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Course Outline
Introduction to AI in Manufacturing
- Current trends in smart manufacturing and Industry 4.0.
- Overview of AI applications in operational settings.
- Key performance metrics and KPIs.
Data Collection and Preparation
- Identifying manufacturing data sources (sensors, PLCs, MES).
- Cleaning and formatting time-series data.
- Utilizing Pandas and Jupyter for data preprocessing.
Descriptive and Diagnostic Analytics
- Data exploration and visualization techniques.
- Conducting correlation analysis and identifying root causes.
- Building custom dashboards using Power BI.
Machine Learning for Process Optimization
- Exploring supervised and unsupervised learning methods.
- Applying clustering for pattern discovery.
- Using regression and classification for predictive modeling.
AI for Predictive Maintenance and Quality Control
- Implementing anomaly detection and generating predictive alerts.
- Developing failure prediction models.
- Enhancing product quality through actionable model insights.
Real-Time Analytics and Feedback Loops
- Handling streaming data and real-time processing.
- Integrating with SCADA and MES systems.
- Establishing feedback mechanisms for automatic process adjustments.
Case Study and Capstone Project
- Hands-on analysis of real-world data sets.
- Designing and validating an optimization model.
- Delivering a final presentation of an AI-driven improvement plan.
Summary and Next Steps
Requirements
- A foundational understanding of manufacturing processes or operations management.
- Prior experience with data analysis or Excel-based reporting.
- Basic familiarity with programming or scripting languages.
Target Audience
- Process engineers.
- Plant supervisors.
- Lean Six Sigma professionals.
21 Hours
