Digital Model Research Articles

Identification and classification of surface defects for digital twin models of the workpiece.

Workpiece surface defect detection is an indispensable part of intelligent production. The surface information obtained by traditional 2D image detection has some limitations due to the influence of environmental light factors and part complexity. However, the digital twin model has the characteristics of high fidelity and scalability, and the digital twin surface can be obtained by a device with a scanning accuracy of 0.02mm to achieve the representation of the real surface of the workpiece. The surface defect detection system for digital twin models is proposed based on the improved YOLOv5 model in this paper. Firstly, the digital twin model of the workpiece is reconstructed by the point cloud data obtained by the scanning device, and the surface features with defects are captured. Subsequently, the training dataset is calibrated based on the defect surface, where the defect types include Inclusion, Perforation, pitting surface and Rolled-in scale. Finally, the improved YOLOv5 model with CBAM mechanism and BiFPN module was used to identify the surface defects of the digital twin model and compare it with the original YOLOv5 model and other common models. The results show that the improved YOLOv5 model can realize the identification and classification of surface defects. Compared with the original YOLOv5 model, the mAP value of the improved YOLOv5 model has increased by 0.2%, and the model has high precision. On the basis of the same data set, the improved YOLOv5 model has higher recognition accuracy than other models, improving 11.7%, 3.4%, 6.2%, 33.5%, respectively. As a result, this study provides a practical and systematic detection method for digital twin model surface during the intelligent production process, and realizes the rapid screening of the workpiece with defects.

Knowledge-based digital twin system: Using a knowlege-driven approach for manufacturing process modeling

The Knowledge-Based Digital Twin System is a digital twin system developed on the foundation of a knowledge graph, aimed at serving the complex manufacturing process. This system embraces a knowledge-driven modeling approach, aspiring to construct a digital twin model for the manufacturing process, thereby enabling precise description, management, prediction, and optimization of the process. The core of this system lies in the comprehensive knowledge graph that encapsulates all pertinent information about the manufacturing process, facilitating dynamic modeling and iteration through knowledge matching and inference within the knowledge, geometry, and decision model. This approach not only ensures consistency across models but also addresses the challenge of coupling multi-source heterogeneous information, creating a holistic and precise information model. As the manufacturing process deepens and knowledge accumulates, the model's understanding of the process progressively enhances, promoting self-evolution and continuous optimization. The developed knowledge-decision-geometry model acts as the ontological layer within the digital twin framework, laying a foundational conceptual framework for the digital twin of the manufacturing process. Validated on an aero-engine blade production line in a factory, the results demonstrate that the knowledge model, as the core driver, enables continuous self-updating of the geometric model for an accurate depiction of the entire manufacturing process, while the decision model provides deep insights for decision-makers based on knowledge. The system not only effectively controls, predicts, and optimizes the manufacturing process but also continually evolves as the process advances. This research offers a new perspective on the realization of the digital twin for the manufacturing process, providing solid theoretical support with a knowledge-driven approach.

Integration of UAV Digital Surface Model and HEC-HMS Hydrological Model System in iRIC Hydrological Simulation—A Case Study of Wu River

This research investigates flood susceptibility in the mid- and downstream areas of Taiwan’s Wu River, historically prone to flooding in central Taiwan. The study integrates the Hydrologic Engineering Center—Hydrologic Modeling System (HEC-HMS) for flow simulations with unmanned aerial vehicle (UAV)-derived digital surface models (DSMs) at varying resolutions. Flood simulations, executed through the International River Interface Cooperative (iRIC), assess flood depths using diverse DSM resolutions. Notably, HEC-HMS simulations exhibit commendable Nash–Sutcliffe efficiency (NSE) exceeding 0.88 and a peak flow percentage error (PEPF) below 5%, indicating excellent suitability. In iRIC flood simulations, optimal results emerge with a 2 m resolution UAV-DSM. Furthermore, the study incorporates rainfall data at different recurrence intervals in iRIC flood simulations, presenting an alternative flood modeling approach. This research underscores the efficacy of integrating UAV-DSM into iRIC flood simulations, enabling precise flood depth assessment and risk analysis for flood control management.

Review of Advanced Digital Technologies, Modeling and Control Applied in Various Processes

This special issue reviews advanced digital technologies in modeling and control of technological processes [...]

How do Porosity, Size and Interconnectivity of Pores Influence The Values of the Archie Equation Exponents in Carbonate and Sandy Rocks?

Objective: The aim of this study is to investigate the exponents of the Archie equation in order to support the evaluation of hydrocarbon or groundwater reserves through electrical resistivity measurements of subsurface rocks. Theoretical Framework: The Archie equation describes the relationship between the electrical resistivity of a rock and its water saturation and depends on the cementation (m) and saturation (n) exponents. Several authors state that electrical resistivity and the exponents m and n depend on the size, geometry, and connectivity of the pores. Method: This article applies the finite element method to simulate the propagation of the electric field in digital rock models and thus evaluate the Archie exponents. Results and Discussion: Our results indicate that the relationship between these exponents and pore attributes is different for values below or above a threshold. The variables with the greatest influence on m are the porosity of macropores and their interconnectivity, as well as the volumetric fraction of the microporous phase. As for the saturation exponent, the variables with the greatest influence are the porosity of macropores and the interconnectivity of both potentially conductive domains. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of underground natural resource assessment. These implications may cover the sectors of hydrocarbon production, groundwater, geo-environmental investigations, and geotechnical studies. Originality/Value: This study contributes to the literature by presenting an innovative method for determining the exponents of the Archie equation. The relevance and value of this research are evidenced by its high economic impact on the aforementioned sectors and by its applicability anywhere.

The Digital Transformation Model for Innovative Marketing Maturity in the Oil Companies

Purpose: The manufacturing industry has been at the cutting-edge of technology advancements, which has Objectives: The objective of this research is to examine digital transformation within the oil and gas industry, focusing on the transition to renewable energy sources and the role of innovative technologies. It aims to explore how digital technology transforms operational strategies and the importance of data utilization, brand recognition, and ethical data practices in this transformation process. Methods: This study employs a qualitative investigation methodology to delve into digital transformation in the oil and gas sector. Through interviews with industry decision-makers, the research gathers insights into the challenges and opportunities faced by firms adopting digital solutions and strategies. Results: The findings of the research highlight the transformative nature of digital technology within the oil and gas industry. They underscore the significance of managing internal and external boundaries during the transformation process. Key factors such as data utilization, brand recognition, and ethical data practices emerge as crucial for successful digital transformation. The study provides empirically grounded directions for developing frameworks to guide digital transformation practices in the industry. Conclusion: In conclusion, this research contributes to the understanding of digital transformation in the oil sector. By shedding light on the challenges and opportunities inherent in adopting digital solutions, the study aims to facilitate sustainable development and enhance innovation potential in the energy landscape. The findings offer valuable insights for firms navigating the complexities of digital transformation and pave the way for future research in this area.

Comparison of Dentoalveolar Expansion with Clear Aligners by Using Two Different Treatment Protocols in the Early Mixed Dentition: A Retrospective Study

Objectives: To evaluate the effect of different expansion protocols on tooth movements during maxillary arch expansion with Invisalign First System® (IFS) in the mixed dentition. Materials and Methods: This study was conducted using digital models of 20 patients treated with sequential expansion (MFE, mean age: 7.88±0.60; 9 girls, 11 boys) and 20 patients treated with simultaneous expansion (SIE, mean age: 7.94±0.51; 12 girls, 8 boys) acquired before (T0) and after (T1) treatment. Maxillary parameters were measured on each model at T0 and T1. Mann Whitney U tests were used for comparisons between groups, and Wilcoxon tests were used for the analysis of intra-group. Results: In both groups, the greatest increase of maxillary arch width (AW) was determined at both the cusp tips and gingival levels of the upper first deciduous molars followed by the second deciduous molars and by the deciduous canines (p < 0.01). The lowest increase of AW was found in the at the permanent first molars (p < 0.01). Conclusion: Both protocols with IFS can be considered effective in patients in early mixed dentition. The greatest increase was detected at the level of the upper first deciduous molars, whereas the least increase was observed at the level of the first permanent molars. Key Words: Clear Aligners, Invisalign® First system, Mixed dentition

A method for generating the digital twin models of plants

In the process of creating digital twin models of plants, the simulation accuracy of models generated by conventional 3D reconstruction methods is insufficient. Considering the increasing number of models over time, adopting deep learning-based models not only consume a significant amount of time but also require substantial computational resources consumption. Therefore, we proposed a method for generating digital twin models of plants, aiming to ensure the simulation credibility while effectively reducing resource costs. Experimental results show that this method is capable of generating plant models that closely resemble physical entities at different growth periods. Compared with other construction methods, this approach achieves higher simulation credibility (CD=0.089, EMD=0.034, KNN=0.005, RGB=4.97) with lower computational resources consumption (ROM=7.46MB, VRAM=2456MB, Time=0h5min48s) when generating a digital twin model.

Meta-photonics: A bridge between physical association and digital models in photonics

Recently, the burgeoning field of metaverse has facilitated the convergence of digital scenarios and the real world to achieve unprecedented interaction patterns. Inspired by this digital approach, we propose the concept of meta-photonics to gain insight into the photonics underlying increasingly complex phenomena, while traditional physics theory is not keeping pace with current observations. Here, we are armed with different deep learning models to build intrinsic dimensions among different physical elements and bring together amplitude, phase, polarization conversion ratio, extinction ratio, unwrapping phase, surface electric field, and connected domain, most of which have never been reported, based on the results of the forward and inverse models. This approach reveals an opportunity to catalyze the discovery of more enigmatic associations among different concepts and paves the way for tailoring more flexible optical responses in the vibrant world of meta-photonics.

Examining electrical equivalent circuit models of insulators in transmission lines under pollution conditions: a comprehensive study and analysis

This research presents a comprehensive study investigating the behavior of insulators in transmission lines under various pollution conditions. Utilizing equivalent electrical circuits, the analysis aims to understand the impact of pollution on insulator performance, which is crucial for maintaining the reliability and efficiency of power transmission systems. By providing valuable insights into the relationship between pollution and insulator behavior, this study contributes to the development of improved strategies for mitigating the effects of pollution on transmission lines, ultimately enhancing the overall safety and stability of electrical power networks. The study focuses on a digital model that enables the visualization of potential and electric field distribution within a series of glass insulators utilized in Algerian electrical grids. Initially, a parallel RC network equivalent circuit was devised and its parameters were determined through finite element analysis using Comsol Multiphysics. This equivalent circuit was subsequently integrated into ATP/EMTP software to simulate leakage currents, yielding satisfactory outcomes. The model was then incorporated into the isolator's equivalent circuit. Results from various simulations indicate that the occurrence of discharge along the dry strip impacts leakage current. Furthermore, the re-initiation of discharge on the contaminated insulator's surface is influenced by the pollutant deposit's conductivity and distribution. Therefore, understanding the pollution level is vital for accurately evaluating and sizing the insulator chain at the installation site.

PROTOTYPING OF A TWO WHEELER CLUTCH HUB USING REVERSE ENGINEERING TECHNOLOGY

A clutch hub is a critical component in a two wheeler manual transmission system, responsible for transferring rotational power from the engine to the transmission gears and providing a mechanism for disengaging the gears to allow for smooth gear changes. The clutch hub is placed between clutch basket and pressure plate. The clutch plates are mounted on it. It has teeth at center hole which rotate with main shaft. The prototyping of clutch hub using reverse engineering technique, making it essential to understand its design and functionality reverse engineering is employed to deconstruct an existing clutch hub, analyze its dimensions and materials, and create an accurate digital 3D model is created using computer aided design software, by scanning the part and prototyping of clutch hub is made by using 3D printing technology. Key Words: Clutch hub, Reverse engineering, 3D Model, Prototyping, 3D printing.

The impact of apicobasal ridges on dental load-bearing capacity in aquatic-feeding predatory amniotes

Abstract Apicobasal ridges are longitudinal ridges of enamel that are particularly common in several clades of aquatic-feeding predatory amniotes, including Plesiosauria, Ichthyosauria, Mosasauridae, Crocodylia, and Spinosauridae, as well as some early members of Cetacea. Although the repeated evolution of these dental ridges in unrelated clades suggests an adaptive benefit, their primary function in feeding is debated. Hypothesized functions range from increasing tooth strength to improving prey puncture or removal efficiency, but these have never been quantitatively tested. This study utilizes finite element analysis (FEA) to assess the impact of apicobasal ridges upon tooth crown strength in aquatic-feeding amniotes. Drawing on morphometric data from fossilized tooth crowns, a set of digital models was constructed to calculate the performance of smooth and ridged tooth variants under simulated bite force loadings. The similarities in overall stress distribution patterns across models of the same tooth shape, regardless of the presence or morphology of ridges, indicate that apicobasal ridges have little impact on stress reduction within the tooth crown. Ultimately, these findings suggest that apicobasal ridges have a minimal role in improving crown strength and form a framework for future research into the remaining hypotheses.

Quantitative prediction methods and applications of digital ore deposit models

With the depletion of surface mineral deposits, attention has shifted to deep mineral exploration prospecting. The emergence of three-dimensional digital ore deposit models, facilitated by the advancement of computer technology, has transformed traditional two-dimensional geological models into computer-recognizable numerical-symbolic digital models. This transformation allows qualitative geological concepts, originally expressed in natural language, to be quantitatively expressed, thereby enhancing the accuracy of deep mineral exploration. This paper first outlines the development from traditional ore deposit models to digital ore deposit models, asserting that digital ore deposit models can encompass a more comprehensive array of multidisciplinary geological information, including mineralogy, ore deposit types, structural geology, geochemistry, and geophysics. Building upon digital ore deposit models, a three-dimensional geological model is constructed, combining regional metallogenic regularities, identifying ore-forming favorable information, and conducting quantitative extraction. Subsequently, a comprehensive prospecting model is established, followed by the integration of deep learning methods for deep-seated mineral exploration, forming a unified system of quantitative prospecting methodology for hidden mineral deposits under the framework of geological prospecting models, three-dimensional geological modeling, and three-dimensional quantitative prospecting. Finally, using the Zaozigou gold deposit in Gansu Province as a case study, this approach is applied to mineral exploration prospecting, delineating prospective target areas and providing valuable reference for future exploration efforts.

Sustainable energy transition in cities: A deep statistical prediction model for renewable energy sources management for low-carbon urban development

This study proposes a novel approach to sustainable energy transition in urban environments, focusing on the prediction and management of renewable energy outputs for low-carbon urban development. We introduce a new intelligent prediction model based on Gated Recurrent Unit (GRU) Networks to forecast the output power of both tidal and biomass units. The GRU model, known for its capability in capturing sequential dependencies, is employed to enhance the accuracy of renewable energy predictions in the context of urban sustainability. To address the interpretability challenges inherent in complex deep learning models, we leverage LIME (Local Interpretable Model-agnostic Explanations). By integrating LIME into our GRU-based prediction model, we enhance transparency and understandability. This approach facilitates the identification of key factors influencing predictions, providing stakeholders and decision-makers with valuable insights for effective sustainable energy management. The proposed hybrid model is applied to the digital model of real tidal and biomass sites in China, allowing for a statistical examination of renewable integration and energy management strategies. The results offer a comprehensive understanding of the dynamics between renewable energy sources and urban development, paving the way for informed decision-making in the pursuit of low-carbon and resilient cities. This research contributes to the broader discourse on sustainable urban development by offering a robust statistical framework for renewable energy integration and management in urban environments.

3D DEWS digital parametric modeling and manufacturing for obtaining the post-cracking parameters of SFRC

A pullout analytical model is introduced to simulate the Double Edge Wedge Splitting (DEWS) test to obtain the tensile post-peak response of steel fiber reinforced concretes. One of the main barriers to the widespread utilization of FRC as a structural system is the anisotropy of the material. This augments the uncertainty of the material behavior (fibers random distribution and orientation in the bulk concrete), increases the dispersion of the test results, and requires the performance of characterization tests to check the residual tensile stresses at specific crack opening intervals. The characterization tests proposed in the literature (e.g., Barcelona, Montevideo, Wedge Splitting Test, EN 14651) are time-consuming and relatively complex, demanding careful preparation and hydraulic presses with data acquisition systems. This paper aims to contribute to the discussion of alternatives for FRC characterization procedures by the introduction of a pullout analytical model to predict FRC constitutive law based on DEWS tests. The pullout analytical model is calibrated with pullout tests considering straight and hooked-end steel fibers with different strengths, embedment lengths, inclinations, and diameters. A 3D digital model reproducing the experimental DEWS samples is developed, considering the fibers’ randomness distribution, orientation, and wall effect. The analyses are compared with four different experimental tests. The results define a linear post-peak tensile constitutive law, considering the mean residual stress values at different COD values. The model was able to reproduce the specimens’ production procedures and the fibers’ distribution and orientation correctly. The results demonstrated a good agreement with the experimental data.

Urban Building Height Extraction from Gaofen-7 Stereo Satellite Images Enhanced by Contour Matching

The traditional method for extracting the heights of urban buildings involves utilizing dense matching algorithms on stereo images to generate a digital surface model (DSM). However, for urban buildings, the disparity discontinuity issue that troubles the dense matching algorithm makes the elevations of high-rise buildings and the surrounding areas inaccurate. The occlusion caused by trees in greenbelts makes it difficult to accurately extract the ground elevation around the building. To tackle these problems, a method for building height extraction from Gaofen-7 (GF-7) stereo images enhanced by contour matching is presented. Firstly, a contour matching algorithm was proposed to extract accurate building roof elevation from GF-7 images. Secondly, a ground filtering algorithm was employed on the DSM to generate a digital elevation model (DEM), and ground elevation can be extracted from this DEM. The difference between the rooftop elevation and the ground elevation represents the building height. The presented method was verified in Yingde, Guangzhou, Guangdong Province, and Xi’an, Shaanxi Province. The experimental results demonstrate that our proposed method outperforms existing methods in building height extraction concerning accuracy.

Digital topographic analysis and lineament interpretation south of the Lena Delta, North Siberia: Landscape expression of its tectonic activity

This study applies semi-automated techniques to analyze the landforms, morphostructure, and geology of the Kharaulakh and Chekanovsky sectors, south of the Lena Delta, northern Siberia. High-resolution data, including TanDEM-X 30 m spatial resolution Digital Elevation Models (DEM), 2 m spatial resolution ArcticDEM Digital Surface Models (DSM), and Sentinel-2 satellite imagery, serve as the basis for the analysis. Digital terrain classification and identification of tectonic lineaments are performed using the Benthic Terrain Modeler (BTM) and Linear Extraction (LINE) algorithm, respectively. Additionally, a hypsometric analysis is conducted to assess areas of potential neotectonic activity. 873 lineaments are identified, primarily in NE-SW and E-W orientations. NW-SE lineaments perpendicular to the lithological and fold belt strikes in the Kharaulakh Ridge suggest inheritance from original depositional settings or later compressional tectonics, now appearing as valleys and weak zones facilitating erosion and river incision. Longer N-S or NNW-SSE lineaments in the Kharaulakh Sector are associated with thrust sheets of the Verkhoyansk fold-and-thrust belt and Cenozoic graben structures linked to the formation of the Laptev Sea Rift System and ongoing regional deformation. The Chekanovsky Sector, marked by epiplatform blocks and hills, presents a dissected plateau with steep valleys. Here, high hypsometric integral values are attributed to compression and uplift near the southwestern boundary of the Laptev Sea Microplate.

Strokes2Surface: Recovering Curve Networks From 4D Architectural Design Sketches

AbstractWe present Strokes2Surface, an offline geometry reconstruction pipeline that recovers well‐connected curve networks from imprecise 4D sketches to bridge concept design and digital modeling stages in architectural design. The input to our pipeline consists of 3D strokes' polyline vertices and their timestamps as the 4th dimension, along with additional metadata recorded throughout sketching. Inspired by architectural sketching practices, our pipeline combines a classifier and two clustering models to achieve its goal. First, with a set of extracted hand‐engineered features from the sketch, the classifier recognizes the type of individual strokes between those depicting boundaries (Shape strokes) and those depicting enclosed areas (Scribble strokes). Next, the two clustering models parse strokes of each type into distinct groups, each representing an individual edge or face of the intended architectural object. Curve networks are then formed through topology recovery of consolidated Shape clusters and surfaced using Scribble clusters guiding the cycle discovery. Our evaluation is threefold: We confirm the usability of the Strokes2Surface pipeline in architectural design use cases via a user study, we validate our choice of features via statistical analysis and ablation studies on our collected dataset, and we compare our outputs against a range of reconstructions computed using alternative methods.

Effects of Illumination Conditions on Individual Tree Height Extraction Using UAV LiDAR: Pilot Study of a Planted Coniferous Stand

Tree height is one of the key dendrometric parameters for indirectly estimating the timber volume or aboveground biomass of a forest. Field measurement is time-consuming and labor-intensive, while unmanned aerial vehicle (UAV)-borne LiDAR is a more efficient tool for acquiring tree heights of large-area forests. Although individual tree heights extracted from point cloud data are of high accuracy, they are still affected by some weather and environment factors. In this study, taking a planted M. glyptostroboides (Metasequoia glyptostroboides Hu & W.C. Cheng) stand as the study object, we preliminarily assessed the effects of various illumination conditions (solar altitude angle and cloud cover) on tree height extraction using UAV LiDAR. The eight point clouds of the target stand were scanned at four time points (sunrise, noon, sunset, and night) in two consecutive days (sunny and overcast), respectively. The point clouds were first classified into ground points and aboveground vegetation points, which accordingly produced digital elevation model (DEM) and digital surface model (DSM). Then, the canopy height model (CHM) was obtained by subtracting DEM from DSM. Subsequently, individual trees were segmented based on the seed points identified by local maxima filtering. Finally, the individual tree heights of sample trees were separately extracted and assessed against the in situ measured values. As results, the R2 and RMSEs of tree heights obtained in the overcast daytime were commonly better than those in the sunny daytime; the R2 and RMSEs at night were superior among all time points, while those at noon were poorest. These indicated that the accuracy of individual tree height extraction had an inverse correlation with the intensity of illumination. To obtain more accurate tree heights for forestry applications, it is best to acquire point cloud data using UAV LiDAR at night, or at least not at noon when the illumination is generally strongest.

Digital twin-driven smelting process management method for converter steelmaking

AbstractThe converter is an indispensable key equipment in the steel manufacturing industry. With the increasing demand for high-quality steel, there is an increasing demand for monitoring and controlling the status of the converter during the smelting process. Compared to other manufacturing industries, such as food processing and textile, converter steelmaking requires a larger keep-out zone due to its ultra-high temperatures and harsh smelting environment. This makes it difficult for personnel to fully understand, analyze, and manage the smelting process, resulting in low production efficiency and the inability to achieve consistently high-quality results. Aiming at the low virtual visualization level and insufficient monitoring ability of the converter steelmaking process, a process management method based on digital twin technology is proposed. Firstly, a digital twin system framework for full-process monitoring of converter steelmaking is proposed based on the analysis of the process characteristics of converter steelmaking. The proposed framework provides critical enabling technologies such as point cloud-based digital twin model construction, visual display, and steel endpoint analysis and prediction, to support full-process, high-fidelity intelligent monitoring. After conducting experiments, a digital twin-driven smelting process management system was developed to manage the entire smelting process. The system has proven to be effective as it increased the monthly production capacity by 77.7%. The waste of smelting materials has also been greatly reduced from 34% without the system to 7.8% with the system. Based on these results, it is evident that this system significantly enhances smelting efficiency and reduces both the costs and waste associated with the process.