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Duration 14 hours
Course Outline
Foundations of Generative AI
- The landscape of AI within modern manufacturing.
- Core theoretical underpinnings of Generative AI.
- Examining real-world case studies and practical applications.
Design Optimization via Generative AI
- Leveraging AI capabilities in product design and development lifecycles.
- In-depth case study: Applying generative design methodologies.
- Fostering innovation and creative problem-solving in design processes.
Predictive Maintenance Strategies
- Forecasting equipment needs through AI-driven maintenance models.
- Workshop: Constructing a functional predictive maintenance model.
- Minimizing operational downtime and reducing maintenance expenditures with AI.
Elevating Quality Control
- Integrating AI into robust quality assurance workflows.
- Exercise: Detecting and analyzing defects using AI algorithms.
- Enhancing product integrity through machine learning techniques.
Data-Driven Decision Making
- Translating AI insights into actionable production improvements.
- Group scenario analysis: Making data-centric operational decisions.
- Employing data visualization techniques to clarify AI outputs.
Integrating AI into Manufacturing Ecosystems
- Strategic approaches for embedding AI into current manufacturing workflows.
- Panel discussion: Navigating common integration challenges.
- Industry best practices for AI adoption in factory environments.
Future Directions in Manufacturing AI
- Assessing emerging technologies and their potential industrial impact.
- Interactive session: Positioning your organization for the future of AI in manufacturing.
- Maintaining competitive advantage through continuous AI learning.
Applied Practice Sessions
- Executing hands-on projects with Generative AI tools.
- Conducting peer reviews and presenting group findings.
- Capstone project: Formulating a holistic AI strategy for a complex manufacturing scenario.
Recap & Future Actions
Requirements
- Foundational experience in manufacturing engineering or process improvement methodologies.
- Working knowledge of basic AI and machine learning principles.
- Fundamental programming skills, with a preference for proficiency in Python.
Target Audience
- Manufacturing Engineers
- Process Improvement Specialists
- AI Developers
Testimonials (3)
Comprehensiveness about the backround; how to do a prompt.
Olaf Kannt - Helios Klinikum Krefeld
Course - Generative AI and Prompt Engineering in Healthcare
the tips and recommended prompts that we can take away from this training
Lee Mei Lin - ST Engineering IHQ Pte Ltd
Course - InVideo AI: Creating Engaging Short-Form Videos
use of proper and effective prompt
