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Microscale Structure to Property Prediction for Additively Manufactured IN625 through Advanced Identification of Material Law

Published

Author(s)

Sourav Saha, Orion Kafka, Ye Lu, Cheng Yu, Wing Kam Liu

Abstract

Challenge 4 of the Air Force Research Laboratory (AFRL) Additive Manufacturing Modeling Challenge Series asks the participants to predict the grain-average elastic strain tensors of a few specific \textitchallenge grains} during tensile loading, based on experimental data and extensive characterization of the IN 625 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 faster and can be used to identify complex material model in the broad field of mechanics. The results in terms of absolute error have been reported for the original prediction and re-calibrated material model. The predictions show that the method is capable of handling large scale computational problems for local response identification. Re-calibrated results and speed-up shows promise for using PGD for material model calibration in the field of computational mechanics.
Citation
Integrating Materials and Manufacturing Innovation
Volume
10

Keywords

Additive Manufacturing, IN 625, Elastic Strain, Data-driven Method, Proper Generalized Decomposition

Citation

Saha, S. , Kafka, O. , Lu, Y. , Yu, C. and Liu, W. (2021), Microscale Structure to Property Prediction for Additively Manufactured IN625 through Advanced Identification of Material Law, Integrating Materials and Manufacturing Innovation, [online], https://doi.org/10.1007/s40192-021-00208-5, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932009 (Accessed May 21, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created May 11, 2021, Updated November 29, 2022