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dc.contributor.author
Karathanasopoulos, Nikos
dc.contributor.author
Pandya, Kedar Sanjay
dc.contributor.author
Mohr, Dirk
dc.date.accessioned
2021-01-22T10:11:30Z
dc.date.available
2021-01-22T09:25:24Z
dc.date.available
2021-01-22T10:11:30Z
dc.date.issued
2021-06
dc.identifier.other
10.1016/j.jajp.2020.100040
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/464672
dc.identifier.doi
10.3929/ethz-b-000464672
dc.description.abstract
Developing robust numerical simulation tools to investigate the self-piercing riveting process is critical, since the feasibility, quality and strength of riveted connections relies on the successful formation of mechanical interlocks between the rivet and sheet materials. In the present study, we investigate the riveting of aluminum alloy and dual-phase steel sheets (AA7075-F/DP600 and AA6016-T4/DP600) over a wide range of sheet thicknesses, as a function of the rivet and die geometries employed. More specifically, we study the dependence of the probability of successful joint formation, defined as the ratio of the number of acceptable riveted connections to the total number of test cases, on the rivet leg inner radius and die central tip depth. Towards this, we use experimentally calibrated Hosford-Coulomb fracture surfaces for each deformable part, incorporated in dedicated axisymmetric 2D finite element (FE) process models. The FE predictions are validated through comparisons with experimentally obtained riveted joints. Moreover, we analyze the relation between the mechanical interlocks achieved and the rivet and die geometries employed, deriving practice-relevant conclusions with respect to the most favorable design parameters. In particular, we show that while the probability of successful joint formation decreases upon increasing rivet leg inner radius, the joint quality, in terms of effectuated interlock distance per rivet mean residual equivalent plastic strain, increases. Furthermore, we show that machine-learning techniques can be employed to classify with remarkable accuracy the successful joint formation for a wide range of possible self-piercing riveting scenarios, accounting for both rivet and die-related geometrical attributes, as well as for the thickness of the metal sheets.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
Self-pierce riveting
en_US
dc.subject
Joint quality
en_US
dc.subject
Joint feasibility
en_US
dc.subject
Numerical simulation
en_US
dc.subject
Machine learning
en_US
dc.title
Self-piercing riveting process: Prediction of joint characteristics through finite element and neural network modeling
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
dc.date.published
2020-12-15
ethz.journal.title
Journal of Advanced Joining Processes
ethz.journal.volume
3
en_US
ethz.pages.start
100040
en_US
ethz.size
13 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Amsterdam
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02622 - Institut für virtuelle Produktion / Institute of Virtual Manufacturing::09473 - Mohr, Dirk / Mohr, Dirk
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02622 - Institut für virtuelle Produktion / Institute of Virtual Manufacturing::09473 - Mohr, Dirk / Mohr, Dirk
en_US
ethz.date.deposited
2021-01-22T09:25:32Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-01-22T10:11:38Z
ethz.rosetta.lastUpdated
2021-02-15T23:29:34Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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