Optimisation of the Thin-Walled Composite Structures in Terms of Critical Buckling Force.
ANN
FEM
artificial neural network
composites
laminates
optimisation
parametric studies
ply orientation
thin-walled
Journal
Materials (Basel, Switzerland)
ISSN: 1996-1944
Titre abrégé: Materials (Basel)
Pays: Switzerland
ID NLM: 101555929
Informations de publication
Date de publication:
02 Sep 2020
02 Sep 2020
Historique:
received:
28
07
2020
revised:
16
08
2020
accepted:
28
08
2020
entrez:
5
9
2020
pubmed:
6
9
2020
medline:
6
9
2020
Statut:
epublish
Résumé
The paper presents the optimisation of thin-walled composite structures on a representative sample of a thin-walled column made of carbon laminate with a channel section-type profile. The optimisation consisted of determining the configuration of laminate layers for which the tested structure has the greatest resistance to the loss of stability. The optimisation of the layer configuration was performed using two methods. The first method, divided into two stages to reduce the time, was to determine the optimum arrangement angle in each laminate layer using finite element methods (FEM). The second method employed artificial neural networks for predicting critical buckling force values and the creation of an optimisation tool. Artificial neural networks were combined into groups of networks, thereby improving the quality of the obtained results and simplifying the obtained neural networks. The results from computations were verified against the results obtained from the experiment. The optimisation was performed using ABAQUS
Identifiants
pubmed: 32887452
pii: ma13173881
doi: 10.3390/ma13173881
pmc: PMC7504342
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Ministerstwo Nauki i Szkolnictwa Wyższego
ID : 030/RID/2018/19