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2nd Edition of International Conference on

Materials Science and Engineering

March 28 -30, 2022 | Singapore

2022 Speakers

Fatigue behavior of polychloroprene rubber (CR) with tungsten nano-particles considering stress triaxiality based on semi-empirical and machine learning models

Joeun Choi, Speaker at Speaker at Materials Conferences: Joeun Choi
Sogang University, Korea, Republic of
Title : Fatigue behavior of polychloroprene rubber (CR) with tungsten nano-particles considering stress triaxiality based on semi-empirical and machine learning models

Abstract:

This paper presents a strain-based semi-empirical fatigue life prediction model of tungsten (W) nano-particles reinforced polychloroprene rubber (CR) material (W-CR material) developed for nuclear decommissioning manufactured by rolling process. The developed semi-empirical fatigue life prediction model is based on Mason-Coffin formulation considering the anisotropic mechanical behaviour and the stress triaxiality. To consider various multiaxial stress states, a novel fatigue experiment method which utilizes limiting dome height test (LDH test) has been developed. In addition, three types of specimens have been cut along 0º (rolling), 45º and 90º to consider the W-CR material’s anisotropic behaviour. In the semi-empirical model, the effect of stress triaxiality is characterized as the form of an exponent function. Moreover, the anisotropy is represented by the ratio between the maximum stress of the specimen made in the rolling direction and each direction. The developed model is able to consider low cycle fatigue (LCF) and high cycle fatigue (HCF) together. The semi-empirical fatigue life prediction model shows a high correlation factor equal to 91.4%. Furthermore, a machine learning (ML) models have been applied for the fatigue life prediction. The accuracy of the adopted ML models with five training set compositions have been compared. The machine learning method shows a lower mean relative error than the semi-empirical model. The advantage of the proposed models in this paper is the anisotropic behavior of rubber composites can be considered easily. It is concluded that the proposed models are reliable and useful to predict fatigue life for various stress states of rubber composites.

Biography:

Miss. Choi earned her bachelor’s degree in Sogang University, Republic of Korea and graduated in 2020. She then joined the research group of Prof. Kim at the department of Mechanical Engineering, Sogang University. Miss. Choi’s research interests are composite materials, computer aided process analysis, machine learning, and optimal design.

 

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