Author ORCID Identifier
Semester
Spring
Date of Graduation
2025
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Industrial and Managements Systems Engineering
Committee Chair
Thorsten Wuest
Committee Co-Chair
Zhichao Liu
Committee Member
Zhichao Liu
Committee Member
Omar Al-Shebeeb
Abstract
Digital Twin (DT) technology, a cornerstone of Industry 4.0, facilitates real-time synchronization between virtual models and physical manufacturing systems, enhancing operational efficiency and decision-making. However, its widespread adoption is hindered by the absence of standardized methods for selecting Development Environments (DEs) for DTs, compounded by challenges in cost, interoperability, and connectivity with Industrial Internet of Things (IIoT) protocols. This thesis proposes a Systematic Selection Framework to address this gap, offering a structured methodology to evaluate DEs based-on visualization quality, scalability, interoperability, and cost-effectiveness for manufacturing applications. The framework categorizes and compares sixteen DEs into Game Engines, Robotics Engines, and Simulation Engines, employing a multi-step process involving a persona-based analysis, weighted criteria filtering, and implementation guidelines. The framework was evaluated on the use case of the Smart Manufacturing Lab and identified the Unity Engine as a match for the setup, calling for a high-fidelity visual DT of the Cyber-Physical Lab (CP-Lab), an 8-station assembly line at the Smart Manufacturing Lab.
The CP-Lab DT, developed using Unity and integrated with MQTT-driven Testbed-as-a-Service (TaaS) data as the communication protocol, transforms a static CAD model into a dynamic simulation. It replicates conveyor motion across seven pallets and Turning station flipping operations, leveraging open-source tools like Mosquitto and C# scripting for real-time synchronization. Validation confirms the framework’s functionality and partial scalability, though data inconsistencies limit full station simulation, underscoring the need for robust datasets. This replicable, flexible framework mitigates trial-and-error DE selection, advancing DT adoption in Industry 4.0. Future work will explore real-time CP-Lab integration, Machine Learning (ML) integration, full integration of MQTT data, and refined criteria weighting to enhance precision and scalability across diverse manufacturing contexts.
Recommended Citation
Dodero Fernandez, Carlos, "Framework for Development Environment Selection in Digital Twin Applications" (2025). Graduate Theses, Dissertations, and Problem Reports. 12776.
https://researchrepository.wvu.edu/etd/12776