incorporating inherent SMS capabilities into scheduling; (ii) present the main SMS alignment axes of each methodological trend; (iii) provide a map to classify the literature that comes the closest to the SMS concept; (iv) discuss the main findings and research gaps identified by this study. Finally, managerial implications and opportunities for further research are identified.     

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.     

Parallel Manufacturing for Industrial Metaverses: A New Paradigm in Smart Manufacturing

To tackle the complexity of human and social factors in manufacturing systems, parallel manufacturing for industrial metaverses is proposed as a new paradigm in smart manufacturing for effective and efficient operations of those systems, where Cyber-Physical-Social Systems (CPSSs) and the Internet of Minds (IoM) are regarded as its infrastructures and the "Artificial systems", "Computational experiments" and "Parallel execution" (ACP) method is its methodological foundation for parallel evolution, closed-loop feedback, and collaborative optimization. In parallel manufacturing, social demands are analyzed and extracted from social intelligence for product R & D and production planning, and digital workers and robotic workers perform the majority of the physical and mental work instead of human workers, contributing to the realization of low-cost, high-efficiency and zero-inventory manufacturing. A variety of advanced technologies such as Knowledge Automation (KA), blockchain, crowdsourcing and Decentralized Autonomous Organizations (DAOs) provide powerful support for the construction of parallel manufacturing, which holds the promise of breaking the constraints of resource and capacity, and the limitations of time and space. Finally, the effectiveness of parallel manufacturing is verified by taking the workflow of customized shoes as a case, especially the unmanned production line named FlexVega.      

A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective

This paper addresses the problems of data management and analytics for decision-aid by proposing a new vision of Digital Shadow (DS) which would be considered as the core component of a future Digital Twin. Knowledge generated by experts and artificial intelligence, is transformed into formal business rules and integrated into the DS to enable the characterization of the real behavior of the physical system throughout its operation stage. This behavior model is continuously enriched by direct or derived learning, in order to improve the digital twin. The proposed DS relies on data analytics (based on unsupervised learning) and on a knowledge inference engine. It enables the incidents to be detected and it is also able to decipher its operational context. An example of this application in the aeronautic machining industry is provided to stress both the feasibility of the proposition and its potential impact on shop floor performance.     

Industrial Internet Platform (IIP) enabled Smart Product Lifecycle-Service System (SPLSS) for manufacturing model transformation: From an industrial practice survey

Nowadays, the focus of enterprises is gradually expanding from product manufacturing system to Product service system (PSS). In the existing research, scholars mainly focus on the in-depth study of a certain method or technology, and the research on traditional PSS is relatively mature. In addition, it is a tendency that transform towards SPLSS with the characteristics of industrial internet platform (IIP) and sustainable operation theory. Based on this, this paper explores the research path from traditional product service system (PSS) to smart product service system (SPSS), and then to smart product lifecycle service system (SPLSS), which can endow the products with intelligent perception, intelligent analysis and intelligent decision-making capabilities based on Industrial Internet Platform (IIP). A reference model of IIP enabled SPLSS for industrial services based on system engineering method and theory from a very detailed practical investigation is proposed. Moreover, on the basis of this model, its three dimensional subsystems are expanded, including: supporting environment subsystem, evolution path subsystem, and added value subsystem. The study can provide a reference for the enterprise's manufacturing strategy and manufacturing path. It will promote the transformation and upgrading of enterprise manufacturing model to improve service efficiency and reduce operating costs, and then realize the value-added of products and services.     

Digital twin-driven optimization of gas exchange system of 2-stroke heavy fuel aircraft engine

Multiple parts in gas exchange system of 2-stroke heavy fuel aircraft engine with poppet valves lead to complicated manufacturing and inefficient assembly. Besides, real experimental optimization wastes lots of time and cost due to the increased valve parameters. To address the above issues, the paper proposes a digital twin (DT)-driven optimization method with several DT modules for the system to virtually simulate and optimize the parameters, performance and manufacturing with data interaction and recorded. The DT modules receive real-time feedback data from manufacturing measurements and performance tests to conduct the correction throughout the optimization process. The results demonstrate that the virtual engine model with feedback and correction is quite precise and credible compared with test results, and iterative calculation for optimal parameters is performed efficiently. With the guidance of virtual manufacturing, real manufacturing and assembly are arranged more reasonably and efficiency has been promoted. Real-world test found both power and gas exchange performance improved about 4% under various engine speeds and loads, which verified the effectiveness of DT-driven optimization. The study achieves the integration between virtual and real worlds of the system performance and manufacturing, which facilitates the development of aircraft engine smart manufacturing.     

From simple digital twin to complex digital twin Part I: A novel modeling method for multi-scale and multi-scenario digital twin

In recent years, the digital twin has attracted widespread attention as an important means of digitalization and intelligence. However, the digital twin is becoming more and more complex due to the expansion of need on the simulation of multi-scale and multi-scenario in reality. The instance of digital twin in references mostly concentrates a particular application, while it is still a lack of a method for constructing the complex digital twin in the total elements, the variable scale of working environments, changeable process, not even the coupling effects. In this paper, a novel modeling method for such a complex digital twin is proposed based on the standardized processing on the model division and assembly. Firstly, the complex model of digital twin is divided into several simple models according to the composition, context, component, and code in 4C architecture. Composition and context make the digital twin focus on the effective elements in a specific scale and scenario. Component and code develop the digital twin in standard-based modularization. Secondly, assemble the simple models of digital twins into the complex model through information fusion, multi-scale association and multi-scenarios iterations. Ontology establishes the complete information library of the entities on different digital twins. Knowledge graph bridges the structure relationship between the different scales of digital twins. The scenario iterations realize the behavior interaction and the accuracy calculation results. It provides an implementable method to construct a complex model of digital twin, and the reuse of components and code also enables rapid development of digital twins.     

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.     

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.     

Control Strategies for Digital Twin Systems

With the continuous breakthrough in information technology and its integration into practical applications, industrial digital twins are expected to accelerate their development in the near future. This paper studies various control strategies for digital twin systems from the viewpoint of practical applications.To make full use of advantages of digital twins for control systems, an architecture of digital twin control systems, adaptive model tracking scheme, performance prediction scheme, perfo...     

Digital twin-driven service model and optimal allocation of manufacturing resources in shared manufacturing

The sharing economy has been recognized a mutually beneficial economic mode. Deriving from the concept of sharing economy, shared manufacturing was proposed under the support of advanced information and manufacturing technologies. As a core part of implementing shared manufacturing, manufacturing resource allocation aims to coordinate cross-organizational resources to provide on-demand services for personalized manufacturing requirements. However, some challenges still hinder effective and efficient resource allocation in shared manufacturing. Traditional centralized optimization methods with only one decision model are difficult to maintain autonomous decision rights of resource providers. Thus, they could hardly adapt to the situation of cross-organizational resource coordination. In addition, the credit of resource providers is rarely considered in the resource allocation process, which is unfavorable for promoting more reliable trades in shared manufacturing. To address these issues, this study proposes an integrated architecture to promote the resource allocation in shared manufacturing. A digital twin-driven service model is built to perform the seamless monitoring and control of shared manufacturing resources. The resource allocation model is constructed based on the consideration of the credit of resource providers. To keep the decision autonomy of resource providers, augment Lagrangian coordination is adopted to analyze the constructed resource allocation model. A case study is further employed to validate the effectiveness and efficiency of the proposed method in performing the resource allocation in shared manufacturing.     

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.     

Digital twins-based smart manufacturing system design in Industry 4.0: A review

A smart manufacturing system (SMS) is a multi-field physical system with complex couplings among various components. Usually, designers in various fields can only design subsystems of an SMS based on the limited cognition of dynamics. Conducting SMS designs concurrently and developing a unified model to effectively imitate every interaction and behavior of manufacturing processes are challenging. As an emerging technology, digital twins can achieve semi-physical simulations to reduce the vast time and cost of physical commissioning/reconfiguration by the early detection of design errors/flaws of the SMS. However, the development of the digital twins concept in the SMS design remains vague. An innovative Function-Structure-Behavior-Control-Intelligence-Performance (FSBCIP) framework is proposed to review how digital twins technologies are integrated into and promote the SMS design based on a literature search in the Web of Science database. The definitions, frameworks, major design steps, new blueprint models, key enabling technologies, design cases, and research directions of digital twins-based SMS design are presented in this survey. It is expected that this survey will shed new light on urgent industrial concerns in developing new SMSs in the Industry 4.0 era.     

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.     

基于数字孪生技术的大型煤矿远程智能监控研究

为了保证大型煤矿开采工作的安全性与质量,利用数字孪生技术优化设计大型煤矿远程智能监控方法。利用数字孪生技术构建大型煤矿虚拟模型,在该模型下确定测点位置,远程采集大型煤矿实时运行数据。通过对数据特征的提取与匹配,从煤矿开挖设备和施工环境两个方面,监测大型煤矿运行状态。改装大型煤矿远程智能控制器,以运行状态的监测结果作为控制程序的启动条件,实现对大型煤矿的远程智能监控任务。通过与传统监控方法的对比得出结论:优化设计方法对煤矿开挖设备的监控性能明显升高,对环境中瓦斯浓度和温度的监测误差分别降低了0.34%和0.19℃,控制误差分别降低0.09%和0.145℃,同时监控范围扩大27.4%。     

Mutual information-enhanced digital twin promotes vision-guided robotic grasping

Vision-guided learning for autonomous robotic manipulations is a wide-ranging and high-impact topic in the context of smart manufacturing. Most learning strategies are object-centered or prior information-dependent, which likely lead to the problems of generalization across objects or scenes. To alleviate this, this work presented an embodiment decision-making method by the marriage between the digital twin epistemology and information-theoretic approach. The initial insight was that the mutual information generated in the interactions between the available vision models and real-world perceptions could decrease the uncertainty of sensing-action processes. Further, the real-time interactive information gains and visual templates constitute the digital twin through bidirectional data flowing and real-time optimization. As a demonstration of concept, on the conveyor-based and vision-guided robotic grasping platform, the robotic grasping experiments of freely placed and moving parts were performed. Experimental results indicated that the autonomous and real-time optimization of the conveyor-based and vision-guided robotic grasping system happens and the adaptability to the real-world changes had been clearly increased. This research suggested that the representation and dynamic capture of the complex interactions between both sides of cyber-physical system could generate new possibilities to the evolution of decision-making paradigm in more complex industrial processes.     

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.     

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.