How Unsupervised Learning Can Help in Defect Detection & Quality Control in Manufacturing

As the American Society of Quality reports, many organizations have quality-related costs of up to 40% of their total production revenue. A large part of this cost comes from the inefficiency of manual inspection, which is the most common way to provide quality control in manufacturing. The application of artificial intelligence for quality control automation presents a more productive and accurate way of doing a visual inspection of production lines. However, traditional machine learning methods present several limitations to how we can train and utilize models for defect detection. So in this article, we'll discuss the advantages of unsupervised learning for defect detection, and elaborate on the approaches MobiDev uses in our practical experience. AI defect detection is based on computer vision that provides capabilities for automating the whole AI quality inspection process using machine learning algorithms.