Automated Defect Detection of Screws in the Manufacturing Industry Using Convolutional Neural Networks

Date
2022-01-04
Authors
Breitenbach, Johannes
Eckert, Isabelle
Mahal, Vanessa
Baumgartl, Hermann
Buettner, Ricardo
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Defect detection in industrial production processes is an important and necessary part of quality control. Many defects can occur during the manufacturing process, causing high manufacturing costs. Thus the inspection of screws, which represent an indispensable element of many mechanical components, is a critical process. To reduce manufacturing costs and increase efficiency, a reliable method for inspection is Deep Learning. It can help simplify the process of quality control and increase the velocity and volume of detected defects in screws. This approach uses a CNN model to classify non-defective and defective screws with different types of defects. Instead of manual quality control methods, which can be easily biased, our CNN approach is accurate, cost-efficient, and fast, with an accuracy of over 97 percent. With this approach corresponding to industrial production processes, different defects in screws and non-defective screws can be classified from images according to a real-world industrial inspection scenario.
Description
Keywords
Case studies of Artificial Intelligence, Business Intelligence, Analytics Technologies for Industry Platforms, deep learning, manufacturing industry, quality control, screws
Citation
Rights
Access Rights
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.