Search: id:"swepub:oai:DiVA.org:bth-24477" >
Non-destructive mon...
Non-destructive monitoring of forming quality of self-piercing riveting via a lightweight deep learning
-
- Lin, Sen (author)
- Shenzhen Polytechnic, China
-
- Zhao, Lun (author)
- Shenzhen Polytechnic, China
-
- Wang, Sen (author)
- Kunming University of Science and Technology, China
-
show more...
-
- Islam, Md. Shafiqul, 1984- (author)
- Blekinge Tekniska Högskola,Institutionen för maskinteknik
-
- Wei, Wu (author)
- Guizhou University, Guiyang, China
-
- Huo, Xiaole (author)
- Shenzhen Polytechnic, China
-
- Guo, Zixin (author)
- Shenzhen Polytechnic, China
-
show less...
-
(creator_code:org_t)
- Springer Nature, 2023
- 2023
- English.
-
In: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13:1
- Related links:
-
https://doi.org/10.1...
-
show more...
-
https://bth.diva-por... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- Self-piercing riveting (SPR) has been widely used in automobile body jointing. However, the riveting process is prone to various forming quality failures, such as empty riveting, repeated riveting, substrate cracking, and other riveting defects. This paper combines deep learning algorithms to achieve non-contact monitoring of SPR forming quality. And a lightweight convolutional neural network with higher accuracy and less computational effort is designed. The ablation and comparative experiments results show that the lightweight convolutional neural network proposed in this paper achieves improved accuracy and reduced computational complexity. Compared with the original algorithm, the algorithm's accuracy in this paper is increased by 4.5[Formula: see text], and the recall is increased by 1.4[Formula: see text]. In addition, the amount of redundant parameters is reduced by 86.5[Formula: see text], and the amount of computation is reduced by 47.33[Formula: see text]. This method can effectively overcome the limitations of low efficiency, high work intensity, and easy leakage of manual visual inspection methods and provide a more efficient solution for monitoring the quality of SPR forming quality. © 2023. The Author(s).
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Teknisk mekanik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Applied Mechanics (hsv//eng)
Publication and Content Type
- ref (subject category)
- art (subject category)
Find in a library
To the university's database