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