中文版 | English
Title

A new lightweight deep neural network for surface scratch detection

Author
Corresponding AuthorZhang,Liangchi
Publication Years
2022
DOI
Source Title
ISSN
0268-3768
EISSN
1433-3015
Abstract
This paper aims to develop a lightweight convolutional neural network, WearNet, to realise automatic scratch detection for components in contact sliding such as those in metal forming. To this end, a large surface scratch dataset obtained from cylinder-on-flat sliding tests was used to train the WearNet with appropriate training parameters such as learning rate, gradient algorithm and mini-batch size. A comprehensive investigation on the network response and decision mechanism was also conducted to show the capability of the developed WearNet. It was found that compared with the existing networks, WearNet can realise an excellent classification accuracy of 94.16% with a much smaller model size and faster detection speed. Besides, WearNet outperformed other state-of-the-art networks when a public image database was used for network evaluation. The application of WearNet in an embedded system further demonstrated such advantages in the detection of surface scratches in sheet metal forming processes.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Corresponding
Funding Project
Baosteel Australia Research and Development Centre (BAJC) portfolio with Project[BA17001] ; ARC Hub for Computational Particle Technology[IH140100035] ; Chinese Guangdong Specific Discipline Project["2020ZDZX2006","ZDSYS20200810171201007"]
WOS Research Area
Automation & Control Systems ; Engineering
WOS Subject
Automation & Control Systems ; Engineering, Manufacturing
WOS Accession No
WOS:000875077300006
Publisher
ESI Research Field
ENGINEERING
Scopus EID
2-s2.0-85140654490
Data Source
Scopus
Citation statistics
Cited Times [WOS]:14
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/407154
DepartmentInstitute for Manufacturing Innovation
工学院_力学与航空航天工程系
Affiliation
1.School of Mechanical and Manufacturing Engineering,The University of New South Wales,Kensington,2052,Australia
2.Shenzhen Key Laboratory of Cross-Scale Manufacturing Mechanics,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
3.SUSTech Institute for Manufacturing Innovation,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
4.Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
5.Baoshan Iron & Steel Co.,Ltd.,Shanghai,200941,China
Corresponding Author AffilicationSouthern University of Science and Technology;  Institute for Manufacturing Innovation;  Department of Mechanics and Aerospace Engineering
Recommended Citation
GB/T 7714
Li,Wei,Zhang,Liangchi,Wu,Chuhan,et al. A new lightweight deep neural network for surface scratch detection[J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY,2022.
APA
Li,Wei,Zhang,Liangchi,Wu,Chuhan,Cui,Zhenxiang,&Niu,Chao.(2022).A new lightweight deep neural network for surface scratch detection.INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY.
MLA
Li,Wei,et al."A new lightweight deep neural network for surface scratch detection".INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2022).
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