Advancements in 3D digital model generation for digital twins in industrial environments: Knowledge gaps and future directions

IF 8 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

Abstract

Digital twins are considered a transformative paradigm for industrial environments, providing a dynamic, digital, and intelligent representation of industrial assets. The necessity of digital twins in industrial settings is underscored by their ability to enhance asset monitoring, operational efficiency, and maintenance activities. The 3D digital model is fundamental for digital twins, serving not only as a digital representation of industrial environment but also facilitating the simulation of real-world scenarios. Although there have been extensive studies on the application of digital twins in industrial environments, the creation of 3D digital model for digital twins in existing industrial environments is still overlooked, primarily due to the complexity of these environments. This article aims to propose a workflow to create a 3D digital model for digital twins in existing industrial environments that includes four key components: 1) Data capturing, 2) 3D modeling, 3) Asset localization, and 4) Information integration. A significant body of literature on each component is surveyed to identify current knowledge gaps in harnessing 3D digital models for digital twins in industrial environments. In response to these gaps, this study proposes a series of future research directions, including automated data validation, real-time processing, semi-supervised or unsupervised learning-based 3D reconstruction methods, and 3D visualization approaches for industrial assets.
为工业环境中的数字双胞胎生成三维数字模型的进展:知识差距与未来方向
数字孪生被认为是工业环境的变革范例,它为工业资产提供了动态、数字化和智能化的表现形式。数字孪生能够提高资产监控、运营效率和维护活动,因此在工业环境中使用数字孪生非常必要。三维数字模型是数字孪生的基础,它不仅是工业环境的数字表示,还有助于模拟真实世界的场景。虽然已有大量关于数字孪生在工业环境中应用的研究,但在现有工业环境中创建数字孪生的三维数字模型仍被忽视,这主要是由于这些环境的复杂性。本文旨在提出一种在现有工业环境中创建数字双胞胎三维数字模型的工作流程,其中包括四个关键部分:1) 数据采集;2) 三维建模;3) 资产定位;4) 信息集成。我们调查了有关每个组成部分的大量文献,以确定当前在工业环境中利用三维数字模型进行数字孪生方面的知识差距。针对这些差距,本研究提出了一系列未来研究方向,包括自动数据验证、实时处理、基于半监督或无监督学习的三维重建方法,以及工业资产的三维可视化方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
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