An optical system for identifying and classifying defects of metal parts
Author:
Affiliation:
1. Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, Greece
Publisher
Informa UK Limited
Subject
Electrical and Electronic Engineering,Computer Science Applications,Mechanical Engineering,Aerospace Engineering
Link
https://www.tandfonline.com/doi/pdf/10.1080/0951192X.2021.1992660
Reference44 articles.
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3. A Machine Learning Approach for Visual Recognition of Complex Parts in Robotic Manipulation
4. Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural networks
5. Batsuuri, S., J. Ahn, and J. Ko. 2012. “Steel Surface Defects Detection and Classification Using SIFT and Voting Strategy.” International Journal of Software Engineering and Its Applications 6: 161–166.
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