The connotation of digital twin,and the construction and application method of shop-floor digital twin

Digital twin (DT) technology provides a novel, feasible, and clear implementation path for the realization of smart manufacturing and cyber-physical systems (CPS). Currently, DT is applied to all stages of the product lifecycle, including design, production, and service, although its application in the production stage is not yet extensive. Shop-floor digital twin (SDT) is a digital mapping model of the corresponding physical shop-floor. How to build and apply SDT has always been challenging when applying DT technology in the production phase. To address the existing problems, this paper first reviews the origin and evolution of DT, including its application status in the production stage. Then, an implementation framework for the construction and application of SDT is proposed. Three key implementation techniques are explained in detail: the five-dimensional modeling of SDT; DT-based 3D visual and real-time monitoring of shop-floor operating status; and prediction of shop-floor operating status based on SDT using Markov chain. A DT-based visual monitoring and prediction system (DT-VMPS) for shop-floor operating status is developed, and the feasibility and effectiveness of the proposed method are demonstrated through the use of an engineering case study. Finally, a summary of the contributions of the paper is given, and future research issues are discussed.     

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.     

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.     

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

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

Understanding digital transformation in advanced manufacturing and engineering: A bibliometric analysis,topic modeling and research trend discovery

Digital transformation (DT) is the process of combining digital technologies with sound business models to generate great value for enterprises. DT intertwines with customer requirements, domain knowledge, and theoretical and empirical insights for value propagations. Studies of DT are growing rapidly and heterogeneously, covering the aspects of product design, engineering, production, and life-cycle management due to the fast and market-driven industrial development under Industry 4.0. Our work addresses the challenge of understanding DT trends by presenting a machine learning (ML) approach for topic modeling to review and analyze advanced DT technology research and development. A systematic review process is developed based on the comprehensive DT in manufacturing systems and engineering literature (i.e., 99 articles). Six dominant topics are identified, namely smart factory, sustainability and product-service systems, construction digital transformation, public infrastructure-centric digital transformation, techno-centric digital transformation, and business model-centric digital transformation. The study also contributes to adopting and demonstrating the ML-based topic modeling for intelligent and systematic bibliometric analysis, particularly for unveiling advanced engineering research trends through domain literature.     

Ontology-based information modeling method for digital twin creation of as-fabricated machining parts

The Digital Twin concept, as the cutting edge of digital manufacturing solution for modern industries, plays a significant role in the Industry 4.0 era. One key enabling technology for developing a DT is the information modeling of physical products, so as to combine the physical world with the cyberspace more extensively and closely. Therefore, the modeling approach to managing as-fabricated data of physical products, which faithfully reflects the product's physical status, emerges to be pivotal. This paper addresses the problem of modeling as-fabricated parts in the machining process, which is difficult to accomplish by relevant methods, and hinders the long-term data archiving and reuse of process data. Furthermore, to fill the gap, an ontology-based information modeling method of as-fabricated parts is proposed as the recommendation to create DTs for as-fabricated parts. It provides a simple and standardized process for companies to create DTs of as-fabricated parts by specifying the information classification, the contents to be modeled and the modeling method. To validate the effectiveness of the proposed approach, a case study is undertaken in an aviation manufacturing plant at last. The result shows that the proposed information modeling methodology is readily to DT creation of as-fabricated parts.     

A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach

Virtual commissioning is a key technology in Industry 4.0 that can address issues faced by engineers during early design phases. The process of virtual commissioning involves the creation of a Digital Twin—a dynamic, virtual representation of a corresponding physical system. The digital twin model can be used for testing and verifying the control system in a simulated virtual environment to achieve rapid set-up and optimization prior to physical commissioning. Additionally, the modular production control systems, can be integrated and tested during or prior to the construction of the physical system. This paper describes the implementation of a digital twin emulator of an automated mechatronic modular production system that is linked with the running programmable logic controllers and allow for exchanging near real-time information with the physical system. The development and deployment of the digital twin emulator involves a novel hybrid simulation- and data-driven modeling approach that combines Discrete Event Simulation and Agent Based Modeling paradigms. The Digital Twin Emulator can support design decisions, test what-if system configurations, verify and validate the actual behavior of the complete system off-line, test realistic reactions, and provide statistics on the system’s performance.

     

Digital twin-based industrial cloud robotics: Framework,control approach and implementation

Industrial cloud robotics (ICR) integrates cloud computing with industrial robots (IRs). The capabilities of industrial robots can be encapsulated as cloud services and used for ubiquitous manufacturing. Currently, the digital models for process simulation, path simulation, etc. are encapsulated as cloud services. The digital models in the cloud may not reflect the real state of the physical robotic manufacturing systems due to inaccurate or delayed condition update and therefore result in inaccurate simulation and robotic control. Digital twin can be used to realize fine sensing control of the physical manufacturing systems by a combination of high-fidelity digital model and sensory data. In this paper, we propose a framework of digital twin-based industrial cloud robotics (DTICR) for industrial robotic control and its key methodologies. The DTICR is divided into physical IR, digital IR, robotic control services, and digital twin data. First, the robotic control capabilities are encapsulated as Robot Control as-a-Service (RCaaS) based on manufacturing features and feature-level robotic capability model. Then the available RCaaSs are ranked and parsed. After manufacturing process simulation with digital IR models, RCaaSs are mapped to physical robots for robotic control. The digital IR models are connected to the physical robots and updated by sensory data. A case is implemented to demonstrate the workflow of DTICR. The results show that DTICR is capable to synchronize and merge digital IRs and physical IRs effectively. The bidirectional interaction between digital IRs and physical IRs enables fine sensing control of IRs. The proposed DTICR is also flexible and extensible by using ontology models.     

Conceptual digital twin modeling based on an integrated five-dimensional framework and TRIZ function model

Digital twin represents a fusion of the informational and physical domains, to bridge the material and virtual worlds. Existing methods of digital twin modeling are mainly based on modular representation, which limits guidance of the modeling process. Such methods do not consider the components or operational rules of the digital twin in detail, thereby preventing designers from applying these methods in their fields. With the increasing application of digital twin to various engineering fields, an effective method of modeling a multi-dimensional digital twin at the conceptual level is required. To such an end, this paper presents a method for the conceptual modeling of a digital twin based on a five-dimensional digital twin framework to represent the complex relationship between digital twin objects and their attributes. The proposed method was used to model the digital twin of an intelligent vehicle at the concept level.     

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.     

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.     

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 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 twins-based flexible operating of open architecture production line for individualized manufacturing

Individualized manufacturing implies high flexibility of both the hardware and software of the production lines based on a fast physical and logical system (de)commissioning. This paper proposes an open architecture production line (OAPL) design together with a digital twins-based flexible operating approach for individualized manufacturing. Firstly, an OAPL is designed and implemented with physical reconfigurability by orchestrating different open architectural platforms together with open architecture machine tools (OAMTs). Secondly, an open architectural style modeling and configuration method is presented to enable the software reconfigurability of the controls of the OAPL. Thirdly, a digital twin-based online process emulating and multi-physics simulation is integrated to aid the comprehensive characterizing of the operation status of the OAPL. Based on the system reconfigurability and digital twins system, a triple-layer Learning-Optimization-Reacting approach together with an ensemble algorithm for flexible operating of the OAPL is proposed. The digital twins are formed with the ability to flexibly operate the OAPL for catering to different individualized requirements. A demonstrative implementation of a stepping-motor assembly OAPL is presented finally.     

A proactive material handling method for CPS enabled shop-floor

Cyber physical system (CPS) enables companies to keep high traceability and controllability in manufacturing for better quality and improved productivity. However, several challenges including excessively long waiting time and a serious waste of energy still exist on the shop-floor where limited buffer exists for each machine (e.g., shop-floor that manufactures large-size products). The production logistics tasks are released after work-in-processes (WIPs) are processed, and the machines will be occupied before trolleys arrival when using passive material handling strategy. To address this issue, a proactive material handling method for CPS enabled shop-floor (CPS-PMH) is proposed. Firstly, the manufacturing resources (machines and trolleys) are made smart by applying CPS technologies so that they are able to sense, act, interact and behave within a smart environment. Secondly, a shop-floor digital twin model is created, aiming to reflect their status just like real-life objects, and key production performance indicators can be analysed timely. Then, a time-weighted multiple linear regression method (TWMLR) is proposed to forecast the remaining processing time of WIPs. A proactive material handling model is designed to allocate smart trolleys optimally. Finally, a case study from Southern China is used to validate the proposed method and results show that the proposed CPS-PMH can largely reduce the total non-value-added energy consumption of manufacturing resources and optimize the routes of smart trolleys.     

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.     

Unified modeling for digital twin of a knowledge-based system design

While Model-Based Systems Engineering (MBSE) improves the ambiguity problem of the conventional document-based way, it brings management complexity. Faced with the complexity, one of the core issues that companies care about is how to effectively evaluate, predict, and manage it in the early system design stage. The inaccuracy of contemporary complexity measurement approaches still exits due to the inconsistency between the actual design process in physical space and the theoretical simulation in virtual space. Digital Twin (DT) provides a promising way to alleviate the problem by bridging the physical space and virtual space. Aiming to integrate DT with MBSE for the system design complexity analysis and prediction, based on previous work, an integration framework named System Design Digital Twin in 5 Dimensions was introduced from a knowledge perspective. The framework provides services for design complexity measurement, effort estimation, and change propagation prediction. Then, to represent the system design digital twin in a unified way, a modeling profile is constructed through SysML stereotypes. The modeling profile includes System design digital model in virtual space profile, system services profile, relationships profile and digital twin data profile. Finally, the system design of a cube-satellite space mission demonstrates the proposed unfiled modeling approach.