Can I read Grain Boundary Slip Transfer Classification and Metric Selection with Artificial Neural Networks on EtoBox?

Grain Boundary Slip Transfer Classification and Metric Selection with Artificial Neural Networks by Zhao, Zhuowen; Bieler, Thomas R.; LLorca, Javier; Eisenlohr, Philip is a scholarly article available to read on EtoBox.

What is Grain Boundary Slip Transfer Classification and Metric Selection with Artificial Neural Networks about?

An artificial neural network is used to evaluate the effectiveness of six metrics and their combinations to assess whether slip transfers across grain boundaries in coarse-grained oligocrystalline Al foils \citep{Bieler_etal2019_2,Alizadeh_etal2020}. This approach extends the one- or two-dimensional projections formerly applied to analyze slip transfer. The accuracy of this binary classification reaches around \pcnt{87} for the best single metric and around \pcnt{90} when considering two or more metrics simultaneously. The results suggest slip transfer mostly depends on the geometric relationship between grains. Training a double-layer network having \num{10} nodes per hidden layer with \num{40} measurements is sufficient to render the maximum accuracy.

Author

Zhao, Zhuowen; Bieler, Thomas R.; LLorca, Javier; Eisenlohr, Philip

Published

2020

Language

EN