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Course Outline
Introduction to AI in Manufacturing
- Trends in smart manufacturing and Industry 4.0.
- Overview of AI applications in operational settings.
- Key performance indicators (KPIs) and metrics.
Data Collection and Preparation
- Sources of manufacturing data (sensors, PLC, MES).
- Cleaning and structuring time-series data.
- Preprocessing techniques using Pandas and Jupyter.
Descriptive and Diagnostic Analytics
- Exploratory data analysis and visualization.
- Correlation analysis and root cause identification.
- Building custom dashboards with Power BI.
Machine Learning for Process Optimization
- Supervised and unsupervised learning paradigms.
- Clustering techniques for pattern recognition.
- Regression and classification methods for predictive modeling.
AI for Predictive Maintenance and Quality
- Anomaly detection and proactive alert systems.
- Developing failure prediction models.
- Enhancing product quality through model-driven insights.
Real-Time Analytics and Feedback Loops
- Streaming data and real-time processing capabilities.
- Integration with SCADA/MES systems.
- Automated feedback loops for dynamic process adjustments.
Case Study and Capstone Project
- Practical analysis of real-world datasets.
- Designing and validating optimization models.
- Presenting a comprehensive AI-driven improvement plan.
Summary and Future Directions
Requirements
- Foundational knowledge of manufacturing processes or operations management.
- Practical experience with data analysis or Excel-based reporting.
- Basic proficiency in programming or scripting languages.
Target Audience
- Process engineers.
- Plant supervisors.
- Lean Six Sigma practitioners.
21 Hours
