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Kursplan
Introduction to AI in Quality Control
- Overview of AI in manufacturing quality processes
- Applications in inspection, defect detection, and compliance
- Benefits and limitations of AI-powered QA
Collecting and Preparing Quality Data
- Types of data used in QA (images, sensors, production logs)
- Labeling visual datasets with LabelImg
- Data storage and structure for training models
Introduction to Computer Vision for QA
- Basics of image processing with OpenCV
- Preprocessing techniques for industrial images
- Extracting visual features for analysis
Machine Learning for Anomaly Detection
- Training simple classifiers for defect detection
- Using convolutional neural networks (CNNs)
- Unsupervised learning for anomaly identification
Yield Forecasting with AI Models
- Introduction to regression techniques
- Building models to forecast production yields
- Evaluating and improving prediction accuracy
Integrating AI with Production Systems
- Deployment options for inspection models
- Edge AI vs. cloud-based analysis
- Automating alerts and quality reporting
Practical Case Study and Final Project
- Developing an end-to-end AI inspection prototype
- Training and testing with sample QA datasets
- Presenting a functional quality control AI solution
Summary and Next Steps
Krav
- An understanding of basic manufacturing or QA processes
- Familiarity with spreadsheets or digital forms of reporting
- Interest in data-driven quality control methods
Audience
- Quality assurance specialists
- Production leads
21 timer
