Course Code: aiqca
Duration: 21 hours
Prerequisites:
  • 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
Overview:

AI for Quality Control is the use of computer vision and machine learning techniques to identify defects, anomalies, and deviations in production processes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level quality professionals who wish to apply AI tools to automate inspections and improve product quality in manufacturing environments.

By the end of this training, participants will be able to:

  • Understand how AI is applied in industrial quality control.
  • Collect and label image or sensor data from production lines.
  • Use machine learning and computer vision to detect defects.
  • Develop simple AI models for anomaly detection and yield forecasting.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
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

Sites Published:

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