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Digital Twin Architecture of a Cyber-physical Assembly Transfer System
... Digital Twin as the enabler for CPPS has influenced the practical implementation of CPPS. Although, its initial implementation has been mostly carried out in laboratory settings [16] [17]. One of the main benefits of digital twins is real-time supervision [18] of the processes. ...
Setting up new doors for smart production is to fill the gaps between virtual and physical systems. Using a digital twin to represent production cells, simulate system behavior, forecast process failures, and alter variables adaptively. This paper presents a novel digital twin training model to automate intelligent production systems using digital transformation techniques. First, the cell is customized to computer-aided applications, industrial Product Lifecycle Management solutions, and automation platforms. Second, a network of interfaces between the environments is developed to allow communications between the digital world and the factory to achieve near-synchronous controls. Thirdly, the skills of some members of the deep reinforcement learning (DRL) family are addressed in combination with Smart Manufacturing's manufacturing aspects. Thus the experimental results show the new variety of data science and the production sectors will form the DRL method to automated production control issues under straightforward optimization environments.
In recent years, the use of digital twins (DT) to improve maintenance procedures has increased in various industrial sectors (e.g., manufacturing, energy industry, aerospace) but is more limited in the construction industry. However, the operation and maintenance (O&M) phase of a building’s life cycle is the most expensive. Smart buildings already use BIM (Building Information Modeling) for facility management, but they lack the predictive capabilities of DT. On the other hand, the use of extended reality (XR) technologies to improve maintenance operations has been a major topic of academic research in recent years, both through data display and remote collaboration. In this context, this paper focuses on reviewing projects using a combination of these technologies to improve maintenance operations in smart buildings. This review uses a combination of at least three of the terms “Digital Twin”, “Maintenance”, “BIM” and “Extended Reality”. Results show how a BIM can be used to create a DT and how this DT use combined with XR technologies can improve maintenance operations in a smart building. This paper also highlights the challenges for the correct implementation of a BIM-based DT combined with XR devices. An example of use is also proposed using a diagram of the possible interactions between the user, the DT and the application framework during maintenance operations.
The current introduction of Industry 4.0 is very challenging for industrial companies. On the one hand, there is an urge to implement concepts such as digital worker assistance systems or cyber-physical production systems, but besides theoretical work, there is very little research that shows examples of its practical implementation. Furthermore, there is currently a lack of a clear model of how sensor-based worker assistance systems for data acquisition and analytics can be designed and systematically implemented. In the present research, a model for a vision-based worker assistance system for assembly was developed based on an industrial case study regarding a manual assembly line. The proposed model consists of five integrated modules: data acquisition, data preprocessing, data storage, data analysis, and simulation. The data acquisition module was constructed in the assembly workstation of the production line by implementing a depth camera, which together with an algorithm developed in Python for preprocessing, tracks the activities of the operator and inserts the processing times into a SQL table of the data storage module. This module contains all the relevant information of the production system, from the shop floor to the Manufacturing Execution System, enabling vertical integration. The data analysis module, aimed at the streaming and predictive analytics, was deployed in the RStudio platform. Likewise, the simulation module was conceptualized to retrieve real-time data from the shop floor and to select the best strategy. To evaluate the model testing of the proposed system in real production was performed. The results of this use case provide useful information for academia as well as practitioners how to implement vision-based worker assistance systems.
With the rapid development of smart manufacturing, some challenges are emerging in the production management, including the utilization of information technology and the elimination of dynamic disturbance. A digital twin-driven production management system (DTPMS) can dynamically simulate and optimize production processes in manufacturing and achieve real-time synchronization, high fidelity, and real-virtual fusion in cyber-physical production. This paper focuses on establishing DTPMS for production life-cycle management. First, we illustrate how to integrate digital twin technology and simulation platforms. Second, a framework of DTPMS is proposed to support a cyber-physical system of production workshop, including product design, product manufacturing, and intelligent service management. Finally, the proposed DTPMS is applied to the production process of a heavy-duty vehicle gearbox. The experimental results indicate that the defective rate of products and the in-process inventory are reduced by 34% and 89%, respectively, while the one-time pass rate of product inspection is increased by 14.2%, which demonstrates the feasibility and effectiveness of the DTPMS.
Various kinds of engineering software and digitalized equipment are widely applied through the lifecycle of industrial products. As a result, massive data of different types are being produced. However, these data are hysteretic and isolated from each other, leading to low efficiency and low utilization of these valuable data. Simulation based on theoretical and static model has been a conventional and powerful tool for the verification, validation, and optimization of a system in its early planning stage, but no attention is paid to the simulation application during system run-time. With the development of new-generation information and digitalization technologies, more data can be collected, and it is time to find a way for the deep application of all these data. As a result, the concept of digital twin has aroused much concern and is developing rapidly. Dispute and discussions around concepts, paradigms, frameworks, applications, and technologies of digital twin are on the rise both in academic and industrial communities. After a complete search of several databases and careful selection according to the proposed criteria, 240 academic publications about digital twin are identified and classified. This paper conducts a comprehensive and in-depth review of these literatures to analyze digital twin from the perspective of concepts, technologies, and industrial applications. Research status, evolution of the concept, key enabling technologies of three aspects, and fifteen kinds of industrial applications in respective lifecycle phase are demonstrated in detail. Based on this, observations and future work recommendations for digital twin research are presented in the form of different lifecycle phases.
Roller conveyor line (RCL) plays an important role in smart manufacturing workshop and modern logistics industry. RCL transfers the specified type of workpiece box to specified exports according to the personalized demands. It greatly improves the sorting speed and delivery speed of workpiece. However, the construction of RCL is very time-consuming and difficult because of the unintuitive design and complex control. In order to reduce the difficulty of constructing RCL and eliminate the gap between design and control, a research about cyber-physical system (CPS) for design and control driven by digital twin (DT) is conducted. In this research, three key enabling technologies of constructing five-dimensional DT for RCL are illustrated in detail as follows: (1) multi-scale modeling method of RCL; (2) extensible distributed communication framework; (3) fast mapping method of distributed controllers. Finally, this paper provides a case study of using the CPS driven by DT to design and control RCL. Experimental results show that the CPS in this paper can achieve the rapid design and distributed control of RCL.
When, in 1956, Artificial Intelligence (AI) was officially declared a research field, no one would have ever predicted the huge influence and impact its description, prediction, and prescription capabilities were going to have on our daily lives. In parallel to continuous advances in AI, the past decade has seen the spread of broadband and ubiquitous connectivity, (embedded) sensors collecting descriptive high dimensional data, and improvements in big data processing techniques and cloud computing. The joint usage of such technologies has led to the creation of digital twins, artificial intelligent virtual replicas of physical systems. Digital Twin (DT) technology is nowadays being developed and commercialized to optimize several manufacturing and aviation processes, while in the healthcare and medicine fields this technology is still at its early development stage. This paper presents the results of a study focused on the analysis of the state-of-the-art definitions of DT, the investigation of the main characteristics that a DT should possess, and the exploration of the domains in which DT applications are currently being developed. The design implications derived from the study are then presented: they focus on socio-technical design aspects and DT lifecycle. Open issues and challenges that require to be addressed in the future are finally discussed.
This paper presents a methodology to calculate the Remaining Useful Life (RUL) of machinery equipment by utilising physics-based simulation models and Digital Twin concept, in order to enable predictive maintenance for manufacturing resources using Prognostics and health management (PHM) techniques. The resources and the properties of them are first modelled in a digital environment able to simulate the real machine’s behaviour. Data are gathered by machines’ controllers and external sensors to be used for the synchronous tuning of the digital models and their simulation. The outcome of the simulation is then used to assess the resource’s condition and to calculate RUL. In this way, the condition and the status of the machines can be monitored and predicted as a result from the simulation of physics-based models, without invasive techniques of common predictive maintenance solutions. A case study is presented in this paper where the proposed methodology is validated by predicting the RUL of an industrial robot.
The Digital Twin (DT) is one of the main concepts associated to the Industry 4.0 wave. This term is more and more used in industry and research initiatives; however, the scientific literature does not provide a unique definition of this concept. The chapter aims at analyzing the definitions of the DT concept in scientific literature, retracing it from the initial conceptualization in the aerospace field, to the most recent interpretations in the manufacturing domain and more specifically in Industry 4.0 and smart manufacturing research. DT provides virtual representations of systems along their lifecycle. Optimizations and decisions making would then rely on the same data that are updated in real-time with the physical system, through synchronization enabled by sensors. The chapter also proposes the definition of DT for Industry 4.0 manufacturing, elaborated by the European H2020 project MAYA, as a contribution to the research discussion about DT concept.
Mass customization and shortening product life cycles pose a heightened set of requirement on modern production systems. Fast response to changing conditions has been found to be a key to competitive advantage for manufacturing firms. In order to cope with this, modern firms are entering in the fourth industrial revolution. Smart pieces of equipment, based on ubiquitous computation and reliable communication, are being deployed in the shop-floors and supply information to the whole enterprise. The work presented in this paper belong to a pilot project to explore promising technologies in this domain and deploying them in a test-line at a large vehicle manufacturer in Sweden. Traceability and visibility of assets plays a critical role in the process of improving shop-floor performance, contributing to better control, planning and scheduling decisions. In detail this article focuses on the collection of requirement and design of a real-time positioning system for asset tracking including its implementation with other technologies in the company demonstrator.
The increasing change of production leads to differences between the current shop floor and the state of planning. This difference causes significant challenges for production planners while integrating new products into existing production systems. To tackle this issue, this paper presents a concept for the automated creation of a digital twin of a body-in-white production system based on current resources, products as well as process information from the cyber-physical system. The paper focuses on the different data sources and information in cyber-physical systems necessary for integration planning. Furthermore, major parts of the concept are evaluated in a real body-in-white production system. The resulting digital twin enables faster product integration and Industry 4.0 concepts.
Digital twin is a significant way to achieve smart manufacturing, and provides a new paradigm for fault diagnosis. Traditional data-based fault diagnosis methods mostly assume that the training data and test data are following the same distribution and can acquire sufficient data to train a reliable diagnosis model, which is unrealistic in the dynamic changing production process. In this paper, we present a two-phase Digital-twin-assisted Fault Diagnosis method using Deep transfer learning (DFDD), which realizes fault diagnosis both in the development and maintenance phases. At first, the potential problems that are not considered at design time can be discovered through front running the ultra-high-fidelity model in the virtual space, while a Deep Neural Network (DNN) based diagnosis model will be fully trained. In the second phase, the previously trained diagnosis model can be migrated from the virtual space to physical space using Deep Transfer Learning (DTL) for real-time monitoring and predictive maintenance. This ensures the accuracy of the diagnosis as well as avoids wasting time and knowledge. A case study about fault diagnosis using DFDD in a car body-side production line is presented. The results show the superiority and feasibility of our proposed method.
Factory design offers many promising capabilities regarding productivity and floor utilization. To evaluate the design and help the designer to escape design flaws, digital twin is proposed to support factory design. With considering of frequently changing in design phase, a modular approach was proposed to help building flexible digital twin and conducting corresponding changes. By using flexible digital twin, designer can quickly evaluate different designs and find design flaws in an easy way. And consequently time saving can be benefited. A case study of application on real factory is presented to illustrate the advantage.
Today´s manufacturing companies need to adapt more than ever to changing customer needs, rising resource costs and increasing uncertainties. A promising way to face these issues is the digitalization of the manufacturing system. Key elements of the digitalization are cyber-physical systems (CPS) and the cyber-physical production system (CPPS). In the last years, the Digital Twin has become a synonym for the cyber-part of CPS and CPPS. In this paper, a conceptual framework and potential applications of a decision support system for the order management process are discussed, that is based on the Digital Twin of the manufacturing system.
The Digital Twin (DT) is one of the main concepts associated to the Industry 4.0 wave. This term is more and more used in industry and research initiatives; however, the scientific literature does not provide a unique definition of this concept. The paper aims at analyzing the definitions of the DT concept in scientific literature, retracing it from the initial conceptualization in the aerospace field, to the most recent interpretations in the manufacturing domain and more specifically in Industry 4.0 and smart manufacturing research. DT provides virtual representations of systems along their lifecycle. Optimizations and decisions making would then rely on the same data that are updated in real-time with the physical system, through synchronization enabled by sensors. The paper also proposes the definition of DT for Industry 4.0 manufacturing, elaborated by the European H2020 project MAYA, as a contribution to the research discussion about DT concept.
Inside smart factories, Cyber-Physical Systems (CPS) have to be synchronized one with another and with the external world to share information and trigger actions. In this work, the conceptual development of such a network to implement a Smart Factory at the Mini-Factory Laboratory of the Free University of Bolzano is presented. The objective is to set up a framework for an Industrial Internet System (IIS), first by homogenizing and integrating the communication systems of the end-nodes of the laboratory, i.e., sensors, robots, etc., through the necessary hardware and middleware, and second, by constructing a centralized backbone network where all valuable information is collected for further big data analysis.
Nowadays, along with the application of new-generation information technologies in industry and manufacturing, the big data-driven manufacturing era is coming. However, although various big data in the entire product lifecycle, including product design, manufacturing, and service, can be obtained, it can be found that the current research on product lifecycle data mainly focuses on physical products rather than virtual models. Besides, due to the lack of convergence between product physical and virtual space, the data in product lifecycle is isolated, fragmented, and stagnant, which is useless for manufacturing enterprises. These problems lead to low level of efficiency, intelligence, sustainability in product design, manufacturing, and service phases. However, physical product data, virtual product data, and connected data that tie physical and virtual product are needed to support product design, manufacturing, and service. Therefore, how to generate and use converged cyber-physical data to better serve product lifecycle, so as to drive product design, manufacturing, and service to be more efficient, smart, and sustainable, is emphasized and investigated based on our previous study on big data in product lifecycle management. In this paper, a new method for product design, manufacturing, and service driven by digital twin is proposed. The detailed application methods and frameworks of digital twin-driven product design, manufacturing, and service are investigated. Furthermore, three cases are given to illustrate the future applications of digital twin in the three phases of a product respectively.
This paper introduces the concept of a " Digital Twin " as a virtual representation of what has been produced. Compare a Digital Twin to its engineering design to better understand what was produced versus what was designed, tightening the loop between design and execution.
Interoperable enterprise systems (be they supply chains, extended enterprises, or any form of virtual organizations) must be designed, controlled, and appraised from a holistic and systemic point of view. Systems interoperability is a key to enterprise integration, which recommends that the IT architecture and infrastructure be aligned with business process organization and control, themselves designed according to a strategic view expressed in an enterprise architecture. The paper discusses architectures and methods to build interoperable enterprise systems, advocating a mixed service and process orientation, to support synchronous and/or asynchronous operations, both at the business level (business events, business services, business processes) and at the application level (workflow, IT and Web services, application programs).
In the era of digitalization, there are many emerging technologies, such as the Internet of Things (IoT), Digital Twin (DT), Cloud Computing and Artificial Intelligence (AI), which are quickly developped and used in product design and development. Among those technologies, DT is one promising technology which has been widely used in different industries, especially manufacturing, to monitor the performance, optimize the progresses, simulate the results and predict the potential errors. DT also plays various roles within the whole product lifecycle from design, manufacturing, delivery, use and end-of-life. With the growing demands of individualized products and implementation of Industry 4.0, DT can provide an effective solution for future product design, development and innovation. This paper aims to figure out the current states of DT research focusing on product design and development through summarizing typical industrial cases. Challenges and potential applications of DT in product design and development are also discussed to inspire future studies.
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.
Personalised production allows the supply chain (SC) to exist in various dynamic fluctuations within a make-to-order (MTO) environment. An SC for personalised production has redundant inventory and operation capacity; therefore, it requires a system that can achieve recoverability for operation resilience. Thus, a standalone cyber physical system (CPS) has limitation for SC control with MTO. To solve this problem, the CPS must be coordinated, and a systematic approach is required. This study proposes a cyber physical logistics system (CPLS) that is coordinated with the agent cyber physical production systems in a multi-level CPS structure. This multi-level architectural framework
is designed to provide technical functionalities for resilient SC control. The service composition procedures of technical functionalities on the distributed digital twin (DT) simulation are divided into type and instance stages. The operation procedures of the DT application and technical functionality of the DT engine are suggested. The proposed CPLS has appropriate service composition and operation for the bullwhip and ripple effects, which are the two main SC-related problems. This study illustrates an early case of CPLS that can minimise differences among assets using distributed DT simulation;
furthermore, the study establishes an SC and production plan based on the DT simulation
results.
As the modern society is in the middle of its fourth industrial revolution, many enabling technologies are now viable to use in an engineering context. Several of these technologies are mature and available off the shelf; however, in the industrial setting they are rather novel. Two of these are virtual reality (VR), which has grown immensely in the gaming sector, and 3-D imaging, which is commonly used in archeology and construction. This study presents lessons learned from combining these two technologies in an industrial context with the digital twin concept. Three industrial case studies have been performed, and several observations have been identified in all three aspects of sustainability. For example, improved solution fidelity at an early stage can be achieved by externalizing tacit knowledge, and multiple issues during planning and installation phases have been avoided by utilizing the hybrid digital twin models. This type of digital twin enables highly detailed production system access, enabling engineering abilities from anywhere, anytime. Furthermore, the model becomes a powerful communication tool, which has reduced the resistance to workplace changes, as stakeholders lacking computer-aided design (CAD) knowledge can be involved in the change process. The highly detailed models have also allowed more focus to be put on safety and regulations, as these aspects naturally are more suited to experience in immersive VR. In conclusion, the hybrid digital twin concept developed in this study is a promising tool for decision makers and stakeholders alike, bound to benefit those who use it in all three aspects of sustainability.
The digital twin is a rather new industrial control and automation systems concept. While the approach so far has gained interest mainly due to capabilities to make advanced simulations and optimizations, recently the possibilities for enhanced security have got attention within the research community. In this paper, we discuss how a digital twin replication model and corresponding security architecture can be used to allow data sharing and control of security-critical processes. We identify design-driving security requirements for digital twin based data sharing and control. We show that the proposed state synchronization design meet the expected digital twin synchronization requirements and give a high level design and evaluation of other security components of the architecture. We also make performance evaluations of a proof of concept for protected software upgrade using the proposed digital twin design. Our new security framework provides a foundation for future research work in this promising new area.
This paper discusses an object-oriented event-driven simulation as a digital twin of a flexible assembly cell coordinated with a robot to perform assembly tasks alongside human. The digital twin extends the use of virtual simulation models developed in the design phase of a production system to operations for real-time control, dynamic skill-based tasks allocation between human and robot, sequencing of tasks and developing robot program accordingly. The methodology combines lean methods of manual assembly in human–robot collaboration paving path towards flexible human–robot work teams. The study is validated with an industrial case study involving dexterous assembly tasks.
https://www.sciencedirect.com/science/article/pii/S000785061930037X
As the applications of real-time computing technology are becoming more and more popular in many safety critical domains such as avionics, national defense and transportation systems, field network faces new requirements and challenges because of the coexistence of different kinds of synchronous real-time, asynchronous real-time and non-real-time communication requirements. The realization approach and schedulability analysis of safety critical real-time network are very crucial because of giving a way to solve the problem. Firstly, the shortcomings of the existing COTS network such as Ethernet are described. And then, a high reliable and real time Ethernet communication protocol known as E&TTE (event- and time-triggered Ethernet) based on event- and time-triggered approach is presented. The E&TTE based real time network is flexible, and the communication time is predictable. At the same time, the network scheduling model is constructed, and the global schedulability analysis is done in this paper. The rationality and feasibility of E&TTE protocol is also proven by a concrete example. With E&TTE protocol, the diverse requirements of safety critical real time network can be met better. In addition, the E&TTE protocol can be widely applied to the field control network of ordinary industry systems.
Considering the urgency of the need for standards which would allow constitution of heterogeneous computer networks, ISO created a new subcommittee for "Open Systems Interconnection" (ISO/ TC97/SC 16) in 1977. The first priority of subcommittee 16 was to develop an architecture for open systems interconnection which could serve as a framework for the definition of standard protocols. As a result of 18 months of studies and discussions, SC16 adopted a layered architecture comprising seven layers (Physical, Data Link, Network, Transport, Session, Presentation, and Application). In July 1979 the specifications of this architecture, established by SC16, were passed under the name of "OSI Reference Model" to Technical Committee 97 "Data Processing" along with recommendations to start officially, on this basis, a set of protocols standardization projects to cover the most urgent needs. These recommendations were adopted by T.C97 at the end of 1979 as the basis for the following development of standards for Open Systems Interconnectlon within ISO. The OSI Reference Model was also recognized by CCITT Rapporteur's Group on "Layered Model for Public Data Network Services." This paper presents the model of architecture for Open Systems Interconnection developed by SC16. Some indications are also given on the initial set of protocols which will-likely be developed in this OSI Reference Model.
In this paper, we describe the influence of Richard Bellman's work on our research. The influence is primarily felt in the attitude we take towards the research, which is that whenever the right mathematical tool for a problem that we are considering is not available, then some mathematical invention is required. We describe our research studies in software and system engineering for what we have called Constructed Complex Systems (we call them that to distinguish them from the mathematically more popular use of the term to refer to studies of fractals, chaos, and other behaviors in dynamical systems), which are large systems mediated or managed by computing systems, and the Integration Science that we believe is necessary for adequately designing, building, and analyzing such systems. The two technical topic areas we describe are Wrappings, which is our knowledge-based integration infrastructure for Constructed Complex Systems, and Virtual Worlds, which provide a very interesting testbed for integrating a wide range of processes, from human activities to formal systems. In both cases, we describe some of the mathematical implications and requirements of our approach.
We propose a framework for component-based modeling using an abstract layered model for components. A component is the superposition of two models: a behavior model and an interaction model. Interaction models describe architectural constraints induced by connectors between components. We propose and analyze general requirements for component composition that motivated and guided the development of the framework. We define an associative and commutative composition operator on components encompassing heterogeneous interaction. As a particular instance of the proposed framework, we consider components where behavior models are transition systems and interaction models are described by priority relations on interactions. This leads to a concept of "flexible" composition different from usual composition in that it preserves deadlock-freedom and is appropriate for correctness by construction. Nevertheless, flexible composition is a partial operation. Product systems should be interaction safe in the sense that they do not violate constraints of the interaction model. We propose results ensuring correctness by construction of a system from properties of its interaction model and of its components. The properties considered include global deadlock-freedom, individual deadlock-freedom of components, and interaction safety.
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