TY - JOUR
T1 - Macroscale Property Prediction for Additively Manufactured IN625 from Microstructure Through Advanced Homogenization
AU - Saha, Sourav
AU - Kafka, Orion L.
AU - Lu, Ye
AU - Yu, Cheng
AU - Liu, Wing Kam
N1 - Funding Information:
The authors would like to acknowledge the support of National Science Foundation (NSF, USA) Grants CMMI-1762035 and CMMI-1934367; and Award No. 70NANB19H005 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD), United States. This research was completed while Orion Kafka held a National Research Council Postdoctoral Research Associateship at the National Institute of Standards and Technology.
Publisher Copyright:
© 2021, The Minerals, Metals & Materials Society.
PY - 2021/9
Y1 - 2021/9
N2 - Design of additively manufactured metallic parts requires computational models that can predict the mechanical response of the parts considering the microstructural, manufacturing, and operating conditions. This article documents our response to Air Force Research Laboratory (AFRL) Additive Manufacturing Modeling Challenge 3, which asks the participants to predict the mechanical response of tensile coupons of IN625 as function of microstructure and manufacturing conditions. A representative volume element (RVE) approach was coupled with a crystal plasticity material model, solved within the fast Fourier transformation (FFT) framework for mechanics, to address the challenge. During the competition, material model calibration proved to be a challenge, prompting the introduction in this manuscript of an advanced material model identification method using proper generalized decomposition (PGD). Finally, a mechanistic reduced order method called self-consistent clustering analysis (SCA) is shown as a possible alternative to the FFT method for solving these problems. Apart from presenting the response analysis, some physical interpretation and assumptions associated with the modeling are discussed.
AB - Design of additively manufactured metallic parts requires computational models that can predict the mechanical response of the parts considering the microstructural, manufacturing, and operating conditions. This article documents our response to Air Force Research Laboratory (AFRL) Additive Manufacturing Modeling Challenge 3, which asks the participants to predict the mechanical response of tensile coupons of IN625 as function of microstructure and manufacturing conditions. A representative volume element (RVE) approach was coupled with a crystal plasticity material model, solved within the fast Fourier transformation (FFT) framework for mechanics, to address the challenge. During the competition, material model calibration proved to be a challenge, prompting the introduction in this manuscript of an advanced material model identification method using proper generalized decomposition (PGD). Finally, a mechanistic reduced order method called self-consistent clustering analysis (SCA) is shown as a possible alternative to the FFT method for solving these problems. Apart from presenting the response analysis, some physical interpretation and assumptions associated with the modeling are discussed.
KW - Additive manufacturing
KW - Homogenization
KW - IN625
KW - Proper generalized decomposition
KW - Self-consistent clustering analysis
UR - http://www.scopus.com/inward/record.url?scp=85111493958&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85111493958&partnerID=8YFLogxK
U2 - 10.1007/s40192-021-00221-8
DO - 10.1007/s40192-021-00221-8
M3 - Article
AN - SCOPUS:85111493958
VL - 10
SP - 360
EP - 372
JO - Integrating Materials and Manufacturing Innovation
JF - Integrating Materials and Manufacturing Innovation
SN - 2193-9764
IS - 3
ER -