Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Non-equibiaxial residual stress evaluation methodology using simulated indentation behavior and machine learning

  • Seongin Moon (Korea Atomic Energy Research Institute) ;
  • Minjae Choi (Korea Atomic Energy Research Institute) ;
  • Seokmin Hong (Korea Atomic Energy Research Institute) ;
  • Sung-Woo Kim (Korea Atomic Energy Research Institute) ;
  • Minho Yoon (Department of Mechanical and Information Engineering, University of Seoul)
  • Received : 2023.05.09
  • Accepted : 2023.11.20
  • Published : 2024.04.25
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Abstract

Measuring the residual stress in the components in nuclear power plants is crucial to their safety evaluation. The instrumented indentation technique is a minimally invasive approach that can be conveniently used to determine the residual stress in structural materials in service. Because the indentation behavior of a structure with residual stresses is closely related to the elastic-plastic behavior of the indented material, an accurate understanding of the elastic-plastic behavior of the material is essential for evaluation of the residual stresses in the structures. However, due to the analytical problems associated with solving the elastic-plastic behavior, empirical equations with limited applicability have been used. In the present study, the impact of the non-equibiaxial residual stress state on indentation behavior was investigated using finite element analysis. In addition, a new nonequibiaxial residual-stress prediction methodology is proposed using a convolutional neural network, and the performance was validated. A more accurate residual-stress measurement will be possible by applying the proposed residual-stress prediction methodology in the future.

Keywords

Acknowledgement

This work was supported by the Ministry of Science and ICT and a National Research Foundation of Korea (NRF) grant funded by the Korean government (2021M2E4A1037979).

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