340
Views
6
CrossRef citations to date
0
Altmetric
Research Article

Optimization of adhesive single-lap joints under bending moment

, & ORCID Icon
Pages 1687-1712 | Received 17 Mar 2021, Accepted 13 May 2021, Published online: 06 Jun 2021
 

ABSTRACT

This paper presents the optimization of stacking sequence of composite laminate adherends in an adhesive single lap joint under bending moment in order to minimize the amount of maximum peel and shear stress of the adhesive layer. The effects of different assumptions on the equations of the stress and their results are presented. The adhesive single lap joint is modelled as an adherend-adhesive sandwich panel. The finite element method is used to verify the analytical method and the structure is modelled by ANSYS software version 14. The results of the analytical method and the finite element method are compared, and there was a good agreement between two methods. For the optimization part, the single-objective and multi-objective bees algorithm are used to search for the best and worst stacking sequence with and without permutation. The results of the optimization indicate that the maximum shear and peel stress highly depend on the extensional, coupling and bending stiffness in a way that in the optimum model, adherends have high and almost equal stiffness. In the worst models, there is a huge difference in the stiffness of the adherends causing destructive high stress in the thinner adherend.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 868.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.