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

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.

Automated summary

In this study, an artificial neural network is combined with finite element nanoindentation to evaluate free volume model parameters for bulk metallic glass. A material database is generated based on finite element analysis, and an artificial neural network is trained to correlate the free volume model and indentation parameters. The feasibility of this approach is experimentally validated.