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Soil carbon content is vital to the way ecosystems function, and it plays an important role in soil properties in producing food, storing water, and mitigating climate change. Soil carbon is the key to unlocking the soil’s various economic and environmental benefits, often known as the multi-functionality of soil carbon content. Human activities, particularly those that include the use of fire to burn biomass, deplete soil cover and result in immediate and enduring losses of soil organic carbon. We must minimize soil carbon loss due to erosion and greenhouse gas (GHG) emissions into the environment. As a way to capture and store carbon dioxide in the atmosphere, carbon sequestration can be used as a way to adapt to climate change because it has been shown to have positive effects on temperature, gross primary production, and the ability of soil to hold water. However, it is still a challenging problem to quantify the effect of how much carbon (biochar) is applied to the soil in terms of carbon sequestration accurately, quickly, cost-effectively, and on a large-scale field. Many studies use expensive element analyzers with intensive labor costs to measure soil carbon content. Several research efforts include utilizing artificial intelligence methods or applications to acquire carbon information from the soil and the atmosphere but lacking proper human interpretation (domain knowledge), the results might not be reliable. Therefore, Explainable Artificial Intelligence of the Internet of Things (XAIoT) is proposed to address the reliability issue and define a set of AI models that are interpretable by domain knowledge from environmental and engineering domain experts. Furthermore, the objectives that we would like to achieve are: 1) the XAIoT empowered microwave and millimeter-wave radar sensing as systematic data acquisition platforms for measuring soil carbon content; 2) in situ site-specific management (SSM) systems to manage soil carbon content and using machine learning algorithms; 3) the Digital Twin-enabled optimal soil carbon content management strategy in large-scale fields using mobile platforms such as UAVs and UGVs for the application of biochar and manure mixtures; 4) a cognitive battery management system for extending battery life for XAIoT smart sensing and smart actuation subsystems. The proposed applications and solutions have the positive potential to fight climate change, reduce carbon emissions, strengthen the quality of life on low-income and disadvantaged farms and adjacent communities, and identify means to gain acceptance among stakeholders in soil carbon management.

A transparent digital twin (DT) is designed for output control using the belief rule base (BRB), namely, DT-BRB. The goal of the transparent DT-BRB is not only to model the complex relationships between the system inputs and output but also to conduct output control by identifying and optimizing the key parameters in the model inputs. The proposed DT-BRB approach is composed of three major steps. First, BRB is adopted to model the relationships between the inputs and output of the physical system. Second, an analytical procedure is proposed to identify only the key parameters in the system inputs with the highest contribution to the output. Being consistent with the inferencing, integration, and unification procedures of BRB, there are also three parts in the contribution calculation in this step. Finally, the data-driven optimization is performed to control the system output. A practical case study on the Wuhan Metro System is conducted for reducing the building tilt rate (BTR) in tunnel construction. By comparing the results following different standards, the 80% contribution standard is proved to have the highest marginal contribution that identifies only 43.5% parameters as the key parameters but can reduce the BTR by 73.73%. Moreover, it is also observed that the proposed DT-BRB approach is so effective that iterative optimizations are not necessarily needed.

This article introduces a closed-loop control strategy for maintaining consistency of liquid temperature in commercial drop-on-demand (DoD) inkjet printing. No additional sensors or additional actuators are installed in the printhead while achieving consistency in liquid temperature. To this end, this article presents a novel in situ sensing-actuation policy at every individual liquid nozzle, where the jetting mechanism has three distinct roles. It is used for jetting liquid droplet onto the print media based on the print job. It is used as a soft sensor to estimate the real-time liquid temperature of the jetting nozzle. While not jetting liquid, it is used as a heating actuator to minimize the gradient of liquid temperature among nozzles. The soft sensing-based in situ controller is implemented in an experimentally validated digital twin that models the thermofluidic processes of the printhead. The digital twin is scalable and flexible to incorporate an arbitrary number of liquid nozzles, making the control strategy applicable for future designs of the printhead.

Background Breakthrough imaging technologies may challenge the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. In this context, X-Ray CT (computed tomography) technology can accurately obtain the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial–temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting to spatially quantify several traits. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA). Results Roots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, several traits were computed. Two of them were accurately validated using the root digital twin as a ground truth against the cylindrical model: number of branches (RRMSE better than 9%) and volume, reaching a coefficient of determination (R2) of 0.84 and a P Conclusions The experimental results support the viability of the developed methodology, being able to provide scalability to a comprehensive analysis in order to perform high throughput root phenotyping.

Journal of intelligent manufacturing (2021). doi:10.1007/s10845-021-01860-6, Published by Springer Science + Business Media B.V, Dordrecht [u.a.]

Continuous plastics compounding processes are highly complex from a physicochemical point of view and correspondingly difficult to optimize. Model-based digital twins suitable for this purpose require complex simulation approaches, trained personnel and input variables that are difficult to determine. However, data-based digital twins, which are in principle suitable for this purpose, often fail because of the enormous trial effort which is required to generate a sufficiently large database. To overcome this problem this paper describes a hybrid approach for generating the necessary database for a data-based digital twin. By intelligent combination of real experiments, adaptation of the physical-chemical process model to these few experimental data and subsequent data cloud generation with the adapted process model results in a sufficient data base for the digital twin.

The application of Internet of Things (IoT) within industrial environments is fostering the adoption of the digital twin (DT) approach, applied at the edge of the network to handle heterogeneity stemming from siloed application management solutions and from protocols originated by different manufacturing tools and enterprise services. In this challenging context, network heterogeneity also represents a critical element that can significantly limit the design and deployment of DT-oriented applications. This article proposes the Application-driven digital twin networking middleware with the twofold objective of: 1) Simplifying the interaction among heterogeneous devices by allowing DTs to exploit IP-based protocols instead of specialized industrial ones and to enhance packet content expressiveness, by enriching data via well-defined standards. 2) Dynamically managing network resources in edge industrial environments, applying software defined networking to exploit the communication mechanisms most suitable to application requirements, ranging from native IP to more articulated based on packet content.

With the ongoing Industry 4.0 (I4.0) revolution, plant management and supervision play a key role in the development and (re-)design of industrial plants. In the arising scenarios, the need to coordinate human workers and autonomous systems, sharing the same environment, teaming together, becomes a fundamental requirement. Indeed, even though automation in standard assembly lines has reached high efficiency and reliability, for complex and new applications, a certain amount of failure must be considered for future addressing. This paper presents a framework for the flexible coordination of such a complex and heterogeneous cyber-physical system. A digital Ttwin mirrors in real-time the plant system, while a dashboard displays plant status, providing the human operators with fundamental tools for supervision and prompt intervention in case of failure. The framework was developed and tested in an industrially relevant environment, specifically for the assembly of the interior of an aircraft fuselage.

Metallized Film Capacitors (MFC) are vital devices in many important fields such as energy, transportation, and aviation, whilst Digital Twin (DT) technology opens a new channel to leverage existing data resources of metallized film capacitors. This paper review current knowledge about metallized film capacitors and digital twin, list the key issues, propose frameworks, and provide the outlook to clarify the potential of digital twin’s application in metallized film capacitors. First, based on the development history and research status of metallized film capacitors, this review sorts out the key research issues of data utilization. Then, the connotation and typical characteristics of the digital twin definition are combed and refined, and the typical architecture of digital twin implementation is presented. Furthermore, the overall application architecture of digital twin in metallized film capacitors is constructed, and the technology system of each application scenario and the role of digital twin are analyzed. Finally, the main challenges in the application of the digital twin in metallized film capacitors and the future development directions are discussed. This review promotes the performance improvement, cost reduction and application expansion of metallized film capacitors, which can be used as a guide for the application of digital twin in other power equipment of energy system.

A dynamic digital twin is a feasible solution that can be employed to build real‐time connectivity between virtual and physical objects. Industries like manufacturing, aerospace and healthcare utilise dynamic digital twins for simulation, monitoring and control purposes, but recently, this nascent technology has also attracted the interest of urban designers. Due to the novelty of the dynamic digital twin in urban design, this research study addresses the concept of digital twin technology and investigates its applicability in so‐called smart city settings. Drawing on results from research interviews and examples from the Digital Twin project in Helsinki city, the research illustrates that solid data infrastructure forms the foundation for urban digital twins and the development of future smart city applications and services. Furthermore, data‐enriched digital twins evidently accelerate smart city experimentations and strengthen both learning and knowledge‐based decision‐making. Digital twins have also proved that they offer an environment in which smart city practitioners can bridge multi‐stakeholder urban design teams through one digital platform.

This letter proposes a realistic setup for a digital twin of an IoT deployment, which makes it possible to measure the impact of IoT networking and computing slices on the physical resources of edge computing hosts. The proposed setup relies on a configurable IoT system, capable of emulating the behavior of a LWM2M broker, along with a large number of devices, both real and emulated. Through our digital twin, we can measure IoT resource utilization and its variation over time, quantifying utilization peaks that may occur at transients, when devices attach to or leave the network.

The project aims to create a Greenhouse Industry 4.0 Digital Twin software platform for combining the Industry 4.0 technologies (IoT, AI, Big Data, cloud computing, and Digital Twins) as integrated parts of the greenhouse production systems. The integration provides a new disruptive approach for vertical integration and optimization of the greenhouse production processes to improve energy efficiency, production throughput, and productivity without compromising product quality or sustainability. Applying the Industry 4.0 Digital Twin concept to the Danish horticulture greenhouse industry provides digital models for simulating and evaluating the physical greenhouse facility’s performance. A Digital Twin combines modeling, AI, and Big Data analytics with IoT and traditional sensor data from the production and cloud-based enterprise data to predict how the physical twin will perform under varying operational conditions. The Digital Twins support the co-optimization of the production schedule, energy consumption, and labor cost by considering influential factors, including production deadlines, quality grading, heating, artificial lighting, energy prices (gas and electricity), and weather forecasts. The ecosystem of digital twins extends the state-of-the-art by adopting a scalable distributed approach of “system of systems” that interconnects Digital Twins in a production facility. A collection of specialized Digital Twins are linked together to describe and simulate all aspects of the production chain, such as overall production capacity, energy consumption, delivery dates, and supply processes. The contribution of this project is to develop an ecosystem of digital twins that collectively capture the behavior of an industrial greenhouse facility. The ecosystem will enable the industrial greenhouse facilities to become increasingly active participants in the electricity grid.

This report summarises the lessons learned during the three-year CDBB West Cambridge Digital Twin project.

This paper deals with the current topic of computer vision which is used in quality control, accurate measurement or robot guidance. Templates are a key component of template matching algorithms. Creating them is a complex and demanding process. The authors of the paper try to describe the possibilities of automated template generation from information that is already available about the product. A digital version in the CAD system exists of virtually every product nowadays. An experiment has shown that with the use of this information, it is possible to create a template that will replace templates created through standard procedures.

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), Artificial Intelligence (AI) — including Machine Learning (ML) and Big Data analytics — as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This paper provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas where IoT can contribute are discussed, namely, i) tracking and tracing, ii) Remote Patient Monitoring (RPM) by Wearable IoT (WIoT), iii) Personal Digital Twins (PDT), and iv) real-life use case: ICT/IoT solution in Korea. Second, the role and novel applications of AI are explained, namely: i) diagnosis and prognosis, ii) risk prediction, iii) vaccine and drug development, iv) research dataset, v) early warnings and alerts, vi) social control and fake news detection, and vii) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including i) crowd surveillance, ii) public announcements, iii) screening and diagnosis, and iv) essential supply delivery. Finally, we discuss how Distributed Ledger Technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.

In this paper, we propose a new approach for the simulation-based support of tryout operations in deep drawing which can be schematically classified as automatic knowledge acquisition. The central idea is to identify information maximising sensor positions for draw-in as well as local blank holder force sensors by solving the column subset selection problem with respect to the sensor sensitivities. Inverse surrogate models are then trained using the selected sensor signals as predictors and the material and process parameters as targets. The final models are able to observe the drawing process by estimating current material and process parameters, which can then be compared to the target values to identify process corrections. The methodology is examined on an Audi A8L side panel frame using a set of 635 simulations, where 20 out of 21 material and process parameters can be estimated with an R-2 value greater than 0.9. The result shows that the observational models are not only capable of estimating all but one process parameters with high accuracy, but also allow the determination of material parameters at the same time. Since no assumptions are made about the type of process, sensors, material or process parameters, the methodology proposed can also be applied to other manufacturing processes and use cases., The International Journal of Advanced Manufacturing Technology, 117 (3), ISSN:0268-3768, ISSN:1433-3015

Industry 4.0 is the fourth industrial revolution for decentralized production through shared facilities to achieve on-demand manufacturing and resource efficiency. It evolves from Industry 3.0 which focuses on routine operation. Data analytics is the set of techniques focus on gain actionable insight to make smart decisions from a massive amount of data. As the performance of routine operation can be improved by smart decisions and smart decisions need the support from routine operation to collect relevant data, there is an increasing amount of research effort in the merge between Industry 4.0 and data analytics. To better understand current research efforts, hot topics, and tending topics on this critical intersection, the basic concepts in Industry 4.0 and data analytics are introduced first. Then the merge between them is decomposed into three components: industry sectors, cyber-physical systems, and analytic methods. Joint research efforts on different intersections with different components are studied and discussed. Finally, a systematic literature review on the interaction between Industry 4.0 and data analytics is conducted to understand the existing research focus and trend.

Due to the falling costs of computational resources and the increasing potential of data acquisition, interest in digital twins, a virtual copy of the physical original, and their industrial application is increasing. Nevertheless, there is limited published work on how to support the process of physical to virtual twinning and what its key aspects are. The aim of this study is to present insights with regards to physical to virtual twinning gained from modelling projects in mechatronic product development. We conducted a survey and in-depth interviews with members of modelling projects. In the surveys and interviews we identified how physical products and virtual models were linked, which virtual models were used and which general challenges and key aspects are considered important by the project members. Our findings show that the key characteristics that pose challenges to modelling regarding physical to virtual twinning are model granularity, model validation, and model integration and interconnectivity.

This paper presents a novel method for rapidly addressing the earthquake-induced damage identification task in historic masonry towers. The proposed method, termed DORI, combines operational modal analysis (OMA), FE modeling, rapid surrogate modeling (SM) and non-linear Incremental dynamic analysis (IDA). While OMA-based Structural Health Monitoring methods using statistical pattern recognition are known to allow the detection of small structural damages due to earthquakes, even far-field ones of moderate intensity, the combination of SM and IDA-based methods for damage localization and quantification is here proposed. The monumental bell tower of the Basilica of San Pietro located in Perugia, Italy, is considered for the validation of the method. While being continuously monitored since 2014, the bell tower experienced the main shocks of the 2016 Central Italy seismic sequence and the on-site vibration-based monitoring system detected changes in global dynamic behavior after the earthquakes. In the paper, experimental vibration data (continuous and seismic records), FE models and surrogate models of the structure are used for post-earthquake damage localization and quantification exploiting an ideal subdivision of the structure into meaningful macroelements. Results of linear and non-linear numerical modeling (SM and IDA, respectively) are successfully combined to this aim and the continuous exchange of information between the physical reality (monitoring data) and the virtual models (FE models and surrogate models) effectively enforces the Digital Twin paradigm. The earthquake-induced damage identified by both data-driven and model-based strategies is finally confirmed by in-situ visual inspections.

In order to make full use of the information provided in the physical reservoirs, including the production history and environmental conditions, the whole life cycle of reservoir discovery and recovery should be considered when mapping in the virtual space. A new concept of reservoir digital twin and the exploratory multi-scale framework is proposed in this paper, covering a wide range of engineering processes related with the reservoirs, including the drainage, sorption and phase change in the reservoirs, as well as extended processes like injection, transportation and on-field processing. The mathematical tool package for constructing the numerical description in the digital space for various engineering processes in the physical space is equipped with certain advanced models and algorithms developed by ourselves. For a macroscopic flow problem, we can model it either in the Navier-Stokes scheme, suitable for the injection, transportation and oil-water separation processes, or in the Darcy scheme, suitable for the drainage and sorption processes. Lattice Boltzmann method can also be developed as a special discretization of the Navier-Stokes scheme, which is easy to be coupled with multiple distributions, for example, temperature field, and a rigorous Chapman-Enskog expansion is performed to show the equivalence between the lattice Bhatnagar-Gross-Krook formulation and the corresponding Navier-Stokes equations and other macroscopic models. Based on the mathematical toolpackage, for various practical applications in petroleum engineering related with reservoirs, we can always find the suitable numerical tools to construct a digital twin to simulate the operations, design the facilities and optimize the processes. Cited as: Zhang, T., Sun, S. An exploratory multi-scale framework to reservoir digital twin. Advances in Geo-Energy Research, 2021, 5(3): 239-251, doi: 10.46690/ager.2021.03.02

Lithium-ion batteries have always been a focus of research on new energy vehicles, however, their internal reactions are complex, and problems such as battery aging and safety have not been fully understood. In view of the research and preliminary application of the digital twin in complex systems such as aerospace, we will have the opportunity to use the digital twin to solve the bottleneck of current battery research. Firstly, this paper arranges the development history, basic concepts and key technologies of the digital twin, and summarizes current research methods and challenges in battery modeling, state estimation, remaining useful life prediction, battery safety and control. Furthermore, based on digital twin we describe the solutions for battery digital modeling, real-time state estimation, dynamic charging control, dynamic thermal management, and dynamic equalization control in the intelligent battery management system. We also give development opportunities for digital twin in the battery field. Finally we summarize the development trends and challenges of smart battery management.

The paper describes the concept of a Digital Twin (DT) creation, which could be used as part of a centralized ground traffic control system in the airport’s apron area. The work is focused on the automatic control systems expected in the future, which will completely replace the ground traffic controllers that solve the centralized control’s tasks in our days. It is assumed that the developed DT can be used both at the stage of training the control system and at the stage of its use. A special feature of the proposed DT concept is the clearly formulated principles of the observed process’ time discretization and the space in which the movement of ground vehicles and aircraft occurs. The experimental verification of the developed concept was carried out using a special simulation model designed to generate data streams on the airport’s transport network’s traffic participants’ location.

Nowadays, digital twins exist everywhere in various fields. However, an analysis of existing applications in manufacturing and logistics revealed that many entirely apply the concept. To identify when a complete implementation of the concept is beneficial, we analyse the need and the implications within production logistics. This study also presents an architecture supporting integrating a digital twin into production logistics and a corresponding application scenario. Based on this, we have derived practical applications. Each application is applied to different situations, and actual benefits can overcome the limitations of the previous studies.

Progress in applied research for sustainable machine tools and forming technologies bases upon industrial and environmental requirements for resource efficiency. Relevant technical trends base upon impact studies and applied research projects on the lifecycle resource consumption for manufacturing processes and systems. This paper gives an overview about a unified methodological approach of the evaluation of resource efficiency of machine tools. It answers the scientific question on sustainability: which technological parameters and machine tool characteristics lead to their lowest resource consumption/losses and part manufacturing costs. Therefore, the method allows to consider them as an energy-information model, in which the transformation of any forms and types of energy, material, and information takes place. It is shown that innovative hollow shaft forming technologies become sustainable alternatives to cutting technologies. A smart factory uses digitalization, manufacturing data management, and self-learning methods for resource efficiency. Sustainable production requires robust and error-free machining processes. Therefore, a collision prevention system protects machining centers and work pieces from collisions in real time will be presented. The gathered information about the product and its properties as well as manufacturing data builds a digital twin and enables a prediction of the resource consumption in smart factories.

Development of Embedded Real-Time Systems is prone to error, and developing bug-free applications is expensive and no guarantees can be provided. We introduce the concept of Digital Quadruplet which includes: a 3D virtual representation of the physical world (a Digital Twin), a Discrete-Event formal model of the system of interest (called the “Digital Triplet”), which can be used for formal analysis as well as simulation studies, and a physical model of the real system under study for experimentation (called the “Digital Quadruplet”). We focus on the definition of the idea of a Digital Quadruplet and how to make these four apparati consistent and reusable. To do so, we use the Discrete-Event formal model as a center for both simulation and execution of the real-time embedded components with timing constraints, as well as a common mechanism for interfacing with the digital counterparts, providing model continuity throughout the process. Here we focus on a principal part of the Digital Quadruplet idea: the provision of an environment to allow models to be used for simulation (in virtual time), visualization, or execution in real-time. A Discrete-EVent Systems specifications (DEVS) kernel runs on bare-metal hardware platforms, avoiding the use of an Operating RTOS in the platform, and the combination with discrete-event modeling engineering.

Latest trends and developments in digital technologies have enabled a new manufacturing model. Digital systems can monitor, optimize and control processes by creating a virtual copy of the physical...

In this letter, we present a data-driven condition-monitoring system for a moka pot aiming at anomaly detection in the coffee-preparation process. A data-acquisition system and the corresponding generation process of a comprehensive dataset (including data from ideal and anomalous brewing scenarios) are described. Supervised and unsupervised machine learning algorithms are trained and tested on the dataset aiming at detecting anomalies in the process and showing the relevance of the considered framework.

With the new industrial revolution of digital transformation, more intelligence and autonomous systems can be adopted in the manufacturing transportation processes. Safety and security of autonomous vehicles (AVs) have obvious advantages of reducing accidents and maintaining a cautious environment for drivers and pedestrians. Therefore, the transformation to data-driven vehicles is associated with the concept of digital twin, especially within the context of AV design. This also raises the need to adopt new safety designs to increase the resiliency and security of the whole AV system. To enable secure autonomous systems for smart manufacturing transportation in an end-to-end fashion, this article presents the main challenges and solutions considering safety and security functions. This article aims to identify a standard framework for vehicular digital twins that facilitate the data collection, data processing, and analytics phases. To demonstrate the effectiveness of the proposed approach, a case study for a vehicle follower model is analyzed when radar sensor measurements are manipulated in an attempt to cause a collision. Perceptive findings of this article can pave the way for future research aspects related to employing digital twins in the AV industry.

Digital twins (DTs) offer the potential for improved understanding of current and future manufacturing processes. This can only be achieved by DTs consistently and accurately representing the real processes. However, the robustness and resilience of the DT itself remain an issue. Accordingly, this paper offers an approach to deal with uncertainty and disruptions, as the DT detects these effectively and self-adapts as needed to maintain representativeness. The paper proposes an intelligent agent-based architecture to improve the robustness (including accuracy of representativeness) and resilience (including timely update) of the DT. The approach is demonstrated on a case of cryogenic secondary manufacturing.

International audience; This paper addresses the problems of data management and analytics for decision-aid by proposing a new vision of Digital Shadow (DS) which would be considered as the core component of a future Digital Twin. Knowledge generated by experts and artificial intelligence, is transformed into formal business rules and integrated into the DS to enable the characterization of the real behavior of the physical system throughout its operation stage. This behavior model is continuously enriched by direct or derived learning, in order to improve the digital twin. The proposed DS relies on data analytics (based on unsupervised learning) and on a knowledge inference engine. It enables the incidents to be detected and it is also able to decipher its operational context. An example of this application in the aeronautic machining industry is provided to stress both the feasibility of the proposition and its potential impact on shop floor performance.

This article describes the preparation of a clinical study on a connected medical device dedicated to facilitate the prevention of lymphedema for patients treated for breast cancer after axillary lymph node dissection (ALND), and to alert them of any manifestation or resurgence in order to prevent its aggravation. A simulator of the entire physical process called digital twin has been developed for this purpose, in a hardware-in-the-loop framework. Statistical calibration of its input parameters, by stochastic inversion and using sensitivity studies, led to establish one or more measurement protocols allowing to capture the signal on a mobile device (phone or tablet) and to detect signal breaks that are physically significant. The measured signal makes it possible to report quickly on the worsening of the patient’s condition and to warn the therapists within a very reasonable period of time. The general methodology of this work seems to us to be easily reproducible in the preparation of clinical studies of other types for connected devices, which aim to develop measurement protocols limiting the often significant cost of such studies. One of the immediate prospects of this work is the initiation of a clinical study on different patients who have been treated by surgery for breast cancer, after improving the robustness of the design of the prototype to take into account the uncertainties affecting the measurements.

The massive data generated by large-scale dynamic systems makes its optimization facing a tough challenge. Traditional White Box-based methods directly model the internal operating mechanism of the system, so massive amounts of measured data need to be handled, which is costly and time-consuming. The poor interpretability of the Black Box-based methods makes it difficult to adapt to the dynamic environment. Thus we propose a novel Gray Box-based approach namely Deep Reinforcement Learning-enabled Constraint Set Inversion Algorithm (DRESIA), which establishes a quantitative model of the nonlinear interoperability effects of system internal states which simplifies the White Box's complex mechanism of reconstruction and prediction and retains the interpretability of the model, therefore improves the prediction efficiency of feasible region while also improving the generalization ability. It further improves the dynamic adaptability of the modeling environment, which provides a new performance balancing scheme for system modeling. Under the premise that the large-scale 5G Cyber-Twin system satisfies the given Quality of Service (QoS) requirements, we perform DRESIA to realize the efficient and dynamic optimal search of feasible region, the results show that the DRESIA reduces the computational cost, and balances the accuracy and robustness of the feasible region, which validate the effectiveness and superiority of Gray Box-based approach.

An aircraft is made up of a number of complicated systems which work in harmony to ensure safe and trouble-free flight. In order to maintain such a platform, many diagnostic and prognostic techniques have been suggested, mostly aimed at components but some at the system level. Together these form a patchwork approach to the overall problem of efficiently informing aircraft maintenance to the Original Equipment Manufacturers, the operators /airlines, and the Maintenance, Repair, and Overhaul organisations. It involves these organisations having to support several different approaches to aircraft health management, and is therefore inefficient and costly. In the current work, a streamlined methodology is put forward. This is based on OSA-CBM (Open System Architecture for Condition Based Maintenance) and can be applied to any aircraft system. Integral with this is the use of mRMR (minimum redundancy maximum relevance) for feature selection, the resulting symptom vector being used for fault diagnosis. This approach is demonstrated on three test cases: the engine, the environmental control system, and the fuel system. In each case, the digital twin setup, simulation conditions for healthy and faulty scenarios, a methodology based on OSA-CBM up to diagnostics are detailed. Diagnostics is carried out for each system in turn, using four machine learning supervised algorithms. The best performing algorithm for each system will then subsequently be used in a vehicle level reasoner called FAVER (A Framework for Aerospace Vehicle Reasoning), which requires these system diagnoses as a starting point for vehicle reasoning and fault ambiguity resolution.

In production systems, digital twins must be always aligned with the real system to guarantee an effective decision making process in a continuously changing environment. To allow the alignment, digital models can be updated with process mining techniques through data collected by sensors. This paper addresses the issue of detecting changes in the production system by analyzing data collected from sensors. Using raw collected data, a procedure is proposed to compute and plot relevant system measures that could help change identification. Simulation is used to test the effectiveness of the procedure in a realistic medium size production line.

With intensified market competition, product development procedure is accelerated, requiring rapid product innovation and efficient collaboration between design and manufacturing. However, there still exist information islands, prohibiting integration of product life cycle processes. To address this issue, bionics and digital twin (DT) are combined as a potential solution. The concepts, framework, and features of digital twin bionics (DTB) is originally proposed, with co-evolution mechanism of product-twins (including virtual and physical products) and production-twins (including virtual and physical production) elaborated. A symbiotic co-evolution mechanism is presented to integrate the processes of product development and manufacturing. In detail, the supporting technologies, such as industrial internet of things, cloud edge computing, big data, and artificial intelligence, as well as the potential key technologies, are illustrated. A case study for rapid development of automotive body-in-white in an industrial robotics welding production line is investigated to verify the applicability of the proposed framework. The results suggest that the integration of bionics and DTs can accelerate the innovations and developments of new products and also help to achieve efficient management of production construction.

The digital twin can reconstruct the scenes of real traffic operation of inland waterways in virtual space, giving birth to new ideas for the digital transformation and upgrading of water safety management. Aiming at the problems of video fragmentation, sub-system data separation and untimely emergency response in the current inland waterway safety monitoring, this paper proposes a new method for reconstructing and applying a digital twin of inland waterways based on 3D video fusion which consists of four parts: (1) Combining drone tilt photography and BIM modeling technology to construct a three-dimensional scene model of inland waterways; (2) Proposing a multi-channel video three-dimensional registration and rendering method to integrate in real time discrete surveillance videos of different angles of view with three-dimensional scene model to establish the spatial correlation between different video images in the scene; (3) Integrating multi-source IoT sensing data to form a scene of digital twin of inland waterways; (4) Exploring innovative application methods for water safety management based on digital twins to meet the water safety management requirements and business logic of inland waterways. A certain segment of the Changhu Shen Line was selected for demonstration and application. The application results show that the construction and application of digital twins for inland waterways combined with 3D video reduces the pressure on managers to understand real-time video footage and improves the efficiency of daily monitoring, evidence collection, and emergency response.

Complex manufacturing and supply chain systems consist of concurrent labour-intensive processes and procedures with repetitive time-consuming tasks and multiple quality checks. These features may pose challenges for the efficient operation and management, while manual tasks may significantly increase human errors or near misses, having impact on the propagation of effects and parallel interactions within these systems. In order to handle the aforementioned challenges, a digital twin (DT) integrated in a multi-agent cyber-physical manufacturing system (CPMS) with the help of RFID technology is proposed. The proposed reference architecture tends to improve the trackability and traceability of complex manufacturing processes. In this research work, the interactions occurring both within a single complex manufacturing system and between multiple sites within a supply chain are considered. For the implementation of the integrated DT-CPMS, a simulation model employing the agent-based modelling technique is developed. A case study from a cryogenic supply chain in the UK is also selected to show the application and validity of the proposed digital solution. The results prove that the DT-CPMS architecture can improve system’s performance in terms of human, equipment and space utilisations.

International audience; During successive industrial revolutions, the objective has not only been for industry to improve and satisfy its direct needs, it has also been to improve society’s standard of living and make life easier for the consumer. Therefore, economic growth should always go hand in hand with each industrial revolution. The medical industry, energy conservation and, in particular, production technologies will be transformed through new value chain models. Globalization, urbanization, demographic change and energy transformation are the transforming forces estimating the technological impetus for a better identification of solutions for a world on the move. In recent years, successive revolutions have made remarkable contributions to a person’s quality of life, safety, industrial economy, comfort and health. This work aims at improving the energy consumption of buildings. To achieve this goal, we have created a Digital Twin that accurately reflects the behavior and characteristics of a future or existing building. To reproduce a copy of the future or existing building, we chose to use the Autodesk REVIT solution to meet several constraints. These have a significant influence on the energy behavior of the building.

Requirement conversion (RC) is an important activity in product customized design. Nowadays, the RC process is mostly driven by designers’ knowledge, the RC model is passive to dynamic customer requirements, and the RC behavior is a black box to customers. Digital twin (DT) is characterized as a self-reinforcing mechanism driven by mirroring between the physical and virtual spaces, which is powerful in addressing these challenges for RC. This paper proposes a digital twin driven requirement conversion (DTRC) architecture and a tri-model-based approach integrated by digital model, behavior model, and evolution model for DTRC development. Firstly, the digital model based on the artificial neural network can simulate the virtual twin data to compensate for the absence of real-world data. Then, driven by the virtual-reality integration data, the behavior model mirrors and visualizes the RC behavior in real world based on the decision tree. Finally, a genetic algorithm based evolution model optimizes the RC rules via physical data throughout the whole product life cycle. A case study of DTRC for elevator customized design is further conducted to validate feasibility and effectiveness of the proposed approach. Experimental results show that DTRC outperforms other RC approaches in terms of conversion accuracy. Meanwhile, DTRC can visualize and optimize the conversion path through the tree topology, which is beneficial to the customer participation and proactive to the dynamic environment.

This work introduces a novel algorithm for the reconstruction of rolling stocks from a sequence of images. The research aims at producing an accurate and wide image model that can be used as a Digital Twin (DT) for diagnosis, fault prediction, maintenance, and other monitoring operations. When observing large surfaces with nearly constant textures, metallic reflections, and repetitive patterns, motion estimation algorithms based on whole image error minimization and feature pairing with Random Sampling and Consensus (RANSAC) or Least Median of Squares (LMedS) fail to provide appropriate associations. To overcome such an issue, we propose a custom Kalman Filter (KF) modified by adding multiple input-noise sources represented as a Gaussian mixture distribution (GM), and specific algorithms to select appropriate data and variance to use for state prediction and correction. The proposed algorithm has been tested on images of train vessels, having a high number of windows, and large metallic paintings with constant or repetitive patterns. The approach here presented showed to be robust in the presence of high environmental disturbances and a reduced number of features. A large set of rolling stocks has been collected during a six months campaign. The set was employed to demonstrate the validity of the proposed algorithm by comparing the reconstructed twin versus known data. The system showed an overall accuracy in length estimation above 99%.

The problems of CNC machine tool (CNCMT) fault diagnosis and production rescheduling have attracted continuous attention because of their great significance to the manufacturing industry. Digital twin is a supporting technology for achieving smart manufacturing and provides a new paradigm for solving these problems. This paper explores a digital twin-driven interaction and cooperation framework and proposes the architecture and implementation mechanism to enable the sharing of data, knowledge, and resource, to realize the fusion of physical space and cyber space, and to improve the accuracy of fault diagnosis. Under this framework, aiming at the influence of CNCMT failure on the initial production planning, a self-adaptation rescheduling method based on Monte Carlo Tree Search (MCTS) algorithm is proposed to provide support for developing more efficient production planning. Finally, the effectiveness of the proposed framework is validated by experimental study. The framework and integrated rescheduling approach can provide guidance for enterprises in implementing CNCMT maintenance and production scheduling to meet high accuracy and reliability requirements.

Smart spindles extend conventional electromechanical machine tool sub-systems with additional sensing and local data processing capabilities, according to the Cyber-Physical Systems paradigm, in the framework of Industry 4.0. The article investigates the possibility and advantages of performing local elaboration of spindle-specific data, which allows embedding and protecting the expertise of component creator in form of proprietary data processing algorithms and numerical models. The main elements and functionalities of a smart spindle are discussed, focusing on how they can deliver a competitive performance exploiting edge computing, the convenience of use for the end user and aspects related to data privacy.

The construction industry lacks a comprehensive and holistic way of utilizing Building Information Modeling (BIM) throughout a building’s life-cycle, where the transition to Asset Management (AM) and Facility Management (FM) is particularly lacking. Enterprise BIM (EBIM) is an emerging, unexplored, holistic organizational concept that is designed to support and optimize business management throughout the entire life-cycles of buildings and infrastructure. However, current understanding and implementation of EBIM is rare. To fix this gap in knowledge, this paper defines the EBIM concept and assesses associated perspectives from research and practice in order to integrate BIM more strongly into the enterprise’s core processes and other needs at different levels within the organization and externally. To achieve a higher and more efficient utilization of building data, however, EBIMs need to develop a clear life-cycle-based information management strategy, including coordination and communication between all stakeholders. The paper highlights and discusses the importance of both available and missing standards related to the effective implementation of EBIM. Several existing open standards have been identified to help strengthen the EBIM concept. The paper identifies a need to develop standards to integrate BIM and IoT (the Internet of Things) and standards that can handle both structured and unstructured data. The authors have conducted a wide-ranging review of research, practice and existing standards and considers today’s potential for using EBIM, as well as discusses existing challenges and future research needs. Furthermore, the EBIM concept is contextualized by providing a case study with focus on cleaning. This study identifies a need to develop best practices in interdisciplinary collaborative projects. Such practical implementation requires integrated and standardized information and technology management and the exploitation of the available technologies in interdisciplinary interaction with those involved in the various processes and the flow of information throughout the life-cycle.

Working at an oil and gas facility, such as a drilling rig, production facility, processing facility, or storage facility, involves various challenges, including health and safety risks. It is possible to leverage emerging digital technologies such as smart sensors, wearable or mobile devices, big data analytics, cloud computing, extended reality technologies, robotic systems, and drones to mitigate the challenges faced by oil and gas workers. While these technologies are not new to the oil and gas industry, most of its existing digital transformation initiatives follow business or process-centric approaches, in which the critical driver of the technology adoption is the enhancement of production, efficiency, and revenue. As a result, they may not address the challenges faced by the workers. As oil and gas workers are among the essential assets in the oil and gas industry, it is vital to address the challenges faced by these workers. This paper proposes a human-centric digital transformational framework for the oil and gas industry to deploy existing digital technologies to enhance their workers’ health, safety, and working conditions. The paper outlines the critical challenges faced by oilfield workers, introduces a system architecture to implements a human-centric digital transformation, discusses the opportunities of the proposed framework, and summarizes the key impediment for the proposed framework.

In the era of the fourth industrial revolution, technological frameworks aim to arrange information and communication technologies into valuable assets to support the management of operations. One of these frameworks is the digital twin, which enables abstractions of a physical resource in the virtual space, allowing for behavior simulations and performance assessments. Consensus regarding which key features manufacturing digital twins should encompass has not yet been reached. This study summarizes a variety of features proposed by recent models in the literature. Four features are identified, followed by expert analyses that assess how valuable they are to the success of implementations.

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.