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Course Outline
Introduction to Digital Twins
- Concepts and evolution of digital twins
- Use cases in manufacturing, energy, and logistics
- Digital twin architecture and lifecycle
System Modelling and Simulation
- Modelling dynamic systems with Simulink
- Physics-based vs. data-driven modelling
- Visualising systems with Unity
Real-Time Data Integration
- Using MQTT and OPC-UA for connectivity
- Streaming data with Node-RED
- Ingesting sensor and machine data into the twin
AI and Machine Learning in Digital Twins
- Integrating AI models for prediction and optimisation
- Using TensorFlow or PyTorch with live data
- Training models on simulation outputs
Visualisation and Dashboards
- Designing user interfaces for twin monitoring
- 3D and 2D visualisation 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
- An understanding of system modelling or industrial operations
- Experience with Python or similar programming languages
- Familiarity with data integration concepts
Audience
- Digital transformation leaders
- Plant IT personnel
- Data architects
21 Hours
