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Course Outline
Introduction to AI in Quality Control
- Overview of AI applications 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 using LabelImg.
- Data storage and structuring for model training.
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 fundamental understanding of manufacturing or QA processes.
- Familiarity with spreadsheets or digital reporting tools.
- An interest in data-driven quality control methodologies.
Target Audience
- Quality assurance specialists.
- Production team leaders.
21 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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