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Course Outline
Introduction to Digital Twins
- Core concepts and the evolution of digital twins.
- Use cases across manufacturing, energy, and logistics sectors.
- Digital twin architecture and lifecycle stages.
System Modelling and Simulation
- Modelling dynamic systems using Simulink.
- Comparing physics-based versus data-driven modelling approaches.
- Visualising 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
- Incorporating AI models for prediction and optimisation.
- Utilising TensorFlow or PyTorch with live data streams.
- Training models on simulation outputs.
Visualization and Dashboards
- Designing user interfaces for monitoring twins.
- Exploring 3D and 2D visualisation options.
- 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 components.
- Deploying and testing in a simulated environment.
Maintaining and Scaling Digital Twins
- Lifecycle management and update protocols.
- Ensuring interoperability and adhering to standards.
- Scaling solutions to encompass multiple assets or processes.
Summary and Next Steps
Requirements
- Knowledge of system modelling or industrial operations.
- Experience with Python or comparable programming languages.
- Familiarity with data integration concepts.
Target Audience
- Leaders driving digital transformation.
- IT personnel within plant operations.
- Data architects.
21 Hours
