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.
- An overview of AI applications in operational workflows.
- Essential performance metrics and KPIs.
Data Collection and Preparation
- Identifying sources of manufacturing data (sensors, PLC, MES).
- Cleaning and structuring time-series data.
- Utilizing Pandas and Jupyter for data preprocessing.
Descriptive and Diagnostic Analytics
- Exploring and visualizing data effectively.
- Conducting correlation analysis and identifying root causes.
- Building custom dashboards using Power BI.
Machine Learning for Process Optimization
- Understanding supervised and unsupervised learning methods.
- Applying clustering for pattern discovery.
- Using regression and classification for predictive modeling.
AI for Predictive Maintenance and Quality
- Implementing anomaly detection and predictive alerts.
- Developing failure prediction models.
- Enhancing product quality through model-driven insights.
Real-Time Analytics and Feedback Loops
- Handling streaming data and real-time processing.
- Integrating with SCADA/MES systems.
- Establishing feedback loops for automatic process adjustments.
Case Study and Capstone Project
- Performing 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.
- Fundamental familiarity with programming or scripting languages.
Target Audience
- Process engineers.
- Plant supervisors.
- Lean Six Sigma professionals.
21 Hours
