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Course Outline
AI Foundations in Manufacturing
- Trends in smart manufacturing and Industry 4.0.
- Overview of AI applications in operations.
- Essential performance metrics and KPIs.
Data Acquisition and Preparation
- Origins of manufacturing data (sensors, PLC, MES).
- Processing and structuring time-series data.
- Preprocessing using Pandas and Jupyter.
Descriptive and Diagnostic Analytics
- Data exploration and visualization techniques.
- Correlation studies and identifying root causes.
- Building custom dashboards with Power BI.
Machine Learning for Process Optimization
- Supervised and unsupervised learning methods.
- Clustering for uncovering patterns.
- Regression and classification for forecasting.
AI for Predictive Maintenance and Quality Control
- Anomaly detection and predictive alerts.
- Models for predicting failures.
- Enhancing product quality via model insights.
Real-Time Analytics and Feedback Mechanisms
- Streaming data and real-time processing.
- Integration with SCADA/MES systems.
- Feedback loops for automatic process adjustments.
Case Studies and Capstone Project
- Practical analysis of real-world datasets.
- Developing and validating an optimization model.
- Presenting the final AI-driven improvement strategy.
Conclusion and Future Directions
Requirements
- Knowledge of manufacturing processes or operations management.
- Familiarity with data analysis or Excel-based reporting.
- Basic proficiency in programming or scripting.
Target Audience
- Process engineers.
- Plant supervisors.
- Lean Six Sigma practitioners.
21 Hours
