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Course Outline
Introduction to AI in Quality Control
- Overview of AI applications in manufacturing quality processes.
- Uses in inspection, defect identification, and regulatory compliance.
- Advantages and constraints of AI-driven QA.
Collecting and Preparing Quality Data
- Categories of data utilized in QA (images, sensors, production logs).
- Annotating visual datasets using LabelImg.
- Organizing data storage and structure for model training.
Introduction to Computer Vision for QA
- Fundamentals of image processing with OpenCV.
- Preprocessing methods for industrial imagery.
- Extracting visual features for deeper analysis.
Machine Learning for Anomaly Detection
- Training basic classifiers for identifying defects.
- Utilizing convolutional neural networks (CNNs).
- Applying unsupervised learning for anomaly recognition.
Yield Forecasting with AI Models
- Overview of regression techniques.
- Creating models to predict production yields.
- Assessing and refining prediction accuracy.
Integrating AI with Production Systems
- Deployment strategies for inspection models.
- Comparing Edge AI and cloud-based analysis.
- Automating alerts and quality reporting mechanisms.
Practical Case Study and Final Project
- Building an end-to-end AI inspection prototype.
- Training and testing using sample QA datasets.
- Presenting a functional AI solution for quality control.
Summary and Next Steps
Requirements
- A foundational understanding of standard manufacturing or QA workflows.
- Proficiency with spreadsheets or digital reporting tools.
- A genuine interest in data-driven quality assurance methodologies.
Target Audience
- Quality assurance specialists.
- Production team leads.
21 Hours
