Digital Twin Framework Research Articles

Process Simulation and Optimization of Arc Welding Robot Workstation Based on Digital Twin

For the welding cell in the manufacturing process of large excavation motor arm workpieces, a system framework, based on a digital twin welding robot cell, is proposed and constructed in order to optimize the robotic collaboration process of the welding workstation with digital twin technology. For the automated welding cell, combined with the actual robotic welding process, the physical entity was digitally modeled in 3D, and the twin welding robot operating posture process beats and other data were updated in real time, through real-time interactive data drive, to achieve real-time synchronization and faithful mapping of the virtual twin as well as 3D visualization and monitoring of the system. For the robot welding process in the arc welding operation process, a mathematical model of the kinematics of the welding robot was established, and an optimization method for the placement planning of the initial welding position of the robot base was proposed, with the goal of smooth operation of the robot arm joints, which assist in the process simulation verification of the welding process through the virtual twin scenario. The implementation and validation process of welding process optimization, based on this digital twin framework, is introduced with a moving arm robot welding example.

A digital twin framework to support vehicle interaction risk management in the mining industry

In recent years, transport-related accidents, notably those involving trackless mobile machinery (TMM), have consistently ranked among the top three causes of fatalities and injuries in the South African mining industry (SAMI) [1]. These accidents arise from a combination of mechanical and technical malfunctions, environmental factors, and human or machine operator errors. Remarkably, these incidents persist despite the existence of specific regulations, standards, and codes of practice for transportation and machinery. This paper introduces a digital twin framework for TMM, which employs a systems engineering approach combined with software tools and computational analysis. This framework aims to enhance the current regulations by offering a continuous, quantitative risk assessment. By modelling and detecting non-conformance and adverse vehicle interaction events, the framework provides a quantitative risk analysis that complements the prevailing qualitative methods reliant on historical data and operational experience. A case study conducted at the CSIR main campus in Pretoria showcases the potential of the TMM Digital Twin.

An ontology to integrate process-based approach in ZDM strategies in a Digital Twin framework

The current context of Industry 4.0 is characterized by challenges that were not present in the past like greater variability, higher customization and greater complexity. To address these challenges and the increasing need from companies to focus on sustainability-related issues is necessary to adopt a quality improvement method. In this paper, the method considered is Zero Defect Manufacturing (ZDM) a “tool” which shows considerable potential, but needs some auxiliary technologies to operate. In this regard, the model proposed is an ontology based on a pre-existent ontology. This new ontology is capable of applying Detect and Repair strategies to go with the creation of a Digital Twin of the product. The realized ontology can be used to support decision-making in an industrial context: in fact, it provides to any operator in the production process, an indication about the quality of the product, also advising some corrective actions if needed, like the repair or the disassembly of the product and the subsequent recycling of the good quality components. To obtain this outcome, an analysis of the literature was performed to determine the gaps present in the literature, then an ontology editor allowed the creation of the ontology and, finally, the ontology was validated in the context of Industry 4.0 Laboratory at the Politecnico di Milano. In this environment, the proposed solution was populated with the data coming from the servers, determining the quality of the product as a function of the state of product components and the condition of one of the assets installed in the production line.

Development of Digital Twin of a Compact Bulk Feeder to Optimise its Functionality

Increased international competition is intensifying pressure on companies such as Atlas Copco to improve the performance of their products; for example, the Compact Bulk Feeder (CBF) element of the Company's ‘Self Pierced Riveting Machine’. Digital technologies are advancing rapidly to support the development of new products and processes. This has encouraged Atlas Copco to exploit digitalisation to improve the CBF's functionality and enhance the product's competitiveness in the market. This paper is presenting the results of research of modelling the digital twin of the CBF to enhance its functionalities and operation. A digital twin framework is presented using suitable hardware and software technologies as well as the results of the modelling from physical to the virtual world of two of the CBF functions.

Machine Learning Agents Augmented by Digital Twinning for Smart Production Scheduling

Digital manufacturing tools aim to provide intelligent solutions that can support manufacturing industry to adapt to the volatile operational environment. The successful implementation of such tools highly depends on the capabilities of the digital frameworks or platforms they are deployed upon as well as the quality of their intelligence. The objective of this work is to develop and discuss a framework for training and deploying Machine Learning (ML) agents for production scheduling with the augmentation of Digital Twin (DT) technologies. Two types of ML production scheduling agents have been developed and integrated with the DT framework: a Deep Learning agent and a Deep Reinforcement Learning agent. In order to increase interoperability, Asset Administration Shell Industry4.0 standard has been utilized for the integration and deployment of the proposed DT framework into industrial practice. The proposed framework is tested and validated upon an industrial case study from the bicycles’ production industry.

Digital Twins towards Microwave-assisted 3D Printing of Continuous Carbon Fiber Reinforced Composites

Microwave-assisted 3D printing based on the fused filament fabrication (FFF) method is an emerging technology to print lightweight continuous carbon fiber reinforced thermoplastics (CCFRP) at high speed. Different from traditional FFF, microwave offers selective and volumetric heating properties to melt thermoplastic materials instantaneously, and the microwave printing head and nozzle remain at room temperature. These advantages can increase the printing speed of CCFRP significantly, while the belt slippage of a printing bed is noticed during microwave-assisted 3D printing. The slippage of the moving belt happens because the cold nozzle moves at high speed and encounters resistance from the rough surface of the printed filament. To solve this problem, this paper presents a hierarchical digital twin (DT) framework, consisting of core and basic DTs, for the prevention of belt slippage induced printing malfunction. The core DTs use MATLAB Simscape multibody models to simulate the printing process virtually and the printing G-code is corrected before printing. In addition, an accelerometer installed on the printing bed is connected to the core DTs. Abnormal vibration signals due to belt slippage can be measured and communicated with core DTs for interrupting and correcting the process. By monitoring the temperature of the heated filament and the output microwave power, the service life of the nozzle and microwave cable are evaluated in the basic DTs.

Holistic Digital Twin Framework: Designing Human-Machine Systems with an Overall Situation Awareness

Context Awareness refers to the use of context to provide task-relevant information and/or services to a user. The Holistic Framework for Human-Machine System (HMS) is an architecture based on multiple factors, including a surroundings consideration, to design Digital Twins (DTs). This paper discusses some of the research challenges in understanding the context and developing context-aware DT systems. In particular, the authors gave some solutions that can improve Human-Machines Systems (HMS), such as the use of human data, meta-data and peri-data or the definition of Capacity, Availability, Possibility levels. Finally, a critical discussion is brought to caution future designs of systems such as HMS in a high risk context.

Digital Twin Framework for Reconfiguration Management: Concept & Evaluation

Digital Twin Framework for Reconfiguration Management: Concept & Evaluation

State Perception and Prediction of Digital Twin Based on Proxy Model

The maintenance of critical components plays a crucial role in ensuring the overall stable operation of equipment and minimizing damages caused by functional errors. However, Traditional operation and maintenance (O&M) modes suffer from problems such as reliance on empirical judgment, lack of data support, insufficient preventive maintenance, and inadequate collaborative management. To address these issues, a viable approach is to adopt more intelligent O&M modes. Based on the characteristics of digital twin technology, such as virtual interaction and real-time feedback, a digital twin framework for critical component maintenance of equipment is proposed, providing a new approach for the practical application of digital twin in intelligent maintenance processes. This framework consists of two key components: the digital twin maintenance model and the proxy model. The process of establishing the digital twin model is elaborated in detail, and a mechanism that integrates digital twin technology and the proxy model is proposed, along with a prediction process based on the fusion of simulation and monitoring data. Finally, based on the summary of the modeling process and the proxy model, a visualization interface for intelligent maintenance of components is built using relevant engineering software.

Digital twins for electro-physical, chemical, and photonic processes

Manufacturing processes are becoming increasingly data-driven. Integrating manufacturing data and process models in real-time, a digital twin (DT) may function as an autonomous and dynamic digital replica. This, in turn, may enable manufacturers to not only understand and monitor a process but also proactively control it in real-time or a product over its life cycle. This paper examines the DT concept and its evolution and presents a future DT framework. DTs’ key components (e.g., process models) and implementation are focused on additive manufacturing, electrical discharge machining, and electrochemical machining. Furthermore, current challenges and future research directions are summarized.

Digital Twin (DT) Smart City for Air Quality Management

Air pollution is a pressing issue in cities, and managing air quality poses a challenge for urban designers and decision-makers. This study proposes a Digital Twin (DT) Smart City integrated with Mixed Reality technology to enhance visualization and collaboration for addressing urban air pollution. The research adopts an applied research approach, with a focus on developing a DT framework. A use case of DT development for Jakarta, the capital of Indonesia, is presented. By integrating air quality data, meteorological information, traffic patterns, and urban infrastructure data, the DT provides a comprehensive understanding of air pollution dynamics. The visualization capabilities of the DT, utilizing Mixed Reality technology, facilitate effective decision-making and the identification of strategies for managing air quality. However, further research is needed to address data management challenges to build a DT for Smart City at scale.

Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildings

Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance of buildings in terms of occupant comfort is evaluated using a probabilistic model based on Bayesian networks (BNs). The BN model is founded on an in-depth analysis of satisfaction survey responses and a thorough study of building performance parameters. This study also presents a user-friendly visualization compatible with BIM to simplify data collecting in two case studies from Norway with data from 2019 to 2022. This paper proposes a novel Digital Twin approach for incorporating building information modeling (BIM) with real-time sensor data, occupants’ feedback, a probabilistic model of occupants’ comfort, and HVAC faults detection and prediction that may affect occupants’ comfort. New methods for using BIM as a visualization platform, as well as a predictive maintenance method to detect and anticipate problems in the HVAC system, are also presented. These methods will help decision-makers improve the occupants’ comfort conditions in buildings. However, due to the intricate interaction between numerous equipment and the absence of data integration among FM systems, CMMS, BMS, and BIM data are integrated in this paper into a framework utilizing ontology graphs to generalize the Digital Twin framework so it can be applied to many buildings. The results of this study can aid decision-makers in the facility management sector by offering insight into the aspects that influence occupant comfort, speeding up the process of identifying equipment malfunctions, and pointing toward possible solutions.

Beyond digital shadows: A Digital Twin for monitoring earthwork operation in large infrastructure projects

Current research on Digital Twin (DT) is largely focused on the performance of built assets in their operational phases as well as on urban environment. However, Digital Twin has not been given enough attention to construction phases, for which this paper proposes a Digital Twin framework for the construction phase, develops a DT prototype and tests it for the use case of measuring the productivity and monitoring of earthwork operation. The DT framework and its prototype are underpinned by the principles of versatility, scalability, usability and automation to enable the DT to fulfil the requirements of large-sized earthwork projects and the dynamic nature of their operation. Cloud computing and dashboard visualisation were deployed to enable automated and repeatable data pipelines and data analytics at scale and to provide insights in near-real time. The testing of the DT prototype in a motorway project in the Northeast of England successfully demonstrated its ability to produce key insights by using the following approaches: (i) To predict equipment utilisation ratios and productivities; (ii) To detect the percentage of time spent on different tasks (i.e., loading, hauling, dumping, returning or idling), the distance travelled by equipment over time and the speed distribution; and (iii) To visualise certain earthwork operations.

Digital Twin for a Collaborative Painting Robot

A collaborative painting robot that can be used as an alternative to workers has been developed using a digital twin framework and its performance was demonstrated experimentally. The digital twin of the automatic painting robot simulates the entire process and estimates the paint result before the real execution. An operator can view the simulated process and result with an option to either confirm or cancel the task. If the task is accepted, the digital twin generates all the parameters, including the end effector trajectory of the robot, the material flow to the collaborative robot, and a spray mechanism. This ability means that the painting process can be practiced in a virtual environment to decrease set costs, waste, and time, all of which are highly demanded in single-item production. In this study, the screen was fixtureless and, thus, a camera was used to capture it in a physical environment, which was further analyzed to determine its pose. The digital twin then builds the screen in real-time in a virtual environment. The communication between the physical and digital twins is bidirectional in this scenario. An operator can design a painting pattern, such as a basic shape and/or letter, along with its size and paint location, in the resulting procedure. The digital twin then generates the simulation and expected painting result using the physical twin's screen pose. The painting results show that the root mean square error (RMSE) of the painting is less than 1.5 mm and the standard deviation of RMSE is less than 0.85 mm. Additionally, the initial benefits of the technique include lower setup costs, waste, and time, as well as an easy-to-use operating procedure. More benefits are expected from the digital twin framework, such as the ability of the digital twin to (1) find a solution when a fault arises, (2) refine the control or optimize the operation, and (3) plan using historic data.

Cloud-based process design in a digital twin framework with integrated and coupled technology models for blisk milling

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.

Development and validation of a digital twin framework for SMT manufacturing

Electronics manufacturing is a global industry and key to innovation in the virtual world because it is the physical backbone. The cost-effective development and manufacture of such electronic modules are critical to maintaining the competitiveness of high-wage manufacturing countries. Therefore, two approaches suggest themselves: Design for Manufacturing (DfM) and manufacturing optimization. To apply both approaches in industry, it is necessary to introduce models that describe the relationship between process input and process output. Applied to electronics manufacturing processes, this means that design, process parameters, and material properties must be mapped to process quality criteria in end-of-line testing. Recently, machine learning (ML) algorithms have been emerging and dominating other modeling methods such as numerical simulation. To develop such complex ML models, a unified data structure for each input and output must be defined. This paper proposes an extensible ML-enabled framework that provides direct and structured access to the printed circuit board (PCB) design and process parameters. This framework is used to perform structured data acquisition using a custom data mining board. During the manufacturing of these PCBs on a full surface mount technology (SMT) process line, all available process machine-level data is collected, archived, and parsed into a uniform, standardized, and flat data structure. This enables fast analysis of the correlation between inputs and quality criteria, direct access by ML algorithms, and training of models. Through these measures, the quality of the framework is validated and correlations are revealed and compared to previous literature. This shows the great importance of a data framework for the integration of data analysis technologies into industrial processes.

A digital twin framework for large comprehensive ports and a case study of Qingdao Port

A digital twin framework for large comprehensive ports and a case study of Qingdao Port

A digital twin based framework for detection, diagnosis, and improvement of throughput bottlenecks

Digitalization through Industry 4.0 technologies is one of the essential steps for the complete collaboration, communication, and integration of heterogeneous resources in a manufacturing organization towards improving manufacturing performance. One of the ways is to measure the effective utilization of critical resources, also known as bottlenecks. Finding such critical resources in a manufacturing system has been a significant focus of manufacturing research for several decades. However, finding a bottleneck in a complex manufacturing system is difficult due to the interdependencies and interactions of many resources. In this work, a digital twin framework is developed to detect, diagnose, and improve bottleneck resources using utilization-based bottleneck analysis, process mining, and diagnostic analytics. Unlike existing bottleneck detection methods, this novel approach is capable of directly utilizing enterprise data from multiple levels, namely production planning, process execution, and asset monitoring, to generate event-log which can be fed into a digital twin. This enables not only the detection and diagnosis of bottleneck resources, but also validation of various what-if improvement scenarios. The digital twin itself is generated through process mining techniques, which can extract the main process map from a complex system. The results show that the utilization can detect both sole and shifting bottlenecks in a complex manufacturing system. Diagnosing and managing bottleneck resources through the proposed approach yielded a minimum throughput improvement of 10% in a real factory setting. The concept of a custom digital twin for a specific context and goal opens many new possibilities for studying the strong interaction of multi-source data and decision-making in a manufacturing system. This methodology also has the potential to be exploited for multi-objective optimization of bottleneck resources.

A digital twin of electrical tomography for quantitative multiphase flow imaging

Multiphase flow is ubiquitous in nature, industry and research, and accurate flow imaging is critical to understanding this complex phenomenon. Electrical tomography (ET) is a promising technique for multiphase flow visualization and characterization which provides a non-invasive and non-radiative way to unravel the internal physical properties at high temporal resolution. However, existing ET-based multiphase flow imaging methods are inadequate for quantitative imaging of multiphase flows due to inversion errors and limited ground truth data of fluid phases distribution. Here we report a digital twin (DT) framework of ET to address the challenges of real-time quantitative multiphase flow imaging. The proposed DT framework, building upon a synergistic integration of 3D field coupling simulation, model-based deep learning, and edge computing, allows ET to dynamically learn the flow features in the virtual space and implement the model in the physical system, thus providing excellent resolution and accuracy. The proposed DT framework is demonstrated using electrical capacitance tomography (ECT) of a gas-liquid two-phase flow. It can be readily extended to a broader range of tomography modalities, scenarios, and scales in biomedical, energy, and aerospace applications.

Empowering digital twins with eXtended reality collaborations

BackgroundThe advancements of Artificial Intelligence, Big Data Analytics, and the Internet of Things paved the path to the emergence and use of Digital Twins (DTs) as technologies to “twin” the life of a physical entity in different fields, ranging from industry to healthcare. At the same time, the advent of eXtended Reality (XR) in industrial and consumer electronics has provided novel paradigms that may be put to good use to visualize and interact with DTs. XR technologies can support human-to-human interactions for training and remote assistance and could transform DTs into collaborative intelligence tools. MethodsWe here present the Human Collaborative Intelligence empowered Digital Twin framework (HCLINT-DT) integrating human annotations (e.g., textual and vocal) to allow the creation of an all-in-one-place resource to preserve such knowledge. This framework could be adopted in many fields, supporting users to learn how to carry out an unknown process or explore others’ past experiences. ResultsThe assessment of such a framework has involved implementing a DT supporting human annotations, reflected in both the physical world (Augmented Reality) and the virtual one (Virtual Reality). ConclusionsThe outcomes of the interface design assessment confirm the interest in developing HCLINT-DT-based applications. Finally, we evaluated how the proposed framework could be translated into a manufacturing context.