Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-24T09:45:29.987Z Has data issue: false hasContentIssue false
  • English
  • Français

“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
...Show all authors
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]
Get access

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
Check for updates
Copyright
Copyright © Materials Research Society 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References:

P Kruth, J., Wang, X., Laoui, T., and Froyen, L., Assembly Automation 23 (4), 357-371 (2003).CrossRefGoogle Scholar
Kumar, S., The Journal of The Minerals, Metals & Materials Society 55 (10), 43-47 (2003).CrossRefGoogle Scholar
Tapia, G. and Elwany, A., Journal of Manufacturing Science and Engineering 136 (6), 060801 (2014).CrossRefGoogle Scholar
Hu, Z. and Mahadevan, S., The International Journal of Advanced Manufacturing Technology 93, 2855-2874 (2017).CrossRefGoogle Scholar
Nath, P., Hu, Z., and Mahadevan, S., in Multi-Level Uncertainty Quantification in Additive Manufacturing, (International Solid Freeform Fabrication Symposium 28, University of Texas at Austin, Austin, TX, 2017), pp. 7-9.Google Scholar
Delgado, J., Ciurana, J., and Rodriguez, C.A., The International Journal of Advanced Manufacturing Technology 60, 601-610 (2012).CrossRefGoogle Scholar
King, W.E. et. al., Applied Physics Reviews 2 (4), 041304 (2015).10.1063/1.4937809CrossRefGoogle Scholar
Ma, L. et. al., in Using Design of Experiments in Finite Element Modeling to Identify Critical Variables for Laser Powder Bed Fusion , (International Solid Freeform Fabrication Symposium 26, University of Texas at Austin, Austin, TX, 2015), pp. 219-228.Google Scholar
Abi-Mansour, A., SoftwareX 9, 168-174 (2019).10.1016/j.softx.2019.01.016CrossRefGoogle Scholar
Wang, Y. U., Acta Materialia 54, 953-961 (2006).CrossRefGoogle Scholar
Deng, J., Materials Transactions 53 (2), 385-389 (2012).CrossRefGoogle Scholar
Chockalingam, K., Kouznetsova, V.G., van der Sluis, O., and Geers, M.G.D., Computer Methods in Applied Mechanics and Engineering 312, 492-508 (2016).CrossRefGoogle Scholar
Landis, E.N. and Keane, D.T., Materials Characterization 61 (12), 1305-1316 (2010).CrossRefGoogle Scholar
du Plessis, A., Yadroitsev, I., Yadroitsava, I., and Le Roux, S.G. , 3D Printing and Additive Manufacturing 5 (3), 227-247 (2018).CrossRefGoogle Scholar
Thompson, A., Maskery, I., and Leach, R.K., Measurement Science and Technology 27 (7), 072001 (2016).CrossRefGoogle Scholar
Feldkamp, L.A., Davis, L.C., and Kress, J.W., Journal of the Optical Society of America 1 (6), 612–619 (1984).CrossRefGoogle Scholar
Gordon, N. J., Salmond, D. J., Smith, A. F. M.. “Novel approach to nonlinear/non-Gaussian Bayesian state estimation.” IEE Proceedings-F 140 (2) (1993).Google Scholar