Machine vision-based segmentation and classification method for intelligent roller surface monitoring |
|
Author: | Shi, Depeng1; Zhou, Jiehan2; Xu, Jirui3; |
Organizations: |
1Department of MEE of Southern University of Science and Technology. China 2Information Technology and Electrical Engineering in Oulu University. Finland 3Department of Mechanical Engineering, Tsinghua University, China
4epartment of Mechanical Engineering, Tsinghua university, China
5Department of Mechanical Engineering and the State Key Lab of Tribology, Tsinghua University. China 6HIECISE Precision Equipment Co. Ltd. China 7Mechanical and Energy Engineering, Southern University of Science and Technology, China |
Format: | article |
Version: | accepted version |
Access: | open |
Online Access: | PDF Full Text (PDF, 0.6 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe2020042922900 |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers,
2018
|
Publish Date: | 2020-04-29 |
Description: |
AbstractThe surface quality of steel rollers is a key factor determining the quality of final products such as metal sheets and foils in the rolling industry. It is important to examine the surface quality of rollers since rollers with optical defects will always duplicate the defects onto the metal sheet or foil during rolling. The typical optical defects of rollers after finish grinding include speckles, chatter marks, swirl marks and combination of all of the above. They can hardly be modeled or shaped by the approach of micro topography or SEM (scanning electrical microscope). In this paper, an on-site machine vision system is firstly applied for stable inspection for the optical defects on roller surfaces. Then, an improved optical defect segmentation algorithm is developed based on the active contour model and the images including chatter marks and swirl marks. The normal surface state is classified by the combination of methods of Gabor filters, KPCA method and ELM neural networks. Finally, experiment are carried out to verify the efficiency of the improved segmentation method and the recognition rate of the combined classification algorithm. see all
|
ISBN: | 978-1-5386-9380-3 |
ISBN Print: | 978-1-5386-9381-0 |
Pages: | 1811 - 1817 |
Article number: | 8560283 |
DOI: | 10.1109/SmartWorld.2018.00305 |
OADOI: | https://oadoi.org/10.1109/SmartWorld.2018.00305 |
Host publication: |
4th IEEE SmartWorld, 15th IEEE International Conference on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018 |
Host publication editor: |
Loulergue, F. Wang, G. Bhuiyan, M. Z. A. Ma, X. Li, P. Roveri, M. Han, Q. Chen, L. |
Conference: |
IEEE International Conference on Cloud and Big Data Computing |
Type of Publication: |
A4 Article in conference proceedings |
Field of Science: |
213 Electronic, automation and communications engineering, electronics |
Subjects: | |
Funding: |
Resrach supported by Tsinghua University Initiative Scientific Research Program Tsinghua-RWTH Aachen Collaborative Innovation and Southern University of Science and Technology. |
Copyright information: |
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |