Atomistic studies of interactions between the dominant lattice dislocations and γ/γ-lamellar boundaries in lamellar γ-TiAl

This paper reports on atomistic simulations of the interactions between the dominant lattice dislocations in ?-TiAl (<1 0 1] superdislocations) with all three kinds of ?/?-lamellar boundaries in polysynthetically twinned (PST) TiAl. The purpose of this study is to clarify the early stage of lamellar boundary controlled plastic deformation in PST TiAl. The interatomic interactions in our simulations are described by a bond order potential for L10-TiAl which provides a proper quantum mechanical description of the bonding. We are interested in the dislocation core geometries that the lattice produces in proximity to lamellar boundaries and the way in which these cores are affected by the elastic and atomistic effects of dislocation-lamellar boundary interaction. We study the way in which the interfaces affect the activation of ordinary dislocation and superdislocation slip inside the ?-lamellae and transfer of plastic deformation across lamellar boundaries. We find three new phenomena in the atomic-scale plasticity of PST TiAl, particularly due to elastic and atomic mismatch associated with the 60° and 120° ?/?-interfaces: (i) two new roles of the ?/?-interfaces, i.e. decomposition of superdislocations within 120° and 60° interfaces and subsequent detachment of a single ordinary dislocation and (ii) blocking of ordinary dislocations by 60° and 120° interfaces resulting in the emission of a twinning dislocation.

A fast method for fuzzy neural modeling and refinement

In the identification of complex dynamic systems using fuzzy neural networks, one of the main issues is the curse of dimensionality, which makes it difficult to retain a large number of system inputs or to consider a large number of fuzzy sets. Moreover, due to the correlations, not all possible network inputs or regression vectors in the network are necessary and adding them simply increases the model complexity and deteriorates the network generalisation performance. In this paper, the problem is solved by first proposing a fast algorithm for selection of network terms, and then introducing a refinement procedure to tackle the correlation issue. Simulation results show the efficacy of the method.