Search Results (63)

This review analyzes the Electronic Digital Twin (EDT) tools characterizing the industrial transformation phase from Industry 4.0 to Industry 5.0. The goal is to provide innovative research EDT solutions to integrate in manufacturing production processes. Specifically, this research is focused on the possibility of combining the advanced technologies and electronics and mechatronics of industrial machines with Artificial Intelligence (AI) algorithms. Furthermore, this review provides important elements about possible future implementations of AI-EDTs and some circuital examples to support the understanding of the concept of circuit simulation in EDT models. EDTs are useful to comprehend the modeling concepts functional to the AI application using the output of the circuit simulations. The output of the circuit is used to train the AI model, thus strengthening the capability to classify and predict the real behavior of production machines with a good accuracy. This review discusses perspectives, limits, and advantages of EDTs and is useful to define new research patterns integrating structured EDTs in advanced industrial environments. The focus of this paper is the definition of possible perspectives of EDT implementations, including AI, in data-driven processes in specific strategic areas of industrial research by classifying the scientific topics in six main pillars. This paper is also suitable for the researcher to develop innovative topics for projects scaled into different work packages based on EDT facilities. Full article

Most of the failures during the actual operation of equipment are caused by improper human operation, tools, spare parts, and environmental factors. These faults are routine. Conventional faults have been validated during equipment development, testing, identification, and maintenance processes, with clear definitions and clear fault tree analysis (FTA) conclusions. Digital twins can offer rapid and interactive diagnostic capabilities for routine equipment failures. To enhance the efficiency of routine fault diagnosis and the interactive experience of the diagnosis process, this paper proposes a digital twin-based equipment routine fault diagnosis model. On this basis, considering the excellent interactivity of the Interactive Electronic Technical Manual (IETM), a conventional equipment fault diagnosis scheme based on twin data and IETM is designed. This scheme converts the equipment fault tree into an IETM fault data model (DM), which is structured and stored in a database to form a fault database. Using real-time twin data of equipment as input, the FTA method is adopted to perform step-by-step fault diagnosis and isolation guidance operation through the IETM process DM combined with fault, while providing maintenance operation guidance. When the real-time twin data of the equipment is not completely consistent with the fault information in the fault library, the case analysis method is used to calculate the similarity between the real-time twin data of the equipment and the clearly defined fault symptom information in the fault library. Based on the set similarity threshold, IETM pushes fault DMs above the threshold for corresponding fault diagnosis isolation guidance. Full article

The rapid development of digital technologies is opening up new avenues for transforming education, particularly in fields that require practical training, such as electronic circuit design. In this context, this paper presents the development of a multiplayer virtual learning platform that makes use of digital twins technology to offer a realistic, collaborative experience in a simulated environment. Users can interact in real time through synchronized avatars, voice communication, and multiple viewing angles, simulating a physical classroom. Evaluation of the platform with undergraduate students showed positive results in terms of usability, collaboration, and learning effectiveness. Despite the limitations of the sample, the findings reinforce the prospect of virtual laboratories as a modern tool in technical education. Full article

Sanicro 25 (X7NiCrWCuCoNb25-23-3-3-2) steel is specifically designed for use in superheater components within the latest generation of conventional power plants. These power plants operate under conditions often referred to as super-ultra-supercritical, with steam parameters that can reach up to 30 MPa and temperatures of 653 °C for fresh steam and 672 °C for reheated steam. While last-generation supercritical power plants still rely on fossil fuels, they represent a significant step forward in more sustainable energy production. The most sophisticated facilities of this kind can achieve thermodynamic efficiencies exceeding 47%. This study aimed to conduct a detailed analysis of the initial precipitation processes occurring in Sanicro 25 steel within the temperature range of 700–900 °C. The temperature of 700 °C corresponds to the operational conditions of this material, particularly in secondary steam superheaters in thermal power plants that operate under ultra-supercritical parameters. Understanding precipitation processes is crucial for optimizing mechanical performance, particularly in terms of long-term strength and creep resistance. To accurately assess the microstructural changes that occur during the early stages of service, a digital twin approach was employed, which included CALPHAD simulations and experimental heat treatments. Experimental annealing tests were conducted in air within the temperature range of 700–900 °C. Precipitation behavior was simulated using the Thermo-Calc 2025a with Dictra software package. The results from Prisma simulations correlated well with the experimental data related to the kinetics of phase transformations; however, it was noted that the predicted sizes of the precipitates were generally smaller than those observed in experiments. Additionally, computational limitations were encountered during some simulations due to the complexity arising from the numerous alloying elements present in Sanicro 25 steel. The microstructural evolution was investigated using various methods, including light microscopy (LM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Full article

The properties of Hardware-in-the-Loop (HIL) for the development of controllers, together with electronic emulation of physical process by Digital Twins (DT) significantly enhance the optimization of design and implementation in nonlinear control applications. The study emphasizes the use of the Raspberry Pi (RBP), a low-cost and portable electronic board for two interrelated goals: (a) the Electronic Control Unit (ECU-RBP) implementing a Lyapunov-based Controller (LBC) for nonlinear trajectory tracking of P3DX wheeled robots, and (b) the Digital Twin (DT-RPB) emulating the real robot behavior, which is remotely connected to the control unit. ECU-RBP, DT-RBP and real robot are connected as nodes within the same wireless network, enhancing interaction between the three physical elements. The development process is supported by the Matlab/Simulink environment and the associated packages for the specified electronic board. Following testing of the real robot from the ECU-RBP in an open loop, the model is identified and integrated into the DT-RBP to replicate its functionality. The LBC solution, which has also been validated through simulation, is implemented in the ECU-RBP to examine the closed-loop control according to the HIL strategy. Finally, the study evaluates the effectiveness of the HIL approach by comparing the results obtained from the application of the LBC, as implemented in the ECU-RBP to both the real robot and its DT. Full article

This review investigates recent developments in cardiac mechano-electrical-fluid interaction (MEFI) modeling, with a focus on multiphysics simulation platforms and digital twin frameworks developed between 2015 and 2025. The purpose of the study is to assess how computational modeling methods—particularly finite element and immersed boundary techniques, monolithic and partitioned coupling schemes, and artificial intelligence (AI)-enhanced surrogate modeling—capture the integrated dynamics of cardiac electrophysiology, tissue mechanics, and hemodynamics. The goal is to evaluate the translational potential of MEFI models in clinical applications such as cardiac resynchronization therapy (CRT), arrhythmia classification, atrial fibrillation ablation, and surgical planning. Quantitative results from the literature demonstrate <5% error in pressure–volume loop predictions, >0.90 F1 scores in machine-learning-based arrhythmia detection, and <10% deviation in myocardial strain relative to MRI-based ground truth. These findings highlight both the promise and limitations of current MEFI approaches. While recent advances improve physiological fidelity and predictive accuracy, key challenges remain in achieving multiscale integration, model validation across diverse populations, and real-time clinical applicability. The review concludes by identifying future milestones for clinical translation, including regulatory model certification, standardization of validation protocols, and integration of patient-specific digital twins into electronic health record (EHR) systems. Full article

Background: The increasing use of technologies operating between 10 and 200 kHz, such as RFID, wireless power transfer systems, and induction cooktops, raises concerns about electromagnetic interference (EMI) with cardiac implantable electronic devices (CIEDs). The mechanisms of interaction within this frequency range have been only partially addressed by both the scientific and regulatory communities. Methods: A physical twin of a pacemaker/implantable defibrillator (PM/ICD) was developed to experimentally assess voltages induced at the input stage by low-to-mid-frequency magnetic fields. The setup simulates the two sensing modalities programmable in PMs/ICDs and allows for the analysis of different implant configurations, lead geometries, and positions within a human body phantom. Results: Characterization of the physical twin demonstrated its capability to reliably measure induced voltages in the range of 5 mV to 1.5 V. Its application enabled the identification of factors beyond the implant’s induction area that contribute to the induced voltage, such as the electrode-tissue interface and body-induced currents. Conclusions: This physical twin represents a valuable tool for experimentally validating the mechanisms of EMI in CIEDs, providing insights beyond current standards. The data obtained can serve as a reference for the validation of numerical models and patient-specific digital twins. Moreover, it offers valuable information to guide future updates and revisions of international electromagnetic compatibility standards for CIEDs. Full article

Thermoplastic fiber metal laminates (FMLs) are hybrid material systems that consist of a thin aluminum alloy sheet bonded to plies of fiber-reinforced adhesive. They provide excellent properties like fatigue strength, damage-tolerant properties, and inherent resistance to corrosion. However, they are still challenging materials in terms of the metal–composite interface, which is the weakest link in this material system. In this paper, an experimental–numerical method for the determination of the fracture stress and energy for metal–composite interlayer is presented and verified. The proposed method utilizes four different experimental tests: DCB test (interface opening—mode I), ENF test (interface shearing—mode II), MMB test (mixed-mode I+II—opening with the shearing of the interface) and three-point bending test (3PB). For each test, digital twin in the form of a numerical model is prepared. The established numerical models for DCB and ENF allowed us to determine fracture stress and energy for mode I and mode II, respectively. On the basis of the numerical and experimental (from the MMB test) data, the B-K exponent is determined. Finally, the developed material model is verified in a three-point bending test, which results in mixed-mode conditions. The research is conducted on the thermoplastic FML made of aluminum alloy sheet and glass fiber reinforced polyamide 6. The research presented is complemented by fundamental mechanical tests, image processing and Scanning Electron Microscopy (SEM) analysis. As an effect, for the tested material, fracture parameters are determined using the described method. Full article

The study is focused on the application of the electronic proof of concept Digital Twin (DT) model supporting Electroencephalogram (EEG) signal detection and interpretation. The EEG DT model integrates two open source tools: a first tool used for the circuit modeling and simulation of the electrodes, and a second one implementing an Artificial Intelligence (AI)-supervised algorithm to classify and adjust a noisy EEG signal. Specifically, the DT model adopts the Random Forest (RF) AI-supervised algorithm, replacing the signal filtering process and facilitating the time–domain peak and the wave shape morphology reading of a noisy detection. In order to prove the DT’s efficacy, the RF model is trained by considering the specific case of detections of EEG of patients under the effects of alcohol. The choice of the RF algorithm is justified by its good performance parameters. For the specific dataset, the RF exhibits a probabilistic error slightly lower than that of the ANN and a better cleaning action. The goal of the paper is to provide a methodology to use ‘intelligent’ electrodes supporting EEG data processing during data acquisition and to optimize the measurement’s interpretation through a data post-processing process. The proposed EEG DT could represent an alternative to the traditional denoising signal processing approaches. Full article

Recovering copper from end-of-life electronics, especially from printed circuit boards, provides significant economic benefits, reduces environmental impact, and supports a circular economy. This case study presents a data-driven approach to predicting copper recovery in the electrolysis stage of a bioleaching process by utilizing RGB sensor readings. We tested nine regression models using RGB values from experimental data. The gradient boosting model, optimized via response surface methodology (RSM), outperformed the others, with predictions matching 84% of observed patterns. These results demonstrate strong predictive capabilities, with scope for further accuracy enhancements. We offer an open-source, web-based digital twin designed specifically to monitor the bioleaching plant, enabling real-time and historical data analysis to support predictive maintenance. Our results underscore the potential to optimize the entire bioleaching process, marking a significant advancement for large-scale copper recovery. This study is the first to investigate predictive bioleaching continuous processes in a semi-industrial e-waste plant using RGB sensors, presenting a novel approach in the field. Full article

The paper is focused on the modeling of a digital twin (DT) through a circuit simulation and artificial intelligence (AI) analysis to determine the effects of disturbances and noise in optocoupler devices integrated into programmable logic controller (PLC) systems. Specifically, the DT analyzes the parametric and the predicted simulations about the sensitivity of the optocouplers versus noise and interference to provide possible corrective actions, compensating for the distortion of the output signal. The model is structured into two main data processing steps: the first is based on the circuit simulation of the optocoupler noise coupling by highlighting the time-domain sensitivity aspects and the frequency behavior of the coupled signals; the second one estimates the predicted disturbed signal by means of supervised random forest (RF) and unsupervised K-Means algorithms to provide further elements to prevent corrective solutions by means of risk maps. This work is suitable for Industry 5.0 scenarios involving machine control supported by AI-based DT platforms. The innovative elements of the proposed model are the DT features of scalability and modularity; the spatial multidimensionality, able to couple the effects of different undesired signals; and the possibility to simulate the whole PLC system, including its control circuits. Full article

This review examines the significant influence of Digital Twins (DTs) and their variant, Digital Human Twins (DHTs), on the healthcare field. DTs represent virtual replicas that encapsulate both medical and physiological characteristics—such as tissues, organs, and biokinetic data—of patients. These virtual models facilitate a deeper understanding of disease progression and enhance the customization and optimization of treatment plans by modeling complex interactions between genetic factors and environmental influences. By establishing dynamic, bidirectional connections between the DTs of physical objects and their digital counterparts, these technologies enable real-time data exchange, thereby transforming electronic health records. Leveraging the increasing availability of extensive historical datasets from clinical trials and real-world sources, AI models can now generate comprehensive predictions of future health outcomes for specific patients in the form of AI-generated DTs. Such models can also offer insights into potential diagnoses, disease progression, and treatment responses. This remarkable progression in healthcare paves the way for precision medicine and personalized health, allowing for high-level individualized medical interventions and therapies. However, the integration of DTs into healthcare faces several challenges, including data security, accessibility, bias, and quality. Addressing these obstacles is crucial to realizing the full potential of DHTs, heralding a new era of personalized, precise, and accurate medicine. Full article

Semiconductor detectors for high-energy sensing ($X/\gamma$-rays) play a critical role in fields such as astronomy, particle physics, spectroscopy, medical imaging, and homeland security. The increasing need for precise detector characterization highlights the importance of developing advanced digital twins, which help optimize the design and performance of imaging systems. Current simulation frameworks primarily focus on modeling electron–hole pair dynamics within the semiconductor bulk after the photon absorption, leading to the current signals at the nearby electrodes. However, most simulations neglect charge diffusion and Coulomb repulsion, which spatially expand the charge cloud during propagation due to the high complexity they add to the physical models. Although these effects are relatively weak, their inclusion is essential for achieving a high-fidelity replication of real detector behavior. There are some existing methods that successfully incorporate these two phenomena with minimal computational cost, including those developed by Gatti in 1987 and by Benoit and Hamel in 2009. The present work evaluates these two approaches and proposes a novel Monte Carlo technique that offers higher accuracy in exchange for increased computational time. Our new method enables more realistic performance predictions while remaining within practical computational limits. Full article

Side-channel attacks (SCAs) are powerful techniques used to recover keys from electronic devices by exploiting various physical leakages, such as power, timing, and heat. Although heat is one of the less frequently analyzed channels due to the high noise associated with thermal traces, it poses a significant and growing threat to the security of very large-scale integrated (VLSI) microsystems, particularly system in package (SiP) technologies. Thermal side-channel attacks (TSCAs) exploit temperature variations, risking not only hardware damage from excessive heat dissipation but also enabling the extraction of sensitive data, like cryptographic keys, by observing thermal patterns. This dual threat underscores the need for a synergistic approach to thermal management and security in designing integrated microsystems. In response, this paper presents a novel approach that improves the early detection of abnormal thermal fluctuations in SiP designs, preventing cybercriminals from exploiting such anomalies to extract sensitive information for malicious purposes. Our approach employs a new concept called Thermal Digital Twin (TDT), which integrates two previously separate methods and techniques, resulting in successful outcomes. It combines the gradient direction sensor scan (GDSSCAN) to capture thermal data from the physical field programmable gate array (FPGA), which guarantees rapid thermal scan with a measurement period that could be close to $10 \mathsf{\mu }\mathrm{s},$ a resolution of ${0.5 }^{\circ }$C, and a temperature range from −${40 }^{\circ }$C to ${140 }^{\circ }$C; once the data are transmitted in real time to a Digital Twin created in COMSOL Multiphysics^® 6.0 for simulation using the Finite Element Method (FEM), the real time required by the CPU to perform all the necessary calculations can extend to several seconds or minutes. This integration allows for a detailed analysis of thermal transfer within the SiP model of our FPGA. Implementation and simulations demonstrate that the Thermal Digital Twin (TDT) approach could reduce the risks associated with TSCA by a significant percentage, thereby enhancing the security of FPGA systems against thermal threats. Full article

Digital Twins (DT) play a key role in Industry 4.0 applications, and the technology is in the process of being mature. Since its conceptualisation, it has been heavily contextualised and often misinterpreted as being merely a virtual model. Thus, it is crucial to define it clearly and have a deeper understanding of its architecture, workflow, and implementation scales. This paper reviews the notion of a Digital Twin represented in the literature and analyses different kinds of descriptions, including several definitions and architectural models. A new fit-for-all definition is proposed which describes the underlying technology without being context-specific and also overcomes the pitfalls of the existing generalised definitions. In addition, the existing three-dimensional and five-dimensional models of the DT architecture and their characteristic features are analysed. A new simplified two-branched model of DT is introduced, which retains a clear separation between the real and virtual spaces and outlines the latter based on the two key modelling approaches. This model is then extended for condition monitoring of electronic components and systems, and a hybrid approach to Prognostics and Health Management (PHM) is further elaborated on. The proposed framework, enabled by the two-branched Digital Twin model, combines the physics-of-degradation and data-driven approaches and empowers the next generation of reliability assessment methods. Finally, the benefits, challenges, and outlook of the proposed approach are also discussed. Full article