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Duration 21 hours
Course Outline
Introduction to AI in Manufacturing
- Trends in smart manufacturing and Industry 4.0
- Overview of AI applications in operations
- Key performance metrics and KPIs
Data Collection and Preparation
- Manufacturing data sources (sensors, PLC, MES)
- Cleaning and formatting time-series data
- Preprocessing using Pandas and Jupyter
Descriptive and Diagnostic Analytics
- Data exploration and visualization techniques
- Correlation analysis and root cause identification
- Building custom dashboards with Power BI
Machine Learning for Process Optimization
- Supervised and unsupervised learning methods
- Clustering for pattern discovery
- Regression and classification for predictive tasks
AI for Predictive Maintenance and Quality
- Anomaly detection and predictive alert systems
- Developing failure prediction models
- Enhancing product quality through model insights
Real-Time Analytics and Feedback Loops
- Streaming data and real-time processing
- Integration with SCADA/MES systems
- Feedback mechanisms for automatic process adjustments
Case Study and Capstone Project
- Hands-on analysis of real-world datasets
- Designing and validating an optimization model
- Presenting a final AI-driven improvement plan
Summary and Next Steps
Requirements
- Familiarity with manufacturing processes or operations management
- Experience in data analysis or Excel-based reporting
- Basic proficiency in programming or scripting
Target Audience
- Process engineers
- Plant supervisors
- Lean Six Sigma professionals
Testimonials (1)
How to use and explore AI for daily works
