Insights into twinning in $\mathrm{Mg AZ31: A}$ combined $\mathrm{EBSD}$ and machine learning study

Orme, Andrew D.; Chelladurai, Isaac; Rampton, Travis M.; Fullwood, David T.; Khosravani, Ali; Miles, Michael P.; Mishra, Raja K.

doi:10.1016/j.commatsci.2016.08.011

Title: Insights into twinning in $$\mathrm{Mg AZ31: A}$$ combined $$\mathrm{EBSD}$$ and machine learning study

Journal Article · Sat Aug 20 00:00:00 EDT 2016 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2016.08.011· OSTI ID:1533666

Orme, Andrew D. ^[1]; Chelladurai, Isaac ^[1]; Rampton, Travis M. ^[1]; Fullwood, David T. ^[1]; Khosravani, Ali ^[2]; Miles, Michael P. ^[1]; Mishra, Raja K. ^[3]

Brigham Young University, Provo, UT (United States)
Georgia Institute of Technology, Atlanta, GA (United States)
General Motors R&D Center, Warren, MI (United States)

To explore the driving forces behind deformation twinning in Mg AZ31, a machine learning framework is utilized to mine data obtained from electron backscatter diffraction (EBSD) scans in order to extract correlations in physical characteristics that cause twinning. The results are intended to inform physics-based models of twin nucleation and growth. A decision tree learning environment is selected to capture the relationships between microstructure and twin formation; this type of model effectively highlights the more influential characteristics of the local microstructure. Trees are assembled to analyze both twin nucleation in a given grain, and twin propagation across grain boundaries. Each model reveals a unique combination of crystallographic attributes that affect twinning in the Mg. Twin nucleation is found to be mostly controlled by a combination of grain size, basal Schmid factor, and bulk dislocation density while twin propagation is affected most by grain boundary length, basal Schmid factor, angle from grain boundary plane to the RD plane, and grain boundary misorientation. The machine learning framework can be readily adapted to investigate other relationships between microstructure and material response.

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Research Organization:: Brigham Young Univ., Provo, UT (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF)

Grant/Contract Number:: SC0012587; CMMI 1404771; -SC0012587; BES

OSTI ID:: 1533666

Alternate ID(s):: OSTI ID: 1397554

Journal Information:: Computational Materials Science, Vol. 124, Issue C; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 28 works

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36 MATERIALS SCIENCE
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Title: Insights into twinning in $$\mathrm{Mg AZ31: A}$$ combined $$\mathrm{EBSD}$$ and machine learning study

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