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.
Duration 21 hours
Course Outline
Fundamentals of Digital Twins
- Core concepts and the historical evolution of digital twins
- Applications in manufacturing, energy, and logistics sectors
- Architectural frameworks and lifecycle management
System Modeling and Simulation
- Simulating dynamic systems using Simulink
- Comparing physics-based and data-driven modeling approaches
- System visualization techniques with Unity
Live Data Integration
- Establishing connectivity via MQTT and OPC-UA
- Data streaming workflows using Node-RED
- Incorporating sensor and machine data into the twin model
AI and Machine Learning Applications in Digital Twins
- Embedding AI models for predictive analytics and optimization
- Utilizing TensorFlow or PyTorch with live data feeds
- Model training based on simulation outputs
Visualization and Dashboard Creation
- Designing user interfaces for twin monitoring
- Exploring 3D and 2D visualization capabilities
- Building custom dashboards featuring real-time insights
Case Study: Developing a Digital Twin Prototype
- End-to-end design process for a manufacturing asset twin
- Setting up data integration and machine learning pipelines
- Deployment and validation in a simulated environment
Digital Twin Maintenance and Scalability
- Managing lifecycle updates and maintenance
- Ensuring interoperability and adhering to standards
- Scaling solutions across multiple assets or processes
Conclusion and Future Directions
Requirements
- Foundational knowledge of system modeling or industrial operations
- Proficiency in Python or comparable programming languages
- Working knowledge of data integration principles
Target Audience
- Leaders driving digital transformation initiatives
- IT specialists in industrial or plant settings
- Data architects
Testimonials (1)
How to use and explore AI for daily works
