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Duration 21 hours
Course Outline
Introduction to AI in Manufacturing
- Emerging trends in smart manufacturing and Industry 4.0
- Overview of AI applications in operational contexts
- Essential performance metrics and KPIs
Data Collection and Preparation
- Data sources in manufacturing (sensors, PLC, MES)
- Cleaning and structuring time-series data
- Preprocessing workflows using Pandas and Jupyter
Descriptive and Diagnostic Analytics
- Data exploration and visualization techniques
- Correlation analysis and root cause identification
- Building custom dashboards with Power BI
Machine Learning for Process Optimization
- Supervised and unsupervised learning approaches
- Clustering methods for pattern discovery
- Regression and classification models for prediction
AI for Predictive Maintenance and Quality
- Anomaly detection and predictive 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
- Implementing feedback loops for automatic process adjustments
Case Study and Capstone Project
- Hands-on analysis of real-world datasets
- Designing and validating optimization models
- Presenting a final AI-driven improvement plan
Summary and Next Steps
Requirements
- Familiarity with manufacturing processes or operations management principles.
- 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 professionals.
