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In this paper, Artificial Intelligence/Knowledge Representation methods are used for the digital modeling of cultural heritage elements. Accordingly, the new concept of digital cultural heritage twin is presented as composed of a physical component and an immaterial component of the cultural entity. The former concerns the physical aspects, i.e. style, name of the artist, execution time, dimension, etc. The latter represents the emotional and intangible aspects transmitted by the entity, i.e. emotions, thoughts, opinions. In order to digitally model the physical and immaterial components of the twin, the Narrative Knowledge Representation Language has been formally introduced and described. It is particularly suitable for representing the immaterial aspects of the cultural entity, as it is capable of modeling in a simple but rigorous and efficient way complex situations and events, behaviours, attitudes, etc. As an experiment, NKRL has been adopted for representing some of the most relevant intangible items of the visual narrative underlying the hidden painting that lies beneath the Mona Lisa (La Gioconda) image painted by Leonardo Da Vinci on the same poplar panel. Real-time application of the resulting knowledge base opens up novel possibilities for the development of virtual objects, chatbots and expert systems, as well as the definition of semantic search platforms related to cultural heritage. [ABSTRACT FROM AUTHOR]

Fish processing is an indispensable part of fish food production. It mainly involves de-heading, gutting, filleting, skinning, trimming, and slicing, with the cutting operations holding a critical role. Unfortunately, inefficiency, low quality, and poor safety are the primary problems facing the fish processing industry today, dramatically hindering the automation and intelligence of fish processing. Consequently, it is vital to develop intelligent cutting in current fish processing in an efficient, high-quality, and safe manner. This review summarizes the main cutting techniques for fish processing. The critical techniques to achieve intelligent cutting in fish processing from imaging, image processing, and modeling dimensions are outlined, with their applications in practical fish processing. Fish characteristics, cutting mechanisms, and cutting process control are emphasized. In addition, Industry 4.0 technologies, especially the Internet of Things (IoT), big data analytics, and digital twins (DT), are emphasized. Finally, challenges and future work are highlighted, which will serve as references for subsequent researchers and enterprises engaged in this field to promote the automation and intelligence of fish processing production, ultimately realizing the high-efficiency, high-quality, and safe production of fish food products. [ABSTRACT FROM AUTHOR]

The coronavirus has created significant disruptions and exposed supply chain (SC) vulnerabilities. This crisis started a discussion about SC sustainability and performance. Therefore, the implementation of advanced technologies and supply chain collaboration could mitigate this disruption with the help of government support and policies. Considering this situation, this paper examines how COVID-19 influences advanced technologies (Artificial Intelligence, the Internet of Things, Blockchain, Digital twins, and Big Data Analytics) and supply chain collaboration (SCC) with a moderating role of government support and policies (GSP) in Pakistan. The study encompasses a comprehensive assessment carried out via structural equation modeling and data collected from Pakistani companies engaged in SCM or those operating within the SC divisions of manufacturing enterprises. According to the empirical findings, it is evident that COVID-19 outbreaks have a significant influence on SSCP; However, they do not show a similar impact on advanced technologies (AI, IoT, Blockchain, DT, and BDA) and supply chain collaboration, the influence of COVID-19 on SSCP was effectively mediated through advance technologies (AI, IoT, Blockchain, DT, and BDA) and supply chain collaboration. This research contributes to the existing literature on SSCP by emphasizing the importance of the resource-based view, dynamic capability view, and institutional theories. SC and logistics managers can apply the theoretical framework proposed in this study to mitigate the impact of the COVID-19 epidemic or disruptions in logistics and SC operations, thereby improving profitability in the context of an epidemic. [ABSTRACT FROM AUTHOR]

Purpose: The industry of the future through digital twins is constantly seeking efficiency, competitiveness, and innovation, particularly in the case of the diagnosis of rotating structures. Their use in the maintenance of systems has been the subject of several research in recent years by studying simulated vibration behaviour of several operating modes, as well as building a substantial database. Method: However, the limitations in its implementation lie in the accuracy and reliability of the simulated data, which can lead to a misdiagnosis of the state of the system. The study presented in this paper proposes a method of diagnosis based on the digital twin using a digital and experimental database of a gearbox classified with the Multiclass Support Vector Machine (MSVM). To come up with an outcome, experimental data are collected on the test bench in different modes of operation, then for each of these modes, simulations are made on the numerical model created to generate data, which will be updated on those from the physical model. Results: Using a classification algorithm, data from the numerical model are used to train the model and experimental data are used for testing. The results provide an average of 81% of classification accuracy. Conclusion: Finally, from the dynamic model of a gearbox developed in this study, several operating modes can be generated and used in case of lack of experimental data, to do a diagnostic. [ABSTRACT FROM AUTHOR]

The metaverse is seen as a future generation of the Internet in which the virtual and the real merge into a common world. Technologies such as IoT, cloud computing, artificial intelligence or the reality–virtuality continuum underpin the metaverse and condition its evolution. This paper presents a bibliometric study on the WoS database (from 1995 to 2022) to obtain a comprehensive and non-subjective understanding of the metaverse. The study identifies the main subject areas and sources, the leading countries and authors. It also analyzes the evolution over time and the core and future research themes. Extended reality, blockchain, artificial intelligence and sensors are identified as core themes, while building information modeling, digital twins and governance emerge as future themes. Based on the bibliometric study, a general layered metaverse architecture is proposed that streamlines open challenges in the metaverse to assist researchers and companies in introducing innovative and disruptive improvements. [ABSTRACT FROM AUTHOR]

Objective: To understand the current status of research in the field of metaverse, and to analyze the research progress and evolutionary trends in this field. Methods: Based on the bibliometric analysis, a total of 921 papers were obtained by searching the Web of science core database for the keyword "metaverse". CiteSpace was used to visualize and analyze the current status and trends of metaverse research in China. Results: Ireland is currently the country with the highest research impact. China is currently the country with the largest number of publications in this field, but the impact of the research is insufficient. The current research in the highly cited literature focuses on technical and history reviews of the metaverse as well as its development in the field of education. Artificial Intelligence and utaut2 are the underlying clusters of cited literature in this research area. Several research hotspots have been formed, such as virtual reality, augmented reality, mixed reality, digital twins and artificial intelligence. Conclusion: The current research on metaverse in various fields is basically in its infancy, but has a great potential for future development and will gradually penetrate into many different directions with many challenges. [ABSTRACT FROM AUTHOR]

With the rapid development of industrial manufacturing, the operation and maintenance problems of the power system are becoming increasingly prominent, and traditional manual maintenance methods are no longer able to meet the needs. This article aims to provide better power support for manufacturing enterprises. This article establishes a digital twin model of the power system, which corresponds the physical information and operational data of the actual power system to the digital model. Based on this model for simulation and prediction, machine learning and artificial intelligence algorithms are used to analyze historical data, extract features and patterns, and establish a monitoring and prediction model for the operation status of the power system. Finally, through real-time monitoring and prediction, timely detection and handling of faults and anomalies in the power system, the results can effectively improve the operational efficiency and reliability of the power system in manufacturing enterprises. Through real-time monitoring and prediction, faults and anomalies in the power system can be detected in a timely manner, reducing downtime and maintenance costs. Digital twin technology can also provide analysis and optimization suggestions for operating data for manufacturing enterprises, helping them improve production efficiency and competitiveness. [ABSTRACT FROM AUTHOR]

Industry 4.0 focuses on the realization of smart manufacturing based on cyber-physical systems (CPS). However, emerging Industry 5.0 and Society 5.0 reaches beyond CPS and covers the entire value chain of manufacturing, and faces economic, environmental, and social challenges. To meet such challenges, we regard Industry 5.0 as a socio-technical revolution based on the socio-cyber-physical system (SCPS), and propose a socio-technically enhanced wisdom manufacturing architecture and framework beyond CPS-based Industry 4.0/smart manufacturing with especially concerning transition enabling technologies such as artificial intelligence, social Internet of Things (SIoT), big data, machine learning, edge computing, social computing, 3D printing, blockchains, digital twins, and cobots. Finally we address the roadmap to blockchainized value-added SCPS-based Industrial Metaverse for Industry/Society 5.0, which will achieve high utilization of resources and provide products and services to satisfy experience-driven individual needs via metamanufacturing cloud services towards smart, resilient, sustainable, and human-centric solutions. [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]

Artificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision-making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI-intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI-intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease- or injury-specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high-risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application. Level of evidence IV [ABSTRACT FROM AUTHOR]

The relevant problem of analyzing the features of measurement systems and their metrological support is formulated. Promising areas for the development of measurement systems, including virtual systems, are considered. The article analyzes such development areas as remote and synchronized vector measurements, cloud technologies, the Internet of Things, big data, and artificial intelligence. Several innovative solutions employing both biosensors and hybrid metrology are described (introduction of structural redundancy into measurement systems, as well as the introduction of temporal and algorithmic redundancies into the software of measurement systems). It is shown that the integration of component functions objectively complicates the identification of measurement systems in complex technical systems, yet providing new possibilities for technical systems with measurement functions. The following areas for improving the metrological support of measurement systems are analyzed: self-diagnostics and self-monitoring; remote and automated calibration and verification; use of digital twins, big data, and artificial intelligence; establishment of adaptive calibration and verification intervals. The study reveals the need to minimize bureaucratic procedures and automate metrological procedures while showing practicability in switching from periodic to adaptive procedures requiring no human involvement. [ABSTRACT FROM AUTHOR]

Cutting-edge technologies like big data analytics (BDA), artificial intelligence (AI), quantum computing, blockchain, and digital twins have a profound impact on the sustainability of the production system. In addition, it is argued that turbulence in technology could negatively impact the adoption of these technologies and adversely impact the sustainability of the production system of the firm. The present study has demonstrated that the role of technological turbulence as a moderator could impact the relationships between the sustainability the of production system with its predictors. The study further analyses the mediating role of operational sustainability which could impact the firm performance. A theoretical model has been developed that is underpinned by dynamic capability view (DCV) theory and firm absorptive capacity theory. This model was verified by PLS-SEM with 412 responses from various manufacturing firms in India. There exists a positive and significant influence of AI and other cutting-edge technologies for keeping the production system sustainable. [ABSTRACT FROM AUTHOR]

Demands for more accurate machine learning models have given rise to rethinking current modeling approaches that were deemed unsuitable, primarily due to their computational complexity and the lack of availability and accessibility to representative data. In Industry 4.0, rapid advancements in Digital Twin (DT) technologies and the pervasiveness of cost-effective sensor technologies have pushed the incorporation of artificial intelligence, particularly data-driven machine learning models, for use in smart manufacturing. However, the persistent issue with such models is their high sensitivity to the training data and the lack of interpretability in the outcomes, at times generating unrealistic results. The incorporation of knowledge into the machine learning pipeline has been earmarked as the most promising approach to address such issues. This paper aims to answer this call through a Knowledge-embedded Machine Learning (KML) framework for smart manufacturing, which embeds knowledge from experience and, or physics information into the machine learning pipeline, thus making the outcomes from these models more representative of real applications. The merits of KML were then presented through comparative studies showing its capability to outperform knowledge-based and data-driven models. This promising outcome led to the development of frameworks that can potentially incorporate KML for smart manufacturing applications such as Prognostics and Health Management (PHM) and DT, further supporting the usefulness of the proposed KML framework. [ABSTRACT FROM AUTHOR]

In recent years, there have been concentrations on the Digital Twin from researchers and companies due to its advancement in IT, communication systems, Cloud Computing, Internet-of-Things (IoT), and Blockchain. The main concept of the DT is to provide a comprehensive tangible, and operational explanation of any element, asset, or system. However, it is an extremely dynamic taxonomy developing in complication during the life cycle that produces an enormous quantity of the engendered data and information from them. Likewise, with the development of the Blockchain, the digital twins have the potential to redefine and could be a key strategy to support the IoT-based digital twin's applications for transferring data and value onto the Internet with full transparency besides promising accessibility, trusted traceability, and immutability of transactions. Therefore, the integration of digital twins with the IoT and blockchain technologies has the potential to revolutionize various industries by providing enhanced security, transparency, and data integrity. Thus, this work presents a survey on the innovative theme of digital twins with the integration of Blockchain for various applications. Also, provides challenges and future research directions on this subject. In addition, in this paper, we propose a concept and architecture for integrating digital twins with IoT-based blockchain archives, which allows for real-time monitoring and control of physical assets and processes in a secure and decentralized manner. We also discuss the challenges and limitations of this integration, including issues related to data privacy, scalability, and interoperability. Finally, we provide insights into the future scope of this technology and discuss potential research directions for further improving the integration of digital twins with IoT-based blockchain archives. Overall, this paper provides a comprehensive overview of the potential benefits and challenges of integrating digital twins with IoT-based blockchain and lays the foundation for future research in this area. [ABSTRACT FROM AUTHOR]

The growing relevance of artificial intelligence (AI) for technical systems offers significant potential for the realization and operation of autonomous systems in complex and potentially unknown environments. However, unlike classical solution approaches, the functionality of an AI system cannot be verified analytically, which is why data-driven approaches such as scenario-based testing are used. With the increasing complexity of the required functionality of the AI-based system, the quantity, and quality of the data needed for development and validation also increase. To meet this demand, data generated synthetically using simulation is increasingly being used. Compared to the acquisition of real-world reference data, simulation offers the major advantage that it can be configured to test specific scenarios of interest. This paper presents an architecture for the systematic generation of virtual test scenarios to establish synthetically generated test data as an integral part of the development and validation process for AI systems. Key aspects of this architecture are the consistent use of digital twins as virtual 1-to-1 replicas and a simulation infrastructure that enables the generation of training and validation data for AI-based systems in appropriate quantity, quality, and time. In particular, this paper focuses on the application of the architecture in the context of two use cases from different application domains. [ABSTRACT FROM AUTHOR]

Product surface quality which significantly influences a product's wear resilience and fatigue strength is an important technical characteristic. A frequently used indicator of surface quality, precise mating surface fit, and fatigue life is average surface roughness. Unstable machining leads to an unacceptable surface roughness of the workpieces. Effective surface quality control is significant in improving machining efficiency and reducing costs. A method for controlling the quality of product surfaces based on an offline digital twin and artificial intelligence is developed. To reduce average surface roughness, genetic algorithms are used to optimize machining settings. Actual machining data is used to test and validate the digital twin developed. The simulated results agree with the machining trials results and the root means square error is found to be between 0.128 and 0.135 µm. The developed offline digital twin accurately predicts the surface finish and significantly improves manufacturing and production intelligence. [ABSTRACT FROM AUTHOR]

This research work demonstrates that new cast iron materials can be developed aided by an expert digital twin (manufacturing twin) and model predictive controls developed based on artificial intelligent tools. The well-known EN GJS-550 high strength standard material grade with yield strength exceeding 370 MPa, commonly used for safety components, can be replaced with the new HS420/250 grade with a yield strength higher than 420 MPa, elongation higher than 5%, with the resulting hardness maintained below 250 HB (Brinell Hardness). The new high yield strength material grade was developed by expert analysis of industrial manufacturing data with monitored automotive brake anchors for more than one year. This 13.5% increase in yield strength opens new design opportunities for lightweight cast iron components. Using the model predictive control and the corrective protocols developed, a light weight designed automotive brake anchors component was successfully manufactured with the new high yield strength material. [ABSTRACT FROM AUTHOR]

The high demand for machining process automation has placed real-time tool condition monitoring as one of the top priorities of academic and industrial scholars in the past decade. But the presence of numerous known and unknown machining variables and challenging operating conditions such as high temperature and pressure makes it a daunting task. However, recent advancements in sensor and digital technologies have enabled in-process condition monitoring and real-time process optimization a highly accurate, robust, and effective process. Hence, the objective of the article is to provide a summary of the factors influencing the performance of cutting tools, critical machining variables to be monitored, techniques applied to monitor tool conditions, and artificial intelligence algorithms used to predict tool performance by analyzing and reviewing the literature. The future direction of intelligent cutting tools and how they would help in building the foundation for advanced smart factory ecosystems such as digital twins and Industry 4.0 are also discussed. [ABSTRACT FROM AUTHOR]

The author explores crucial focus areas where physical intelligence is reshaping architecture which are explored in the issue. These include physical intelligence in spatial perception, physical intelligence and artificial intelligence (AI) design, and physical intelligence for digital construction.

This document is a comment on an article about the use of AI and digital modeling for precision in positive end-expiratory pressure (PEEP) settings in intensive care medicine. The authors agree with the previous letter that the relationship between recruitability and PEEP setting is complex and requires further understanding. They suggest exploring options such as automated multi-parametric monitoring and modeling, as well as algorithms based on AI, to predict treatment response and minimize harm. However, they acknowledge challenges in determining input variables and understanding the systemic consequences of PEEP changes. They emphasize that technology can provide monitoring capabilities and potential solutions, but the goals of care should still be determined by clinicians in partnership with patients and their families. [Extracted from the article]

Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field—integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research. [ABSTRACT FROM AUTHOR]

The Industrial Revolution can be termed as the transformation of traditional industrial practices into new techniques dominated by the technologies available at that time. The first three industrial revolutions were driven respectively by mechanization, electrification, and automation which had gradually transformed the agrarian economy into a manufacturing-based economy. It helped in enhancing the lifestyle of the factory workers and the healthcare system, which improved the overall quality of living. The industries that adapted to the change witnessed a tremendous increase in the production of goods, competitive advantage, and cross-border business opportunities. While we are currently living to see the fourth industrial revolution (also known as Industry 4.0) unfolding around us, the world is poised for the next big leap, the fifth industrial revolution or Industry 5.0. Hence, the first half of the paper outlines the enabling technologies of Industry 4.0 and conceptualizes how they would act as the foundation for the fifth industrial revolution. The socio-economic challenges of the technologies and the need for Industry 5.0 technologies are also discussed. The second half of the paper outlines the prospective technologies of Industry 5.0, their potential applications from the perspective of industry leaders and scholars and conceptualizes how they can overcome the challenges of Industry 4.0. The definition of "sustainability trilemma" a new term coined by the authors, and the reasoning for calling the next industrial revolution "Industry 4.0S" (another new term) rather than Industry 5.0 are also presented. [ABSTRACT FROM AUTHOR]

The advancement of digital twin technology has significantly impacted the utilization of virtual cities in the realm of smart cities and mobility. Digital twins provide a platform for the development and testing of various mobility systems, algorithms, and policies. In this research, we introduce DTUMOS, a digital twin framework for urban mobility operating systems. DTUMOS is a versatile, open-source framework that can be flexibly and adaptably integrated into various urban mobility systems. Its novel architecture, combining an AI-based estimated time of arrival model and vehicle routing algorithm, allows DTUMOS to achieve high-speed performance while maintaining accuracy in the implementation of large-scale mobility systems. DTUMOS exhibits distinct advantages in terms of scalability, simulation speed, and visualization compared to current state-of-the-art mobility digital twins and simulations. The performance and scalability of DTUMOS are validated through the use of real data in large metropolitan cities including Seoul, New York City, and Chicago. DTUMOS' lightweight and open-source environment present opportunities for the development of various simulation-based algorithms and the quantitative evaluation of policies for future mobility systems. [ABSTRACT FROM AUTHOR]

Several studies involving the implementation of artificial neural network (ANN) technology for process design, monitoring, and control are under active research. This new technology has shown great potential in advancing chemical processes through the development of digital twins and smart factories. In joining this race, the current study explores the capability of physics-based modeling (CFD) and artificial neural networks for advanced process data visualization. Here, 20 CFD simulations of a multi-tubular reactor equipped with a Zn-Fe-Cr catalyst for synthesizing butadiene were executed. The simulation result was extracted as 3-D data with XYZ coordinates and imported into a python-based DNN model for training and cross-validation. An accuracy of 99.2% was obtained from the ANN surrogate model. The trained model was used to predict 3D data in terms of the process temperature, concentration, etc. The 3D data was then imported into a Paraview® VTK for detailed virtualization. Cross-sectional, longitudinal, and radial distribution of the various process variables, such as concentration profiles and pressure contour, were effectively visualized. A graphic user interface was further developed using Python for real-time visualization of the equipment. This implementation is analogous to the digital twin and is employable for online system optimization, high accuracy, low computational cost, and seamlessly integrable 3D real-time visualization system design for efficient, quick, and easy plant decision-making. [ABSTRACT FROM AUTHOR]

The specialization of different urban sectors, theories, and technologies and their confluence in city development have led to a greatly accelerated growth in urban informatics, the transdisciplinary field for understanding and developing the city through new information technologies. While this young and highly promising field has attracted multiple reviews of its advances and outlook for its future, it would be instructive to probe further into the research initiatives of this rapidly evolving field, to provide reference to the development of not only urban informatics, but moreover the future of cities as a whole. This article thus presents a collection of research initiatives for urban informatics, based on the reviews of the state of the art in this field. The initiatives cover three levels, namely the future of urban science; core enabling technologies including geospatial artificial intelligence, high-definition mapping, quantum computing, artificial intelligence and the internet of things (AIoT), digital twins, explainable artificial intelligence, distributed machine learning, privacy-preserving deep learning, and applications in urban design and planning, transport, location-based services, and the metaverse, together with a discussion of algorithmic and data-driven approaches. The article concludes with hopes for the future development of urban informatics and focusses on the balance between our ever-increasing reliance on technology and important societal concerns. [ABSTRACT FROM AUTHOR]

Self-configuration is the preparation required to facilitate smart-manufacturing (SM) with the inputs derived without user intervention for engineering applications. Thus, it is vital for achieving the highest maturity level of SM technologies. In context, digital twin (DT) is an advanced virtual factory with simulation as its core technical functionality. However, the requirement of several inputs limits the implementation of DT on a physical asset without user intervention. Moreover, surpassing this limitation requires extraction methods for deriving the necessary inputs for DT application. Therefore, this study proposes information fusion and systematic logic library (SLL)-generation methods to facilitate the self-configuration of an autonomous DT. The information fusion aggregates and extracts the information elements required for DT application from heterogeneous information sources. In addition, the SLL generation method created the SLL required for reflecting the functional units of agents within the physical asset. Both methods were proposed from available SM standards such as ISA-95, automation markup language, and OPC unified architecture. Furthermore, an autonomous DT-supporting framework was designed by analyzing the relationship between asset description and SM standards, which facilitated the artificial intelligence-based extraction of the asset description object and SLL. Additionally, the core functional engines within this framework were designed using machine learning and process-mining techniques. Consequently, the proposed methods reduced the input pre-processing time required for constructing and synchronizing an autonomous DT to aid the application of autonomous DT on the physical asset without user intervention. [ABSTRACT FROM AUTHOR]

The potential benefits of Industry 4.0 have led to an increased interest in smart manufacturing. To facilitate the self-diagnosis and adaptive ability in smart milling system, a digital twin–driven intelligent algorithm for monitoring in-process milling parameters is proposed here. The algorithm can extract the radial width of cut, axial depth of cut, cutter runout parameters, and cutting constants in the end milling process at the same time only by using force sensor. It is an important breakthrough in this paper to converge two different force models to realize cyber-physical fusion for identifying milling parameters in the milling process. By using the convolution force model, digital twin technology can extract the approximate solution of milling parameters in the machining process in advance, so as to narrow the range of solution. Furthermore, the subsequent artificial intelligence algorithm can find the accurate solution of the current milling parameters in a short calculation time by cyber-physical fusion with the numerical force model considering cutter runout effect. Milling experiments are carried out to validate the proposed algorithm. It is shown that due to the complementary advantages of the convolution force model and numerical force model, the algorithm proposed in this paper can give consider to the identification accuracy and calculation efficiency. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, big data, Internet of Things, and other technologies, digital twin has gained great attention and become a current research topic. Using digital twin technology, the digital twin model can be constructed in the cyber space that is fully equivalent to the physical entity. It is always consistent with the physical entity in the operation process, which greatly improves the dynamic perception and prediction ability of the real world. After the development in recent years, digital twin has gradually changed from the initial concept discussion to the study of model framework and implementation method. However, because the research objects in different industries have great differences in their own composition, service conditions, and application scenarios, they have personalized characteristics in modeling strategies and usage methods. Therefore, based on different industries, this paper reviews the current articles on digital twins and distinguishes the focus of digital twin modeling research; subsequently, the relevant supporting techniques and methods are summarized according to their different importance for digital twin modeling. Based on the review in this paper, future researchers can conduct targeted research on digital twin technology in term of the characteristics of the objects in their industry. [ABSTRACT FROM AUTHOR]

As health-care budgets are continuously under increasing demands, Artificial Intelligence resources such as digital heart twins could save millions of dollars by predicting results and preventing unnecessary surgery. Can we start to make digital human body twins to plant and predict health outcomes for a patient? By using a way to design competent simulation models from real objects, digital twins were created through IoT. But the digital twin is a complicated system and a very long-drawn step away from its possibilities. Researchers must design all components of entities or structures. There is a need to collect and merge various types of data. Many engineering researchers and participants aren't sure about which technologies and resources to use. The 3D digital twin model offers a reference guide for digital twin comprehension and implementation. This paper aims to investigate and outline the recent technologies and tools used for digital twin applications from a 3-D digital model perspective, such as references to technologies and tools for future digital twin applications. [ABSTRACT FROM AUTHOR]

The Journal of Intelligent Manufacturing celebrates its 35th anniversary and reflects on the progress made in automation since its inception in 1990. The author emphasizes the importance of software in enhancing the economic value of hardware automation and the emergence of expert systems and machine learning in modeling manufacturing systems. The concept of hyper-automation, which focuses on automating processes beyond the traditional automation layer, is discussed, along with the use of digital twins and different models in the two layers of automation. The article concludes by acknowledging the contributions of authors, associate editors, and reviewers in shaping the field of intelligent manufacturing. [Extracted from the article]

Purpose: Axial alignment of the talar implant in total ankle arthroplasty remains a major issue, since the real axis of motion of each patient is impossible to determine with usual techniques. Further knowledge regarding individual axis of motion of the ankle is therefore needed. Material and methods: Therefore, digital twins, artificial intelligence, and machine learning technology were used to identify a real personalized motion axis of the tibiotalar joint. Three-dimensional (3D) models of distal extremities were generated using computed tomography data of normal patients. Digital twins were used to reproduce the mobility of the ankles, and the real ankle of the patients was matched to the digital twin with machine learning technology. Results: The results showed that a personalized axis can be obtained for each patient. When the origin of the axis is the centre of mass of the talus, this axis can be represented in a geodesic system. The mean value of the axis is a line passing in first approximation through the centre of the sphere (with a variation of 3 mm from the centre of the mass of the talus) and through a point with the coordinates 91.6° west and 7.4° north (range 84° to 98° west; − 2° to 12° north). This study improves the understanding of the axis of the ankle, as well as its relationship to the possibility to use the geodesic system for robotic in ankle arthroplasty. Conclusion: The consideration of a personalized axis of the ankle might be helpful for better understanding of ankle surgery and particularly total ankle arthroplasty. [ABSTRACT FROM AUTHOR]

In particular, the concept of a digital twin and a manufacturing metaverse share some commonality as both are models (most likely derived from data), however, each serves its own purpose (see Table 1). The term metaverse is attributed to the science fiction book (Stephenson, [3]) published in 1992, just a year after the Internet was established. 2The developments in simulation leading of a manufacturing metaverse The metaverse journey in manufacturing can be traced back to analog simulation, followed by the numerical 1D (D = dimension), and then 2D and 3D digital simulation. [Extracted from the article]

With the development of manufacturing, machining data applications are becoming a key technological component of enhancing the intelligence of manufacturing. The new generation of machine tools should be digitalized, highly efficient, network-accessible and intelligent. An intelligent machine tool (IMT) driven by the digital twin provides a superior solution for the development of intelligent manufacturing. In this paper, a real-time machining data application and service based on IMT digital twin is presented. Multisensor fusion technology is adopted for real-time data acquisition and processing. Data transmission and storage are completed using the MTConnect protocol and components. Multiple forms of HMIs and applications are developed for data visualization and analysis in digital twin, including the machining trajectory, machining status and energy consumption. An IMT digital twin model is established with the aim of further data analysis and optimization, such as the machine tool dynamics, contour error estimation and compensation. Examples of the IMT digital twin application are presented to prove that the development method of the IMT digital twin is effective and feasible. The perspective development of machining data analysis and service is also discussed. [ABSTRACT FROM AUTHOR]

In the book, he introduces the reader to the metaverse, a phrase he coined as a successor to the Internet and constitutes his early 1990s vision of how a virtual reality-based Internet might evolve in the near future. Graph From digital data collection, distributed information sharing, adaptive machine controls, and artificial intelligence (AI)-based decision-making, are we now on the cusp of creating our own "Metalverse"? As with the internet evolving in the industrial sector, the recently held AFS 2022 Foundry Industry 4.0 Conference broached many of the topics that are key facets of metaverse and how our industry might integrate them into a more digital-centric manufacturing environment. [Extracted from the article]