Optimization of fatigue life of pearlitic Grade 900A steel based on the combination of genetic algorithm and artificial neural network

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Affiliation of Author(s):University of Electronic Science and Technology of China

Place of Publication:International Journal of Fatigue

Key Words:Artificial neural network; Fatigue life; Genetic algorithm; Railway rail; Stress intensity factor

Abstract:In this paper, the fatigue life of pearlitic Grade 900A steel used in railway applications is investigated. To predict the fatigue life of pearlitic Grade 900A steel based on the number of cycles of the particular stress level in the load block, occurrence ratio and overload ratio, a feed-forward neural network is designed. The results of this artificial neural network are compared to the surface fitting method. Then sensitivity analysis is applied to the obtained artificial neural network values to measure the effect of each input parameter on the fatigue life. Finally, the genetic...

All the Authors:Nima Sina,Wenchen Ma,Zhiliang Liu,Filippo Berto,Aboozar Gholami

Indexed by:Journal Paper

Correspondence Author:Reza Masoudi Nejad

Document Code:106975

Discipline:Engineering

First-Level Discipline:Mechanical engineering

Volume:162

Page Number:106975

ISSN No.:0142-1123

Translation or Not:no

Date of Publication:2022-05-06

Date of Publication:2022-05-06