Building Digital Twins with AI and Real-Time Data Training Course

Introduction to Digital Twins

• Fundamental concepts and the evolution of digital twins
• Applications in manufacturing, energy, and logistics
• Architectural design and lifecycle management

System Modelling and Simulation

• Modelling dynamic systems using Simulink
• Comparing physics-based and data-driven modelling approaches
• Visualising systems with Unity

Real-Time Data Integration

• Employing MQTT and OPC-UA for connectivity
• Streaming data using Node-RED
• Ingesting sensor and machine data into the twin

AI and Machine Learning within Digital Twins

• Integrating AI models for prediction and optimisation
• Utilising TensorFlow or PyTorch with live data feeds
• Training models based on simulation outputs

Visualisation and Dashboards

• Designing user interfaces for monitoring the twin
• Options for 3D and 2D visualisation
• Creating custom dashboards with real-time insights

Case Study: Developing a Digital Twin Prototype

• End-to-end design of a manufacturing asset twin
• Setting up data integration and machine learning
• Deployment and testing within a simulated environment

Maintenance and Scaling of Digital Twins

• Lifecycle management and system updates
• Ensuring interoperability and adhering to standards
• Scaling solutions to multiple assets or processes

Conclusion and Future Directions