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 Duration 14 hours

Course Outline

Introduction to Edge AI in Industrial Automation

  • Exploring Edge AI and its industrial applications
  • Analyzing the advantages and challenges of Edge AI in industrial contexts
  • Examining case studies of successful Edge AI implementations in manufacturing

Establishing the Edge AI Environment

  • Installation and configuration of Edge AI tools
  • Setting up industrial sensors and data acquisition systems
  • Introduction to key Edge AI frameworks and libraries
  • Practical exercises for environment configuration

Predictive Maintenance via Edge AI

  • Fundamentals of predictive maintenance
  • Creating AI models for monitoring equipment health
  • Enabling real-time fault detection and prediction
  • Hands-on practical tasks for predictive maintenance

Quality Control with Edge AI

  • Overview of quality control in manufacturing
  • AI methods for detecting and classifying defects
  • Implementing vision-based quality inspection systems
  • Practical exercises for quality control applications

Process Optimization through Edge AI

  • Introduction to industrial process optimization
  • Leveraging AI for real-time monitoring and control
  • Building AI-driven decision-making systems
  • Practical exercises for process optimization

Deployment and Management of Edge AI Solutions

  • Deploying AI models on industrial edge devices
  • Monitoring and upkeep of Edge AI systems
  • Troubleshooting and refining deployed models
  • Hands-on exercises focused on deployment and management

Tools and Frameworks for Industrial Edge AI

  • Survey of tools and frameworks (e.g., TensorFlow Lite, OpenVINO)
  • Utilizing TensorFlow Lite for industrial AI tasks
  • Practical exercises using optimization tools

Real-World Applications and Case Studies

  • Review of successful industrial Edge AI projects
  • Discussion of industry-specific use cases
  • Capstone project: building and optimizing a real-world industrial AI application

Conclusion and Future Directions

Requirements

  • Familiarity with fundamental AI and machine learning principles
  • Practical experience with industrial automation systems
  • Foundational programming proficiency (Python is recommended)

Target Audience

  • Industrial engineers
  • Manufacturing specialists
  • AI developers

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