Assembly process optimization for reducing the dimensional error of antenna assembly with abundant rivets

Jun Ni (), Wen Cheng Tang () and Yan Xing ()
Additional contact information
Jun Ni: Southeast University
Wen Cheng Tang: Southeast University
Yan Xing: Southeast University

Journal of Intelligent Manufacturing, 2018, vol. 29, issue 1, No 16, 245-258

Abstract: Abstract This paper proposes a process optimization method to improve the dimensional precision of riveted assemblies. The method representation and investigation use an assembly with 1093 rivets yielded from the double curved reflector. Firstly the static and dynamic finite element (FE) models respectively represent the global large-scale assembly and the local riveting process. The dimensional precision is denoted by the root mean square (RMS) of the deformations of the key points selected from the static FE nodes. Then the quantitation between RMS and process parameters equates to the iterative static FE analyses interpolating the dynamic FE analysis result and the possible former static FE analysis result. Finally the integration of the genetic and ant colony algorithms optimizes the process parameters, i.e. the rivet upsetting directions (UDs) and the assembly sequence (AS). Investigation indicates (1) both the rivet UDs and AS are the main RMS influence factors; (2) the proposed method can efficiently optimize the specific process parameters for the large-scale assembly with abundant rivets; and (3) the effective optimization prefers to solve rivet UDs and AS step by step.

Keywords: Sheet metal; Rivet; Process; Upsetting; Stamp; Sequence; Optimization (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-015-1105-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:29:y:2018:i:1:d:10.1007_s10845-015-1105-x

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-015-1105-x

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().