JOURNAL ARTICLE

A VR-based interactive teaching and practice environment for supporting the whole process of mining engineering education.

  • Published In: Mining Technology (2572-6668), 2023, v. 132, n. 2. P. 89 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Xie, Jiacheng; Yan, Zewen; Wang, Xuewen 3 of 3

Abstract

The article focuses on the development and application of a virtual reality (VR)-based interactive teaching and practice environment for mining equipment processes to enhance practical training under laboratory conditions. This system integrates digital design, virtual simulation, and VR human–computer interaction (HCI) hardware to simulate the structure, operation, and electronic control of mining machinery, enabling students to experience realistic underground production scenarios safely. The environment supports virtual assembly, collaborative networked operation, stereoscopic display, and real-time mapping between virtual and actual equipment data, facilitating comprehensive learning of mechanical, electrical, and control systems. Testing with student groups demonstrated that VR-enhanced teaching, especially when combined with real-time virtual-to-physical equipment interaction, significantly improves understanding of equipment structure, electrical control, and coordinated operation compared to traditional methods. The study concludes that VR integration offers a novel, immersive model for mining equipment education and training, with potential for broader industrial adoption.

Additional Information

  • Source:Mining Technology (2572-6668). 2023/06, Vol. 132, Issue 2, p89
  • Document Type:Article
  • Subject Area:Earth and Atmospheric Sciences
  • Publication Date:2023
  • ISSN:2572-6668
  • DOI:10.1080/25726668.2023.2177737
  • Accession Number:163954966
  • Copyright Statement:Copyright of Mining Technology (2572-6668) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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