Real-Time-Capable Synchronization of Digital Twins

Digital twins are rigorous mathematical models that can be used to represent the operation of real systems. This connection allows for deeper understanding of the actual states of the analyzed system through estimation of variables that are difficult to measure otherwise. In this context, the present manuscript describes the successful implementation of a digital twin to represent a four-stage multi-effect evaporation train from an industrial sugar-cane processing unit. Particularly, the complex phenomenological effects, including the coupling between thermodynamic and fluid dynamic effects, and the low level of instrumentation in the plant constitute major challenges for adequate process operation. For this reason, dynamic mass and energy balances were developed, implemented and validated with actual industrial data, in order to provide process information for decision-making in real time. For example, the digital twin was able to indicate failure of process sensors and to provide estimates for the affected variables in real time, improving the robustness of the operation and constituting an important tool for process monitoring.

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When Digital Twin Meets Network Softwarization in the Industrial IoT: Real-Time Requirements Case Study

Sensors ◽

10.3390/s21248194 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8194

Author(s):

Mehdi Kherbache ◽

Moufida Maimour ◽

Eric Rondeau

Keyword(s):

Real Time ◽

Industrial Systems ◽

Digital Twin ◽

Digital Twins ◽

Industrial Internet ◽

Industrial Iot ◽

Time Requirements ◽

Loop Network ◽

Sdn Controller

The Industrial Internet of Things (IIoT) is known to be a complex system because of its severe constraints as it controls critical applications. It is difficult to manage such networks and keep control of all the variables impacting their operation during their whole lifecycle. Meanwhile, Digital Twinning technology has been increasingly used to optimize the performances of industrial systems and has been ranked as one of the top ten most promising technological trends in the next decade. Many Digital Twins of industrial systems exist nowadays but only few are destined to networks. In this paper, we propose a holistic digital twinning architecture for the IIoT where the network is integrated along with the other industrial components of the system. To do so, the concept of Network Digital Twin is introduced. The main motivation is to permit a closed-loop network management across the whole network lifecycle, from the design to the service phase. Our architecture leverages the Software Defined Networking (SDN) paradigm as an expression of network softwarization. Mainly, the SDN controller allows for setting up the connection between each Digital Twin of the industrial system and its physical counterpart. We validate the feasibility of the proposed architecture in the process of choosing the most suitable communication mechanism that satisfies the real-time requirements of a Flexible Production System.

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A Generic Asset Model for Implementing Product Digital Twins in Smart Remanufacturing

10.21203/rs.3.rs-186288/v1 ◽

2021 ◽

Author(s):

Mairi Kerin ◽

Duc Truong Pham ◽

Jun Huang ◽

Jeremy Hadall

Keyword(s):

Real Time ◽

Manufacturing Industry ◽

Integrated System ◽

Embedded Sensors ◽

Functional Requirements ◽

Digital Twin ◽

Digital Twins ◽

The Impact ◽

The Internet Of Things

Abstract A digital twin is a “live” virtual replica of a sensorised component, product, process, human, or system. It accurately copies the entity being modelled by capturing information in real time or near real time from the entity through embedded sensors and the Internet-of-Things. Many applications of digital twins in manufacturing industry have been investigated. This article focuses on the development of product digital twins to reduce the impact of quantity, quality, and demand uncertainties in remanufacturing. Starting from issues specific to remanufacturing, the article derives the functional requirements for a product digital twin for remanufacturing and proposes a UML model of a generic asset to be remanufactured. The model has been demonstrated in a case study which highlights the need to translate existing knowledge and data into an integrated system to realise a product digital twin, capable of supporting remanufacturing process planning.

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A Deep Lifelong Learning Method for Digital-Twin Driven Defect Recognition With Novel Classes

Journal of Computing and Information Science in Engineering ◽

10.1115/1.4049960 ◽

2021 ◽

Vol 21 (3) ◽

Author(s):

Gao Yiping ◽

Li Xinyu ◽

Liang Gao

Keyword(s):

Lifelong Learning ◽

Real Time ◽

Learning Strategy ◽

Development Trend ◽

Smart Manufacturing ◽

Learning Method ◽

Time Data ◽

Time Efficiency ◽

Digital Twins ◽

Defect Recognition

Abstract Recently, digital twins (DTs) have become a research hotspot in smart manufacturing, and using DTs to assist defect recognition has also become a development trend. Real-time data collection is one of the advantages of DTs, and it can help the realization of real-time defect recognition. However, DT-driven defect recognition cannot be realized unless some bottlenecks of the recognition models, such as the time efficiency, have been solved. To improve the time efficiency, novel defect class recognition is an essential problem. Most of the existing methods can only recognize the known defect classes, which are available during training. For new incoming classes, known as novel classes, these models must be rebuilt, which is time-consuming and costly. This greatly impedes the realization of DT-driven defect recognition. To overcome this problem, this paper proposes a deep lifelong learning method for novel class recognition. The proposed method uses a two-level deep learning architecture to detect and recognize novel classes, and uses a lifelong learning strategy, weight imprinting, to upgrade the model. With these improvements, the proposed method can handle novel classes timely. The experimental results indicate that the proposed method achieves good results for the novel classes, and it has almost no delay for production. Compared with the rebuilt methods, the time cost is reduced by at least 200 times. This result suggests that the proposed method has good potential in the realization of DT-driven defect recognition.

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Accurate, Real-Time Replication of Governing Equations of Physical Systems with Transpose CNNs — for Industry 4.0 and Digital Twins

Management and Industrial Engineering - Machine Learning in Industry ◽

10.1007/978-3-030-75847-9_7 ◽

2021 ◽

pp. 139-158

Author(s):

Hritik Narayan ◽

Arya K. Bhattacharya

Keyword(s):

Real Time ◽

Industry 4.0 ◽

Governing Equations ◽

Physical Systems ◽

Digital Twins

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Building a Trustworthy Product-level Shape-performance Integrated Digital Twin with Multi-fidelity Surrogate Model

Journal of Mechanical Design ◽

10.1115/1.4052390 ◽

2021 ◽

pp. 1-28

Author(s):

Shuo Wang ◽

Xiaonan Lai ◽

Xiwang He ◽

Yiming Qiu ◽

Xueguan Song

Keyword(s):

Real Time ◽

Sensor Data ◽

Test Case ◽

Digital Twin ◽

Digital Twins ◽

Training Samples ◽

Practical Engineering ◽

Simulation Results ◽

Visualization Techniques ◽

Quantities Of Interest

Abstract Digital twin has the potential for increasing production, achieving real-time monitor, and realizing predictive maintenance by establishing a real-time high-fidelity mapping between the physical entity and its digital model. However, the high accuracy and instantaneousness requirements of digital twins have hindered their applications in practical engineering. This paper presents a universal framework to fulfill the requirements and to build an accurate and trustworthy digital twin by integrating numerical simulations, sensor data, multi-fidelity surrogate (MFS) models, and visualization techniques. In practical engineering, the number of sensors available to measure quantities of interest is often limited, complementary simulations are necessary to compute these quantities. The simulation results are generally more comprehensive but not as accurate as the sensor data. Therefore, the proposed framework combines the benefits of both simulation results and sensor data by using an MFS model based on moving least squares, named MFS-MLS. The MFS-MLS was developed as an essential part to calibrate the continuous field of the simulation by limited sensor data to obtain accurate results for the digital twin. Then single-fidelity surrogate models are built on the whole domain using the calibrated results of the MFS-MLS as training samples and sensor data as inputs to predict and visualize the quantities of interest in real-time. In addition, the framework was validated by a truss test case, and the results demonstrate that the proposed framework has the potential to be an effective tool to build accurate and trustworthy digital twins.

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A processing architecture for real-time predictive smart city digital twins

10.1109/bigdata52589.2021.9671660 ◽

2021 ◽

Author(s):

Sven Van Den Berghe

Keyword(s):

Real Time ◽

Smart City ◽

Digital Twins ◽

Processing Architecture

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Machining Digital Twin using real-time model-based simulations and lookahead function for closed loop machining control

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-021-07867-w ◽

2021 ◽

Author(s):

Rob Ward ◽

Chao Sun ◽

Javier Dominguez-Caballero ◽

Seun Ojo ◽

Sabino Ayvar-Soberanis ◽

...

Keyword(s):

Residual Stress ◽

Machine Tool ◽

Real Time ◽

Closed Loop ◽

Machining Process ◽

Time Model ◽

Stress Control ◽

Digital Twin ◽

Model Based ◽

Digital Twins

AbstractThe future of machining lies in the fully autonomous machine tool. New technologies must be developed that predict, sense and action intelligent decisions autonomously. Digital twins are one component on this journey and are already having significant impact in the manufacturing industries. Despite this, the implementation of machining Digital Twins has been slow due to the computational burden of simulating cutting forces online resulting in no commercially available Digital Twin that can automatically control the machining process in real time. Addressing this problem, this research presents a machining Digital Twin capable of real-time adaptive control of intelligent machining operations. The computational bottleneck of calculating cutter workpiece engagements online has been overcome using a novel method which combines a priori calculation with real-time tool centre point position data. For the first time, a novel online machine-induced residual stress control system is presented which integrates real-time model-based simulations with online feedback for closed loop residual stress control. Autonomous Digital Twin technologies presented also include chatter prediction and control and adaptive feed rate control. The proposed machining Digital Twin system has been implemented on a large-scale CNC machine tool designed for high-speed machining of aerostructure parts. Validation case studies have been conducted and are presented for each of the machining Digital Twin applications.

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SIoT for cognitive logistics: Leveraging the social graph of digital twins for effective operations on real-time events

ITU Journal on Future and Evolving Technologies ◽

10.52953/onrk8179 ◽

2021 ◽

Vol 2 (5) ◽

pp. 69-79

Author(s):

Miha Cimperman ◽

Angela Dimitriou ◽

Kostas Kalaboukas ◽

Aziz S. Mousas ◽

Salvatore Quattropani

Keyword(s):

Real Time ◽

Lead Time ◽

Ad Hoc ◽

Vast Number ◽

Novel Approach ◽

Digital Twins ◽

The Social ◽

Social Internet Of Things ◽

Integrated Logistics ◽

First Time

Over the years, with the migration of organizations towards the concepts of logistics 4.0, a paradigm shift was necessary to guarantee logistics efficiency. The challenge is to dynamically cope in real time with vast number of shipments and destinations, which need to be realigned both with a determined lead time and with a finite of available resources. Although a number of standards have already been adopted for the management of transport and logistics operations, taking advantage, for instance, of Decision Support Systems and Geographic Information Systems, new models are required for achieving effective handling of the dynamic logistics environment that is shaped today. In this paper, an integrated logistics framework addressing the previous challenges is presented, for the first time, as a result of the activities of the H2020 COG-LO project. This novel approach exploits Social Internet of Things (SIoT) and the digital twins technique to realize the concept of the Cognitive Logistics Object (CLO). A CLO is defined as an entity that is augmented with cognitive capabilities, it is autonomous, and bears social-like capabilities, which enable the formulation of ad hoc communities for negotiating optimal solutions in logistics operations.

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