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Quality prediction and rivet/die selection for SPR joints with artificial neural network and genetic algorithm

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Zhao, Huan, Han, Li, Liu, Yunpeng and Liu, Xianping (2021) Quality prediction and rivet/die selection for SPR joints with artificial neural network and genetic algorithm. Journal of Manufacturing Processes, 66 . pp. 574-594. doi:10.1016/j.jmapro.2021.04.033

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Official URL: https://doi.org/10.1016/j.jmapro.2021.04.033

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Abstract

In this study, artificial neural network (ANN) was adopted to predict the quality of SPR joints. Three ANN models were developed respectively for the key joint quality indicators: the interlock, the remaining bottom sheet thickness at the joint center (Tcen) and under the rivet tip (Ttip). Experimental SPR tests were performed and the results verified the high prediction accuracy of the ANN models. The mean absolute errors (MAE) between the experimental and prediction results for the interlock, Tcen and Ttip reached 0.058mm, 0.075mm and 0.059mm respectively, and the corresponding mean absolute percentage errors (MAPE) were 14.2%, 22.4% and 10.9%. Moreover, two innovative approaches were proposed to simplify the selection of rivet and die for new joint designs. One was realized by combining the genetic algorithm (GA) with the ANN models, and can generate optimal rivet and die combinations for different joint quality standards. The second was achieved by plotting application range maps of different rivet and die combinations with the help of ANN models, and can quickly select the suitable and accessible rivet and die. Furthermore, interaction effects between different joining parameters on the joint quality were also discussed by analyzing the contour graphs plotted with the ANN models.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Library of Congress Subject Headings (LCSH): Rivets and riveting, Neural networks (Computer science), Genetic algorithms, Punching machinery
Journal or Publication Title: Journal of Manufacturing Processes
Publisher: Elsevier
ISSN: 1526-6125
Official Date: June 2021
Dates:
DateEvent
June 2021Published
30 April 2021Available
15 April 2021Accepted
Volume: 66
Page Range: pp. 574-594
DOI: 10.1016/j.jmapro.2021.04.033
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: JLR
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDJaguar Land Rover (Firm)http://viaf.org/viaf/305209406

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