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Course Outline
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).
- Labelling 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 versus 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
Requirements
- A basic understanding of manufacturing or quality assurance (QA) processes.
- Familiarity with spreadsheets or digital reporting tools.
- An interest in data-driven quality control methods.
Audience
- Quality assurance specialists.
- Production leads.
21 Hours
