Construction method of shop-floor digital twin based on MBSE

Digital twin (DT) technology is essential for achieving the fusion of virtual-real cyber-physical systems. Academics and companies have made great strides in the theoretical research and case studies of constructing the shop-floor digital twin (SDT), which is the premise of applying DT technology on the shop floor. A shop floor is a large complex system that involves many elements including people, machines, materials, methods, and the environment and processes, such as the technical flow, business process, logistics, and control flow. However, most of the developed cases lack a hierarchical, structured and modularized implementation framework for the development of an SDT system, which leads to problems such as a low reuse rate of the system blocks, lack of scalability, and high upgrade and maintenance costs. In response to these issues, we propose a construction method of the DT for the shop floor based on model-based systems engineering from the perspective of the system. In this method, a comprehensive DT model for the shop floor is gradually constructed by using system modeling language, the modeling method “MagicGrid,” and the “V model” of systems engineering. The model includes four dimensions of the shop-floor requirements, structure, behavior, and parameters, as well as three stages (the problem domain, solution domain, and implementation domain), and connects nine steps of the “V model,” including the system requirements, system architecture, subsystem implementation, subsystem integration, and system verification. Then, based on an example of a real NC machining shop floor, subsystems including a visualization system, synchronization system, and simulation system, are discussed. Finally, the functions of the integrated systems are verified based on the requirements, including the real-time synchronization of “man, machine, material, and method” and the transient simulation in real time. The numerical indicators of the integrated system are verified, including the model completeness and synchronization timeliness.     

A digital twin-enhanced system for engineering product family design and optimization

Engineering product family design and optimization in complex environments has been a major bottleneck in today’s industrial transformation towards smart manufacturing. Digital twin (DT), as a core part of cyber-physical system (CPS), can provide decision support to enhance engineering product lifecycle management workflows via remote monitoring and control, high-fidelity simulation, and solution generation functionalities. Although many studies have proven DT to be highly suited for industry needs, little has been reported on the product family design and optimization capabilities specifically with context awareness, which could be leaving many enterprises ambivalent on its adoption. To fill this gap, a reusable and transparent DT capable of situational recognition and self-correction is essentially required. This paper develops a generic DT architecture reference model to enable the context-aware product family design optimization process in a cost-effective manner. A case study featuring asset re-/configuration within a dynamic environment is further described to demonstrate its in-context decision-aiding capabilities. The authors hope this study can provide valuable insights to both academia and industry in improving their engineering product family management process.     

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.     

Building a right digital twin with model engineering

In recent years, the concept of digital twin (DT) is attracting more and more attention from researchers and engineers. But there is still no consensus on what a right DT is. On one hand, some common models are renamed as DTs. On the other hand, some DTs extremely pursue ‘the same’ as physical objects, which bring unnecessary complexities to them. In this paper, we try to answer two questions from the point of view of model engineering: how to define a right digital twin, and how to build a right digital twin. The concept and related technologies of model engineering are introduced. Some basic principles and a set of metrics for a right DT are given. An evolutionary concurrent modeling method for DT (ECoM4DT) is proposed not only inheriting the theory from classic M&S methods but also highlighting the characteristics of DT compared with traditional models to systemically guide the DT modeling process.     

Integrating crowd-/service-sourcing into digital twin for advanced manufacturing service innovation

In order to support advanced collaborations among smart products, services, users and service providers in a smart product and service ecosystem (S-PSS), this paper proposed a service-oriented hybrid digital twin (DT) and digital thread platform-based approach with embedded crowd-/service-sourcing mechanism for enabling advanced manufacturing services. This approach is well supported by the ecosystem interaction intelligence of digitally connected products, services, users, and service providers via Internet of Beings (IoB) (Things, Users and Service providers). First, driven by industrial application needs in heating industry, a conceptual model of the service-oriented hybrid platform integrated with crowdsourcing mechanism is developed, which supports the concepts of product DT, service DT and human user DT. Second, the key system realization techniques are developed to integrate service crowdsourcing and service recommendation for realizing smart services. Finally, a case study is carried out for evaluating and confirming its feasibility.     

面向软件定义网络的可变粒度数字孪生构建技术

软件定义网络（software defined network,SDN）应用范围的扩大带来了应用需求多样化的挑战。利用数字孪生（digital twin,DT）增强SDN的实时分析、推演和控制能力,能更好地满足各种应用场景需求。然而,当前SDN的数字孪生构建面临着时延需求高、计算开销大、资源协调难的问题。因此,以应用需求为导向,在网络可用计算资源约束下,提出了一种新型的可变粒度数字孪生（variable granularity digital twin,VGDT）思想及其构建技术。VGDT结合网络可用计算资源分布特征,建立了保证数字孪生时延和完整度的多节点资源协同优化模型。在此基础上,利用混合编码遗传算法对该模型进行求解,获得最佳映射数据粒度和数字孪生部署方案,指导数字孪生的构建过程。仿真结果表明,与现有模式相比,在网络计算资源约束下,VGDT具有更高的数字孪生模型完整度和有效性。     

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.     

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.     

Digital Twin for Human-Robot Interactive Welding and Welder Behavior Analysis

This paper presents an innovative investigation on prototyping a digital twin (DT) as the platform for human-robot interactive welding and welder behavior analysis. This human-robot interaction (HRI) working style helps to enhance human users’ operational productivity and comfort; while data-driven welder behavior analysis benefits to further novice welder training. This HRI system includes three modules: 1) a human user who demonstrates the welding operations offsite with her/his operations recorded by the motion-tracked handles; 2) a robot that executes the demonstrated welding operations to complete the physical welding tasks onsite; 3) a DT system that is developed based on virtual reality (VR) as a digital replica of the physical human-robot interactive welding environment. The DT system bridges a human user and robot through a bi-directional information flow: a) transmitting demonstrated welding operations in VR to the robot in the physical environment; b) displaying the physical welding scenes to human users in VR. Compared to existing DT systems reported in the literatures, the developed one provides better capability in engaging human users in interacting with welding scenes, through an augmented VR. To verify the effectiveness, six welders, skilled with certain manual welding training and unskilled without any training, tested the system by completing the same welding job; three skilled welders produce satisfied welded workpieces, while the other three unskilled do not. A data-driven approach as a combination of fast Fourier transform (FFT), principal component analysis (PCA), and support vector machine (SVM) is developed to analyze their behaviors. Given an operation sequence, i.e., motion speed sequence of the welding torch, frequency features are firstly extracted by FFT and then reduced in dimension through PCA, which are finally routed into SVM for classification. The trained model demonstrates a 94.44% classification accuracy in the testing dataset. The successful pattern recognition in skilled welder operations should benefit to accelerate novice welder training.      

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.     

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.     

断路器柔性装配车间数字孪生系统设计

为优化断路器装配车间的产线结构和作业方法,结合数字孪生技术,提出一种基于多机器人运动控制的断路器柔性自动化车间装配方案。面向自动化装配单元,结合工业机器人的柔性装配工艺及方法,对实体装配车间进行全物理属性的数字化建模,同时建立多机器人的运动学控制模型,将机器人的三维运动模型应用于虚拟孪生场景。通过数据的交互传递,实现物理单元与虚拟单元的实时链接,将车间机器人的运动轨迹、装配状态、作业运送流程等数据信息进行实时显示,从而实现断路器柔性装配数字孪生系统的搭建与同步映射。实验结果证明,所提方案对实现断路器柔性装配有显著效果。     

A digital twin-driven human-robot collaborative assembly approach in the wake of COVID-19

In the wake of COVID-19, the production demand of medical equipment is increasing rapidly. This type of products is mainly assembled by hand or fixed program with complex and flexible structure. However, the low efficiency and adaptability in current assembly mode are unable to meet the assembly requirements. So in this paper, a new framework of human-robot collaborative (HRC) assembly based on digital twin (DT) is proposed. The data management system of proposed framework integrates all kinds of data from digital twin spaces. In order to obtain the HRC strategy and action sequence in dynamic environment, the double deep deterministic policy gradient (D-DDPG) is applied as optimization model in DT. During assembly, the performance model is adopted to evaluate the quality of resilience assembly. The proposed framework is finally validated by an alternator assembly case, which proves that DT-based HRC assembly has a significant effect on improving assembly efficiency and safety.     

四旋翼无人机飞行过程孪生仿真研究

为了满足无人机在实际飞行过程中的虚实交互、实时响应和精确控制等要求,以四旋翼无人机的飞行过程作为任务需求,提出基于数字孪生技术的四旋翼无人机飞行过程仿真研究。搭建了四旋翼无人机飞行数字孪生系统架构,分析了数字孪生体仿真数据的流向及其作用,并对四旋翼无人机飞行数字孪生系统的功能和意义进行了介绍。从几何、物理、行为和规则等四个方面融合构建了四旋翼无人机的数字孪生体模型。最后进行了四旋翼无人机巡航过程的案例研究,通过仿真案例中的各项参数分析,考察了虚实无人机之间的交互性,证明了数字孪生体模型的精确性,验证了四旋翼无人机飞行数字孪生系统的可行性。     

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.     

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.     

How to model and implement connections between physical and virtual models for digital twin application

Digital twin (DT) is a virtual mirror (representation) of a physical world or a system along its lifecycle. As for a complex discrete manufacturing system (DMS), it is a digital model for emulating or reproducing the functions or actions of a real manufacturing system by giving the system simulation information or directly driven by a real system with proper connections between the DT model and the real-world system. It is a key building block for smart factory and manufacturing under the Industry 4.0 paradigm. The key research question is how to effectively create a DT model during the design stage of a complex manufacturing system and to make it usable throughout the system’s lifecycle such as the production stage. Given that there are some existing discussions on DT framework development, this paper focuses on the modeling methods for rapidly creating a virtual model and the connection implementation mechanism between a physical world production system at a workshop level and its mirrored virtual model. To reach above goals, in this paper, the discrete event system (DES) modeling theory is applied to the three-dimension DT model. First, for formally representing a manufacturing system and creating its virtual model, seven basic elements: controller, executor, processor, buffer, flowing entity, virtual service node and logistics path of a DMS have been identified and the concept of the logistics path network and the service cell is introduced to uniformly describe a manufacturing system. Second, for implementing interconnection and interaction, a new interconnection and data interaction mechanism between the physical system and its virtual model for through-life applications has been designed. With them, each service cell consists of seven elements and encapsulates input/output information and control logic. All the discrete cells are constructed and mapped onto different production-process-oriented digital manufacturing modules by integrating logical, geometric and data models. As a result, the virtual-physical connection is realized to form a DT model. The proposed virtual modeling method and the associated connection mechanism have been applied to a real-world workshop DT to demonstrate its practicality and usefulness.     

A generic methodology and a digital twin for zero defect manufacturing (ZDM) performance mapping towards design for ZDM

Over recent years, the manufacturing industry has seen constant growth and change. From one side, it has been affected by the fourth industrial revolution (Industry 4.0). From the other side, it has had to enhance its ability to meet higher customer expectations, such as producing more customized products in a shorter time. In the contemporary competitive market of manufacturing, quality is a criterion of primary importance for winning market share. Quality improvement must be coupled with a concern for high performance. One of the most promising concepts for quality control and improvement is called zero defect manufacturing (ZDM), which utilizes the benefits of Industry 4.0 technologies. ZDM imposes the rule that any event in the production process should have a counter-action to mitigate it. In light of this, the current research developed a methodology the manufacturer can use to correctly select or design appropriate ZDM strategies and equipment to implement at each manufacturing stage. This methodology consists of several steps. The first step is to conduct several simulations using a dynamic scheduling tool with specific data sets to develop a digital twin (DT). The data sets are created using the Taguchi design of experiments methodology. The DT model is created for use in predicting the results of the developed scheduling tool without actually using said tool. Using the DT, multiple ZDM parameter-combination sets can be created and plugged into the model. This process generates ZDM performance maps that show the effect of each ZDM strategy at each manufacturing stage under different control parameters. These maps are intended to provide information for comparing different ZDM-oriented equipment to help manufacturers reach a final decision on correct and efficient ZDM implementation or to assist in the design phase of a ZDM strategy implementation.     

质子交换膜燃料电池系统数字孪生故障诊断模型研究

基于数字孪生的平行故障诊断方法通过检测和评估真实系统与数字孪生系统之间的残差进行故障诊断,相关数字孪生体基于质子交换膜燃料电池动力学模型和数据集合建立.如果残差向量超过故障检测阈值,则利用故障残差的相对敏感度执行故障隔离.本文将平行故障诊断方法引入质子交换膜燃料电池系统,构建了基于数字孪生估计器的平行故障诊断模型.仿真...     

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.