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Course Outline
Introduction to Artificial Intelligence in Manufacturing
- Emerging trends in smart manufacturing and Industry 4.0.
- Overview of AI applications in operational contexts.
- Key performance metrics and KPIs.
Data Collection and Preparation
- Identifying manufacturing data sources (sensors, PLC, MES).
- Cleaning and formatting time-series data.
- Utilizing Pandas and Jupyter for preprocessing tasks.
Descriptive and Diagnostic Analytics
- Exploring and visualizing data.
- Conducting correlation analysis and identifying root causes.
- Building custom dashboards with Power BI.
Machine Learning for Process Optimization
- Understanding supervised and unsupervised learning approaches.
- Employing clustering techniques for pattern discovery.
- Applying regression and classification for predictive modeling.
AI Applications in Predictive Maintenance and Quality Control
- Implementing anomaly detection and predictive alerting.
- Developing failure prediction models.
- Enhancing product quality through insights derived from models.
Real-Time Analytics and Feedback Mechanisms
- Processing streaming data in real time.
- Integrating with SCADA/MES systems.
- Enabling automatic process adjustments via feedback loops.
Case Study and Capstone Project
- Conducting hands-on analysis of real-world datasets.
- Designing and validating optimization models.
- Presenting a final AI-driven improvement plan.
Summary and Next Steps
Requirements
- Fundamental knowledge of manufacturing processes or operations management.
- Prior experience with data analysis or Excel-based reporting.
- Basic proficiency in programming or scripting.
Target Audience
- Process engineers.
- Plant supervisors.
- Lean Six Sigma practitioners.
21 Hours
