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

Autor: Marvin G. Warner, Amra Peles, W. Steven Rosenthal, T. R. Pope, Tamas Varga, Christopher A. Barrett, Yulan Li, Anil K. Battu, Josef F. Christ, Francesca Grogan, Erin I. Barker
Rok vydání: 2020
Předmět:
Zdroj: MRS Advances. 5:1593-1601
ISSN: 2059-8521
DOI: 10.1557/adv.2020.125
Popis: 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.
Databáze: OpenAIRE
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