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教育案例研究:在 TIA 门户、Unity 和 Game4Automation 框架中创建生产线的数字孪生。

Educational Case Studies: Creating a Digital Twin of the Production Line in TIA Portal, Unity, and Game4Automation Framework.

机构信息

Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, 812 19 Bratislava, Slovakia.

出版信息

Sensors (Basel). 2023 May 22;23(10):4977. doi: 10.3390/s23104977.

DOI:10.3390/s23104977
PMID:37430895
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10222384/
Abstract

In today's industry, the fourth industrial revolution is underway, characterized by the integration of advanced technologies such as artificial intelligence, the Internet of Things, and big data. One of the key pillars of this revolution is the technology of digital twin, which is rapidly gaining importance in various industries. However, the concept of digital twins is often misunderstood or misused as a buzzword, leading to confusion in its definition and applications. This observation inspired the authors of this paper to create their own demonstration applications that allow the control of both the real and virtual systems through automatic two-way communication and mutual influence in context of digital twins. The paper aims to demonstrate the use of digital twin technology aimed at discrete manufacturing events in two case studies. In order to create the digital twins for these case studies, the authors used technologies as Unity, Game4Automation, Siemens TIA portal, and Fishertechnik models. The first case study involves the creation of a digital twin for a production line model, while the second case study involves the virtual extension of a warehouse stacker using a digital twin. These case studies will form the basis for the creation of pilot courses for Industry 4.0 education and can be further modified for the development of Industry 4.0 educational materials and technical practice. In conclusion, selected technologies are affordable, which makes the presented methodologies and educational studies accessible to a wide range of researchers and solution developers tackling the issue of digital twins, with a focus on discrete manufacturing events.

摘要

在当今的工业界,第四次工业革命正在进行,其特点是人工智能、物联网和大数据等先进技术的融合。这场革命的一个关键支柱是数字孪生技术,它在各个行业中的重要性正在迅速提高。然而,数字孪生的概念常常被误解或滥用为一个流行词,导致其定义和应用混淆不清。这一观察结果促使本文作者创建了自己的演示应用程序,允许通过自动双向通信和数字孪生环境中的相互影响来控制真实和虚拟系统。本文旨在通过两个案例研究展示数字孪生技术在离散制造事件中的应用。为了为这些案例研究创建数字孪生,作者使用了 Unity、Game4Automation、Siemens TIA portal 和 Fishertechnik 模型等技术。第一个案例研究涉及创建生产线模型的数字孪生,第二个案例研究涉及使用数字孪生对仓库堆垛机进行虚拟扩展。这些案例研究将为工业 4.0 教育创建试点课程提供基础,并可进一步修改,以开发工业 4.0 教育材料和技术实践。总之,所选技术价格实惠,这使得解决数字孪生问题的广泛研究人员和解决方案开发人员都能够使用所提出的方法和教育研究,重点是离散制造事件。

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本文引用的文献

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2
Intelligent Analysis of Abnormal Vehicle Behavior Based on a Digital Twin.基于数字孪生的车辆异常行为智能分析
J Shanghai Jiaotong Univ Sci. 2021;26(5):587-597. doi: 10.1007/s12204-021-2348-7. Epub 2021 Oct 28.
3
Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing.
基于物联网的传感器、大数据处理和机器学习模型在汽车制造实时监控系统中的性能分析。
Sensors (Basel). 2018 Sep 4;18(9):2946. doi: 10.3390/s18092946.