Microstructural damage sensitivity prediction using spatial statistics
Name
s41598-019-39315-x.pdf
Description
Published version
Size
1.64 MB
Format
Unknown
Checksum (MD5)
eb7cf01b70eb42dac6b59922a8734f60
Author(s)
Cameron, Benjamin Clive
Tasan, Cemal
Date Issued
2019
Journal
Scientific Reports
Publisher
Springer Nature
Version
Final published version
Abstract
© 2019, The Author(s). The vast compositional space of metallic materials provides ample opportunity to design stronger, more ductile and cheaper alloys. However, the substantial complexity of deformation micro-mechanisms makes simulation-based prediction of microstructural performance exceedingly difficult. In absence of predictive tools, tedious experiments have to be conducted to screen properties. Here, we develop a purely empirical model to forecast microstructural performance in advance, bypassing these challenges. This is achieved by combining in situ deformation experiments with a novel methodology that utilizes n-point statistics and principle component analysis to extract key microstructural features. We demonstrate this approach by predicting crack nucleation in a complex dual-phase steel, achieving substantial predictive ability (84.8% of microstructures predicted to crack, actually crack), a substantial improvement upon the alternate simulation-based approaches. This significant accuracy illustrates the utility of this alternate approach and opens the door to a wide range of alloy design tools.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41598-019-39315-x
