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Title: Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data

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:
 [1] ;  [2] ;  [3] ;  [4] ;  [5] ;  [6]
  1. Georgia Inst. of Technology, Atlanta, GA (United States). Woodruff School of Mechanical Engineering
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computational Materials and Data Science
  3. Georgia Inst. of Technology, Atlanta, GA (United States). School of Computational Science and Engineering
  4. Georgia Inst. of Technology, Atlanta, GA (United States). School of Computational Science and Engineering
  5. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Materials Mechanics
  6. 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:
Report Number(s):
SAND2016-12785J
Journal ID: ISSN 2193-9764; PII: 88; TRN: US1702975
Grant/Contract Number:
AC04-94AL85000; 1435237
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
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF)
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
OSTI Identifier:
1399880