A Modular, Logistics-Centric Digital Twin Framework for Construction: From Concept to Prototype

This paper proposes a digital twin (DT) framework for point source applications in environmental sensing (ES). The DT concept has become quite popular among process and manufacturing industries for improving performance and estimating remaining useful life (RUL). However, environmental behavior, such as in gas dispersion, is ever changing and hard to model in real-time. The DT framework is applied to the point source environmental monitoring problem, through the use of hybrid modeling and optimization techniques. A controlled release case study is overviewed to illustrate our proposed DT framework and several spatial interpolation techniques are explored for source estimation. Future research efforts and directions are discussed.

This paper explores developing and implementing a Digital Twin (DT) framework for real-time optimization and process enhancement in energy operations. The DT concept, which originated in manufacturing and has gained significant traction across industries, is particularly valuable in the context of energy systems. By providing a dynamic digital representation of physical assets, the DT framework enables continuous monitoring, predictive analytics, and decision support, enhancing operational efficiency, reducing costs, and improving system reliability. This paper outlines conceptualizing a DT framework tailored for energy operations, integrating it with existing infrastructure such as smart grids, IoT devices, and renewable energy systems. The study delves into real-time data integration, optimization techniques, and modeling methods that empower the framework to simulate various operational scenarios and optimize system performance. It also discusses challenges such as data quality, scalability, and system integration, proposing solutions for overcoming these obstacles. The proposed framework is compared with traditional optimization methods to demonstrate its advantages, including improved energy efficiency, reduced downtime, and enhanced resilience. Finally, the paper offers recommendations for implementing the framework and directions for future research to further enhance its capabilities through advancements in AI, machine learning, and augmented reality. The integration of DT technology in energy operations represents a transformative step toward more sustainable and efficient energy management.

We introduce a novel digital twin (DT) framework for the predictive maintenance of long-term physical systems. Using monitoring tire health as an application, we show how the DT framework can be used to enhance automotive safety and efficiency, and how the technical challenges can be overcome using a three-step approach. First, to manage the data complexity over a long operation span, we employ data reduction techniques to concisely represent physical tires using historical performance and usage data. Relying on these data, for fast real-time prediction, we train a transformer-based model offline on our concise dataset to predict future tire health over time, represented as remaining casing potential (RCP). Based on our architecture, our model quantifies both epistemic and aleatoric uncertainties, providing reliable confidence intervals around predicted RCP. Second, to incorporate real-time data, we update the predictive model in the DT framework, ensuring its accuracy throughout its lifespan with the aid of hybrid modeling and the use of the discrepancy function. Third, to assist decision-making in predictive maintenance, we implement a tire state decision algorithm, which strategically determines the optimal timing for tire replacement based on RCP forecasted by our transformer model. This approach ensures that our DT accurately predicts system health, continually refines its digital representation, and supports predictive maintenance decisions. Our framework effectively embodies a physical system, leveraging big data and machine learning (ML) for predictive maintenance, model updates, and decision-making.

The Fourth Industrial Revolution drives industries from traditional manufacturing to the smart manufacturing approach. In this transformation, existing equipment, processes, or devices are retrofitted with some sensors and other cyber-physical systems (CPS), and adapted towards digital production, which is a blend of critical enabling technologies. In the current scenario of Industry 4.0, industries are shaping themselves towards the development of customized and cost-effective processes to satisfy customer needs with the aid of a digital twin framework, which enables the user to monitor, simulate, control, optimize, and identify defects and trends within, ongoing process, and reduces the chances of human prone errors. This paper intends to make an appraisal of the literature on the digital twin (DT) framework in the domain of smart manufacturing with the aid of critical enabling technologies such as data-driven systems, machine learning and artificial intelligence, and deep learning. This paper also focuses on the concept, evolution, and background of digital twin and the benefits and challenges involved in its implementation. The Scopus and Web of Science databases from 2016 to 2021 were considered for the bibliometric analysis and used to study and analyze the articles that fall within the research theme. For the systematic bibliometric analysis, a novel approach known as Proknow-C was employed, including a series of procedures for article selection and filtration from the existing databases to get the most appropriate articles aligned with the research theme. Additionally, the authors performed statistical and network analyses on the articles within the research theme to identify the most prominent research areas, journal/conference, and authors in the field of a digital twin. This study identifies the current scenarios, possible research gaps, challenges in implementing DT, case studies and future research goals within the research theme.

Towards a digital twin framework in additive manufacturing: Machine learning and bayesian optimization for time series process optimization

Saltwater intrusion (SWI) poses a significant environmental challenge for coastal aquifers in Pacific Island nations, including Port Vila, Vanuatu. This study utilised a 3D numerical simulation model to evaluate SWI in the Tagabe coastal aquifer under current pumping regimes. To address SWI, optimal pumping patterns were identified through machine learning-based surrogate ensemble models and a simulation-optimisation (S–O) management model. A digital twin (DT) framework of the Tagabe coastal aquifer was developed, incorporating a 3D numerical model, surrogate ensemble models, and the S–O approach. The DT framework, linked with illustrative field data, was used to generate and analyse five illustrative scenarios based on varying salt concentrations (0.45, 0.55, 0.75, 0.90, and 1.15 kg/m3; Scenarios 1 to 5, respectively). The results indicated that scenario 3 (salt concentration of 0.75 kg/m3) led to the highest pumping rates from production wells (17,317 m3/d) and the lowest from barrier wells (202 m3/d), while scenario 5 showed maximum pumping of 31,676 m3/d from production wells and 5000 m3/d from barrier wells. The S–O model results were validated with less than 10% relative error compared to the numerical model outputs. To the author’s best knowledge, the application of DT in managing SWI has not been applied before. This study is the first to apply a DT framework for managing SWI in coastal aquifers, showcasing its potential for predicting future scenarios and optimising water management strategies. The results from the study indicated that DT can be successfully employed in a coastal aquifer for managing the SWI. The methodology developed and implemented in this study is of global significance and could be used to manage water resources wisely. The study demonstrated that with the help of the S–O approach, the DT is vital in predicting future scenarios, changes in pumping patterns, and other uncertainties.

4SQR-Code: A 4-state QR code generation model for increasing data storing capacity in the Digital Twin framework

In this publication, the application of an implemented Digital Twin (DT) framework is presented by orchestration of CAM-integrated and containerized technology models carrying out FEM-coupled simulations for the finishing process of a simplified blade integrated disk (blisk) demonstrator. As a case study, the continuous acquisition, processing and usage of virtual process planning and simulation data as well as real machine and sensor data along the value chain is presented. The use case demonstrates the successful application of the underlying DT framework implementation for the prediction of the continuously changing dynamic behavior of the workpiece and according stable spindle speeds in the process planning phase as well as their validation in the actual manufacturing phase.

Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement

The development of a digital twin (DT) framework for fast online adaptive proton therapy planning in prostate stereotactic body radiation therapy (SBRT) with dominant intraprostatic lesion (DIL) boost represents a significant advancement in personalized radiotherapy. This framework integrates deep learning-based multi-atlas deformable image registration, daily patient anatomy updates via cone-beam CT (CBCT), and knowledge-based plan quality evaluation using the ProKnow scoring system to achieve clinical-equivalent plan quality with substantially reduced reoptimization times compared to traditional clinical workflows. Drawing on a database of 43 prior prostate SBRT cases, the DT framework predicts interfractional anatomical variations for new patients and pre-generates multiple probabilistic treatment plans. Upon acquiring daily CBCT, it enables rapid plan reoptimization, achieving an average reoptimization time of 5.5 [2.8, 8.2] minutes, compared to 19.8 [7.9, 31.7] minutes for clinical plans. The DT-based plans yielded a plan quality score of 157.2 [151.6, 162.8], surpassing or matching clinical plans, with superior dose coverage for the DIL (V100: 99.5%) and clinical target volume (CTV V100: 99.8%). Additionally, the framework minimized doses to organs at risk (OARs), achieving bladder V20.8Gy of 11.4 [7.2, 15.6] cc, rectum V23Gy of 0.7 [0.3, 1.1] cc, and urethra D10 of 90.9% [88.6%, 93.2%], aligning with clinical standards. By addressing interfractional variations efficiently, the DT framework enhances treatment precision, reduces OAR toxicity, and supports real-time adaptive radiotherapy. This transformative approach not only streamlines the planning process but also improves clinical outcomes, offering a scalable solution for prostate SBRT with DIL boost and paving the way for broader applications in adaptive proton therapy.

Applying methods such as deep learning improves the efficiency of bearing fault diagnosis and reduces trains’ operation and maintenance costs. However, in practical applications, the deficiency of historical data and the imbalance of data types often limit the effectiveness of the diagnosis. The variability between operating conditions also restricts the availability of transfer learning including domain adaptation. To address this challenge, a digital twin (DT) framework is established to fill the data for train fault diagnosis. A train bearing dynamics model is optimized using virtual-reality mapping in the DT framework with measured health data as a baseline to generate data closer to reality. Finally, the fault diagnosis uses a hybrid dataset that mixes measured and simulated data as a source domain for transfer learning. The Case Western Reserve University dataset is used as an example, and the accuracy reaches up to 99.40%, which verifies the method’s effectiveness.

We present our recent development of an integrated mesoscale digital twin (DT) framework for relating processing conditions, microstructures, and mechanical responses of additively manufactured (AM) metals. In particular, focusing on the laser powder bed fusion technique, we describe how individual modeling and simulation capabilities are coupled to investigate and control AM microstructural features at multiple length and time scales. We review our prior case studies that demonstrate the integrated modeling schemes, in which high-fidelity melt pool dynamics simulations provide accurate local thermal profiles and histories to subsequent AM microstructure simulations. We also report our new mechanical response modeling results for predicted AM microstructures. In addition, we illustrate how our DT framework has been validated through modeling–experiment integration, as well as how it has been practically utilized to guide and analyze AM experiments. Finally, we share our perspectives on future directions of further development of the DT framework for more efficient, accurate predictions and wider ranges of applications.

Digital twin-driven framework for fatigue life prediction of welded structures considering residual stress

With the rapid development of communication networks, the complexity, heterogeneity, dynamic change and customization of networks bring unprecedented challenges to intelligent network management. To help address such challenges, we propose a digital twin (DT) framework to map the physical networks (e.g., 5G networks, campus networks) to the digital space, and to orchestrate the digital models to establish a complete digital network system. In addition, a set of control strategies is presented to help make the network system operate intelligently within this DT framework. Furthermore, we present a case study of Access Point (AP) channel planning to illustrate the process of using our proposed DT framework to manage a WLAN network.

An ultrasonic guided wave-based structural health monitoring system has potential applications in mainy domains such as, the oil and gas industry, civil engineering, and aerospace. However, there are some inherent challenges, such as the sensitivity of the Guided Waves (GW) to environmental and operational conditions (EOCs), defect(s) size and location, and sensor(s) placement. Therefore, the reliability of detection systems based on GW requires validation. Simulation tools are often used to study the impact of the above-mentioned factors. However, the computational burden associated to extensive simulation campaigns is excessive. To increase the computational efficiency, this work proposes a machine learning-based Digital Twin (DT) framework. More specifically, the DT framework employed in this paper comprises a linear dimensionality reduction algorithm and fully connected neural networks that work as a metamodel. The performance of the DT is evaluated on a simulation configuration of Aluminum with uncertainties in instrumentation and damage size. The simulation data required for training are obtained from CIVA simulation platform. The predicted signals from the DT are quantified using misfit-based criteria targeting amplitude and phase aspects based on time-frequency transformation. The assessment of the results suggests that DT has captured all the dynamics of the signals, and the predicted signals are in good agreement with the simulated ones. Furthermore, the developed DT has been employed to efficiently carry out the probability of detection study for reliability assessment and sensitivity analysis based on the propagation of uncertainties.