Defects and Methods for Quality Control

SUMMARY

Manufacturing defects come in many forms, from tiny surface imperfections to critical assembly errors. But how can we effectively detect and classify these issues using machine learning?

In this lesson, you’ll dive into the complexities of defect detection, understanding the unique challenges posed by surface defects, dimensional inaccuracies, material inconsistencies, and faulty assemblies. You’ll also explore how different machine learning models—such as Convolutional Neural Networks (CNNs) for image-based defects and Decision Trees for structured numerical data—can be leveraged to enhance quality control.

Beyond model selection, we’ll assess the strengths and limitations of key machine learning (ML) techniques, including CNNs, Random Forests, Autoencoders, and Support Vector Machines (SVMs), providing insight into their real-world applications. By the end of this lesson, you’ll have a clear understanding of how to align the right ML approach with specific defect types, ensuring more reliable and efficient manufacturing processes.

About The Author

Dilek Dustegor is a Professor of Computing Science at the University of Groningen in the Netherlands. She is interested in bridging the gaps between research, development and implementation using AI and automation. She is pursuing research about modeling, design and analysis of large scale / networked systems using Internet of Things (IoT) and Machine Learning (ML) techniques, with a special interest in smart city applications. She is a seasoned educator, and loves using the newest educational technologies for an enhanced learning experience.