Exploring the Intersection of Metaverse, Digital Twins, and Artificial Intelligence in Training and Maintenance

IF 2.6 3区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computing and Information Science in Engineering Pub Date : 2023-05-03 DOI:10.1115/1.4062455

M. Bordegoni, F. Ferrise

引用次数: 5

Abstract

As technology advances, we are surrounded by more complex products that can be challenging to use and troubleshoot. We often turn to online resources and the help of others to learn how to use a product's features or fix malfunctions. This is a common issue in both everyday life and industry. The key to be able to use a product or fix malfunctions is having access to accurate information and instructions and to gain the necessary skills to perform the tasks correctly. This paper offers an overview of how Artificial Intelligence, Digital Twins, and the Metaverse - currently popular technologies - can enhance the process of acquiring knowledge, know-how, and skills, with a focus on industrial maintenance. However, the concepts discussed may also be applicable to the maintenance of consumer products.

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探索虚拟世界、数字孪生和人工智能在培训和维护中的交集

随着技术的进步，我们被更复杂的产品所包围，这些产品的使用和故障排除可能具有挑战性。我们经常求助于在线资源和他人的帮助来学习如何使用产品的功能或修复故障。这在日常生活和工业中都是一个常见的问题。能够使用产品或修复故障的关键是能够访问准确的信息和说明，并获得正确执行任务所需的技能。本文概述了当前流行的人工智能、数字孪生和元宇宙技术如何增强获取知识、专有技术和技能的过程，重点是工业维护。然而，所讨论的概念也可能适用于消费品的维护。

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

Journal of Computing and Information Science in Engineering 工程技术-工程：制造

CiteScore

6.30

自引率

12.90%

发文量

100

审稿时长

6 months

期刊介绍： The ASME Journal of Computing and Information Science in Engineering (JCISE) publishes articles related to Algorithms, Computational Methods, Computing Infrastructure, Computer-Interpretable Representations, Human-Computer Interfaces, Information Science, and/or System Architectures that aim to improve some aspect of product and system lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, recycling etc.). Applications considered in JCISE manuscripts should be relevant to the mechanical engineering discipline. Papers can be focused on fundamental research leading to new methods, or adaptation of existing methods for new applications. Scope: Advanced Computing Infrastructure; Artificial Intelligence; Big Data and Analytics; Collaborative Design; Computer Aided Design; Computer Aided Engineering; Computer Aided Manufacturing; Computational Foundations for Additive Manufacturing; Computational Foundations for Engineering Optimization; Computational Geometry; Computational Metrology; Computational Synthesis; Conceptual Design; Cybermanufacturing; Cyber Physical Security for Factories; Cyber Physical System Design and Operation; Data-Driven Engineering Applications; Engineering Informatics; Geometric Reasoning; GPU Computing for Design and Manufacturing; Human Computer Interfaces/Interactions; Industrial Internet of Things; Knowledge Engineering; Information Management; Inverse Methods for Engineering Applications; Machine Learning for Engineering Applications; Manufacturing Planning; Manufacturing Automation; Model-based Systems Engineering; Multiphysics Modeling and Simulation; Multiscale Modeling and Simulation; Multidisciplinary Optimization; Physics-Based Simulations; Process Modeling for Engineering Applications; Qualification, Verification and Validation of Computational Models; Symbolic Computing for Engineering Applications; Tolerance Modeling; Topology and Shape Optimization; Virtual and Augmented Reality Environments; Virtual Prototyping