Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to AI in Manufacturing
- Trends in smart manufacturing and Industry 4.0.
- Overview of AI applications in operational contexts.
- Key performance metrics and KPIs.
Data Collection and Preparation
- Sources of manufacturing data (sensors, PLC, MES).
- Cleaning and formatting time-series data.
- Preprocessing using Pandas and Jupyter.
Descriptive and Diagnostic Analytics
- Data exploration and visualization techniques.
- Correlation analysis and root cause identification.
- Creating custom dashboards with Power BI.
Machine Learning for Process Optimization
- Supervised and unsupervised learning approaches.
- Clustering for pattern discovery.
- Regression and classification for predictive modeling.
AI for Predictive Maintenance and Quality
- Anomaly detection and predictive alerts.
- Developing failure prediction models.
- Enhancing product quality through model insights.
Real-Time Analytics and Feedback Loops
- Streaming data and real-time processing capabilities.
- Integration with SCADA/MES systems.
- 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.
- Presenting a final AI-driven improvement plan.
Summary and Next Steps
Requirements
- A solid understanding of manufacturing processes or operations management.
- Practical experience with data analysis or Excel-based reporting.
- Basic proficiency in programming or scripting languages.
Target Audience
- Process engineers.
- Plant supervisors.
- Lean Six Sigma professionals.
21 Hours
