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Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

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Towards the Augmentation of Digital Twin Performance.

Quentin Charrier¹, Nisar Hakam¹, Khaled Benfriha¹

¹Arts et Métiers Institute of Technology (AMIT), 75013 Paris, France.

Sensors (Basel, Switzerland)

|November 25, 2023

Summary

This study enhances Digital Twin (DT) technology for industrial process supervision by introducing a new framework for data analysis and Key Performance Indicators (KPIs). This improves monitoring of physical processes via their cyber representations.

Keywords:

Digital Twin Industry 4.0 analysis sensors

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Area of Science:

Industrial Engineering
Computer Science
Cyber-Physical Systems

Background:

Digital Twin (DT) technology offers visualization, analysis, and simulation for industrial production.
Existing DT methodologies face challenges in data contextualization for effective process supervision.
Cyber-Physical Systems (CPS) generate performance data crucial for industrial monitoring.

Purpose of the Study:

To extend Digital Twin capabilities for enhanced process supervision.
To introduce a novel framework for data identification, collection, and analysis within DT.
To integrate Key Performance Indicators (KPIs) for real-time monitoring.

Main Methods:

Literature review of existing Digital Twin methodologies and data contextualization.
Development of a framework to identify, collect, and analyze performance data from production systems.
Implementation of Key Performance Indicators (KPIs) within an immersive DT environment for case study monitoring.

Main Results:

A novel framework was developed and implemented to enhance DT functionalities for process supervision.
Key Performance Indicators (KPIs) were successfully integrated into the DT's immersive environment.
The methodology enables monitoring of physical processes through their cyber representations, improving performance insights.

Conclusions:

The proposed methodology extends DTs for effective process supervision by integrating data analysis and KPIs.
The framework facilitates the monitoring of machine and production line performance via DTs.
This approach addresses industrial challenges and opens possibilities for applications like predictive maintenance.