Home > Research > Publications & Outputs > A microstructure sensitive model for deformatio...

Associated organisational unit

Links

Text available via DOI:

View graph of relations

A microstructure sensitive model for deformation of Ti-6Al-4V describing Cast-and-Wrought and Additive Manufacturing morphologies

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
Close
<mark>Journal publication date</mark>15/12/2018
<mark>Journal</mark>Materials and Design
Volume160
Number of pages13
Pages (from-to)350-362
Publication StatusPublished
Early online date15/09/18
<mark>Original language</mark>English

Abstract

Microstructural variations affect deformation response of materials and it is not presented in most of plastic flow prediction models. This work presents a unified description for the deformation response of Ti-6Al-4V (Ti-64) that successfully captures the differences in strength between microstructures produced by conventional cast & wrought routes (C&W) and those obtained by Additive Manufacturing (AM), under various deformation conditions. In the developed model the grain morphology, grain size, phase volume fractions and phase chemical compositions have been linked to the mechanical properties of the studied Ti-64 alloys to predict the effect of processing routes on deformation behaviour of the materials. The model performance has been tested on seven different microstructures from C&W to AM processing routs. It has been found that altering the microstructure greatly affects the yield strength of the tested materials. Additionally, the strength of Ti-64 was found to be mostly affected by the relative volume fraction of α β and α′ and their respective morphology. The results showed that the model not only successfully predicts the strength of martensitic structures generated through AM but also those obtained by quenching in conventional C&W processing. The findings from this study also suggest that the model could be extended to other titanium alloys within the α + β family. © 2018 Elsevier Ltd