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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 workflows.
- Critical performance metrics and KPIs.
Data Collection and Preparation
- Identifying sources of manufacturing data (sensors, PLC, MES).
- Cleaning and structuring time-series data.
- Preprocessing using Pandas and Jupyter.
Descriptive and Diagnostic Analytics
- Exploring and visualising data effectively.
- Conducting correlation analysis and identifying root causes.
- Creating custom dashboards with Power BI.
Machine Learning for Process Optimisation
- Understanding supervised and unsupervised learning.
- Utilising clustering for pattern discovery.
- Applying regression and classification for predictions.
AI for Predictive Maintenance and Quality
- Implementing anomaly detection and predictive alerts.
- Developing failure prediction models.
- Enhancing product quality through model-derived insights.
Real-Time Analytics and Feedback Loops
- Handling streaming data and real-time processing.
- Integrating with SCADA/MES systems.
- Establishing feedback mechanisms for automatic process adjustments.
Case Study and Capstone Project
- Hands-on analysis of real-world datasets.
- Designing and validating an optimisation model.
- Presenting a final AI-driven improvement plan.
Summary and Next Steps
Requirements
- A solid grasp of manufacturing processes or operations management.
- Practical experience in data analysis or Excel-based reporting.
- Foundational knowledge of programming or scripting.
Target Audience
- Process engineers.
- Plant supervisors.
- Lean Six Sigma specialists.
21 Hours
