Digital Twin Course Materials
The "Introduction to Digital Twins for Mechanical Engineers" course, an elective, is designed for Senior and graduate students interested in learning about digital twins, with a heavy emphasis on practical, hands-on activities, Python script examples, and multimodal content integration, blending theoretical knowledge with practical application, including an innovative component on voice-enabled LLMs.
Key Highlights:
Comprehensive Course Content:
Detailed syllabi (MEEN 5303 and MEEN 4336) outlining course descriptions, learning outcomes, grading, and weekly schedules.
Lecture presentations covering fundamental concepts of Digital Twins, their evolution, classifications (product, system, process), and core elements.
Practical guides for essential Linux commands (file operations, process management, networking) and setting up NVIDIA Jetson Nano for local LLMs.
Hands-on Learning Activities:
Exercises for concept mapping, Linux command practice, IoT communication pipeline setup, and virtual dashboard design.
Activities focused on real-time data logging and visualization, system behavior simulation, digital twin platform comparison, and basic machine learning integration.
Later modules address ethical considerations, cybersecurity audits, and multi-twin architecture design.
Enabling Technologies:
Lectures on the critical roles of the Internet of Things (IoT) for real-time data collection, cloud computing for scalable data storage and processing, and edge computing for localized data analytics.
Voice-Enabled LLM Development:
Python scripts demonstrating the integration of speech-to-text (Whisper) and local LLM (Ollama) capabilities, suggesting a practical project or assignment in the curriculum.