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Course Outline

Introduction to Edge AI in Industrial Automation

  • Overview of Edge AI and its industrial applications
  • Advantages and challenges of implementing Edge AI in industrial contexts
  • Analysis 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 pertinent Edge AI frameworks and libraries
  • Practical exercises focused on environment setup

Predictive Maintenance with Edge AI

  • Fundamentals of predictive maintenance
  • Creating AI models for monitoring equipment health
  • Executing real-time fault detection and prediction
  • Hands-on exercises dedicated to predictive maintenance

Quality Control Using Edge AI

  • Overview of quality control practices in manufacturing
  • AI techniques for detecting and classifying defects
  • Building vision-based quality control systems
  • Practical exercises for quality control applications

Process Optimization with Edge AI

  • Introduction to process optimization strategies
  • Leveraging AI for real-time process monitoring and control
  • Implementing AI-driven decision-making systems
  • Hands-on exercises for process optimization

Deploying and Managing Edge AI Solutions

  • Deploying AI models onto industrial edge devices
  • Monitoring and maintaining Edge AI systems
  • Troubleshooting and refining deployed models
  • Practical exercises for 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 use cases
  • Hands-on exercises involving optimization tools

Real-World Applications and Case Studies

  • Review of successful industrial Edge AI projects
  • Exploration of industry-specific use cases
  • Capstone project to build and optimize a practical industrial AI application

Summary and Next Steps

Requirements

  • A solid grasp of AI and machine learning fundamentals
  • Prior experience with industrial automation systems
  • Foundational programming proficiency (Python is recommended)

Audience

  • Industrial engineers
  • Manufacturing professionals
  • AI developers
 14 Hours

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