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Struttura del corso
Introduction to AI in Manufacturing
- Trends in smart manufacturing and Industry 4.0
- Overview of AI use cases in operations
- Key performance metrics and KPIs
Data Collection and Preparation
- Sources of manufacturing data (sensors, PLC, MES)
- Cleaning and formatting time-series data
- Using Pandas and Jupyter for preprocessing
Descriptive and Diagnostic Analytics
- Data exploration and visualization
- Correlation analysis and root cause identification
- Custom dashboards with Power BI
Machine Learning for Process Optimization
- Supervised and unsupervised learning
- Clustering for pattern discovery
- Regression and classification for prediction
AI for Predictive Maintenance and Quality
- Anomaly detection and predictive alerts
- Failure prediction models
- Improving product quality through model insights
Real-Time Analytics and Feedback Loops
- Streaming data and real-time processing
- Integrating with SCADA/MES systems
- Feedback 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 AI-driven improvement plan
Summary and Next Steps
Requisiti
- An understanding of manufacturing processes or operations management
- Experience with data analysis or Excel-based reporting
- Basic familiarity with programming or scripting
Audience
- Process engineers
- Plant supervisors
- Lean Six Sigma professionals
21 ore
