Campus Units

Materials Science and Engineering

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

2016

Journal or Book Title

Materials Science and Engineering: A

Volume

660

First Page

172

Last Page

180

DOI

10.1016/j.msea.2016.02.052

Abstract

A constituent-based phenomenological equation to predict yield strength values from quantified measurements of the microstructure and composition of β processed Ti-6Al-4V alloy was developed via the integration of artificial neural networks and genetic algorithms. It is shown that the solid solution strengthening contributes the most to the yield strength (~80% of the value), while the intrinsic yield strength of the two phases and microstructure have lower effects (~10% for both terms). Similarities and differences between the proposed equation and the previously established phenomenological equation for the yield strength prediction of the α+β processed Ti-6Al-4V alloys are discussed. While the two equations are very similar in terms of the intrinsic yield strength of the two constituent phases, the solid solution strengthening terms and the ‘Hall-Petch’-like effect from the alpha lath, there is a pronounced difference in the role of the basketweave factor in strengthening. Finally, Monte Carlo simulations were applied to the proposed phenomenological equation to determine the effect of measurement uncertainties on the estimated yield strength values.

Comments

This is a manuscript of an article published as Ghamarian, I., B. Hayes, P. Samimi, B. A. Welk, H. L. Fraser, and P. C. Collins. "Developing a phenomenological equation to predict yield strength from composition and microstructure in β processed Ti-6Al-4V." Materials Science and Engineering: A 660 (2016): 172-180. doi: 10.1016/j.msea.2016.02.052. Posted with permission.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Elsevier, B.V.

Language

en

File Format

application/pdf

Published Version

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