Search

Digital Twins (DTs) hold significant promise in addressing the challenges faced by food supply chains (FSCs). This paper aims to provide critical insights into the potential for Digital Twins to meet the key challenges of the FSC and establish a comprehensive conceptual framework for their implementation. Following a systematic literature review (SLR), the study identified 81 peer-reviewed, high-quality papers published over the last decade (2012–2023). The typology-driven thematic analysis emphasises the emergent nature of DTs within FSCs, highlighting their key characteristics including monitoring, real-time simulation, and scenario analysis. The identified characteristics, applications, implementation drivers and barriers of Digital Twin form the basis for a novel conceptual framework for implementing DTs in FSCs. Leveraging insights from Innovation Adoption Theory and the Technology-Organization-Environment (TOE) framework, the study outlines a structured five step implementation process divided into three stages. Notably, technology assessment and performance evaluation emerge as two innovative steps necessary for the successful implementation of DTs specifically, not previously considered by the theory. The study identifies promising avenues for future research. These findings provide invaluable guidance to researchers and practitioners seeking to embrace the potential of Digital Twin within the food industry. [ABSTRACT FROM AUTHOR]

The increasing complexity of industrial systems demands more effective and intelligent maintenance approaches to address manufacturing defects arising from faults in multiple asset modules. Traditional digital twin (DT) systems, however, face limitations in interoperability, knowledge sharing, and causal inference. As such, cognitive digital twins (CDTs) can add value by managing a collaborative web of interconnected systems, facilitating advanced cross-domain analysis and dynamic context considerations. This paper introduces a CDT system that leverages industrial knowledge graphs (iKGs) to support maintenance planning and operations. By employing a design structure matrix (DSM) to model dependencies and relationships, a semantic translation approach maps the knowledge into a graph-based representation for reasoning and analysis. An automatic solution generation mechanism, utilising graph sequencing with Louvain and PageRank algorithms, derives feasible solutions, which can be validated via simulation to minimise production disruption impacts. The CDT system can also identify potential disruptions in new product designs, thus enabling preventive actions to be taken. A case study featuring a print production manufacturing line illustrates the CDT system's capabilities in causal inference and solution explainability. The study concludes with a discussion of limitations and future directions, providing valuable guidelines for manufacturers aiming to enhance reactive and predictive maintenance strategies. [ABSTRACT FROM AUTHOR]

Nowadays many processes in the food industry are monitored in an automatic way, with the purpose of minimising the need for workforce and of ensuring the proper control of the quality and safety of the foodstuff. All the sensors share data with a centralised management unit, where often a Manufacturing Execution System collects and evaluates them. As reported in recent research, however, a further step that can be undertaken, exploiting Industry 4.0 enabling technologies, is the implementation of digital twin approaches, with the additional aim to prevent possible issues during production. In line with these considerations, this work aims at showing two different digital twin models intended for improving the control of as many real food systems. Liquid and powder fluids are taken as examples for highlighting the differences in the optimization of the two food processes, as well as for fully exploring the potential of the digital twin approach. Finally, based on the real data taken from two pilot plants, a framework for the selection of the best digital twin tool in the food sector is delineated. [ABSTRACT FROM AUTHOR]

Photogrammetry systems are widely used in industrial manufacturing applications as an assistance measurement tool. Not only does it provide high-precision feedback for assembly process inspection and product quality assessment, but also it can improve the flexibility and robustness of manufacturing systems and production lines. However, with growing global competition and demands, companies are forced to enhance production efficiency, shorten production lifecycle and increase product variety by incorporating reconfigurable factory design that can meet challenging timeline and requirements. Although dynamic facility layout is widely investigated, the position selection for the photogrammetry system in dynamic manufacturing environment is usually overlooked. In this paper, dynamic layout of the V-STARS photogrammetry system is investigated and optimised in a digital-twin environment using deep reinforcement learning. The learning objectives are derived from the field of view (FoV) evaluation from point clouds 3D reconstruction, and collision detection from the digital twin simulated in Visual Components. The application feasibility of the proposed dynamic layout optimisation of the V-STARS photogrammetry system is verified with a real world industrial application. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore adaptation-based and stability-based views of supply chain resilience to analyse what insights these different perspectives, individually and collectively, offer for theory and practice. In the stability-based view, resilience is triggered by disruptions and performance deviationsto return to some 'normal' states. This view accounts for known-known uncertainty. The adaptation-based view shifts the focus from avoiding oscillations and recovering some stable states toward proactive adaptation and performance persistence. The adaptation-based view aims at designing structurally adaptable networks with process flexibility and actively used redundancy. It considers resilience from the value-creation perspective accounting for unknown-unknown uncertainties. Stability-based approach views resilience as an outcome or quantity. Adaptation-based approach considers resilience as a property or quality. A combination of stability- and adaptationbased approaches is imperative for building a strong supply chain immunity through an integration of general protection and adaptability. These approaches complement each other depending on the knowledge of and attitude to uncertainty by decision-makers. A combination of the two views helps consider resilience both as a quantity to measure how sick the supply chain is and to understand how the resilience comes about to ensure the quality of the network health and viability. [ABSTRACT FROM AUTHOR]

Supply chains operate in a highly distuptive environment where a SC master plan should be updated in line with disruptions to ensure that a high service level is provided to customers while total cost is minimised. There is an absence of knowledge of how a SC master plan should be updated to cope with disruptions using hybrid modelling. To fill this gap, we present a hybrid modelling framework to update a SC master plan in presence of disruptions. The proposed framework, which is a precursor to a SC digital twin, integrates simulation, machine learning, and optimisation to identify the production, storage, and distribution values that maximise SC service level while minimising total cost under disruptions. This approach proves effective in a SC disrupted by demand increase and lead time extension. Results show that employing hybrid modelling leads to a noticeable improvement in service level and total cost. The outcome of the new knowledge on using hybrid modelling for managing disruptions provides essential learning for the extension of modelling through a digital twin for SC master planning. We observe that in the presence of disruptions it is more economical to keep higher inventory at downstream SC members than the upstream SC members. [ABSTRACT FROM AUTHOR]

Rapid advances in new-generation information technologies have been the main driving force for the transformation of manufacturing enterprises in Industry 4.0. Digital twin (DT), as a key technology to promote intelligent manufacturing, has shown great potential for manufacturing enterprises to create an industrial intelligence-driven production equipment through in-depth integration of cyber-physical systems. However, the lack of a systematic effective DT modelling method with a supporting evaluation metric is the most important factor restricting the application of DT in manufacturing enterprises. To bridge the gap, this paper proposes a novel multi-level modelling and fidelity evaluation (MLM&FE) method of DT for creating smart production equipment in manufacturing enterprises, which could help enterprises establish an industrial intelligence-driven production environment to quickly respond to changes in the customised global market, thus greatly improving competitiveness of the enterprises. Specifically, this paper firstly designs a reference framework for DT-enhanced smart production equipment, on which an MLM&FE architecture is proposed. Then, key implementation methodologies and tools for MLM&FE are introduced from the perspective of data space modelling, virtual space modelling, knowledge space modelling, model integration and evaluation. Finally, the developed smart production equipment prototype demonstrates the feasibility and effectiveness of DT MLM&FE. [ABSTRACT FROM AUTHOR]

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]

The adoption of simulation models as Digital Twins (DTs) has been standing out in recent years and represents a revolution in decision-making. In this context, we note increasingly faster and more efficient decisions by mirroring the behaviour of physical systems. On the other hand, we highlight the challenges to ensure the simulation models validity over time since traditional validation approaches have limitations when we consider the periodic update of the model. Thus, the present work proposes an approach based on the constant assessment of these models through Machine Learning and control charts. To this end, we suggest a monitoring tool using the K-Nearest Neighbors (K-NN) classifier, combined with a p-control chart, to periodically assess the validity of DT simulation models. The proposed approach was tested in several theoretical cases and also implemented in a real case study. The findings suggest that the proposed tool can monitor the DT functioning and identify possible special causes that could compromise its results. Finally, we highlight the wide applicability of the proposed tool, which can be used in different DT models, including near/real-time models with different characteristics regarding connection, integration, and complexity. [ABSTRACT FROM AUTHOR]

Digital twins became of greater interest to researchers and practitioners in supply chain and operations management (SCOM). Literature has addressed the need to understand digital twins in SCOM, mostly focusing on fragmented technological solutions and use cases. We start with an integrative literature review to determine which elements belong to research on digital twins in SCOM. We define the seven major elements of a digital twin in SCOM: technology, people, management, organisation, scope, task, and modelling. We also distinguish five major types of digital twins in SCOM: product, process, organisation, supply chain and network-of-networks. Illustration of a SCOM digital twin is provided using an anyLogistix example. We conclude that digital twins in SCOM are not merely a simulation-based replica of a real object but a complex socio-technical phenomenon involved in continuous human-artificial intelligence interactions. This leads to an understanding of the role of digital twins through the lens of Industry 5.0, reconfigurable and viable supply chains. Researchers and practitioners alike can use our framework to structure the knowledge on SCOM digital twins and consider all seven elements when designing and using digital twins. [ABSTRACT FROM AUTHOR]

High quality, safe, and reliable batteries are essential for widespread adoption of electric vehicles. Current Li-ion battery pack manufacturing processes rely on manual inspections to ensure electric vehicle battery quality. Such manual quality control is prone to errors, increasing the chances of defective batteries. This is likely to increase safety and reliability concerns in the public imagination, slowing down the adoption of electric vehicles. Furthermore, manual inspection is time-consuming and likely to become a bottleneck in scaling up electric vehicle battery production. A potential solution to address this need for fast and accurate inspection of batteries is the use of machine vision and robotics. In this study, we use digital twin design and simulation to develop a battery module inspection system that uses cobots and machine vision to inspect electric vehicle batteries for defects. Our proposed system can automate visual quality checks that are currently being done by human operators. The proposed cobotic system has been simulated and validated for a variety of battery defects to achieve fast and reliable detection. Since a digital twin of the cobotic inspection workcell has been used, the battery inspection system, as designed and validated, is ready for immediate implementation. [ABSTRACT FROM AUTHOR]

As electric vehicle adoption accelerates and demand increases, the inability to produce batteries in sufficient quantities has emerged as a critical bottleneck in the electric vehicle supply chain. Given the impending climate change crisis, resolving this bottleneck is imperative to accelerate the transition to a zero-emission electric mobility future. One potential solution is the use of robotics for fast and cost-effective assembly of batteries at scale. This study proposes a three-stage digital twin design and analysis method to develop robotic workcells for fast and cost-effective assembly of electric vehicle battery modules. Using digital twin design and simulation, robotic assembly line configurations have been developed for battery module production at different scales. Digital twin analytics was used to evaluate and optimise the proposed robotic battery assembly system for speed and cost. Industrial automation experts were consulted to further improve robotic work cell layouts to minimise investment in robots. Because digital twins of robotic workcells have been used, the configurations of the battery assembly line, as designed and validated, are ready for immediate implementation. For practitioners, this study offers heuristic methods to determine the appropriate assembly line configuration, the required number of robots and humans, for a desired production volume. For researchers, this study outlines promising areas for future investigation. [ABSTRACT FROM AUTHOR]

The paper describes a smart model for palletising as main intra-logistics task for product-on-pallet distribution, and its development in Logistics 4.0 framework aligned to Industry 4.0. The model is developed in holonic paradigm as a 2-layer Holonic Logistics Execution System (HLES) in semiheterarchical topology. Scheduling of logistics activities and allocation to resources are optimised for global efficiency on compact time periods. The logistics model virtualises physical entities: resources, pallets and orders as holons implemented with digital twin software, and categories of task workloads: resource health monitoring, dispatching and tracking pallet orders, re-assigning jobs at resource failure, which grants reality-awareness and robustness. Global workload optimisation uses Constraint Programming as decision making technology with ILOG optimiser engine as situation-specific solver tool in SaaS cloud model, and delegate multi-agent system (MAS) technology for intelligence distribution. The optimised objective function is a combination of palletising cost weighted by robot speed limits and pallet storage cost in payable stocks. The main constructs are exemplified and validated on a real-life structure with multiple palletising resources in which programmable logic controllers (PLC) coordinate locally the parallel execution of order holons according to their globally optimised sequence. [ABSTRACT FROM AUTHOR]

In recent years, the emerging Digital Twin (DT) paradigm under Industry 4.0 has been attracting more attention from both practitioners and academia due to its dynamic capabilities. Most DT studies are theoretical and deal with hypothetical analysis, whereas fewer studies are available on real-life empirical cases. Due to dynamic problem-solving capabilities, DT technologies are widely used in performance improvement analysis in food processing companies (FPC) despite limited implication's for business strategies. Our study incorporates the DT technologies with an implementation case study on FPC and accomplishes the real-life problem of the food processing company (FPC). Moreover, the proposed DT research framework demonstrates the various DT implementation stages, such as strategic mapping and physical-virtual space replica, with rigorous analysis. The results show that DT enhances the existing system's machine availability, allocation efficiency, technical efficiency, worker efficiency, utilization rate, effectiveness, step ratio, and throughput rate. The proposed physical-virtual interface model is executed using AnyLogic software with JAVA-enabled programming. [ABSTRACT FROM AUTHOR]

Numerous studies have focused on digital twins (DTs) and their applications. However, a common method for evaluating the performance of DTs is lacking. Digital twins performance assessment mechanism is crucial when improving or monitoring any process or system in a business, such a strategy could serve as a guide to help researchers and practitioners create more effective digital twins. Guided by these challenges, we propose a structured method for measuring the performance and flexibility of digital twins, and our study provides a procedure for quantitative calculation of the performance of DTs based on four key performance indicators (KPIs). Moreover, we propose a new KPI, DTflex, for determining the flexibility of digital twins. We evaluated the performance of DTflex as a KPI to demonstrate its use as a simple tool for designers and practitioners to quickly compare different DT methodologies, which might provide valuable input for the iterative improvement of DTs to increase their efficiency in future applications. The knowledge provided along with the method proposed in this paper is expected to help researchers and practitioners working in different fields to compare DT methodologies and DT performance which will lead to higher efficiency DT solutions and increased re-usage of existing DTs. [ABSTRACT FROM AUTHOR]

The Industry 4.0 revolution is changing the manufacturing landscape. A broad set of new technologies emerged (including software and connected equipment) that digitise manufacturing systems. These technologies bring new vitality and opportunities to the manufacturing industry, but they also bring new challenges. This paper focuses on the impact of Industry 4.0 on production planning approaches and software. We first propose a digital twin framework that integrates production planning systems and frontier technologies. The frontier technologies that may impact production planning software are the internet of things, cloud manufacturing, blockchain, and big data analytics. Second, we provide a state-of-the-art on the application of each technology in the production planning, as well as a detailed analysis of the benefit and application status. Finally, this paper discusses the future research and application directions in the production planning. We conclude that Industry 4.0 will lead to the construction of data-driven models for production planning software. These tools will include models built accurately from data, account for uncertainty, and partially actuate the decision autonomously. [ABSTRACT FROM AUTHOR]

Data has become a high-value commodity in manufacturing. There is a growing realisation that the data-driven applications could become strong differentiators of manufacturing enterprises. To guide the developments in digitisation, a widely accepted framework is needed. In the absence of the universal framework, the components making a digital enterprise are captured in an example framework that is introduced in the paper. The adoption of new technology and software solutions has increased complexity of manufacturing systems. In addition, new product introductions have become more frequent and the demand more variable. A digital space enables optimisation and simulation of decisions before their realisation in the physical space. Predictive modelling with its time dimension is a valuable actor in the digital space. Three challenges of predictive modelling such as model complexity, model interpretability, and model reuse are identified in this paper. The coverage of each challenge in the literature is illustrated with the recently published papers. The main aspects of these challenges and the synthesis of the developments in digital manufacturing are articulated in the form of eight observations that could guide the future research. [ABSTRACT FROM AUTHOR]

Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with the development of novel technologies. Every medical institution is now equipped at varying degrees of sophistication with the ability to monitor human health in both non-invasive and invasive modes. The multiple scales at which human physiology can be interrogated provide a profound perspective on health and disease. We are at the nexus of creating avatars (herein defined as an extension of digital twins) of human patho/physiology to serve as paradigms for interrogation and potential intervention. Motivated by the emergence of these new capabilities, the IEEE Engineering in Medicine and Biology Society, the Departments of Biomedical Engineering at Johns Hopkins University and Bioengineering at University of California at San Diego sponsored an interdisciplinary workshop to define the grand challenges that face biomedical engineering and the mechanisms to address these challenges. The Workshop identified five grand challenges with cross-cutting themes and provided a roadmap for new technologies, identified new training needs, and defined the types of interdisciplinary teams needed for addressing these challenges. The themes presented in this paper include: 1) accumedicine through creation of avatars of cells, tissues, organs and whole human; 2) development of smart and responsive devices for human function augmentation; 3) exocortical technologies to understand brain function and treat neuropathologies; 4) the development of approaches to harness the human immune system for health and wellness; and 5) new strategies to engineer genomes and cells.

Traditional manufacturing workshop processes are challenging to change, and passive monitoring methods, such as quality traceability and completion rate monitoring, pose challenges for discrete workshops in achieving real-time and accurate production control. Therefore, combined with the digital twin system, a multi-level production status monitoring model is established to monitor discrete manufacturing workshops actively. This article focuses on discrete manufacturing workshops' modeling and self-evolution production processes. It also proposes an active monitoring architecture for production status in discrete workshops using digital twin technology. A full-information model is constructed for a discrete manufacturing workshop. Then, a multi-node interaction logic is established for the discrete manufacturing workshop. Finally, based on the fuzzy C-means clustering algorithm, production anomalies are monitored, and real-time monitoring of disturbances caused by various production factors, including material status, equipment status, and work-in-progress status, is performed to assess their impact on production. Based on deep Bayesian network learning algorithms, we can monitor production performance and analyze workshop capacity, equipment utilization, bottleneck rhythm, etc. This allows us to actively monitor production performance through the self-evolution of digital twin models. This article aims to proactively predict changes in workshop production status and provide support for further plan completion rate improvement, capacity prediction, and capacity optimization. [ABSTRACT FROM AUTHOR]

An adjoint‐based procedure to determine weaknesses, or, more generally, the material properties of structures is developed and tested. Given a series of load cases and corresponding displacement/strain measurements, the material properties are obtained by minimizing the weighted differences between the measured and computed values. The present paper proposes and tests techniques to minimize the number of load cases and sensors. Several examples show the viability, accuracy and efficiency of the proposed methodology and its potential use for high fidelity digital twins. [ABSTRACT FROM AUTHOR]

Accurate prediction of the remaining useful life (RUL) of proton exchange membrane fuel cells (PEMFCs) is essential for maximizing their operational lifespan. However, existing methods often face limitations in two key areas: long-term prediction (beyond 168 h, or one week) and adaptability to varying operating conditions. To address these challenges, we propose a novel self-adaptive digital twin (SADT) model for RUL prediction of PEMFCs. Our approach uniquely integrates a deep convolutional neural network to generate robust health indicators (HIs) that maintain consistent monotonicity across diverse operating conditions. Additionally, we introduce a novel quantile Huber loss (QH-loss) function to enhance prediction accuracy and incorporate a transfer learning technique to improve adaptability under varying operational scenarios. Experimental results on PEMFC degradation datasets demonstrate that our method outperforms state-of-the-art techniques in long-term prediction accuracy, highlighting its potential to significantly extend fuel cell lifetimes. • A novel self-adaptive digital twin for predicting RUL of the PEMFC. • Transfer Learning enhanced RUL prediction accuracy and adaptability. • Universal monotonic HI applicable to various PEMFC operating conditions. [ABSTRACT FROM AUTHOR]

Engineering education at the Institute for Information Management in Engineering integrates product lifecycle management (PLM), computer‐aided design (CAD), eXtended reality (XR), and artificial intelligence (AI) to enhance learning and prepare students for modern challenges. Our interdisciplinary approach, emphasizing digital twins and virtual twins, fosters immersive, hands‐on experiences. This paper reviews our strategies, comparing them with global initiatives, highlighting the transformative impact of our curriculum on preparing future engineers for complex industrial environments. [ABSTRACT FROM AUTHOR]

An important step in implementing geospatial digital twins is to enhance the expressiveness of virtual geographical scenes for the physical world. However, the existing virtual geographical scenes cannot quickly express the dynamically changing geographic environment for remote users due to the inefficient handling of modeling processes, user perception, and remote sharing. The research analysed the concept and characteristics of geospatial digital twins, and constructed the virtual geographical scene ontology, based on which we developed geographical spatiotemporal semantic rules and designed a dynamic annotation algorithm to enhance the representation of virtual geographical scenes. Finally, we investigated a real-time transmission method of panoramic video based on 5 G and used immersive virtual reality (IVR) to realize the user experience of remote immersion in geographical scenes. We selected a specific geographic environment containing multiple typical geographic entities to develop three prototype systems for experimental analyses. The results showed that the proposed method enabled users to view the virtual geographical scene on a VR device. The average latency for this process was 14.72 seconds. Compared with the virtual geographical scenes constructed by traditional methods, the experiments showed the proposed method advantageous in comprehensiveness, timeliness, and photorealism and abilities to enhance the user's geographical scene perception. [ABSTRACT FROM AUTHOR]

A digital twin-driven lifecycle management method is proposed to monitor the lifespan of motorized spindle. The lifecycle management system is designed and developed based on the combined programming of MATLAB, ANSYS, and LabVIEW. The prediction models of lifespan are built according to the digital twin (DT) data of thermal characteristics of the motorized spindle. The DT for thermal characteristics is realized through correcting the thermal boundary conditions according to the correction models which are established according to the measured and simulated temperatures at thermal key points. The temperature domain and threshold models of the cooling channel are constructed using the exponential fitting method to monitor the working status of the cooling system. The experimental results show that the DT-driven lifecycle management system can effectively monitor the status and remaining useful life of the motorized spindle, which provides a basis for the lifecycle management of the motorized spindle. [ABSTRACT FROM AUTHOR]

The manufacturing process of light-curing additive manufacturing leads to the inevitable presence of steps on the surface of the produced models, which affects the surface quality of the product. In the context of mass production, evaluating the surface quality of a model requires a large number of experimental samples to obtain data, which leads to material waste and extended production cycles. To facilitate the acquisition of surface feature data of a solid model, a digital twin system is proposed in this study. The system is specifically applied to light-curing additive manufacturing to generate a digital model with the surface features of a solid model. It aims to realize the replacement of the solid model with a virtual model for surface quality inspection. To realize the concept of generating high-fidelity digital models, a digital twin light-curing molding process was designed. It simulates the principle of light-curing molding and generates a digital model containing the surface features of a solid product based on the slice profile data. Case studies are conducted to build a digital twin system for the liquid crystal display light-curing additive manufacturing molding process to verify the feasibility of the proposed scheme and the fidelity of the digital model. The results of the comparison test between the digital model and the solid model show that the digital model produced by the digital twin system of the LCD light-curing additive manufacturing molding process contains the key features of the solid model. The digital model serves as the foundational data to verify whether the physical model meets the requirements. [ABSTRACT FROM AUTHOR]

This study addresses the significant gap in the literature regarding the heat transfer coefficient (HTC) under near-solidus forming (NSF) conditions, where materials are shaped close to their solidus state, presenting complex behaviour compared to traditional hot forming processes. Despite the pivotal role of heat transfer in developing a reliable material model for the digital twin (DT), limited data exist particularly regarding HTC characterization at NSF. Additionally, testing methodologies suitable for the high-temperature conditions, crucial for NSF processes, have not been adequately addressed. To fill this gap, this study aims to characterize HTC under NSF conditions using a columnar pressing test. The test was conducted at three different temperatures such as 1250, 1300, and 1360 °C and two different pressures, 2 and 8 MPa. During the test, temperature data was collected at the centre of the sample using a k-type thermocouple. Furthermore, the DT of the pressing test was developed and the three-dimensional finite element model of 42CrMo4 steel was constructed using FORGE NxT® 4.0 FEM software. The simulations were performed with varying HTC values to replicate the experimental test data. Inverse modelling techniques were then applied to compare experimental and simulated data, enabling the characterization and optimization of HTC values under NSF testing conditions. The results demonstrated that HTC in the NSF process is primary impacted by the forming pressure, whereas temperature change showed no variation at the studied ranges. The HTC value of 500 W/m2K and 800 W/m2K was identified at 2 MPa and 8 MPa, respectively. The conclusion of this study aims for a better understanding of heat transfer phenomena in NSF processes, enhancing the reliability of DT for industrial applications. [ABSTRACT FROM AUTHOR]

Digital twins (DT) have increasingly garnered attention in today's manufacturing industry for their potential to enhance productivity, efficiency, and decision-making processes. This study presents a human-centric digital twin framework (HCDT) specifically designed for a three-axis vertical milling machine (VMC3), following the ISO 23247 standard. Our system seeks to offer a comprehensive virtual depiction of a tangible milling machine, facilitating immediate monitoring, analysis, and enhancement of machining operations. This study targets specifically the prediction of power consumption in three-axis vertical machining using advanced machine learning. Power consumption during modified Kakino toolpath cutting was examined through a series of trials that involved adjusting spindle speed, depth of cut, and feed rate. The analysis was conducted utilizing physical sensors, mathematical modeling, and machine learning. The utilization of machine learning methods, specifically random forest, exhibited encouraging outcomes, as evidenced by a mean absolute error (MAE) of 17.30. In addition, virtual simulations were performed to forecast power usage. The proposed applied digital twin (ADT) incorporates human intelligence and artificial intelligence to effectively integrate physical and virtual environments, offering a unique approach that adheres to the ISO 23247 framework. The cross-system entity of the digital twin showcases that the calculated power has large variations with the experimental power. However, the proposed ADT approach has resulted in higher similarity to the calculated, experimental, and predicted power consumption respectively in the proposed digital twin scenario. Also, the addition of explainability to the result has developed the trustworthiness in the digital twin environment. [ABSTRACT FROM AUTHOR]

Robotic manipulators play an innovative role as a new method for high-precision, large-scale manufacturing of composite components. However, machining composite materials with these systems presents unique challenges. Unlike traditional monolithic materials, composites exhibit complex behaviour and inconsistent results during machining. Additionally, robotic manipulator as a machine tool often associates with stiffness and vibration issues which adds another layer of complexity to this approach. By employing a comprehensive analysis and a combination of quantitative and qualitative review methodology, this review paper aims to survey diverse properties of composite materials by different categories and their interaction with machining processes. Subsequently, a survey of manufacturing techniques for composite machining following with a review in various modeling practices to capture material machining behaviour under a systematic framework is presented. Thereafter, the reviewed literature examines the errors inherent in robotic systems, alongside ongoing research efforts in modeling to characterise robot behaviour and enhance its performance. Afterward, the paper explores the application of data-driven modelling methods, with a primary focus on digital twins, in enabling real-time monitoring and process optimisation. Finally, this paper aims to identify the gap in this field and suggests the potential routes for future research and application as well as their challenges. [ABSTRACT FROM AUTHOR]

Offsite construction (OSC) techniques are argued to provide superior quality and shorter schedules compared with traditional techniques. Nonetheless, the pace of OSC implementation has been slow due to the influence of several barriers. In recent years, virtual reality (VR) applications have been used to address many of these barriers and promote the implementation of OSC projects. However, a comprehensive and coherent literature review that establishes the current state and categorizes VR applications in OSC projects is still lacking. To address this research gap, this study provides a state-of-the-art review of VR applications in OSC (VR–OSC) using the scientometric and systematic review methods. This study characterizes the synthesis between VR and OSC and identifies research trends and gaps that can be studied in future VR–OSC research. The scientometric review focuses on identifying the main topics of both research domains separately and combined based on the collected articles. The systematic review, meanwhile, qualitatively evaluates these articles, highlighting the existing research gaps and anticipating future research frontiers. The scientometric results indicate that VR applications in OSC can be organized into a number of clusters, such as Crane Operations and Onsite Planning, Educational Applications, Safety and Ergonomics, and Evaluation of Design Alternatives. The qualitative analysis identifies several future research directions to advance the field of VR–OSC, including (1) multiuser VR models in crane operation planning, (2) consideration of the role of human emotions in VR safety training by adopting biometric sensors, (3) decentralized web-VR platforms for remote OSC planning, and (4) VR-solutions for modeling robotic movements in OSC factories. This study can serve as a useful point of reference for VR–OSC researchers and provides a sound foundation for future research on VR–OSC. Practical Applications: As the market share of OSC techniques continues to grow, it is vital for construction professionals to stay up to date with the latest advances and technologies so that they are well-positioned to identify and address adoption barriers. This study provides a comprehensive analysis of the two topics (i.e., OSC and VR) both independently and integrally. The review of VR applications and their categorization into several OSC subdomains, such as crane operations management, safety management, processes planning, and educational practices, provides OSC practitioners with a holistic understanding on the basis of which to employ VR solutions more efficiently and effectively in their projects. Furthermore, the integration of VR with other technologies, such as eye-tracking technologies, motion capture sensors, and robotic tools, illustrates how VR tools can be used to address many OSC-related problems, including complex production lines, inefficient safety training, and overlapping crane operations. Finally, the improved understanding of current gaps in the domain of VR–OSC can help to galvanize industry support for more in-depth research on the use of VR in OSC. [ABSTRACT FROM AUTHOR]

With the rapid advancement of Digital Twins (DTs) technologies, they have emerged as viable solutions for enhancing the control and optimization of thermal power combustion processes. This paper presents a pioneering approach that leverages DTs to revolutionize the combustion process control system within thermal power generating units. Focused on bridging the physical and digital realms, our research establishes DTs of the combustion process control system (CPCS), situated within the Networked Control System Laboratory (NCSLab). We propose a novel five-dimensional (5D) DTs model encompassing the physical, data, service, connection, and virtual models, thereby facilitating a comprehensive fusion of physical and digital information. By analyzing the demands and technical intricacies of the combustion process control system, we design and implement a virtual 3D model, enriching the visualization and analytical capabilities of the DT environment. Furthermore, through controlled experiments on the combustion process and rigorous comparison with offline simulations, we validate the efficacy and feasibility of our approach. Importantly, our research opens avenues for future exploration, particularly in harnessing industrial real-time data to drive the virtual 3D model. Through the integration of Artificial Intelligence (AI) techniques such as Deep Learning, we elevate the capabilities of our DT framework. This enhanced predictive analytics, dynamic optimization, human–machine interaction, and virtual model realism. This research contributes to the advancement of DT technology in the context of thermal power plants and holds promise for driving innovation and efficiency across diverse industrial sectors. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) in the electricity sector is increasing as part of the twin digital and green transitions in the European Union (EU). AI technologies can be used for optimisation, prediction and scheduling purposes, offering possible solutions to the challenges presented by the ongoing energy transition. At the same time, AI technologies in general, not only in the electricity sector, also raise concerns about (unintended) harms and risks for people. To respond to these concerns, the EU has taken steps to regulate the responsible development and use of AI. A significant milestone in this endeavour is the recent adoption of the AI Act, the first horizontal EU legislation introducing harmonised rules for AI systems. In this paper, we examine different uses of AI in the electricity domain and investigate the extent to which the upcoming AI Act applies to them. In addition, we identify the shortcomings of the AI Act in the context of the electricity sector, as well as aspects that are not covered by this legislation. We show that the AI Act will only cover a narrow subset of AI systems in the electricity sector and that addressing some of the challenges that these systems pose may require further regulation. [ABSTRACT FROM AUTHOR]

In the sintering of advanced ceramics, a digital twin consisted of a finite element model can predict the final shape and microstructure of a sintered ceramic. However, to minimise uncertainties, it is essential to continually update the mechanical properties in the digital twin using data collected from the manufacturing process. One promising approach for achieving this is through artificial neural networks (ANN). This study introduces a machine learning strategy to update the constitutive behaviour of advanced ceramics in finite element analysis of sintering deformation. A major challenge in implementing machine learning in material processing is the huge amount of data required by the training and validation of an ANN, which are often unavailable or incomplete in a real manufacturing process. This study demonstrates that the data requirement can be reduced by employing a two-step training technique. Firstly, the ANN is trained using a nonlinear constitutive law, which describes a general relationship between the strain rates and stresses. Subsequently, the weights and biases of the ANN are transferred for the retraining using limited experimental data for an actual ceramic. It is shown that such approach can successfully capture the nonlinear constitutive behaviour of fine-grained alumina without demanding for large amount of experimental data. A case study is provided, highlighting the feasibility of implementing the ANN in a commercial finite element package, to replace the constitutive law and predict the shrinkage and distortion of a sintering part. In particular, the sintered dumb-bell shape part, simulated using the retrained ANN, showed grain size and relative density that markedly different from those using the nonlinear constitutive law. It is important to note that the proposed methodology is generic and can be used to create ANNs to replace constitutive laws in finite element analysis in digital twins for a wide range of other engineering processes. [ABSTRACT FROM AUTHOR]

A new method was proposed to address fault diagnosis by applying the digital twin (DT) high-fidelity behavior and the deep learning (DL) data mining capabilities. Subsequently, the proposed fault distribution GAN (FDGAN) was built to map virtual and physical entities for the data from the established test platform. Finally, the MobileViG was employed to validate the model and diagnose faults. The accuracy of the proposed method with training samples of 600 and 800 were 88.4% and 99.5%, respectively. These accuracies surpass those of other methods based on CycleGAN (98.86%), CACGAN (94.92%), ACGAN (86.45%), ML1D-GAN (82.33%), and transfer learning (99.38%). Therefore, with the integration of global connectivity, an innovative network structure, and training methods, FDGAN can effectively address challenges such as network degradation, limited feature extraction in small windows, and insufficient model robustness. [ABSTRACT FROM AUTHOR]

The rapid expansion of artificial intelligence (AI) across various fields has significantly impacted how individuals construct and present their identities, particularly on social media. This brief communication explains the concept of digital twinning within the public sphere, where AI-generated images create virtual twins. We examine the phenomenon of attachment to AI-generated self-image, proposing that a perceived sense of emptiness predicts attachment to AI-generated self-image. Using the empty self-theory as a theoretical foundation, we argue that individuals experiencing feelings of emptiness are more likely to form deep emotional bonds with their AI-generated self-image. They may find solace by associating their sense of emptiness with AI-generated self-images, similar to idealised images in advertising. This attachment can manifest as either subjective, where users believe the AI-generated image truly represents them, or objective, where the image is seen as a possession. In future research, it would be valuable to investigate the various factors that contribute to an individual's inclination to form an attachment to AI-generated self-images. Deep exploration of the difference between objective and subjective attachment and quantifying this proposed relationship through empirical studies could provide valuable insights into the interplay between technology and human psychology. [ABSTRACT FROM AUTHOR]

Human induced pluripotent stem cell‐derived cardiomyocytes (iPSC‐CMs) offer potential as an in vitro model for studying drug cardiotoxicity and patient‐specific cardiovascular disease. The inherent electrophysiological heterogeneity of these cells limits the depth of insights that can be drawn from well‐designed experiments. In this review, we provide our perspective on some sources and the consequences of iPSC‐CM heterogeneity. We demonstrate the extent of heterogeneity in the literature and explain how such heterogeneity is exacerbated by patch‐clamp experimental artifacts in the manual and automated set‐up. Finally, we discuss how this heterogeneity, caused by both intrinsic and extrinsic factors, limits our ability to build digital twins of patient‐derived cardiomyocytes. [ABSTRACT FROM AUTHOR]

The Industry 5.0 initiative seeks the sustainability and resilience of production systems through digital technologies. Derived from such an initiative, the Operator 5.0 concept surged to place the operator as the main value-creation contributor of sustainable and resilient human-machine systems. Smart service systems are data-driven services that provide intelligent capabilities to support decision-making in business processes. Despite previous research on smart services driven by multiple technologies in different contexts, further studies approaching Digital Twins as enablers of smart services in machine use phases are yet to be explored. Digital Twin, an Industry 5.0 enabler, is a technology that virtually represents physical assets that collect and analyze data from actual operations to make predictions for decision-making. Simulation-based Digital Twins generate simulation models that are continuously upgraded with asset data in real time, improving the accuracy of predictions. This paper aims to investigate how the Simulation-based Digital Twins (SDT) can enable the development of smart services in the Operator 5.0 context. We build on a multiple case study of 13 interviews from nine heavy mobile machinery manufacturers. We capture our results in categorizing SDT-enabled smart services for the use phases of the machine lifecycle. The categorization identifies the capabilities of each smart service for decision-making support and value co-creation. [ABSTRACT FROM AUTHOR]

Challenges related to data integration and interoperability were raised recently under the auspice of the Urban Digital Twin (UDT). This new paradigm shows its potential to address current city challenges. However, to maximize its outcomes at the city scale, we should tackle the fundamental issues related to data integration. Indeed, various Digital Twin (DT) frameworks are developed in practice. Their implementations led to the identification of three main levels of data integration. The first level involves the extension of the data model to handle new information. The second level supports data by default, and the data needs to be transformed to meet the model requirements. The third level performs the integration at the front-end level with the help of system architectures. The aim of this work is to analyze, illustrate, and guide the effectiveness of different data integration approaches. This exploratory review unpacks the levels of integration according to the corresponding UDT lifecycle phases (i.e., creation, use, and update phases). It highlights the challenges and potentialities of data integration levels and offers the DT designer conceptual guidelines related to data integration. Furthermore, current and theoretical data integration scenarios are extracted and investigated, considering several types and sources of data. This research provides a comprehensive analytical framework for data integration within UDTs, where some of the current operational UDT are examined based on the various integration levels of life cycle data. While the state-of-the-art identifies data integration as a major challenge for the full implementation of UDT, it is not explored in depth, and the integration is only addressed from a case study-specific perspective, according to the data availability and the UDT requirement. Hence, this framework provides a generic and urban application-independent overview of the different levels of data integration based on the UDT lifecycle inspired by the Spatial Data Infrastructure lifecycle. This article provides first conceptual insights of data integration levels to build, use, and update UDT. However, from a practical perspective, the list of UDT initiatives used to illustrate the work is not exhaustive, and future initiatives should be documented. Furthermore, the current emphasis is on the creation and use phases of the lifecycle, which lacks a concrete illustration of the update phase. Indeed, it limits the practicability of the data integration levels in the maintenance phase. [ABSTRACT FROM AUTHOR]