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Course Outline
Introduction to AI in Manufacturing
- Emerging trends in smart manufacturing and Industry 4.0
- Broad overview of AI applications in operational contexts
- Critical performance metrics and KPIs
Data Acquisition and Preparation
- Origins of manufacturing data (sensors, PLC, MES)
- Cleaning and structuring time-series data
- Preprocessing workflows using Pandas and Jupyter
Descriptive and Diagnostic Analytics
- Exploratory data analysis and visualization techniques
- Correlation studies and root cause analysis
- Developing custom dashboards with Power BI
Machine Learning for Process Optimization
- Supervised and unsupervised learning methodologies
- Clustering techniques for pattern recognition
- Regression and classification for predictive modeling
AI for Predictive Maintenance and Quality Assurance
- Anomaly detection and proactive alert systems
- Building models for failure prediction
- Enhancing product quality through model-derived insights
Real-Time Analytics and Feedback Mechanisms
- Streaming data ingestion and real-time processing
- Integration with SCADA and MES systems
- Feedback loops for automated process adjustments
Case Studies and Capstone Project
- Practical analysis of industry-specific datasets
- Designing and validating optimization models
- Presenting a comprehensive AI-driven improvement strategy
Conclusion and Future Directions
Requirements
- Foundational knowledge of manufacturing processes or operations management
- Practical experience with data analysis or Excel-based reporting
- Basic familiarity with programming or scripting languages
Target Audience
- Process engineers
- Plant supervisors
- Lean Six Sigma professionals
21 Hours
