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Course Outline
Introduction to AI in Manufacturing
- Evolving trends in smart manufacturing and Industry 4.0
- Overview of practical AI applications in operations
- Essential performance metrics and KPIs
Data Acquisition and Preparation
- Identifying manufacturing data sources (sensors, PLC, MES)
- Refining and structuring time-series data
- Applying Pandas and Jupyter for data preprocessing
Descriptive and Diagnostic Analytics
- Exploring data patterns and visualizations
- Conducting correlation analysis and identifying root causes
- Developing custom dashboards using Power BI
Machine Learning Strategies for Process Optimization
- Techniques in supervised and unsupervised learning
- Utilizing clustering for pattern recognition
- Applying regression and classification for predictive insights
AI Applications in Predictive Maintenance and Quality Control
- Detecting anomalies and generating predictive alerts
- Building failure prediction models
- Enhancing product quality through actionable model insights
Real-Time Analytics and Feedback Mechanisms
- Managing streaming data and real-time processing
- Integrating systems with SCADA/MES platforms
- Implementing feedback loops for automatic process adjustments
Case Studies and Capstone Project
- Hands-on analysis of real-world datasets
- Designing and validating optimization models
- Presenting a comprehensive AI-driven improvement plan
Conclusion and Future Directions
Requirements
- Familiarity with manufacturing processes or operations management principles.
- Practical experience with data analysis or Excel-based reporting.
- Foundational knowledge of programming or scripting languages.
Target Audience
- Process Engineers
- Plant Supervisors
- Lean Six Sigma Professionals
21 Hours
