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Course Outline
Fundamentals of Digital Twins
- Key concepts and the evolution of digital twins
- Applications in manufacturing, energy, and logistics sectors
- Digital twin structure and lifecycle management
System Modeling and Simulation
- Modeling dynamic systems using Simulink
- Physics-based versus data-driven modeling approaches
- System visualization using Unity
Integrating Live Data
- Utilizing MQTT and OPC-UA for connectivity
- Data streaming with Node-RED
- Collecting sensor and machine data into the twin
AI and Machine Learning within Digital Twins
- Integrating AI models for predictive analytics and optimization
- Using TensorFlow or PyTorch with live data
- Training models based on simulation outputs
Visualization and Dashboards
- Developing user interfaces for twin monitoring
- 3D and 2D visualization capabilities
- Creating custom dashboards with live insights
Case Study: Developing a Digital Twin Prototype
- End-to-end design of a manufacturing asset twin
- Setting up data integration and machine learning
- Deployment and testing in a simulated setting
Maintenance and Scaling of Digital Twins
- Lifecycle management and updates
- Interoperability and industry standards
- Scaling to multiple assets or processes
Recap and Future Directions
Requirements
- A solid understanding of system modeling or industrial operations
- Proficiency with Python or comparable programming languages
- Knowledge of data integration principles
Target Audience
- Leaders in digital transformation
- IT staff in plant operations
- Data architects
21 Hours
