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Course Outline
Introduction to Digital Twins
- Concepts and the evolution of digital twins
- Application scenarios in manufacturing, energy, and logistics
- Digital twin architecture and lifecycle
System Modeling and Simulation
- Modeling dynamic systems using Simulink
- Distinctions between physics-based and data-driven modeling
- Visualizing systems via Unity
Real-Time Data Integration
- Leveraging MQTT and OPC-UA for connectivity
- Managing data streaming 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 the twin
- Options for 3D and 2D visualization
- Creating custom dashboards with real-time insights
Case Study: Developing a Digital Twin Prototype
- End-to-end design of a manufacturing asset twin
- Configuring data integration and machine learning setup
- Deployment and testing within a simulated environment
Maintaining and Scaling Digital Twins
- Lifecycle management and updates
- Interoperability and standards
- Scaling to accommodate multiple assets or processes
Summary and Next Steps
Requirements
- A foundational understanding of system modeling or industrial operations
- Practical experience with Python or comparable programming languages
- Familiarity with data integration concepts
Target Audience
- Leaders driving digital transformation
- IT staff within plant operations
- Data architects
21 Hours
