scholarly journals Retraction Note to: Application of artificial neural networks for analytical modeling of Charpy impact energy of functionally graded steels

2020 ◽  
Vol 32 (23) ◽  
pp. 17813-17813
Author(s):  
Ali Nazari
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Impact Energy ◽  
Analytical Modeling ◽  
Charpy Impact ◽  
Functionally Graded ◽  
Charpy Impact Energy ◽  
Artificial Neural

Multiple objective crashworthiness optimization of circular tubes with functionally graded thickness via artificial neural networks and genetic algorithms

2016 ◽  
Vol 231 (11) ◽  
pp. 2005-2016 ◽  
Author(s):  
Adil Baykasoğlu ◽  
Cengiz Baykasoğlu
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Functionally Graded ◽  
Design Parameters ◽  
Multiple Objective ◽  
Circular Tubes ◽  
Crashworthiness Optimization ◽  
Artificial Neural ◽  
Graded Thickness

The objective of this paper is to develop a multiple objective optimization procedure for crashworthiness optimization of circular tubes having functionally graded thickness. The proposed optimization approach is based on finite element analyses for construction of sample design space and verification; artificial neural networks for predicting objective functions values (peak crash force and specific energy absorption) for design parameters; and genetic algorithms for generating design parameters alternatives and determining optimal combination of them. The proposed approach seaminglesly integrates artificial neural networks and genetic algorithms. Artificial neural network acts as an objective function evaluator within the multiple objective genetic algorithms. We have shown that the proposed approach is able to generate Pareto optimal designs which are in a very good agreement with the finite element results.

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