Immersive learning system for manufacturing industries

被引：7
|
作者：
Fernandes, KJ [1 ]
Raja, VH
Eyre, J
机构：
[1] Univ Warwick, Int Mfg Ctr, Warwick Mfg Grp, Coventry CV4 7AL, W Midlands, England
[2] VR Syst UK Ltd, Southampton SO52 9DL, Hants, England
来源：
COMPUTERS IN INDUSTRY | 2003年 / 51卷 / 01期
关键词：
virtual reality; manufacturing companies; cybersphere;
D O I：
10.1016/S0166-3615(03)00027-7
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Virtual reality-based training systems are advanced computer-assisted training systems using virtual reality (VR) technology. To have better structure and easier implementation, a virtual training system can be modeled as an integrated system consisting of a training visualization suite, an interface module and instruction module. This paper discusses how a fully immersive VR visualization suite, called "Cybersphere", can be used in conjunction with a collaborative product suite to achieve an ideal training environment for manufacturing industries. The design and development of the system, and expert- and user-based evaluations are reported. (C) 2003 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：31 / 40
页数：10
相关论文
共 50 条
  • [21] Design of an Augmented Reality System for Immersive Learning of Digital Electronic
    Martin, Sergio
    Parra, Gerardo
    Cubillo, Joaquin
    Quintana, Blanca
    Gil, Rosario
    Perez, Clara
    Castro, Manuel
    [J]. 2020 XIV TECHNOLOGIES APPLIED TO ELECTRONICS TEACHING CONFERENCE (TAEE2020), 2020,
  • [22] MILSDeM: Guiding immersive learning system development and taxonomy evaluation
    Mat Sanusi, Khaleel Asyraaf
    Majonica, Daniel
    Iren, Deniz
    Fanchamps, Nardie
    Klemke, Roland
    [J]. EDUCATION AND INFORMATION TECHNOLOGIES, 2024,
  • [23] Handheld Augmented Reality Supported Immersive Ubiquitous Learning System
    Liu, Tsung-Yu
    Chu, Yu-Ling
    [J]. 2008 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), VOLS 1-6, 2008, : 2453 - +
  • [24] Analyzing and Predicting Overall Equipment Effectiveness in Manufacturing Industries using Machine Learning
    de Souza, Bruno Vilela
    Barros dos Santos, Sergio R.
    de Oliveira, Andre M.
    Givigi, Sidney N.
    [J]. SYSCON 2022: THE 16TH ANNUAL IEEE INTERNATIONAL SYSTEMS CONFERENCE (SYSCON), 2022,
  • [25] An Immersive VR Interactive Learning System for Tenon Structure Training
    Chen, Liang
    Wu, Lingling
    Li, Xuwei
    Xu, Jin
    [J]. 2019 2ND INTERNATIONAL CONFERENCE ON DATA INTELLIGENCE AND SECURITY (ICDIS 2019), 2019, : 115 - 119
  • [26] ANALYSIS OF BRAIN ACTIVITY INFLUENCED BY REPLICATION ACCURACY IN IMITATION LEARNING IN MANUFACTURING INDUSTRIES
    Asaka, Yusuke
    Watanuki, Keiichi
    Fukuda, Shuichi
    Muramatsu, Keiichi
    Hou, Lei
    [J]. PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2016, VOL 1A, 2016,
  • [27] Production Time Prediction for Contract Manufacturing Industries Using Automated Machine Learning
    Sousa, Afonso
    Ferreira, Luis
    Ribeiro, Rui
    Xavier, Joao
    Pilastri, Andre
    Cortez, Paulo
    [J]. ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2022, PART II, 2022, 647 : 262 - 273
  • [28] FLDID: Federated Learning Enabled Deep Intrusion Detection in Smart Manufacturing Industries
    Verma, Priyanka
    Breslin, John G.
    O'Shea, Donna
    [J]. SENSORS, 2022, 22 (22)
  • [29] Critical factors for the dimensional management system (DMS) implementation in manufacturing industries
    Teoi, Augusto Yassuo
    Anholon, Rosley
    da Silva, Dirceu
    Goncalves Quelhas, Osvaldo Luiz
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2017, 88 (1-4): : 1053 - 1063
  • [30] Integrated Evaluation System for the Strategic Management of Innovation Initiatives in Manufacturing Industries
    Kohl, H.
    Orth, R.
    Riebartsch, O.
    Hecklau, F.
    [J]. 13TH GLOBAL CONFERENCE ON SUSTAINABLE MANUFACTURING - DECOUPLING GROWTH FROM RESOURCE USE, 2016, 40 : 335 - 340
12345