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Course Outline
Introduction to AI in Manufacturing
- Current trends in smart manufacturing and Industry 4.0.
- Overview of AI applications in operational settings.
- Core performance metrics and KPIs.
Data Acquisition and Preprocessing
- Sources of manufacturing data (sensors, PLCs, MES).
- Cleaning and structuring time-series data.
- Leveraging Pandas and Jupyter for data preparation.
Descriptive and Diagnostic Analytics
- Exploratory data analysis and visualization techniques.
- Correlation studies and root cause analysis.
- Developing custom dashboards using Power BI.
Machine Learning for Process Optimization
- Supervised and unsupervised learning methodologies.
- Clustering techniques for pattern recognition.
- Regression and classification for predictive insights.
AI for Predictive Maintenance and Quality Control
- Anomaly detection and proactive alert systems.
- Building failure prediction models.
- Enhancing product quality via model-driven insights.
Real-Time Analytics and Feedback Mechanisms
- Streaming data and real-time processing capabilities.
- Integration with SCADA/MES systems.
- Enabling automatic process adjustments through feedback loops.
Case Study and Capstone Project
- Practical analysis of real-world datasets.
- Designing and validating an optimization model.
- Presenting an AI-driven improvement strategy.
Summary and Future Directions
Requirements
- Familiarity with manufacturing workflows or operational management.
- Prior exposure to data analysis or Excel-based reporting.
- Fundamental knowledge of programming or scripting languages.
Target Audience
- Process engineers.
- Plant supervisors.
- Lean Six Sigma practitioners.
21 Hours
