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Course Outline
Introduction to AI in the Manufacturing Sector
- Developments in smart manufacturing and Industry 4.0
- Overview of AI applications in operational settings
- Key performance metrics and KPIs
Data Collection and Preparation
- Sources of manufacturing data (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
- Custom dashboards created with Power BI
Machine Learning for Process Optimization
- Supervised and unsupervised learning methods
- Clustering techniques for pattern discovery
- Regression and classification for predictive modeling
AI Applications in Predictive Maintenance and Quality Control
- Anomaly detection and predictive alerts
- Models for failure prediction
- Enhancing product quality through model insights
Real-Time Analytics and Feedback Mechanisms
- Streaming data handling and real-time processing
- Integration with SCADA and MES systems
- Feedback loops for automatic process adjustments
Case Study and Capstone Project
- Practical analysis of real-world datasets
- 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 principles
- Experience in data analysis or creating Excel-based reports
- Basic knowledge of programming or scripting languages
Target Audience
- Process engineers
- Plant supervisors
- Lean Six Sigma practitioners
21 Hours
