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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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WRAP-quality-prediction-rivet-die-selection-SPR-joints-artificial-neural-network-genetic-algorithm-Liu-2021.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only until 30 April 2022. Contact author directly, specifying your specific needs. - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (4Mb) |
Official URL: https://doi.org/10.1016/j.jmapro.2021.04.033
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 | ||||||||
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| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TJ Mechanical engineering and machinery |
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| Divisions: | Faculty of Science > 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: |
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| 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: |
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