| Title: | OS9-4 Machine Vision-Based Chamfer Detection for Metal Parts |
|---|---|
| Publication: | ICAROB2025 |
| Volume: | 30 |
| Pages: | 258-263 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2025.OS9-4 |
| Author(s): | Shangying Han, Kaili Guo, Yanzi Kong, Yanliang Kong, Junjin Chen, Ce Bian, Mengfan Zhang |
| Publication Date: | February 13, 2025 |
| Keywords: | visual inspection, machine learning, image segmentation, contour curve |
| Abstract: | This paper introduces a detection system specifically designed for chamfering in metal holes, aimed at achieving precise detection of the chamfers. Chamfering, as a process of beveling the edges or corners of metal parts, plays a crucial role in the subsequent machining and assembly stages. Through multiple experimental validations, this paper employs an industrial camera with a telecentric lens to capture images of the metal chamfers, achieving optimal results. This paper utilizes computer vision techniques to accurately identify the location of the chamfers and delineate their dimensions. A comprehensive analysis of the chamfer radius effectively determines the presence of defects. |
| PDF File: | https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS9/OS9-4.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
(c)2008 Copyright The Regents of ALife Robotics Corporation Ltd. All Rights Reserved.