Digital Twin Research Articles

Virtual-real mapping for the scraper conveyor S-shaped bending in a fully mechanized mining face production system based on digital twin

ABSTRACT The human-centered paradigm of intelligent manufacturing is reshaping traditional production models. The fully mechanized coal mining face production system (FMCMFPS) represents a typical application of complex manufacturing systems (MS) in the coal mining industry, with an increased emphasis on the safety of personnel within the system. Among its key components, the real-time monitoring of the scraper conveyor S-shaped bending is critical to ensuring safe and efficient coal mining operations. This monitoring also aligns with the principles of Industry 5.0, which emphasize human-centric, safe and intelligent manufacturing practices. However, the underground environment is harsh, with moisture, dust and high vibrations. These conditions, along with the structure of the scraper conveyor, complicate the accurate capture of data. Issues such as data misalignment, missing information and frame retraction errors lead to inconsistencies between sensor data and actual conditions. To tackle these challenges, the paper presents a virtual-real mapping method for the scraper conveyor S-shaped bending using digital twin technology. A framework and mechanistic model for the S-shaped bending are established, and techniques like the Interquartile Range (IQR), sliding window and filtering algorithms are used to handle anomalous data. The proposed approach is validated through a prototype system at the Ulanmulun Mine in Inner Mongolia.

Biomechanics and digital twins for carbon neutral realisation in the digital economy driving smart agriculture

Biomechanics and digital twins for carbon neutral realisation in the digital economy driving smart agriculture

Artificial Neural Networks as Digital Twins for Whispering Gallery Mode Optical Sensors in Robotics Applications

This paper investigates the use of artificial neural networks (ANNs) as a viable digital twin or alternative to the typical whispering gallery mode (WGM) optical sensors in engineering systems, especially in dynamic environments like robotics. Because of its fragility and limited endurance, the WGM sensor which is based on micro-optical resonators is inappropriate in these kinds of situations. In order to address these issues, the paper suggests an ANN that is specifically designed for the system and makes use of the WGM sensor’s high-quality factor (Q-factor). By extending the applicability and endurance to dynamic contexts and reducing fragility problems, the ANN seeks to give high-resolution measurement. In order to minimize post-processing requirements and maintain system robustness, the study goal is for the ANN to function as a representative predictor of the WGM sensor output. The GUCnoid 1.0 humanoid robot is used in the paper as an example to show how the WGM optical sensors may improve humanoid robot performance for a variety of applications. The results of the experiments demonstrate that the sensitivity, precision, and resolution of ANN outputs and actual WGM shifts are equivalent. As a consequence, current obstacles to the widespread use of high-precision sensing in the robotics industry are removed, and the potential of ANNs as virtual substitutes or the digital twin for genuine WGM sensors in robotics systems is validated. So, this paper can be very beneficial not only to the sensing technologies that are used in robotics, which are subjected to the dynamic environments, but also to the industrial automation and human-machine interface.

Software Practice and Experience on Smart Mobility Digital Twin in Transportation and Automotive Industry: Toward SDV-empowered Digital Twin through EV Edge-Cloud and AutoML

Software Practice and Experience on Smart Mobility Digital Twin in Transportation and Automotive Industry: Toward SDV-empowered Digital Twin through EV Edge-Cloud and AutoML

Applications of Electrical Load Modelling in Digital Twins of Power Systems

Applications of Electrical Load Modelling in Digital Twins of Power Systems

Machine learning-driven prediction and digital twins implementation for VOC emissions in VAT photopolymerization additive manufacturing

Machine learning-driven prediction and digital twins implementation for VOC emissions in VAT photopolymerization additive manufacturing

Towards a digital twin of extrusion-based additive manufacturing: an experimentally validated numerical model of ironing process

Towards a digital twin of extrusion-based additive manufacturing: an experimentally validated numerical model of ironing process

Ten years of the digital twin brain: Perspectives and challenges

Ten years of the digital twin brain: Perspectives and challenges

State of the Art of Digital Twins in Improving Supply Chain Resilience

Background: In today’s complex and rapidly changing global markets, supply chain resilience (SCR) has become critical for businesses aiming to maintain continuity and competitive advantage. Disruptions and challenges in the supply chain will always exist; therefore, preparing in advance and improving resilience for the upcoming consequences should be the utmost important goal. Methods: Digital twins (DTs) provide a comprehensive view of product performance, enabling companies to adopt an end-to-end approach to product management. To maximize product and service value, supply chains must also be managed holistically. Results: Therefore, companies will need smarter strategies to balance inventory costs, availability and lead times. The optimal setup of suppliers, manufacturing, logistics and stock locations will ensure high service levels and meet customer expectations. Additionally, supply chains must be resilient, and capable of maintaining performance during disruptions and adapting to demand changes. Conclusions: This paper aims to provide a comprehensive review of the state of the art in digital twin applications within supply chains, focusing on their role in improving visibility, agility and decision-making. This paper explores how digital twins might integrate with emerging technologies such as IoT, AI and blockchain, fostering a more adaptive and robust supply chain ecosystem.

Knowledge Graph-Based Multi-Objective Recommendation for a 6G Air Interface: A Digital Twin Empowered Approach

In future sixth-generation (6G) communication systems, it is foreseen that complex communication scenarios and critical performance requirements will necessitate more flexible air interface configurations. Traditional air interface adaptation will no longer be applicable to 6G due to issues such as high computational complexity, sub-optimal trade-offs among multi-objective performance metrics, outdated configurations due to fast-varying channels, etc. In this paper, the relevant user behaviors, communication environment, and system are virtualized via the digital twinning technique. Then, a knowledge graph-based multi-objective recommendation framework is proposed to configure the digital twinning air interface to adapt to channel conditions, while balancing various service requirements. First, the knowledge graph is applied to reveal complex dependencies between the air interface and the service requirements, and more importantly, to reconcile possibly contradictory performance targets. Furthermore, the air interface configuration, empowered by the digital twin technique, is able to exploit predicted prior knowledge about user behavior and the channel characteristics, thus improving the utilization efficiency of wireless resources promptly. Moreover, the digital twin technique allows the candidate air interfaces to be virtually verified and compared with little effort. Finally, two case studies are presented to demonstrate the potential of the knowledge graph-based recommendation method for the digital twinning air interface.

Barriers and Challenges for Digital Twin Adoption in Healthcare Supply Chain and Operations Management

Barriers and Challenges for Digital Twin Adoption in Healthcare Supply Chain and Operations Management

DTL-GNN: Digital Twin Lightweight Method Based on Graph Neural Network

In the digital twin system of mechatronics engineering, the scale and accuracy of models are continually improving. Nevertheless, this growth can hinder real-time interaction and decision-making accuracy within digital twins. The resulting delay impacts the entire system’s reliability by reducing its response speed and real-time decision-making. Consequently, there is an imperative demand for a lightweight approach to tackle the challenges arising from the escalating scale of digital twin virtual entity models. This paper presents a digital twin methodology that is lightweight. The procedure comprises three primary phases: graph data modeling, graph neural network analysis, and hierarchical simplification of virtual entities. Specifically, the graph neural network method proposed in this article is used to classify the graph data of virtual entities. Then, the model is hierarchically simplified based on the classification. Finally, experiments were conducted on factory and robotic arm datasets to evaluate the proposed method. The experimental results indicate that the DTL-GNN method can reduce system redundancy while preserving the essential features of virtual entities.

Improving Image Matching Between Digital Twin and Real-World Using Cross-Domain Geo-Localization Techniques

Digital twins, as virtual replicas of real-world entities, have gained attention for their ability to synchronize with reality and facilitate simulations. However, inherent differences between digital twin and real-world datasets challenge traditional image-based matching methods. This paper evaluates the performance of cross-domain image geo-localization methods applied to digital twins and the real world, focusing on visual and semantic feature matching. Experimental results show that both methods perform well, with semantic feature matching outperforming visual feature matching, indicating its advantage in handling differing visual characteristics between domains. These findings confirm the potential of semantic feature matching for effective cross-domain matching between digital twins and the real world.

DIGITAL TWIN APPLICATION IN CONSTRUCTION COST MANAGEMENT

In line with the current construction revolution, it is time for the construction industry to embrace innovation and technology. This is corresponding with the National Construction Policy 2030 (NCP 2030) that comes out with the aim to digitalize the entire construction industry towards the IR 4.0. The focus is to boost the nation's construction industry's competitiveness and recognition worldwide. The construction industry has undergone a significant transformation in recent years such as BIM, IoT including Digital Twin due to the incorporation of digital technologies. A digital twin is a virtual representation of a physical asset. It is still a relatively new concept in the construction industry, but it offers an innovative method for improving cost management strategies in construction projects. Applications of the digital twin in construction cost management have the potential to revolutionize conventional methods. Therefore, this study seeks to determine the level of understanding of construction industry players on the concept of digital twin applications in construction cost management by providing the concept and to explore the challenges and strategies in implementing the digital twin applications in construction cost management. This research employed a mixed-method approach by means of questionnaire survey and interview for data collection. 35 samples that consist of construction industry players from different organizations participated in this study. The data collected from the survey and interview are analyses through descriptive and content analysis. Overall, the findings find out the understanding of digital twin applications with its challenges and strategies to overcome it. This research contributes to the body of knowledge regarding digital twin applications and construction cost management.

Integrating Social Dimensions into Urban Digital Twins: A Review and Proposed Framework for Social Digital Twins

Integrating Social Dimensions into Urban Digital Twins: A Review and Proposed Framework for Social Digital Twins

Globalization and Architecture: Urban Homogenization and Challenges for Unprotected Heritage: The Case of Postmodern Buildings with Complex Geometric Shapes in the Ensanche of San Sebastián

Globalization has profoundly impacted architecture by promoting urban homogenization, where global styles and materials overshadow local character. This shift prioritizes standardized functionality and energy efficiency over cultural identity, erasing regional architectural distinctiveness. In historical urban centers, globalization-driven interventions—such as ventilated facades or external thermal insulation systems (ETISs)—often simplify original compositions and alter building materiality, texture, and color. The Ensanche of San Sebastián serves as a case study highlighting this issue. Despite its architectural richness, which includes neoclassical and modernist buildings primarily constructed with sandstone from the Igeldo quarry, unprotected buildings are at risk of unsympathetic renovations. Such changes can distort the identity of what is considered “everyday heritage”, encompassing the residential buildings and public spaces that shape the collective memory of cities. This study presents a replicable methodology for assessing the vulnerability of buildings to facade interventions. By utilizing tools like digital twins, point cloud modeling, and typological analysis, the research establishes criteria for interventions aimed at preserving architectural values. It emphasizes the importance of collaborative efforts with urban planning authorities and public awareness campaigns to safeguard heritage. Ultimately, protecting architectural identity requires balancing the goals of energy efficiency with cultural preservation. This approach ensures that urban landscapes maintain their historical and social significance amidst globalization pressures.

Estimation of Directional Wave Spectra with Motion Data of Floating Structure Using Complex-Valued Neural Networks

This study presents the development and validation of a complex-valued neural network model to predict wave conditions—such as wave direction, height, and period—surrounding floating structures. Accurate wave predictions are crucial for optimizing the design, operation, and maintenance of offshore platforms and floating offshore wind turbines, particularly in the context of digital twins. The proposed methodology leverages motion data obtained through numerical simulations of a floating structure to train the prediction model, enabling it to predict both the amplitude and phase information of the surrounding waves. The model successfully addresses the challenges of representing wave direction data in polar coordinates and capturing phase differences between motion components, which are difficult for traditional real-valued neural networks. The performance of the model was validated through various test cases, with the maximum prediction error found to be less than 10% and most predictions showing an error of less than 5%. Wave direction predictions demonstrated high accuracy, with errors consistently below 2%. While the model was trained using pseudo-measurement data, the results suggest that high-accuracy predictions can be achieved using real-world measurement data. This work contributes to enhancing wave prediction models for floating structures and is expected to improve the safety, performance, and long-term stability of such systems.

Leveraging Digital Twins for Enhancing Building Energy Efficiency: A Literature Review of Applications, Technologies, and Challenges

Leveraging Digital Twins for Enhancing Building Energy Efficiency: A Literature Review of Applications, Technologies, and Challenges

Data-knowledge hybrid driven intelligent prediction method of tunnel excavation profiles geometric deformation

ABSTRACT Applying digital twin technology to the tunnel excavation process enables intelligent tunnel construction, which is critical for ensuring construction safety and advancing intelligent building methods. However, the complex construction environment, involving numerous environmental factors and dynamic changes, poses challenges for traditional deformation prediction and analysis methods. These methods fail to systematically integrate the elements of the excavation scene to achieve accurate prediction. Therefore, this paper proposes a data-knowledge hybrid driven intelligent prediction method of tunnel excavation profile geometric deformation. A dynamic data-driven tunnel excavation knowledge extraction method is designed, along with the construction of a multi-process coupled tunnel excavation knowledge base. Additionally, a deep learning-based excavation profile deformation prediction model is developed, integrating the data-knowledge hybrid driven method to improve prediction performance. The experimental results demonstrate that the method proposed in this paper can significantly integrate and analyse the deformation elements of the tunnel excavation scene. It achieves intelligent prediction of the tunnel excavation profile geometric deformation, with reductions of 57.46%, 76.00%, and 53.12% in MAE, MSE, and RMSE, respectively, compared with the traditional prediction model. This method effectively enhances the deformation prediction accuracy of the tunnel excavation profile twin model.

A Self-Adaptive Traffic Signal System Integrating Real-Time Vehicle Detection and License Plate Recognition for Enhanced Traffic Management

Traffic management systems play a crucial role in smart cities, especially because increasing urban populations lead to higher traffic volumes on roads. This results in increased congestion at intersections, causing delays and traffic violations. This paper proposes an adaptive traffic control and optimization system that dynamically adjusts signal timings in response to real-time traffic situations and volumes by applying machine learning algorithms to images captured through video surveillance cameras. This system is also able to capture the details of vehicles violating signals, which would be helpful for enforcing traffic rules. Benefiting from advancements in computer vision techniques, we deployed a novel real-time object detection model called YOLOv11 in order to detect vehicles and adjust the duration of green signals. Our system used Tesseract OCR for extracting license plate information, thus ensuring robust traffic monitoring and enforcement. A web-based real-time digital twin complemented the system by visualizing traffic volume and signal timings for the monitoring and optimization of traffic flow. Experimental results demonstrated that YOLOv11 achieved a better overall accuracy, namely 95.1%, and efficiency compared to previous models. The proposed solution reduces congestion and improves traffic flow across intersections while offering a scalable and cost-effective approach for smart traffic and lowering greenhouse gas emissions at the same time.