Using machine-learning to understand complex microstructural effects on the mechanical behavior of Ti-6Al-4V alloys
Abstract
Structural materials properties are highly dependent on their microstructure. Their microstructure is in turn affected by multiple fabrication and thermo-mechanical treatment parameters, all of which conform a highly-dimensional parametric space with often hidden correlations that are difficult to extract by experimentation alone. This is particularly true for alloys of the dual-phase Ti-6Al-4V family, with their greatly complex and rich microstructures, which combine several intrinsic length scales associated with multiple grain and subgrain structures, grains with different crystal lattices (α and β phases), and complex chemistry. In this paper we use a comprehensive set of machine learning techniques to develop predictive tools relating the yield strength and hardening rate of these alloys to a set of input parameters covering extensive ranges. The data generator is a finite-element crystal plasticity model for polycrystal deformation that takes into account slip anisotropy and employs standard dislocation evolution models for the α and β phases of Ti-based alloys. Our dataset includes over two thousand independent simulations and is used to train the machine learning models, which are then used to establish correlations between microstructural parameters and the alloys’ mechanical response. Our results point to the most influential parameters affecting yield strength and hardening rate, informationmore »
- Publication Date:
- Research Org.:
- Univ. of California, Los Angeles, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Fusion Energy Sciences (FES); National Science Foundation (NSF)
- OSTI Identifier:
- 1977003
- Grant/Contract Number:
- SC0012774; DMR-1611342
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Computational Materials Science
- Additional Journal Information:
- Journal Volume: 208; Journal Issue: C; Journal ID: ISSN 0927-0256
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; Ti-6Al-4V alloys; yield strength; polycrystal plasticity; dual-phase Ti alloys; machine learning; hardening rate
Citation Formats
McElfresh, Cameron, Roberts, Collin, He, Sicong, Prikhodko, Sergey, and Marian, Jaime. Using machine-learning to understand complex microstructural effects on the mechanical behavior of Ti-6Al-4V alloys. United States: N. p., 2022.
Web. doi:10.1016/j.commatsci.2022.111267.
McElfresh, Cameron, Roberts, Collin, He, Sicong, Prikhodko, Sergey, & Marian, Jaime. Using machine-learning to understand complex microstructural effects on the mechanical behavior of Ti-6Al-4V alloys. United States. https://doi.org/10.1016/j.commatsci.2022.111267
McElfresh, Cameron, Roberts, Collin, He, Sicong, Prikhodko, Sergey, and Marian, Jaime. Thu .
"Using machine-learning to understand complex microstructural effects on the mechanical behavior of Ti-6Al-4V alloys". United States. https://doi.org/10.1016/j.commatsci.2022.111267. https://www.osti.gov/servlets/purl/1977003.
@article{osti_1977003,
title = {Using machine-learning to understand complex microstructural effects on the mechanical behavior of Ti-6Al-4V alloys},
author = {McElfresh, Cameron and Roberts, Collin and He, Sicong and Prikhodko, Sergey and Marian, Jaime},
abstractNote = {Structural materials properties are highly dependent on their microstructure. Their microstructure is in turn affected by multiple fabrication and thermo-mechanical treatment parameters, all of which conform a highly-dimensional parametric space with often hidden correlations that are difficult to extract by experimentation alone. This is particularly true for alloys of the dual-phase Ti-6Al-4V family, with their greatly complex and rich microstructures, which combine several intrinsic length scales associated with multiple grain and subgrain structures, grains with different crystal lattices (α and β phases), and complex chemistry. In this paper we use a comprehensive set of machine learning techniques to develop predictive tools relating the yield strength and hardening rate of these alloys to a set of input parameters covering extensive ranges. The data generator is a finite-element crystal plasticity model for polycrystal deformation that takes into account slip anisotropy and employs standard dislocation evolution models for the α and β phases of Ti-based alloys. Our dataset includes over two thousand independent simulations and is used to train the machine learning models, which are then used to establish correlations between microstructural parameters and the alloys’ mechanical response. Our results point to the most influential parameters affecting yield strength and hardening rate, information that can then be used to guide experimental synthesis and characterization efforts to save time and resources.},
doi = {10.1016/j.commatsci.2022.111267},
journal = {Computational Materials Science},
number = C,
volume = 208,
place = {United States},
year = {Thu Mar 17 00:00:00 EDT 2022},
month = {Thu Mar 17 00:00:00 EDT 2022}
}
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