Study on Online Monitor System for Surface Quality of Rolling Rail Based on Machine Vision
- DOI
- 10.2991/978-94-6463-242-2_74How to use a DOI?
- Keywords
- Machine Vision; Hot Rolled Rail; Online Supervision; Quality Control
- Abstract
In view of quality control process of the heavy rail on universal rolling line, based on method of machine vision, a system can be used to monitor surface defect of hot rolled heavy rail has been developed and it’s configurations of hardware and software are introduced in this paper. This system adopted gigabit Ethernet cable for data communication to ensure stability and reliability of the transmission system for super large data, and high speed linear CCD camera used to get effective reliable heavy rail surface image, and image resolution can reach 0.5 mm/pixel, defect size of hot rail larger than 5 mm can be collected and displayed. In the system, with the aid of intelligent discriminant algorithm to recognize the surface defects of heavy rail, it is successful to implement real-time monitoring for surface quality and to reduce leave out and mistake rate of the hot rolled heavy rail.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Xingqiang Tan AU - Benwen Zhang PY - 2023 DA - 2023/09/22 TI - Study on Online Monitor System for Surface Quality of Rolling Rail Based on Machine Vision BT - Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023) PB - Atlantis Press SP - 604 EP - 610 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-242-2_74 DO - 10.2991/978-94-6463-242-2_74 ID - Tan2023 ER -