Grain boundary slip transfer classification and metric selection with artificial neural networks. (August 2020)

• Record Type: Journal Article
• Authors: Zhao, Zhuowen; Bieler, Thomas R.; LLorca, Javier; Eisenlohr, Philip
• Is Part Of: Scripta materialia. Number 185(2020)
• Page Start: 71
• Page End: 75
• Publication Date: 2020-08
• Subjects: Grain boundary -- Slip transfer -- Classification -- Metric selection -- Artificial neural network; Materials -- Periodicals; Metallurgy -- Periodicals; Metalen; Legeringen; Materiaalkunde; Metals, metalworking and machinery industries; Metals; Electronic journals; 620.11
• DOI: 10.1016/j.scriptamat.2020.04.029
• Languages: English
• ISSNs: 1359-6462

Abstract

Graphical abstract: Abstract: 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 [1, 2]. This approach extends the one- or two-dimensional projections formerly applied to analyze slip transfer. The accuracy of this binary classification reaches around 87% for the best single metric and around 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 10 nodes per hidden layer with 40 measurements is sufficient to render the maximum accuracy.