Multi-scale evolution mechanism and knowledge construction of a digital twin mimic model

Metal products are susceptible to factors such as cutting force, clamping force and heat in the machining process, resulting in product quality problems, such as geometric deformation and surface defects. The real-time observation and control of product quality are integral to optimizing machining process. Digital twin technologies can be used to monitor and control the quality of products via multi-scale based quality analysis. However, previous research on digital twin lacks a fine-grained expression and generation method for product multi-scale quality, making it impossible to carry out an in-depth analysis of product quality. Aiming at addressing this challenge, we study the multi-scale evolution mechanism of the digital twin model and explore the knowledge generation method of the digital twin data. The proposed method constructed the digital twin quality knowledge model from the macro, meso, and micro levels by utilizing the data of the digital twin mimic model. These multi-scale quality knowledge models can express product quality in a fine-grained way and provide data support for digital twin-based decision-making. Finally, we tested the method in monitoring and controlling the machining quality of an air rudder to verify the feasibility of the proposed method.     

A digital twin-driven approach for the assembly-commissioning of high precision products

High precision products (HPPs) with multidisciplinary coupling are widely used in aerospace, marine, chemical and other fields. Since the internal structure of HPPs is complex and compact, the assembly process requires high precision and involves multidisciplinary coupling. Traditional assembly process of HPPs is based on manual experience, which results in low assembly efficiency and poor-quality consistency. Given the above problems, this research proposes a digital twin-driven assembly-commissioning approach for HPPs. Firstly, this paper introduces the theoretical framework of digital twin-driven assembly-commissioning. Secondly, we introduce the construction method of assembly-commissioning total factor information model based on digital twin technology; the fusion method of twin data and the interoperability method between digital twin models; in addition, the assembliability prediction and assembly-commissioning process optimization methods. Finally, a case study product is used to verify the effectiveness and feasibility of the proposed method.     

A digital twin-driven approach towards traceability and dynamic control for processing quality

Processing quality is the basis for ensuring product quality, and reflects the development needs and application value of realizing intelligent manufacturing. Aiming at the low efficiency of quality problems traceability, poor timeliness and unpredictability of quality control in the machining process, digital twin technology can provide a new intelligent solution based on interaction and integration between physical workshop and virtual workshop. Therefore, a digital twin-driven approach towards traceability and dynamic control for processing quality is proposed in the paper. Firstly, a Bayesian network model for the analysis of factors affecting processing quality (BN_PQ) is introduced, which determines the relevance and influence weight of each factor to processing quality. Secondly, in order to integrate multi-source heterogeneous data to trace the processing quality, a multi-level scalable information model and association mechanism are established. Moreover, the construction method of the IoT system in manufacturing unit for dynamic control of processing quality are introduced, in which the collection method of real-time data is discussed. The contents of digital twin data for processing quality constraints (DTD_PQ) and the management method are elaborated. Then, the digital twin-driven dynamic control method of processing quality is proposed. The conceptual model of the digital twin database and the operating logic for dynamic control of processing quality are described in detail. Finally, the interactive operation and core technologies of DTD_PQ towards traceability and dynamic control of processing quality are analyzed. By choosing examples of machining the connecting rod of diesel engine and the prototype system that has been developed, the effectiveness of the proposed method is verified.     

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.     

Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation

Digital twin (DT) and artificial intelligence (AI) technologies are powerful enablers for Industry 4.0 toward sustainable resilient manufacturing. Digital twins of machine tools and machining processes combine advanced digital techniques and production domain knowledge, facilitate the enhancement of agility, traceability, and resilience of production systems, and help machine tool builders achieve a paradigm shift from one-time products provision to on-going service delivery. However, the adaptability and accuracy of digital twins at the shopfloor level are restricted by heterogeneous data sources, modeling precision as well as uncertainties from dynamical industrial environments. This article proposes a novel modeling framework to address these inadequacies by in-depth integrating AI techniques and machine tool expertise using aggregated data along the product development process. A data processing procedure is constructed to contextualize metadata sources from the design, planning, manufacturing, and quality stages and link them into a digital thread. On this consistent data basis, a modeling pipeline is presented to incorporate production and machine tool prior knowledge into AI development pipeline, while considering the multi-fidelity nature of data sources in dynamic industrial circumstances. In terms of implementation, we first introduce our existing work for building digital twins of machine tool and manufacturing process. Within this infrastructure, we developed a hybrid learning-based digital twin for manufacturing process following proposed modeling framework and tested it in an external industrial project exemplarily for real-time workpiece quality monitoring. The result indicates that the proposed hybrid learning-based digital twin enables learning uncertainties of the interaction of machine tools and machining processes in real industrial environments, thus allows estimating and enhancing the modeling reliability, depending on the data quality and accessibility. Prospectively, it also contributes to the reparametrization of model parameters and to the adaptive process control.     

Digital 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.     

Digital twin-enabled Graduation Intelligent Manufacturing System for fixed-position assembly islands

The layout of fixed-position assembly islands is widely used in the heavy equipment industry, where the product remains at one assembly island for its entire assembly period, while required workers, equipment, and materials are moved to the island according to the assembly plan. Such layout is not only suitable for producing bulky or fragile products, but also offers considerable flexibility and competitive operational efficiency for products with medium variety and volumes. However, due to inherent complexity of the product, sophisticated assembly operations heavily rely on skilled operators, and the complexity and uncertainty are high and amplified by such massive manual interventions as well as the unique routing patterns of the fixed-position assembly process. Aiming at reducing the complexity and uncertainty, this paper introduces a digital twin-enabled Graduation Intelligent Manufacturing System (DT-GiMS) for fixed-position assembly islands. Inspired by the success of graduation ceremony, an assembly system-Graduation Manufacturing System (GMS) is proposed for fixed-position assembly islands, in which job tickets, setup tickets, operation tickets, and logistics tickets are designed to organize the production activities. Following the concept of digital twin, unified digital representations with appropriate sets of information are created at object level, product level, and system level, respectively. Through Internet of Things (IoT), smart gateway, Web 3D and industrial wearable technologies, vital information including identity, status, geometric model, and production process can be captured and mapped in physical space, and converged and synchronized with their digital representations in twin (cloud) space on a real-time basis. The overall framework of DT-GiMS is presented with physical layer, digital layer, and service layer. Real-time convergence and synchronization among them ensure that right resources are allocated and utilized to the right activities at the right time with enhanced visibility. Considering customer demand and production capacity constraints, real-time ticket pool management mechanisms are proposed to manage production activities in a near-optimal way under DT-GiMS. With the support of cloud-based services provided in service layer in DT-GiMS, managers could easily make production decisions, and onsite operators could efficiently complete their daily tasks with nearly error-free operations with enhanced visibility. A demonstrative case is carried out to verify the effectiveness of the proposed concept and approach.     

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.     

面向复杂产品研制的MBSE体系架构及其发展趋势研究

基于模型的系统工程(model based systems engineering,MBSE)是一种将系统工程理论与数字化技术相结合的复杂产品研制技术,能够以系统工程思维有效驱动复杂产品研制流程,并以系统模型方式形式化地表达系统复杂交互作用.对此,首先以复杂产品研制背景、MBSE国内外发展状况为理论基础,总结并介绍目前MBSE在复杂产品研制过程中存在的问题;其次,以MBSE模型域、技术域、功能域、跨域支撑要素和应用域为研究层面,结合人工智能、数字孪生、数字主线等数字化技术综合考虑MBSE工程实践所需的关键要素,提出一种面向复杂产品研制的MBSE体系架构并进行详细论述;最后,基于该体系架构研判目前MBSE发展形势,并以数据模型为核心生产要素的角度进一步探讨未来MBSE的发展趋势.     

A digital twin-based multidisciplinary collaborative design approach for complex engineering product development

With the ever-increasing demand for personalized product functions, product structure becomes more and more complex. To design a complex engineering product, it involves mechanical, electrical, automation and other relevant fields, which requires a closer multidisciplinary collaborative design (MCD) and integration. However, the traditional design method lacks multidisciplinary coordination, which leads to interaction barriers between design stages and disconnection between product design and prototype manufacturing. To bridge the gap, a novel digital twin-enabled MCD approach is proposed. Firstly, the paper explores how to converge the MCD into the digital design process of complex engineering products in a cyber-physical system manner. The multidisciplinary collaborative design is divided into three parts: multidisciplinary knowledge collaboration, multidisciplinary collaborative modeling and multidisciplinary collaborative simulation, and the realization methods are proposed for each part. To be able to describe the complex product in a virtual environment, a systematic MCD framework based on the digital twin is further constructed. Integrate multidisciplinary collaboration into three stages: conceptual design, detailed design and virtual verification. The ability to verify and revise problems arising from multidisciplinary fusions in real-time minimizes the number of iterations and costs in the design process. Meanwhile, it provides a reference value for complex product design. Finally, a design case of an automatic cutting machine is conducted to reveal the feasibility and effectiveness of the proposed approach.     

Blockchain-based application for the traceability of complex assembly structures

Production processes are becoming increasingly complex and decentralized, spanning across multiple manufacturing areas, factories, and even countries. Thereby is the development of a holistic and data-continuous traceability system, which provides transparency of records for the entire manufacturing flow from raw materials to the final product, one of the main challenges. While some solutions already address traceability issues in supply chain management and manufacturing, customized and volatile industries, in which each product features a unique configuration, are still lacking appropriate models and proofs of concept. In this research, we develop a decentralized blockchain application called TokenTrail, which focuses on the specific traceability requirements of multi-hierarchical assembly structures. The proposed blockchain architecture is based on a consortium Ethereum network and a Proof of Authority consensus, which delivers a trusted and shared database within an economic processing framework. To overcome the challenges of managing liaison transformations, we develop an assembly token manager based on the semi-fungible ERC 1155 token, which enables a direct representation of complex assembly processes and structures containing unique parts and batches within one smart contract. A user-friendly front-end application simplifies the querying of the recorded traceability data and visualizes existing dependencies.     

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.     

产品溯源研究综述及前景展望

产品溯源体系建设是完善经济全球化治理架构的重要方面,对保证产品的质量和安全、保护消费者权益、促进社会发展具有重要的意义.从现有的产品溯源框架、方法和系统平台3方面对产品溯源相关研究成果进行综述.首先,从溯源信息存储、溯源深度提升、溯源模块化设计和溯源监管4个角度,对产品溯源框架进行介绍;其次,将现有产品溯源方法分为产品标识溯源方法和区块链溯源方法两个类别,并分别对各个类别的溯源方法进行详述和分析;进而,围绕在食品畜牧、供应链、数字凭证和知识产权4个应用方向,对现有的产品溯源系统平台进行介绍;最后,基于对产品溯源研究工作的综述和分析,从异构多源溯源数据管理架构、成员共享激励与可信协作机制、溯源数据安全保障、溯源实时性保障、溯源过程有效监管5个方面,对当前产品溯源面临的挑战和未来的发展前景进行分析和展望.     

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.     

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.     

Digital twins for automotive development: Two wheelers application

Shortening product lifecycles and increasing customer demands are forcing manufacturers to increase the efficiency and effectiveness of their product development process to stay competitive in an increasingly global setting. The effectiveness of current day product development is hampered by the availability of real-world data that allows the deduction of comprehensive customer requirements. Key to solving this is a tight connection between the manufacturer and the vehicle in-use. Founded by technological advances put forward by Industry 4.0, the digital twin provides the tools needed to strengthen this connection. Driven by the difficulties automotive manufacturers face in generating insights in customer usage of the vehicle, the present work develops a general digital twin framework for the automotive industry, characterizing the digital twin as a holistic representation of a physical vehicle with the appropriate fidelity throughout its lifecycle. Further highlighting that this holistic portrayal requires models which depict the human interaction with the vehicle. To illustrate this, a digital twin model capable of capturing human interaction with automotive products, more specifically motorcycles, is presented. Data generated by this digital twin model provides a data-based foundation for the development of customer requirements, increasing the effectiveness of next-generation vehicle development.     

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.     

Adaptive generic product structure modelling for design reuse in engineer-to-order products

In product lifecycle management, the efficiency of information reuse relies on the definition and management of equivalence information between various product data and structure representations. Equivalence information ensures the consistency and traceability of product information throughout the product lifecycle. The sales-delivery process of engineer-to-order (ETO) products presents a great potential for design reuse, i.e. the reuse of previously validated design solutions in the design of new product variants according to customer-specific requirements. A product family data model that focuses on the interdependencies of viewpoints on information will therefore improve the setup of design reuse mechanisms such as modularity. This paper describes the Adaptive Generic Product Structure (AGPS), a dynamic structure-based product family modelling approach that enables the systematic aggregation of product variants and their distinctive components. The purpose of the approach is to capitalize on the expanding component variety developed within previous product variants as early as the sales lead phase of the sales-delivery process, in order to reduce customer-driven design costs and shorten lead-times. An illustrative example based on the aerospace industry is presented.     

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.     

Digital twin-driven intelligent production line for automotive MEMS pressure sensors

The equipment and technological processes used in manufacturing electronic products are gradually being automated and networked. Currently, digital twin technology continues to evolve and mature. The electronics manufacturing industry is undergoing an intelligent and digital transformation. Micro-electro-mechanical system (MEMS) sensors have been widely used in the automotive field due to their small size, low cost, and high reliability. In this study, a new intelligent production line for automotive MEMS pressure sensors driven by digital twin is individually designed. The intelligent production line system consists of physical production lines, digital production lines, twin data, and data service systems. The technology of multi-source heterogeneous data acquisition is used to process and analyze data collected in real time in a physical production line. Based on the technology of parallel control, the physical and digital production lines are synchronized. To obtain optimal process parameters, a process database is established through the analysis of the key processes of the production line. Three types of automotive MEMS pressure sensors are successfully manufactured in the constructed digital twin-driven intelligent production line. The intelligent production line can realize 24-h unattended operation. The product yield is above 98 %, and the takt time is less than 16 s.