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March 27, 2026Sensors3 citationsOpen Access

Real-Time Digital Twin Architecture for Immersive Industrial Automation Training

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JOJessica S. OrtizVAVíctor H. AndaluzCCChristian P. Carvajal

Key Points

  • This research aims to create a Digital Twin architecture that enhances training in industrial automation through real-time interaction.
  • Developed a multi-layer Digital Twin architecture integrating physical PLC and virtual environment.
  • Used Unity-based platform for immersive training experiences.
  • Evaluated system performance through communication latency and synchronization metrics.
  • Conducted comparative evaluation with engineering students for effectiveness.
  • Demonstrated average communication latency below 15 ms and jitter under 0.5 ms.
  • Confirmed high perceived usability with a score of 86 out of 100.
  • Reported reduced cognitive workload with a NASA-TLX score of 34 out of 100.
  • Showed scalability and reliable real-time interaction during continuous operation.

Abstract

Industrial automation laboratories often face limitations related to restricted access to industrial equipment, safety constraints, and limited scalability for hands-on experimentation. To address these challenges, this work proposes a real-time multi-layer Digital Twin architecture integrating a physical Siemens S7-1500 PLC, an immersive Unity-based virtual environment, HMI supervision, and IoT-enabled remote monitoring within a unified communication framework. The architecture is structured into physical, digital, and integration layers, enabling modular scalability and bidirectional synchronization between the physical process and its virtual representation through Ethernet TCP/IP communication. System performance was evaluated using synchronization metrics including communication latency, jitter, deterministic timing deviation, and event synchronization accuracy. Experimental results demonstrated stable PLC–Digital Twin communication with average latencies below 15 ms and jitter below 0.5 ms, ensuring reliable real-time interaction during continuous operation. A comparative evaluation with engineering students also showed improved learning conditions, achieving high perceived usability (SUS = 86/100) and reduced cognitive workload (NASA-TLX = 34/100). These results confirm the effectiveness of the proposed architecture as a scalable platform for Industry 4.0 training environments.

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Cite This Study

Ortiz et al. (2026) studied this question.

synapsesocial.com/papers/69c6202f15a0a509bde18a84https://doi.org/10.3390/s26072023
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