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Course Outline
Introduction to Digital Twins
- Concepts and the evolution of digital twins.
- Application use cases in manufacturing, energy, and logistics sectors.
- Overview of digital twin architecture and lifecycle management.
System Modeling and Simulation
- Modeling dynamic systems using Simulink.
- Comparing physics-based approaches versus data-driven modeling.
- Visualizing systems with Unity.
Real-Time Data Integration
- Establishing connectivity via MQTT and OPC-UA.
- Managing streaming data with Node-RED.
- Ingesting sensor and machine data into the digital twin.
AI and Machine Learning in Digital Twins
- Integrating AI models for prediction and optimization tasks.
- Utilizing TensorFlow or PyTorch with live data feeds.
- Training models on simulation output data.
Visualization and Dashboards
- Designing user interfaces for twin monitoring.
- Exploring 3D and 2D visualization options.
- Creating custom dashboards with real-time insights.
Case Study: Building a Digital Twin Prototype
- End-to-end design of a manufacturing asset twin.
- Configuring data integration and machine learning setups.
- Deployment and testing within a simulated environment.
Maintaining and Scaling Digital Twins
- Lifecycle management and regular updates.
- Ensuring interoperability and adhering to standards.
- Scaling solutions to 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 in digital transformation.
- IT personnel in plant environments.
- Data architects.
21 Hours
