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Course Outline
Introduction to AI in Manufacturing
- Current trends in smart manufacturing and Industry 4.0
- An overview of AI applications in operational contexts
- Essential performance metrics and KPIs
Data Collection and Preparation
- Origin points for manufacturing data (sensors, PLCs, MES)
- Cleaning and structuring time-series data
- Leveraging Pandas and Jupyter for data preprocessing
Descriptive and Diagnostic Analytics
- Data exploration and visualization techniques
- Correlation analysis and identifying root causes
- Creating custom dashboards using Power BI
Machine Learning for Process Optimization
- Concepts of supervised and unsupervised learning
- Applying clustering to discover patterns
- Using regression and classification for predictive tasks
AI for Predictive Maintenance and Quality
- Detecting anomalies and generating predictive alerts
- Developing failure prediction models
- Enhancing product quality through insights from model outputs
Real-Time Analytics and Feedback Loops
- Processing streaming data in real-time
- Integration with SCADA/MES systems
- Implementing feedback mechanisms for automatic process adjustments
Case Study and Capstone Project
- Hands-on analysis of real-world datasets
- Designing and validating an optimization model
- Presenting a final AI-driven improvement plan
Summary and Next Steps
Requirements
- Familiarity with manufacturing processes or operations management
- Practical experience with data analysis or Excel-based reporting tools
- Basic knowledge of programming or scripting languages
Target Audience
- Process engineers
- Plant supervisors
- Lean Six Sigma practitioners
21 Hours
