4.7 Article

A mathematical prediction model to establish the role of stacking fault energy on the cryo-deformation behavior of FCC materials at different strain levels

期刊

INTERNATIONAL JOURNAL OF PLASTICITY
卷 97, 期 -, 页码 159-177

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijplas.2017.05.014

关键词

Severe plastic deformation; Cryo-deformation; Stacking fault energy; Shockley partials; Structure-property correlation; Modeling of deformation mechanism; Crystal plasticity

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In the present work, a mathematical model has been developed to predict the deformation mechanism (slip/twin) of materials corresponding to three different levels (high, medium and low) of stacking fault energy (SFE) processed in subzero (cryogenic) temperature. These materials were processed through severe plastic deformation (SPD) route at various levels of deformation strain. According to the developed model, the deformation of the SPDed material involves three stages: (i) release of Shockley partials, (ii) relative movement between the leading and trailing partial, and (iii) transformation of the stacking fault into twin/subcell. Each of this stage is associated with a certain critical stress of activation, which can be calculated from the proposed mathematical model as a function of deformation strain and SFE. Depending on the critical stress values, this model presents five different case scenarios, which encompasses the different deformation mechanism of a SPDed material: (i) amalgamation of leading and trailing partials in the grain interior resulting dislocation subcell formation, (ii) merging of leading and trailing partials in the grain interior/boundaries resulting in dislocation annihilation, (iii) transformation of the partial into twin in the grain interior, (iv) transformation of partial into twin at grain boundary, and (v) expansion of stacking fault till the grain boundary leading to formation of dislocation subcell. The propensity of twin/slip as predicted from the model has been correlated with the empirical defect densities obtained from X-ray diffraction analysis and transmission electron microscopy. These defect densities have been further used to develop a strength prediction model and have been correlated with the experimental tensile test data. (c) 2017 Elsevier Ltd. All rights reserved.

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