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Macroscale Property Prediction for Additively Manufactured IN625 from Microstructure through Advanced Homogenization

Published

Author(s)

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

Abstract

Design of additively manufactured metallic parts requires computational models that can predict the mechanical response of parts considering the microstructural, manufacturing, and operating conditions. The article discusses the authors' response to Air Force Research Laboratory (AFRL) Additive Manufacturing Modeling Challenge 3 that asks the participants to predict the mechanical response of tensile coupons of IN625 as function of microstructure and manufacturing conditions. The group used representative volume element (RVE) approach coupled with crystal plasticity FFT method to solve the problem. During the competition, material law calibration proved to be a challenge which prompted the authors to introduce an advanced material law identification method using proper generalized decomposition (PGD). Finally, the article proposes a mechanistic reduced order method called Self-consistent Clustering Analysis (SCA) for solving the problems paving the pathway for multiscale simulation. Apart from presenting the response analysis, some physical interpretation and assumptions associated with the modeling are discussed.
Citation
Integrating Materials and Manufacturing Innovation

Keywords

Additive Manufacturing, IN625, Proper Generalized Decomposition, Data-driven Science, Homogenization

Citation

Saha, S. , Kafka, O. , Lu, Y. , Yu, C. and Liu, W. (2021), Macroscale Property Prediction for Additively Manufactured IN625 from Microstructure through Advanced Homogenization, Integrating Materials and Manufacturing Innovation, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932078 (Accessed May 18, 2024)

Issues

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Created July 30, 2021, Updated November 29, 2022