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Course Outline
Introduction to AI in Manufacturing
- Emerging trends in smart manufacturing and Industry 4.0
- Overview of AI applications in operational contexts
- Key performance metrics and KPIs
Data Collection and Preparation
- Manufacturing data sources (sensors, PLC, MES)
- Cleaning and formatting time-series data
- Utilizing Pandas and Jupyter for preprocessing
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 modeling
AI for Predictive Maintenance and Quality Assurance
- Anomaly detection and predictive alert systems
- 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 data sets
- Designing and validating an optimization model
- Final presentation of an AI-driven improvement plan
Summary and Next Steps
Requirements
- Familiarity with manufacturing processes or operations management
- Proficiency in data analysis or Excel-based reporting
- Basic knowledge of programming or scripting
Target Audience
- Process engineers
- Plant supervisors
- Lean Six Sigma professionals
21 Hours
