W. Bernstein, Andrew Bowman, R. Durscher, A. Gillman, S. Donegan
{"title":"Towards Data and Model Interoperability for Industrial Extended Reality in Manufacturing","authors":"W. Bernstein, Andrew Bowman, R. Durscher, A. Gillman, S. Donegan","doi":"10.1115/1.4062328","DOIUrl":null,"url":null,"abstract":"\n Extended reality (XR) technologies have realized significant value for design, manufacturing, and sustainment processes. However, Industrial XR, or XR implemented within industrial applications, suffers from scalability and flexibility challenges due to fundamental gaps with interoperability between data, models, and platforms. Though there has been a number of recent efforts to improve the interoperability of industrial XR technologies, progress has been hindered by an innate separation between the domain-specific models (e.g., manufacturing execution data, material specifications, and product manufacturing information) with XR (often-standard) processes (e.g., multi-scale spatial representations and data formats optimized for run-time presentation). In this paper, we elaborate on promising research directions and opportunities around which the manufacturing and visualization academic community can rally. To establish such research directions, we (1) conducted a meta-review on well-established state-of-the-art review articles that have already presented in-depth surveys on application areas for industrial XR, such as maintenance, assembly and inspection and (2) mapped those findings to publicly published priorities from across the US Department of Defense. We hope that our presented research agenda will spur interdisciplinary work across academic silos, i.e., manufacturing and visualization communities, and engages either community within work groups led by the other, e.g., within standards development organizations.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computing and Information Science in Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4062328","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 1
Abstract
Extended reality (XR) technologies have realized significant value for design, manufacturing, and sustainment processes. However, Industrial XR, or XR implemented within industrial applications, suffers from scalability and flexibility challenges due to fundamental gaps with interoperability between data, models, and platforms. Though there has been a number of recent efforts to improve the interoperability of industrial XR technologies, progress has been hindered by an innate separation between the domain-specific models (e.g., manufacturing execution data, material specifications, and product manufacturing information) with XR (often-standard) processes (e.g., multi-scale spatial representations and data formats optimized for run-time presentation). In this paper, we elaborate on promising research directions and opportunities around which the manufacturing and visualization academic community can rally. To establish such research directions, we (1) conducted a meta-review on well-established state-of-the-art review articles that have already presented in-depth surveys on application areas for industrial XR, such as maintenance, assembly and inspection and (2) mapped those findings to publicly published priorities from across the US Department of Defense. We hope that our presented research agenda will spur interdisciplinary work across academic silos, i.e., manufacturing and visualization communities, and engages either community within work groups led by the other, e.g., within standards development organizations.
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