A new lightweight deep neural network for surface scratch detection

Li，Wei1; Zhang，Liangchi2,3,4; Wu，Chuhan1; Cui，Zhenxiang5; Niu，Chao5

2022

10.1007/s00170-022-10335-8

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY   Impact Factor & Quartile

0268-3768

1433-3015

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.

Contact sliding Convolutional neural network Sheet metal forming Surface scratch detection

SCI

English

Corresponding

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"]

Automation & Control Systems ; Engineering

Automation & Control Systems ; Engineering, Manufacturing

WOS:000875077300006

SPRINGER LONDON LTD

ENGINEERING

2-s2.0-85140654490

Scopus

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

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.

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.

Li，Wei,et al."A new lightweight deep neural network for surface scratch detection".INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2022).