Digital twin-enabled machining process modeling

Considering the new generation of information technology, the digitalization and intellectualization of the machining process have become the major core in intelligent manufacturing. The complex and diverse requirements, as well as the processing sites force the machining sequence to move towards cyber-physical integration. This paper presents a multidimensional modeling approach for machining processes, by introducing Digital Twin (DT) technology. The method is oriented towards the design and execution phases of the machining process and is used to support intelligent machining. The working mechanism of modeling, simulation, prediction and control of machining process is described based on the interpretation of the modeling and application methods of machining process design, inspection process, fault diagnosis and quality prediction, as based on digital twin technology. Finally, key components of diesel engines are targeted as test objects, demonstrating increased material removal rate by 5.1%, reduced deformation by 22.98% and 30.13%, respectively, verifying the effectiveness of the applied framework and the proposed method.     

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 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.     

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 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.     

An effective MBSE approach for constructing industrial robot digital twin system

Recently, the rapid development of digital twin (DT) technology has been regarded significant in Cyber-physical systems (CPS) promotion. Scholars are focusing on the theoretical architecture and implementing applications, in order to establish a high-fidelity, dynamic, and full-lifecycle DT model and achieve a deep fusion of real and virtual. As a typical complex system with multi-disciplines, multi-physics, and multi-domain characteristics, industrial robot (IR) involves various processes and elements from the two other levels of the system: components and production lines. Their complex relationships lead to a huge challenge to build a comprehensive DT model. Current researchers usually concentrates on single-layer services because of limited construction methodology, which results in enormous isolated models, and leads to low reusable system blocks, finite scalability, and high costs of design, adjustment, upgrade, and maintenance. To address these issues, a standardized methodology and a hierarchical, modular, and generic architecture are proposed to depict comprehensive and variable industrial robot digital twin (IRDT). Firstly, the ontology information model is presented by analyzing variable factors systematically. Then, model-based system engineering (MBSE) based methodology is introduced, including construction process and variants management. After modeling process of three levels (problem domain, solution main, and implementation domain) and four viewpoints (requirement, structure, behavior, and parameter), a generic architecture of IRDT is constructed and a feature-based variants management method is described. Besides, a six-axis IRDTS is implemented to illustrate the mapping of logical architecture and physical system as a multi-level elements and processes representation example. And the steps of numerical evaluations consist of system delay and derivation. Finally, results show the effectiveness and the potential of the proposed theoretical methodology for constructing IRDTS and other industrial applications.     

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 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.     

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.     

The digital twin of the quality monitoring and control in the series solar cell production line

With the development of intelligent manufacturing (IM), the Digital twin (DT) has become an important means to the evolution mechanism of the process. Many researchers pay attention on the realization of DT in different industries. Based on the DT and Digital Twin Shop Floor (DTS) model, a novel, high throughput metrology method is proposed in the process quality monitoring and control of the Series Solar Cell Production Line (SSCPL) for detailed performance analysis. The variance of individual loss parameters and their impact on quality performance are quantified and mapped into the virtual space. The nature of their distributions and correlations provide great insights about quality loss mechanisms in process monitoring, helping to prioritize efforts for optimizing the control of the SSCPL in the physical space. Additionally, the parameters can be tied back to the physical space, allowing the data to be used directly for the control in the manufacturing. The data-loop of “Autonomous perception of process parameters - Dynamic behaver mapping - Online monitoring - Online data analysis - Parameters configuration & control” can be obtained in the model. This paper provides an application paradigm for DT and IM.     

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.     

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.     

Knowledge graph and function block based Digital Twin modeling for robotic machining of large-scale components

Robotic machining is a potential method for machining large-scale components (LSCs) due to its low cost and high flexibility. However, the low stiffness of robots and complex machining process of LSCs result in a lack of alignment between the physical process and digital models, making it difficult to realize the robotic machining of LSCs. The recent Digital Twin (DT) concept shows potential in terms of representing and modeling physical processes. Therefore, this study proposes a robotic machining DT for LSCs. However, the current DT is not capable of knowledge representation, multi-source data integration, optimization algorithm implementation, and real-time control. To address these issues, Knowledge Graph (KG) and Function Block (FB) are employed in the proposed robotic machining DT. Here, robotic machining related information, such as the machining parameters and errors, is represented in the virtual space by building the KG, whereas the FBs are responsible for integrating and applying the algorithms for process execution and optimization based on real-world events. Moreover, a novel adaptive process adjustment strategy is proposed to improve the efficiency of the process execution. Finally, a prototype system of the robotic machining DT is developed and validated by an experiment on robotic milling of the assembly interface for an LSC. The results demonstrate that the robotic machining is successfully optimized and improved by the proposed method.     

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

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

Design of digital twin applications in automated storage yard scheduling

A digital twin-enabled automated storage yard scheduling framework for uncertain port dispatching is proposed in this paper. Digital twin technology is employed to establish the virtual yet realistic storage yard and the connection between them. In the proposed framework, disturbed scenarios during practical operation are monitored, and real-time data is visualized in the virtual space to adapt to the time-varying environment. The proposed framework focuses on the optimization of three main resources, viz. storage area, automated stacking cranes (ASCs), and automated guided vehicles (AGVs). In addition, three key technologies, the Internet of Things (IoT), virtual reality, and digital thread, are adopted to develop the proposed scheduling system. A case study of ASC rescheduling due to dynamic arrival is used to demonstrate the effectiveness of the proposed framework and the significance of obtaining uncertainties in port optimization. Sensitivity analysis is conducted to define the appropriate configuration required to handle all tasks. The results show that digital twin applications in automated storage yard scheduling help operators make optimization decisions.     

基于数字孪生的产线设备实时监控方法与实现

为推动国家智能制造发展,面向生产过程中设备实时监控困难、透明性差、管控效率低、跨学科交叉情况复杂等问题,融合MBSE思想,提出一种基于数字孪生的产线设备监控方法并实现。首先,提出基于数字孪生的监控方法架构;基于SysML建模语言对产线系统和设备进行统一描述建模,建立结构图和行为图,形成数据模型;通过SysML模型与OPC UA联合,以位移数据和任务数据双通道驱动的方式进行虚实映射,并建立异常报警追溯机制;以仓储系统中核心设备堆垛机为例,构建其SysML模型、数字孪生模型,并以仓储产线实时缓存数据库redis为数据源,通过OPC UA获取并实时更新数据驱动数字孪生模型,实现其三维可视化监控,验证了方法的可行性和实时性     

《数字孪生水利工程建设技术导则（试行）》解析

2022 年 3 月 30 日,水利部印发《数字孪生水利工程建设技术导则(试行)》,以指导数字孪生水利工程建设。从数字孪生水利工程建设的重要性、内涵、框架,以及数据底板、模型库、知识库、孪生引擎、监测感知、通信网络、工程自动化控制、工程安全智能分析预警、网络安全体系、运维体系等数字孪生水利工程重点内容进行解读,并对建设指标适用情况和共建共享要求进行说明。