Twin System Research Articles

Digital Twin System of Pest Management Driven by Data and Model Fusion

Protecting crops from pests is a major issue in the current agricultural production system. The agricultural digital twin system, as an emerging product of modern agricultural development, can effectively achieve intelligent control of pest management systems. In response to the current problems of heavy use of pesticides in pest management and over-reliance on managers’ personal experience with pepper plants, this paper proposes a digital twin system that monitors changes in aphid populations, enabling timely and effective pest control interventions. The digital twin system is developed for pest management driven by data and model fusion. First, a digital twin framework is presented to manage insect pests in the whole process of crop growth. Then, a digital twin model is established to predict the number of pests based on the random forest algorithm optimized by the genetic algorithm; a pest control intervention based on a twin data search strategy is designed and the decision optimization of pest management is conducted. Finally, a case study is carried out to verify the feasibility of the system for the growth state of pepper and pepper pests. The experimental results show that the virtual and real interactive feedback of the pepper aphid management system is achieved. It can obtain prediction accuracy of 88.01% with the training set and prediction accuracy of 85.73% with the test set. The application of the prediction model to the decision-making objective function can improve economic efficiency by more than 20%. In addition, the proposed approach is superior to the manual regulatory method in pest management. This system prioritizes detecting population trends over precise species identification, providing a practical tool for integrated pest management (IPM).

Research on Key Technology of Wind Turbine Drive Train Fault Diagnosis System Based on Digital Twin

Fault diagnosis of wind turbines has always been a challenging problem due to their complexity and harsh working conditions. Although data-mining-based fault diagnosis methods can accurately and efficiently diagnose potential faults, the visibility is extremely poor. In this paper, digital twin technology is introduced into the fault diagnosis of wind turbine drive train systems, and a wind turbine drive train fault diagnosis method based on digital twin technology is proposed, which monitors and simulates the actual operating condition in real-time by establishing a digital twin model of the wind turbine drive train. In addition, an improved variational modal decomposition combined with particle swarm optimization least squares support vector machine (IVMD-PSO-LSSVM) fault diagnosis method is proposed, which not only improves the accuracy of fault diagnosis but also effectively shortens the diagnosis time and strengthens the response speed of the system. Finally, a digital twin system for condition monitoring and fault diagnosis of wind turbine drive trains is developed based on the Unity 3D platform. Experiments show that the proposed IVMD-PSO-LSSVM can accurately identify fault types with an accuracy rate of 99.1%, which is an improvement of 3.4% compared to before. The proposed digital twin model can be used for real-time monitoring of wind turbine vibration data and provide a more intuitive real-time simulation of the wind turbine’s operating status. This facilitates quick fault location and enables more accurate and efficient maintenance.

Digital twin system framework and information model for industry chain based on industrial Internet

Digital twin system framework and information model for industry chain based on industrial Internet

Expert Twin: A Digital Twin with an Integrated Fuzzy-Based Decision-Making Module

Digitalization and the application of modern Industry 4.0 solutions are becoming increasingly important to remain competitive as product ranges expand and global supply chains grow. This paper presents a new Digital Twin framework to achieve robustness in manufacturing process optimization and enhance the efficiency of decision support. Most digital twins in the literature synchronously represent the real system without any control elements despite the bidirectional data link. The proposed approach combines the advantages of traditional process simulations with a real-time communication and data acquisition method using programmable logic controllers designed to control automated systems. In addition, it complements this by utilizing human experience and expertise in modeling using Fuzzy Logic to create a control-enabled digital twin system. The resulting "Expert Twin" system reduces the reaction time of the production to unexpected events and increases the efficiency of decision support; it generates and selects alternatives, therefore creating smart manufacturing. The Expert Twin framework was integrated, tested, and validated on an automated production sample system in a laboratory environment. In the experimental scenarios carried out, the method production increased production line utility by up to 28% and the number of re-schedules can be halved.

Comprehensive Application Analysis of Digital Twin System in Human Robot Collaboration

Comprehensive Application Analysis of Digital Twin System in Human Robot Collaboration

Reorientation induced plasticity in full αʺ martensite Ti-9V-1Fe-4Al alloy

The microstructure and deformation mechanism of a full orthorhombic αʺ martensite Ti-9V-1Fe-4Al (wt.%) alloy were investigated by XRD, TEM, EBSD analyses and tensile tests. The results show that the quenched alloy has a twinned-martensite microstructure and {111}αʺ type I internal twins are formed as the lattice invariant shear for martensite transformation. The alloy plastically deforms by a complex reorientation of αʺ martensite variants via the movement of the inter-variant boundaries of <211>α′′ type Ⅱ twin, and deformation twinning of {111}α′′ type I internal twin and of newly generated {011}αʺ compound twin in primary twinned martensites. {130}α′′<3¯ 10>α′′ and {110}α′′<1 1‾ 0>α′′ plastic twins were not observed in the alloy. Such a kind of deformation induced martensite reorientation results in a good combination of strength and plasticity with yielding strength of 480 MPa, tensile strength of 1030 MPa and elongation of 23% in the alloy. The calculated results further show that the shears of {111}α′′ type I, <211>α′′ type II and {011}α′′ compound twins and the shuffle of {111}α′′ type I twin increase, while the shears of {130}α′′<3‾ 10>α′′ and {110}α′′<1 1‾ 0>α′′ twins and the shuffle of {130}α′′<3‾ 10>α′′ twin decrease with an increase of the lattice parameter ratio bα′′/aα′′. The shears of these activated twinning systems are larger than those of {130}α′′<3‾ 10>α′′ and {110}α′′<1 1‾ 0>α′′ twins, and the shuffle of {111}α′′ type I twin is also bigger than that of {130}α′′<3‾ 10>α′′ twin in the present alloy, which implies that the operative twining modes cannot be derived simply in terms of the shear and shuffle magnitude in full αʺ martensite Ti based alloys.

Blockchain empowered access control for digital twin system with attribute-based encryption

Digital twin is a pivotal and burgeoning technique that plays a crucial role in the realms of digital transformation and intelligent advancement. To bolster diverse applications and realize digital transformation, it is imperative to share the generated device data among multiple stakeholders involved in the digital twin system product life cycle. Since the device data contains sensitive and secret information, strict access control is required. Attribute-based encryption (ABE) is commonly employed for the construction of a cryptographic-enforced data access control scheme. ABE enables data confidentiality and one-to-many fine-grained access control due to its flexibility and high scalability. Unfortunately, traditional ABE schemes adopted in digital twin system has been hindered by the single point of failure and trust problems. Blockchain is a secure distributed ledger technology with decentralization and tamper-proofing properties. In this article, we present a blockchain-empowered data access control scheme for digital twin system with attribute-based encryption. Firstly, blockchain is employed to construct a complete, tamper-resistant, and efficient architecture for digital twin system to address challenges of access control. Subsequently, to ensure the confidentiality of digital twin data, we employ ciphertext-policy ABE (CP-ABE) to achieve fine-grained and one-to-many access control. Finally, we combine blockchain-based architecture with the interplanetary file system (IPFS) to propose a data storage scheme to release storage pressure on the blockchain and improve system efficiency. Security analysis and performance evaluation exhibit that the proposed access control scheme can effectively protect the data security of digital twin system with efficient storage.

Early-warning of unsafe hoisting operations: An integration of digital twin and knowledge graph

Unsafe hoisting operations have been consistently associated with numerous safety incidents involving tower cranes. Currently, the predominant measures to mitigate these operations center around comprehensive training and education, emphasizing standardized protocols prior to hoisting activities. Despite concerted efforts in this direction, a conspicuous research gap persists in early-warning mechanisms during the construction phase. This paper aims to address this gap by proposing an innovative early-warning methodology, inspired by the principles of digital twin and knowledge graph. We firstly introduce a digital twin framework designed to mirror the real-time operational status of the tower crane. This framework enables the immediate detection of deviations or infractions as they occur. Subsequently, we develop a knowledge graph capable of promptly identifying unsafe hoisting operations by leveraging real-time data obtained from the digital twin. To validate the efficacy of our proposed methodology, we construct a scaled-down replica of a tower crane and establish a tailored digital twin system. The findings of a series of experimental trials prominently underscore the system's capability to generate timely alerts in response to unsafe hoisting operations while maintaining an impressively low rate of false alarms.

Reliability and risk assessment of digital twin system based on improved failure mode and effects analysis

Reliability and risk assessment of digital twin system based on improved failure mode and effects analysis

Classroom Teaching of Engineering Courses Assisted by Artificial Intelligence and Digital Twin Systems

This paper explores the integration of Artificial Intelligence (AI) and Digital Twin systems in the classroom teaching of engineering courses, with a focus on the Industrial Robot Technology course1. It addresses the challenges of traditional practical teaching methods and proposes a new model that leverages AI and Digital Twin technology to enhance student engagement, improve learning outcomes, and better prepare students for the demands of the industry. The paper discusses the implementation of AI-assisted teaching, the creation of a ubiquitous teaching ecology, and the strengthening of vocational skills through digital twin.

A Review of Multi-source Heterogeneous Twin Data Processing Methods in Traffic Scenes

As the complexity of intelligent public transportation systems continues to increase and the degree of interconnection of equipment at all levels gradually deepens, building a digital twin system will generate a large amount of multi-source heterogeneous data. Effective processing and in-depth mining of data can provide real-time and intelligent decision-making optimization for the construction of digital twin systems, thereby efficiently handling complex emergencies. This paper conducts a systematic review on the processing methods and technologies of multi-source heterogeneous twin data in traffic dynamic scenarios. First, the content and classification of multi-source heterogeneous twin data in the operation process of intelligent bus systems are clarified; secondly, the multi-source The data processing methods and technologies applied in each stage of data collection, data storage, data fusion and data analysis in heterogeneous twin data processing are analyzed, and the advantages, disadvantages and applications of various methods and technologies are analyzed; finally, corresponding to the multiple This paper summarizes the methods and technologies for processing multi-source heterogeneous twin data, and points out the challenges and development trends faced by the current multi-source heterogeneous twin data processing methods and technologies.

Digital thread-driven cloud-fog-edge collaborative disturbance mitigation mechanism for adaptive production in digital twin discrete manufacturing workshop

The collaborative capacity among various computing layers within a digital twin workshop directly impacts the handling of disturbances in discrete manufacturing processes. However, the intricate nature of the cross-space (physical and virtual space) and cross-layer (cloud, fog, and edge) interactions within a digital twin system poses challenges to construct a comprehensive data model and interoperable data flow, leading to a fragmented state of computing layers. To tackle these challenges, this study presents a digital thread-driven theory and method for cloud-fog-edge collaboration in digital twin discrete manufacturing workshops. Firstly, the five-dimensions production information that cross spaces and layers is concatenated and integrated by constructing digital thread models. Subsequently, a disturbance response model is conducted based on these digital thread models to detect, track, and evaluate disturbances. Furthermore, corresponding cloud-fog-edge collaboration strategies are devised for different types and severity of disturbances, facilitating the dynamic production tasks adjustments. Finally, a case study to discuss the performance of the proposed method regarding the maturity of the digital thread model, disturbance response capability, and production self-adaptive adjustment capability. The results demonstrate that the proposed method effectively mitigates makespan and machine idle time under gradual and abrupt disturbances, and saves 46.22% of computing processing time.

Research on the construction method of virtual mapping digital twin system for substation

Abstract At present, there are problems in the process of fault location and cause analysis in the actual work of substations, such as long time consumption, high investment in manpower and material resources, and low returns. A Digital Twin (DT) system construction method that can be used for virtual mapping in substations is proposed to address this problem. Firstly, the design principles and overall structure of the substation DT system were formulated by comprehensively considering the needs of transient modeling, debugging, and verification calculations in the substation. Then, the construction method and process of the three-dimensional twin model of the primary system (PS) in the substation were analyzed by using SolidWorks and Unity3D, and the graphical modeling method and process of the general protection and secondary circuit logic modules were analyzed by using the Cybersim integrated simulation support system. Finally, a three-dimensional twin model of the PS and secondary system (SS) equipment inside the substation was constructed. The reliability of the proposed method was verified through simulation experiments, and the results showed that the fault identification accuracy, fault location accuracy, and fault cause judgment accuracy were the highest during bus faults, with 96.10%, 94.17%, and 92.93%, respectively. The capacitor had the lowest accuracy, with 94.86%, 92.96%, and 91.73%, respectively. The DT system can accurately achieve functions such as status viewing and alarm of the substation, precise positioning and navigation of equipment, and positioning of faulty and abnormal equipment.

Optimal decision-making method for equipment maintenance to enhance the resilience of power digital twin system under extreme disaster

Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events, causing surveillance and energy loss. This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters. Initially, the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes. Subsequently, it delves into communication and data processing mechanisms, specifically focusing on central data processing (CDP), communication routers (CRs), and phasor measurement units (PMUs), to re-establish an equipment recovery model based on these data transmission methodologies. Furthermore, it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution. The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system. The findings suggest that the proposed branch- and-bound algorithm significantly augments the observational capabilities of a power system with limited resources, thereby bolstering its stability and emergency response mechanisms.

Research on Digital Twin System Platform Framework and Key Technologies of Unmanned Ground Equipment

Research on Digital Twin System Platform Framework and Key Technologies of Unmanned Ground Equipment

Stressful crystal histories recorded around melt inclusions in volcanic quartz

Magma ascent and eruption are driven by a set of internally and externally generated stresses that act upon the magma. We present microstructural maps around melt inclusions in quartz crystals from six large rhyolitic eruptions using synchrotron Laue X-ray microdiffraction to quantify elastic residual strain and stress. We measure plastic strain using average diffraction peak width and lattice misorientation, highlighting dislocations and subgrain boundaries. Quartz crystals across studied magma systems preserve similar and relatively small magnitudes of elastic residual stress (mean 53–135 MPa, median 46–116 MPa) in comparison to the strength of quartz (~ 10 GPa). However, the distribution of strain in the lattice around inclusions varies between samples. We hypothesize that dislocation and twin systems may be established during compaction of crystal-rich magma, which affects the magnitude and distribution of preserved elastic strains. Given the lack of stress-free haloes around faceted inclusions, we conclude that most residual strain and stress was imparted after inclusion faceting. Fragmentation may be one of the final strain events that superimposes stresses of ~ 100 MPa across all studied crystals. Overall, volcanic quartz crystals preserve complex, overprinted deformation textures indicating that quartz crystals have prolonged deformation histories throughout storage, fragmentation, and eruption.

A multiscale constitutive model of magnesium-shape memory alloy composite

In this work, a multiscale constitutive model is established to describe the deformation behaviors of magnesium-shape memory alloy (Mg-SMA) composite in a wide temperature range and reveal the strengthening mechanism of SMA reinforcement on Mg. The model is established at the grain scale firstly and gradually transited to the macroscopic scale by employing a newly developed three-level scale transition rule. At the grain scale, the thermodynamic-consistent constitutive models of Mg and SMA are, respectively, constructed by addressing different inelastic deformation mechanisms. The basal, prismatic, pyramidal, slip systems and extension twinning system are considered for the Mg phase, and the martensite transformation (MT) and austenitic plasticity are addressed for SMA reinforcement. Thermodynamic driving forces of each inelastic deformation mechanism are derived from the dissipative inequality and the constructed Gibbs free energies. At the polycrystalline scale, to evaluate the interactions among the grains and pores, and obtain the whole responses of the polycrystalline Mg and SMA, a thermo-mechanically coupled self-consistent homogenization scheme is employed. At the mesoscopic scale, a modified thermo-mechanically coupled Mori-Tanaka's homogenization scheme is adopted to evaluate the interaction between the Mg phase and SMA phase, and predict the whole responses for the representative volume element (RVE) of the composite. According to the geometrical features and mechanical loadings applied on the specimen, a hypothesis of homogeneous stress and strain fields at the macroscopic scale is adopted to achieve the scale transition from the RVE of the composite to the whole specimen. The capacity of the multiscale model is verified by comparing the predictions with the existing experimental data (Aydogmus, 2015). Moreover, the influences of characteristic information for the microstructures at different spatial scales on the deformation behaviors of the composite are predicted and discussed.

Predicting and compensating for small-sample thermal information data in precision machine tools: A spatial-temporal interactive integration network and digital twin system approach

The thermal errors in ball screws present complex spatial-temporal characteristics and exhibit persistent long-term memory, significantly affecting the machining accuracy of the whole machine tool in applications. Deep learning has emerged as a promising approach to predict these errors using thermal data. However, the scarcity of extensive thermal datasets, typically due to the prohibitive costs and time required for experimental acquisition, hampers the comprehensive exploitation of spatial-temporal and long-term memory attributes. Moreover, the current thermal error compensation system does not support the deployment of deep learning-based thermal error models due to its weak real-time performance. These limitations lead to suboptimal prediction accuracy and inadequate error compensation. Addressing this challenge, a novel spatial-temporal interactive integration network is proposed, and this is a sophisticated model that synergistically blends a time memory gate and a spatial-temporal fusion gate within an advanced attention-based spatial-temporal graph convolutional framework. Our network ingeniously leverages spatial data to anchor long-term memory while employing temporal data for selective memory feature extraction, facilitating a refined integration of spatial-temporal features. Concurrently, the integration of gated recurrent units and a time attention layer meticulously extracts and enhances temporal features, bolstered by our innovative time memory gate, which is adept at handling small-sample scenarios and refining thermal error predictions. Critically, the digital twin system for thermal error compensation is constructed to improve the system’s real-time performance. The integration of this network into the digital twin system marks a pivotal advancement in thermal error compensation. Our empirical results underscore the system's remarkable robustness and superior predictive accuracy, demonstrating a significant reduction in positioning and machining errors (over 90% and 80% respectively) even with constrained thermal data inputs. These advancements not only delineate a substantial leap in predictive accuracy but also underscore our contribution to reducing operational costs and enhancing the efficacy of machine tools.

Social Learning with Actor–Critic for dynamic grasping of underwater robots via digital twins

Dynamic grasping is a crucial technology in the field of underwater robotics, playing an essential role in executing complex tasks. However, traditional control methods encounter challenges in achieving an efficient and stable dynamic grasping process due to the complexities and uncertainties of underwater environments. This paper introduces a novel black-box intelligent optimization algorithm named Social Learning with Actor–Critic (SLAC) for the dynamic grasping of underwater robots. The core architecture of SLAC is based on the integration of two key algorithms: Intelligent Social Learning (ISL) for intelligent optimization and Soft Actor–Critic (SAC) for reinforcement learning. ISL enhances SAC by supplying a larger number of transitions, whereas SAC improves ISL with more effective strategies. These algorithms interact synergistically, augmenting their respective strengths throughout the learning process. To evaluate SLAC’s performance, a comparison is made with six state-of-the-art methods across eight continuous control benchmark cases. The results highlight SLAC’s exceptional learning capability and performance benefits. Furthermore, virtual reality software for the underwater robot and a corresponding digital twin system have been developed. The SLAC algorithm is trained in the digital twin environment before its application in the actual underwater setting. Through interactive training and iterative learning, both simulated and experimental results demonstrate the robot’s proficiency in achieving efficient and stable dynamic grasping, effectively adapting to various underwater environments’ variations and complexities.

Design and development of robotic technology through microcontroller system with machine learning techniques

Through the integration of AI and IoT, the digital twin transforms industrial sectors by virtually portraying physical systems. Simulation and the application of lifecycle management improve decision-making. In this paper, a virtual prototype system a digital twin framework that integrates robotic devices is proposed. Created using debugging platforms, they track every robotic activity, supported by real-time microcontroller structural design systems. Machine learning methods are the fundamental engine of the digital twin system. Because of this connection, robotic actions may be seamlessly controlled and monitored, guaranteeing effectiveness and adaptability in changing contexts. Robotics has advanced significantly with the combination of digital twin technology, machine learning, and microcontroller systems, offering improved performance and versatility in a range of applications.