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Course Outline

Introduction to Generative AI

  • Overview of AI in manufacturing
  • Principles of Generative AI
  • Real-world applications and case studies

Design Optimization with Generative AI

  • Using AI for product design and development
  • Case study: Generative design in practice
  • Enhancing creativity and innovation in product design

Predictive Maintenance

  • Implementing AI for equipment maintenance forecasting
  • Workshop: Building a predictive maintenance model
  • Reducing downtime and maintenance costs with AI

Quality Control Enhancement

  • Applying AI in quality assurance processes
  • Exercise: AI-driven defect detection and analysis
  • Improving product quality with machine learning algorithms

Data Analysis and Decision Making

  • Interpreting AI-generated insights for production improvement
  • Group activity: Data-driven decision-making scenarios
  • Utilizing data visualization for better understanding AI outputs

Integrating AI into Manufacturing Systems

  • Strategies for adopting AI in existing manufacturing workflows
  • Panel discussion: Overcoming challenges in AI integration
  • Best practices for implementing AI in manufacturing environments

Future Trends in Manufacturing AI

  • Exploring emerging technologies and their potential impact
  • Interactive session: Preparing for the future of manufacturing AI
  • Staying ahead of the curve with continuous learning in AI

Practical Sessions

  • Hands-on projects using Generative AI tools
  • Peer reviews and group presentations
  • Final project: Developing a comprehensive AI strategy for a manufacturing scenario

Summary and Next Steps

Requirements

  • Background in manufacturing engineering or process improvement
  • Familiarity with basic AI and machine learning concepts
  • Basic programming knowledge, preferably in Python

Audience

  • Manufacturing engineers
  • Process improvement specialists
  • AI developers
 14 Hours

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