Stress Analysis of 2D-FG Rectangular Plates with Multi-Gene Genetic Programming

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Tarih

16.08.2022

Yazarlar

Arslan, Sibel
Demirbas, Munise Didem
Çakır, Didem
Ozturk,Celal

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

MDPIST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND

Erişim Hakkı

info:eu-repo/semantics/embargoedAccess

Özet

Functionally Graded Materials (FGMs) are designed for use in high-temperature applications. Since the mass production of FGM has not yet been made, the determination of its thermomechanical limits depends on the compositional gradient exponent value. In this study, an efficient working model is created for the thermal stress problem of the 2D-FG plate using Multi-gene Genetic Programming (MGGP). In our MGGP model in this study, data sets obtained from the numerical analysis results of the thermal stress problem are used, and formulas that give equivalent stress levels as output data, with the input data being the compositional gradient exponent, are obtained. For the current problem, efficient models that reduce CPU processing time are obtained by using the MGGP method.

Açıklama

Anahtar Kelimeler

multi-gene genetic programming, genetic programming, thermal stress problem, compositional gradient exponent, functionally graded material

Kaynak

APPLIED SCIENCES-BASEL

WoS Q Değeri

Q2

Scopus Q Değeri

N/A

Cilt

12

Sayı

16

Künye

Functionally Graded Materials (FGMs) are designed for use in high-temperature applications. Since the mass production of FGM has not yet been made, the determination of its thermomechanical limits depends on the compositional gradient exponent value. In this study, an efficient working model is created for the thermal stress problem of the 2D-FG plate using Multi-gene Genetic Programming (MGGP). In our MGGP model in this study, data sets obtained from the numerical analysis results of the thermal stress problem are used, and formulas that give equivalent stress levels as output data, with the input data being the compositional gradient exponent, are obtained. For the current problem, efficient models that reduce CPU processing time are obtained by using the MGGP method.