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Integrating Materials and Manufacturing Innovation
Erschienen in: Integrating Materials and Manufacturing Innovation 1/2017

13.03.2017 | Research

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

verfasst von: Evdokia Popova, Theron M. Rodgers, Xinyi Gong, Ahmet Cecen, Jonathan D. Madison, Surya R. Kalidindi

Erschienen in: Integrating Materials and Manufacturing Innovation | Ausgabe 1/2017

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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.
Vorheriger Artikel Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials
Nächster Artikel Microstructure-Based Multiscale Analysis of Hot Rolling of Duplex Stainless Steel Using Various Simulation Software

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Metadaten
Titel
Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data
verfasst von
Evdokia Popova
Theron M. Rodgers
Xinyi Gong
Ahmet Cecen
Jonathan D. Madison
Surya R. Kalidindi
Publikationsdatum
13.03.2017
Verlag
Springer International Publishing
Erschienen in
Integrating Materials and Manufacturing Innovation / Ausgabe 1/2017
Print ISSN: 2193-9764
Elektronische ISSN: 2193-9772
DOI
https://doi.org/10.1007/s40192-017-0088-1

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