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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 operational contexts
- Essential performance indicators and KPIs
Data Acquisition and Preparation
- Manufacturing data sources (sensors, PLC, MES)
- Cleaning and structuring time-series data
- Preprocessing using Pandas and Jupyter
Descriptive and Diagnostic Analytics
- Data exploration and visualization techniques
- Correlation analysis and identifying root causes
- Building custom dashboards with Power BI
Machine Learning for Process Optimization
- Supervised and unsupervised learning approaches
- Clustering methods for pattern discovery
- Regression and classification for predictive modeling
AI for Predictive Maintenance and Quality Control
- Anomaly detection and predictive alert systems
- Models for predicting equipment failure
- Enhancing product quality through model-driven insights
Real-Time Analytics and Feedback Loops
- Streaming data and real-time processing capabilities
- Integration with SCADA/MES systems
- Implementing feedback for automatic process adjustments
Case Study and Capstone Project
- Practical analysis of real-world datasets
- Designing and validating optimization models
- Presenting an AI-driven improvement strategy
Summary and Future Directions
Requirements
- Foundational knowledge of manufacturing processes or operations management
- Practical experience in data analysis or Excel-based reporting
- Basic proficiency in programming or scripting
Target Audience
- Process engineers
- Plant supervisors
- Lean Six Sigma practitioners
Testimonials (1)
How to use and explore AI for daily works
