A tyre-rim interaction digital twin for biaxial loading conditions

Biopharma Is Going Digital … Bit by Bit

Participation and influence of citizens are crucial requirements to ensure sustainable and responsible land use planning. Notwithstanding, both documented and anecdotal evidence indicate that citizens’ influence in planning is still limited, especially in the agenda-setting phase. One explanation is that the design of geospatial tools and participation in land use planning is often limited to elites and experts and less to ordinary citizens. Recent studies propose that digital twins could ensure sustainable and responsible land use planning where the influence of citizens can be significantly observed. Adapting Kingdon's multiple streams framework to include elements of sociotechnical artefacts, this study aims to test this assertion and verify how citizens interacting with government institutions at different levels can employ digital twins to find agenda status for their land use issues and proposals. This study employed a systematic review following the PRISMA process to identify 34 articles about applying digital twins for citizen participation in planning decision-making. The study reveals that to improve the participation and influence of citizens, the development of digital twins needs to be citizen-centric. Data from digital twins should be presented in a form non-experts can understand. Qualities for digital twins to promote citizen participation include being interactive and user-friendly, smooth visualisation and immersive experience, inclusiveness, and privacy-oriented. In line with Kingdon’s framework, citizens could frame, convince, and propose land use interventions. The findings also reveal two ways of citizen participation: (1) providing data for developing digital twins and (2) utilising digital twins to analyse and identify key land use challenges and to push forward land use claims and proposals within a policy system.

The paper discusses industrial production management in an Industry 4.0 enterprise based on digital twins of manufacturing process and products. The principles of applying digital twins in the production cycle are defined. We present an example structure of an integrated automated production management system that includes the digital twins and a number of other systems. The functions of digital twins in such a system are indicated. The order of formation and development of digital twins in the course of the enterprise design and construction is considered. At the initial stage of collecting requirements and drawing up technical specifications for the enterprise design, we propose to create an approximate simulation model of the production cycle, which allows the feasibility assessment of key requirements. At the subsequent stages of the enterprise life cycle, the simulation model is enriched up to a full-fledged digital twin and augmented with other components of the management system. We show that the digital twin development can be performed at a high level of formal rigor and automation utilizing the mathematical device of category theory. For this purpose, the novel construction called flexible multicomma category is introduced, representing all possible enterprise architecture variants from a given viewpoint in the sense of the ISO/IEC/IEEE 42010 standard. A flexible multicomma is defined by a signature composed of functors that represent the architecture components from the selected viewpoint. We prove that a multicomma of any fixed shape derived from the signature is embedded into the flexible multicomma.

With the development of digital twin, the research on the fusion of intelligent manufacturing system and digital twin technology attracts more attention. In this paper, the realization of digital twin is studied for geometric feature online inspection system of body-in-white (BIW). A three-level virtual modeling approach of Element-Behavior-Rule is proposed for the physical environment modeling in a digital environment. An element model, behavior model and rule model are defined, respectively, and the relationship among the three-layer model is given for real-time communication. A three-layer communication architecture of the digital twin system is designed for the real-time mapping between physical and virtual space, and the process of the online inspection digital twin system is analyzed. Finally, on the basis of the online inspection physical experimental platform and JHIM (Jiangheng Intelligent Manufacturing) software platform, the digital twin system for geometric feature online inspection of BIW is developed, which shows that the virtual model can be driven in real time by real-time physical inspection data. Digital twin technology provides a feasible way for real-time monitoring of the online inspection system.

9 - Digital Twin Development and cloud deployment for a DC Motor Control embedded system

A digital twin is a real-time, dynamic digital representation of a physical system, enabling continuous monitoring, simulation, and analysis.It plays a crucial role in optimizing system performance, predictive maintenance, and decision-making across various engineering domains.Simulink, a MATLABbased software environment, is widely utilized for modelling and simulating complex dynamical systems through block diagrams.This paper aims to explore the development process of digital twins within Simulink, highlighting its capabilities for system-level design and validation.Simulink provides engineers and researchers with powerful tools to create, simulate, and optimize digital twins of physical systems.By integrating real-time data, these models enable accurate performance evaluation, fault detection, and predictive analytics, making them essential for industries such as aerospace, automotive, robotics, civil engineering, and manufacturing.The study outlines key methodologies for developing digital twins in Simulink, emphasizing their role in enhancing system reliability, improving operational efficiency, and accelerating innovation.Additionally, it discusses Simulink's features, such as its multi-domain modelling capabilities, simulation accuracy in real time, and integration with real-world data sources, which contribute to the improvement of digital twin technology.It is demonstrated that Simulinkbased digital twins facilitate engineering decision-making, streamline system optimization, and drive technological advancements in industrial applications.

The objective of this work is to develop and validate a Digital Twin (DT) for a multistage production line of tapered roller bearings. The manufacturing process consists of ring machining and component assembly, including intensive quality controls. This work proposes the integration of machine learning models associated with the manufacture of the double outer ring and the two inner rings in the DT. The models are trained with real data, so that the DT can predict the behavior of the production process under changing conditions of ongoing processes, machines or materials, and optimal operating conditions can be predicted. The DT has been developed and integrated with the aim of guiding production by proposing optimal machine configurations. To this end, different stations have been modeled and integrated into the DT as independent modules: grinding machines, inner and outer rings pairing module, and a module for calculating the optimal family of rings to be ground. After integrating the DT in the line, results show not only a raise in the line efficiency but also a decrease in the overall scrap ratio.

The concept of Digital Twins is still in an infant state of development. Digital Twins are often built as a tool to aid in better understanding of physical systems through simulation. They can be used to visualize information during operations and provide instructions during training or execution of procedures. The use of Digital Twins to test is appealing as it can be done quickly and safely. However, testing without inclusion of the physical system can lead to a reality gap. The reality gap can lead to high risks when applying concepts tested on digital Twins to the physical system directly. Sometimes interaction with the physical system is unfeasible. In this paper, we present an experimental laboratory that we built to provide a platform for the development of high quality Digital Twins through a feedback loop. The physical system is a Palfinger crane. Our replicate physical twin is a Universal collaborative industrial robot model UR16e due to its similar anatomy to the crane. The RoboDK simulation software was used to rapidly develop a digital twin of the UR16e. We demonstrate a solution to the interoperability problem in digital Twins using the monitoring adaptation loop from the Autonomic Adaptation System of the Arrowhead Framework.

An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient’s characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters – including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient’s life circumstances and plans, and medical procedures – a digital twin paired to the patient’s characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients’ well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life.

This article examines the current issues of the development of digital counterparts of mechanical engineering products in the context of advanced manufacturing technologies, with special emphasis on the use of specialized software for processing scanned external surfaces of physical objects. The research presented in this paper is aimed at identifying the key factors affecting the complexity of creating digital counterparts of mechanical engineering products. As part of the work, techniques have been developed to increase the accuracy of creating digital counterparts of physical objects with damaged or worn work surfaces. The article provides real-world examples of the use of software tools to solve practical problems related to the creation of digital twins. A detailed analysis of the factors influencing the complexity and labor intensity of the work is carried out, with an emphasis on the influence of the modes of software processing of polygonal models of scans of parts and their impact on the accuracy of 3D modeling. The results of the study have a wide range of practical applications in various industries, including mechanical engineering, automotive, tractor construction, information technology and others. The data obtained allows us to optimize the processes of creating digital twins, increasing the quality and efficiency of work. The paper also highlights the impact of software and work organization on the qual ity of digital counterparts of machine parts being developed. The study allows us to deter mine the optimal approaches to software selection, workflow optimization and resource management, which ultimately helps to reduce the complexity and increase the accuracy of creating digital twins. It is important to note that the development of digital twins is a com plex process that requires an integrated approach. It is necessary to take into account many factors, ranging from the type of object being scanned and its condition, ending with the functionality of the software and the qualifications of specialists. The application of the developed techniques will improve the accuracy and speed of creating digital twins, which in turn will lead to a reduction in the development and implementation of new products, as well as increase the efficiency of production processes.

Through the development of digital twins, the Industrial Internet of Things (IIoT) has fundamentally changed how the physical and digital worlds are integrated. The purpose of this study is to examine how digital twins can improve production control in smart consumer electronics factories. In particular, the study examines how well digital twins work as a production control tool and suggests a novel strategy that combines real-time data, sophisticated analytics, and predictive capabilities. The proposed method uses a digital twin-based methodology to model and simulate the behavior of the production system in real-time, enabling the early identification of potential problems and proactive decision-making. The proposed method optimizes resource utilization, lowers energy consumption, and adapts to changing conditions by using cutting-edge algorithms and machine-learning techniques. However, existing approaches face limitations in energy efficiency, scalability, reliability, robustness, cost-effectiveness, and interoperability. Addressing these challenges is essential to advance production control in smart consumer electronics factories.

The fatigue of steel bridges poses a great threat to their safety and functionality. However, current approaches for fatigue management are largely based on heuristic design philosophies, physical testing, and bridge managers' experience. This paper proposes a closed lifecycle fatigue management driven by Digital Twin for steel bridges. To provide clarity around the concept, the definition of Digital Twin for steel bridges is given at first. Then eight functional modules supporting Digital Twin are outlined in detail, aiming to provide a reference for the future development of Digital Twin in fatigue management. Finally, the implementation mechanism of Digital Twin is further described over different phases during the bridge lifecycle. This paper also identifies two main obstacles for the development of Digital Twin: i) the lack of understanding of steel bridge fatigue, and ii) the insufficiency of the present technologies.

Digital twin technology consists of creating virtual replicas of objects or processes that simulate the behavior of their real counterparts. The objective is to analyze its effectiveness or behavior in certain cases to improve its effectiveness. Applied to products, machines and even complete business ecosystems, the digital twin model can reveal information from the past, optimize the present and even predict the future performance of the different areas analyzed. In the context of supply chains, digital twins are changing the way they do business, providing a range of options to facilitate collaborative environments and data-based decision making and making business processes more robust. This paper proposes the design and development of a digital twin for a case study of a pharmaceutical company. The technology used is based on simulators, solvers and data analytic tools that allow these functions to be connected in an integral interface for the company.

Collective reinforcement learning based resource allocation for digital twin service in 6G networks

The operation of fuel cell stacks on test benches today is typically monitored by constantalarm threshold values for selected operating conditions. However, due to the wide range ofoperating conditions of the stacks, this type of monitoring only works for extreme maximumand minimum operating conditions. Wide setting ranges for thresholds prevent the detectionof minor faults, whereas too narrow limits will interrupt the tests unnecessarily. A particularchallenge is the monitoring of faults that either do not result in a directly measured responsefrom the fuel-cell stack or that only become noticeable with a time delay. The continuousimprovement of fuel cell stacks over the last years necessarily requires much-improvedmonitoring methods, especially for durability tests with an operating time of thousands ofhours. For this reason, a novel monitoring concept using AI-based methods for the operationof PEM fuel cells on test benches was developed and is being presented.Firstly, machine-learning based mechanisms for monitoring the operating conditions as setand controlled by the test bench are shown. Due to the cyclic operation during durabilitytests, the operating conditions set at a load point can be compared to the past operatingconditions at the same load point. This proceeding allows the early and accurate detection offaults caused by the test bench and thereby ensures the usability of the measured data aswell as an early alarm in case of problems.In addition, a digital twin based method for monitoring the condition of the fuel cell stack ispresented. The deep-learning based digital twin calculates a probabilistic prediction of theexpected voltage of the fuel cell stack based on the current and past operating conditions.The comparison of expected and measured cell voltage taking the model’s confidence intoaccount enables the early and precise detection of unforeseen events such as contaminationand thus averts consequential damage to the fuel cell stack. In contrast to physical models,the digital twin represents a data driven model-ling approach for fuel cells.The presented methods are applied to real testing data to demonstrate the detection of faultsduring the operation of fuel cell stacks on test benches that would have remainedundiscovered by today’s monitoring mechanisms. It shows that the data driven digital twin isable to predict the fuel cell’s stack voltage with an accuracy of 2.5 mV over 1000 h of unseendata. Figure 1