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)
  • 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

Requirements

  • 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 Hours

Delivery Options

Private Group Training

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  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

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