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Towards Aircraft Maintenance Metaverse Using Speech Interactions with Virtual Objects in Mixed Reality.

Aziz Siyaev1, Geun-Sik Jo1,2

  • 1Artificial Intelligence Laboratory, Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea.

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Summary

This study introduces a mixed reality (MR) system for aircraft maintenance training, using smart glasses and deep learning for speech control. This enhances virtual aircraft interaction and provides intuitive, hands-free operation for trainee engineers.

Keywords:
Boeing 737Industry 4.0aircraft maintenance educationdeep learningmetaversemixed reality (MR)smart maintenancespeech interaction

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Area of Science:

  • Aerospace Engineering
  • Computer Science
  • Educational Technology

Background:

  • Metaverses offer virtual experiences integrated with the physical world.
  • Mixed Reality (MR) presents opportunities for virtual aircraft interaction (digital twins) in aviation maintenance.
  • Current aviation training often relies on outdated physical models, limiting practical experience.

Purpose of the Study:

  • To propose a mixed reality (MR) education and training system for Boeing 737 aircraft maintenance.
  • To enhance trainee engineers' interaction with virtual assets using speech commands via smart glasses.
  • To enable hands-free operation and intuitive control in a virtual aircraft maintenance environment.

Main Methods:

  • Development of a mixed reality (MR) training module for aircraft maintenance using smart glasses.
  • Integration of a deep learning speech interaction module with Convolutional Neural Network (CNN) architecture.
  • Implementation of speech command recognition for English and Korean languages to control virtual assets and workflow.

Main Results:

  • The speech interaction module demonstrated high accuracy in prediction, with an average F1-Score of 95.7% for command prediction and 99.6% for language identification.
  • The system enables intuitive and efficient control over virtual aircraft maintenance operations.
  • Enhanced interaction with virtual objects in MR was achieved through speech commands.

Conclusions:

  • The proposed MR system with speech interaction significantly improves aircraft maintenance education and training.
  • Deep learning-based speech control offers an efficient and hands-free method for operating virtual assets in aviation.
  • This technology bridges the gap between theoretical knowledge and practical skills in aircraft maintenance training.