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Production systems have to be adapted continuously to changing circumstances. This means that the responsible production planner has to frequently make decisions about reconfiguring complex systems under uncertainty whose outcome greatly affects the company's business success. Discrete-event simulation is one powerful tool to support the necessary analysis and scenario evaluation, but still remains time-consuming and tricky to set up and to maintain. When implemented as a digital twin of the production system, the simulation model can maintain a high degree of accuracy over a long time period. An approach to realise this potential is presented and illustrated in this paper with a use case from the automotive industry. This paper contributes several new methods and findings to the development of digital twins of production systems: Firstly, it demonstrates how exceptional events in the validation of the digital twin can be handled. Secondly, it shows how structural changes in the system can be discovered using data on machine activity and process mining. Thirdly, the paper introduces a possibility on how to assess the accuracy of the digital twin. Furthermore, it demonstrates how to assess the robustness of the digital twin to estimation errors in machine processing times. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive analysis of recent advancements in additive manufacturing, a transformative approach to industrial production that allows for the layer-by-layer construction of complex parts directly from digital models. Focusing specifically on Directed Energy Deposition, it begins by clarifying the fundamental principles of metal additive manufacturing as defined by International Organization of Standardization and American Society for Testing and Materials standards, with an emphasis on laser- and powder-based methods that are pivotal to Directed Energy Deposition. It explores the critical process mechanisms that can lead to defect formation in the manufactured parts, offering in-depth insights into the factors that influence these outcomes. Additionally, the unique mechanisms of defect formation inherent to Directed Energy Deposition are examined in detail. The review also covers the current landscape of process evaluation and non-destructive testing methods essential for quality assurance, including both traditional and contemporary in situ monitoring techniques, with a particular focus given to advanced machine-vision-based methods for geometric analysis. Furthermore, the integration of process monitoring, multiphysics simulation models, and data analytics is discussed, charting a forward-looking roadmap for the development of Digital Twins in Laser–Powder-based Directed Energy Deposition. Finally, this review highlights critical research gaps and proposes directions for future research to enhance the accuracy and efficiency of Directed Energy Deposition systems. [ABSTRACT FROM AUTHOR]

This paper outlines the main idea and approach of the H2020 ASSISTANT (LeArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments) project. ASSISTANT is aimed at the investigation of AI-based tools for adaptive manufacturing environments, and focuses on the development of a set of digital twins for integration with, management of, and decision support for production planning and control. The ASSISTANT tools are based on the approach of extending generative design, an established methodology for product design, to a broader set of manufacturing decision making processes; and to make use of machine learning, optimisation, and simulation techniques to produce executable models capable of ethical reasoning and data-driven decision making for manufacturing systems. Combining human control and accountable AI, the ASSISTANT toolsets span a wide range of manufacturing processes and time scales, including process planning, production planning, scheduling, and real-time control. They are designed to be adaptable and applicable in a both general and specific manufacturing environments. [ABSTRACT FROM AUTHOR]

As an important enabling technology in the era of Industry 4.0, the intelligent connected vehicle (ICV) facilitates robust data interaction with the outside through sensors and communication technologies, ultimately making scientific decisions based on environmental perception information. However, due to constraints such as limited communication bandwidth and computing resources, the influx of data simultaneously impedes the sustainable optimisation of the vehicle decision making process at the same time. As a novel technology that effectively connects physical and virtual space, the special ability of the digital twin (DT) is to identify characteristics within a certain lifecycle, thereby garnering widespread attention across various industries. The purpose of this paper is to review the contribution of digital twins in the application field of intelligent vehicles and explore its potential for development. First, the key technologies of ICV provide a basis for the embedding of digital twins. Then, by analysing the development process and technical composition of digital twins, readers can better understand the concept of digital twins. Finally, the application of DTs in ICV is reviewed from the perspective of vehicles, traffic facilities, and occupants. Future challenges and opportunities in this direction are described at the same time.

Civil structural health monitoring (CSHM) tracks different aspects of an infrastructure system's service and safety condition by utilizing reliably measured data and physics-based model simulations. Data and physical models are coupled with heuristic experience to proactively represent current and expected future performance. In the past two decades, more bridges and dams have been instrumented and monitored during and after construction to determine their performances and responses to various loading, material, boundary, and environmental conditions. Furthermore, bridge and dam owners increasingly utilize civionics systems to obtain essential data for developing data-driven asset management programs and addressing the state of good repair requirements. [ABSTRACT FROM AUTHOR]

Recently, Digital Twin (DT) has a growth revolution by increasing Artificial Intelligence (AI) techniques and relative technologies as the Internet of Things (IoT). They may be considered as the panacea for DT technology for various applications in the real world such as manufacturing, healthcare, and smart cities. The integration of DT and AI is a new avenue for open research in the upcoming days. However, for exploring the issues of developing Digital Twins, there are interesting in identifying challenges with standardization ensures future developments in this innovative theme. This paper first presents the Digital Twins concept, challenges, and applications. Afterward, it discusses the incorporation of AI and DT for developing various IoT-based applications with exploring the challenges and opportunities in this innovative arena. Then, developing tools are presented for exploring the digital twins' system implementation. Further, a review of recent DT-based AI approaches is presented. Finally, a discussion of open research directions in this innovative theme is presented. [ABSTRACT FROM AUTHOR]

This article examines how digital twins have been used in a multi-national corporation, what technologies have been used, what benefits have been delivered, and the significance of people- and process-related issues in achieving successful implementation. A qualitative, inductive research method is used, based on interviews provided by key personnel involved in three digital twin projects. The article concludes that digital twin projects are likely to involve incremental rather than disruptive change, and that successful implementation is usually underpinned by ensuring technology, people, and process change factors are progressed in a balanced and integrated fashion. Building upon existing frameworks, three "properties" are identified as being of particular value in digital twin projects—workforce adaptability, technology manageability, and process agility—and a related set of steps and actions is put forward as a template and point of reference for future digital twin implementations. The combination of assessing digital properties and following a set of key actions represents a novel approach to digital twin project planning, and overall the findings are a contribution to the developing theory around digital twins and digitalization, in general, and are also of relevance to professionals embarking on DT projects. [ABSTRACT FROM AUTHOR]

Current research on Digital Twin (DT) is largely focused on the performance of built assets in their operational phases as well as on urban environment. However, Digital Twin has not been given enough attention to construction phases, for which this paper proposes a Digital Twin framework for the construction phase, develops a DT prototype and tests it for the use case of measuring the productivity and monitoring of earthwork operation. The DT framework and its prototype are underpinned by the principles of versatility, scalability, usability and automation to enable the DT to fulfil the requirements of large-sized earthwork projects and the dynamic nature of their operation. Cloud computing and dashboard visualisation were deployed to enable automated and repeatable data pipelines and data analytics at scale and to provide insights in near-real time. The testing of the DT prototype in a motorway project in the Northeast of England successfully demonstrated its ability to produce key insights by using the following approaches: (i) To predict equipment utilisation ratios and productivities; (ii) To detect the percentage of time spent on different tasks (i.e., loading, hauling, dumping, returning or idling), the distance travelled by equipment over time and the speed distribution; and (iii) To visualise certain earthwork operations. [ABSTRACT FROM AUTHOR]

This review discusses the links between the newly introduced concepts of digital twins and more classical finite element modeling, reduced order models, parametric modeling, inverse analysis, machine learning, and parameter identification. The purpose of this article is to demonstrate that development, as almost always is the case, is based on previously developed tools that are currently exploited since the technological tools for their implementation are available and the needs of their usage appear. This fact has rarely been declared clearly in the available literature. The need for digital twins in infrastructures arises due to the extreme loadings applied on energy-related infrastructure and to the higher importance that fatigue effects have. Digital twins promise to provide reliable and affordable models that accompany the structure throughout its whole lifetime, make fatigue and degradation prediction more reliable, and support effective predictive maintenance schemes. [ABSTRACT FROM AUTHOR]

Digital Twin technology is an emerging concept that has quickly gained traction in both industry and academia. A digital twin is a virtual representation of a real-world object or system. It is used to evaluate performance, and inefficiencies and design solutions to improve the efficiency of its physical counterpart. The Digital Twin is described as the integration of data between a physical and virtual asset. It is nothing but a replica of the physical object, known as a logical object, which reflects all the major characteristics and properties of the original product. To model a digital twin for a physical object or process, it uses artificial intelligence, the Internet of Things, and other supporting technologies like cloud and analytics. The review of digital twins in the manufacturing, health, and industrial sectors is mainly represented in this paper. It also discusses the significance, characteristics, and fundamentals of creating a digital twin for any physical asset or process. [ABSTRACT FROM AUTHOR]

The Value Stream Method (VSM) is widely used in manufacturing to analyze and redesign value streams. The aim is to improve processes, reduce waste and create a thorough product flow. Despite having many benefits, VSM also comes with disadvantages regarding modern dynamic production environments. It fails to meet the requirement of providing reliable information for a realistic Value Stream Design (VSD) followed by targeted improvement activities. As a result, the VSM is usually subject to uncertainty and relies on expert knowledge. Digitalization, on the other hand, is leading to an increasing availability of production data. The use of data has the potential to support the VSM with targeted data preparation. In this regard, the concept of Digital Twin (DT) offers the capability of providing the required database to systematically collect and condense this data. This paper provides a framework for the Digital Value Stream Twin (DVST). In addition, requirements for the implementation of a DVST in practice will be elaborated. [ABSTRACT FROM AUTHOR]

Purpose: The global pandemic COVID-19 unveils transforming the supply chain (SC) to be more resilient against unprecedented events. Identifying and assessing these risk factors is the most significant phase in supply chain risk management (SCRM). The earlier risk quantification methods make timely decision-making more complex due to their inability to provide early warning. The paper aims to propose a model for analyzing the social media data to understand the potential SC risk factors in real-time. Design/methodology/approach: In this paper, the potential of text-mining, one of the most popular Artificial Intelligence (AI)-based data analytics approaches for extracting information from social media is exploited. The model retrieves the information using Twitter streaming API from online SC forums. Findings: The potential risk factors that disrupt SC performance are obtained from the recent data by text-mining analyses. The outcomes carry valuable insights about some contemporary SC issues due to the pandemic during the year 2021. The most frequent risk factors using rule mining techniques are also analyzed. Originality/value: This study presents the significant role of Twitter in real-time risk identification from online SC platforms like "Supply Chain Dive", "Supply Chain Brain" and "Supply Chain Digest". The results indicate the significant role of data analytics in achieving accurate decision-making. Future research will extend to represent a digital twin for identifying potential risks through social media analytics, assessing risk propagation and obtaining mitigation strategies. [ABSTRACT FROM AUTHOR]

The emergence of digital twin technology presents tremendous opportunities for several industry sectors. A digital twin is defined as the virtual representation of a physical asset that collects and sends real-time information. A digital twin collects data from the physical asset in real-time and uses this data to create a virtual model of the physical object. Its functionality depends on the bi-directional coordination of data between the physical and virtual models. This is likened to cyber-physical systems, which seek to provide bi-directional coordination between the physical and virtual worlds. While digital twins have found applications in the various industrial sectors such as aerospace, manufacturing, and industrial engineering, their applications in the construction industry are relatively limited. Although some level of progress has been made in the construction industry with the application of a digital twin, it still lags in other sectors. Virtual models of constructed facilities are developed and used to plan and construct the actual facility, with changes in the physical facility being automatically reflected in the virtual model based on real-time data and vice-versa. The digital twin shows promising possibilities in the design, construction, operation, and maintenance of a facility. This paper reviews the development and implementation of digital twin technology in the construction industry and compares its use with other industries while assessing the benefits of DT to the construction industry. A systematic literature review including a thematic analysis was employed to address the purpose of this study. Limitations associated with the existing and emerging applications are also identified. It concludes by highlighting the importance of DT applications in the construction sector. [ABSTRACT FROM AUTHOR]

This review discusses the links between the newly introduced concepts of digital twins and more classical finite element modeling, reduced order models, parametric modeling, inverse analysis, machine learning, and parameter identification. The purpose of this article is to demonstrate that development, as almost always is the case, is based on previously developed tools that are currently exploited since the technological tools for their implementation are available and the needs of their usage appear. This fact has rarely been declared clearly in the available literature. The need for digital twins in infrastructures arises due to the extreme loadings applied on energy-related infrastructure and to the higher importance that fatigue effects have. Digital twins promise to provide reliable and affordable models that accompany the structure throughout its whole lifetime, make fatigue and degradation prediction more reliable, and support effective predictive maintenance schemes.

We theorize a notion of a digital supply chain (SC) twin – a computerized model that represents network states for any given moment in real time. We explore the conditions surrounding the design and implementation of the digital twins when managing disruption risks in SCs. The combination of model-based and data-driven approaches allows uncovering the interrelations of risk data, disruption modeling, and performance assessment. The SC shocks and adaptations amid the COVID-19 pandemic along with post-pandemic recoveries provide indisputable evidences for the urgent needs of digital twins for mapping supply networks and ensuring visibility. The results of this study contribute to the research and practice of SC risk management by enhancing predictive and reactive decisions to utilize the advantages of SC visualization, historical disruption data analysis, and real-time disruption data and ensure end-to-end visibility and business continuity in global companies. [ABSTRACT FROM AUTHOR]

Digital twin technology can be considered as an innovation accelerator. By providing a live copy of physical systems, digital twins bring to the table numerous advantages such as accelerated business processes, enhanced productivity, and faster innovation with reduced costs. For these numerous advantages digital twin is an ideal solution for several problems in domains such as Industry 4.0, education, healthcare and smart cities. However, to make sure the digital twin contributes effectively to these domains by representing a synchronized real-time copy of the physical system, the network connecting the physical and digital twins should fulfill a set of requirements such as low latency of real-time communication, data security and quality. This paper provides an overview on the technology of digital twin and its application domains with a detailed discussion on its networking requirements and proposed enabling technologies to fulfill them. [ABSTRACT FROM AUTHOR]

The goal of this work is to achieve the defect-free production of parts made using Additive Manufacturing (AM) processes. As a step towards this goal, the objective is to detect flaws in AM parts during the process by combining predictions from a physical model (simulation) with in-situ sensor signatures in a machine learning framework. We hypothesize that flaws in AM parts are detected with significantly higher statistical fidelity (F-score) when both in-situ sensor data and theoretical predictions are pooled together in a machine learning model, compared to an approach that is based exclusively on machine learning of sensor data (black-box model) or physics-based predictions (white-box model). We test the hypothesized efficacy of such a gray-box model or digital twin approach in the context of the laser powder bed fusion (LPBF) and directed energy deposition (DED) AM processes. For example, in the DED process, we first predicted the instantaneous spatiotemporal distribution of temperature in a thin-wall titanium alloy part using a computational heat transfer model based on graph theory. Subsequently, we combined the preceding physics-derived thermal trends with in-situ temperature measurements obtained from a pyrometer in a readily implemented supervised machine learning framework (support vector machine). We demonstrate that the integration of temperature predictions from an ab initio heat transfer model and in-situ sensor data is capable of detecting flaws in the DED-produced thin-wall part with F-score approaching 90%. By contrast, the F-score decreases to nearly 80% when either temperature measurements from the in-situ sensor or temperature distribution predictions from the theoretical model are used alone by themselves. This work thus demonstrates an early foray into the digital twin paradigm for real-time process monitoring in AM via seamless integration of physics-based modeling (simulation), in-situ sensing, and data analytics (machine learning). [ABSTRACT FROM AUTHOR]

• Developed a generic digital twin (DT) framework for smart water grids (SWG). • Integrated data analytics and hydraulic model calibration for constructing high-fidelity SWG DT. • Automated model training including data cleansing, outlier detection and event classification. • Applied DT-based approach to three supply zone with pipeline length of more than 1000 km. • Achieved event detection accuracy of greater than 80% with lead time of 1 to 3 days. Smart Water Grid (SWG) plays a critical role in sustaining cities economic and social development, but challenges remain in fully realizing the benefits of SWG. While Digital twin (DT) has been discussed in some literature for possible SWG applications, there has been limited, or no technical framework developed to facilitate SWG operation and management. In this paper, a generic framework is developed for constructing SWG high fidelity Digital Twin (DT) by integrating digital thread with various digital models for visualization, data-driven analysis, physics-based simulations, and decision-making support. Both the physics-based models and data-driven models are trained/retrained and calibrated/re-calibrated respectively by using the data collected with the sensors installed throughout a SWG. The information derived from the SWG DT can be diagnostic, predictive, and prescriptive to significantly augment users' intelligence for improving SWG operation and management. One important application of digital twin augmented intelligence is illustrated to timely detect and localize anomaly events, which may include, but not be limited to, pipe bursts and unauthorized water usages. The solution is tested on the selected areas in Singapore to construct the ever-green DT by calibrating and recalibrating the models for near real-time SWG operation management. The case study was conducted for three supply zones with the total pipeline length of more than 1000 km and 40 weeks of monitoring data, collected by 89 pressure monitoring stations and 8 flow meters at inlets and boundary. More than 3300 data-driven models are trained for optimizing the model performance to achieve accuracy of greater than 80% F1 score for detecting anomaly events, which are subsequently localized within 400 m with 2–3 days lead time. [ABSTRACT FROM AUTHOR]

• Key application areas of deep learning are identified in urban water and wastewater management. • Popular application areas include anomaly detection, system state forecasting, asset monitoring and assessment. • Industrial application of deep learning is still at an early stage with few implementations reported. • Research challenges include data privacy, algorithm development, explainability, multi-agent system and digital twin. • Deep learning should drive urban water systems towards high intelligence and autonomy. Deep learning techniques and algorithms are emerging as a disruptive technology with the potential to transform global economies, environments and societies. They have been applied to planning and management problems of urban water systems in general, however, there is lack of a systematic review of the current state of deep learning applications and an examination of potential directions where deep learning can contribute to solving urban water challenges. Here we provide such a review, covering water demand forecasting, leakage and contamination detection, sewer defect assessment, wastewater system state prediction, asset monitoring and urban flooding. We find that the application of deep learning techniques is still at an early stage as most studies used benchmark networks, synthetic data, laboratory or pilot systems to test the performance of deep learning methods with no practical adoption reported. Leakage detection is perhaps at the forefront of receiving practical implementation into day-to-day operation and management of urban water systems, compared with other problems reviewed. Five research challenges, i.e., data privacy, algorithmic development, explainability and trustworthiness, multi-agent systems and digital twins, are identified as key areas to advance the application and implementation of deep learning in urban water management. Future research and application of deep learning systems are expected to drive urban water systems towards high intelligence and autonomy. We hope this review will inspire research and development that can harness the power of deep learning to help achieve sustainable water management and digitalise the water sector across the world. [Display omitted] [ABSTRACT FROM AUTHOR]