Microscale Structure to Property Prediction for Additively Manufactured IN625 through Advanced Material Model Parameter Identification

Thumbnail Image

Files

s40192-021-00208-5.pdf (2.01 MB)

Links to Files

https://link.springer.com/article/10.1007/s40192-021-00208-5

Permanent Link

https://doi.org/10.1007/s40192-021-00208-5
 http://hdl.handle.net/11603/30073

Collections

UMBC Mechanical Engineering Department

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0003-3698-5596

Date

2021-05-11

Type of Work

journal articles
Text

Department

Program

Citation of Original Publication

Saha, S., Kafka, O.L., Lu, Y. et al. Microscale Structure to Property Prediction for Additively Manufactured IN625 through Advanced Material Model Parameter Identification. Integr Mater Manuf Innov 10, 142–156 (2021). https://doi.org/10.1007/s40192-021-00208-5

Rights

This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0

Subjects

Abstract

Challenge 4 of the Air Force Research Laboratory additive manufacturing modeling challenge series asks the participants to predict the grain-average elastic strain tensors of a few specific challenge grains during tensile loading, based on experimental data and extensive characterization of an IN625 test specimen. In this article, we present our strategy and computational methods for tackling this problem. During the competition stage, a characterized microstructural image from the experiment was directly used to predict the mechanical responses of certain challenge grains with a genetic algorithm-based material model identification method. Later, in the post-competition stage, a proper generalized decomposition (PGD)-based reduced order method is introduced for improved material model calibration. This data-driven reduced order method is efficient and can be used to identify complex material model parameters in the broad field of mechanics and materials science. The results in terms of absolute error have been reported for the original prediction and re-calibrated material model. The predictions show that the overall method is capable of handling large-scale computational problems for local response identification. The re-calibrated results and speed-up show promise for using PGD for material model calibration.