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Course Outline
Foundations of AI in Manufacturing
- Current trends in smart manufacturing and Industry 4.0
- Overview of AI applications in operational workflows
- Core performance metrics and KPIs
Gathering and Preparing Data
- Origins of manufacturing data (sensors, PLCs, MES)
- Sanitising and structuring time-series information
- Applying Pandas and Jupyter for data preprocessing
Descriptive and Diagnostic Analysis
- Exploring and visualising data sets
- Performing correlation studies and identifying root causes
- Building custom dashboards using Power BI
Leveraging Machine Learning for Process Optimisation
- Supervised versus unsupervised learning methodologies
- Applying clustering to uncover patterns
- Using regression and classification for predictive insights
AI for Predictive Maintenance and Quality Control
- Detecting anomalies and generating predictive alerts
- Developing models for failure prediction
- Elevating product quality through model-derived insights
Real-Time Analytics and Feedback Mechanisms
- Managing streaming data and real-time processing
- Integrating with SCADA/MES systems
- Establishing feedback loops for automated process adjustments
Case Studies and Capstone Project
- Conducting hands-on analysis of authentic data sets
- Designing and validating an optimisation model
- Presenting a final AI-driven improvement strategy
Recap and Future Directions
Requirements
- Fundamental knowledge of manufacturing workflows or operations management
- Practical experience with data analysis or reporting via Excel
- Basic familiarity with programming languages or scripting
Target Audience
- Process engineers
- Plant supervisors
- Lean Six Sigma practitioners
21 Hours
