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Course Outline
Introduction to Digital Twins
- Core concepts and the evolution of digital twins
- Applications in manufacturing, energy, and logistics sectors
- Architecture and lifecycle of digital twins
System Modeling and Simulation
- Modeling dynamic systems using Simulink
- Distinguishing between physics-based and data-driven modeling
- Visualizing systems via Unity
Real-Time Data Integration
- Leveraging MQTT and OPC-UA for connectivity
- Handling data streams with Node-RED
- Ingesting sensor and machine 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 based on simulation outputs
Visualization and Dashboards
- Designing user interfaces for monitoring twins
- Exploring 3D and 2D visualization options
- Creating custom dashboards with real-time insights
Case Study: Developing a Digital Twin Prototype
- Comprehensive design of a manufacturing asset twin
- Setting up data integration and machine learning
- Deployment and testing within 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
- Familiarity with data integration principles
Target Audience
- Leaders in digital transformation
- IT staff in manufacturing plants
- Data architects
21 Hours
