Material optimization of functionally graded plates using deep neural network and modified symbiotic organisms search for eigenvalue problems

Do, Dieu.T.T.; Lee, Dongkyu; Lee, Jae Hong

doi:10.1016/j.compositesb.2018.09.087

Cited by 61 publications

(37 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In particular, Vesperini et al [12] proposed an ANN for multi-room speaker localization, in which convolutional neural networks and multi-layer perceptron architectures were investigated. In the engineering field, Do et al [13] presented an ANN for material optimization of functionally graded plates under buckling load or free vibration. Le et al [14] proposed a risk assessment framework using ANN technique.…”

Section: Introductionmentioning

confidence: 99%

Analysis of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using artificial neural networks

Nguyen

et al. 2021

STCE

View full text Add to dashboard Cite

We study static mechanical behavior of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using a non-traditional computational approach based on artificial neural network. One of the main objectives of our approach is to save the computational cost for the optimization process, which is usually time-consuming by using traditional methods such as finite element method (FEM). In this study, 1000 data sets randomly generated by the FEM through iterations are used for the training process to get optimal weights. Based on these obtained optimal weights, beam behaviors under the changes in material distribution through thickness could then be predicted. In this model, the ANN's inputs are the gradation index of the power-law distribution and thickness, while the outputs are compliance and beam displacements. The computed results are verified against those derived from the FEM.

show abstract

Section: Introductionmentioning

confidence: 99%

Analysis of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using artificial neural networks

Nguyen

et al. 2021

STCE

View full text Add to dashboard Cite

show abstract

“…Gu et al (2018) focused on the prediction of the mechanical properties of the hierarchical system and generated new microstructural patterns that led to durable and stronger materials. Do et al (2019) investigated that the combination of deep neural network and modified symbiotic organisms search is very efficient in the optimization of the material distribution in FGP. Abueidda et al (2019) used CNN to predict the mechanical properties of the 2 D checkerboard composite quantitatively.…”

Section: Introductionmentioning

confidence: 99%

Buckling of laminated composite skew plate using FEM and machine learning methods

Mishra

Kumar

Samui

et al. 2020

View full text Add to dashboard Cite

Purpose The purpose of this paper is to attempt the buckling analysis of a laminated composite skew plate using the C0 finite element (FE) model based on higher-order shear deformation theory (HSDT) in conjunction with minimax probability machine regression (MPMR) and multivariate adaptive regression spline (MARS). Design/methodology/approach HSDT considers the third-order variation of in-plane displacements which eliminates the use of shear correction factor owing to realistic parabolic transverse shear stresses across the thickness coordinate. At the top and bottom of the plate, zero transverse shear stress condition is imposed. C0 FE model based on HSDT is developed and coded in formula translation (FORTRAN). FE model is validated and found efficient to create new results. MPMR and MARS models are coded in MATLAB. Using skew angle (α), stacking sequence (Ai) and buckling strength (Y) as input parameters, a regression problem is formulated using MPMR and MARS to predict the buckling strength of laminated composite skew plates. Findings The results of the MPMR and MARS models are in good agreement with the FE model result. MPMR is a better tool than MARS to analyze the buckling problem. Research limitations/implications The present work considers the linear behavior of the laminated composite skew plate. Originality/value To the authors’ best of knowledge, there is no work in the literature on the buckling analysis of a laminated composite skew plate using C0 FE formulation based on third-order shear deformation theory in conjunction with MPMR and MARS. These machine-learning techniques increase efficiency, reduce the computational time and reduce the cost of analysis. Further, an equation is generated with the MARS model via which the buckling strength of the laminated composite skew plate can be predicted with ease and simplicity.

show abstract

“…FKM'nin malzeme özelliklerinin sıcaklığa bağlı değiştiğini kabul ettiler ve hacimsel dağılımın kalınlık yönünde değiştiğini ifade ettiler. Do ve arkadaşları [7], FKM'lerin doğal frekansı ve maksimum burkulma yükünü dikkate alarak optimum hacimsel dağılım için simbiyotik organizmalar arama algoritması ve derin yapay sinir ağı yöntemini kullandılar. Sayısal yöntem olarak SEM kullandılar ve elde ettikleri değerler ile derin yapay sinir ağı yöntemi için model oluşturdular.…”

Section: Introductionunclassified

İki̇ Boyutlu Fonksi̇yonel Kademelendi̇ri̇lmi̇ş Plakalarin Yapay Si̇ni̇r Aği Öğrenme Algori̇tmalari İle Isil Geri̇lme Modellemesi̇

Demirbaş

Çakir

2020

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

View full text Add to dashboard Cite

Belirli bir hacimsel dağılım fonksiyonuna göre oluşturulan fonksiyonel kademelendirilmiş malzemeler (FKM) günümüz yüksek sıcaklık uygulamalarına dayanımlı malzeme üretiminde önemli bir yere sahiptir. Amaç fonksiyonuna bağlı olarak FKM'de maksimum çalışma performansı, yapısal varyasyonlar ve emniyetli gerilme değerleri gibi önemli özelliklerin sağlanabilmesi için hacimsel dağılımın belirlenmesi çok önemlidir. Hacimsel dağılımın belirlenmesi ve uygun hacimsel dağılımın test edilebilmesi için nümerik analiz yöntemleri kullanılmaktadır. Bu çalışmada, sonlu farklar metodu ile iki boyutlu fonksiyonel kademelendirilmiş plakaların ısı akısı tesirinde ısıl-mekanik davranışının tespit edilmesinde önemli bir parametre olan eşdeğer gerilme seviyeleri için malzemenin hacimsel dağılımına bağlı modeller oluşturulmuştur. Bu modeller yapay sinir ağında iki farklı eğitim algoritması ile elde edilmiştir. Bu eğitim algoritmaları Levenberg-Marquart ve Geriye Yayılım algoritmasıdır. FKM'lerin hacimsel dağılımını belirleyen kompozisyonel gradyant üst değerleri n ve m'dir. Sonlu farklar metodu ile sayısal analizde n ve m değerlerini belirlemek 1800 s iken Yapay Sinir ağı modeli ile 900 s olup verimlilik önemli derecede artmaktadır. Kullanılan eğitim algoritmalarının iş-zaman-performans değerleri açısından ileride yapılacak bilimsel çalışmalar için fikir verici nitelikte olması önemlidir. Önerilen eğitimli modeller henüz seri üretimi gerçekleştirilemeyen FKM için hem üretimde hem de yapılacak teorik çalışmalarda optimum hacimsel dağılıma ulaşmada yol gösterici olacaktır.

show abstract

Material optimization of functionally graded plates using deep neural network and modified symbiotic organisms search for eigenvalue problems

Cited by 61 publications

References 72 publications

Analysis of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using artificial neural networks

Analysis of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using artificial neural networks

Buckling of laminated composite skew plate using FEM and machine learning methods

İki̇ Boyutlu Fonksi̇yonel Kademelendi̇ri̇lmi̇ş Plakalarin Yapay Si̇ni̇r Aği Öğrenme Algori̇tmalari İle Isil Geri̇lme Modellemesi̇

Contact Info

Product

Resources

About