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Course Outline
Introduction to AI in Manufacturing
- Current trends in smart manufacturing and Industry 4.0.
- Overview of AI use cases within operational contexts.
- Key performance metrics and KPIs.
Data Collection and Preparation
- Sources of manufacturing data (sensors, PLCs, MES).
- Techniques for cleaning and formatting time-series data.
- Using Pandas and Jupyter for data preprocessing.
Descriptive and Diagnostic Analytics
- Data exploration and visualization techniques.
- Correlation analysis and root cause identification.
- Creating custom dashboards with Power BI.
Machine Learning for Process Optimization
- Supervised and unsupervised learning methods.
- Clustering techniques for pattern discovery.
- Regression and classification for predictive purposes.
AI for Predictive Maintenance and Quality Assurance
- Anomaly detection and predictive alert systems.
- Developing failure prediction models.
- Enhancing product quality through model insights.
Real-Time Analytics and Feedback Loops
- Streaming data handling and real-time processing.
- Integration with SCADA/MES systems.
- Implementing feedback mechanisms 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 an AI-driven improvement plan.
Summary and Next Steps
Requirements
- A foundational understanding of manufacturing processes or operations management.
- Practical experience with data analysis or Excel-based reporting.
- Basic familiarity with programming or scripting languages.
Target Audience
- Process engineers.
- Plant supervisors.
- Lean Six Sigma professionals.
21 Hours
