Determination of material properties of bulk metallic glass using nanoindentation and artificial neural network

Park, Soowan; Fonseca, Joao Henrique; Marimuthu, Karuppasamy Pandian; Jeong, Chanyoung; Lee, Sihyung; Lee, Hyungyil

doi:10.1016/j.intermet.2022.107492

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Determination of material properties of bulk metallic glass using nanoindentation and artificial neural network

Park, Soowan;
Fonseca, Joao Henrique;
Marimuthu, Karuppasamy Pandian;
Jeong, Chanyoung;
Lee, Sihyung;
외 1명

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26

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28

초록

An artificial neural network (ANN) model is combined with finite element (FE) nanoindentation to evaluate free volume model (FVM) parameters for bulk metallic glass (BMG). FVM is numerically implemented with the user material subroutine (UMAT). A material database is generated based on FE analysis, in which indentation parameters are obtained from FVM parameters. An ANN is generated in order to correlate FVM and indentation parameters and trained/tested from the generated database after the application of removal of multicollinearity, sampling, and normalization for computational efficiency. The fully trained ANN inversely evaluates the FVM parameters from the indentation parameters. The ANN approach is experimentally validated by sphero-conical/ Berkovich indentation load-depth curves of Zr55Cu30Ag15 and Zr65Cu15Al10Ni10.

키워드

Bulk metallic glass; Nanoindentation; Free volume model; FEA; Latin hypercube; Artificial neural network; SHEAR-BAND PATTERNS; MECHANICAL-PROPERTIES; INSTRUMENTED INDENTATION; TENSILE PROPERTIES; DEFORMATION; PREDICT; ANN; SENSITIVITY; PERFORMANCE; SIMULATION

제목: Determination of material properties of bulk metallic glass using nanoindentation and artificial neural network

저자: Park, Soowan; Fonseca, Joao Henrique; Marimuthu, Karuppasamy Pandian; Jeong, Chanyoung; Lee, Sihyung; Lee, Hyungyil

DOI: 10.1016/j.intermet.2022.107492

발행일: 2022-05

유형: Article

저널명: Intermetallics

권: 144