Multi-objective Optimization of Material Composition in Functionally Graded Hollow Sphere by Making Use of Genetic Algorithm : Effect of Porosity Distribution in Material Composition

2002 ◽  
Vol 2002.77 (0) ◽  
pp. _7-45_-_7-46_
Author(s):  
Takuhisa Sakaue ◽  
Katsumi Iida ◽  
Ryuusuke Kawamura ◽  
Yoshinobu Tanigawa
Keyword(s):  
Genetic Algorithm ◽  
Hollow Sphere ◽  
Functionally Graded ◽  
Material Composition ◽  
Multi Objective Optimization ◽  
Porosity Distribution ◽  
Multi Objective

Multi-objective Optimization of Geometric Dimensions and Material Composition of Functionally Graded Components

2008 ◽  
Author(s):  
Senthil S. Vel ◽  
Andrew J. Goupee ◽  
Glaucio H. Paulino ◽  
Marek-Jerzy Pindera ◽  
Robert H. Dodds ◽  
...  
Keyword(s):  
Functionally Graded ◽  
Material Composition ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Functionally Graded Components

scholarly journals Multi-Objective Optimization of Functionally Graded Beams Using a Genetic Algorithm with Non-Dominated Sorting

2021 ◽  
Vol 5 (4) ◽  
pp. 92
Author(s):  
Chih-Ping Wu ◽  
Kuan-Wei Li
Keyword(s):  
Genetic Algorithm ◽  
Critical Temperature ◽  
Temperature Change ◽  
Material Properties ◽  
Thermal Buckling ◽  
Buckling Analysis ◽  
Functionally Graded ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Fg Beam

A mixed layer-wise (LW) higher-order shear deformation theory (HSDT) is developed for the thermal buckling analysis of simply-supported, functionally graded (FG) beams subjected to a uniform temperature change. The material properties of the FG beam are assumed to be dependent on the thickness and temperature variables, and the effective material properties are estimated using either the rule of mixtures or the Mori–Tanaka scheme. The results shown in the numerical examples indicate the mixed LW HSDT solutions for critical temperature change parameters are in excellent agreement with the accurate solutions available in the literature. A multi-objective optimization of FG beams is presented to maximize the critical temperature change parameters and to minimize their total mass using a non-dominated sorting-based genetic algorithm. Some specific forms for the volume fractions of the constituents of the FG beam are assumed in advance, such as the one- and three-parameter power-law functions. The former is used in the thermal buckling analysis of the FG beams for comparison purposes, and the latter is used in their optimal design.

MULTI-OBJECTIVE OPTIMIZATION OF OFFSHORE TSUNAMI OBSERVATION POINTS BY GENETIC ALGORITHM

2020 ◽  
Vol 76 (2) ◽  
pp. I_325-I_330
Author(s):  
Kazutoshi KURAMOTO ◽  
Fumiyasu MAKINOSHIMA ◽  
Anawat SUPPASRI ◽  
Fumihiko IMAMURA
Keyword(s):  
Genetic Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective

Multi-objective optimization of injection-molded plastic parts using entropy weight, random forest, and genetic algorithm methods

2020 ◽  
Vol 40 (4) ◽  
pp. 360-371
Author(s):  
Yanli Cao ◽  
Xiying Fan ◽  
Yonghuan Guo ◽  
Sai Li ◽  
Haiyue Huang
Keyword(s):  
Genetic Algorithm ◽  
Random Forest ◽  
Regression Model ◽  
Process Parameters ◽  
Multi Objective Optimization ◽  
Entropy Weight ◽  
Multi Objective ◽  
Injection Molded ◽  
Plastic Parts

AbstractThe qualities of injection-molded parts are affected by process parameters. Warpage and volume shrinkage are two typical defects. Moreover, insufficient or excessively large clamping force also affects the quality of parts and the cost of the process. An experiment based on the orthogonal design was conducted to minimize the above defects. Moldflow software was used to simulate the injection process of each experiment. The entropy weight was used to determine the weight of each index, the comprehensive evaluation value was calculated, and multi-objective optimization was transformed into single-objective optimization. A regression model was established by the random forest (RF) algorithm. To further illustrate the reliability and accuracy of the model, back-propagation neural network and kriging models were taken as comparative algorithms. The results showed that the error of RF was the smallest and its performance was the best. Finally, genetic algorithm was used to search for the minimum of the regression model established by RF. The optimal parameters were found to improve the quality of plastic parts and reduce the energy consumption. The plastic parts manufactured by the optimal process parameters showed good quality and met the requirements of production.

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