Defects and Methods for Quality Control (Pilot)

SUMMARY

“How can machine learning transform quality control in manufacturing?”

Welcome to an exciting journey into the future of defect detection, where machine learning (ML) becomes your toolkit for revolutionizing quality control. This course, ML Methods for Defect Detection in Manufacturing, is designed for forward-thinking manufacturing professionals looking to harness data-driven approaches for smarter, faster, and more accurate anomaly detection.

This training equips you with the knowledge to map ML models to specific defect types, a skill essential for deploying the right tool for each quality control challenge. From identifying surface imperfections to spotting material inconsistencies and dimensional errors, you’ll gain insights into how different ML methods like Convolutional Neural Networks (CNNs), Autoencoders, and Decision Trees can be used to enhance manufacturing accuracy.

Why is this relevant? Traditional quality control methods struggle with the complexity and volume of today’s production environments. Machine learning changes the game by enabling real-time monitoring and predictive insights that go beyond what human inspection can achieve. With each lesson, you’ll build confidence in model selection and application, learning how to collect, preprocess, and leverage data effectively to detect and prevent manufacturing defects.

Through practical exercises and real-world case studies, you’ll develop hands-on skills and leave with a clear roadmap to integrating machine learning into your quality control processes. Join us to transform quality control and be at the forefront of innovation in manufacturing.

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 IoT and 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.