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

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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. This workflow consists of four main steps: data pre-processing, microstructure quantification, dimensionality reduction, and extraction/validation of process-structure linkages. 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. Additionally, the modular workflows developed and presented in this work facilitate easy dissemination and curation by the broader community.

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Acknowledgments

Evdokia Popova and Surya R. Kalidindi would like to acknowledge support from NIST grant 70NANB14H191. Xinyi Gong acknowledges support from NSF award 1435237. Ahmet Cecen acknowledges support from AFOSR award FA9550-12-1-0458. Sandia National Laboratories is a multi-mission laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.

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Correspondence to Surya R. Kalidindi.

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Popova, E., Rodgers, T.M., Gong, X. et al. Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data. Integr Mater Manuf Innov 6, 54–68 (2017). https://doi.org/10.1007/s40192-017-0088-1

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