Pronounced grain boundary network evolution in nanocrystalline Cu subjected to large cyclic strains

Abstract

The grain boundary network of nanocrystalline Cu foils was modified by the application of cyclic loadings and elevated temperatures. Broadly, the changes to the boundary network were directly correlated to the applied temperature and accumulated strain, including a 300% increase in the twin length fraction. By independently varying each treatment variable, a matrix of grain boundary statistics was built to check the plausibility of hypothesized mechanisms against their expected temperature and stress/strain dependences. These comparisons allow the field of candidate mechanisms to be significantly narrowed. Most importantly, the effect of temperature and strain on twin length fraction were found to be strongly synergistic, with the combined effect being ~150% that of the summed individual contributions. Looking beyond scalar metrics, an analysis of the grain boundary network showed that twin related domain formation favored larger sizes and repeated twin variant selection over the creation of many small domains with diverse variants.

Modeling the Effects of Varying the Ti Concentration on the Mechanical Properties of Cu–Ti Alloys

“…The MGS values were computed using the mean linear intercept method. 49 This value for the MGS is in line with the grain sizes of experimentally reported nanocrystalline Cu 50 , 51 and Cu-based alloys. 2 …”

Personalized Teaching Strategy of University Ideology Course Based on Lagrange Neural Network and Big Data Technology

“…Literature [ 21 , 22 ] puts forward a new type of neural network to solve general nonlinear programming problems. Literature [ 23 ] proposed a new type of neural network, LNN (Lagrange neural network), to solve nonlinear programming problems. Literature [ 24 ] studies the stability and convergence time of LNN in solving optimization problems.…”