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.     

From simple digital twin to complex digital twin part II: Multi-scenario applications of digital twin shop floor

The shop floor has always been an important application object for the digital twin. It is well known that production, process, and product are the core business of the shop floor. Therefore, the digital twin shop floor covers multi-dimensional information and multi-scale application scenarios. In this paper, the digital twin shop floor is constructed according to the modeling method of the complex digital twin proposed in Part I. The digital twin shop floor is firstly divided into several simple digital twins that focus on scenarios of different scales. Two simple application scenarios are constructed, including tool wear prediction and spindle temperature prediction. Main functions in different application scenarios, such as data acquisition, data processing, and data visualization, are implemented and encapsulated as components to construct simple digital twins. Secondly, ontology models, knowledge graphs, and message queues are used to assemble these simple digital twins into the complex digital twin shop floor. And two complex application scenarios are constructed, including machining geometry simulation considering spindle temperature and production scheduling considering tool wear. The implementation of the complex digital twin shop floor demonstrates the feasibility of the proposed modeling method.     

Intelligent scheduling of a feature-process-machine tool supernetwork based on digital twin workshop

Modern manufacturing enterprises are shifting toward multi-variety and small-batch production. By optimizing scheduling, both transit and waiting times within the production process can be shortened. This study integrates the advantages of a digital twin and supernetwork to develop an intelligent scheduling method for workshops to rapidly and efficiently generate process plans. By establishing the supernetwork model of a feature-process-machine tool in the digital twin workshop, the centralized and classified management of multiple data types can be realized. A feature similarity matrix is used to cluster similar attribute data in the feature layer subnetwork to realize rapid correspondence of multi-source association information among feature-process-machine tools. Through similarity calculations of decomposed features and the mapping relationships of the supernetwork, production scheduling schemes can be rapidly and efficiently formulated. A virtual workshop is also used to simulate and optimize the scheduling scheme to realize intelligent workshop scheduling. Finally, the efficiency of the proposed intelligent scheduling strategy is verified by using a case study of an aeroengine gear production workshop.     

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.     

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 framework and method for Human-Robot cooperative safe control based on digital twin

Human-robot collaboration (HRC) combines the robot’s mechanical properties and predictability with human experience, logical thinking, and strain capabilities to alleviate production efficiency. However, ensuring the safety of the HRC process in-real time has become an urgent issue. Digital twin extends functions of virtual models in the design phase of the physical counterpart in the production phase through virtual-real interactive feedback, data fusion analysis, advanced computational features, etc. This paper proposes an HRC safety control framework and corresponding method based on the digital twin. In the design phase, virtual simulation and virtual reality technology are integrated to construct virtual twins of various HRC scenarios for testing and analyzing potential safety hazards. In the production phase, the safety distance between humans and robots of the HRC scene is monitored and calculated by an iterative algorithm according to machine vision and a convolutional neural network. Finally, the virtual twin is driven based on real-scene data, real-time online visual monitoring, and optimization of the HRC’s overall process. A case study using ABB-IRB1600 is presented to verify the feasibility of the proposed approach.     

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.     

Product redesign using functional backtrack with digital twin

Digital twin, as a new industrial technology, provides great opportunities in various stages of product development. Product redesign is widely required in the process of product improvement, which is greatly depends on the functional analysis of product. Although traditional functional analysis can identify product design problems, the analyzed information is extremely detailed and verbose, which hinders the opportunity of product innovation. To expand the solution space for improving the innovation chance and ensuring solution quality of the product in the physical space, a digital twin is introduced in the redesign process. This study proposes a product redesign method using the functional backtrack obtained from a relational function model (RFM) to the hierarchical function model (HFM) with the digital twin. Based on a selected target product, the proposed method constructs the product RFM (sub-field) that originates from the reverse fishbone and relationships between components. Related parameters of components are obtained. A digital twin entity is built using the RFM (sub-field) and parameters based on the target physical product, and functions are extracted in the form of “verb + noun.” The RFM is formed considering four relations between functions. Furthermore, functions in the RFM are divided into various levels using the Dempster–Shafer theory based on functional levels and boundaries. In addition, the HFM is formed to indicate the level of problem functions and range area of the solution space. Components and parameters of harmful functions are obtained based on the digital twin entity. Creative ideas of product redesign are generated using the theory of inventive problem solving (TRIZ) to solve inventive problems at different functional levels. Technique for order preference by similarity to an ideal solution (TOPSIS) is introduced to evaluate and select solutions. Finally, the feasibility and effectiveness of the proposed method are verified in the redesign of an antenna mounted on vehicles.     

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.     

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.     

The meta-defect-detection system for gear pitting based on digital twin

Gear pitting is a typical surface defect, and its accurate three-dimensional (3D) detection is significant for the operation and maintenance of equipment. Therefore, this paper develops a 3D gear pitting detection method based on digital twin. Firstly, a geometric model of gear pitting is built to expand the samples which are helpful improving the detection accuracy. Secondly, based on the gear pitting model and Unity, a virtual fringe projection profilometry (FPP) system is established in the metaverse for generating the deformation fringe patterns and retrieving their phases. To improve the accuracy of pitting detection, a gear pitting detection network (GPD-Net) is proposed. It can not only extract local and global characteristics from different spaces and scales but also fuse these features via the proposed attention-based conditional random field modules. Thus GPD-Net can retrieve the high-precision wrapped phase by a single fringe pattern. Meanwhile, an FPP system is built in the physical world, and the reconstructed actual point cloud images are imported into the virtual FPP system for the precise measurement of gear pitting. The developed measurement system can be regarded as a meta-defect-detection system. The experimental results show the superiority of GPD-Net over the state-of-the-art phase retrieval algorithms and the effectiveness of the proposed three-dimensional gear pitting detection method. The proposed 3D detection method based on FPP is not limited by the fault type, and it can be effectively applied to the 3D measurement of various surface defects.     

Logic as an integrated modeling framework

There is a growing interest in the application of Artificial Intelligence (AI) tools and techniques to management science problems. A rich variety of commercial AI software products is now emerging to facilitate this. However, there has been little integration between AI and Management Science (MS) at a theoretical level. This paper proposes the use of formal logic as the basis for this conceptual bridge, and the use of logic programming as a modeling language for management science applications. The framework is illustrated by a logic-based representation, called PM, for specifying production, distribution and inventory problems.     

Consistency retention method for CNC machine tool digital twin model

Computer Numerical Control Machine Tool (CNCMT) Digital Twin (DT) model is a carrier for complex, time-varying, coupled data of CNCMT, which can theoretically provide a time-varying high-fidelity model. However, there are still many difficulties in its implementation process. And the key issue is how to realize the updated DT model with performance attenuation and validate it. In order to solve this problem, a model consistency retention method for CNCMT DT model is studied and proposed in this paper. Firstly, the framework of consistency retention method for DT model is designed including both digital space and physical space. The principles of data management and performance attenuation update in digital space are elaborated. Then, the implementation method for consistency retention of CNCMT DT model is studied in terms of performance attenuation update workflow for wear and other damage separately. Finally, a case study for the establishment and application of high-fidelity test bench DT model that focusing on rolling guide-rail is carried out to show the implementation flow of the proposed method and verify its operability and effectiveness.     

面向数字孪生流域的云网融合架构研究

数字孪生流域建设是智慧水利建设的重要标志,是推动流域水利高质量发展的必然要求。为了应对当前数字孪生流域建设过程中面临的流域物联感知能力、通信传输网络资源、模型算法仿真计算水平等方面的不足而产生的挑战,本文结合云网融合的云网协同、按需互联、敏捷智能的服务能力特性,基于云网融合层次化的设计思想,把流域云和网充分融合,有效调度并应用计算、通信、存储等基础设施资源,提出了面向数字孪生流域的云网融合逻辑架构,梳理并阐述了水利感知网云网融合服务实现、水利信息网云网融合服务实现、数字孪生平台算力服务实现等可应用于数字孪生流域建设的云网融合服务架构,总结分析了面向数字孪生流域的云网融合关键技术。研究对充分发挥新一代信息技术对数字孪生流域建设的支撑驱动作用具有重要参考意义。     

Enabling technologies and tools for digital twin

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

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.     

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.     

Modeling and implementation of a digital twin of material flows based on physics simulation

Cyber-physical production systems enable adaptivity and flexibility when manufacturing customized products in small batches. Due to varying routes and a high variance of workpieces, material flows in cyber-physical production systems can get highly complex, which can lead to physically induced disturbances that can result in accidents or decreased throughput and high costs. This issue can be addressed by applying a physics engine to simulate the physical interaction between workpieces and the material handling systems during the operation. Connecting such a digital model to a real material handling system in order to derive simulation-based decision support leads to the concept of digital twins. To date, few practical implementations of digital twins in manufacturing outside the machine tool domain were reported. Therefore, this paper describes the modeling and the subsequent implementation of an integrated system that consists of a real material handling system and its digital twin, based on physics simulation. A practical use case demonstrates the versatile advantages of the implemented solution for a manufacturing system with respect to the three digital twin functions prediction, monitoring and diagnosis.     

Research on digital characterization and identification process model of functional genes for intelligent innovative design

The inheritance and variation of functional knowledge is an important way to achieve product innovation. Analogous biological genetic inheritance can solve the problems with the inheritance and variation of functional knowledge. However, the different expressions of functional knowledge in different domains lead designers to consuming a lot of time for functional knowledge characterization and identification, which reduces the efficiency of functional knowledge application. Therefore, in order to solve the problem of inefficient application of functional knowledge in the genetic process caused by irregular expression of functional knowledge, the paper proposes a digital characterization and identification process model of functional genes for product intelligent innovative design. The process model enables product intelligent innovative design through digital characterization of functional genes and rapid identification of functional genes using computer assistance. The process model consists of four following steps. First, extract functional knowledge from patents and perform preliminary functional gene characterization. Second, characterize functional genes digitally by using computerized digital coding techniques. Third, identify the required functional genes by using image similarity matching algorithm. It assists designers to quickly identify the functional genes required for the target system. Fourth, obtain the target product design scheme. By “transcribing” and “translating” the required functional genes and using TRIZ tools to solve the translated scheme, the product innovative design scheme is finally obtained. The feasibility and effectiveness of the model are verified by the intelligent innovative design of shared bicycle parking management device.     

Prediction maintenance integrated decision-making approach supported by digital twin-driven cooperative awareness and interconnection framework

Predictive maintenance is one of the important technical means to guarantee and improve the normal industrial production. The existing bottlenecks for popularization and application are analyzed. In order to solve these problems, a cooperative awareness and interconnection framework across multiple organizations for total factors that affect prediction maintenance decision-making is discussed. Initially, the structure and operation mechanism of this framework are proposed. It is designed to support the sharing of data, knowledge and resources. As a key supporting technology, the digital twin is also integrated into it to improve the accuracy of fault diagnosis and prediction and support making a maintenance plan with higher accuracy and reliability. Then, under this framework, an integrated mathematical programming model is established with considering the parameter uncertainty and an NSGA-II hybrid algorithm is utilized to solve it. Moreover, an adjustment strategy for a maintenance plan is discussed in response to the dynamic characteristics of the actual maintenance environment. Finally, a case, prediction maintenance decision-making for bearings in grinding rolls of the large vertical mill, is studied. Analysis results verify the advantage of the integrated solving mechanism based on the proposed framework. The framework and integrated decision-making approach can guide the implementation of predictive maintenance with higher accuracy and reliability for industrial enterprises.