Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Digital Twins
- Concepts and the evolution of digital twins
- Use cases across manufacturing, energy, and logistics sectors
- Digital twin architecture and lifecycle management
System Modeling and Simulation
- Modeling dynamic systems using Simulink
- Comparing physics-based and data-driven modeling approaches
- Visualizing systems with Unity
Real-Time Data Integration
- Leveraging MQTT and OPC-UA for connectivity
- 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
- Utilizing TensorFlow or PyTorch with live data streams
- Training models using simulation outputs
Visualization and Dashboards
- Designing user interfaces for twin monitoring
- Options for 3D and 2D visualization
- 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 workflows
- Deployment and testing within a simulated environment
Maintaining and Scaling Digital Twins
- Lifecycle management and updates
- Interoperability and adherence to standards
- Scaling to encompass multiple assets or processes
Summary and Next Steps
Requirements
- A foundational understanding of system modeling or industrial operations
- Proficiency in Python or comparable programming languages
- Knowledge of data integration concepts
Target Audience
- Leaders driving digital transformation
- Plant IT staff
- Data architects
21 Hours
