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Course Outline
Introduction to Digital Twins
- Concepts and historical development of digital twins
- Applications in manufacturing, energy, and logistics sectors
- Digital twin architecture and lifecycle stages
System Modeling and Simulation
- Modeling dynamic systems using Simulink
- Contrast between physics-based and data-driven modeling approaches
- System visualization with Unity
Real-Time Data Integration
- Leveraging MQTT and OPC-UA for connectivity
- Handling data streams with Node-RED
- Ingesting sensor and machinery data into the twin
AI and Machine Learning in Digital Twins
- Integrating AI models for prediction and optimization
- Utilizing TensorFlow or PyTorch with live data
- Training models on simulation outputs
Visualization and Dashboards
- Designing user interfaces for twin monitoring
- 3D and 2D visualization options
- Custom dashboards with real-time insights
Case Study: Building a Digital Twin Prototype
- End-to-end design of a manufacturing asset twin
- Data integration and machine learning setup
- Deployment and testing in a simulated environment
Maintaining and Scaling Digital Twins
- Lifecycle management and updates
- Interoperability and standards
- Scaling to multiple assets or processes
Summary and Next Steps
Requirements
- Knowledge of system modeling or industrial operations
- Proficiency in Python or comparable programming languages
- Understanding of data integration principles
Target Audience
- Leaders driving digital transformation
- IT staff within plant operations
- Data architects
21 Hours
