Optimization of flexure stiffness of FGM beams via artificial neural networks by mixed FEM
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Techno-Press
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Artificial neural networks (ANNs) are known as intelligent methods for modeling the behavior of physical phenomena because of it is a soft computing technique and takes data samples rather than entire data sets to arrive at solutions, which saves both time and money. ANN is successfully used in the civil engineering applications which are suitable examining the complicated relations between variables. Functionally graded materials (FGMs) are advanced composites that successfully used in various engineering design. The FGMs are nonhomogeneous materials and made of two different type of materials. In the present study, the bending analysis of functionally graded material (FGM) beams presents on theoretical based on combination of mixed-finite element method, Gateaux differential and Timoshenko beam theory. The main idea in this study is to build a model using ANN with four parameters that are: Young's modulus ratio (E-t/E-b), a shear correction factor (k(s)), power-law exponent (n) and length to thickness ratio (L/h). The output data is the maximum displacement (w). In the experiments: 252 different data are used. The proposed ANN model is evaluated by the correlation of the coefficient (R), MAE and MSE statistical methods. The ANN model is very good and the maximum displacement can be predicted in ANN without attempting any experiments.
Açıklama
Anahtar Kelimeler
Functionally Graded Material Beam, Artificial Neural Networks, Mixed Finite Element Method, Displacement Data
Kaynak
Structural Engineering And Mechanics
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
75
Sayı
5
