Microscale Structure to Property Prediction for Additively Manufactured IN625 through Advanced Identification of Material Law
Sourav Saha, Orion Kafka, Ye Lu, Cheng Yu, Wing Kam Liu
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.
Integrating Materials and Manufacturing Innovation
, Kafka, O.
, Lu, Y.
, Yu, C.
and Liu, W.
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 December 4, 2023)