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Course Outline
Introduction to Digital Twins
- Core concepts and the evolution of digital twin technology
- Applications in manufacturing, energy, and logistics sectors
- Overview of digital twin architecture and its lifecycle
System Modeling and Simulation
- Simulating dynamic systems using Simulink
- Comparing physics-based and data-driven modeling approaches
- Visualizing system behavior with Unity
Real-Time Data Integration
- Establishing connectivity via MQTT and OPC-UA
- Managing data streams with Node-RED
- Incorporating sensor and machine data into the digital twin
AI and Machine Learning in Digital Twins
- Embedding AI models for predictive analytics and optimization
- Utilizing TensorFlow or PyTorch with live data feeds
- Training models based on simulation results
Visualization and Dashboards
- Creating user interfaces for monitoring twin performance
- Exploring 3D and 2D visualization capabilities
- Building custom dashboards with live insights
Case Study: Constructing a Digital Twin Prototype
- Full-cycle design of a manufacturing asset twin
- Setting up data integration and machine learning components
- Deployment and validation in a simulated environment
Maintaining and Scaling Digital Twins
- Managing lifecycle updates and maintenance
- Ensuring interoperability and adherence to standards
- Scaling solutions across multiple assets or processes
Recap and Future Directions
Requirements
- A foundational grasp of system modeling or industrial processes
- Proficiency in Python or comparable programming languages
- Knowledge of data integration principles
Target Audience
- Leaders driving digital transformation initiatives
- IT professionals in plant and manufacturing environments
- Data architects and engineers
21 Hours
