Ontology of 3D virtual modeling in digital twin: a review, analysis and thinking

Wang, Yuchen; Wang, Xinheng; Liu, Ang; Zhang, Junqing; Zhang, Jinhua

doi:10.1007/s10845-023-02246-6

Cited by 2 publications

(1 citation statement)

References 115 publications

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“…Consequently, a hierarchical modeling method for DT models of mechanical products based on graph databases has emerged. This approach involves creating product subassemblies or component nodes, establishing node relationships in a predefined graph database, storing feature information in the nodes, and conducting postprocessing on the established DT models [91][92][93]. Additionally, a dynamic data modeling method based on timesequence databases is proposed.…”

Section: Model Constructionmentioning

confidence: 99%

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

Hu,

Li,

et al. 2024

Intelligent and Sustainable Manufacturing

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Digital twin technology develops virtual models of objects digitally, simulating their real-world behavior based on data. It aims to reduce product development cycles and costs through feedback between the virtual and real worlds, data fusion analysis, and iterative decision-making optimization. Traditional manufacturing processes often face challenges such as poor real-time monitoring and interaction during machining, difficulties in diagnosing equipment failures, and significant errors in machining. Digital twin technology offers a powerful solution to these issues. Initially, a comprehensive review of the research literature was conducted to assess the current research scope and trends. This was followed by an explanation of the basic concepts of digital twins and the technical pathway for integrating digital twins into intelligent manufacturing including outlining the essential technologies for creating a system of interaction between the virtual and real worlds, enabling multimodel fusion, data sensing, algorithm-based prediction, and intelligent decision-making. Moreover, the application of digital twins in intelligent manufacturing throughout the product life cycle was detailed, covering product design, manufacturing, and service stages. Specifically, in the manufacturing phase, a model based on heat conduction theory and visualization was used to construct a time-varying error model for the motion axis, leading to experiments predicting the time-varying error in the hole spacing of a workpiece. These experiments achieved a minimum prediction error of only 0.2 μm compared to the actual error. By compensating for time-varying errors in real time, the variability in the hole spacing error decreased by 69.19%. This paper concludes by summarizing the current state of digital twins in intelligent manufacturing and projecting future trends in key technologies, application areas, and data use, providing a basis for further research.

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Section: Model Constructionmentioning

confidence: 99%

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

Hu,

Li,

et al. 2024

Intelligent and Sustainable Manufacturing

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Enabling additive manufacturing part inspection of digital twins via collaborative virtual reality

Chheang,

Narain,

Hooten

et al. 2024

Sci Rep

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Digital twins (DTs) are an emerging capability in additive manufacturing (AM), set to revolutionize design optimization, inspection, in situ monitoring, and root cause analysis. AM DTs typically incorporate multimodal data streams, ranging from machine toolpaths and in-process imaging to X-ray CT scans and performance metrics. Despite the evolution of DT platforms, challenges remain in effectively inspecting them for actionable insights, either individually or in a multidisciplinary, geographically distributed team setting. Quality assurance, manufacturing departments, pilot labs, and plant operations must collaborate closely to reliably produce parts at scale. This is particularly crucial in AM where complex structures require a collaborative and multidisciplinary approach. Additionally, the large-scale data originating from different modalities and their inherent 3D nature pose significant hurdles for traditional 2D desktop-based inspection methods. To address these challenges and increase the value proposition of DTs, we introduce a novel virtual reality (VR) framework to facilitate collaborative and real-time inspection of DTs in AM. This framework includes advanced features for intuitive alignment and visualization of multimodal data, visual occlusion management, streaming large-scale volumetric data, and collaborative tools, substantially improving the inspection of AM components and processes to fully exploit the potential of DTs in AM.

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Ontology of 3D virtual modeling in digital twin: a review, analysis and thinking

Cited by 2 publications

References 115 publications

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

Enabling additive manufacturing part inspection of digital twins via collaborative virtual reality

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