Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data
Abstract
A novel data science workflow is developed and demonstrated to extract process-structure linkages (i.e., reduced-order model) for microstructure evolution problems when the final microstructure depends on (simulation or experimental) processing parameters. Our workflow consists of four main steps: data pre-processing, microstructure quantification, dimensionality reduction, and extraction/validation of process-structure linkages. These methods that can be employed within each step vary based on the type and amount of available data. In this paper, this data-driven workflow is applied to a set of synthetic additive manufacturing microstructures obtained using the Potts-kinetic Monte Carlo (kMC) approach. Additive manufacturing techniques inherently produce complex microstructures that can vary significantly with processing conditions. Using the developed workflow, a low-dimensional data-driven model was established to correlate process parameters with the predicted final microstructure. In addition, the modular workflows developed and presented in this work facilitate easy dissemination and curation by the broader community.
- Authors:
-
- Georgia Inst. of Technology, Atlanta, GA (United States). Woodruff School of Mechanical Engineering
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computational Materials and Data Science
- Georgia Inst. of Technology, Atlanta, GA (United States). School of Computational Science and Engineering
- Georgia Inst. of Technology, Atlanta, GA (United States). School of Computational Science and Engineering
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Materials Mechanics
- Georgia Inst. of Technology, Atlanta, GA (United States). Woodruff School of Mechanical Engineering, School of Materials Science and Engineering, School of Computational Science and Engineering
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF)
- OSTI Identifier:
- 1399880
- Report Number(s):
- SAND2016-12785J
Journal ID: ISSN 2193-9764; PII: 88; TRN: US1702975
- Grant/Contract Number:
- AC04-94AL85000; 1435237
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Integrating Materials and Manufacturing Innovation
- Additional Journal Information:
- Journal Volume: 6; Journal Issue: 1; Journal ID: ISSN 2193-9764
- Publisher:
- Springer
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; 97 MATHEMATICS AND COMPUTING; PSP linkages; workflows; microstructure quantification; additive manufacturing; monte carlo simulation
Citation Formats
Popova, Evdokia, Rodgers, Theron M., Gong, Xinyi, Cecen, Ahmet, Madison, Jonathan D., and Kalidindi, Surya R. Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data. United States: N. p., 2017.
Web. doi:10.1007/s40192-017-0088-1.
Popova, Evdokia, Rodgers, Theron M., Gong, Xinyi, Cecen, Ahmet, Madison, Jonathan D., & Kalidindi, Surya R. Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data. United States. https://doi.org/10.1007/s40192-017-0088-1
Popova, Evdokia, Rodgers, Theron M., Gong, Xinyi, Cecen, Ahmet, Madison, Jonathan D., and Kalidindi, Surya R. Mon .
"Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data". United States. https://doi.org/10.1007/s40192-017-0088-1. https://www.osti.gov/servlets/purl/1399880.
@article{osti_1399880,
title = {Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data},
author = {Popova, Evdokia and Rodgers, Theron M. and Gong, Xinyi and Cecen, Ahmet and Madison, Jonathan D. and Kalidindi, Surya R.},
abstractNote = {A novel data science workflow is developed and demonstrated to extract process-structure linkages (i.e., reduced-order model) for microstructure evolution problems when the final microstructure depends on (simulation or experimental) processing parameters. Our workflow consists of four main steps: data pre-processing, microstructure quantification, dimensionality reduction, and extraction/validation of process-structure linkages. These methods that can be employed within each step vary based on the type and amount of available data. In this paper, this data-driven workflow is applied to a set of synthetic additive manufacturing microstructures obtained using the Potts-kinetic Monte Carlo (kMC) approach. Additive manufacturing techniques inherently produce complex microstructures that can vary significantly with processing conditions. Using the developed workflow, a low-dimensional data-driven model was established to correlate process parameters with the predicted final microstructure. In addition, the modular workflows developed and presented in this work facilitate easy dissemination and curation by the broader community.},
doi = {10.1007/s40192-017-0088-1},
journal = {Integrating Materials and Manufacturing Innovation},
number = 1,
volume = 6,
place = {United States},
year = {Mon Mar 13 00:00:00 EDT 2017},
month = {Mon Mar 13 00:00:00 EDT 2017}
}
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