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
- Emerging trends in smart manufacturing and Industry 4.0.
- Overview of AI use cases within operational contexts.
- Key performance metrics and KPIs.
Data Collection and Preparation
- Sources of manufacturing data, including sensors, PLCs, and MES systems.
- Techniques for cleaning and formatting time-series data.
- Utilizing Pandas and Jupyter for data preprocessing.
Descriptive and Diagnostic Analytics
- Data exploration and visualization techniques.
- Correlation analysis and root cause identification.
- Creating custom dashboards using Power BI.
Machine Learning for Process Optimization
- Supervised and unsupervised learning methodologies.
- Clustering techniques for pattern discovery.
- Applying regression and classification for predictive insights.
AI for Predictive Maintenance and Quality Control
- Anomaly detection and predictive alert systems.
- Developing failure prediction models.
- Enhancing product quality through actionable model insights.
Real-Time Analytics and Feedback Loops
- Processing streaming data in real-time.
- Integration with SCADA and MES systems.
- Implementing feedback mechanisms for automatic process adjustments.
Case Study and Capstone Project
- Hands-on analysis of real-world datasets.
- Designing and validating an optimization model.
- Delivering a final presentation of the 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.
Audience
- Process engineers.
- Plant supervisors.
- Lean Six Sigma professionals.
21 Hours
