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“Sintering” Models and In-Situ Experiments: Data Assimilation for Microstructure Prediction in SLS Additive Manufacturing of Nylon Components

Published online by Cambridge University Press:  20 February 2020

W. Steven Rosenthal ,
Francesca C. Grogan ,
Yulan Li ,
Erin I. Barker ,
Josef F. Christ ,
Timothy R. Pope ,
Anil K. Battu ,
Tamas Varga ,
Christopher A. Barrett  and
Marvin G. Warner
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W. Steven Rosenthal*
Affiliation:
Pacific Northwest National Laboratory, Richland, WA 99352, U.S.A.
Francesca C. Grogan
Affiliation:
Pacific Northwest National Laboratory, Richland, WA 99352, U.S.A.
Yulan Li
Affiliation:
Pacific Northwest National Laboratory, Richland, WA 99352, U.S.A.
Erin I. Barker
Affiliation:
Pacific Northwest National Laboratory, Richland, WA 99352, U.S.A.
Josef F. Christ
Affiliation:
Pacific Northwest National Laboratory, Richland, WA 99352, U.S.A.
Timothy R. Pope
Affiliation:
Pacific Northwest National Laboratory, Richland, WA 99352, U.S.A.
Anil K. Battu
Affiliation:
Pacific Northwest National Laboratory, Richland, WA 99352, U.S.A.
Tamas Varga
Affiliation:
Pacific Northwest National Laboratory, Richland, WA 99352, U.S.A.
Christopher A. Barrett
Affiliation:
Pacific Northwest National Laboratory, Richland, WA 99352, U.S.A.
Marvin G. Warner
Affiliation:
Pacific Northwest National Laboratory, Richland, WA 99352, U.S.A.
Amra Peles
Affiliation:
Pacific Northwest National Laboratory, Richland, WA 99352, U.S.A.
*
†E-mail: [email protected]
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Abstract

Selective laser sintering methods are workhorses for additively manufacturing polymer-based components. The ease of rapid prototyping also means it is easy to produce illicit components. It is necessary to have a data-calibrated in-situ physical model of the build process in order to predict expected and defective microstructure characteristics that inform component provenance. Toward this end, sintering models are calibrated and characteristics such as component defects are explored. This is accomplished by assimilating multiple data streams, imaging analysis, and computational model predictions in an adaptive Bayesian parameter estimation algorithm. From these data sources, along with a phase-field model, bulk porosity distributions are inferred. Model parameters are constrained to physically-relevant search directions by sensitivity analysis, and then matched to predictions using adaptive sampling. Using this feedback loop, data-constrained estimates of sintering model parameters along with uncertainty bounds are obtained.

Keywords

3D printingadditive manufacturingin situpolymersintering
Type
Articles
Information
MRS Advances , Volume 5 , Issue 29-30: Materials Theory, Computation and Characterization , 2020 , pp. 1593 - 1601
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Copyright
Copyright © Materials Research Society 2020

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