Non-equibiaxial residual stress evaluation methodology using simulated indentation behavior and machine learning

Seongin Moon, Minjae Choi, Seokmin Hong, Sung Woo Kim, Minho Yoon

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)1347-1356
Number of pages10
JournalNuclear Engineering and Technology
Volume56
Issue number4
DOIs
StatePublished - Apr 2024

Keywords

  • Convolutional neural network
  • Finite element analysis
  • Instrumented indentation technique
  • Non-equiaxial residual stress

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